Author response:

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

Recommendations for the authors:

Reviewer #1 (Recommendations For The Authors):

I praise the authors for their impressive work; all my major concerns have been addressed. I believe the revised article is much stronger and will surely raise the interest of a broad readership.

I list in the following a few minor points that the authors might want to consider when finalizing the work:

- It might be helpful for the reader to know if EPIC-ATAC can also be used on tissues different from tumors and PBMC/blood, and how (i.e. which reference should they use). 

We thank the reviewer for this comment. In the discussion, we have clarified this point as follows:

“Although not tested in this work, the TME marker peaks and profiles could be used on normal tissues where immune cells are expected to be present. In cases where specific cell types are expected in a sample but are not part of our list of reference profiles (e.g., neuronal cells in brain tumors or tissues other than human PBMCs or tumor samples), custom marker peaks and reference profiles can be provided to EPIC-ATAC to perform cell-type deconvolution. To this end, users should select markers that are cell-type specific, which could be identified using pairwise differential analysis performed on ATAC-Seq data from sorted cells from the populations of interest, following the approach developed in this work (Figure 1, see Code availability).”

- In Fig 2 the numbers are hard to read as they are too close or overlapping.We have updated Figure 2 to avoid the overlap between the numbers.

- In Fig 5 I see some squared around the sub-panels, but it might be due to the PDF compression. 

We do not see these squares on the Figure 5 but have seen such squares on Figure 1. We have checked that all the PDF files uploaded on the eLife submission system do not contain the previously mentioned squares.

- In the Introduction, some "deconvolution concepts" are introduced (e.g. Line 63-65), but not explained/illustrated. It might be helpful to refer to a "didactic" review. 

We have added two references to these sentences in the introduction:

“As described in more details elsewhere (Avila Cobos et al., 2018; Sturm et al., 2019), many of these tools model bulk data as a mixture of reference profiles either coming from purified cell populations or inferred from single-cell genomic data for each cell type.”

Author response:

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

General Response

We are grateful for the constructive comments from reviewers and the editor.

The main point converged on a potential alternative interpretation that top-down modulation to the visual cortex may be contributing to the NC connectivity we observed. For this revision, we address that point with new analysis in Fig. S8 and Fig. 6. These results indicate that top-down modulation does not account for the observed NC connectivity.

We performed the following analyses.

(1) In a subset of experiments, we recorded pupil dynamics while the mice were engaged in a passive visual stimulation experiment (Fig. S8A). We found that pupil dynamics, which indicate the arousal state of the animal, explained only 3% of the variance of neural dynamics. This is significantly smaller than the contribution of sensory stimuli and the activity of the surrounding neuronal population (Fig. S8B). In particular, the visual stimulus itself typically accounted for 10-fold more variance than pupil dynamics (Fig. S8C). This suggests that the population neural activity is highly stimulus-driven and that a large portion of functional connectivity is independent of top-down modulation. In addition, after subtracting the neural activity from the pupil-modulated portion, the cross-stimulus stability of the NC was preserved (Fig. S8D).

We note that the contribution from pupil dynamics to neural activity in this study is smaller than what was observed in an earlier study (Stringer et al. 2019 Science). That can be because mice were in quiet wakefulness in the current study, while mice were in spontaneous locomotion in the earlier study. We discuss this discrepancy in the main text, in the subsection “Functional connectivity is not explained by the arousal state”.

(2) We performed network simulations with top-down input (Fig. 6F-H). With multidimensional top-down input comparable to the experimental data, recurrent connections within the network are necessary to generate cross-stimulus stable NC connectivity (Fig. 6G). It took increasing the contribution from the top-down input (i.e., to more than 1/3 of the contribution from the stimulus), before the cross-stimulus NC connectivity can be generated by the top-down modulation (Fig. 6H). Thus, this analysis provides further evidence that top-down modulation was not playing a major role in the NC connectivity we observed.

These new results support our original conclusion that network connectivity is the principal mechanism underlying the stability of functional networks.

Public Reviews:

Reviewer #1 (Public Review):

Using multi-region two-photon calcium imaging, the manuscript meticulously explores the structure of noise correlations (NCs) across the mouse visual cortex and uses this information to make inferences about the organization of communication channels between primary visual cortex (V1) and higher visual areas (HVAs). Using visual responses to grating stimuli, the manuscript identifies 6 tuning groups of visual cortex neurons and finds that NCs are highest among neurons belonging to the same tuning group whether or not they are found in the same cortical area. The NCs depend on the similarity of tuning of the neurons (their signal correlations) but are preserved across different stimulus sets - noise correlations recorded using drifting gratings are highly correlated with those measured using naturalistic videos. Based on these findings, the manuscript concludes that populations of neurons with high NCs constitute discrete communication channels that convey visual signals within and across cortical areas.

Experiments and analyses are conducted to a high standard and the robustness of noise correlation measurements is carefully validated. However, the interpretation of noise correlation measurements as a proxy from network connectivity is fraught with challenges. While the data clearly indicates the existence of distributed functional ensembles, the notion of communication channels implies the existence of direct anatomical connections between them, which noise correlations cannot measure.

The traditional view of noise correlations is that they reflect direct connectivity or shared inputs between neurons. While it is valid in a broad sense, noise correlations may reflect shared top-down input as well as local or feedforward connectivity. This is particularly important since mouse cortical neurons are strongly modulated by spontaneous behavior (e.g. Stringer et al, Science, 2019). Therefore, noise correlation between a pair of neurons may reflect whether they are similarly modulated by behavioral state and overt spontaneous behaviors. Consequently, noise correlation alone cannot determine whether neurons belong to discrete communication channels.

Behavioral modulation can influence the gain of sensory-evoked responses (Niell and Stryker, Neuron, 2010). This can explain why signal correlation is one of the best predictors of noise correlations as reported in the manuscript. A pair of neurons that are similarly gain-modulated by spontaneous behavior (e.g. both active during whisking or locomotion) will have higher noise correlations if they respond to similar stimuli. Top-down modulation by the behavioral state is also consistent with the stability of noise correlations across stimuli. Therefore, it is important to determine to what extent noise correlations can be explained by shared behavioral modulation.

We thank the reviewer for the constructive and positive feedback on our study.

The reviewer acknowledged the quality of our experiments and analysis and stated a concern that the noise correlation can be explained by top-down modulation. We have addressed this concern carefully in the revision, please see the General Response above.

Reviewer #2 (Public Review):

Summary:

This groundbreaking study characterizes the structure of activity correlations over a millimeter scale in the mouse cortex with the goal of identifying visual channels, specialized conduits of visual information that show preferential connectivity. Examining the statistical structure of the visual activity of L2/3 neurons, the study finds pairs of neurons located near each other or across distances of hundreds of micrometers with significantly correlated activity in response to visual stimulation. These highly correlated pairs have closely related visual tuning sharing orientation and/or spatial and/or temporal preference as would be expected from dedicated visual channels with specific connectivity.

Strengths:

The study presents best-in-class mesoscopic-scale 2-photon recordings from neuronal populations in pairs of visual areas (V1-LM, V1-PM, V1-AL, V1-LI). The study employs diverse visual stimuli that capture some of the specialization and heterogeneity of neuronal tuning in mouse visual areas. The rigorous data quantification takes into consideration functional cell groups as well as other variables that influence trial-to-trial correlations (similarity of tuning, neuronal distance, receptive field overlap). The paper convincingly demonstrates the robustness of the clustering analysis and of the activity correlation measurements. The calcium imaging results convincingly show that noise correlations are correlated across visual stimuli and are strongest within cell classes which could reflect distributed visual channels. A simple simulation is provided that suggests that recurrent connectivity is required for the stimulus invariance of the results. The paper is well-written and conceptually clear. The figures are beautiful and clear. The arguments are well laid out and the claims appear in large part supported by the data and analysis results (but see weaknesses).

Weaknesses:

An inherent limitation of the approach is that it cannot reveal which anatomical connectivity patterns are responsible for observed network structure. The modeling results presented, however, suggest interestingly that a simple feedforward architecture may not account for fundamental characteristics of the data. A limitation of the study is the lack of a behavioral task. The paper shows nicely that the correlation structure generalizes across visual stimuli. However, the correlation structure could differ widely when animals are actively responding to visual stimuli. I do think that, because of the complexity involved, a characterization of correlations during a visual task is beyond the scope of the current study.

An important question that does not seem addressed (but it is addressed indirectly, I could be mistaken) is the extent to which it is possible to obtain reliable measurements of noise correlation from cell pairs that have widely distinct tuning. L2/3 activity in the visual cortex is quite sparse. The cell groups laid out in Figure S2 have very sharp tuning. Cells whose tuning does not overlap may not yield significant trial-to-trial correlations because they do not show significant responses to the same set of stimuli, if at all any time. Could this bias the noise correlation measurements or explain some of the dependence of the observed noise correlations on signal correlations/similarity of tuning? Could the variable overlap in the responses to visual responses explain the dependence of correlations on cell classes and groups?

With electrophysiology, this issue is less of a problem because many if not most neurons will show some activity in response to suboptimal stimuli. For the present study which uses calcium imaging together with deconvolution, some of the activity may not be visible to the experimenters. The correlation measure is shown to be robust to changes in firing rates due to missing spikes. However, the degree of overlap of responses between cell pairs and their consequences for measures of noise correlations are not explored.

Beyond that comment, the remaining issues are relatively minor issues related to manuscript text, figures, and statistical analyses. There are typos left in the manuscript. Some of the methodological details and results of statistical testing also seem to be missing. Some of the visuals and analyses chosen to examine the data (e.g., box plots) may not be the most effective in highlighting differences across groups. If addressed, this would make a very strong paper.

We thank the reviewer for acknowledging the contributions of our study.

We agree with the reviewer that future studies on behaviorally engaged animals are necessary. Although we also agree with the reviewer that behavior studies are out the scope of the current manuscript, we have included additional analysis and discussion on whether and how top-down input would affect the NC connectivity in the revision. Please see the General Response above.

Reviewer #3 (Public Review):

Summary:

Yu et al harness the capabilities of mesoscopic 2P imaging to record simultaneously from populations of neurons in several visual cortical areas and measure their correlated variability. They first divide neurons into 65 classes depending on their tuning to moving gratings. They found the pairs of neurons of the same tuning class show higher noise correlations (NCs) both within and across cortical areas. Based on these observations and a model they conclude that visual information is broadcast across areas through multiple, discrete channels with little mixing across them.

NCs can reflect indirect or direct connectivity, or shared afferents between pairs of neurons, potentially providing insight on network organization. While NCs have been comprehensively studied in neuron pairs of the same area, the structure of these correlations across areas is much less known. Thus, the manuscripts present novel insights into the correlation structure of visual responses across multiple areas.

Strengths:

The study uses state-of-the art mesoscopic two-photon imaging.

The measurements of shared variability across multiple areas are novel.

The results are mostly well presented and many thorough controls for some metrics are included.

Weaknesses:

I have concerns that the observed large intra-class/group NCs might not reflect connectivity but shared behaviorally driven multiplicative gain modulations of sensory-evoked responses. In this case, the NC structure might not be due to the presence of discrete, multiple channels broadcasting visual information as concluded. I also find that the claim of multiple discrete broadcasting channels needs more support before discarding the alternative hypothesis that a continuum of tuning similarity explains the large NCs observed in groups of neurons.

Specifically:

Major concerns:

(1) Multiplicative gain modulation underlying correlated noise between similarly tuned neurons

(1a) The conclusion that visual information is broadcasted in discrete channels across visual areas relies on interpreting NC as reflecting, direct or indirect connectivity between pairs, or common inputs. However, a large fraction of the activity in the mouse visual system is known to reflect spontaneous and instructed movements, including locomotion and face movements, among others. Running activity and face movements are some of the largest contributors to visual cortex activity and exert a multiplicative gain on sensory-evoked responses (Niell et al, Stringer et al, among others). Thus, trial-by-fluctuations of behavioral state would result in gain modulations that, due to their multiplicative nature, would result in more shared variability in cotuned neurons, as multiplication affects neurons that are responding to the stimulus over those that are not responding ( see Lin et al, Neuron 2015 for a similar point).<br /> As behavioral modulations are not considered, this confound affects most of the conclusions of the manuscript, as it would result in larger NCs the more similar the tuning of the neurons is, independently of any connectivity feature. It seems that this alternative hypothesis can explain most of the results without the need for discrete broadcasting channels or any particular network architecture and should be addressed to support its main claims.

(1b) In Figure 5 the observations are interpreted as evidence for NCs reflecting features of the network architecture, as NCs measured using gratings predicted NC to naturalistic videos. However, it seems from Figure 5 A that signal correlations (SCs) from gratings had non-zero correlations with SCs during naturalistic videos (is this the case?). Thus, neurons that are cotuned to gratings might also tend to be coactivated during the presentation of videos. In this case, they are also expected to be susceptible to shared behaviorally driven fluctuations, independently of any circuit architecture as explained before. This alternative interpretation should be addressed before concluding that these measurements reflect connectivity features.

We thank the reviewer for acknowledging the contributions of our study.

The reviewer suggested that gain modulation might be interfering with the interpretation of the NC connectivity. We have addressed this issue in the General Response above.

Here, we will elaborate on one additional analysis we performed, in case it might be of interest. We carried out multiplicative gain modeling by implementing an established method (Goris et al. 2014 Nat Neurosci) on our dataset. We were able to perform the modeling work successfully. However, we found that it is not a suitable model for explaining the current dataset because the multiplicative gain induced a negative correlation. This seemed odd but can be explained. First, top-down input is not purely multiplicative but rather both additive and multiplicative. Second, the top-down modulation is high dimensional. Third, the firing rate of layer 2/3 mouse visual cortex neurons is lower than the firing rates for non-human primate recordings used in the development of the method (Goris et al. 2014 Nat Neurosci). Thus, we did not pursue the model further. We just mention it here in case the outcome might be of interest to fellow researchers.

(2) Discrete vs continuous communication channels

(2a) One of the author's main claims is that the mouse cortical network consists of discrete communication channels. This discreteness is based on an unbiased clustering approach to the tuning of neurons, followed by a manual grouping into six categories in relation to the stimulus space. I believe there are several problems with this claim. First, this clustering approach is inherently trying to group neurons and discretise neural populations. To make the claim that there are 'discrete communication channels' the null hypothesis should be a continuous model. An explicit test in favor of a discrete model is lacking, i.e. are the results better explained using discrete groups vs. when considering only tuning similarity? Second, the fact that 65 classes are recovered (out of 72 conditions) and that manual clustering is necessary to arrive at the six categories is far from convincing that we need to think about categorically different subsets of neurons. That we should think of discrete communication channels is especially surprising in this context as the relevant stimulus parameter axes seem inherently continuous: spatial and temporal frequency. It is hard to motivate the biological need for a discretely organized cortical network to process these continuous input spaces.

(2b) Consequently, I feel the support for discrete vs continuous selective communication is rather inconclusive. It seems that following the author's claims, it would be important to establish if neurons belong to the same groups, rather than tuning similarity is a defining feature for showing large NCs.

Thanks for pointing this out so that we can clarify.

We did not mean to argue that the tuning of neurons is discrete. Our conclusions are not dependent on asserting a particular degree of discreteness. We performed GMM clustering to label neurons with an identity so that we could analyze the NC connectivity structure with a degree of granularity supported by the data. Our analysis suggested that communication happens within a class, rather than through mixed classes. We realized that using the term “discrete” may be confusing. In the revised text we used the term “unmixed” or “non-mixing” instead to emphasize that the communication happens between neurons belonging to the same tuning cluster, or class. 

However, we do see how the question of discreteness among classes might be interesting to readers. To provide further information, we have included a new Fig. S2 to visualize the GMM classes using t-SNE embedding.

Finally, as stated in point 1, the larger NCs observed within groups than across groups might be due to the multiplicative gain of state modulations, due to the larger tuning similarity of the neurons within a class or group.

We have addressed this issue in the General Response above and the response to comment (1).

Recommendations for the authors:

Reviewing Editor (Recommendations For The Authors):

A general recommendation discussed with the reviewers is to make use of behavioural recording to assess whether shared behaviourally driven modulations can explain the observed relation between SC and NC, independently of the network architecture. Alternatively, a simulation or model might also address this point as well as the possibility that the relation of SC and NC might be also independent of network architecture given the sparseness of the sensory responses in L2/3.

We have addressed this in the General Response above.

Broadly speaking, inferring network architecture based on NCs is extremely challenging. Consequently, the study could also be substantially improved by reframing the results in terms of distributed co-active ensembles without insinuation of direct anatomical connectivity between them.

We agree that the inferring network architecture based on NCs is challenging. The current study has revealed some principles of functional networks measured by NCs, and we showed that cross-stimulus NC connectivity provides effective constraints to network modeling. We are explicit about the nature of NCs in the manuscript. For example, in the Abstract, we write “to measure correlated variability (i.e., noise correlations, NCs)”, and in the Introduction, we write “NCs are due to connectivity (direct or indirect connectivity between the neurons, and/or shared input)”. We are following conventions in the field (e.g., Sporns 2016; Cohen and Kohn 2011).

Notice also that the abstract or title should make clear that the study was made in mice.

Sorry for the confusion, we now clearly state the study was carried out in mice in the Abstract and Introduction.

Reviewer #1 (Recommendations For The Authors):

The manuscript presents a meticulous characterization of noise correlations in the visual cortical network. However, as I outline in the public review, I think the use of noise correlations to infer communication channels is problematic and I urge the authors to carefully consider this terminology. Language such as "strength of connections" (Figure 4D) should be avoided.

We now state in the figure legend that the plot in Fig. 4D shows the average NC value.

My general suggestion to the authors, which primarily concerns the interpretation of analyses in Figures 4-6, is to consider the possible impact of shared top-down modulation on noise correlations. If behavioral data was recorded simultaneously (e.g. using cameras to record face and body movements), behavioral modulation should be considered alongside signal correlation as a possible factor influencing NCs.

We have addressed this issue in the General Response above.

I may be misunderstanding the analysis in Figure 4C but it appears circular. If the fraction of neurons belonging to a particular tuning group is larger, then the number of in-group high NC pairs will be higher for that group even if high NC pairs are distributed randomly. Can you please clarify? I frankly do not understand the analysis in Figure 4D and it is unclear to me how the analyses in Figure 4C-D address the hypotheses depicted in the cartoons.

Sorry for the confusion, we have clarified this in the Fig. 4 legend.

Each HVA has a SFTF bias (Fig. 1E,F; Marshel et al., 2011; Andermann et al., 2011; Vries et al., 2020). Each red marker on the graph in Fig. 4C is a single V1-HVA pair (blue markers are within an area) for a particular SFTF group (Fig. 1). The x-axis indicates the number of high NC pairs in the SFTF group in the V1-HVA pair divided by the total number of high NC pairs per that V1-HVA pair (summed over all SFTF groups). The trend is that for HVAs with a bias towards a particular SFTF group, there are also more high NC pairs in that SFTF group, and thus it is consistent with the model on the right side. This is not circular because it is possible to have a SFTF bias in an HVA and have uniformly low NCs. The reviewer is correct that a random distribution of high NCs could give a similar effect, which is still consistent with the model: that the number of high NC pairs (and not their specific magnitudes) can account for SFTF biases in HVAs.

To contrast with that model, we tested whether the average NC value for each tuning group varies. That is, can a small number of very high NCs account for SFTF biases in HVAs? That is what is examined in Fig. 4D. We found that the average NC value does not account for the SFTF biases. Thus, the SFTF biases were not related to the modulation in NC (i.e., functional connection strength). 

I found the discussion section quite odd and did not understand the relevance of the discussion of the coefficient of variation of various quantities to the present manuscript. It would be more useful to discuss the limitations and possible interpretations of noise correlation measurements in more detail.

We have revised the discussion section to focus on interpreting the results of the current study and comparing them with those of previous studies.

Figure 3B: please indicate what the different colors mean - I assume it is the same as Figure 3A but it is unclear.

We added text to the legend for clarification.

Typos: Page 7: "direct/indirection wiring", Page 11: "pooled over all texted areas"

We have fixed the typos.

Reviewer #2 (Recommendations For The Authors):

The significance of the results feels like it could be articulated better. The main conclusion is that V1 to HVA connections avoid mixing channels and send distinctly tuned information along distinct channels - a more explicit description of what this functional network understanding adds would be useful to the reader.

Thanks for the suggestion. We have edited the introduction section and the discussion section to make the take-home message more clear.

Previous studies with anatomical data already indicate distinctly tuned channels - several of which the authors cite - although inconsistently:

• Kim et al 2018 https://doi.org/10.1016/j.neuron.2018.10.023

• Glickfeld et al., 2013 (cited)

• Han et al., 2022 (cited)

• Han and Bonin 2023 (cited)

Thanks for the suggestion, we now cite the Kim et al. 2018 paper.

I think the information you provide is valuable - but the value should be more clearly spelled out - This section from the end of the discussion for example feels like abdicates that responsibility:<br /> "In summary, mesoscale two-photon imaging techniques open up the window of cellular-resolution functional connectivity at the system level. How to make use of the knowledge of functional connectivity remains unclear, given that functional connectivity provides important constraints on population neuron behavior."

A discussion of how the results relate to previous studies and a section on the limitations of the study seems warranted.

Thanks for the suggestion, we have extensively edited the discussion section to make the take-home message clear and discuss prior studies and limitations of the present study.

Details:

Analyses or simulations showing that the dependency of correlations on similarity of tuning is not an artifact of how the data was acquired is in my mind missing and if that is the case it is crucial that this be addressed.

At each step of data analysis, we performed control analysis to assess the fidelity of the conclusion. For example, on the spike train inference (Fig. S4), GMM clustering (Fig. S1), and noise correlation analysis (Figs. 2, S5).

None of the statistical testing seems to use animals as experimental units (instead of neurons). This could over-inflate the significance of the results. Wherever applicable and possible, I would recommend using hierarchical bootstrap for testing or showing that the differences observed are reproducible across animals.

We analyzed the tuning selectivity of HVAs (Fig. 1F) using experimental units, rather than neurons. It is very difficult to observe all tuning classes in each experiment, so pooling neurons across animals is necessary for much of the analysis. We do take care to avoid overstating statistical results, and we show the data points in most figure to give the reader an impression of the distributions.

Page 2. "The number of neurons belonged to the six tuning groups combined: V1, 5373; LM, 1316; AL, 656; PM, 491; LI, 334." Yet the total recorded number of neurons is 17,990. How neurons were excluded is mentioned in Methods but it should be stated more explicitly in Results.

We have added text in the Fig. 1 legend to direct the audience to the Methods section for information on the exclusion / inclusion criteria.

Figure 1C, left. I don't understand how correlation is the best way to quantify the consistency of class center with a subset of data. Why not use for example as the mean square error. The logic underlying this analysis is not explained in Methods.

Sorry for the confusion, we have clarified this in the Methods section.

We measured the consistency of the centers of the Gaussian clusters, which are 45-dimensional vectors in the PC dimensions. We measured the Pearson correlation of Gaussian center vectors independently defined by GMM clustering on random subsets of neurons. We found the center of the Gaussian profile of each class was consistent (Fig. 1C). The same class of different GMMs was identified by matching the center of the class.

Figure 1E. There are statements in the text about cell groups being more represented in certain visual areas. These differences are not well represented in the box plots. Can't the individual data points be plotted? I have also not found the description and results of statistical testing for these data.

We have replotted the figure (now Fig. 1F) with dot scatters which show all of the individual experiments.

Figure 2A, right, since these are paired data, I am not quite sure why only marginal distributions are shown. It would be interesting to know the distributions of correlations that are significant.

This is only for illustration showing that NCs are measurable and significantly different from zero or shuffled controls. The distribution of NCs is broad and has both positive and negative values. We are not using this for downstream analysis.

Figure 4A, I wonder if it would not be better to concentrate on significant correlations.

We focused on large correlation values rather than significant values because we wanted to examine the structure of “strongly connected” neuron pairs. Negative and small correlation values can be significant as well. Focusing on large values would allow us to generate a clear interpretation.  

Figure 4B, 'Mean strength of connections' which I presume mean correlations is not defined anywhere that I can see.

I believe the reviewer means Fig. 4D. It means the average NC value. We have edited the figure legend to add clarity.

Figure 4F, a few words explaining how to understand the correlation matrix in text or captions would be helpful.

Sorry for the confusion, we have clarified this part in figure legend for Fig. 4F.

Page 5, right column: Incomplete sentence: "To determine whether it is the number of high NC pairs or the magnitude of the NCs,".

We have edited this sentence.

Page 5, right column: "Prior findings from studies of axonal projections from V1 to HVAs indicated that the number of SF-TF-specific boutons -rather than the strength of boutons- contribute to the SF-TF biases among HVAs (Glickfeld et al., 2013)." Glickfeld et al. also reported that boutons with tuning matched to the target area showed stronger peak dF/F responses.

Thank you. We have revised this part accordingly.

Page 9, the Discussion and Figure 7 which situates the study results in a broader context is welcome and interesting, but I have the feeling that more words should be spent explaining the figure and conceptual framework to a non-expert audience. I am a bit at a loss about how to read the information in the figure.

Sorry for the confusion, we have added an explanation about this section (page 10, right column).

As far as I can see, data availability is not addressed in the manuscript. The data, code to analyze the data and generate the figures, and simulation code should be made available in a permanent public repository. This includes data for visual area mapping, calcium imaging data, and any data accessory to the experiments.

We have stated in the manuscript that code and data are available upon request. We regularly share data with no conditions (e.g., no entitlement to authorship), and we often do so even prior to publication.

The sex of the mice should be indicated in Figure T1.

The sex of the mice was mixed. This is stated in the Methods section.

Methods:

Section on statistical testing, computation of explained variance missing, etc. I feel many analyses are not thoroughly described.

Sorry for the confusion, we have improved our method section.

Signal correlation (similarity between two neurons' average responses to stimuli) and its relation to noise correlation is not formally defined.

We have included the definition of signal correlation in the Methods.

Number of visual stimulation trials is not stated in Methods. Only stated figure caption.

The number of visual stimulus trials is provided in the last paragraph of the Methods section (Visual Stimuli).

Fix typos: incorrect spelling, punctuation, and missing symbols (e.g. closing parentheses).

We have carefully examined the spelling, punctuation, and grammar. We have corrected errors and we hope that none remain.

Why use intrinsic imaging to locate retinotopic boundaries in mice already expressing GCaMP6s?

We agree with the reviewer that calcium imaging of visual cortex can be used to identify the visual cortex.

It is true that areas can be mapped using the GCaMP signals. That is not our preferred approach. Using intrinsic imaging to define the boundary between V1 and HVAs has been a well refined routine in our lab for over a decade. It is part of our standard protocol. One advantage is that the data (from intrinsic signals) is of the same nature every time. This enables us to use the same mapping procedure no matter what reporters mice might be expressing (and the pattern, e.g., patchy or restricted to certain cell types).

Reviewer #3 (Recommendations For The Authors):

The possibilty that larger intra-group NCs observed simply reflect a multiplicative gain on cotuned neurons could be addressed using pupil and/or face recordings: Does pupil size or facial motion predict NCs and if factored out, does signal correlation still predict NCs?

Perhaps a variant of the network model presented in Figure 6 with multiplicative gain could also be tested to investigate these issues.

We have addressed this issue in general response.

Here, we will elaborate on one additional analysis we performed, in case it might be of interest. We carried out multiplicative gain modeling by implementing an established method (Goris et al. 2014 Nat Neurosci) on our dataset. We were able to perform the modeling work successfully. However, we found that it is not a suitable model for explaining the current dataset because the multiplicative gain induced a negative correlation. This seemed odd but can be explained. First, top-down input is not purely multiplicative but rather both additive and multiplicative. Second, the top-down modulation is high dimensional. Third, the firing rate of layer 2/3 mouse visual cortex neurons is lower than the firing rates for non-human primate recordings used in the development of the method (Goris et al. 2014 Nat Neurosci). Thus, we did not pursue the model further. We just mention it here in case the outcome might be of interest to fellow researchers.

Similarly further analyses can be done to strengthen support for the claims that the observed NCs reflect discrete communication channels. A direct test of continuous vs categorical channels would strengthen the conclusions. One possible analysis would be to compare pairs with similar tuning (same SC) belonging to the same or different groups.

Thanks for pointing this out so that we can clarify.

We did not mean to argue that the tuning of neurons is discrete. Our conclusions are not dependent on asserting a particular degree of discreteness. We performed GMM clustering to label neurons with an identity so that we could analyze the NC connectivity structure with a degree of granularity supported by the data. Our analysis suggested that communication happens within a class, rather than through mixed classes. We realized that using the term “discrete” may be confusing. In the revised text we used the term “unmixed” or “non-mixing” instead to emphasize that the communication happens between neurons belonging to the same tuning cluster, or class. 

However, we do see how the question of discreteness among classes might be interesting to readers. To provide further information, we have included a new Fig. S2 to visualize the GMM classes using t-SNE embedding.

I also found many places where the manuscript needs clarification and /or more methodological details:<br /> • How many times was each of the stimulus conditions repeated? And how many times for the two naturalistic videos? What was the total duration of the experiments?

The number of visual stimulus trials is provided in the last paragraph of the Methods section entitled Visual Stimuli. About 15 trials were recorded for each drifting grating stimulus, and about 20 trials were recorded for each naturalistic video.

• Typo: Suit2p should be Suite2p (section Calcium image processing - Methods).

We have fixed the typo.

• What do the error bars in Figure 1E represent? Differences in group representation across areas from Figure 1E are mentioned in the text without any statistical testing.

We have revised the Figure 1E (current Fig. 1F), and we now show all data points.

• The manuscript would benefit from a comparison of the observed area-specific tuning biases across areas (Figure 1E and others) with the previous literature.

We have included additional discussion on this in the last paragraph of the section entitled Visual cortical neurons form six tuning groups.

• Why are inferred spike trains used to calculate NCs? Why can't dF/F be used? Do the results differ when using dF/F to calculate NC? Please clarify in the text.

We believe inferred spike trains provide better resolution and make it easier to compare with quantitative values from electrical recordings. Notice that NC values computed using dF/F can be much larger than those computed by inferred spike trains. For example, see Smith & Hausser 2010 Nat Neurosci. Supplementary Figure S8.

• The sentence seems incomplete or unclear: "That is, there are more high NC pairs that are in-group." Explicit vs what?

We have revised this sentence.

• Figure 1E is unclear to me. What is being plotted? Please add a color bar with the metric and the units for the matrix (left) and in the tuning curves (right panels). If the Y and X axes represent the different classes from the GMM, why are there more than 65 rows? Why is the matrix not full?

We have revised this figure. Fig. 1D is the full 65 x 65 matrix. Fig. 1F has small 3x3 matrices mapping the responses to different TF and SF of gratings. We hope the new version is clearer.

• How are receptive fields defined? How are their long and short axes calculated? How are their limits defined when calculating RF overlap?

We have added further details in the Methods section entitled “Receptive field analysis”.

Author response:

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

Public Reviews:

Reviewer #1 (Public Review):

Summary:

This study uses an online cognitive task to assess how reward and effort are integrated in a motivated decision-making task. In particular the authors were looking to explore how neuropsychiatric symptoms, in particular, apathy and anhedonia, and circadian rhythms affect behavior in this task. Amongst many results, they found that choice bias (the degree to which integrated reward and effort affect decisions) is reduced in individuals with greater neuropsychiatric symptoms, and late chronotypes (being an 'evening person').

Strengths:

The authors recruited participants to perform the cognitive task both in and out of sync with their chronotypes, allowing for the important insight that individuals with late chronotypes show a more reduced choice bias when tested in the morning.<br /> Overall, this is a well-designed and controlled online experimental study. The modelling approach is robust, with care being taken to both perform and explain to the readers the various tests used to ensure the models allow the authors to sufficiently test their hypotheses.

Weaknesses:

This study was not designed to test the interactions of neuropsychiatric symptoms and chronotypes on decision making, and thus can only make preliminary suggestions regarding how symptoms, chronotypes and time-of-assessment interact.

Reviewer #2 (Public Review):

Summary:

The study combines computational modeling of choice behavior with an economic, effort-based decision-making task to assess how willingness to exert physical effort for a reward varies as a function of individual differences in apathy and anhedonia, or depression, as well as chronotype. They find an overall reduction in effort selection that scales with apathy, anhedonia and depression. They also find that later chronotypes are less likely to choose effort than earlier chronotypes and, interestingly, an interaction whereby later chronotypes are especially unwilling to exert effort in the morning versus the evening.

Strengths:

This study uses state-of-the-art tools for model fitting and validation and regression methods which rule out multicollinearity among symptom measures and Bayesian methods which estimate effects and uncertainty about those estimates. The replication of results across two different kinds of samples is another strength. Finally, the study provides new information about the effects not only of chronotype but also chronotype by timepoint interactions which are previously unknown in the subfield of effort-based decision-making.

Weaknesses:

The study has few weaknesses. The biggest drawback is that it does not provide evidence for the idea that a match between chronotype and delay matters is especially relevant for people with depression or continuous measures like anhedonia and apathy. It is unclear whether disorders further interact with chronotype and time of day to determine a bias against effort. On the other hand, the study does provide evidence that future studies should consider such interactions when examining questions about effort expenditure in psychiatric disorders.

Reviewer #3 (Public Review):

Summary:

In this manuscript, Mehrhof and Nord study a large dataset of participants collected online (n=958 after exclusions) who performed a simple effort-based choice task. They report that the level of effort and reward influence choices in a way that is expected from prior work. They then relate choice preferences to neuropsychiatric syndromes and, in a smaller sample (n<200), to people's circadian preferences, i.e., whether they are a morning-preferring or evening-preferring chronotype. They find relationships between the choice bias (a model parameter capturing the likelihood to accept effort-reward challenges, like an intercept) and anhedonia and apathy, as well as chronotype. People with higher anhedonia and apathy and an evening chronotype are less likely to accept challenges (more negative choice bias). People with an evening chronotype are also more reward sensitive and more likely to accept challenges in the evening, compared to the morning.

Strengths:

This is an interesting and well-written manuscript which replicates some known results and introduces a new consideration related to chronotype relationships which have not been explored before. It uses a large sample size and includes analyses related to transdiagnostic as well as diagnostic criteria.

Weaknesses:

The authors do not explore how chronotype and depression are related (does one mediate the effect of the other etc). Both variables are included in the same model in the revised article now which is a great improvement, but it also means psychopathology and circadian rhythms are treated as distinct phenomena and their relationship in predicting effort-reward preferences is not examined.

Recommendations for the authors:

Reviewer #3 (Recommendations For The Authors):

Two points in response to changes the authors made:

(1) "motivational tendency" is in our opinion not an improved phrase over "choice bias". A paper by Jon Roiser calls it "overall bias to accept effortful challenges" (but that's maybe too long?)

We thank the reviewer for their suggestion of renaming our computational parameter and agree it would be of value to introduce and label this parameter in line with other work, improving consistency across the literature. Hence, we have updated our manuscript and now introduce the parameter as bias to accept effortful challenges for reward and refer to the parameter as acceptance bias thereafter.

We have updated this nomenclature throughout the manuscript text, figures and supplement.

(2) The new title "Both neuropsychiatric symptoms and circadian rhythm alter effort-based decision-making" sounds slightly causal (as would be the case in a longitudinal or intervention study). Maybe instead the authors could use "are associated with" or similar?

We agree with the reviewers that our current title could be interpreted in a causal manner. We have updated our title to now read A common alteration in effort-based decision-making in apathy, anhedonia, and late circadian rhythm.

Author response:

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

Public Reviews:

Reviewer #1 (Public Review):

Summary:

This important study investigated the role of oxytocin (OT) neurons in the paraventricular nucleus (PVN) and their projections to the medial prefrontal cortex (mPFC) in regulating pup care and infanticide behaviors in mandarin voles. The researchers used techniques like immunofluorescence, optogenetics, OT sensors, and peripheral OT administration. Activating OT neurons in the PVN reduced the time it took pup-caring male voles to approach and retrieve pups, facilitating pup-care behavior. However, this activation had no effect on females. Interestingly, this same PVN OT neuron activation also reduced the time for both male and female infanticidal voles to approach and attack pups, suggesting PVN OT neuron activity can promote pup care while inhibiting infanticide behavior. Inhibition of these neurons promoted infanticide. Stimulating PVN->mPFC OT projections facilitated pup care in males and in infanticide-prone voles, activation of these terminals prolonged latency to approach and attack. Inhibition of PVN->mPFC OT projections promoted infanticide. Peripheral OT administration increased pup care in males and reduced infanticide in both sexes. However, some results differed in females, suggesting other mechanisms may regulate female pup care.

Strengths:

This multi-faceted approach provides converging evidence, strengthens the conclusions drawn from the study, and makes them very convincing. Additionally, the study examines both pup care and infanticide behaviors, offering insights into the mechanisms underlying these contrasting behaviors. The inclusion of both male and female voles allows for the exploration of potential sex differences in the regulation of pup-directed behaviors. The peripheral OT administration experiments also provide valuable information for potential clinical applications and wildlife management strategies.

Weaknesses:

While the study presents exciting findings, there are several weaknesses that should be addressed. The sample sizes used in some experiments, such as the Fos study and optogenetic manipulations, appear to be small, which may limit the statistical power and generalizability of the results. Effect sizes are not reported, making it difficult to evaluate the practical significance of the findings. The imaging parameters and analysis details for the Fos study are not clearly described, hindering the interpretation of these results (i.e., was the entire PVN counted?). Also, does the Fos colocalization align with previous studies that look at PVN Fos and maternal/ paternal care? Additionally, the study lacks electrophysiological data to support the optogenetic findings, which could provide insights into the neural mechanisms underlying the observed behaviors. 

In some previous studies (He et al., 2019; Mei, Yan, Yin, Sullivan, & Lin, 2023), the sample size in morphological studies is also small and may be representative. We agree with reviewer’s opinion that results from larger sample size may be more statistically powerful and generalizable. We will pay attention to this issue in the future study. As reviewer suggested, we have added effect size both in the source data and in the main text, including d, η2  and odds ratio. We have added the objective magnification used in the figure legend. The imaging parameters and analysis details for the Fos study have also been added in the revised manuscript. Brain slices of 40 µm thick were collected consecutively on 4 slides, each slide had 6 brain slices spaced 160 µm apart from each other. PVN area were determined based on the Allen Mouse Brain Atlas and our previous study, and Fos, OT and merged positive neurons were counted. Our result about Fos and OT colocalization is consistent with previous study. In a previous study on virgin male prairie voles, OT and Fos colabeled neurons in the PVN increased after exposure to conspecific pups and experiencing paternal care (Kenkel et al., 2012). In another study of prairie voles, OT and c-fos colabeled neurons in PVN significantly increased after becoming parents which may be due to a shift from virgin to parents (Kelly, Hiura, Saunders, & Ophir, 2017). To support the optogenetic findings, we used c-Fos expression as a marker of neuron activity and revealed significant increases/decreases of c-Fos positive neurons induced by optogenetic activation/inhibition (Supplementary Data Fig. 1), and additionally we found that optogenetic inhibition of OT neurons reduced levels of OT release using OT1.0 sensors. Based on these two experiments, we verified that optogenetic manipulation in the present study is validate and results of optogenetic experiment are reliable (Supplementary Data Fig. 5).

The study has several limitations that warrant further discussion. Firstly, the potential effects of manipulating OT neurons on the release of other neurotransmitters (or the influence of other neurochemicals or brain regions) on pup-directed behaviors, especially in females, are not fully explored. Additionally, it is unclear whether back-propagation of action potentials during optogenetic manipulations causes the same behavioral effect as direct stimulation of PVN OT cells. Moreover, the authors do not address whether the observed changes in behavior could be explained by overall increases or decreases in locomotor activity.

We agree with reviewer’s suggestion that several limitations should be discussed. Although we used a virus strategy to specifically activate or inhibit PVN OT neurons, other neurochemical may also be released during optogenetic manipulations because OT neurons may also release other neurochemicals. In one of our previous studies, activation of the OT neuron projections from the PVN to the VTA as well as to the Nac brain also altered pup-directed behaviors, which may also be accompanied by dopamine release (He et al., 2021). In addition, backpropagation of action potentials during optogenetic manipulations may also causes the same behavioral effect as direct stimulation of PVN OT cells. These effects on pup-directed behaviors should also be investigated further in the future study. For the optogenetics experiments, we have referred to some of the previous research (Mei et al., 2023; Murugan et al., 2017), and in our study we have also carried out the verification of the reliability of the methods. To exclude effects of locomotor activity on pup directed behaviors, we also investigated effect of optogenetic manipulations on the locomotor activity of experimental animals and found that optogenetic manipulation did not change levels of locomotor activity (Supplementary Data Fig. 6).

The authors do not specify the percentage of PVN->mPFC neurons labeled that were OT-positive, nor do they directly compare the sexes in their behavioral analysis (or if they did, it is not clear statistically). While the authors propose that the sex difference in pup-directed behaviors is due to females having greater OT expression, they do not provide evidence to support this claim from their labeling data. It is also uncertain whether more OT neurons were manipulated in females compared to males. The study could benefit from a more comprehensive discussion of other factors that could influence the neural circuit under investigation, especially in females.

AAV11-Ef1a-EGFP virus can infect fibers and retrogradely reach to cell body, thus this virus can be used to retrogradely trace neurons. We injected this virus (green, AAV11-Ef1a-EGFP) in the mPFC and observed virus infected and OT (red) positive neuron in the PVN (Yellow), and we also counted the OT neurons that project from PVN to mPFC and found that approximately 45.16% and 40.79% of cells projecting from PVN to the mPFC were OT-positive, and approximately 18.48% and 18.89% of OT cells in the PVN projected to the mPFC in females and males, respectively (Supplementary Data Fig. 4). In addition, as reviewers suggested, we compared the numbers of OT neurons, activated OT neurons (OT and Fos double-labeled neurons) and level of OT release between males and females. We found that females have more activated OT neurons (Figure1, d, g) and released higher levels of OT into the mPFC (Figure 4 d, e) than males. This part has been added in the result and discussion. We did not analyze whether more OT neurons were manipulated in females compared to males, which is indeed a limitation of this study that requires our attention. 

As the reviewers suggested, we also discussed other factors that could influence the neural circuit under investigation. In addition to OT neurons, OTR neurons may also regulate behavioral responses to pups. In a study of virgin female mice, pup exposure was found to activate oxytocin and oxytocin receptor expressing neurons (Okabe et al., 2017). Other brain regions such as preoptic area (POA) may also be involved in parental behaviors. For example, virgin female mice repeatedly exposed to pups showed shorter retrieval latencies and greater c-Fos expression in the preoptic area (POA), concentrations of OT in the POA were also significantly increased, and the facilitation of alloparental behavior by repeated exposure to pups occurred through the organization of the OT system (Okabe et al., 2017). A recent study suggests that OT of the PVN is involved in the care of pups by male voles (He et al., 2021). This study suggests that PVN to ventral tegumental area (VTA) OT projections as well as VTA to nucleus accumbens (NAc) DA projections are involved in the care of pups by male voles. Inhibition of OT projections from the PVN to the VTA reduces DA release in the NAc during licking and grooming of pups (He et al., 2021). The effects of these factors on pup-directed responses should also be considered in the future study. 

Reviewer #2 (Public Review):

Summary:

This series of experiments studied the involvement of PVN OT neurons and their projection to the mPFC in pup-care and attack behavior in virgin male and female Mandarin voles. Using Fos visualization, optogenetics, fiber photometry, and IP injection of OT the results converge on OT regulating caregiving and attacks on pups. Some sex differences were found in the effects of the manipulations.

Strengths:

Major strengths are the modern multi-method approaches and involving both sexes of Mandarin vole in every experiment.

Weaknesses:

Weaknesses include the lack of some specific details in the methods that would help readers interpret the results. These include:

(1) No description of diffusion of centrally injected agents.

Thanks for your professional consideration. Individuals with appropriate viral expression and optical fiber implant location were included in the statistical analysis, otherwise excluded. For optogenetic experiments, the virus (AAV2/9-mOXT-hCHR2(H134R)–mCherry-ER2-WPRE-pA or rAAV-mOXT-eNpHR3.0-mCherry-WPRE-hGH-pA) was designed and constructed to only infect OT neurons, which limited the diffusion of the virus. For fiber photometric experiments, the OT1.0 sensor was largely able to restrict expression within the mPFC brain region, and additionally individuals with incorrect optical fiber embedding position were not included in the statistical analysis. The diffusion of central optogenetic viruses and OT1.0 sensors are shown in the supplemental figure (Supplementary Data Fig. 7).

(2) Whether all central targets were consistent across animals included in the data analyses. This includes that is not stated if the medial prelimbic mPFC target was in all optogenetic study animals as shown in Figure 4 and if that is the case, there is no discussion of that subregion's function compared to other mPFC subregions.

As shown in Figure 4 and in the schematic diagram of the optogenetic experiment, the central targets of virus infection and fiber location remain consistent in the data analysis, otherwise the data would be excluded. In the present study, viruses were injected into the prelimbic (PrL). The PrL and infralimbic (IL) regions of the mPFC play different roles in different social interaction contexts (Bravo-Rivera, Roman-Ortiz, Brignoni-Perez, Sotres-Bayon, & Quirk, 2014; Moscarello & LeDoux, 2013). A study has shown that the PrL region of the mPFC contributes to active avoidance in situations where conflict needs to be mitigated, but also contributes to the retention of conflict responses for reward (Capuzzo & Floresco, 2020). This may reveal that the suppression of infanticide by PVN to mPFC OT projections is a behavioral consequence of active conflict avoidance. In a study on pain in rats, OT neurons projections from the PVN to the PrL were found to increase the responsiveness of cell populations in the PrL, suggesting that OT may act by altering the local excitation-inhibition (E/I) balance in the PrL (Liu et al., 2023). A study on anxiety-related behaviors in male rats suggests that the anxiolytic effects of OT in the mPFC are PrL-specific but not infralimbic or anterior cingulate and that this is achieved primarily through the engagement of GABAergic neurons, which ultimately modulate downstream anxiety-related brain regions, including the amygdala (Sabihi, Dong, Maurer, Post, & Leuner, 2017). This finding may provide possible downstream pathways for further research. 

(3) How groups of pup-care and infanticidal animals were created since there was no obvious pretest mentioned so perhaps there was the testing of a large number of animals until getting enough subjects in each group.  

Before the experiments, we exposed the animals to pups, and subjects may exhibit pup care, infanticide, or neglect; we grouped subjects according to their behavioral responses to pups, and individuals who neglected pups were excluded.

(4) The apparent use of a 20-minute baseline data collection period for photometry that started right after the animals were stressed from handling and placement in the novel testing chamber.

In fiber photometric experiments, all experimental animals were required to acclimatize to the environment for at least 20 minutes prior to the experiment as described in the Methods section. The time 0 in Fig. 4 represents the point in time when a behavior or a segment of behavior started and is not the actual time 0 at which the test was started.

(5) A weakness in the results reporting is that it's unclear what statistics are reported (2 x 2 ANOVA main effect of interaction results, t-test results) and that the degrees of freedom expected for the 2 X 2 ANOVAs in some cases don't appear to match the numbers of subjects shown in the graphs; including sample sizes in each group would be helpful because the graph panels are very small and data points overlap.

Thanks for your suggestion. We displayed analysis methods for the data statistics and the sample sizes for each group of experiments in the figure legends.

The additional context that could help readers of this study is that the authors overlook some important mPFC and pup caregiving and infanticide studies in the introduction which would help put this work in better context in terms of what is known about the mPFC and these behaviors. These previous studies include Febo et al., 2010; Febo 2012; Peirera and Morrell, 2011 and 2020; and a very relevant study by Alsina-Llanes and Olazábal, 2021 on mPFC lesions and infanticide in virgin male and female mice. The introduction states that nothing is known about the mPFC and infanticide. In the introduction and discussion, stating the species and sex of the animals tested in all the previous studies mentioned would be useful. The authors also discuss PVN OT cell stimulation findings seen in other rodents, so the work seems less conceptually novel. Overall, the findings add to the knowledge about OT regulation of pup-directed behavior in male and female rodents, especially the PVN-mPFC OT projection.

We appreciate you very much to provide so many valuable references. We have cited them in the introduction and discussion. We agree with the reviewer’s opinion that nothing is known about the mPFC and infanticide is incorrect. It should be whether mPFC OT projections are involved in paternal cares and infanticide remains unclear. A study in mother rats indicated that inactivation or inhibition of neuronal activity in the mPFC largely reduced pup retrieval and grouping (Febo, Felix-Ortiz, & Johnson, 2010). In a subsequent study on firing patterns in the mPFC of mother rats suggested that sensory-motor processing occurs in the mPFC that may affect decision making of maternal care to their pups (Febo, 2012). In a study on new mother rats examining different regions of the mPFC (anterior cingulate (Cg1), PrL, IL), they identified a involvement of the IL cortex in biased preference decision-making in favour of the offspring (Pereira & Morrell, 2020). A study on maternal motivation in rats suggests that in the early postpartum period, the IL and Cg1 subregion in mPFC, are the motivating circuits for pup-specific biases (Pereira & Morrell, 2011), while the PrL subregion, are recruited and contribute to the expression of maternal behaviors in the late postpartum period (Pereira & Morrell, 2011).

Reviewer #3 (Public Review):

Summary:

Here Li et al. examine pup-directed behavior in virgin Mandarin voles. Some males and females tend towards infanticide, others tend towards pup care. c-Fos staining showed more oxytocin cells activated in the paraventricular nucleus (PVN) of the hypothalamus in animals expressing pup care behaviors than in infanticidal animals. Optogenetic stimulation of PVN oxytocin neurons (with an oxytocin-specific virus to express the opsin transgene) increased pup-care, or in infanticidal voles increased latency towards approach and attack.

Suppressing the activity of PVN oxytocin neurons promoted infanticide. The use of a recent oxytocin GRAB sensor (OT1.0) showed changes in medial prefrontal cortex (mPFC) signals as measured with photometry in both sexes. Activating mPFC oxytocin projections increased latency to approach and attack in infanticidal females and males (similar to the effects of peripheral oxytocin injections), whereas in pup-caring animals only males showed a decrease in approach. Inhibiting these projections increased infanticidal behaviors in both females and males and had no effect on pup caretaking.

Strengths:

Adopting these methods for Mandarin voles is an impressive accomplishment, especially the valuable data provided by the oxytocin GRAB sensor. This is a major achievement and helps promote systems neuroscience in voles.

Weaknesses:

The study would be strengthened by an initial figure summarizing the behavioral phenotypes of voles expressing pup care vs infanticide: the percentages and behavioral scores of individual male and female nulliparous animals for the behaviors examined here. Do the authors have data about the housing or life history/experiences of these animals? How bimodal and robust are these behavioral tendencies in the population?

As our response to reviewer 2, animals generally exhibit three types of behavioral responses toward pups, and data on the percentage of these different behavioral types occurring in the group will be included in another study in our lab. The reviewer's suggestion of scoring the behaviors is an inspiring idea that will help us to more fully parse these behaviors. Mandarin voles were captured from the wild in Henan, China. The experimental subjects were F2 generation voles reared in the Experimental Animal Centre of Shaanxi Normal University. In our observations, pup care and infanticide behaviors were conserved across several pup exposures, especially pup care behaviors, whereas for infanticide behaviors we did not conduct more pup exposures in order to protect the pups. 

Optogenetics with the oxytocin promoter virus is a nice advance here. More details about their preparation and methods should be in the main text, and not simply relegated to the methods section. For optogenetic stimulation in Figure 2, how were the stimulation parameters chosen? There is a worry that oxytocin neurons can co-release other factors- are the authors sure that oxytocin is being released by optogenetic stimulation as opposed to other transmitters or peptides, and acting through the oxytocin receptor (as opposed to a vasopressin receptor)?

As reviewer suggested, more detailed information about virus construction and choice of optogenetic stimulation parameter have been added in the revised manuscript. The details about the construction of CHR2 and mCherry viruses used in optogenetic manipulation can refer to a previous study in which they constructed an rAAV-expressing Venus from a 2.6 kb region upstream of OT exon 1, which is conserved in mammalian species (Knobloch et al., 2012). For details about construction of the eNpHR 3.0 virus, expression of the vector is driven by the mouse OXT promoter, a 1kb promoter upstream of exon 1 of the OXT gene, which has been shown to induce cell type-specific expression in OXT cells (Peñagarikano et al., 2015). Details about the construction of OT1.0 sensor can be referred to the research of Professor Li's group (Qian et al., 2023). The mapping of the viral vectors and OT1.0 sensor is shown below. 

The optogenetic stimulation parameters were used based on a previous study (He et al., 2021). However, our description of the parameters in the experiment is still not in detail, so some information about optogenetic stimulation parameters has been added in the method. In pupdirected pup care behavioral test, light stimulation lasted for 11 min. Parameters used in optogenetic manipulation of PVN OT neurons were ~ 3 mW, 20 Hz, 20 ms, 8 s ON and 2 s OFF and parameters used in optogenetic manipulation of PVN OT neurons projecting to mPFC were ~ 10 mW, 20 Hz, 20 ms, 8 s ON and 2 s OFF to cover the entire interaction. We performed fiber photometric experiments to determine the role that OT plays in behavior, and these results were able to support each other with optogenetic experiments. In addition, we further confirmed the role of optogenetic manipulation on OT release in combination with optogenetic inhibition and OT1.0 sensors (Supplementary Data Fig. 2). It has been previously shown that OT is able to act specifically on OTR in mPFC-PL (Sabihi et al., 2017). Our study focuses on oxytocin neurons as well as oxytocin release, and more research is needed to construct a more complex and complete network regarding the involvement of the OTR and other factors in the mPFC in these behaviors.

Author response image 1.

Author response image 2.

 

Given that they are studying changes in latency to approach/attack, having some controls for motion when oxytocin neurons are activated or suppressed might be nice. Oxytocin is reported to be an anxiolytic and a sedative at high levels.

As our response to reviewer 1, to exclude effects of locomotor activity on pup directed behaviors, we also investigated effect of optogenetic manipulations on the locomotor activity of experimental animals and found that optogenetic manipulation did not change levels of locomotor activity (Supplementary Data Fig. 6).

The OT1.0 sensor is also amazing, these data are quite remarkable. However, photometry is known to be susceptive to motion artifacts and I didn't see much in the methods about controls or correction for this. It's also surprising to see such dramatic, sudden, and large-scale suppression of oxytocin signaling in the mPFC in the infanticidal animals - does this mean there is a substantial tonic level of oxytocin release in the cortex under baseline conditions?

The optical fiber recording system used in the present study can automatically exclude effects of motion artifacts by simultaneously recording signals stimulated by a 405nm light source. As shown in the formula below, the z-score data were calculated and presented, and the increase and decline of the OT signal is a trend relative to the baseline. For a smooth baseline, the decreasing signal is generally amplified after calculation. In our experiments combining optogenetic inhibition and OT1.0 sensors, we were able to find that there was a certain level of OT release at baseline, on which there was room for a decrease in the signal recorded by the OT1.0 sensor.

Figure 5 is difficult to parse as-is, and relates to an important consideration for this study: how extensive is the oxytocin neuron projection from PVN to mPFC?

AAV11-Ef1a-EGFP virus can infect fiber and retrogradely reach to cell body, thus this virus can be used to retrogradely trace neurons. We injected the this virus (green, AAV11-Ef1aEGFP) in the mPFC and observed virus infected and OT (red) positive neuron in the PVN (Yellow), and we also counted the OT neurons that project from PVN to mPFC and found that approximately 45.16% and 40.79% of cells projecting from PVN to the mPFC were OT-positive, and approximately 18.48% and 18.89% of OT cells in the PVN projected to the mPFC in females and males, respectively (Supplementary Data Fig. 4).  

In Figures 6 and 7, the authors use the phrase 'projection terminals'; however, to my knowledge, there have not been terminals (i.e., presynaptic formations opposed to a target postsynaptic site) observed in oxytocin neuron projections into target central regions.

According your suggestion, we replaced the ‘terminals’ with ‘fibers’ to describe it more accurately..

Projection-based inhibition as in Figure 7 remains a controversial issue, as it is unclear if the opsin activation can be fast enough to reduce the fast axonal/terminal action potential. Do the authors have confirmation that this works, perhaps with the oxytocin GRAB OT sensor?

Thanks for your suggestion. We measured the OT release using OT1.0 sensors when the OT neuron projections in the mPFC were optogenetically inhibited. The result showed that optogenetic inhibition of OT neuron fibers in the mPFC significantly reduced OT release that validate the method of projection-based inhibition (Supplementary Data Fig. 5).

As females and males had similar GRAB OT1.0 responses in mPFC, why would the behavioral effects of increasing activity be different between the sexes?

In the present study, females released higher levels of OT into the mPFC (Figure 4 d, e) than males upon occurrence of different behaviors. In addition, females already exhibited more rapid approach and retrieval of pups than male before the optogenetic activation this may be the reason no effects of this manipulation were found in female.

Recommendations for the authors:

Reviewer #1 (Recommendations For The Authors):

(1) Check for spelling and grammar errors throughout.

Thanks to the reviewer's suggestion, we have checked and revised the article.

(2) Report effect sizes for all significant findings to allow evaluation of practical significance.

As reviewer suggested, we have added effect size both in the source data and in the main text, including d, η2  and odds ratio.

(3) Provide detailed information on the imaging parameters and analysis methods used in the Fos study.

The imaging parameters and analysis details for the Fos study have also been added in the revised manuscript. Brain slices of 40 µm thick were collected consecutively on 4 slides, each slide had 6 brain slices spaced 160 µm apart from each other. PVN area were determined based on the Allen Mouse Brain Atlas and our previous study, andFos, OT and merged positive neurons were counted.

(4) Compare the Fos colocalization results with previous studies examining PVN Fos and maternal/paternal care to contextualize the findings.

Our result about Fos and OT colocalization is consistent with previous study. In a previous study on virgin male prairie voles, OT and Fos colabeled neurons in the PVN increased after exposure to conspecific pups and experiencing paternal care (Kenkel et al., 2012). In another study of prairie voles, OT and c-fos colabeled neurons in PVN significantly increased after becoming parents which may be due to a shift from virgin to parents (Kelly et al., 2017).

(5) Discuss the limitations of the study, such as the potential effects of manipulating OT neurons on the release of other transmitters or the influence of other neurochemicals or brain regions on pupdirected behaviors, especially in females.

We agree with reviewer’s suggestion that several limitations should be discussed. Although we used a virus strategy to specifically activate or inhibit PVN OT neurons, other neurochemical may also be released during optogenetic manipulations because OT neurons may also release other neurochemicals. In one of our previous studies, activation of the OT neuron projections from the PVN to the VTA as well as to the Nac brain also altered pup-directed behaviors, which may also be accompanied by dopamine release (He et al., 2021). In addition, backpropagation of action potentials during optogenetic manipulations may also causes the same behavioral effect as direct stimulation of PVN OT cells. These effects on pup-directed behaviors should also be investigated further in the future study.

(6) Address the possibility of back-propagation of action potentials in the optogenetic manipulations causing the same behavioral effects as PVN OT cell stimulation.

We agree with the reviewer’s opinion hat optogenetic manipulation may possibly induce back-propagation of action potentials that may result in same behavioral effects as OT cell stimulation. We will pay attention to this issue in the future study.  

(7) Investigate whether changes in locomotor behavior could explain the observed effects on pupdirected behaviors.

To exclude effects of locomotor activity on pup directed behaviors, we also investigated effect of optogenetic manipulations on the locomotor activity of experimental animals and found that optogenetic manipulation did not change levels of locomotor activity (Supplementary Data Fig. 6).

(8) Report the percentage of PVN->mPFC neurons labeled that were OT-positive.

AAV11-Ef1a-EGFP virus can infect fiber and retrogradely reach to cell body, thus this virus can be used to retrogradely trace neurons. We injected this virus (green, AAV11-Ef1a-EGFP) in the mPFC and observed virus infected and OT (red) positive neuron in the PVN (Yellow), and we also counted the OT neurons that project from PVN to mPFC and found that approximately 45.16% and 40.79% of cells projecting from PVN to the mPFC were OT-positive, and approximately 18.48% and 18.89% of OT cells in the PVN projected to the mPFC in females and males, respectively (Supplementary Data Fig. 4).

(9)  Directly compare the sexes in the behavioral analysis and discuss any potential sex differences.

We agree with the reviewer's suggestion and have added comparisons between two sexes and discussion about relevant results. 

(10) If available, report and discuss the OT expression levels and the number of OT neurons manipulated in each sex.

In the present study, we have counted the number of OT cells, but did not measure the level of OT expression using WB or qPCR. In addition, the percentages of CHR2(H134R) and eNpHR3.0 virus infected neurons in total OT positive neurons were presented (Supplementary Data Fig. 7), but we did not know how many cells were actually manipulated during the optogenetic experiment.

(11) Expand the discussion to include what could be regulating or interacting with the OT circuit under investigation, particularly in females where the effects were less pronounced.

As the reviewers suggested, we have also added relevant discussion. In addition to OT neurons, OTR neurons may also regulate behavioral responses to pups. In a study of virgin female mice pup exposure was found to activate oxytocin and oxytocin receptor expressing neurons (Okabe et al., 2017). Other brain regions such as preoptic area (POA) may also be involved in parental behaviors. For example, virgin female mice repeatedly exposed to pups showed shorter retrieval latencies and greater c-Fos expression in the preoptic area (POA), concentrations of OT in the POA were also significantly increased, and the facilitation of alloparental behavior by repeated exposure to pups occurred through the organization of the OT system (Okabe et al., 2017). A recent study suggests that OT of the PVN is involved in the care of pups by male voles (He et al., 2021). This study suggests that PVN to ventral tegumental area (VTA) OT projections as well as VTA to nucleus accumbens (NAc) DA projections are involved in the care of pups by male voles. Inhibition of OT projections from the PVN to the VTA reduces DA release in the NAc during licking and grooming of pups (He et al., 2021).

Reviewer #2 (Recommendations For The Authors):

A few additional things the authors may want to consider:

(1) I don't understand the subject numbers in the peripheral OT study data shown in Figure 8. Panels p and q have 69 females shown and 50 males. Was there a second, much larger, IP injection study conducted that was different than the subjects shown in panels a-o that had ~5 subjects per treatment group per sex?

Sorry for the confusing. More animals were used to test effects of OT on infanticide behaviors in our pre-test. These data combined with data from formal pharmacological experiment were presented in Fig. 8p, q. After OT treatment, the changes in detailed and specific behaviors were only collected in several animals. We have clarified that in the revised manuscript. 

(2) The authors suggest higher baseline OT release in the female mPFC, which makes sense and helps explain some of their results. It seems that the data in Figure 1 show what is probably no sex difference in OT cell numbers in the PVN of Mandarin voles, which is unlike the old studies in mice or rats. If readers look at the data in Figure 1 showing what seems to be no sex difference in OT cell number, the authors' argument in the discussion about mPFC OT release levels higher in females would be inconsistent with their own data shown. The authors have the brain sections they need to help support or undermine this argument in the discussion, so maybe it would be useful to analyze the OT cell numbers across the PVN and report it in this paper or briefly mention it in the discussion.

We compared the numbers of OT neurons, activated OT neurons (OT and Fos doublelabeled neurons) and level of OT release between males and females. We found that females have more activated OT neurons (Figure1, d, g) and released higher levels of OT into the mPFC (Figure 4 d, e) than males. This part has been added in the result and discussion. The inconsistency of the OT cell numbers with previous studies may be due to the method of cell counting, as we did not count all slides consecutively.  

(3) The discussion suggests visual cues are involved in mPFC OT release relevant for pup care or infanticide, but this is a very odd claim for nocturnal animals that live and nest with their pups in underground burrows.

Sorry for the confusing. Here, we cited the finding in mice that activation of PVN OT neurons induced by visual stimulation promoted pup care to support our finding that the activity of OT cells of the PVN is involved in pup care, rather than to illustrate the role of visual stimulation in voles. We have clarified that in the revised manuscript.

(4) The lack of decrease in mPFC OT release in the 2nd and 3rd approaches to pups is probably because the release was so high after the 1st approach that it didn't have time to drop before the subsequent approaches. The authors don't state how long those between-approach intervals were on average to help readers interpret this result.

As described in our methods, we spaced about 60 s between each behavioral test to allow the signal return back to the baseline level.

(5) Do PVN-mPFC OT somata collateralize to other brain sites? Could mPFC terminal stimulation activate entire PVN cells and every site they project to? A caveat could be mentioned in the discussion if there's support for this from other optogenetic and PVN OT cell projection studies.

We verified the OT projections from PVN to mPFC, to validate the optogenetic manipulation of this pathway, but did not investigate whether the OT neurons projecting from PVN to mPFC also project collaterally to other brain regions. It is suggested that mPFC terminal stimulation only activate PVN OT cells projecting mPFC, whether other OT neurons were activated remains unclear. 

(6) I don't see an ethics statement related to the experiments obviously having to involve pup injury or death. Nothing is said in methods about what happened after adult subjects attacked pups. I assumed the tests were quickly terminated and pups euthanized.

In case the pups were attacked, we removed them immediately to avoid unnecessary injuries, and injured pups were euthanized.

(7) The authors could be more specific about what psychological diseases they refer to in the abstract and elsewhere that are relevant to this study. Depression? Rare cases of psychosis? Even within the already rare parental psychosis, infanticide is tragic but rare.

Infanticide is caused by a variety of factors, mental illness, especially depression and psychosis, is often a very high risk factor among them (Milia & Noonan, 2022; Naviaux, Janne, & Gourdin, 2020). In human, infanticide has been used to refer to the killing, neglect or abuse of newborn babies and older children (Jackson, 2006). Here, we believe that research on the neural mechanisms of infanticide can also contribute to the understanding and treatment of attacks on children, physical and verbal abuse, and direct killing of babies. 

(8) Figure 8 - in one case the "*" is a chi-square result , correct?

Thanks for your careful checking. In Figure 8p, q, we applied the chi-square test and  added it in the legend.

Reviewer #3 (Recommendations For The Authors):

The only other thing is a typo on line 135: the authors mean 'stimulation' instead of 'simulation'.

Corrected.

References

Bravo-Rivera, C., Roman-Ortiz, C., Brignoni-Perez, E., Sotres-Bayon, F., & Quirk, G. J. (2014). Neural structures mediating expression and extinction of platform-mediated avoidance. J Neurosci, 34(29), 9736-9742. doi:10.1523/jneurosci.0191-14.2014

Capuzzo, G., & Floresco, S. B. (2020). Prelimbic and Infralimbic Prefrontal Regulation of Active and Inhibitory Avoidance and Reward-Seeking. J Neurosci, 40(24), 4773-4787. doi:10.1523/jneurosci.0414-20.2020

Febo, M. (2012). Firing patterns of maternal rat prelimbic neurons during spontaneous contact with pups. Brain Res Bull, 88(5), 534-542. doi:10.1016/j.brainresbull.2012.05.012

Febo, M., Felix-Ortiz, A. C., & Johnson, T. R. (2010). Inactivation or inhibition of neuronal activity in the medial prefrontal cortex largely reduces pup retrieval and grouping in maternal rats. Brain Res, 1325, 77-88. doi:10.1016/j.brainres.2010.02.027

He, Z., Young, L., Ma, X. M., Guo, Q., Wang, L., Yang, Y., . . . Tai, F. (2019). Increased anxiety and decreased sociability induced by paternal deprivation involve the PVN-PrL OTergic pathway. Elife, 8. doi:10.7554/eLife.44026

He, Z., Zhang, L., Hou, W., Zhang, X., Young, L. J., Li, L., . . . Tai, F. (2021). Paraventricular Nucleus Oxytocin Subsystems Promote Active Paternal Behaviors in Mandarin Voles. J Neurosci, 41(31), 66996713. doi:10.1523/jneurosci.2864-20.2021

Jackson, M. (2006). Infanticide. The Lancet, 367(9513), 809. doi:https://doi.org/10.1016/S01406736(06)68323-2

Kelly, A. M., Hiura, L. C., Saunders, A. G., & Ophir, A. G. (2017). Oxytocin Neurons Exhibit Extensive Functional Plasticity Due To Offspring Age in Mothers and Fathers. Integr Comp Biol, 57(3), 603618. doi:10.1093/icb/icx036

Kenkel, W. M., Paredes, J., Yee, J. R., Pournajafi-Nazarloo, H., Bales, K. L., & Carter, C. S. (2012). Neuroendocrine and behavioural responses to exposure to an infant in male prairie voles. J Neuroendocrinol, 24(6), 874-886. doi:10.1111/j.1365-2826.2012.02301.x

Knobloch, H. S., Charlet, A., Hoffmann, L. C., Eliava, M., Khrulev, S., Cetin, A. H., . . . Grinevich, V. (2012). Evoked axonal oxytocin release in the central amygdala attenuates fear response. Neuron, 73(3), 553-566. doi:10.1016/j.neuron.2011.11.030

Liu, Y., Li, A., Bair-Marshall, C., Xu, H., Jee, H. J., Zhu, E., . . . Wang, J. (2023). Oxytocin promotes prefrontal population activity via the PVN-PFC pathway to regulate pain. Neuron, 111(11), 17951811.e1797. doi:10.1016/j.neuron.2023.03.014

Mei, L., Yan, R., Yin, L., Sullivan, R. M., & Lin, D. (2023). Antagonistic circuits mediating infanticide and maternal care in female mice. Nature, 618(7967), 1006-1016. doi:10.1038/s41586-023-061479

Milia, G., & Noonan, M. (2022). Experiences and perspectives of women who have committed neonaticide, infanticide and filicide: A systematic review and qualitative evidence synthesis. J Psychiatr Ment Health Nurs, 29(6), 813-828. doi:10.1111/jpm.12828

Moscarello, J. M., & LeDoux, J. E. (2013). Active avoidance learning requires prefrontal suppression of amygdala-mediated defensive reactions. J Neurosci, 33(9), 3815-3823. doi:10.1523/jneurosci.2596-12.2013

Murugan, M., Jang, H. J., Park, M., Miller, E. M., Cox, J., Taliaferro, J. P., . . . Witten, I. B. (2017). Combined Social and Spatial Coding in a Descending Projection from the Prefrontal Cortex. Cell, 171(7), 1663-1677.e1616. doi:10.1016/j.cell.2017.11.002

Naviaux, A. F., Janne, P., & Gourdin, M. (2020). Psychiatric Considerations on Infanticide: Throwing the Baby out with the Bathwater. Psychiatr Danub, 32(Suppl 1), 24-28. 

Okabe, S., Tsuneoka, Y., Takahashi, A., Ooyama, R., Watarai, A., Maeda, S., . . . Kikusui, T. (2017). Pup exposure facilitates retrieving behavior via the oxytocin neural system in female mice. Psychoneuroendocrinology, 79, 20-30. doi:10.1016/j.psyneuen.2017.01.036

Peñagarikano, O., Lázaro, M. T., Lu, X. H., Gordon, A., Dong, H., Lam, H. A., . . . Geschwind, D. H. (2015). Exogenous and evoked oxytocin restores social behavior in the Cntnap2 mouse model of autism. Sci Transl Med, 7(271), 271ra278. doi:10.1126/scitranslmed.3010257

Pereira, M., & Morrell, J. I. (2011). Functional mapping of the neural circuitry of rat maternal motivation: effects of site-specific transient neural inactivation. J Neuroendocrinol, 23(11), 1020-1035. doi:10.1111/j.1365-2826.2011.02200.x

Pereira, M., & Morrell, J. I. (2020). Infralimbic Cortex Biases Preference Decision Making for Offspring over Competing Cocaine-Associated Stimuli in New Mother Rats. eNeuro, 7(4). doi:10.1523/eneuro.0460-19.2020

Qian, T., Wang, H., Wang, P., Geng, L., Mei, L., Osakada, T., . . . Li, Y. (2023). A genetically encoded sensor measures temporal oxytocin release from different neuronal compartments. Nat Biotechnol, 41(7), 944-957. doi:10.1038/s41587-022-01561-2

Sabihi, S., Dong, S. M., Maurer, S. D., Post, C., & Leuner, B. (2017). Oxytocin in the medial prefrontal cortex attenuates anxiety: Anatomical and receptor specificity and mechanism of action. Neuropharmacology, 125, 1-12. doi:10.1016/j.neuropharm.2017.06.024

Author response:

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

Reviewer #1 Public:

- The authors should carefully address the potential confounding of not counterbalancing the conditions of the first trial in both interoceptive tasks for the 9-month and 18-month age groups. The results of these groups could indeed be driven by having seen the synchronous trial first. 

Upon addressing this comment, we noticed an error in our presentation scripts that resulted in a fixed-experimental design for most of the infants. Therefore, it is crucial to investigate the impact of the fixed-experimental design on our results. We have conducted extensive additional analyses comparing data from infants with the inadvertent fixed design to data from infants for whom the randomization was achieved as intended, which can be found in Supplementary Materials A. In summary, we do not find that the fixed order design had a strong impact on the findings, as we do not find that looking behavior differed systematically between different randomization orders, while also looking patterns across ages and tasks indicate that we were able to adequately capture variance associated with these features. Further, we have adapted the interpretation of the results across the manuscript to acknowledge the experimental error and its implications on the interpretation of the results.

For instance, on pages 30 and 31 we have added the following paragraphs:

“The data presented in this study holds several limitations. First, due to an error in our experimental scripts we unintentionally used a fixed-order design, in which almost all infants saw the same fixed order of condition (always starting with a synchronous trial), image assigned to condition, and location of the image (left/right) instead of a semi-randomized design. Such a fixed-order design holds several important limitations as visual preferences might be influenced by the experimental design, i.e., the first trial always being synchronous might have influenced a mean group preference. Further, we cannot rule out that mean group preferences were influenced by the stimuli used (as in most cases the same stimuli were used for synchronous/asynchronous trials) or by the location of the image in a given trial (left/right). Still, there is no strong theoretical argument as to why image used or location should have an impact on infants’ preferences. The stimuli were selected to be similar to each other, in order not to evoke a piori preferences. To further illustrate the impact of the fixed order design we have conducted several additional analyses, which can be found in Supplementary Materials A, which do not indicate that there was a strong impact of the fixed-order design. Specifically, we find no evidence for systematic differences between infants tested with the fixed design and infants tested with a randomized design.

Despite these limitations fixed-order designs also hold advantages, as they are more suitable to investigate individual differences (Dang et al., 2020; Hedge et al., 2018). When each participant is exposed to the same procedure, individual differences are less likely to be attributed to effects of randomization but are more likely to reflect real differences between participants. Also, when considering the impact of the randomization, one must consider our results in relation to earlier studies (Maister et al. 2017, Weijs et al. 2022, Imafuku et al. 2023), some of which used the exact same stimuli as we did (Maister et al., 2017), with fully randomized designs. Results of these studies indicate no looking times differences depending on the stimulus assigned to each condition or systematic preferences for one of the stimuli.”

- The conclusion that cardiac interoception remains stable across infancy is not fully warranted by the data. Given the small sample size of 18-month-old toddlers included in the final analyses, it might be misleading to state this without including the caveat that the study may be underpowered. In other words, the small sample size could explain the direction of the results for this age group. 

We agree with the reviewer and explicitly acknowledge this issue now in the discission, p.  23: 

“However, due to the small sample size at 18 months the results regarding changes and stability of interoceptive sensitivity in the second year of life must be considered speculative and need to be validated in further research.”

Reviewer #1 (Recommendations For The Authors): 

Below are some comments that the authors may wish to take into account: 

- Why did the authors choose to apply different statistical analyses across the dataset (i.e. Bayesian t-test is used with the 3-month-old sample, whereas a paired t-test is used for the 9 and 18-month-olds)? 

The use of different statistical analyses was driven by the timeline of the project, as we had to update our initial plans. Due to challenges related to the Covid-19 pandemic, it was not possible to recruit 3-month-old babies for out study at the time we started the data collection. Thus, we first collected the 9- and 18-month-olds, and the 3-month-olds later. For the 9- and 18-month-old samples we aimed at directly replicating the approach by Maister et al. (2017). However, for the 3-month-olds we wanted to focus more on classification of the strength of evidence in favor/against an effect, taking the results of the equivalence tests for the 9- and 18-month-olds into account.

The following parts have been added to the manuscript to clarify our approach:

Sample (p 33): “The 3-month-old sample was tested after completion of the 9- and 18-monthold samples. Initially, we had planned to start data collection with the 3-month-old sample.

However, due to the Covid-19 pandemic this was not possible.”

Statistical analysis (p. 41): “At 3 months we used a Bayesian paired t-test as the data collection was done after having collected the 9- and 18-month-old samples. Our intention in the analysis of the 3-month-old sample was to focus more strongly on strength of evidence in favor of/against an effect instead of a binary classification for/against an effect.”

- I found the way in which sample sizes are reported a little unclear. This may be due to having the Results section before the Methods section (in line with journal requirements), but it would be helpful if the authors could clarify their sample size from the outset. For example, sample size for the 3-month-olds first says N = 80 (page 9), but then it becomes apparent that N = 53 completed the iBEAT and N = 40 completed the iBREATH. I think for the purpose of explaining the results, it might be more helpful to the reader to only know the final sample size and then specify recruited participants and dropout in the Methods. 

We have adapted the description of sample sizes in the Results section. We now only refer to the number of infants included in a given analysis when reporting the results of the analysis. In addition, we have added the following clarification for the MEGA analysis (p. 11): “This approach allowed us to include 135 observations for the iBEATs from 125 infants, and 120 observations for the iBREATH from 107 infants. The sample size differs slightly from our preregistered approach given that we used the same preprocessing approach for the MEGAanalysis for all samples. “ 

In addition, we now refer to the sample of the MEGA-analysis in the abstract, to make the understanding of our approach more intuitive.

- I think the sentence "Interestingly, we find evidence for a positive relationship between cardiac and respiratory perception in our 18-month-old sample" at page 25 could be deleted given that the small sample size of 18-month-olds suggests this result should be interpreted with caution. The authors already explained this in the earlier paragraph (page 24) and simply re-stating this (weak) effect without further elaborating may not be necessary. 

We have removed the sentence.

- In multiple places in the manuscript, the authors hint at the association between interoception and certain social and self-related abilities (e.g. joint attention, mirror self-recognition), however, these are not fully elaborated on. Could the authors elaborate on the relation between mirror self-recognition and respiratory interoception (page 30)? Why would the ability to recognise the self-face be associated with the individual's ability to perceive their breathing pattern? How these two processes may be linked is not immediately obvious. 

We have rephrased the sentence on page 30 to highlight that the increase in respiratory perception found in our results happens at a similar age as increases in other domains that might be related to interoception. “A hypothesis to be tested in future research is that developmental improvement in respiratory perception might be related to increases in other domains that show links to interoception. For instance, self-perception matures towards the end of the second year of life and has been conceptually related to interoception (Fotopoulou & Tsakiris, 2017; Musculus et al., 2021). Further, gross motor development may be considered in future research, which drastically matures in the first two years of life (WHO Multicentre Growth Reference Study Group, 2006) and has been shown to be related to respiratory function in children with cerebral palsy (Kwon & Lee, 2014).”

- Aren't the 18-month-old infants effectively 19-month-olds? The mean age is 576.65 days, and the age window of recruitment was between 18 and 20 months. 

We have added a sentence clarifying how we refer to the infants age ranges. “To stay coherent, we refer to each age group throughout the manuscript with regard to the lower end of the age range in which we included infants (e.g., we tested infants between 9 and 10 months, but refer to them as the 9-month-old group).”

Reviewer #2 Public:

Weaknesses: 

(1) My primary concern is that this study did not counterbalance the conditions of the first trial in both iBEAT and iBREATH tests for the 9-month and 18-month age groups. In these tests, the first trial invariably involved a synchronous stimulus. I believe that the order of trials can significantly influence an infant's looking duration, and this oversight could potentially impact the results, especially where a marked preference for synchronous stimuli was observed among infants. 

Upon conducting further analyses to address this comment, we noticed an error in our presentation scripts that resulted in the inadvertent use of a fixed-experimental design for most infants. Therefore, we have conducted extensive additional analysis which can be found in Supplementary Materials A. Specifically, we compared data from infants who were tested with the inadvertent fixed design to data from infants for whom the randomization was achieved as intended. Further, we have adapted the interpretation of the results across the manuscript to acknowledge the experimental error and its potential implications for the interpretation of the results.

(2) The analysis indicated that the study's sample size was too small to effectively assess the effects within each age group. This limitation fundamentally undermines the reliability of the findings. 

We have added a statement addressing this issue to the limitation section: “The reduced sample size might have impacted the statistical power to detect mean preferences for some age groups. Still, it must be noted that even the smaller sample sizes included were of similar size as used in previous studies on infant interoceptive sensitivity (Imafuku et al., 2023; Maister et al., 2017; Weijs et al., 2023).”

(3) The authors attribute the infants' preferential-looking behavior solely to the effects of familiarity and novelty. However, the meaning of "familiarity" in relation to external stimuli moving in sync with an infant's heartbeat or breathing is not clearly defined. A deeper exploration of the underlying mechanisms driving this behavior, such as from the perspectives of attention and perception, is necessary. 

We have adapted the respective paragraph in the discussion to clarify the term familiarity, and to also address that other aspects of attention and perception, might be relevant (p. 25): 

“In this context familiarity might refer to the infant’s perception of congruence between internal signal and external stimuli which might drive the infant’s attention. Specifically, the synchronous condition should be easier to process due to the intersensory redundancy and predictability between interoceptive and external signals. “

“However, it is important to consider that other cognitive and attentional mechanisms could also influence these responses.”

Reviewer #2 (Recommendations For The Authors):  

Introduction: 

(1) The relevance of respiration to self-regulation and social interaction was not clearly described. 

We have rephrased the relevant section to highlight that the increase in respiratory perception found in our results happens at a similar age as increases in other domains that might be related to interoception. “A hypothesis to be tested in future research is that developmental improvement in respiratory perception might be related to increases in other domains that show links to interoception. For instance, self-perception matures towards the end of the second year of life and has been conceptually related to interoception (Fotopoulou & Tsakiris, 2017; Musculus et al., 2021). Further, gross motor development may be considered in future research, which drastically matures in the first two years of life (WHO Multicentre Growth Reference Study Group, 2006) and has been shown to be related to respiratory function in children with cerebral palsy (Kwon & Lee, 2014).”

(2) In the last line of page 5, it might be more appropriate to use the term "meta-cognitive awareness" instead of "meta-perception," as the latter can refer to a different concept. 

We have changed the word as recommended. 

(3) The authors predicted a positive correlation in sensitivity between the cardiac and respiratory domains, despite studies in adults suggesting these are not related. How did the authors arrive at this prediction, and how do they interpret the results showing a correlation only in 18-montholds, the age group closest to adults in this study? 

We have elaborated on our reasoning for our prediction (p. 7): “Adult cardiac and respiratory interoception paradigms typically use two conceptually different paradigms. Thus, null results in the adult literature might be due to the unique characteristics of those paradigms.”

Further, we have expanded on this result in the discussion (p. 24): “Still, we find a relationship between cardiac and respiratory signals in the oldest sample tested here, the 18-month-olds, which is closest to adults. Although this effect needs to be interpreted with caution due to the small sample size, this might indicate that using conceptually similar experimental paradigms might be a promising avenue to investigate relationships between different interoceptive modalities in adults.”

Results: 

(4) Please provide the descriptive statistics (means and standard deviations of looking time) for each independent condition, especially for the 18-month and 3-month age groups where this information is missing and only differences in looking times between conditions were mentioned. Furthermore, since the asynchronous condition includes both fast and slow stimuli, descriptive statistics for each should be included to help readers determine whether effects are due to synchronicity or stimulus speed. 

We have added the information on mean and sd of looking times to synch and asynch trials to the results section. Mean looking times to both types of asynchronous trials can be found in supplementary materials C. We have added the information about standard deviations to this part. 

(5) Regarding the MEGA analysis for iBEATs, where a main effect of condition was found (OR = 1.13, t(1769) = 2.541, p = .011), are these t-value and p-value based on the GLMM analysis, or did the authors conduct a separate t-test? This query arises because the p-value of the main effect differs from that in Table 2. Also, is it conventional to present GLMM results in the manner of Table 2, comparing specific level combinations (i.e., synchronous condition and 3month age group), instead of listing main effects and interactions? 

Thank you very much for pointing out that the results of the GLMM were not reported as precise as possible, which might lead to confusion over the presented p-values. The main effect of condition refers to a post-hoc comparison using estimated marginal means from the GLMM across all age groups, while Table 2 refers to the main effect of condition for age group 3 months. 

To make the results more accessible we have restructured parts of the manuscript following your suggestions: In the main manuscript we now focus on the interaction effects for condition and age, as well as the post hoc comparison, while we now report null-full model comparison, and tables for all age groups in the supplements. 

We have added the following clarifying sentences to the manuscript, p. 12:

“In reporting these results we focus on whether we found evidence for interactions between age groups, and whether we found evidence for a general effect across age groups. In-depth results and tables can be found in Supplementary Materials C. 

[…]

Next, we computed post hoc comparisons using estimated marginal means from the MEGAanalysis across all age groups to investigate whether we find indications for a similar effect across ages.”

(6) I am confused about the results indicating a significant effect of condition for the iBREATH dataset excluding 18-month-olds (Table 5, OR = 1.15, t(1050) = 2.397, p = .017), as the description in Table 5 suggests no statistical significance (p = .070). The decision to exclude the 18-month group seems arbitrary, particularly since the age-by-condition interaction was not significant in the GLMM across all three age groups. 

Thank you very much for the comment, we have removed the analysis excluding the 18-month-old group

(7) Regarding the relationship between cardiac and respiratory interoceptive sensitivity, the statement "However, we found a significant interaction between iBEATs scores and age at the 18-month level" (p16) seems unclear. Clarification is needed, as mentioning age interaction at a specific age stage is unusual. A pairwise comparison between 3 and 9 months should also be included. 

Thank you for pointing out that the results could be presented more clearly! Similar to the other MEGA analyses we have put detailed tables of the results of the beta regression in the supplements and have kept a single table with the most important results in the main manuscript. Further, we have clarified the text passage as follows: “However, we found a significant interaction between the iBEATs scores and age, specifically comparing the 3- and 18-month-old groups (β = 3.13, SE = 1.41, p = .027). This interaction indicates that the relationship between iBEATs and iBREATH scores changes between 3 and 18 months of age.”  Also, we have now included a pairwise comparison between 3- and 9-month-olds. 

Discussion: 

(8) In pages 27-28, the authors discuss the results of the specification curve analysis, but there is no explanation for the 7th entry (statistical analysis) in Table 9. This entry seems particularly important. 

We did not include an explanation for the 7th entry, as the impact of the statistical test used was comparatively less pronounced. However, to acknowledge this result we have added the following sentence to the discussion: “Moreover, the statistical test used (paired t-test vs linear mixed model, Table 9, 7th entry) had a rather small impact on the results. However, given the large number of analyses conducted, this might be related to not being able to precisely formulate the model to fit the complexity of the data for each specification.”

Methods: 

(9) What were the colors of the stimuli? 

We have added the colors of the stimuli to the methods section. Further, the stimuli can be found in the osf project associated with the manuscript.

(10) The percentage of trials excluded during preprocessing should be stated. Additionally, the number of trials included in the statistical analyses for each condition (including synchronous, fast, and slow) should be detailed separately. 

We have added information on numbers of trials completed and included in Table 7.

Author response:

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

Reviewer #1 (Public Review):

Amason et al. investigated the formation of granulomas in response to Chromobacterium violaceum infection, aiming to uncover the cellular mechanisms governing the granuloma response. They identify spatiotemporal gene expression of chemokines and receptors associated with the formation and clearance of granulomas, with a specific focus on those involved in immune trafficking. By analyzing the presence or absence of chemokine/receptor RNA expression, they infer the importance of immune cells in resolving infection. Despite observing increased expression of neutrophil-recruiting chemokines, treatment with reparixin (an inhibitor of CXCR1 and CXCR2) did not inhibit neutrophil recruitment during infection. Focusing on monocyte trafficking, they found that CCR2 knockout mice infected with C. violaceum were unable to form granulomas, ultimately succumbing to infection.

The spatial transcriptomics data presented in the figures could be considered a valuable resource if shared, with the potential for improved and clarified analyses. The primary conclusion of the paper, that C. violaceum infection in the liver cannot be contained without macrophages, would benefit from clarification.

We thank the reviewer for their time and effort in evaluating our manuscript.

While the spatial transcriptomic data generated in the figures are interesting and valuable, they could benefit from additional information. The manual selection of regions of granulomas for analysis could use additional context - was the rest of the liver not sequenced, or excluded for other reasons? Including a healthy liver in the analysis could serve as a control for any lasting effects at the final time point of 21 days.

We revised the text in the methods section to include additional information about manual selection of regions. The entire tissue section was sequenced, but using H&E as a guide, we manually selected each representative lesion and a surrounding layer of healthy hepatocytes at each timepoint. We agree that an uninfected control could be useful, however we did not include an uninfected mouse in the experiment because we were most interested in the cells that make up the granuloma, not hepatocytes outside the lesion. Additionally, we find that in the 21 DPI timepoint the surrounding hepatocytes appear to have returned to a homeostatic transcriptional state; at 21 DPI the majority of mice have undetectable CFU burdens.

Providing more context for the scalebars throughout the spatial analyses, such as whether the data are raw counts or normalized based on the number of reads per spatial spot, would be helpful for interpretation, as changes in expression could signal changes in the numbers of cells or changes in the gene expression of cells.

The scalebars for the SpatialFeaturePlots display the normalized gene expression values. The data are normalized based on the number of reads per spatial spot, using the sctransform method published in (Hafemeister & Satija, 2019). We agree that the changes in expression could result from changes in cell numbers and/or changes in gene expression on a per cell basis. However, the sctransform method is designed to preserve biological variation while minimizing technical effects observed in transcriptomics platforms. Regardless of the heterogeneity of sequencing depth, it is clear from these plots that gene expression changes dynamically over time and space, which was the focus of our analysis. We have updated the figure legends to clarify scalebar units, and revised the methods section. 

In Figure 4, qualitative measurements are valuable, but having an idea of the raw data for a few of the pursued chemokines/receptors would aid interpretation

All of the SpatialFeaturePlots utilized to generate Figure 4 have been included in the manuscript, either in the main figures or in the supplemental figures. For example, the SpatialFeaturePlots of Cxcl4, Cxcl9, and Cxcl10 are all in Figure 4 – figure supplement 1.

In Figure 4 it would also be beneficial to clarify whether the reported values are across all clusters and consider focusing on clusters with the greatest change in expression.

Figure 4 summarizes the expression of each gene at each timepoint for the entire selected area, independently of cluster identity. Different clusters do show variability in the relative change in expression. To better show these data, we have included an additional graphic that summarizes the top twenty upregulated genes for each cluster, many of which include chemokines (new Table 4). The average log2FC values for each of these genes can be found in Table 4 – source data 1.   

Figures 5E and F would benefit from clarification regarding the x-axis units and whether the expression levels are summed across all clusters for each time point

Figures 5E and 5F display the normalized gene expression values for all spots (independent of cluster identity) at each timepoint. We have updated the figure legend to reflect this clarification.

Additionally, information on the sequencing depth of the samples would be helpful, particularly as shallow sequencing of RNA can result in poor capture of low-expression transcripts.

We agree with the reviewer that sequencing depth is an additional factor to take into consideration. We have included an additional supplemental figure (Figure 1 – figure supplement 1A-B) to display raw counts spatially at the various timepoints, and within each cluster.

Regarding the conclusion of the essentiality of macrophages in granuloma formation, it may be prudent to further investigate the role of macrophages versus CCR2. Consideration of experiments deleting macrophages directly, instead of CCR2, could provide more definitive evidence of the necessity of macrophage migration in containing infections.

While CCR2 is expressed on a number of other cells besides monocytes, it is well-documented that loss of CCR2 results in accumulation of monocytes in the bone marrow and a significant reduction in the blood-monocyte population. As a result, monocytes are not recruited to the site of infection in numerous prior publications in the field; we confirm this as shown by flow cytometry and IHC. Nonetheless, future studies will aim to rescue Ccr2–/– mice via adoptive transfer of monocytes to further show that monocyte-derived macrophages are essential for defense against infection. We also intend to perform clodronate depletion experiments at various timepoints, however, clodronate will also deplete Kupffer cells and has off-target effects on neutrophils. Overall, the established importance of CCR2 for monocyte egress from the bone marrow and our observation that the macrophage ring fails to form give us sufficient confidence to conclude that monocyte-derived macrophages are essential for this innate granuloma.

Analyzing total cell counts in the liver after infection could provide insight into whether the decrease in the fraction of macrophages is due to decreased numbers or infiltration of other cell types...

Our flow data suggest that the decrease in macrophages in Ccr2–/– mice is due to both a decrease in macrophage number and an increase in the infiltration of other cell types (namely neutrophils). To better illustrate this, we now include an additional quantification of the total cell counts in the liver and spleen (new Figure 6 – figure supplement 1), which supports our conclusion that Ccr2–/– mice have a defect in granuloma macrophage numbers. We have also repeated the experiment to reach sufficient numbers to perform statistical analysis (revised Figure 6F–K).

Reviewer #2 (Public Review):

Summary:

In this study, Amason et al employ spatial transcriptomics and intervention studies to probe the spatial and temporal dynamics of chemokines and their receptors and their influence on cellular dynamics in C. violaceum granulomas. As a result of their spatial transcriptomic analysis, the authors narrow in on the contribution of neutrophil- and monocyte-recruiting pathways to host response. This results in the observation that monocyte recruitment is critical for granuloma formation and infection control, while neutrophil recruitment via CXCR2 may be dispensable.

We thank the reviewer for their thoughtful comments and suggestions.

Strengths:

Since C. violaceum is a self-limiting granulomatous infection, it makes an excellent case study for 'successful' granulomatous inflammation. This stands in contrast to chronic, unproductive granulomas that can occur during M. tuberculosis infection, sarcoidosis, and other granulomatous conditions, infectious or otherwise. Given the short duration of C. violaceum infection, this study specifically highlights the importance of innate immune responses in granulomas.

Another strength of this study is the temporal analysis. This proves to be important when considering the spatial distribution and timing of cellular recruitment. For example, the authors observe that the intensity and distribution of neutrophil- and monocyte-recruiting chemokines vary substantially across infection time and correlate well with their previous study of cellular dynamics in C. violaceum granulomas.

The intervention studies done in the last part of the paper bolster the relevance of the authors' focus on chemokines. The authors provide important negative data demonstrating the null effect of CXCR1/2 inhibition on neutrophil recruitment during C. violaceum infection. That said, the authors' difficulty with solubilizing reparixin in PBS is an important technical consideration given the negative result...

We agree with the reviewer, and the limited solubility of reparixin and other chemokine-receptor inhibitors is a major caveat of this study and others in the field. In future studies, there are several other inhibitors that could be used to further assess the role of CXCR1/2.

On the other hand, monocyte recruitment via CCR2 proves to be indispensable for granuloma formation and infection control. I would hesitate to agree with the authors' interpretation that their data proves macrophages are serving as a physical barrier from the uninvolved liver. It is possible and likely that they are contributing to bacterial control through direct immunological activity and not simply as a structural barrier.

We agree that macrophages do not form a physical or structural barrier, a word that implies epithelial-like function. Instead, we agree that macrophages mostly act immunologically. We revised the text to remove the term barrier.

Weaknesses:

There are several shortcomings that limit the impact of this study. The first is that the cohort size is very limited. While the transcriptomic data is rich, the authors analyze just one tissue from one animal per time point. This assumes that the selected individual will have a representative lesion and prevents any analysis of inter-individual variability.

Granulomas in other infectious diseases, such as schistosomiasis and tuberculosis, are very heterogeneous, both between and within individuals. It will be difficult to assert how broadly generalizable the transcriptomic features are to other C. violaceum granulomas...

We thank the reviewers for highlighting this key difference between granulomas in other infectious diseases, and granulomas induced by C. violaceum. Based on many prior experiments, we observe that C. violaceum-induced granulomas are very reproducible between and within individuals (highlighted in our previous publication). As this is a major advantage of this model system, we chose specific timepoints based on key events that consistently occur in the majority of lesions assessed at each timepoint, allowing us to be confident in the selection of representative granulomas. However, it is worth noting that granulomas within an individual mouse are seeded and resolved somewhat asynchronously. This did indeed affect our spatial transcriptomic data, as the 7 DPI timepoint was not histologically representative of a typical 7 DPI granuloma. Therefore, we excluded the 7 DPI timepoint from our analyses.

Furthermore, this undermines any opportunity for statistical testing of features between time points, limiting the potential value of the temporal data.

We agree with the reviewer that there is much more characterization and quantification that can be done. As demonstrated by the abundance of spatial and temporal data for the chemokine family alone, the spatial transcriptomics dataset is rich and will likely supply us with many years of analyses and investigations. Our current approach is to use the spatial transcriptomics dataset as a hypothesis-generating tool, followed by in vivo studies that seek to uncover physiological relevance for our observations. In the current paper, the strength of the spatial transcriptomic data for CCL2, CCL7 and their receptor CCR2 prompted us to study Ccr2–/– mice. These mice then prove the relevance of the spatial transcriptomic data. In regard to conclusions about temporal changes in chemokine expression, in this manuscript we do not make conclusions that CCL2 is important at one timepoint but not another. We are characterizing the broad temporal trends of expression in order to cast a broad net to inform future in vivo studies. There is much work for us to do to explore all the induced chemokines and their receptors.

Another caveat to these data is the limited or incompletely informative data analysis. The authors use Visium in a more targeted manner to interrogate certain chemokines and cytokines. While this is a great biological avenue, it would be beneficial to see more general analyses considering Visum captures the entire transcriptome. Some important questions that are left unanswered from this study are:

What major genes defined each spatial cluster?...

The initial characterization of each spatial cluster was performed in Harvest et al., 2023. In brief, we used a mixture of published single-cell sequencing data, histological-based parameters, and ImmGen to define each cluster. We have not re-stated those methods in the current manuscript, but instead reference our prior paper.

What were the top differentially expressed genes across time points of infection?...

Though the top differentially expressed genes for each cluster can be informative in some situations, we chose a more targeted approach because of the obvious importance of chemokines. Nonetheless, we have included an additional graphic that summarizes the top twenty upregulated genes for each cluster (new Table 4). The average log2FC values for each of these genes can be found in Table 4 – source data 1.  

Did the authors choose to focus on chemokines/receptors purely from a hypothesis perspective or did chemokines represent a major signature in the transcriptomic differences across time points?

We chose to focus on chemokines because of their obvious importance for recruitment of immune cells. They were also among the highest induced genes in the spatial transcriptome (new Table 4).

In addition to the absence of deep characterization of the spatial transcriptomic data, the study lacks sufficient quantitative analysis to back up the authors' qualitative assessments...

See above comment regarding statistical comparisons.

Furthermore, the authors are underutilizing the spatial information provided by Visium with no spatial analysis conducted to quantify the patterning of expression patterns or spatial correlation between factors.

Several factors make quantification challenging. Lesions grow considerably in size in the first few days of infection, and then shrink in size in the latter days. This makes quantification challenging between timepoints. Radial quantification is also challenging due to the irregular shapes of each granuloma (see comment below for further discussion). Most importantly, the key next experiments are to validate the importance of each chemokine and receptor in vivo. Once we know which ones are the most important, this will justify putting more effort into spatial quantitative analysis and patterning of expression for those chemokines. 

Impact:

The author's analysis helps highlight the chemokine profiles of protective, yet host protective granulomas. As the authors comment on in their discussion, these findings have important similarities and differences with other notable granulomatous conditions, such as tuberculosis. Beyond the relevance to C. violaceum infection, these data can help inform studies of other types of granulomas and hone candidate strategies for host-directed therapy strategies.

Recommendations for the authors:

Reviewer #2 (Recommendations For The Authors):

The Visium analysis would be strengthened by

(1) Showing several histology examples of granulomas at each timepoint to help aid the reader in seeing how 'representative' each Visium sample is...

These histological analyses are performed in our previous manuscript, and indeed were a crucial aspect of the initial characterization of the spatial transcriptomics dataset, which was performed in Harvest et al., 2023. Full liver sections are shown in that paper at each timepoint, and readers can see that the architecture is highly reproducible.

(2) Validating their results in other tissues, either with Visium or with more targeted assays for their study's key molecules, such as immunohistochemistry or in situ hybridization

We agree on the importance of validation studies and have plans to perform single-cell RNA sequencing experiments to further enhance resolution. With key genes in mind, we then plan to perform more in vivo studies to assess physiological relevance of upregulated genes in specific cell types.

At the very least it would be important to validate the expression of CXCL1 and CXCL2 in other tissues and at the protein level, given the importance of those findings

We think that the reviewer is asking us to validate that CXCL1 and CXCL2 are actually expressed given the negative reparixin data. However, if we do prove that they are expressed, this will not resolve whether they have critical roles in neutrophil recruitment. To prove this, we would need either a better CXCR2 inhibitor or Cxcr2 knockout mice. Therefore, we are saving further exploration for the future. Regarding validating other chemokines, we establish that CCR2 is critical, and we now show by immunofluorescence and ELISA (new Figure 7 – figure supplement 4) that CCL2 is highly expressed in WT mice, and Ccr2–/– mice actually have strongly elevated CCL2 expression at 3 DPI compared to WT mice.

In Figure 1B, the UMAP here is largely uninformative. To display the clusters, the authors should instead show a heatmap or equivalent visualization of which genes defined each cluster. It would be helpful for the authors to also write out the full name of each cluster before using the abbreviations shown.

Please see our previous comment about the initial characterization of clusters performed in Harvest et al., 2023, which details the characteristic genes for each cluster. We have written the full names of each cluster in the legend of Figure 1.

In Figure 1C the authors, use a binary representation of whether a cluster is present or not at a particular time point. However, the spot size is arbitrary, and the colors of the dots are the same as the cluster color code. It is not clear what threshold the authors (or SpatialDimPlots) use to declare a given cluster is present at a given time point. Therefore, this chart does not give any sense of the extent of each cluster's presence at each time. The authors should revisualize these data to display the abundance of each cluster at each timepoint. This could simply be done by adjusting the size of the circle or using a more traditional heatmap.

We have now updated this graphic to display the extent of a cluster’s presence, with the size of each dot corresponding to the abundance of each cluster.

In Figures 2 and 3 the authors describe the kinetics of each chemokine by cluster. While the dynamic expression is evident in the images, it is challenging to determine which clusters are driving expression in the absence of cluster annotation in those figures. The authors should support their visual findings with quantification of each factor in each cluster across time points.

In Figure 5, violin plots are shown for Cxcl1 and Ccl2 that depict gene expression by each cluster. However, because each capture area is approximately 50 µm in diameter, the data do not achieve single-cell resolution and are not as informative as one would hope. Therefore, violin plots for each chemokine were not shown, though we have generated these graphics. We did not add these graphics to the revision because we did not think readers would generally want to see several pages of violin plots in the supplement. As mentioned, we plan to do single-cell RNA sequencing to further assess chemokine expression by each cell type present within the granulomas at key timepoints.

With respect to the lack of spatial analysis, the authors describe certain transcript signals (ie. peripheral region versus central region of the granuloma) across each lesion. To back up these qualitative assertions, the authors could use line profiles from the center of each granuloma to the outside to plot the variation in expression of each transcript over radial space. This would provide a more direct way to determine the spatial coordination between various transcripts.

We considered using line profiles to quantify spatial variation within each lesion at each timepoint. However, this was exceptionally challenging due to the asymmetrical nature of some lesions, and the size discrepancy at different timepoints as the granulomas grow (during infection) and shrink (during resolution). When attempting to decide where to draw the line profiles, we determined that this approach did not enhance our analyses beyond using the cluster overlay and H&E to identify and interrogate different clusters.

The data visualization in Figure 4 seems unnecessarily confusing. The authors put the transcriptomic signal into categories of 'absent', 'low', 'medium', and 'high.' Why not simply use a continuous scale? The data would also benefit from hierarchical clustering of the heatmap rows to highlight chemokines and their receptors with similar expression patterns across time.

We considered using a continuous scale as suggested by the reviewer. However, we chose not to create a continuous scale because quantitation is challenging due to the size changes in the lesions over time, such that larger lesions have greater inclusion of surrounding hepatocytes as well as necrotic cores, which would dilute the signal if averaged with the active immunologic granuloma zones. Figure 4 was intended to simplify the entirety of the SpatialFeaturePlots in an easy-to-digest manner, to aid in hypothesis generation as we consider the potential function of each chemokine and receptor in this model. We chose to organize each chemokine ligand based on family, maintaining a numerical order to allow Figure 4 to serve as a quick reference for anyone who is interested in a particular chemokine ligand or receptor.

Do the authors feel confident in the transcriptomic signal coming from regions of necrosis? Given that many of their bright signals are coming from within clusters annotated as necrosis or necrosis-adjacent this raises an important technical consideration. Can the authors use the H&E image to estimate the cellular density (based on nuclear counts) in each region annotated by Visium? Are there any studies supporting the accurate performance of spatial transcriptomic methods in necrosis? Necrosis can be a source of non-specific binding during in situ hybridization assays.

The reviewer raises a good point. A defining characteristic of the areas of necrosis is the lack of defined cell borders, with faded or absent nuclei. In these regions, it is impossible to estimate cellular density. Given these concerns, we have included an additional figure (new Figure 1 – figure supplement 1A-B) to display raw counts in each cluster across all timepoints. Though regions of necrosis do display lower read quantity compared to other areas, we are still confident in the positive transcriptomic signal coming from adjacent regions because there are plenty of negative examples in which expression is not detected. In other words, temporal and spatial upregulation of key genes is still observed in the tissues, and future experiments will aim to interrogate the physiological relevance of each gene, while validating the spatial transcriptomics data with other methodologies.

The methods should include a much more detailed description of the tissue preparation and collection for the Visium experiment. The section on the computational analysis of the Visium data is also extremely limited. At a minimum, the authors should include details on how they performed clustering of the Visium regions.

The detailed description of tissue preparation, computational analysis, and clustering is in our previous manuscript, from which this dataset originates. We can add a direct quote of the methodology if the reviewer requests.

The cluster labels in Figure 5 A-B are very difficult to see. Furthermore, it would help if the authors displayed the annotated cluster names (ie. Those shown in 5C) instead of their numerical coding for a more direct interpretation of the data.

We agree and have updated this figure with annotated cluster names.

The scale bars in Figure 7 are very difficult to see.

The scale bars in histology images were kept small intentionally so as not to occlude data, and eLife is an online-only, digital media platform which allows readers to sufficiently zoom on high-resolution histology images. We have increased the DPI resolution for histology images to further aid in visualization.

The information presented in Tables 2 and 3 is greatly appreciated and will really help guide the reader through the analyses.

We assembled this information for our own learning about chemokines and hope that it is useful for the reader.

Author response:

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

Public Reviews:

Reviewer #1 (Public review):

…the degree to which the predictions can vary according to environmental composition remains difficult to quantify, and the work does not address the sensitivity of the modeling predictions beyond a simulated medium containing 33 root exudates. I find this especially important given that relatively few (84 of 243) species were predicted to grow even after cross-feeding, suggesting that a richer medium could lead to different interaction network structures. While the authors do state the importance of environmental composition and have carefully designed an in silico medium, I believe that simulating a broader set of resource pools would add necessary insight into both the predictive power of the models themselves and trophic interactions in the rhizosphere more generally.

The original analyses were indeed focused on a single well-defined environment supporting the growth of only a subset of the species. We have added a paragraph to the discussion section dealing with the potential limitations of this approach. 

On line 289 we write:

"Overall, the successive iterations connected 84 out of 243 native members of the apple rhizosphere GSMM community via trophic exchanges. The inability of the remaining bacteria to grow, despite being part of the native root microbiome, possibly reflects the selectiveness of the root environment, which fully supports the nutritional demands of only part of the soil species, whereas specific compounds that might be essential to other species are less abundant1. It is important to note that the specific exudate profile used here represent a snapshot of the root metabolome as root secretion-profiles are highly dynamic, reflecting both environmental and plant developmental conditions. A possible complementary explanation to the observed selective growth might be the partiality of our simulation platform, which examined only plant-bacteria and bacteria-bacteria interactions while ignoring other critical components of the rhizosphere system such as fungi, archaea, protists and mesofauna, as well as less abundant bacterial species, components all known to metabolically interact2. Finally, the MAG collection, while relatively substantial, represents only part of the microbial community. Accordingly, the iterative growth simulations represent a subset of the overall hierarchical-trophic exchanges in the root environment, necessarily reflecting the partiality of the dataset."

In addition, we have tried to better explain the advantages of a limited/defined medium to such an analysis. On Line 231 we add:

"By avoiding the inclusion of non-exudate organic metabolites, the true-to-source rhizosphere environment was designed to reveal the hierarchical directionality of the trophic exchanges in soil, as rich media often mask various trophic interactions taking place in native communities3"

More generally, beyond the above justification of our specific medium selection, we agree that simulating a broader set of resource pools would contribute to a more comprehensive understanding of the trophic interactions. Therefore, we conducted the analysis in an additional environment, in which cellulose was used as an input. We were able to follow its well-documented degradation via multiple steps, conducted by different community members, to serve as a benchmark to our suggested framework. 

On line 357 we add:

"To validate the ability of MCSM to capture trophic dependencies and succession, we further tested whether it can trace the well-documented example of cellulose degradation - a multi-step process conducted by several bacterial strains that go through the conversion of cellulose and its oligosaccharide derivatives into ethanol, acetate and glucose, which are all eventually oxidized to CO24. Here, the simulation followed the trophic interactions in an environment provided with cellulose oligosaccharides (4 and 6 glucose units) on the 1st iteration (Supp. Table 3). The formed trophic successions detected along iterations captured the reported multi-step process (Supp.

Fig.7)." 

Finally, we have included additional text regarding the challenge of defining our simulation environment in the Discussion section. 

On line 532 we add:

"In the current study, the root environment was represented by a single pool of resources (metabolites). As genuine root environments are highly dynamic and responsive to stimuli, a single environment can represent, at best, a temporary snapshot of the conditions. Conductance of simulations with several sets of resource pools (e.g., representing temporal variations in exudation profile) can add insights regarding their effect on trophic interactions and community dynamics. In parallel, confirming predictions made in various environments will support an iterative process that will strengthen the predictive power of the framework and improve its accuracy as a tool for generating testable hypotheses. Similarly, complementing the genomicsbased approaches used here with additional layers of 'omics information (mainly transcriptomics & metabolomics) can further constrain the solution space, deflate the number of potential metabolic routes and yield more accurate predictions of GSMMs' performances5."

And we add in Line 520:

"For these reasons, among others, the framework presented here is not intended to be used as a stand-alone tool for determining microbial function. The framework presented is designed to be used as a platform to generate educated hypotheses regarding bacterial function in a specific environment in conjunction with actual carbon substrates available in the particular ecosystem under study. The hypotheses generated provide a starting point for experimental testing required to gain actual, targeted and feasible applicable insights6,7. While recognizing its limitations, this framework is in fact highly versatile and can be used for the characterization of a variety of microbial communities and environments. Given a set of MAGs derived from a specific environment and environmental metabolomics data, this computational framework provides a generic simulation platform for a wide and diverse range of future applications." 

Reviewer #2 (Public review):

There are two main drawback approaches like the one described here, both related only partially to the authors' work yet with great impact in the presented framework. First, the usage of automatic GSMM reconstruction requires great caution. It is indicative of how the semicurated AGORA models are still considered reconstructions and expect the user to parameterize those in a model. In this study, CarveMe was used. CarveMe is a well-known tool with several pros [1]. Yet, several challenges need to be considered when using it [2]. For example, the biomass function used might lead to an overestimation of auxotrophies. Also, as its authors admit in their reply paper, CarveMe does gap fill in a way [3]; models are constructed to ensure no gaps and also secure a minimum growth. However, curation of such a high number of GSMMs is probably not an option. Further, even if FVA is way more useful than FBA for the authors' aim, it does not yet ensure that when a species secretes one compound (let's say metabolite A), the same flux vector, i.e. the same metabolic functioning profile, secretes another compound (metabolite B) at the same time, even if the FVA solution suggests that metabolite B could be secreted in general.

We thank Reviewer #2 for highlighting this key limitation of our analysis. Below and in the 'recommendations to authors' section we address these concerns. 

Concerning the first point raised (models' accuracy) we have now clearly acknowledged in the text the limitations of using an automated GSMM reconstruction tool such as CarveMe. More generally, the framework applied here was built in order to meet the challenges of analyzing highthroughput data while acknowledging the inherent potential of introducing inaccuracies. Pros & cons are now discussed. 

On line 507 we write:

"Moreover, the use of an automatic GSMM reconstruction tool (CarveMe8), though increasingly used for depicting phenotypic landscapes, is typically less accurate than manual curation of metabolic models9. This approach typically neglects specialized functions involving secondary metabolism10 and introduces additional biases such as the overestimation of auxotrophies11,12. Nevertheless, manual curation is practically non-realistic for hundreds of MAGs, an expected outcome considering the volume of nowadays sequencing projects. As the primary motivation of this framework is the development of a tool capable of transforming high-throughput, low-cost genomic information into testable predictions, the use of automatic metabolic network reconstruction tools was favored, despite their inherent limitations, in pursuit of addressing the necessity of pipelines systematically analyzing metagenomics data." 

Regarding using FVA solutions, indeed such solutions return all potential metabolic fluxes in GSMMs (ranges of all fluxes satisfying the objective function, which by default is set to biomass increase) in a given environment. However, as indicated by the reviewer, predicted fluxes do not necessarily co-occur (i.e., when a metabolite is secreted another metabolite is not necessarily secreted too), yet, they provide the full set of potential solutions (unlike the single solution provided by FBA). A possible strategy to reduce inflated predictions provided by FVA and further constrain the solution space (reduce the set of metabolic fluxes) can be the incorporation of additional `omics data layers, as for example was done in the work of Zampieri et al5. Such approach could allow for instance limiting active reactions (blocking fluxes) from the network reconstructions if not coming to play in situ, and therefore impose further constraints and narrow the solution space. We now refer in the text to this limitation and to potential routes to overcome it. 

On line 541 we now write:

Similarly, complementing the genomics-based approaches done here with additional layers of 'omics information (mainly transcriptomics & metabolomics) can further constrain the solution space, deflate the number of potential metabolic routes and yield more accurate predictions of GSMMs' performances5.  

Reviewer #3 (Public review):

When presenting a computational framework, best practices include running it on artificial (synthetic) data where the ground truth is known and therefore the precision and accuracy of the method may be assessed. This is not an optional step, the same way that positive/negative controls in lab experiments are not optional. Without this validation step, the manuscript is severely limited. The authors should ask themselves: what have we done to convince the reader that the framework actually works, at least on our minimal synthetic data? 

Thank you for this suggestion. To validate the ability of MCSM to capture trophic succession, we conducted an additional analysis testing whether it can track the well documented example of cellulose degradation - a multi-step process conducted by several bacterial strains. This example has been included in the manuscript to serve as a case study (i.e. positive control) for metabolic interactions occurring within the bacterial community (Supp. Fig. 7). 

On line 357 we add:

"To validate the ability of MCSM to capture trophic dependencies and succession, we further tested whether it can track the well-documented example of cellulose degradation - a multi-step process conducted by several bacterial strains that go through the conversion of cellulose and its oligosaccharide derivatives into ethanol, acetate and glucose, which are all eventually oxidized to CO24. Here, the simulation followed the trophic interactions in an environment provided with cellulose oligosaccharides (4 and 6 glucose units) on the 1st iteration (Supp. Table 3). The formed trophic successions detected along iterations captured the reported multi-step process (Supp. Fig.

7)."  

"Supplementary Figure 7. Application of MCSM over the process of cellulose decomposition as described by Kato et al4. 5-partite network exhibiting the uptake of cellulose oligomers (4 and 6 units of connected D-glucose) by primary decomposers, through secretion of intermediate compounds and their metabolization by secondary decomposers to CO2. Distribution of phyla of primary and secondary decomposers is denoted by pie charts. Though MAGs were not constructed for the original species as in Kato et al., among the primary consumers, species corresponding to the Acidobacteria (Acidobacteriales)13, Actinobacteria14, Bacteriodetes15, Proteobacteria (Xanthomonadales)16 and Verrucobacteria17 groups are found to be capable of degrading cellulose compounds via enzymatic mechanisms."

More generally, beyond the above addition, the relevance of the framework to the analysis of the data is discussed throughout the analysis (in the original version of the manuscript). We have scrutinized each of our observations in light of current available information and provided a corroborating evidence as well as a few discrepancies for multiple steps in the analysis.  Examples include the following discussions:

On line 312, we discuss the biological relevance of taxonomic classes classified as primary versus secondary degraders

"As in the full GSMM data set (Community bar, Fig. 3C), most of the species which grew in the 1st iteration belonged to the phyla Acidobacteriota, Proteobacteria, and Bacteroidota. This result concurred with findings from the work of Zhalnina et al, which reported that bacteria assigned to these phyla are the primary beneficiaries of root exudates18. Species from three out of the 17 phyla that did not grow in the first iteration - Elusimicrobiota, Chlamydiota, and Fibrobacterota, did grow on the 2nd iteration (Fig. 3C). Members of these phyla are known for their specialized metabolic dependencies. Such is the case for example with members of the Elusimicrobiota phylum, which include mostly uncultured species whose nutritional preferences are likely to be selective19.

At the order level, bacteria classified as Sphingomonadales (class Alphaproteobacteria), a group known to include typical inhabitants of the root environment20, grew in the initial Root environment. In comparison, other root-inhabiting groups including the orders Rhizobiales and Burkholderiales_20, did not grow in the first iteration. _Rhizobiales and Burkholderiales did, however, grow in the second and third iterations, respectively, indicating that in the simulations, the growth of these groups was dependent on exchange metabolites secreted by other community members (Supp. Fig. 4)."

On line 331, we provide support to the classification of specific metabolites as exchange molecules

"Overall, 158 organic compounds were secreted throughout the MCSM simulation (from which 12 compounds overlapped with the original exudate medium). These compounds varied in their distribution and were mapped into 12 biochemical categories (Fig. 3D). Whereas plant secretions are a source of various organic compounds, microbial secretions provide a source of multiple vitamins and co-factors not secreted by the plant. Microbial-secreted compounds included siderophores (staphyloferrin, salmochelin, pyoverdine, and enterochelin), vitamins (pyridoxine, pantothenate, and thiamin), and coenzymes (coenzyme A, flavin adenine dinucleotide, and flavin mononucleotide) – all known to be exchange compounds in microbial communities21,22. In addition, microbial secretions included 11 amino acids (arginine, lysine, threonine, alanine, serine, phenylalanine, tyrosine, leucine, glutamate, isoleucine, and methionine), also known as a common exchange currency in microbial communities23. Some microbial-secreted compounds, such as phenols and alkaloids, were reported to be produced by plants as secondary metabolites24,25. Additional information regarding mean uptake and secretion degrees of compounds classified to biochemical groups is found in Supp. Fig. 5."

On line 432, we provide corroborative support to the classification of exudates as associated with beneficial/non beneficial root communities

"Notably, the S-classified root exudates included compounds reported to support dysbiosis and ARD progression. For example, the S-classified compounds gallic acid and caffeic acid (3,4-dihidroxy-trans-cinnamate) are phenylpropanoids – phenylalanine intermediate phenolic compounds secreted from plant roots following exposure to replant pathogens26. Though secretion of these compounds is considered a defense response, it is hypothesized that high levels of phenolic compounds can have autotoxic effects, potentially exacerbating ARD. Additionally, it was shown that genes associated with the production of caffeic acid were upregulated in ARD-infected apple roots, relative to those grown in γ-irradiated ARD soil27,28, and that root and soil extracts from replant-diseased trees inhibited apple seedling growth and resulted in increased seedling root production of caffeic acid29."

On line 446, we provide a supporting evidence to the classification of secreted compounds as associated with beneficial/non beneficial root communities

"Several secreted compounds classified as healthy exchanges (H) were reported to be potentially associated with beneficial functions. For instance, the compounds L-Sorbose (EX_srb__L_e) and Phenylacetaladehyde (EX_pacald_e), both over-represented in H paths (Fig. 5C), have been shown to inhibit the growth of fungal pathogens associated with replant disease30,31.

Phenylacetaladehyde has also been reported to have nematicidal qualities32."

On line 453 we discuss the correspondence of specific exudate uptakes and compound secretions via specific subnetwork motifs (PM) and their literature/experimental evidence 

"Combining both exudate uptake data and metabolite secretion data, the full H-classified PM path 4-Hydroxybenzoate; GSMM_091; catechol (Fig. 4C; the consumed exudate, the GSMM, and the secreted compound, respectively) provides an exemplary model for how the proposed framework can be used to guide the design of strategies which support specific, advantageous exchanges within the rhizobiome. The root exudate 4-Hydroxybenzoate is metabolized by GSMM_091 (class Verrucomicrobiae, order Pedosphaerales) to catechol. Catechol is a precursor of a number of catecholamines, a group of compounds which was recently shown to increase apple tolerance to ARD symptoms when added to orchard6,33. This analysis (PM; Fig 4C), leads to formulating the testable prediction that 4-Hydroxybenzoate can serve as a selective enhancer of catecholamine synthesizing bacteria associated with reduced ARD symptoms, and therefore serve as a potential source for indigenously produced beneficial compounds."

Moreover, we perceive our analysis as a strategy for integrating high throughput genomic data into testable predictions allowing narrowing the solution space while acknowledging potential inaccuracies that are inherent to the analysis. We have revised the text in order to clearly acknowledge this limitation.

On line 497 we write: 

"The framework we present is currently conceptual."

On line 520 we write: 

"For these reasons, among others, the framework presented here is not intended to be used as a stand-alone tool for determining microbial function. The framework presented is designed to be used as a platform to generate educated hypotheses regarding bacterial function in a specific environment in conjunction with actual carbon substrates available in the particular ecosystem under study. The hypotheses generated provide a start point for experimental testing required to gain actual, targeted and feasibly applicable insights6,7."

On line 532 we add: 

"In the current study, the root environment was represented by a single pool of resources (metabolites). As genuine root environments are highly dynamic and responsive to stimuli, a single environment can represent, at best, a temporary snapshot of the conditions. Conductance of simulations with several sets of resource pools (e.g., representing temporal variations in exudation profile) can add insights regarding their effect on trophic interactions and community dynamics. In parallel, confirming predictions made in various environments will support an iterative process that will strengthen the predictive power of the framework and improve its accuracy as a tool for generating testable hypotheses. Similarly, complementing the genomicsbased approaches used here with additional layers of 'omics information (mainly transcriptomics & metabolomics) can further constrain the solution space, deflate the number of potential metabolic routes and yield more accurate predictions of GSMMs' performances5."

Recommendations for the authors:

Reviewer #1( Recommendations for the authors):

(1) Line 219: "Feasibility" - this term/concept may be difficult to understand for readers unfamiliar with GSMMs. I would recommend either clarifying or rephrasing, perhaps as "simulations confirmed the existence of a feasible solution space for all the 243 models, as well as their capacity to predict growth in the respective environment."

Thanks, done. We have modified this section as suggested (line 221). 

(2) Line 244: How does MCSM fit within/build upon existing frameworks that simulate patterns of niche construction and cross-feeding with constraint-based modeling?

This is now addressed. On line 250 we write:  

"Unlike tools designed for modelling microbial interactions34,35, MCSM bypasses the need for defining a community objective function as the growth of each species is simulated individually. Trophic interactions are then inferred by the extent to which compounds secreted by bacteria could support the growth of other community members."

(3) Figure 4A: While illustrating the general complexity of the predicted trophic interactions, the density of the network makes it very difficult to interpret specific exchanges. Moreover, the naming conventions of the metabolites make it difficult to understand what they represent. I would recommend either restructuring the graph such that the label of each node is legible, or removing the labels altogether.

Thanks, done. Labels were removed and a zoom-in-window to the exchanges highlighted in Figure 4C were added. Caption was revised to indicate that node colors correspond to differential abundance classification of GSMMs in the different plots (H, S, NA are Healthy, Sick, Not-Associated, respectively).

Reviewer #2 (Recommendations for the authors):

CarveMe solves a Mixed Integer Linear Program (MILP) that enforces network connectivity, thus requiring gapless pathways. It's puzzling how to deal with such a great number of GSMMs that is for sure, especially when coming from such an environment as soil and the vast majority of their corresponding MAGs represent most likely novel taxa. One alternative approach for using CarveMe might be to use the rich medium as a medium to gap-fill during the reconstruction. In this case, the gene annotation scores that CarveMe calculates in its initial step, are used to prioritise the reactions selected for gap-filling. This would lead to a new series of challenges but might be a useful comparison with the current GSMMs of the study.

Though indeed CraveMe includes a gap-filling option, here we have purposely avoided the gapfilling option as we aimed to adhere to genomic content of the corresponding genomes and to avoid masking their metabolic dependencies emerging due to their incompleteness. This is noted in the Methods section, which we revised to emphasize the adherence to the genomic content of the models: 

On line 615 we now write:

"All GSMMs were drafted without gap filling in order to adhere to genomic content and to avoid masking metabolic co-dependencies51"

More generally, we now refer to the limitation of automatic reconstruction in the context of the current analysis. On line 507 we write:

"Moreover, the use of an automatic GSMM reconstruction tool (CarveMe8), though increasingly used for depicting phenotypic landscapes, is typically less accurate than manual curation of metabolic models9. This approach typically neglects specialized functions involving secondary metabolism10 and introduces additional biases such as the overestimation of auxotrophies11,12. Nevertheless, manual curation is practically non-realistic for hundreds of MAGs, an expected outcome considering the volume of nowadays sequencing projects. As the primary motivation of this framework is the development of a tool capable of transforming high-throughput, low-cost genomic information into testable predictions, the use of automatic, semi-curated, metabolic network reconstruction tools was favored, despite their inherent limitations, in pursuit of developing pipelines for the systematic analysis of metagenomics data."

Thermodynamically infeasible loops have been a challenge in constraint-based analysis [1].

However, for the case of FBA and FVA time efficient implementations are already available. Therefore, I would suggest using the loopless flag of the cobrapy package when performing FVA. 

Also, it would be nice to show/discuss how many exchange reactions each GSMM includes and what is the number of those with at least a non-zero minimum or maximum in the FVA using each of the three media.

Done. In Supplementary Figure 4, we added a graphic summary of active FVA ranges for each GSMM in the three different environments (exchange reactions, non-zero flux). Additionally, we analyzed a subset of models and compared their regular FVA results vs loopless FVA results.

On line 217 we write:

"The number of active exchange fluxes in each medium corresponds with the respective growth performances displaying noticably higher number of potentially active fluxes in the rich environment (also when applying loopless FVA) (Supp. Fig. 4). Overall, Simulations confirmed the existence of a feasible solution space for  all the 243 models as well as their capacity to predict growth in the respective environemnt (Supp. Data 5)."

"Supplementary Figure 4. FVA performances of GSMMs in different environments (Supp. Fig.

3; Supp. Data 5). A. Distribution of potentially active exchange reactions (non-zero minimum FVA flux) in the different environments. Solid line inside each violin indicates the interquartile range (IQR). White point in IQR indicates the median value. Whiskers extending from the IQR indicate the range within 1.5 times the IQR from the quartiles. Violin width at a given value represents the density of data points at that value. B. Loopless FVA scores compared to regular FVA for models in the 3 different environments. Bars indicate the count of active fluxes (nonzero minimum FVA flux). Only a subset of models was used for this analysis."

This brings us to the main challenge of your framework in my opinion: FVA returns the minimum and the maximum a flux may get. However, it does not ensure that when a metabolite is being secreted, another does the same too. That could lead to an overrepresentation of secreted metabolites after each iteration. To my understanding, unbiased methods focusing on metabolite exchanges would be a much better alternative for such questions. Unbiased constraint-based methods are known for requiring essential computational requirements, yet when focusing on specific parts of the models, recent implementations support them. A great showcase of such techniques is presented in [2].

Indeed, FVA solutions return all potential metabolic fluxes in GSMMs (ranges of all fluxes satisfying the objective function, which by default is set to biomass increase) but they do not ensure that all fluxes actually co-occur (i.e., when a metabolite is secreted necessarily another metabolite is secreted too). However, though FVA solutions do not necessarily ensure cooccurrence regarding secretion and uptake, they provide a broader metabolic picture (the full set of potential solutions), unlike the arbitrary single solution provided by FBA, which is limited in providing information about potential secretions and uptakes in a specific environment. Here, we tried to elucidate the connection between a specific environment (root exudates) and the growth and metabolic capabilities of native bacteria. To the best of our understanding,  unbiased approaches (such as the one displayed in Wedmark et al.36) are not environment dependent but rather calculate all possible metabolic elements and routes within a metabolic network. Therefore, using FVA is well adapted to explore environment-dependent growth. The sensitivity of FVA predicted active fluxes to the environments is now also implied by Sup. Fig. 3B demonstrating the number of potential active fluxes is proportional to growth performances.  In addition, inquiring all possible metabolic routes across a large dataset of hundreds of MAGS, is central to the current analysis, thus the easy implementation of FVA further justifies its use in the current study.

An alternative strategy to reduce inflated FVA predictions and further constrain the solution space of predicted active fluxes can be the incorporation of additional layers of `omics data, as for example was done in the work of Zampieri et al5. Such approach could allow for instance removing reactions from the network reconstructions if not coming to play in situ, and therefore impose further constraints and narrow down the solution space. Currently, the complexity of the soil community might impede or at least constrain a high coverage recovery of transcriptomic data, though future works utilizing additional layers of `omics data are expected to significantly reduce the number of potential solutions and thus improve the accuracy of GEMs predictions. 

This is now discussed in the text. In line 541 we write:

"Similarly, complementing the genomic-based approaches done here, with additional layers of 'omics information (mainly transcriptomics & metabolomics) can further constrain the solution space, deflate the number of potential metabolic routes and yield more accurate predictions of GSMMs' performances5."  

In case it was the first version of CheckM used, the authors could consider repeating this check with CheckM2. As they state in line 293, Archaea may play an essential role in the community. Yet, among the high-quality MAGs only one corresponded to Archaea. However, that is quite possible to be the case because CheckM underestimates the completeness of archaeal genomes. If CheckM2 suggests that archaeal MAGs could be used, these would probably benefit a lot for the aim of the study.

The analysis was conducted with the first version of CheckM to assess MAGs quality. In future analyses we will use CheckM2. However, also before MAG recovery, we already know from the work of Beirhu et al., that Archaea species have a very low representation in the metagenomics data used here (Berihu et al., Additional data 2. Supp. fig. 4; "others" group)6, with less than 0.5% of the contigs mapped to archaeal genomes. The overall taxonomic distribution of the high-quality MAGs was compared to the distribution inferred from the non-binned data (contigs) and amplicon sequencing and the three different data sets are very similar (Fig. 2). 

On line 130 we write:

"Overall, the taxonomic distribution of the MAG collection corresponded with the profile reported for the same samples using alternative taxonomic classification approaches such as 16S rRNA amplicon sequencing and gene-based taxonomic annotations of the non-binned shotgun contigs

(Fig. 2B)."

The visualisation of the network in Figure 4A is hard to follow. An alternative could be a 5partite plot having taxa in columns one, three, and five and compounds in the other two. An alternative visualisation is necessary.

The full list of the 5 and 3 partite graphs is provided in supplementary data 10 (also noted in the figure legend now). Figure 4 was revised to improve its visualization. Labels were removed and a zoom in to 5 and 3 partite plots were added (PMM and PM subnetworks, respectively). 

Line 509: If I get the point of the authors right, they refer to the "from shotgun data to GEMs" approach. I would suggest skipping this statement. Here is a recent study implementing this: https://doi.org/10.1016/j.crmeth.2022.100383.

Thank you for your comment and reference. The intention behind the phrase in line 509 (in previous version) was to refer to going from metagenomics data to GEMs in soil-rhizosphere microbiome while linking environmental inputs (crop-plants exudates metabolomics data) and the agricultural-related metabolic function of bacteria. This phrase has been modified to clearly make a more modest claim while acknowledging other related studies.

On line 548 we write

"Where recent studies begin to apply GSMM reconstruction and analysis starting from MAGs5,37 , this work applies the MAGs to GSMMs approach to conduct a large-scale CBM analysis over highquality MAGs derived from a native rhizosphere and explore the complex network of interactions in light of the functioning of the respective agro-ecosystem. "

Line 820: Reference format is broken.

Corrected.

In the caption of Figure 4, please add the meaning of H, S, and NA so it is selfexplanatory.

Done. In Figure 4 legend we added:

"Node colors correspond to differential abundance classification of GSMMs in the different plots; H, S, NA are Healthy, Sick, Not-Associated, respectively."

Reviewer #3 (Recommendations for the authors):

(1) Figure 4A is unreadable. It is not clear what insight the reader could gain by examining this figure.

Thanks. Figure was revised. Labels were removed and a zoom-in-window to the exchanges highlighted in Figure 4C were added. Caption was revised to indicate that node colors correspond to differential abundance classification of GSMMs in the different plots (H, S, NA are Healthy, Sick, Not-Associated, respectively).

(2) In Figure 5, it is not apparent what the units of "prevalence" are, that is, what is the scale. What does 140 mean? How does that compare to 350?

Thanks. Prevalence in the context of Figure. 5B,C refers to the count of the compounds in each category (significantly affiliated with either healthy or symptomized soils) in sub-network motifs corresponding to this DA classification. We revised the figures (Y axes) and legend to be more specific (B: # of exudates; C: # of secreted compounds).

"B. Bar plot indicating the number of exudates significantly associated with H or S-classified PM sub-networks (Hypergeometric test; FDR <= 0.05; green: healthy-H, red: sick-S). C. Bar plots indicate the number of secreted compounds in PM sub-networks, which are significantly associated with H-classified (upper, colored green), or S-classified (lower, colored red) (Hypergeometric test; FDR <= 0.05)."

References

(1) Buée, M., de Boer, W., Martin, F., van Overbeek, L. & Jurkevitch, E. The rhizosphere zoo: An overview of plant-associated communities of microorganisms, including phages, bacteria, archaea, and fungi, and of some of their structuring factors. Plant Soil 321, 189– 212 (2009).

(2) Bardgett, R. D. & Van Der Putten, W. H. Belowground biodiversity and ecosystem functioning. Nature 515, 505–511 (2014).

(3) Opatovsky, I. et al. Modeling trophic dependencies and exchanges among insects’ bacterial symbionts in a host-simulated environment. BMC Genomics 19, 1–14 (2018).

(4) Kato, S., Haruta, S., Cui, Z. J., Ishii, M. & Igarashi, Y. Stable coexistence of five bacterial strains as a cellulose-degrading community. Appl. Environ. Microbiol. 71, 7099–7106 (2005).

(5) Zampieri, G., Campanaro, S., Angione, C. & Treu, L. Metatranscriptomics-guided genomescale metabolic modeling of microbial communities. Cell Reports Methods 3, 100383 (2023).

(6) Berihu, M. et al. A framework for the targeted recruitment of crop ‑ beneficial soil taxa based on network analysis of metagenomics data. Microbiome 1–21 (2023) doi:10.1186/s40168-022-01438-1.

(7) Dhakar, K. et al. Modeling-Guided Amendments Lead to Enhanced Biodegradation in Soil. mSystems 7, (2022).

(8) Machado, D., Andrejev, S., Tramontano, M. & Patil, K. R. Fast automated reconstruction of genome-scale metabolic models for microbial species and communities. Nucleic Acids Res. 46, 7542–7553 (2018).

(9) Henry, C. S. et al. High-throughput generation, optimization and analysis of genome-scale metabolic models. Nat. Biotechnol. 28, 977–982 (2010).

(10) Freilich, S. et al. Competitive and cooperative metabolic interactions in bacterial communities. Nat. Commun. 2, (2011).

(11) Price, M. Erroneous predictions of auxotrophies by CarveMe. Nat. Ecol. Evol. 7, 194–195 (2023).

(12) Machado, D. & Patil, K. R. Reply to: Erroneous predictions of auxotrophies by CarveMe. Nat. Ecol. Evol. 7, 196–197 (2023).

(13) Kulichevskaya, I. S. et al. Acidicapsa borealis gen. nov., sp. nov. and Acidicapsa ligni sp. nov., subdivision 1 Acidobacteria from Sphagnum peat and decaying wood. Int. J. Syst. Evol. Microbiol. 62, 1512–1520 (2012).

(14) Depart-, M. & Building, L. S. Lignocellulose-degrading actinomycetes. 46, 145–163 (1987).

(15)Thomas, F., Hehemann, J. H., Rebuffet, E., Czjzek, M. & Michel, G. Environmental and gut Bacteroidetes: The food connection. Front. Microbiol. 2, 1–16 (2011).

(16) Dow, J. M. & Daniels, M. J. Pathogenicity determinants and global regulation of pathogenicity of Xanthomonas campestris pv. campestris. Curr. Top. Microbiol. Immunol. 192, 29–41 (1994).

(17) Bergmann, G. T. et al. The under-recognized dominance of Verrucomicrobia in soil bacterial communities. Soil Biol. Biochem. 43, 1450–1455 (2011).

(18) Zhalnina, K. et al. Dynamic root exudate chemistry and microbial substrate preferences drive patterns in rhizosphere microbial community assembly. Nat. Microbiol. 3, 470–480 (2018).

(19) Uzun, M. et al. Recovery and genome reconstruction of novel magnetotactic Elusimicrobiota from bog soil. ISME J. 1–11 (2022) doi:10.1038/s41396-022-01339-z.

(20) Lei, S. et al. Analysis of the community composition and bacterial diversity of the rhizosphere microbiome across different plant taxa. Microbiologyopen 8, 1–10 (2019).

(21) Ghosh, S. K., Banerjee, S. & Sengupta, C. Bioassay, characterization and estimation of siderophores from some important antagonistic fungi. J. Biopestic. 10, 105–112 (2017).

(22) Lu, X., Heal, K. R., Ingalls, A. E., Doxey, A. C. & Neufeld, J. D. Metagenomic and chemical characterization of soil cobalamin production. ISME J. 14, 53–66 (2020).

(23) Mee, M. T., Collins, J. J., Church, G. M. & Wang, H. H. Syntrophic exchange in synthetic microbial communities. Proc. Natl. Acad. Sci. U. S. A. 111, (2014).

(24) Justin, K., Edmond, S., Ally, M. & Xin, H. Plant Secondary Metabolites: Biosynthesis, Classification, Function and Pharmacological Properties. J. Pharm. Pharmacol. 2, 377–392 (2014).

(25) Yang, W. et al. A Genomic Analysis of Bacillus megaterium HT517 Reveals the Genetic Basis of Its Abilities to Promote Growth and Control Disease in Greenhouse Tomato. Genet. Res. (Camb). 2022, (2022).

(26) Balbín-Suárez, A. et al. Root exposure to apple replant disease soil triggers local defense response and rhizoplane microbiome dysbiosis. FEMS Microbiol. Ecol. 97, 1–14 (2021).

(27) Weiß, S., Liu, B., Reckwell, D., Beerhues, L. & Winkelmann, T. Impaired defense reactions in apple replant disease-Affected roots of Malus domestica ‘M26’. Tree Physiol. 37, 1672–1685 (2017).

(28) Weiß, S., Bartsch, M. & Winkelmann, T. Transcriptomic analysis of molecular responses in Malus domestica ‘M26’ roots affected by apple replant disease. Plant Mol. Biol. 94, 303– 318 (2017).

(29) Sun, N. et al. Effects of Organic Acid Root Exudates of Malus hupehensis Rehd. Derived from Soil and Root Leaching Liquor from Orchards with Apple Replant Disease. Plants 11, (2022).

(30) Howell, C. R. Seed Treatment with L-Sorbose to Control Damping-Off or Cotton Seedlings by Rhizoctonia solani. Phytopathology 68, 1096 (1978).

(31) Zou, C. S., Mo, M. H., Gu, Y. Q., Zhou, J. P. & Zhang, K. Q. Possible contributions of volatile-producing bacteria to soil fungistasis. Soil Biol. Biochem. 39, 2371–2379 (2007).

(32) Gomes, V. A. et al. Activity of papaya seeds (Carica papaya) against Meloidogyne incognita as a soil biofumigant. J. Pest Sci. (2004). 93, 783–792 (2020).

(33) Gao, T. et al. Exogenous dopamine and overexpression of the dopamine synthase gene MdTYDC alleviated apple replant disease. Tree Physiol. 41, 1524–1541 (2021).

(34) Diener, C., Gibbons, S. M. & Resendis-Antonio, O. MICOM: Metagenome-Scale Modeling To Infer Metabolic Interactions in the Gut Microbiota. mSystems 5, (2020).

(35) Dukovski, I. et al. A metabolic modeling platform for the computation of microbial ecosystems in time and space (COMETS). Nat. Protoc. 16, 5030–5082 (2021).

(36) Katarina Wedmark, Y., Olav Vik, J. & Øyås, O. A hierarchy of metabolite exchanges in metabolic models of microbial species and communities. bioRxiv 1–19 (2023).

(37) Zorrilla, F., Buric, F., Patil, K. R. & Zelezniak, A. MetaGEM: Reconstruction of genome scale metabolic models directly from metagenomes. Nucleic Acids Res. 49, (2021).

Author response:

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

Public Reviews:

Reviewer #1 (Public Review):

Summary:

The researchers demonstrated that when cytokine priming is combined with exposure to pathogens or pathogen-associated molecular patterns, human alveolar macrophages and monocyte-derived macrophages undergo metabolic adaptations, becoming more glycolytic while reducing oxidative phosphorylation. This metabolic plasticity is greater in monocyte-derived macrophages than in alveolar macrophages.

Strengths:

This study presents evidence of metabolic reprogramming in human macrophages, which significantly contributes to our existing understanding of this field primarily derived from murine models.

Weaknesses:

The study has limited conceptual novelty.

We acknowledge that the study has limited conceptual novelty, however, the current manuscript provides the field with evidence of the changes in the phenotype and functions of human macrophages in response to IFN-γ or IL-4 which is currently lacking in the literature. Moreover, our data shows for the first time that human airway macrophages change their function in response to IFN-γ.  

Reviewer #2 (Public Review):

Summary:

The authors aimed to functionally characterize primary human airway macrophages and monocytederived macrophages, correlating their glycolytic shift in metabolism. They conducted this macrophage characterization in response to type II interferon and IL-4 priming signals, followed by different stimuli of irradiated Mycobacterium tuberculosis and LPS.

Strengths:

(1) The study employs a thorough measurement of metabolic shift in metabolism by assessing extracellular acidification rate (ECAR) and oxygen consumption rate (OCR) of differentially polarized primary human macrophages using the Seahorse XFe24 Analyzer.

(2) The effect of differential metabolic shift on the expression of different surface markers for macrophage activation is evaluated through immunofluorescence flow cytometry and cytokine measurement via ELISA.

(3) The authors have achieved their aim of preliminarily characterizing the glycolysis-dependent cytokine profile and activation marker expression of IFN-g and IL-4 primed primary human macrophages.

(4) The results of the study support its conclusion of glycolysis-dependent phenotypical differences in cytokine secretion and activation marker expression of Ams and MDMs.

Weaknesses:

(1) The data are presented in duplicates for cross-analyses.

(2) The data presented supports a distinct functional profile of airway macrophages (Ams) compared to monocyte (blood)-derived macrophages (MDMs) in response to the same priming signals. However, the study does not attempt to explore the underlying mechanism for this difference.

(3) The study is descriptive in nature, and the results validate IFN-g-mediated glycolytic reprogramming in primary human macrophages without providing mechanistic insights.

(1) We acknowledge the data is presented in duplicate for cross-analyses. This duplication allowed us to examine both (A) the effect of IFN-γ or IL-4 on primary human airway and monocyte derived macrophages in the presence or absence of distinct stimulations and (B) to directly compare the fold change in function occurring in the AM with the changes in the MDM.

(2 & 3) We acknowledge that our study is descriptive however, by inhibiting glycolysis using 2DG we have demonstrated that increased flux through glycolysis is mechanistically required to mediate enhanced cytokine responses in both primary human AM and MDM primed with IFN-γ. However, we acknowledge that we have not determined the differential molecular mechanisms downstream of IFNγ in the AM versus the MDM. IFN-γ promotes both pro- and anti-inflammatory cytokines in AM and this was reduced by inhibiting glycolysis with 2DG. This identifies glycolysis as a key mechanistic pathway which can be therapeutically targeted in AM to modulate inflammation. Mechanistic studies on human AM are limited due to low number of AM retrieved from BAL samples. Nevertheless, the differences between AM and MDM identified in the current study indicate that future mechanistic studies are warranted to identify why IFN-γ promotes IL-10 in AM and not MDM, and, why TNF is differentially regulated by glycolysis in the two macrophage subpopulations, for example.  

Reviewer #3 (Public Review):

Summary:

In this manuscript, the authors explore the contribution of metabolism to the response of two subpopulations of macrophages to bacterial pathogens commonly encountered in the human lung, as well as the influence of priming signals typically produced at a site of inflammation. The two subpopulations are resident airway macrophages (AM) isolated via bronchoalveolar lavage and monocyte-derived macrophages (MDM) isolated from human blood and differentiated using human serum. The two cell types were primed using IFNγ and Il-4, which are produced at sites of inflammation as part of initiation and resolution of inflammation respectively, followed by stimulation with either irradiated Mycobacterium tuberculosis (Mtb) or LPS to simulate interaction with a bacterial pathogen. The authors use human cells for this work, which makes use of widely reported and thoroughly described priming signals, as well as model antigens. This makes the observations on the functional response of these two subpopulations relevant to human health and disease. To examine the relationship between metabolism and functional response, the authors measure rates of oxidative phosphorylation and glycolysis under baseline conditions, primed using IFNγ or IL-4, and primed and stimulated with Mtb or LPS.

Strengths:

• The data indicate that both populations of macrophages increase metabolic rates when primed, but MDMs decrease their rates of oxidative phosphorylation after IL-4 priming and bacterial exposure while AMs do not.

• It is demonstrated that glycolysis rates are directly linked to the expression of surface molecules involved in T-cell stimulation and while secretion of TNFα in AM is dependent on glycolysis, in MDM this is not the case. IL-1β is regulated by glycolysis only after IFN-γ priming in both MDM and AM populations. It is also demonstrated that Mtb and LPS stimulation produces responses that are not metabolically consistent across the two macrophage populations. The Mtb-induced response in MDMs differed from the LPS response, in that it relies on glycolysis, while this relationship is reversed in AMs. The difference in metabolic contributions to functional outcomes between these two macrophage populations is significant, despite acknowledgement of the reductive nature of the system by the authors.

• The observations that AM and MDM rely on glycolysis for the production of cytokines during a response to bacterial pathogens in the lung, but that only MDM shift to Warburg Metabolism, though this shift is blocked following exposure to IL-4, are supported by the data and a significant contribution the study of the innate immune response.

Weaknesses:

• It is unclear whether changes in glycolysis and oxidative phosphorylation in primed cells are due to priming or subsequent treatments. ECAR and OCR analyses were therefore difficult to interpret.

All data sets have been presented and analysed relative to both unprimed unstimulated to show both the effect of priming and subsequent stimulation. A second analysis was subsequently conducted where each data set was normalised to its own baseline in terms of percentage change. Therefore, each of unprimed, IFN-γ and IL-4 primed cells were set to 100% in order to assess the effect of stimulation independent of the baseline priming effect. For clarity we have removed the following line:

“Percentage change for ECAR and OCR was calculated from the respective baseline of each data set to visualise the differential ability of IFN-γ, IL-4 primed or unprimed AM to respond to stimulation (Figure S1C,D).”

We have amended the text in the manuscript (lines 164-173) to “Since IFN-γ priming increased cellular energetics in the AM at baseline, we calculated percent change in ECAR and OCR from the baseline rate of each group in order to assess if IFN-γ or IL-4 primed AM have altered capacity to change their metabolism in response to stimulation (Figure 1C,D). This was carried out to equalise all the primed data sets at baseline before stimulation (Figure S1C, S1D).  These data indicate that whilst the peak of glycolysis is elevated in IFN-γ primed AM (Figure 1A), all AM have a similar capacity to increase glycolysis upon stimulation when baseline differences in metabolism were adjusted for the effects of cytokine priming (Figure 1C). IFN-γ increased the percent change in OCR of AM in response to both bacterial stimuli compared to the unstimulated IFN-γ primed control (Figure 1D). These data indicate that priming AM alters the metabolic baselines of human tissue resident macrophages and not their ability to respond to bacterial stimuli.”

• The data may not support a claim that AM has greater "functional plasticity" without a direct comparison of antigen presentation. Moreover, MDM secrete more IL-1β than AM. The claim that AM "have increased ability to produce all cytokines assayed in response to Mtb stimulation" does not appear to be supported by the data.

Our data suggests that the MDM are more phenotypically plastic (in terms of their ability to alter expression of cell surface markers in response to cytokine cues), whereas AM have a greater ability to alter cytokine production, our measure of functional plasticity. We have now defined the use of the terms ‘functional plasticity’ and ‘phenotypic plasticity’ in the context of our paper in lines 6063. To consider different culture and plating requirements of MDM versus AM, cytokine production was analysed relative to the average of the unprimed Mtb or LPS control of the respective MDM or AM. This allowed us to draw more accurate comparisons between the two macrophage populations by examining their relative ability to increase their cytokine production (expressed as fold change) rather than defining this functional plasticity only in terms of concentrations of cytokine produced in culture.  

We have therefore added the following sentence into the conclusion of the manuscript. “Cumulatively, the data presented herein suggests that the MDM maybe more phenotypically plastic than the AM, while the AM have enhanced functional plasticity in their ability to modulate cytokine production after exposure Th1 and Th2 cytokines.”

We have edited the discussion (lines 421-423) to clarify the following "have increased ability to produce all cytokines assayed in response to Mtb stimulation" and changed it to “stimulated with Mtb have significantly more production of IL-1β, TNF and IL-10 compared with unprimed controls. This is in contrast with IFN-γ primed MDM which only upregulate TNF compared to their unprimed controls.”   

• The claim that AM are better for "innate training" via IFNγ may not be consistent with increased IL1β and a later claim that MDM have increased production and are "associated with optimal training."

We have removed the word “better” and now simply state that AM are a tractable target to induce innate training in the human lung.

• Statistical analyses may not appropriately support some of the conclusions.

We have consulted with a statistician. Please see response to reviewer 3 recommendations for authors point 1 below.  

• AM populations would benefit from further definition-presumably this is a heterogenous, mixed population.

AM are routinely >97% CD68+CD14+ used in the current study (Author response image 1). However, we acknowledge that tissue resident macrophages represent a spectrum of phenotypes. Given limitations in cell numbers from primary human AM derived from BALF, we have not attempted to define the function of discreet subpopulations of AM.

• The term "functional plasticity" could also be more stringently defined for the purposes of this study.

We are terming functional plasticity to be the macrophages’ ability to alter their production of cytokines in response to external cues like IFN-γ and IL-4 whereas phenotypic plasticity is measured based on ability to alter the cell surface expression of activation markers.  We have now defined this in the manuscript (lines 60-63).

Author response image 1.

Expression of macrophage markers on AM. 

Conclusion:

Overall, the authors succeed in their goals of investigating how inflammatory and anti-inflammatory cytokine priming contributes to the metabolic reprogramming of AM and MDM populations. Their conclusions regarding the relationship between cytokine secretion and inflammatory molecule expression in response to bacterial stimuli are supported by the data. The involvement of metabolism in innate immune cell function is relevant when devising treatment strategies that target the innate immune response during infection. The data presented in this paper further our understanding of that relationship and advance the field of innate immune cell biology.

Recommendations for the authors:

Reviewer #1 (Recommendations For The Authors):

(1)  Authors are suggested to provide rationale for their choice of cytokines as IFN-gamma and IL-4. This will be useful for the readers.

We have updated the following sentence (line 44-46) in the manuscript to add more rationale for the choice of IFN-γ and IL-4.  “There is a paucity of data on the role of metabolism in response to Th1 or Th2 microenvironments induced by cytokines-such as IFN-γ or IL-4 respectively, in human macrophages, especially in tissue resident macrophages, such as AM.”

(2)  Authors have shown the final outcome of metabolic reprogramming in terms of expression of HLADR and CD-40, and cytokine release. What pathways/receptors are activated or associated with IL-4 and IFN-gamma priming as a first line of response?

The relationship between IFN-γ or IL-4 induced expression of CD40 is established in haematological cell lines and fibroblasts as well as APC, with roles for the JAK/STAT pathways and upregulation of IRFs defined (1-3). Similarly, the relationship between exogenous IFN-γ and upregulation of HLA-DR expression on human monocytes or endothelial cells is established (4, 5). Whist our work does not outline the signalling pathways downstream of Th1 or Th2 cytokine priming, we have shown for the first time that glycolysis mechanistically underpins the shift in phenotype and function observed in human macrophages upon priming with IFN-γ or IL-4.

(3)  What are the intracellular signals leading to glycolytic shift?

One of the most likely mechanisms that under pin the shift to glycolytic metabolism is the stabilisation of HIF-1α mediated by activation of mTOR (see response below and rebuttal figure 2).  

(4)  Additional evidence is required to show Warburg effect such as stabilization and activation of HIF1alpha.

We acknowledge that we have not shown the activation and stabilisation of HIF-1α, however, we have provided functional evidence of increased glycolysis with concomitant decreased oxidative phosphorylation indicative of Warburg metabolism.

In order to address this gap in evidence we have reworded the manuscript to describe this functional change to “Warburg-like metabolism” throughout the manuscript. In addition, we have undertaken Western Blotting to provide evidence of mTOR activation when cells are primed with IFN-γ (Author response image 2).

Author response image 2.

IFN-γ activates mTOR in primary human monocytes. Monocytes were isolated from healthy donor PBMC using magnetic separation. Monocytes were left untreated (-), stimulated with rapamycin as a negative control (Rap; 50 nM), IFN-γ (10 ng/ml) or IFN-γ and rapamycin simultaneously (IFN-γ + Rap) for 15 minutes. Phosphorylation of S6 was used as a readout of mTOR activation and measured by western blot using β-actin as a control with a blot (A) and (b) densitometry results are shown as the relative expression of pS6: β-actin from. Graphs show data of n=1 of unprimed (black dot) vs IFN-γ primed (red) with and without rapamycin. ImageLab (Bio-Rad) software was used to perform densitometric analysis. 

(5)  What is the importance of showing percentage change vs fold change in figure 1 (1C vs 1A)?

All data sets have been presented and analysed relative to both unprimed unstimulated to show the effect of first priming and subsequent stimulation (Figure 1A). A second analysis was subsequently conducted where each data set was normalised to its own baseline in terms of percentage change (Figure 1C). Therefore, each of unprimed, IFN-γ or IL-4 primed cells were set to 100% to assess the effect of stimulation independent of the pre-existing effect of priming on the baseline metabolism. For clarity we have removed the following line:

“Percentage change for ECAR and OCR was calculated from the respective baseline of each data set to visualise the differential ability of IFN-γ, IL-4 primed or unprimed AM to respond to stimulation (Figure S1C,D).”

We have amended the text (lines 164-173) in the manuscript to “Since IFN-γ priming increased cellular energetics in the AM at baseline, we calculated percent change in ECAR and OCR from the baseline rate of each group in order to assess if IFN-γ or IL-4 primed AM have altered capacity to change their metabolism in response to stimulation (Figure 1C,D). This was carried out to equalise all the primed data sets at baseline before stimulation (Figure S1C, S1D).  These data indicate that whilst the peak of glycolysis is elevated in IFN-γ primed AM (Figure S1A), all AM have a similar capacity to increase glycolysis upon stimulation when baseline differences in metabolism were adjusted for the effects of cytokine priming (Figure 1C). IFN-γ increased the percent change in OCR of AM in response to both bacterial stimuli compared to the unstimulated IFN-γ primed control (Figure 1D). These data indicate that priming AM alters the metabolic baselines of human tissue resident macrophages and not their ability to respond to bacterial stimuli.”

(6)  Why IL-4 primed cells have lower glycolysis than unprimed control cells even in absence of pathogen in Figure 1A?

IL-4 primed AM do not have statistically significant changes in glycolysis compared with unprimed control cells in the absence of stimulation.  

Reviewer #2 (Recommendations For The Authors):

The manuscript entitled "Human airway macrophages are metabolically reprogrammed by IFN-γ resulting in glycolysis dependent functional plasticity" by Cox et al., characterizes glycolytic-linked cytokine secretion and surface receptor expression of primary human airway macrophages (AM) and monocyte-derived macrophages (MDM). The authors primed the primary macrophages with type II interferon (IFN-γ) or interleukin-4 (IL-4) into Th1 and Th2 polarized states. This was followed by measurement of the shift in macrophage metabolism to glycolysis (ECAR measurement) and/or oxidative phosphorylation (OCR measurement) in response to lipopolysaccharide and irradiated Mycobacterium tuberculosis. The authors then utilize 2-DG (an inhibitor of glycolysis) to show the reliance of glycolytic shift in metabolism to drive the expression of different macrophage activation markers in MDMs and cytokine secretion in AMs.

Significance:

The study provides important validation of IFN-γ-mediated glycolytic shift and its correlated functionalities in primary human macrophage populations.

Highlights: The study characterizes glycolytic-linked cytokine secretion and expression of macrophage activation markers in primary human resident (lung) and monocyte (blood)-derived macrophages. The study also shows data in support of IFN-γ alone in mediating glycolytic reprogramming of human primary macrophages.

Limitations:

The study lacks novelty and does not provide any new or different information in relation to IFN-γmediated glycolytic shift in the metabolism of human macrophages.

Major comments:

(1) The authors have relied on irradiated Mycobacterium tuberculosis (Mtb) and LPS stimulation to measure different correlates of macrophage functions. Additionally, the authors have discussed their results with irradiated Mtb with that of infection with live Mtb. There are also recent reports that show Mtb infection limiting glycolytic reprogramming in murine and human macrophages (PMID: 31914380) in contrast to their observation with irradiated Mtb. The authors should also include live Mtb infection or other replicative live bacterium for the induction of surface activation markers and cytokine release in their setup.

We thank the reviewer for this suggestion; however, this is beyond the scope of the current study which was to assess AM and MDM in the context of immune stimulation in a reductive manner using TLR4 ligand LPS and a more complete whole bacteria stimulation. The selected bacterial ligands were employed in the study to allow us to model an optimal macrophage host response. This minimises the confounding variable of live bacteria which can perturb cellular metabolism and immune responses, which we have highlighted in the discussion. Since both LPS and irradiated Mtb induced similar metabolic and phenotypic profiles, it is likely that the effects of priming are maintained with diverse stimuli.  

(2) The authors should add a quantitative measure (like extracellular lactate secretion or ECAR level) for the extent of glycolytic inhibition by the use of 5 mM 2-DG in their setup.

We would like to draw the attention of the reviewer to the data represented in supplementary figure 2B, demonstrating that 2DG lowers ECAR at 5mM at both 1 and 24 h post stimulation with iH37Rv by an average of approximately 40%. In addition, we have acknowledged that inhibition with 5 mM 2DG does not fully inhibit glycolysis as outlined in the study limitations (lines 477-480).  

(3) Percent change and fold change have been used to show the same or similar result in Fig. 1 and 2. Whereas, supplementary Fig. 1 shows absolute ECAR/OCR values in addition to fold change. The authors can plot either fold change or percent change in different measurements to avoid confusion. For example, do ECAR changes upon LPS stimulation in Fig. 1A and 1C come from the same dataset? One of the data points in percent change shows a decrease in percent ECAR change under no cytokine control, whereas all the data points in fold change show an increase.

We have addressed this comment above in response to reviewer 1 point 5 (recommendations for the authors).

We thank the reviewer for highlighting this single error in the data points for percent change. We have fixed this data point which was a result of a calculation error. All data throughout the manuscript has now been rechecked.   

Minor comments:

(1) The manuscript for review should be line-marked for referencing and commenting during review.

We have now included line-marking on the manuscript.  

(2) The authors can depict marker legends differently for all figures. In all figures, circles to squares or triangles represent treatment/stimulation with iH37Rv or LPS. The authors can depict this as circles to squares/triangles in contrast to different legends.

We have changed the legend to include a more detailed description of data represented inserting additional information regarding the colours and symbols represented in the figures.  

(3) Describe bars in supplementary figure 1A - 1H in its legend?

We thank the reviewer for highlighting this oversight, we have amended the legend to state “error bars represent standard deviation”

(4) Discuss the significant increase in CD86 expression in IFN-γ and IL-4 primed unstimulated AMs in Fig. 3E.

We have updated the results section to state that IFN-γ increased the expression of CD86 when isolated in the absence of bacterial stimulations in Fig. 3E (lines 271-272). There is no significant increase in CD86 by IL-4 primed unstimulated AM. IL-4 primed human AM only upregulated CD86 when treated with 2DG or in the presence of stimulation.  

(5) Contrary to Fig. 2, the data points of unstimulated cells in Fig. 4 vary for different treatment conditions (no cytokine, IFN-γ, and IL-4) for each cytokine measurement. What is the difference between unstimulated cells in Fig. 4 (for each cytokine) from that of Fig. 2 (for each receptor MFI)?

Unstimulated cells change their surface activation markers and phenotype in response to IFN-γ and IL-4 in Fig. 2. For Fig. 4, IFN-γ and IL-4 are not sufficient to induce cytokine secretion in the absence of stimulation with bacterial ligands.  

(6) The methodology for seeding and treatment of cells is reemphasized for almost all results. Defining macrophage priming and stimulation of macrophages in the method section and once at the start of results should be fine.

Plating happens differently for Seahorse compared to the flow cytometric phenotyping and ELISA for cytokine production. For clarity we have stated and reemphasized the seeding and treatment of cells throughout the results section.  

(7) Clarify "IL-4 reduced glycolysis in response to LPS stimulation" in relation to the results depicted in Fig. 1A and 1C. Similarly, clarify "IL-4 resulting in reduced IL-1β and IL-10 production" in relation to Fig. 4E.

For clarity we have added the following lines (157-160, 164-170) to the manuscript:  

“IL-4 primed iH37Rv stimulated AM increased ECAR to similar extent as unprimed controls (Figure 1A; left). Conversely, IL-4 primed AM stimulated with LPS AM did not increase their ECAR to the same extent as controls (Figure 1A; right), suggesting that IL-4 reduces the AM ability to increase ECAR in response to LPS stimulation.”   

“Since IFN-γ priming increased cellular energetics in the AM at baseline, we calculated percent change in ECAR and OCR from the baseline rate of each group in order to assess if IFN-γ or IL-4 primed AM have altered capacity to change their metabolism in response to stimulation (Figure 1C,D). This was carried out to equalise all the primed data sets at baseline before stimulation (Figure S1C, S1D). These data indicate that whilst the peak of glycolysis is elevated in IFN-γ primed AM (Figure S1A), all AM have a similar capacity to increase glycolysis upon stimulation when baseline differences in metabolism were adjusted for the effects of cytokine priming (Figure 1C).”

For clarity we have amended the sentence the reviewer has highlighted (lines 214-215): “IL-4 primed AM had reduced fold change in glycolysis upon stimulation with LPS compared with controls”.

Since IFN-γ priming induced large effect sizes, we statistically analysed the IL-4 primed and unprimed data sets in the absence of the IFN-γ primed data sets to determine how IL-4 influenced macrophage function. The only data where this resulted in any statistical significance was in response to cytokine production. We have now clarified this in the methods and relevant figure legends by stating, “Statistically significant differences were determined using two-way ANOVA with a Tukey post-test (AD); *P≤0.05, **P≤0.01, ***P≤0.001, ****P≤0.0001 or #P≤0.05, ##P≤0.01 (where IFN-γ primed data sets were excluded for post-test analysis to analyse statistical differences between no cytokine and IL4 treated data sets).

To further clarify this, we have amended the text of the manuscript (lines 307-310) to reflect this. “All stimulated AM secreted IL-10 regardless of priming (Figure 4E). IFN-γ significantly enhanced iH37Rv induced IL-10 in AM compared to unprimed or IL-4 primed comparators (Figure 4E). IL-4 priming of human AM significantly reduced IL-10 production in response to iH37Rv compared with unprimed AM (Figure 4E). LPS strongly induced IL-10 production in unprimed MDM, which was significantly attenuated by either IFN-γ or IL-4 priming (Figure 4F).”  

(8) Clarify whether data points in unstimulated, iH37Rv stimulated, and LPS-stimulated control cells in Fig. 3A - 3F are from independent experiments from those in Fig. 2A - 2F? The distribution of data points of control (no 2-DG treatment) in Fig. 3 is highly similar to the corresponding data points in Fig. 2. Similarly, provide clarification for similarity in Fig. 5A - 5F and Fig. 4A - 4F.

The data illustrated in figure 2 and 3 are from one very large dataset, as are the data in figures 4 and 5. This large experiment was designed to test the effect of priming macrophages with IFN- or IL-4 (in the presence or absence of stimulation), and also to determine if the differential responses elicited due to priming were dependent on glycolysis (by inhibiting with 2DG). For clarity and transparency, the same stimulated dataset is repeated in both figures. Given the size and complexity of the experiment, we chose to present the data this way to aid the reader.  

(9) Clarify the statement "where data was reanalyzed in the absence of IFN-γ" in the section pertaining to Statistical analysis. The authors should clearly mention nature of biological and technical replicates for each experiment in its figure legend. The authors should also confirm multiple comparison correction in all 2-way ANOVA tests done in each figure legend."

We have amended the text (lines 133-136) to clarify this point “P-values of ≤0.05 were considered statistically significant and denoted with an asterisk. Alternatively, P-values of ≤0.05 were denoted with a hashtag where data was analysed in the absence of IFN-γ primed data sets, to analyse statistical differences between no cytokine and IL-4 treated data sets.”  

Figures represent biological replicates (which are the average of technical replicates, presented as a single data point). This is indicated by the following sentence in each figure legend: “Each linked data point represents the average of technical duplicates for one individual biological donor”.  

Each legend has been amended to include the multiple comparison post-test applied.

(10) Discuss the differences and similarities of IFN-γ driven metabolic reprogramming of primary murine macrophages with the results of this study relative to cytokine secretion and activation marker expression.

We have added additional discussion and detail comparing human and murine macrophages in lines 381-382, 403, 407 and 412-415 of the manuscript.

(11) The repetitive data plots of similar results can be significantly reduced to improve the interpretation of the results.

The benefit of the plotting the data in this way is for a clearer understanding and representation of the data. The repetitive data plots allow the benefit of being able to first delineate the effect of priming and priming plus stimulation and then, separately, to further examine the differences in AM versus MDM. The repetition of the primed data points then allows of the reader to determine the effect of inhibiting glycolysis with 2DG on unprimed and primed macrophages (with and without stimulation).   

Reviewer #3 (Recommendations For The Authors):

The methods used and data reported in this manuscript contribute to our understanding of the role of metabolism in programming of macrophages during priming. Suggestions for improving the presentation and interpretation of results include:

• Consult with a statistician regarding analyses of the multiple conditions used during these assays. The use of repeated statistical analyses with different comparison groups in the same figure/data set seems atypical and should either be amended or fully justified in the text. Also, use of two-way vs. one-way ANOVA should be evaluated and clarified.

We have now consulted a statistician. We have amended the text (lines 133-136) to clarify this point “P-values of ≤0.05 were considered statistically significant and denoted with an asterisk. Alternatively, P-values of ≤0.05 were denoted with a hashtag where data was analysed in the absence of IFN-γ primed data sets, to analyse statistical differences between no cytokine and IL-4 treated groups.”  

There are two variables in the data sets; cytokine priming as well as stimulation status therefore we opted for a two-way ANOVA rather than a One-way ANOVA. There are three stimulation groups: unstimulated, Mtb-stimulated and LPS-stimulated. Cytokine priming also has three groups: no cytokine, IFN-y, or IL-4. There are two variables (priming and stimulation), each with 3 groups i.e., six treatment conditions in total, therefore two-way AVOVA with multiple comparisons tests help pinpoint exactly which groups (e.g., the 6 different levels of the 'stimulation' and 'cytokine' treatments) are significantly different from each other. This was important for understanding the specific effects of our treatments. The reader can therefore also deduce how these six treatment conditions compare to each other.

In contrast, performing multiple single comparisons independently of the rest of the dataset (e.g. t tests), increases the risk of false positives (type 1 error). Multiple comparisons ANOVA with post-tests adjust for this, helping to reduce the likelihood of a type 1 error. These stats are more stringent, and it is therefore harder to get P values <0.05. Hence, if we compared all six treatment groups without adjustment, you increase the chance of finding false positives due to the sheer number of comparisons, leading to biased and incorrect conclusions.

In our case, multiple comparisons tests were essential after the two-way ANOVA because they helped to objectively identify specific treatment group differences and control the overall error rate when we were extracting our conclusions, thereby reducing any risk of biases in our conclusions.

A one-way ANOVA is used to test the effect of a single variable with more than two groups contained in the dataset. For example, in our case if you only want to test how different 'stimulation' groups affect ECAR or OCR, only in unprimed macrophages, a one-way ANOVA would be used.

The current study used two-way ANOVA to test the effects of two variables (priming and stimulation, or in some cases priming and inhibition) each containing 3 groups, and see if there is any interaction between the two factors. For example, in our case this allowed us to examine how the 'stimulation' and the 'cytokine' priming affect ECAR/OCR levels and to determine if the effect of 'stimulation' depends on the 'cytokine' priming.

• More justification could be given for the dose of IFNγ used for priming. Inflammatory priming is typically performed with a "low-dose" treatment (e.g., ~1 ng/ml), whereas the authors use 10 ng/ml, which would be considered a high dose. It would be useful to repeat select experiments with a more standard low-dose treatment of IFNg to demonstrate that this is also sufficient to induce the observed metabolic changes.

Previous work has identified little difference in the response of AM and peripheral monocytes to low versus high doses of IFN-γ (6). We have inserted the following into the study limitations (lines 479-481).  

“Furthermore, only one dose of IFN-γ was utilised due to limitations in AM yield, however, recently both low and high doses of IFN-γ have been shown to have similar effects on AM in vitro (6).”

• Check for accuracy of the Fig.4 legend. Also check that 4G and 4B math is consistent.

The legend for Figure 4 has been amended for incorrect A,B to state G,H. The math has been double checked for accuracy and is correct. 3 out of 10 MDM donors produced IL-1β in the absence of IFN-γ in Figure 4B, therefore the average used to calculate the data represented in Figure 4G was brought down markedly by donors who produced little or no IL-1β.  

• Functional plasticity is a vague term and difficult to interpret in this context. It is stated that AM have greater functional plasticity, but MDMs appear to have greater capacity to secrete IL-1β and respond more robustly to IL-4 in terms of T cell stimulation. On that note, the claims regarding antigen presentation would be more impactful if a direct comparison of antigen presentation capacity was made between AM and MDM.

Our data suggests that AM have a greater ability to alter cytokine production, such as IL1β. To consider different culture and plating requirements of MDM v AM cytokine concentration was normalised and expressed in terms of fold change.  This gives a more controlled and accurate comparison of the ability of IFN-γ or IL-4 to modulate cytokine production in AM compared with MDM.  

The terms ‘functional plasticity’ and phenotypic plasticity’ have now been defined in the manuscript in lines 60-63.  

We have therefore added the following sentence into the conclusion of the manuscript (lines 490-493). “Cumulatively, the data presented herein suggests that the MDM maybe more phenotypically plastic than the AM, while the AM have enhanced functional plasticity in their ability to produce cytokine after exposure Th1 and Th2 cytokines.”

However, we acknowledge that the MDM may be regarded as more plastic because of their ability to respond robustly to IL-4, whereas the phenotypic and functional changes in the AM in response to IL4 are more limited. Whilst the focus of our work was to determine if AM are a tractable target to promote immunity in the lungs through upregulation of pro-inflammatory effector function, their ability to downregulated inflammation in response to IL-4 is comparatively less profound compared with MDM.  

We acknowledge the shortcomings of our work which did not allow us to directly measure antigen processing in the AM, due to limitations in the cellular yield from BALF. We have edited the text (lines 251-252 and 286) to clarify this for the reader.  

• Inconsistent normalization complicates interpretation of metabolic data. For example, it is unclear, for example, whether changes in glycolysis and oxidative phosphorylation in primed cells are due to priming or subsequent treatments. Check harmony of methods for analysis of "metabolic assays" with Fig.1 data, axis, and legend.

We have addressed this comment, which is similar to points made by the other reviewers and amended the manuscript to increase clarity. These changes are outlined in the response to reviewer 1, point 5 (recommendations for the author). In addition, we have amended the metabolic assay method (lines 111-112) to state that “Post stimulation the ECAR and OCR were continually sampled at 20-minute intervals for times indicated.”

• A direct comparison of cytokine production after priming and stimulation with Mtb or LPS is limited by inconsistent axes. The data may not support a claim that AM has greater "functional plasticity" without a direct comparison of antigen presentation. Moreover, MDM secrete more IL-1β than AM. The claim that that AM "have increased ability to produce all cytokines assayed in response to Mtb stimulation" does not appear to be supported by the data.

We have amended the text to clarify this issue (lines 313-315). “These data suggest that the AM have greater functional plasticity in terms of their ability to upregulate cytokine production in response to IFN-γ, compared with the MDM. IFN-γ primed AM have enhanced IL-10 and TNF production in response to Mtb and LPS, respectively.”  

We have amended the manuscript and have replaced “IFN-γ primed AM have increased ability to produce all cytokines assayed in response to Mtb stimulation” with the following (lines 421-423) “IFNγ primed AM stimulated with Mtb have significantly more production of IL-1β, TNF and IL-10 compared with unprimed controls. This is in contrast with IFN-γ primed MDM which only upregulate TNF compared to their unprimed controls.”

• AM populations could be defined experimentally.

Airway macrophages were adherence purified from bronchoalveolar lavage fluid defined as CD68+CD14+ as per rebuttal figure 1. The purpose of this study was to examine if human peripherally derived or lung resident macrophages were plastic in response to the classical polarising cytokines IFNγ and IL-4. We have identified that the AM and MDM do indeed have different functional and metabolic responses to these cytokines. However, determining functional differences within the AM subpopulations is beyond the scope of the current study and hampered by low cell numbers in human BALF.  

References

(1) Conzelmann M, Wagner AH, Hildebrandt A, Rodionova E, Hess M, Zota A, Giese T, Falk CS, Ho AD, Dreger P, Hecker M, Luft T. IFN-γ activated JAK1 shifts CD40-induced cytokine profiles in human antigen-presenting cells toward high IL-12p70 and low IL-10 production. Biochemical pharmacology 2010; 80: 2074-2086.

(2) Fries KM, Sempowski GD, Gaspari AA, Blieden T, Looney RJ, Phipps RP. CD40 Expression by human fibroblasts. Clinical Immunology and Immunopathology 1995; 77: 42-51.

(3) Gu W, Chen J, Yang L, Zhao KN. TNF-α promotes IFN-γ-induced CD40 expression and antigen process in Myb-transformed hematological cells. TheScientificWorldJournal 2012; 2012: 621969.

(4) Hershman MJ, Appel SH, Wellhausen SR, Sonnenfeld G, Polk HC, Jr. Interferon-gamma treatment increases HLA-DR expression on monocytes in severely injured patients. Clinical and experimental immunology 1989; 77: 67-70.

(5) Maenaka A, Kenta I, Ota A, Miwa Y, Ohashi W, Horimi K, Matsuoka Y, Ohnishi M, Uchida K, Kobayashi T. Interferon-γ-induced HLA Class II expression on endothelial cells is decreased by inhibition of mTOR and HMG-CoA reductase. FEBS open bio 2020; 10: 927-936.

(6) Thiel BA, Lundberg KC, Schlatzer D, Jarvela J, Li Q, Shaw R, Reba SM, Fletcher S, Beckloff SE, Chance MR, Boom WH, Silver RF, Bebek G. Human alveolar macrophages display marked hyporesponsiveness to IFN-γ in both proteomic and gene expression analysis. PLoS One 2024; 19: e0295312.

Author response:

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

Public Reviews:

Reviewer #1 (Public Review):

Summary:

It is suggested that for each limb the RG (rhythm generator) can operate in three different regimes: a non-oscillating state-machine regime, and in a flexor driven and a classical half-center oscillatory regime. This means that the field can move away from the old concept that there is only room for the classic half-center organization

Strengths:

A major benefit of the present paper is that a bridge was made between various CPG concepts ( "a potential contradiction between the classical half-center and flexor-driven concepts of spinal RG operation"). Another important step forward is the proposal about the neural control of slow gait ("at slow speeds ({less than or equal to} 0.35 m/s), the spinal network operates in a state regime and requires external inputs for phase transitions, which can come from limb sensory feedback and/or volitional inputs (e.g. from the motor cortex").

Weaknesses:

Some references are missing

We thank the Reviewer for the thoughtful and constructive comments. We have added additional text to meet the specific Reviewer’s recommendations and several references suggested by the Reviewer.  

Reviewer #2 (Public Review):

Summary:

The biologically realistic model of the locomotor circuits developed by this group continues to define the state of the art for understanding spinal genesis of locomotion. Here the authors have achieved a new level of analysis of this model to generate surprising and potentially transformative new insights. They show that these circuits can operate in three very distinct states and that, in the intact cord, these states come into successive operation as the speed of locomotion increases. Equally important, they show that in spinal injury the model is "stuck" in the low speed "state machine" behavior.

Strengths:

There are many strengths for the simulation results presented here. The model itself has been closely tuned to match a huge range of experimental data and this has a high degree of plausibility. The novel insight presented here, with the three different states, constitutes a truly major advance in the understanding of neural genesis of locomotion in spinal circuits. The authors systematically consider how the states of the model relate to presently available data from animal studies. Equally important, they provide a number of intriguing and testable predictions. It is likely that these insights are the most important achieved in the past 10 years. It is highly likely proposed multi-state behavior will have a transformative effect on this field.

Weaknesses:

I have no major weaknesses. A moderate concern is that the authors should consider some basic sensitivity analyses to determine if the 3 state behavior is especially sensitive to any of the major circuit parameters - e.g. connection strengths in the oscillators or?

We thank the Reviewer for the thoughtful and constructive comments. The sensitivity analysis has been included as Supplemental file.

Reviewer #3 (Public Review):

Summary:

This work probes the control of walking in cats at different speeds and different states (split-belt and regular treadmill walking). Since the time of Sherrington there has been ongoing debate on this issue. The authors provide modeling data showing that they could reproduce data from cats walking on a specialized treadmill allowing for regular and split-belt walking. The data suggest that a non-oscillating state-machine regime best explains slow walking - where phase transitions are handled by external inputs into the spinal network. They then show at higher speeds a flexor-driven and then a classical halfcenter regime dominates. In spinal animals, it appears that a non-oscillating state-machine regime best explains the experimental data. The model is adapted from their previous work, and raises interesting questions regarding the operation of spinal networks, that, at low speeds, challenge assumptions regarding central pattern generator function. This is an interesting study. I have a few issues with the general validity of the treadmill data at low speeds, which I suspect can be clarified by the authors.

Strengths:

The study has several strengths. Firstly the detailed model has been well established by the authors and provides details that relate to experimental data such as commissural interneurons (V0c and V0d), along with V3 and V2a interneuron data. Sensory input along with descending drive is also modelled and moreover the model reproduces many experimental data findings. Moreover, the idea that sensory feedback is more crucial at lower speeds, also is confirmed by presynaptic inhibition increasing with descending drive. The inclusion of experimental data from split-belt treadmills, and the ability of the model to reproduce findings here is a definite plus.

Weaknesses:

Conceptually, this is a very useful study which provides interesting modeling data regarding the idea that the network can operate in different regimes, especially at lower speeds. The modelling data speaks for itself, but on the other hand, sensory feedback also provides generalized excitation of neurons which in turn project to the CPG. That is they are not considered part of the CPG proper. In these scenarios, it is possible that an appropriate excitatory drive could be provided to the network itself to move it beyond the state-machine state - into an oscillatory state. Did the authors consider that possibility? This is important since work using L-DOPA, for example, in cats or pharmacological activation of isolated spinal cord circuits, shows the CPG capable of producing locomotion without sensory or descending input.

We thank the Reviewer for the thoughtful and constructive comments. We have added additional texts, references, and discussed the issues raised by the Reviewer. Particularly, in section “Model limitations and future directions” we now admit that afferent feedback can provide some constant level excitation to the RG circuits after spinal transection which can partly compensate for the lack of supraspinal drive and hence affect (shift) the timing of transitions between the considered regimes. We mentioned that this is one of the limitations of the present model. The potential effects of neuroactive drugs, like DOPA, on CPG circuits after spinal transection were left out because they are outside the scope of the present modeling studies.    

Recommendations for the authors:

Reviewer #1 (Recommendations For The Authors):

specific feedback to the authors:

Nevertheless, there are some minor points, worth considering.

Link to HUMAN DATA

Here the authors may be interested to know that human data supports their proposal. This is relevant since there is ample evidence for the operation of spinal CPG's in humans (Duysens and van de Crommert,1998). The present model predicts that the basic output of the CPG remains even at very slow speeds, thus leading to similarity in EMG output. This prediction fits the experimental data (den Otter AR, Geurts AC, Mulder T, Duysens J. Speed related changes in muscle activity from normal to very slow walking speeds. Gait Posture. 2004 Jun;19(3):270-8). To investigate whether the basic CPG output remains basically the same even at very slow speeds (as also predicted by the current model), humans walked slowly on a treadmill (speeds as slow as 0.28 m s−1). Results showed that the phasing of muscle activity remained relatively stable over walking speeds despite substantial changes in its amplitude. Some minor additions were seen, consistent with the increased demands of postural stability. Similar results were obtained in another study: Hof AL, Elzinga H, Grimmius W, Halbertsma JP. Speed dependence of averaged EMG profiles in walking. Gait Posture. 2002 Aug;16(1):78-86. doi:

10.1016/s0966-6362(01)00206-5. PMID: 12127190.

These authors wrote: "The finding that the EMG profiles of many muscles at a wide range of speeds can be represented by addition of few basic patterns is consistent with the notion of a central pattern generator (CPG) for human walking". The basic idea is that the same CPG can provide the motor program at slow and fast speeds but that the drive to the CPG differs. This difference is accentuated under some conditions in pathology, such as in Parkinson's Kinesia Paradoxa. It was argued that the paradox is not really a paradox but is explained as the CPGs are driven by different systems at slow and at fast speeds (Duysens J, Nonnekes J. Parkinson's Kinesia Paradoxa Is Not a Paradox. Mov Disord. 2021 May;36(5):1115-1118. doi: 10.1002/mds.28550. Epub 2021 Mar 3. PMID: 33656203.)

These ideas are well in line with the current proposal ("Based on our predictions, slow (conditionally exploratory) locomotion is not "automatic", but requires volitional (e.g. cortical) signals to trigger stepby-step phase transitions because the spinal network operates in a state-machine regime. In contrast, locomotion at moderate to high speeds (conditionally escape locomotion) occurs automatically under the control of spinal rhythm-generating circuits receiving supraspinal drives that define locomotor speed, unless voluntary modifications or precise stepping are required to navigate complex terrain").

As mentioned in the present paper, other examples exist from pathology ("...Another important implication of our results relates to the recovery of walking in movement disorders, where the recovered pattern is generally very slow. For example, in people with spinal cord injury, the recovered walking pattern is generally less than 0.1 m/s and completely lacks automaticity 77-79. Based on our predictions, because the spinal locomotor network operates in a state-machine regime at these slow speeds, subjects need volition, additional external drive (e.g., epidural spinal cord stimulation) or to make use of limb sensory feedback by changing their posture to perform phase transitions"). As mentioned above, another example is provided by Parkinson's disease. The authors may also be interested in work on flexible generators in SCI: Danner SM, Hofstoetter US, Freundl B, Binder H, Mayr W, Rattay F, Minassian K. Human spinal locomotor control is based on flexibly organized burst generators. Brain. 2015 Mar;138(Pt 3):577-88. doi: 10.1093/brain/awu372. Epub 2015 Jan 12. PMID: 25582580; PMCID: PMC4408427.

We thank the reviewer for these additional and interesting insights. We added a new paragraph in the Discussion to bolster the link with human data that includes references suggested by the Reviewer.

CHAIN OF REFLEXES

It reads: "... in opposition to the previously prevailing viewpoint of Charles Sherrington 21,22 that locomotion is generated through a chain of reflexes, i.e., critically depends on limb sensory feedback (reviewed in 23)." This is correct but incomplete. The reference cited (23: Stuart, D.G. and Hultborn, H, "Thomas Graham Brown (1882--1965), Anders Lundberg (1920-), and the neural control of stepping," Brain Res. Rev. 59(1), 74-95 (2008)) actually reads: "Despite the above findings, the doctrinaire position in the early 1900s was that the rhythm and pattern of hind limb stepping movements was attributable to sequential hind limb reflexes. According to Graham Brown (1911c) this viewpoint was largely due to the arguments of Sherrington and a Belgian physiologist, Maurice Philippson (1877-1938). Philippson studied stepping movements in chronically maintained spinal dogs, using techniques he had acquired in the Strasbourg laboratory of the distinguished German physiologist, Friedrich Goltz (1834-1902). He also analyzed kinematically moving pictures of dog locomotion, which had been sent to him by the renowned French physiologist, Etienne-Jules Marey (1830-1904). Philippson (1905) certainly presented arguments explaining his perception of how sequential spinal reflexes contributed to the four phases of the step cycle (see Fig. 1 in Clarac, 2008). In retrospect, it is likely that Graham Brown was correct in attributing to Philippson and Sherrington the then-prevailing viewpoint that reflexes controlled spinal stepping. It is puzzling, nonetheless, that far less was said then and even now about Philippson's belief that the spinal control was due to a combination of central and reflex mechanisms (Clarac, 2008),4,5 4 We are indebted to François Clarac for drawing to our attention Philippson's statement on p. 37 of his 1905 article that "Nos expériences prouvent d'une part que la moelle lombaire séparée du reste de l'axe cérébro-spinal est capable de produire les mouvements coordonnés dans les deux types de locomotion, trot et gallop. [Our experiments prove that one side of the spinal cord separated from the cerebro-spinal axis is able to produce coordinated movements in two types of locomotion, trot and gallop]." Then, on p. 39 Philippson (1905) states that "Nous voyons donc, en résumé que la coordination locomotrice est une fonction exclusivement médullaire, soutenue d'une part par des enchainements de réflexes directs et croisés, dont l'excitant est tantot le contact avec le sol, tantot le mouvement même du membre. [In summary, we see that locomotor coordination is an exclusive function of the spinal cord supported by a sequencing of direct and crossed reflexes, which are activated sometimes by contact with the ground and sometimes even by leg movement]. A coté de cette coordination basée sur des excitations périphériques, il y a une coordination centrale provenant des voies d'association intra-médullaires. [In conjunction with this peripherally excited coordination, there is a central coordination arising from intraspinal pathways]." (The English translations have also been kindly supplied by François Clarac.) Clearly, Philippson believed in both a central spinal and a reflex control of stepping! 5 In part 1 of his 1913/1916 review Graham Brown discussed Philippson's 1905 article in much detail (pp. 345-350 in Graham Brown, 1913b). He concludes with the statement that "... Philippson die wesentlichen Factoren des Fortbewegungsaktes in das exterozeptive Nervensystem verlegt. Er nimmt an, dass die zyklischen Bewegungen automatisch durch äussere Reize erhalten werden, welche in sich selbst thythmisch als Folge der Reflexakte welche sie selbst erzeugen, wiederholt werden. [Philippson assigns the important factors of the act of locomotion to the exteroceptive nervous system. He assumes that the cyclic movements are automatically maintained by external stimuli which, by themselves, are rhythmically repeated as a consequence of the reflexive actions that they generate themselves]." (English translation kindly supplied by Wulfila Gronenberg). This interpretation clearly ignores Philippson's emphasis on a central spinal component in the control of stepping....). "

Hence it is a simplification to give all credits to Sherrington and ignoring the role of Philippson concerning the chain of reflexes idea.

We again thank the Reviewer for these additional and interesting insights. We added the Philippson (1905) and Clarac (2008) references. The important contribution of Philippson is now indicated.

GTO Ib feedback

It reads: "This effect and the role of Ib feedback from extensor afferents has been demonstrated and described in many studies in cats during real and fictive locomotion 2,57-59."

These citations are appropriate but it is surprising to see that the Hultborn contribution is limited to the Gossard reference while the even more important earlier reference to Conway et al is missing (Conway BA, Hultborn H, Kiehn O. Proprioceptive input resets central locomotor rhythm in the spinal cat. Exp Brain Res. 1987;68(3):643-56. doi: 10.1007/BF00249807. PMID: 3691733).

Yes, the Conway et al. reference has been added.

Other species

The authors may also look at other species. The flexible arrangement of the CPGs, as described in this article, is fully in line with work on other species, showing cpg networks capable to support gait, but also scratching, swimming ..etc (Berkowitz A, Hao ZZ. Partly shared spinal cord networks for locomotion and scratching. Integr Comp Biol. 2011 Dec;51(6):890-902. doi: 10.1093/icb/icr041. Epub 2011 Jun 22. PMID: 21700568. Berkowitz A, Roberts A, Soffe SR. Roles for multifunctional and specialized spinal interneurons during motor pattern generation in tadpoles, zebrafish larvae, and turtles. Front Behav Neurosci. 2010 Jun 28;4:36. doi: 10.3389/fnbeh.2010.00036. PMID: 20631847; PMCID: PMC2903196.)

Similar ideas about flexible coupling can also be found in: Juvin L, Simmers J, Morin D. Locomotor rhythmogenesis in the isolated rat spinal cord: a phase-coupled set of symmetrical flexion extension oscillators. J Physiol. 2007 Aug 15;583(Pt 1):115-28. doi: 10.1113/jphysiol.2007.133413. Epub 2007 Jun 14. PMID: 17569737; PMCID: PMC2277226. Or zebrafish: Harris-Warrick RM. Neuromodulation and flexibility in Central Pattern Generator networks. Curr Opin Neurobiol. 2011 Oct;21(5):685-92. doi: 10.1016/j.conb.2011.05.011. Epub 2011 Jun 7. PMID: 21646013; PMCID: PMC3171584.

We added a sentence in the Discussion along with supporting references.

Standing

In the view of the present reviewer, the model could even be extended to standing in humans. It reads: "at slow speeds ({less than or equal to} 0.35 m/s), the spinal network operates in a state regime and requires external inputs"; similarly (personal experience) when going from sit to stand: as soon as weight is over support, extension is initiated and the body raises, as one would expect when the extensor center is activated by reinforcing load feedback, replacing GTO inhibition (Faist M, Hoefer C, Hodapp M, Dietz V, Berger W, Duysens J. In humans Ib facilitation depends on locomotion while suppression of Ib inhibition requires loading. Brain Res. 2006 Mar 3;1076(1):87-92. doi:

Yes, we agree that the model could be extended to standing and the transition from standing to walking is particularly interesting. However, for this paper, we will keep the focus on locomotion over a range of speeds.

Reviewer #2 (Recommendations For The Authors):

The presentation is exceedingly well done and very clear.

A moderate concern is that the authors do not make use of the capacity of computer simulations for sensitivity analyses. Perhaps these have been previously published? In any case, the question here is whether the 3 state behavior is especially sensitive to excitability of one of the main classes of neurons or a crucial set of connections.

The sensitivity analysis has been made and included as Supplemental file.

Minor point. I have but two minor points. A bit more explanation should be provided for the use of the terms "state machine" to describe the lowest speed state. Perhaps this is a term from control theory? In any case, it is not clear why this is term is appropriate for a state in which the oscillator circuits are "stuck" in a constant output form and need to be "pushed" by sensory input.

Yes, we now provide a definition in the Introduction.

Minor point: it is of course likely that neuromodulation of multiple types of spinal neurons occurs via inputs that activate G protein coupled receptors. These types of inputs are absent from the model, which is fine, but some sort of brief discussion should be included. One possibility is to note that the circuit achieves transitions between different states without the need for neuromodulatory inputs. This appears to me to be a very interesting and surprising insight.

In section “Model limitations and future directions” in the Discussion, we now mention that the term “supraspinal drive” in our model is used to represent supraspinal inputs providing both electrical and neuromodulator effects on spinal neurons increasing their excitability, which disappear after spinal transection.” We think that it is so far too early to simulate the exact effects of the descending neuromodulation, since there is almost no data on the effect of different modulators on specific types of spinal interneurons.

Reviewer #3 (Recommendations For The Authors):

Minor Comments  

Page numbers would be useful.

Abstract

Following spinal transection, the network can only operate in a state-machine regime. This is a bit strong since it applies to computational data. Clarify this statement.

We agree. Sentence has been changed to: “Following spinal transection, the model predicts that the spinal network can only operate in the state-machine regime.”

Introduction

Intro - "This is somewhat surprising...". It gives the impression that spinal cats are autonomously stable on the belt. They are stabilized by the experimenter.

The text has been changed to: “This is somewhat surprising because intact and spinal cats rely on different control mechanisms. Intact cats walking freely on a treadmill engage vision for orientation in space and their supraspinal structures process visual information and send inputs to the spinal cord to control locomotion on a treadmill that maintains a fixed position of the animal relative to the external space. Spinal cats, whose position on the treadmill relative to the external space is fixed by an experimenter, can only use sensory feedback from the hindlimbs to adjust locomotion to the treadmill speed.”

"Cannot consistently perform treadmill locomotion" - likely a context-dependent result. Certainly, cats can do this easily off a treadmill - stalking, for example. Perhaps somewhere, mention that treadmill locomotion is not entirely similar to overground locomotion.

We completely agree. Stalking is an excellent example showing that during overground locomotion slow movements (and related phase transitions) can be controlled by additional voluntary commands from supraspinal structures, which differs from simple treadmill locomotion, performing out of specific goalor task-dependent contexts. Based on this, we suggest a difference between a relatively slow (exploratory-type, including stalking) and relatively fast (escape-type) overground locomotion. We added the following sentence to the introduction:” This is evidently context dependent and specific for the treadmill locomotion as cats, humans  and other animals can voluntarily decide to perform consistent overground locomotion at slow speeds.”

The authors introduce the concept of the state machine regime. In my opinion, this could use some more explanation and citations to the literature. Was it a term coined by the authors, or is there literature reinforcing this point?

This is a computer science and automata theory term that has already been used in descriptions of locomotion (see our references in the 2nd paragraph of Discussion). We added a definition and corresponding references in the Introduction.

In terms of sensory feedback, particularly group II input, it would be interesting to calculate if the conduction delay to the spinal cord at higher speeds would have a certain cutoff point at which it would no longer be timed effectively for phase transitions. This could reinforce your point.

This is an interesting proposition but it is unlikely to be a factor over the range of speeds that we investigated (0.1 to 1.0 m/s). Assuming that group II afferents transmit their signals to spinal circuits at a latency of 10-20 ms, this is more than enough time to affect phase transitions, even at the highest speed considered. This might be a factor at very high speeds (e.g. galloping) or in small animals with high stepping frequencies.

Results.

The assertion that intact cats are inconsistent in terms of walking at slow speeds needs to be bolstered. For example, if a raised platform were built for a tray of food, would the intact cat consistently walk at slower speeds and eat? I suspect so. By the same token, would they walk slowly during bipedal walking? It is pretty easy to check this. Also, reports from the literature show differential effects of runway versus treadmill gait analysis, specifically when afferent input is removed.

The Reviewer is correct that raising a platform for a food tray or even having intact cats walk with their hindlimbs only (with forelimbs on a stationary platform) may allow for consistent stepping at slow speeds (0.1 – 0.3 m/s). However, this effectively removes voluntary control of locomotion and makes the pattern more automatic (spinal + limb sensory feedback). These examples provide additional specific contexts, and we have already mentioned (see above) that slow locomotion of intact cat is context dependent. 

"We believe that intact animals walking on a treadmill..." Citations for this? Certainly, this is not a new point.

No, this is not new. We changed the sentence and added a reference to the statement: “Intact animals walking on a treadmill use visual cues and supraspinal signals to adjust their speed and maintain a fixed position relative to the external space with reference to Salinas et al. (Salinas, M.M., Wilken, J M, and Dingwell, J B, "How humans use visual optic flow to regulate stepping during walking," Gait. Posture. 57, 15-20, 2017).

The presentation of the results is somewhat disjointed. The intact data is presented for tied and splitbelt results, but this is not addressed explicitly until figure 4. Would it not be better to create a figure incorporating both intact and modelling data and present the intact data where appropriate?

We tried to do this initially, but this way required changing the style of the whole paper and we decided against this idea. Therefore, we prefer to keep the presentation of results as it is now. 

Regarding the role of sensory feedback being especially important at low speeds, it is interesting that egr3+ mice (lacking spindle input) show an inability to walk at high speeds >40 cm/s but can walk at lower speeds (up to 7 cm/s) (Takeoka et al 2014). Similar findings were found with a lesion affecting Group I afferents in general (Takeoka and Arber 2019). Also, Grillner and colleagues show that cats can produce fictive locomotion in the absence of sensory input.

In the Takeoka experiments it is difficult to assess the effect of removing somatosensory feedback because animals can simply decide to not step at higher speeds to avoid injury. Their mice deprived of somatosensory feedback can walk at slow speeds, likely thanks to voluntary commands, and cannot do so at higher speeds because (1) maybe somatosensory feedback is indeed necessary and/or (2) because they feel threatened because of impaired posture and poor control in general. In other words, they choose to not walk at faster speeds to avoid injury.

Fictive locomotion by definition is without phasic somatosensory feedback as the animals are curarized or studies are performed in isolated spinal cord preparations. Depending on the preparation, pharmacology or brainstem stimulation is required to evoke fictive locomotion. If animals are deafferented, pharmacology or brainstem stimulation are required to induce fictive locomotion to offset the loss of spinal neuronal excitability provided by primary afferents. At the same time, our preliminary analysis of old fictive locomotion data in the University of Manitoba Spinal Cord center (Drs. Markin and Rybak had an official access to these data base during our collaboration with Dr. David McCrea) has shown that the frequency of stable fictive locomotion in cats usually exceeded 0.6 - 0.7 Hz, which approximately corresponds to the speed above 0.3 - 0.4 m/s. These data and estimation are just approximate; they have not been statistically analyzed and published and hence have not been included in our paper.

Discussion. The statement that sensory feedback is required for animals to locomote may need to be qualified. Animals need some sensory feedback to locomote is perhaps better. For example, lesion studies by Rossignol in the early 2000s showed that cutaneous feedback from the paw was seemingly quite critical (in spinal cats). Also, see previous comments above.

We changed this to: “… requires some sensory feedback to locomote, …”

Figures

Figure 1C. This figure is somewhat confusing. If intact cats do not walk (arrow), how are the data for swing and stance computed? Also raw traces would be useful to indicate that there is variability. Also, while duration is useful, would you not want to illustrate the co-efficient of variation as well as another way to show that the stepping pattern was inconsistent?

This is probably a misunderstanding. The left panel of Fig. 1C superimposes data of intact cats from panel A (with speed range from 0.4 m/s to 1.0 m/s) and data from spinal cats from panel B (with speed range from 0.1 m/s and 1.0 m/s). Therefore, the left part of this left panel 1C (with speed range from 0.1 m/s to 0.4 m/s (pointed out by the arrow) corresponds only to spinal cats (not to intact cats). The standard deviations of all measurements are shown. All these figures were reproduced from the previous publications. We did not apply new statistical analysis to these previously published data/figures.

Figure 4. 'All supraspinal drives (and their suppression of sensory feedback) are eliminated from the schematic shown in A. ' However, it is labelled 'brainstem drives,' which is confusing. Moreover, many of the abbreviations are confusing. Do you need l-SF-E1 in the figure, or could you call it 'Feedback 1' and then refer to l-SF-E1 in the legend? The same goes for βr, etc. Can they move to the legend?

In the intact model (Fig. 4A), we have supraspinal drives (𝛼𝐿 and 𝛼𝑅, and  𝛾𝐿 and 𝛾𝑅 ), some of which provide presynaptic inhibition of sensory feedback (SF-E1 and SF-E2) as shown in Fig. 4A. In spinaltransected model (Fig. 4B), the above brainstem drives and their effects (presynaptic inhibition) on both feedback types are eliminated (therefore, there is no label “Brainstem drives in Fig. 4B). Also, we do not see a strong reason to change the feedback names, since they are explained in the text.

I appreciate the detail of these figures, but they are difficult to conceptualize. They are useful in the context of 3C. Perhaps move this figure to supplementary and then show the proposed schematics for the system operating at slow, medium, and fast speeds in a replacement figure?

We apologize for the resistance, but we would like to keep the current presentation.

There is a lack of raw data (models or experimental) data reinforcing the figures. I would add these to all figures, which would nicely complement the graphs.

These raw data can be found in the cited manuscripts. It would be the same figures.

Author response:

Public Reviews:

Reviewer #1 (Public Review):

Summary:

In their paper, Zhan et al. have used Pf genetic data from simulated data and Ghanaian field samples to elucidate a relationship between multiplicity of infection (MOI) (the number of distinct parasite clones in a single host infection) and force of infection (FOI). Specifically, they use sequencing data from the var genes of Pf along with Bayesian modeling to estimate MOI individual infections and use these values along with methods from queueing theory that rely on various assumptions to estimate FOI. They compare these estimates to known FOIs in a simulated scenario and describe the relationship between these estimated FOI values and another commonly used metric of transmission EIR (entomological inoculation rate).

This approach does fill an important gap in malaria epidemiology, namely estimating the force of infection, which is currently complicated by several factors including superinfection, unknown duration of infection, and highly genetically diverse parasite populations. The authors use a new approach borrowing from other fields of statistics and modeling and make extensive efforts to evaluate their approach under a range of realistic sampling scenarios. However, the write-up would greatly benefit from added clarity both in the description of methods and in the presentation of the results. Without these clarifications, rigorously evaluating whether the author's proposed method of estimating FOI is sound remains difficult. Additionally, there are several limitations that call into question the stated generalizability of this method that should at minimum be further discussed by authors and in some cases require a more thorough evaluation.

Major comments:

(1) Description and evaluation of FOI estimation procedure.

a. The methods section describing the two-moment approximation and accompanying appendix is lacking several important details. Equations on lines 891 and 892 are only a small part of the equations in Choi et al. and do not adequately describe the procedure notably several quantities in those equations are never defined some of them are important to understand the method (e.g. A, S as the main random variables for inter-arrival times and service times, aR and bR which are the known time average quantities, and these also rely on the squared coefficient of variation of the random variable which is also never introduced in the paper). Without going back to the Choi paper to understand these quantities, and to understand the assumptions of this method it was not possible to follow how this works in the paper. At a minimum, all variables used in the equations should be clearly defined. 

We thank the reviewer for this useful comment. We plan to clarify the method, including all the relevant variables in our revised manuscript. The reviewer is correct in pointing out that there are more sections and equations in Choi et al., including the derivation of an exact expression for the steady-state queue-length distribution and the two-moment approximation for the queue-length distribution. Since only the latter was directly utilized in our work, we included in the first version of our manuscript only material on this section and not the other. We agree with the reviewer on readers benefiting from additional information on the derivation of the exact expression for the steady-state queue-length distribution. Therefore, we will summarize the derivation of this expression in our revised manuscript. Regarding the assumptions of the method we applied, especially those for going from the exact expression to the two-moment approximation, we did describe these in the Materials and Methods of our manuscript. We recognize from this comment that the writing and organization of this information may not have been sufficiently clear. We had separated the information on this method into two parts, with the descriptive summary placed in the Materials and Methods and the equations or mathematical formula placed in the Appendix. This can make it difficult for readers to connect the two parts and remember what was introduced earlier in the Materials and Methods when reading the equations and mathematical details in the Appendix. For our revised manuscript, we plan to cover both parts in the Materials and Methods, and to provide more of the technical details in one place, which will be easier to understand and follow.

b. Additionally, the description in the main text of how the queueing procedure can be used to describe malaria infections would benefit from a diagram currently as written it's very difficult to follow. 

We thank the reviewer for this suggestion. We will add a diagram illustrating the connection between the queueing procedure and malaria transmission.

c. Just observing the box plots of mean and 95% CI on a plot with the FOI estimate (Figures 1, 2, and 10-14) is not sufficient to adequately assess the performance of this estimator. First, it is not clear whether the authors are displaying the bootstrapped 95%CIs or whether they are just showing the distribution of the mean FOI taken over multiple simulations, and then it seems that they are also estimating mean FOI per host on an annual basis. Showing a distribution of those per-host estimates would also be helpful. Second, a more quantitative assessment of the ability of the estimator to recover the truth across simulations (e.g. proportion of simulations where the truth is captured in the 95% CI or something like this) is important in many cases it seems that the estimator is always underestimating the true FOI and may not even contain the true value in the FOI distribution (e.g. Figure 10, Figure 1 under the mid-IRS panel). But it's not possible to conclude one way or the other based on this visualization. This is a major issue since it calls into question whether there is in fact data to support that these methods give good and consistent FOI estimates. 

There appears to be some confusion on what we display in some key figures. We will clarify this further both here and in the revised text. In Figures 1, 2, and 10-14, we displayed the bootstrapped distributions including the 95% CIs. These figures do not show the distribution of the mean FOI taken over multiple simulations. We estimated mean FOI on an annual basis per host in the following sense. Both of our proposed methods require either a steady-state queue length distribution, or moments of this distribution for FOI inference. However, we only have one realization or observation for each individual host, and we do not have access to either the time-series observation of a single individual’s MOI or many realizations of a single individual’s MOI at the same sampling time. This is typically the case for empirical data, although numerical simulations could circumvent this limitation and generate such output. Nonetheless, we do have a queue length distribution at the population level for both the simulation output and the empirical data, which can be obtained by simply aggregating MOI estimates across all sampled individuals. We use this population-level queue length distribution to represent and approximate the steady-state queue length distribution at the individual level. Such representation or approximation does not consider explicitly any individual heterogeneity due to biology or transmission. The estimated FOI is per host in the sense of representing the FOI experienced by an individual host whose queue length distribution is approximated from the collection of all sampled individuals. The true FOI per host per year in the simulation output is obtained from dividing the total FOI of all hosts per year by the total number of all hosts. Therefore, our estimator, combined with the demographic information on population size, is for the total number of Plasmodium falciparum infections acquired by all individual hosts in the population of interest per year.

We evaluated the impact of individual heterogeneity on FOI inference by introducing individual heterogeneity into the simulations. With a considerable amount of transmission heterogeneity across individuals (namely 2/3 of the population receiving more than 90% of all bites whereas the remaining 1/3 receives the rest of the bites), our two methods exhibit a similar performance than those of the homogeneous transmission scenarios.

Concerning the second point, we will add a quantitative assessment of the ability of the estimator to recover the truth across simulations and include this information in the legend of each figure. In particular, we will provide the proportion of simulations where the truth is captured by the entire bootstrap distribution, in addition to some measure of relative deviation, such as the relative difference between the true FOI value and the median of the bootstrap distribution for the estimate. This assessment will be a valuable addition, but please note that the comparisons we have provided in a graphical way do illustrate the ability of the methods to estimate “sensible” values, close to the truth despite multiple sources of errors. “Close” is here relative to the scale of variation of FOI in the field and to the kind of precision that would be useful in an empirical context. From a practical perspective based on the potential range of variation of FOI, the graphical results already illustrate that the estimated distributions would be informative.

d. Furthermore the authors state in the methods that the choice of mean and variance (and thus second moment) parameters for inter-arrival times are varied widely, however, it's not clear what those ranges are there needs to be a clear table or figure caption showing what combinations of values were tested and which results are produced from them, this is an essential component of the method and it's impossible to fully evaluate its performance without this information. This relates to the issue of selecting the mean and variance values that maximize the likelihood of observing a given distribution of MOI estimates, this is very unclear since no likelihoods have been written down in the methods section of the main text, which likelihood are the authors referring to, is this the probability distribution of the steady state queue length distribution? At other places the authors refer to these quantities as Maximum Likelihood estimators, how do they know they have found the MLE? There are no derivations in the manuscript to support this. The authors should specify the likelihood and include in an appendix an explanation of why their estimation procedure is in fact maximizing this likelihood, preferably with evidence of the shape of the likelihood, and how fine the grid of values they tested is for their mean and variance since this could influence the overall quality of the estimation procedure. 

We thank the reviewer for pointing out these aspects of the work that can be further clarified. We will specify the ranges for the choice of mean and variance parameters for inter-arrival times as well as the grid of values tested in the corresponding figure caption or in a separate supplementary table. We maximized the likelihood of observing the set of individual MOI estimates in a sampled population given steady queue length distributions (with these distributions based on the two-moment approximation method for different combinations of the mean and variance of inter-arrival times). We will add a section to either the Materials and Methods or the Appendix in our revised manuscript including an explicit formulation of the likelihood.

We will add example figures on the shape of the likelihood to the Appendix. We will also test how choices of the grid of values influence the overall quality of the estimation procedure. Specifically, we will further refine the grid of values to include more points and examine whether the results of FOI inference are consistent and robust against each other.

(2) Limitation of FOI estimation procedure.

a. The authors discuss the importance of the duration of infection to this problem. While I agree that empirically estimating this is not possible, there are other options besides assuming that all 1-5-year-olds have the same duration of infection distribution as naïve adults co-infected with syphilis. E.g. it would be useful to test a wide range of assumed infection duration and assess their impact on the estimation procedure. Furthermore, if the authors are going to stick to the described method for duration of infection, the potentially limited generalizability of this method needs to be further highlighted in both the introduction, and the discussion. In particular, for an estimated mean FOI of about 5 per host per year in the pre-IRS season as estimated in Ghana (Figure 3) it seems that this would not translate to 4-year-old being immune naïve, and certainly this would not necessarily generalize well to a school-aged child population or an adult population. 

The reviewer is indeed correct about the difficulty of empirically measuring the duration of infection for 1-5-year-olds, and that of further testing whether these 1-5-year-olds exhibit the same distribution for duration of infection as naïve adults co-infected with syphilis. We will nevertheless continue to use the described method for duration of infection, while better acknowledging and discussing the limitations this aspect of the method introduces. We note that the infection duration from the historical clinical data we have relied on, is being used in the malaria modeling community as one of the credible sources for this parameter of untreated natural infections in malaria-naïve individuals in malaria-endemic settings of Africa (e.g. in the agent-based model OpenMalaria, see 1).

It is important to emphasize that the proposed methods apply to the MOI estimates for naïve or close to naïve patients. They are not suitable for FOI inference for the school-aged children and the adult populations of high-transmission endemic regions, since individuals in these age classes have been infected many times and their duration of infection is significantly shortened by their immunity. To reduce the degree of misspecification in infection duration and take full advantage of our proposed methods, we will emphasize in the revision the need to prioritize in future data collection and sampling efforts the subpopulation class who has received either no infection or a minimum number of infections in the past, and whose immune profile is close to that of naïve adults, for example, infants. This emphasis is aligned with the top priority of all intervention efforts in the short term, which is to monitor and protect the most vulnerable individuals from severe clinical symptoms and death.

Also, force of infection for naïve hosts is a key basic parameter for epidemiological models of a complex infectious disease such as falciparum malaria, whether for agent-based formulations or equation-based ones. This is because force of infection for non-naïve hosts is typically a function of their immune status and the force of infection of naïve hosts. Thus, knowing the force of infection of naïve hosts can help parameterize and validate these models by reducing degrees of freedom.

b. The evaluation of the capacity parameter c seems to be quite important and is set at 30, however, the authors only describe trying values of 25 and 30, and claim that this does not impact FOI inference, however it is not clear that this is the case. What happens if the carrying capacity is increased substantially? Alternatively, this would be more convincing if the authors provided a mathematical explanation of why the carrying capacity increase will not influence the FOI inference, but absent that, this should be mentioned and discussed as a limitation. 

Thank you for this question. We will investigate more values of the parameter c systematically, including substantially higher ones. We note however that this quantity is the carrying capacity of the queuing system, or the maximum number of blood-stage strains that an individual human host can be co-infected with. We do have empirical evidence for the value of the latter being around 20 (2). This observed value provides a lower bound for parameter c. To account for potential under-sampling of strains, we thus tried values of 25 and 30 in the first version of our manuscript.

In general, this parameter influences the steady-state queue length distribution based on the two-moment approximation, more specifically, the tail of this distribution when the flow of customers/infections is high. Smaller values of parameter c put a lower cap on the maximum value possible for the queue length distribution. The system is more easily “overflowed”, in which case customers (or infections) often find that there is no space available in the queuing system/individual host upon their arrival. These customers (or infections) will not increment the queue length. The parameter c has therefore a small impact for the part of the grid resulting in low flows of customers/infection, for which the system is unlikely to be overflowed. The empirical MOI distribution centers around 4 or 5 with most values well below 10, and only a small fraction of higher values between 15-20 (2). When one increases the value of c, the part of the grid generating very high flows of customers/infections results in queue length distributions with a heavy tail around large MOI values that are not supported by the empirical distribution. We therefore do not expect that substantially higher values for parameter c would change either the relative shape of the likelihood or the MLE.

Reviewer #2 (Public Review):

Summary:

The authors combine a clever use of historical clinical data on infection duration in immunologically naive individuals and queuing theory to infer the force of infection (FOI) from measured multiplicity of infection (MOI) in a sparsely sampled setting. They conduct extensive simulations using agent-based modeling to recapitulate realistic population dynamics and successfully apply their method to recover FOI from measured MOI. They then go on to apply their method to real-world data from Ghana before and after an indoor residual spraying campaign.

Strengths:

(1) The use of historical clinical data is very clever in this context. 

(2) The simulations are very sophisticated with respect to trying to capture realistic population dynamics. 

(3) The mathematical approach is simple and elegant, and thus easy to understand. 

Weaknesses: 

(1) The assumptions of the approach are quite strong and should be made more clear. While the historical clinical data is a unique resource, it would be useful to see how misspecification of the duration of infection distribution would impact the estimates. 

We thank the reviewer for bringing up the limitation of our proposed methods due to their reliance on a known and fixed duration of infection from historical clinical data. Please see our response to reviewer 1 comment 2a.

(2) Seeing as how the assumption of the duration of infection distribution is drawn from historical data and not informed by the data on hand, it does not substantially expand beyond MOI. The authors could address this by suggesting avenues for more refined estimates of infection duration. 

We thank the reviewer for pointing out a potential improvement to the work. We acknowledge that FOI is inferred from MOI, and thus is dependent on the information contained in MOI. FOI reflects risk of infection, is associated with risk of clinical episodes, and can relate local variation in malaria burden to transmission better than other proxy parameters for transmission intensity. It is possible that MOI can be as informative as FOI when one regresses the risk of clinical episodes and local variation in malaria burden with MOI. But MOI by definition is a number and not a rate parameter. FOI for naïve hosts is a key basic parameter for epidemiological models. This is because FOI of non-naïve hosts is typically a function of their immune status and the FOI of naïve hosts. Thus, knowing the FOI of naïve hosts can help parameterize and validate these models by reducing degrees of freedom. In this sense, we believe the transformation from MOI to FOI provides a useful step.

Given the difficulty of measuring infection duration, estimating infection duration and FOI simultaneously appears to be an attractive alternative, as the referee pointed out. This will require however either cohort studies or more densely sampled cross-sectional surveys due to the heterogeneity in infection duration across a multiplicity of factors. These kinds of studies have not been, and will not be, widely available across geographical locations and time. This work aims to utilize more readily available data, in the form of sparsely sampled single-time-point cross-sectional surveys.

(3) It is unclear in the example how their bootstrap imputation approach is accounting for measurement error due to antimalarial treatment. They supply two approaches. First, there is no effect on measurement, so the measured MOI is unaffected, which is likely false and I think the authors are in agreement. The second approach instead discards the measurement for malaria-treated individuals and imputes their MOI by drawing from the remaining distribution. This is an extremely strong assumption that the distribution of MOI of the treated is the same as the untreated, which seems unlikely simply out of treatment-seeking behavior. By imputing in this way, the authors will also deflate the variability of their estimates. 

We thank the reviewer for pointing out aspects of the work that can be further clarified. It is difficult to disentangle the effect of drug treatment on measurement, including infection status, MOI, and duration of infection. Thus, we did not attempt to address this matter explicitly in the original version of our manuscript. Instead, we considered two extreme scenarios which bound reality, well summarized by the reviewer. First, if drug treatment has had no impact on measurement, the MOI of the drug-treated 1-5-year-olds would reflect their true underlying MOI. We can then use their MOI directly for FOI inference. Second, if the drug treatment had a significant impact on measurement, i.e., if it completely changed the infection status, MOI, and duration infection of drug-treated 1-5-year-olds, we would need to either exclude those individuals’ MOI or impute their true underlying MOI. We chose to do the latter in the original version of the manuscript. If those 1-5-year-olds had not received drug treatment, they would have had similar MOI values than those of the non-treated 1-5-year-olds. We can then impute their MOI by sampling from the MOI estimates of non-treated 1-5-year-olds.

The reviewer is correct in pointing out that this imputation does not add additional information and can potentially deflate the variability of MOI distributions, compared to simply throwing or excluding those drug-treated 1-5-year-olds from the analysis. Thus, we can include in our revision FOI estimates with the drug-treated 1-5-year-olds excluded in the estimation.

- For similar reasons, their imputation of microscopy-negative individuals is also questionable, as it also assumes the same distributions of MOI for microscopy-positive and negative individuals. 

We imputed the MOI values of microscopy-negative but PCR-positive 1-5-year-olds by sampling from the microscopy-positive 1-5-year-olds, effectively assuming that both have the same, or similar, MOI distributions. We did so because there is a weak relationship in our Ghana data between the parasitemia level of individual hosts and their MOI (or detected number of var genes, on the basis of which the MOI values themselves were estimated). Parasitemia levels underlie the difference in detection sensitivity of PCR and microscopy.

We will elaborate on this matter in our revised manuscript and include information from our previous and on-going work on the weak relationship between MOI/the number of var genes detected within an individual host and their parasitemia levels. We will also discuss potential reasons or hypotheses for this pattern.

Reviewer #3 (Public Review):

Summary: 

It has been proposed that the FOI is a method of using parasite genetics to determine changes in transmission in areas with high asymptomatic infection. The manuscript attempts to use queuing theory to convert multiplicity of infection estimates (MOI) into estimates of the force of infection (FOI), which they define as the number of genetically distinct blood-stage strains. They look to validate the method by applying it to simulated results from a previously published agent-based model. They then apply these queuing theory methods to previously published and analysed genetic data from Ghana. They then compare their results to previous estimates of FOI. 

Strengths: 

It would be great to be able to infer FOI from cross-sectional surveys which are easier and cheaper than current FOI estimates which require longitudinal studies. This work proposes a method to convert MOI to FOI for cross-sectional studies. They attempt to validate this process using a previously published agent-based model which helps us understand the complexity of parasite population genetics. 

Weaknesses: 

(1) I fear that the work could be easily over-interpreted as no true validation was done, as no field estimates of FOI (I think considered true validation) were measured. The authors have developed a method of estimating FOI from MOI which makes a number of biological and structural assumptions. I would not call being able to recreate model results that were generated using a model that makes its own (probably similar) defined set of biological and structural assumptions a validation of what is going on in the field. The authors claim this at times (for example, Line 153 ) and I feel it would be appropriate to differentiate this in the discussion. 

We thank the reviewer for this comment, although we think there is a mis-understanding on what can and cannot be practically validated in the sense of a “true” measure of FOI that would be free from assumptions for a complex disease such as malaria. We would not want the results to be over-interpreted and will extend the discussion of what we have done to test the methods. We note that for the performance evaluation of statistical methods, the use of simulation output is quite common and often a necessary and important step. In some cases, the simulation output is generated by dynamical models, whereas in others, by purely descriptive ones. All these models make their own assumptions which are necessarily a simplification of reality. The stochastic agent-based model (ABM) of malaria transmission utilized in this work has been shown to reproduce several important patterns observed in empirical data from high-transmission regions, including aspects of strain diversity which are not represented in simpler models.

In what sense this ABM makes a set of biological and structural assumptions which are “probably similar” to those of the queuing methods we present, is not clear to us. We agree that relying on models whose structural assumptions differ from those of a given method or model to be tested, is the best approach. Our proposed methods for FOI inference based on queuing theory rely on the duration of infection distribution and the MOI distribution among sampled individuals, both of which can be direct outputs from the ABM. But these methods are agnostic on the specific mechanisms or biology underlying the regulation of duration and MOI.

Another important point raised by this comment is what would be the “true” FOI value against which to validate our methods. Empirical MOI-FOI pairs for FOI measured directly by tracking cohort studies are still lacking. There are potential measurement errors for both MOI and FOI because the polymorphic markers typically used in different cohort studies cannot differentiate hyper-diverse antigenic strains fully and well (5). Also, these cohort studies usually start with drug treatment. Alternative approaches do not provide a measure of true FOI, in the sense of the estimation being free from assumptions. For example, one approach would be to fit epidemiological models to densely sampled/repeated cross-sectional surveys for FOI inference. In this case, no FOI is measured directly and further benchmarked against fitted FOI values. The evaluation of these models is typically based on how well they can capture other epidemiological quantities which are more easily sampled or measured, including prevalence or incidence. This is similar to what is done in this work. We selected the FOI values that maximize the likelihood of observing the given distribution of MOI estimates. Furthermore, we paired our estimated FOI value for the empirical data from Ghana with another independently measured quantity EIR (Entomological Inoculation Rate), typically used in the field as a measure of transmission intensity. We check whether the resulting FOI-EIR point is consistent with the existing set of FOI-EIR pairs and the relationship between these two quantities from previous studies. We acknowledge that as for model fitting approaches for FOI inference, our validation is also indirect for the field data.

Prompted by the reviewer’s comment, we will discuss this matter in more detail in our revised manuscript, including clarifying further certain basic assumptions of our agent-based model, emphasizing the indirect nature of the validation with the field data and the existing constraints for such validation.

(2) Another aspect of the paper is adding greater realism to the previous agent-based model, by including assumptions on missing data and under-sampling. This takes prominence in the figures and results section, but I would imagine is generally not as interesting to the less specialised reader. The apparent lack of impact of drug treatment on MOI is interesting and counterintuitive, though it is not really mentioned in the results or discussion sufficiently to allay my confusion. I would have been interested in understanding the relationship between MOI and FOI as generated by your queuing theory method and the model. It isn't clear to me why these more standard results are not presented, as I would imagine they are outputs of the model (though happy to stand corrected - it isn't entirely clear to me what the model is doing in this manuscript alone). 

We thank the reviewer for this comment. We will add supplementary figures for the MOI distributions generated by the queuing theory method (i.e., the two-moment approximation method) and our agent-based model in our revised manuscript.

In the first version of our manuscript, we considered two extreme scenarios which bound the reality, instead of simply assuming that drug treatment does not impact the infection status, MOI, and duration of infection. See our response to reviewer 2 point (3). The resulting FOI estimates differ but not substantially across the two extreme scenarios, partially because drug-treated individuals’ MOI distribution is similar to that of non-treated individuals (or the apparent lack of drug treatment on MOI as pointed by the referee). We will consider potentially adding some formal test to quantify the difference between the two MOI distributions and how significant the difference is. We will discuss which of the two extreme scenarios reality is closer to, given the result of the formal test. We will also discuss in our revision possible reasons/hypotheses underlying the impact of drug treatment on MOI from the perspective of the nature, efficiency, and duration of the drugs administrated.

Regarding the last point of the reviewer, on understanding the relationship between MOI and FOI, we are not fully clear about what was meant. We are also confused about the statement on what the “model is doing in this manuscript alone”. We interpret the overall comment as the reviewer suggesting a better understanding of the relationship between MOI and FOI, either between their distributions, or the moments of their distributions, perhaps by fitting models including simple linear regression models. This approach is in principle possible, but it is not the focus of this work. It will be equally difficult to evaluate the performance of this alternative approach given the lack of MOI-FOI pairs from empirical settings with directly measured FOI values (from large cohort studies). Moreover, the qualitative relationship between the two quantities is intuitive. Higher FOI values should correspond to higher MOI values. Less variable FOI values should correspond to more narrow or concentrated MOI distributions, whereas more variable FOI values should correspond to more spread-out ones. We will discuss this matter in our revised manuscript.

(3) I would suggest that outside of malaria geneticists, the force of infection is considered to be the entomological inoculation rate, not the number of genetically distinct blood-stage strains. I appreciate that FOI has been used to explain the latter before by others, though the authors could avoid confusion by stating this clearly throughout the manuscript. For example, the abstract says FOI is "the number of new infections acquired by an individual host over a given time interval" which suggests the former, please consider clarifying. 

We thank the reviewer for this helpful comment as it is fundamental that there is no confusion on the basic definitions. EIR, the entomological inoculation rate, is closely related to the force of infection but is not equal to it. EIR focuses on the rate of arrival of infectious bites and is measured as such by focusing on the mosquito vectors that are infectious and arrive to bite a given host. Not all these bites result in actual infection of the human host. Epidemiological models of malaria transmission clearly make this distinction, as FOI is defined as the rate at which a host acquires infection. This definition comes from more general models for the population dynamics of infectious diseases in general. (For diseases simpler than malaria, with no super-infection, the typical SIR models define the force of infection as the rate at which a susceptible individual becomes infected).  For malaria, force of infection refers to the number of blood-stage new infections acquired by an individual host over a given time interval. This distinction between EIR and FOI is the reason why studies have investigated their relationship, with the nonlinearity of this relationship reflecting the complexity of the underlying biology and how host immunity influences the outcome of an infectious bite.

We agree however with the referee that there could be some confusion in our definition resulting from the approach we use to estimate the MOI distribution (which provides the basis for estimating FOI). In particular, we rely on the non-existent to very low overlap of var repertoires among individuals with MOI=1, an empirical pattern we have documented extensively in previous work (See 2, 3, and 4). The method of var_coding and its Bayesian formulation rely on the assumption of negligible overlap. We note that other approaches for estimating MOI (and FOI) based on other polymorphic markers, also make this assumption (reviewed in _5). Ultimately, the FOI we seek to estimate is the one defined as specified above and in both the abstract and introduction, consistent with the epidemiological literature. We will include clarification in the introduction and discussion of this point in the revision.

(4) Line 319 says "Nevertheless, overall, our paired EIR (directly measured by the entomological team in Ghana (Tiedje et al., 2022)) and FOI values are reasonably consistent with the data points from previous studies, suggesting the robustness of our proposed methods". I would agree that the results are consistent, given that there is huge variation in Figure 4 despite the transformed scales, but I would not say this suggests a robustness of the method. 

We will modify the relevant sentences to use “consistent” instead of “robust”.

(5) The text is a little difficult to follow at times and sometimes requires multiple reads to understand. Greater precision is needed with the language in a few situations and some of the assumptions made in the modelling process are not referenced, making it unclear whether it is a true representation of the biology. 

We thank the reviewer for this comment. As also mentioned in the response to reviewer 1’s comments, we will reorganize and rewrite parts of the text in our revision to improve clarity.

References and Notes

(1) Maire, N. et al. A model for natural immunity to asexual blood stages of Plasmodium falciparum malaria in endemic areas. Am J Trop Med Hyg., 75(2 Suppl):19-31 (2006).

(2) Tiedje, K. E. et al. Measuring changes in Plasmodium falciparum census population size in response to sequential malaria control interventions. eLife, 12 (2023).

(3) Day, K. P. et al. Evidence of strain structure in Plasmodium falciparum var gene repertoires in children from Gabon, West Africa. Proc. Natl. Acad. Sci. U.S.A., 114(20), 4103-4111 (2017).

(4) Ruybal-Pesántez, S. et al. Population genomics of virulence genes of Plasmodium falciparum in clinical isolates from Uganda. Sci. Rep., 7(11810) (2017).

(5) Labbé, F. et al. Neutral vs. non-neutral genetic footprints of Plasmodium falciparum multiclonal infections. PLoS Comput Biol 19(1) (2023).

Author response:

We are grateful to the reviewers and the editorial team for their feedback and thorough revisions of our paper. We also appreciate their acknowledgement that this study represents a significant advancement in the field of reproductive neuroendocrinology and offers insights on the contribution of obesity vs melanocortin signaling in women’s fertility. In the revised version, we will provide a more detailed clarification of the data and methodology and adhere to the reviewers’ suggestions.

Please find below our answers to specific concerns in the public review:

Given the fact that mice lacking MC4R in Kiss1 neurons remained fertile despite some reproductive irregularities, the overall tone and some of the conclusions of the manuscript (e.g., from the abstract: "... Mc4r expressed in Kiss1 neurons is required for fertility in females") were overstated. Perhaps this can be described as a contributing pathway, but other mechanisms must also be involved in conveying metabolic information to the reproductive system.

We will tone down these statements throughout the manuscript to indicate that MC4R in Kiss1 neurons plays a role in the metabolic control of fertility (rather than “…is required for fertility”)

The mechanistic studies evaluating melanocortin signalling in Kiss1 neurons were all completed in ovariectomised animals (with and without exogenous hormones) that do not experience cyclical hormone changes. Such cyclical changes are fundamental to how these neurons function in vivo and may dynamically alter the way they respond to neuropeptides. Therefore, eliminating this variable makes interpretation difficult.

Mice lack true follicular and luteal phases and therefore it is impossible to separate estrogen-mediated changes from progesterone-mediated changes (e.g., in a proestrous female). Therefore, we use an ovariectomized female model in which we can generate a LH surge with an E2-replacement regimen [1]. This model enables us to focus on estrogen effects, exclude progesterone effects, and minimize variability. Inclusion of cycling females would make interpretation much more difficult.

(1) Bosch et al., 2013 Mol & Cell Endo; https://doi.org/10.1016/j.mce.2012.12.021

Use of the POMC-Cre to target ontogenetic inputs to Kiss1 neurons might have targeted a wider population of cells than intended.

POMC is transiently expressed during embryonic development in a portion of cells fated to be Kiss1 or NPY/AgRP neurons [1-2]. Therefore, this is a valid concern when crossing with a floxed mouse. However, use of AAVs in adult animals avoids this issue and leads to specific expression in POMC neurons [3]. This POMC-Cre mouse has been used extensively with AAVs to drive specific expression in POMC neurons by other laboratories [4-7]. Therefore, we are confident that our optogenetic studies have narrowly targeted POMC inputs.

(1) Padilla et al., 2010 Nat Med; https://doi.org/10.1038/nm.2126

(2) Lam et al., 2017 Mol Metab; https://doi.org/10.1016/j.molmet.2017.02.007

(3) Stincic et al., 2018 eNeuro; https://doi.org/10.1523/eneuro.0103-18.2018

(4) Fenselau et al., 2017 Nat Neuro; https://doi.org/10.1038/nn.4442

(5) Rau & Hentges, 2019 J Neuro; https://doi.org/10.1523/jneurosci.3193-18.2019

(6) Fortin et al., 2021 Nutrients; https://doi.org/10.3390/nu13051642

(7) Villa et al., 2024 J Neuro; https://doi.org/10.1523/jneurosci.0222-24.2024

Author Response:

Reviewer #1 (Public Review):

[...] The conclusions of the in vitro experiments using cultured hippocampal slices were well supported by the data, but aspects of the in vivo experiments and proteomic studies need additional clarification.

(1) In contrast to the in vitro experiments in which a γ-secretase inhibitor was used to exclude possible effects of Aβ, this possibility was not examined in in-vivo experiments assessing synapse loss and function (Figure 3) and cognitive function (Figure 4). The absence of plaque formation (Figure 4B) is not sufficient to exclude the possibility that Aβ is involved. The potential involvement of Aβ is an important consideration given the 4-month duration of protein expression in the in vivo studies.

Response: We appreciate the reviewer for raising this question. While our current data did not exclude the potential involvement of Aβ-induced toxicity in the synaptic and cognitive dysfunction observed in mice overexpressing β-CTF, addressing this directly remains challenging. Treatment with γ-secretase inhibitors could potentially shed light on this issue. However, treatments with γ-secretase inhibitors are known to lead to brain dysfunction by itself likely due to its blockade of the γ-cleavage of other essential molecules, such as Notch[1, 2]. As a result, this approach is unlikely to provide a definitive answer, which also prevents us from pursuing it further in vivo. We hope the reviewer understands this limitation and agrees to a discussion of this issue in the revised manuscript instead.

(2) The possibility that the results of the proteomic studies conducted in primary cultured hippocampal neurons depend in part on Aβ was also not taken into consideration.

Response: We thank the reviewer for raising this interesting question. In the revised manuscript, we plan to address this experimentally by using a γ-secretase inhibitor to investigate the potential contribution of Aβ in this study.

Likely impact of the work on the field, and the utility of the methods and data to the community:

The authors' use of sparse expression to examine the role of β-CTF on spine loss could be a useful general tool for examining synapses in brain tissue.

Response: We thank the reviewer for these comments. Indeed, it is a very robust assay and we would like to share this method with the scientific community as soon as possible.

Additional context that might help readers interpret or understand the significance of the work:

The discovery of BACE1 stimulated an international effort to develop BACE1 inhibitors to treat Alzheimer's disease. BACE1 inhibitors block the formation of β-CTF which, in turn, prevents the formation of Aβ and other fragments. Unfortunately, BACE1 inhibitors not only did not improve cognition in patients with Alzheimer's disease, they appeared to worsen it, suggesting that producing β-CTF actually facilitates learning and memory. Therefore, it seems unlikely that the disruptive effects of β-CTF on endosomes plays a significant role in human disease. Insights from the authors that shed further light on this issue would be welcome.

Response: We would like to express our gratitude to the reviewer for raising this interesting question. It remains puzzling why BACE1 inhibition has failed to yield benefits in AD patients, while amyloid clearance via Aβ antibodies has been shown to slow disease progression. One possible explanation is that pharmacological inhibition of BACE1 may not be as effective as genetic removal. Indeed, genetic depletion of BACE1 leads to the clearance of existing amyloid plaques[3], whereas its pharmacological inhibition slows plaque growth and prevents the formation of new plaques but does not stop the growth of the existing ones[4]. We think the negative results of BACE1 inhibitors in clinical trials may not be sufficient to rule out the potential contribution of β-CTF to AD pathogenesis. Given that cognitive function continues to deteriorate rapidly in plaque-free patients after 1.5 years of treatment with Aβ antibodies in phase three clinical studies[5], it is important to consider the possible role of other Aβ-related fragments, such as β-CTF. We will include some further discussion in the revised manuscript.

Reviewer #2 (Public Review):

Summary:

In this study, the authors investigate the potential role of other cleavage products of amyloid precursor protein (APP) in neurodegeneration. They combine in vitro and in vivo experiments, revealing that β-CTF, a product cleaved by BACE1, promotes synaptic loss independently of Aβ. Furthermore, they suggest that β-CTF may interact with Rab5, leading to endosomal dysfunction and contributing to the loss of synaptic proteins.

Response: We would like to thank the reviewer for his/her insightful suggestions. We have addressed the specific comments in following sections.

Weaknesses:

Most experiments were conducted in vitro using overexpressed β-CTF. Additionally, the study does not elucidate the mechanisms by which β-CTF disrupts endosomal function and induces synaptic degeneration.

Response: We would like to thank the reviewer for this insightful comment. While a significant portion of our experiments were conducted in vitro, the main findings were also confirmed in vivo (Figures 3 and 4). Repeating all the experiments in vivo would be challenging and may not be necessary. Regarding the use of overexpressed β-CTF, we acknowledge that this is a common issue in neurodegenerative disease studies. These diseases progress slowly over many years, sometimes even decades in patients. To model this progression in cell or mouse models within a time frame feasible for research, overexpression of certain proteins is often required. While not ideal, it is sometimes unavoidable. Since β-CTF levels are elevated in AD patients[6], its overexpression is a reasonable approach to investigate its potential effects.

We did not further investigate the mechanisms by which β-CTF disrupted endosomal function because our preliminary results align with previous findings. Kim et al. demonstrated that β-CTF recruits APPL1 (a Rab5 effector) via the YENPTY motif to Rab5 endosomes, where it stabilizes active GTP-Rab5, leading to pathologically accelerated endocytosis, endosome swelling and selectively impaired transport of Rab5 endosomes[6]. In our manuscript, we observed that co-expression of Rab5S34N with β-CTF effectively mitigated β-CTF-induced spine loss in hippocampal slice cultures (Figures 6I-J), indicating that Rab5 overactivation-induced endosomal dysfunction contributed to β-CTF-induced spine loss, which was consistent with their conclusions.

Reviewer #3 (Public Review):

Summary:

Most previous studies have focused on the contributions of Abeta and amyloid plaques in the neuronal degeneration associated with Alzheimer's disease, especially in the context of impaired synaptic transmission and plasticity which underlies the impaired cognitive functions, a hallmark in AD. But processes independent of Abeta and plaques are much less explored, and to some extent, the contributions of these processes are less well understood. Luo et all addressed this important question with an array of approaches, and their findings generally support the contribution of beta-CTF-dependent but non-Abeta-dependent process to the impaired synaptic properties in the neurons. Interestingly, the above process appears to operate in a cell-autonomous manner. This cell-autonomous effect of beta-CTF as reported here may facilitate our understanding of some potentially important cellular processes related to neurodegeneration. Although these findings are valuable, it is key to understand the probability of this process occurring in a more natural condition, such as when this process occurs in many neurons at the same time. This will put the authors' findings into a context for a better understanding of their contribution to either physiological or pathological processes, such as Alzheimer's. The experiments and results using the cell system are quite solid, but the in vivo results are incomplete and hence less convincing (see below). The mechanistic analysis is interesting but primitive and does not add much more weight to the significance. Hence, further efforts from the authors are required to clarify and solidify their results, in order to provide a complete picture and support for the authors' conclusions.

Response: We would like to thank the reviewer for the constructive suggestions. We have addressed the specific comments in following sections.

Strengths:

(1) The authors have addressed an interesting and potentially important question

(2) The analysis using the cell system is solid and provides strong support for the authors' major conclusions. This analysis has used various technical approaches to support the authors' conclusions from different aspects and most of these results are consistent with each other.

Response: We would like to thank the reviewer for these comments.

Weaknesses:

(1) The relevance of the authors' major findings to the pathology, especially the Abeta-dependent processes is less clear, and hence the importance of these findings may be limited.

Response: We would like to thank the reviewer for pointing this out. Phase 3 clinical trial data for Aβ antibodies show that cognitive function continues to decline rapidly, even in plaque-free patients, after 1.5 years of treatment[5]. This suggests that plaque-independent mechanisms may drive AD progression. Therefore, it is crucial to consider the potential contributions of other Aβ species or related fragments, such as alternative forms of Aβ and β-CTF. While it is too early to definitively predict how β-CTF contributes to AD progression, it is notable that β-CTF, rather than Aβ, induced synaptic deficits in mice, which recapitulates a key pathological feature of AD. Ultimately, the true role of β-CTF in AD pathogenesis can only be confirmed through clinical studies.

(2) In vivo analysis is incomplete, with certain caveats in the experimental procedures and some of the results need to be further explored to confirm the findings.

Response: We would like to thank the reviewer for this suggestion. We plan to correct these caveats in the revised manuscript.

(3) The mechanistic analysis is rather primitive and does not add further significance.

Response: We would like to thank the reviewer for this comment. We did not delve further into the underlying mechanisms because our analysis indicates that Rab5 dysfunction underlies β-CTF-induced endosomal dysfunction, which is consistent with another study and has been addressed in detail there[6]. We hope the reviewer could understand that our focus in this paper is on how β-CTF triggers synaptic deficits, which is why we did not investigate the mechanisms of β-CTF-induced endosomal dysfunction further.

References:

1. GüNER G, LICHTENTHALER S F. The substrate repertoire of γ-secretase/presenilin [J]. Seminars in cell & developmental biology, 2020, 105: 27-42.
2. DOODY R S, RAMAN R, FARLOW M, et al. A phase 3 trial of semagacestat for treatment of Alzheimer's disease [J]. The New England journal of medicine, 2013, 369(4): 341-50.
3. HU X, DAS B, HOU H, et al. BACE1 deletion in the adult mouse reverses preformed amyloid deposition and improves cognitive functions [J]. The Journal of experimental medicine, 2018, 215(3): 927-40.
4. PETERS F, SALIHOGLU H, RODRIGUES E, et al. BACE1 inhibition more effectively suppresses initiation than progression of β-amyloid pathology [J]. Acta Neuropathol, 2018, 135(5): 695-710.
5. SIMS J R, ZIMMER J A, EVANS C D, et al. Donanemab in Early Symptomatic Alzheimer Disease: The TRAILBLAZER-ALZ 2 Randomized Clinical Trial [J]. Jama, 2023, 330(6): 512-27.
6. KIM S, SATO Y, MOHAN P S, et al. Evidence that the rab5 effector APPL1 mediates APP-βCTF-induced dysfunction of endosomes in Down syndrome and Alzheimer's disease [J]. Molecular psychiatry, 2016, 21(5): 707-16.

Author Response

eLife assessment

Tilk and colleagues present a computational analysis of tumor transcriptomes to investigate the hypothesis that the large number of somatic mutations in some tumors is detrimental such that these detrimental effects are mitigated by an up-regulation by pathways and mechanisms that prevent protein misfolding. The authors address this question by fitting a model that explains the log expression of a gene as a linear function of the log number of mutations in the tumor and show that specific categories of genes (proteasome, chaperones, ...) tend to be upregulated in tumors with a large number of somatic mutations. Some of the associations presented could arise through confounding, but overall the authors present solid evidence that mutational load is associated with higher expression of genes involved in mitigation of protein misfolding – an important finding with general implications for our understanding of cancer evolution.

We thank the reviewers for these kind words. The summary statement and public review highlight our work in understanding how human tumors phenotypically respond to mutational load by assessing changes in gene expression. This work provides a mechanistic underpinning to our previous finding that the accumulation of passenger mutations in tumors creates a substantial cost because even substantially damaging passenger mutations can fix in non-recombining clonal tumor lineages. At the same time, we believe the summary statement and the public review do not mention a key remaining part of our paper that validates our findings and establishes causal connections between protein misfolding due to coding passenger mutations and tumor fitness. Specifically, we replicate and cross-validate our findings in human tumors by examining expression responses in an independent dataset of cancer cell lines (CCLE), where we demonstrate similar expression responses to an accumulation of mutations, indicating generic, cell intrinsic responses. We then establish a causal link by demonstrating that mitigation of protein misfolding through protein degradation and re-folding is necessary for high mutational load cancer cells to maintain viability through perturbation experiments via shRNA known-down and treatment with targeted agents. These analyses and results are important because they show that the adaptive responses we observe are evidence of a generic, cell intrinsic phenomenon that cannot be explained by organismal effects, such as aging, changes in the immune system or microenvironment. 

Joint Public Review:

Tilk and colleagues present a computational investigation of tumor transcriptomes to investigate the hypothesis that the large number of somatic mutations in some tumors is detrimental and that these detrimental effects are mitigated by an up-regulation by pathways and mechanisms that prevent protein misfolding.

The authors address this question by fitting a model that explains the log expression of a gene as a linear function of the log number of mutations in the tumor and additional effects for tumor homogeneity and type. This analysis identified a large number of genes (5000) that are more highly expressed at high mutational load at a FDR of 0.05. These genes are enriched in many core categories, most prominently in the proteasome, translation, and mitochondral translation. The authors then proceed to investigate specific categories of upregulated genes further.

The individual reviews, and the discussion among the reviewers, raised several issues that could potentially undermine or weaken some of the findings presented in this paper.

1) Systematic differences in expression of some genes from one tumor class to another might generate spurious associations with mutational load (ML), which would affect the results presented in Figs 1 and 3. The case of a causal link between ML and over-expression of genes that mitigate deleterious effects of misfolding would be stronger if these results were replicated within single cancer types with many samples with different ML (similar to how Fig S6 relates to Fig 3). A related concern might be an association between increased variance of expression and ML. The compositional nature of expression data could generate trends like the ones shown in Fig. 2 with changing variance.

We agree with the reviewers that possible confounders should be considered since TCGA data is heterogeneous. In this paper, we investigated possible confounders such as multicollinearity with different mutational types (SNVs and CNVs), controlled for expression responses within cancer types in the GLMM, and used the jackknifing procedure to ensure that no one cancer type dominates the signal. However, in principle unknown hidden confounders could remain, which is why a large part of our paper was focused on validating these effects in an independent dataset (CCLE) where many other covariates are not relevant (immune system, donor variability, stage, age, sex, etc.). Importantly, we also used data from perturbation screens that are completely orthogonal to expression responses in CCLE to get at a cause and effect. 

Our reasoning for using all of the data in Figure 1 while controlling for differences due to cancer type in the GLMM was to maximize the variation in mutational load across all of the samples in this dataset to identify what genes increase in expression as mutational load increases over 5 orders of magnitude. As noted here, we also already further validated that the signal we observe in Figure 1 is still robust for our gene sets of interest within cancer types in Supplemental Figure 6.

2) Fig 4, Fig S5 and Fig S8 show results for the regression coefficient of expression on ML after leaving out one cancer at a time. All of us initially read this as results for 'one cancer at a time', rather than 'leave-one-out'. These figures are used to argue that the results are not driven by specific cancer types. However, this analysis would not reveal if the signal was driven by a (small) subset of cancer types. To justify claims like "significant negative relationship between mutational load and cell viability across almost all cancer types", one needs to analyze individual cancer types. Results for specific genes, rather than broad groups would also help interpret these results.

Our reasoning for grouping together genes in Figure 4 was because the shRNA screen was done on a single gene at a time, and we were interested in measuring the joint effect on viability after knocking down all of the genes in a given complex. 

Given that the expression responses in Figure 3 already validate within cancer types in TCGA in Supplemental Figure 6, we believe that it’s very unlikely that the signal we observe is driven by individual cancer types or smaller groups of cancer types. In addition, we did not perform a within cancer analysis in CCLE for Figure 4, because not all available cancer types in CCLE were profiled evenly in the shRNA screen (Total < 300). The vast majority of cancer types in CCLE for the shRNA screen (23/26) have sample sizes <20 within each group that we believe are unlikely to lead to meaningful results that are not driven by noise.

3) You use different model architecture for the TCGA and CCLE analysis because you suspect that the sample size imbalance in the latter might mean that a GLMM can not capture the different variance components accurately. Did you test this? Could you downsample to avoid this? Cancer type is likely a strong confounder of ML.

That was indeed our reasoning, that within group sample sizes in CCLE are too low to robustly estimate variance within cancer types. Given that many cancer types have <20 samples within each group, we don’t think that evenly downsampling would enable us to get an estimate not driven by noise. As noted above, our approach to control for this was to perform a jackknifing procedure that eliminates a single cancer type at a time and re-estimates the effect. 

4) In the splicing analysis (Fig 2 and Fig S4), you report a 10% variation in splicing for a 100-fold variation in ML. This weak trend is replicated in very similar ways for many different types of alternative splicing events. It is not clear why different events (exon skipping, intron retention, etc) should respond in the same way to ML. A weak but homogeneous effect like the one shown here might result from some common confounder (see point 1). Similarly, it is not clear why with increasing intron retention PSI threshold the fraction of under-expressed transcripts would decrease and not increase.

We agree that the effects of all the different alternative splicing effects are complex. Our focus was on intron retention, which is known to occur in cancer (Lindeboom, et. al 2016, Nature Genetics), and our analysis is consistent with the idea that damaging passenger mutations can shift cellular phenotypic states that require the use of many different mechanisms to mitigate protein misfolding.

For Figure S4, as the PSI threshold for calling an alternative splicing event increases, fewer samples are called as having an intron retention event in the gene. This uniformly decreases the numerator across all the mutational load bins, so that when the threshold is increased the fraction of under-expressed transcripts with intron retention events is lower.

Author Response

We thank the reviewers for their positive comments and constructive feedback following their thorough reading of the manuscript. In this provisional reply we will briefly address the reviewer’s comments and suggestions point by point. In the forthcoming revised manuscript, we will more thoroughly address the reviewer’s comments and provide additional supporting data.

(1) The expression 'randomly clustered networks' needs to be explained in more detail given that in its current form risks to indicate that the network might be randomly organized (i.e., not organized). In particular, a clustered network with future functionality based on its current clustering is not random but rather pre-configured into those clusters. What the authors likely meant to say, while using the said expression in the title and text, is that clustering is not induced by an experience in the environment, which will only be later mapped using those clusters. While this organization might indeed appear as randomly clustered when referenced to a future novel experience, it might be non-random when referenced to the prior (unaccounted) activity of the network. Related to this, network organization based on similar yet distinct experiences (e.g., on parallel linear tracks as in Liu, Sibille, Dragoi, Neuron 2021) could explain/configure, in part, the hippocampal CA1 network organization that would appear otherwise 'randomly clustered' when referenced to a future novel experience.

As suggested by the reviewer, we will revise the text to clarify that the random clustering is random with respect to any future, novel environment. The cause of clustering could be prior experiences (e.g. Bourjaily M & Miller P, Front. Comput. Neurosci. 5:37, 2011) or developmental programming (e.g. Perin R, Berger TK, & Markram H, Proc. Natl. Acad. Sci. USA 108:5419, 2011).

(2) The authors should elaborate more on how the said 'randomly clustered networks' generate beyond chance-level preplay. Specifically, why was there preplay stronger than the time-bin shuffle? There are at least two potential explanations:

(2.1) When the activation of clusters lasts for several decoding time bins, temporal shuffle breaks the continuity of one cluster's activation, thus leading to less sequential decoding results. In that case, the preplay might mainly outperform the shuffle when there are fewer clusters activating in a PBE. For example, activation of two clusters must be sequential (either A to B or B to A), while time bin shuffle could lead to non-sequential activations such as a-b-a-b-a-b where a and b are components of A and B;

(2.2) There is a preferred connection between clusters based on the size of overlap across clusters. For example, if pair A-B and B-C have stronger overlap than A-C, then cluster sequences A-B-C and C-B-A are more likely to occur than others (such as A-C-B) across brain states. In that case, authors should present the distribution of overlap across clusters, and whether the sequences during run and sleep match the magnitude of overlap. During run simulation in the model, as clusters randomly receive a weak location cue bias, the activation sequence might not exactly match the overlap of clusters due to the external drive. In that case, the strength of location cue bias (4% in the current setup) could change the balance between the internal drive and external drive of the representation. How does that parameter influence the preplay incidence or quality?

Based on our finding that preplay occurs only in networks that sustain cluster activity over multiple decoding time bins (Figure 5d-e), our understanding of the model’s function is consistent with the reviewers first explanation. We will provide additional analysis in the forthcoming revised manuscript in order to directly test the first explanation and will also test the intriguing possibility that the reviewer’s second suggestion contributes to above-chance preplay.

(3) The manuscript is focused on presenting that a randomly clustered network can generate preplay and place maps with properties similar to experimental observations. An equally interesting question is how preplay supports spatial coding. If preplay is an intrinsic dynamic feature of this network, then it would be good to study whether this network outperforms other networks (randomly connected or ring lattice) in terms of spatial coding (encoding speed, encoding capacity, tuning stability, tuning quality, etc.)

We agree that this is an interesting future direction, but we see it as outside the scope of the current work. There are two interesting avenues of future work: 1) Our current model does not include any plasticity mechanisms, but a future model could study the effects of synaptic plasticity during preplay on long-term network dynamics, and 2) Our current model does not include alternative approaches to constructing the recurrent network, but future studies could systematically compare the spatial coding properties of alternative types of recurrent networks.

(4) The manuscript mentions the small-world connectivity several times, but the concept still appears too abstract and how the small-world index (SWI) contributes to place fields or preplay is not sufficiently discussed.

For a more general audience in the field of neuroscience, it would be helpful to include example graphs with high and low SWI. For example, you can show a ring lattice graph and indicate that there are long paths between points at opposite sides of the ring; show randomly connected graphs indicating there are no local clustered structures, and show clustered graphs with several hubs establishing long-range connections to reduce pair-wise distance.

How this SWI contributes to preplay is also not clear. Figure 6 showed preplay is correlated with SWI, but maybe the correlation is caused by both of them being correlated with cluster participation. The balance between cluster overlap and cluster isolation is well discussed. In the Discussion, the authors mention "...Such a balance in cluster overlap produces networks with small-world characteristics (Watts and Strogatz, 1998) as quantified by a small-world index..." (Lines 560-561). I believe the statement is not entirely appropriate, a network similar to ring lattice can still have the balance of cluster isolation and cluster overlap, while it will have small SWI due to a long path across some node pairs. Both cluster structure and long-range connection could contribute to SWI. The authors only discuss the necessity of cluster structure, but why is the long-range connection important should also be discussed. I guess long-range connection could make the network more flexible (clusters are closer to each other) and thus increase the potential repertoire.

We agree that the manuscript would benefit from a more concrete explanation of the small-world index. We will revise the text and add illustrative figures.

We note that while our most successful clustered networks are indeed those with small-world characteristics, there are other ways of producing small-world networks which may not show good place fields or preplay. We will test another type of small-world network if time permits.

Our discussion of “cluster overlap” is specific to our type of small-world network in which there is no pre-determined spatial dimension (unlike the ring network of Watts and Strogatz). Therefore, because clusters map randomly to location once a particular spatial context is imposed, the random overlap between clusters produces long-range connections in that context (and any other context) so one can think of the amount of overlap between clusters as representing the number of long-range connections in a Watts-Strogatz model, except, we wish to iterate, such models involve a spatial topology within the network, which we do not include.

(5) What drives PBE during sleep? Seems like the main difference between sleep and run states is the magnitude of excitatory and inhibitory inputs controlled by scaling factors. If there are bursts (PBE) in sleep, do you also observe those during run? Does the network automatically generate PBE in a regime of strong excitation and weak inhibition (neural bifurcation)?

During sleep simulations, the PBEs are spontaneously generated by the recurrent connections in the network. The constant-rate Poisson inputs drive low-rate stochastic spiking in the recurrent network, which then randomly generates population events when there is sufficient internal activity to transiently drive additional spiking within the network.

During run simulations, the spatially-tuned inputs drive greater activity in a subset of the cells at a given point on the track, which in turn suppress the other excitatory cells through the feedback inhibition.

(6) Is the concept of 'cluster' similar to 'assemblies', as in Peyrache et al, 2010; Farooq et al, 2019? Does a classic assembly analysis during run reveal cluster structures?

Yes, we are highly confident that the clusters in our network would correspond to the functional assemblies that have been studied through assembly analysis and will present the relevant data in a revision.

(7) Can the capacity of the clustered network to express preplay for multiple distinct future experiences be estimated in relation to current network activity, as in Dragoi and Tonegawa, PNAS 2013?

We agree this is an interesting opportunity to compare the results of our model to what has been previously found experimentally and will test this if time permits.

Reviewer # 2

Weaknesses:

My main critiques of the paper relate to the form of the input to the network.

First, because the input is the same across trials (i.e. all traversals are the same duration/velocity), there is no ability to distinguish a representation of space from a representation of time elapsed since the beginning of the trial. The authors should test what happens e.g. with traversals in which the animal travels at different speeds, and in which the animal's speed is not constant across the entire track, and then confirm that the resulting tuning curves are a better representation of position or duration.

We agree that this is an important question, and we plan to run further simulations where we test the effects of varying the simulated speed. We will present results in the resubmission.

Second, it's unclear how much the results depend on the choice of a one-dimensional environment with ramping input. While this is an elegant idealization that allows the authors to explore the representation and replay properties of their model, it is a strong and highly non-physiological constraint. The authors should verify that their results do not depend on this idealization. Specifically, I would suggest the authors also test the spatial coding properties of their network in 2-dimensional environments, and with different kinds of input that have a range of degrees of spatial tuning and physiological plausibility. A method for systematically producing input with varying degrees of spatial tuning in both 1D and 2D environments has been previously used in (Fang et al 2023, eLife, see Figures 4 and 5), which could be readily adapted for the current study; and behaviorally plausible trajectories in 2D can be produced using the RatInABox package (George et al 2022, bioRxiv), which can also generate e.g. grid cell-like activity that could be used as physiologically plausible input to the network.

We agree that testing the robustness of our results to different models of feedforward input is important and we plan to do this in our revised manuscript for the linear track and W-track.

Testing the model in a 2D environment is an interesting future direction, but we see it as outside the scope of the current work. To our knowledge there are no experimental findings of preplay in 2D environments, but this presents an interesting opportunity for future modeling studies.

Finally, I was left wondering how the cells' spatial tuning relates to their cluster membership, and how the capacity of the network (number of different environments/locations that can be represented) relates to the number of clusters. It seems that if clusters of cells tend to code for nearby locations in the environment (as predicted by the results of Figure 5), then the number of encodable locations would be limited (by the number of clusters). Further, there should be a strong tendency for cells in the same cluster to encode overlapping locations in different environments, which is not seen in experimental data.

Thank you for making this important point and giving us the opportunity to clarify. We do find that subsets of cells with identical cluster membership have correlated place fields, but as we show in Figure 7b the network place map as a whole shows low remapping correlations across environments, which is consistent with experimental data (Hampson RE et al, Hippocampus 6:281, 1996; Pavlides C, et al, Neurobiol Learn Mem 161:122, 2019). Our model includes a relatively small number of cells and clusters compared to CA3, and with a more realistic number of clusters, the level of correlation across network place maps should reduce even further in our model network. The reason for a low level of correlation is because cluster membership is combinatorial, whereby cells that share membership in one cluster can also belong to separate/distinct other clusters, rendering their activity less correlated than might be anticipated. In our revised manuscript we will address this point more carefully and cite the relevant experimental support.

Reviewer # 3

Weaknesses:

To generate place cell-like activity during a simulated traversal of a linear environment, the authors drive the network with a combination of linearly increasing/decreasing synaptic inputs, mimicking border cell-like inputs. These inputs presumably stem from the entorhinal cortex (though this is not discussed). The authors do not explore how the model would behave when these inputs are replaced by or combined with grid cell inputs which would be more physiologically realistic.

We chose the linearly varying spatial inputs as the minimal model of providing spatial input to the network so that we could focus on the dynamics of the recurrent connections. We agree our results will be strengthened by testing alternative types of border-like input so will present such additional results in our revised version. However, given that a sub-goal of our model was to show that place fields could arise in locations at which no neurons receive a peak in external input, whereas combining input from multiple grid cells produces peaked place-field like input, adding grid cell input (and the many other types of potential hippocampal input) is beyond the scope of the paper.

Even though the authors claim that no spatially-tuned information is needed for the model to generate place cells, there is a small location-cue bias added to the cells, depending on the cluster(s) they belong to. Even though this input is relatively weak, it could potentially be driving the sequential activation of clusters and therefore the preplays and place cells. In that case, the claim for non-spatially tuned inputs seems weak. This detail is hidden in the Methods section and not discussed further. How does the model behave without this added bias input?

First, we apologize for a lack of clarity if we have caused confusion about the type of inputs (linear and cluster-dependent as we had attempted to portray prominently in Figure 1, where it is described in the caption, l. 156-157, and Results, l. 189-190 & l. 497-499, as well as in the Methods, l. 671-683) and if we implied an absence of spatially-tuned information in the network. In the revision we will clarify that for reliable place fields to appear, the network must receive spatial information and that one point of our paper is that the information need not arrive as peaks of external input already resembling place cells or grid cells. We chose linearly ramping boundary inputs as the minimally place-field like stimulus (that still contains spatial information) but in our revision we will include alternatives. We should note that during sleep, when “preplay” occurs, there is no such spatial bias (which is why preplay can equally correlate with place field sequences in any context). In the revision, we will update Figure 1 to show more clearly the cluster-dependent linearly ramping input received by some specific cells with both similar and different place fields.

Unlike excitation, inhibition is modeled in a very uniform way (uniform connection probability with all E cells, no I-I connections, no border-cell inputs). This goes against a long literature on the precise coordination of multiple inhibitory subnetworks, with different interneuron subtypes playing different roles (e.g. output-suppressing perisomatic inhibition vs input-gating dendritic inhibition). Even though no model is meant to capture every detail of a real neuronal circuit, expanding on the role of inhibition in this clustered architecture would greatly strengthen this work.

This is an interesting future direction, but we see it as outside the scope of our current work. While inhibitory microcircuits are certainly important physiologically, we focus here on a minimal model that produces the desired place cell activity and preplay, as measured in excitatory cells.

For the modeling insights to be physiologically plausible, it is important to show that CA3 connectivity (which the model mimics) shares the proposed small-world architecture. The authors discuss the existence of this architecture in various brain regions but not in CA3, which is traditionally thought of and modeled as a random or fully connected recurrent excitatory network. A thorough discussion of CA3 connectivity would strengthen this work.

We agree this is an important point that is missing, and we will revise the text to specifically address CA3 connectivity (Guzman et al., Science 353 (6304), 1117-1123 2016) and the small-world structure therein due to the presence of “assemblies”.

Author response:

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

eLife assessment

This study provides valuable insights into how chromatin-bound PfMORC controls gene expression in the asexual blood stage of Plasmodium falciparum. By interacting with key nuclear proteins, PfMORC appears to affect expression of genes relating to host invasion and subtelomeric var genes. Correlating transcriptomic data with in vivo chromatin insights, the study provides solid evidence for the central role of PfMORC in epigenetic transcriptional regulation through modulation of chromatin compaction.

Public Reviews:

Reviewer #1 (Public Review):

Summary:

The study provides valuable insights into the role of PfMORC in Plasmodium's epigenetic regulation, backed by a comprehensive methodological approach. The overarching goal was to understand the role of PfMORC in epigenetic regulation during asexual blood stage development, particularly its interactions with ApiAP2 TFs and its potential involvement in the regulation of genes vital for Plasmodium virulence. To achieve this, they conducted various analyses. These include a proteomic analysis to identify nuclear proteins interacting with PfMORC, a study to determine the genome-wide localization of PfMORC at multiple developmental stages, and a transcriptomic analysis in PfMORCHA-glmS knockdown parasites. Taken together, this study suggests that PfMORC is involved in chromatin assemblies that contribute to the epigenetic modulation of transcription during the asexual blood stage development.

Strengths:

The study employed a multi-faceted approach, combining proteomic, genomic, and transcriptomic analyses, providing a holistic view of PfMORC's role. The proteomic analysis successfully identified several nuclear proteins that may interact with PfMORC. The genome-wide localization offered valuable insights into PfMORC's function, especially its predominant recruitment to subtelomeric regions. The results align with previous findings on PfMORC's interaction with ApiAP2 TFs. Notably, the authors meticulously contextualized their findings with prior research, including pre-prints, adding credibility to their work.

Weaknesses:

While the study identifies potential interacting partners and loci of binding, direct functional outcomes of these interactions remain an inference. The authors heavily rely on past research for some of their claims. While it strengthens some assertions, it might indicate a lack of direct evidence in the current study for particular aspects. The declaration that PfMORC may serve as an attractive drug target is substantial. While the data suggests its involvement in essential processes, further studies are required to validate its feasibility as a drug target.

Reviewer #2 (Public Review):

Summary:

This is a paper entitled "Plasmodium falciparum MORC protein modulates gene expression through interaction with heterochromatin" describes the role of PfMORC during the intra-erythrocytic cycle of Plasmodium falciparum. Garcia et al. investigated the PfMORC-interacting proteins and PfMORC genomic distribution in trophozoites and schizonts. They also examined the transcriptome of the parasites after partial knockdown of the transcript.

Strengths:

This study is a significant advance in the knowledge of the role of PfMORC in heterochromatin assembly. It provides an in-depth analysis of the PfMORC genomic localization and its correlation with other chromatin marks and ApiAP2 transcription factor binding.

Weaknesses:

However, most of the conclusions are based on the function of interacting proteins and the genomic localization of the protein. The authors did not investigate the direct effects of PfMORC depletion on heterochromatin marks. Furthermore, the results of the transcriptomic analysis are puzzling as 50% of the transcripts are downregulated, a phenotype not expected for a heterochromatin marker.

Recommendations for the authors:

Reviewer #1 (Recommendations For The Authors):

Suggestions for improved or additional experiments, data, or analyses.

• Figure 1A and Table 1: the authors should incorporate a volcano plot in their proteomic results presentation. This graphical representation can provide a more intuitive grasp of the most relevant proteins associated with PfMORC in terms of both their abundance and significance. It will aid in swiftly pinpointing proteins with the most notable differential associations. This will complement the comprehensive overview provided by the authors, referencing past research where PfMORC was detailed.

We thank the reviewer for the suggestion. We agree with the reviewer that the volcano plot we now provide does indeed bring comprehensive information on associations between PfMORC and other cellular proteins. The volcano plot presented in the revised manuscript as Figure 1A, was generated using the normalized MS/MS counts from the anti-GFP and 3D7 (control) proteomics datasets (n=3). The potential PfMORC interacting proteins were determined using the fold changes and p-values between the two datasets, as provided in Table 1.

Several protein interactors were strongly supported by statistical analysis (p-value), while others showed weaker p-value due to variability between replicates. Indeed, the total number of proteins identified in the three replicates, shown in the Venn diagram (Supplemental Figure 1D), exhibits a good overlap between the replicates but a lower number of identified proteins in the GFP-E1 sample. This variability was observed also in the statistical analysis. Indeed, by analyzing the GFP/3D7 ratios, some proteins have a significant difference in abundance (fold change greater than 1.5x) in one of the groups but do not meet the statistical threshold. For more clarity, we have included the -log p-value for the proteins listed in Table 1.

Overall, these results demonstrate that many ApiAP2 proteins and several chromatin-associated factors interact with PfMORC.

• Given the plethora of proteins detected in the PfMORC eluate, it raises the question of how many are genuine MORC interactors versus those that are merely nearby molecules acting adjacently. These might incidentally end up in the immunoprecipitate due to unintended interactions with DNA or chromatin. While the M&M section mentions that the beads were thoroughly washed, there is no specification about the washing buffer or its stringency (i.e., salinity level). At higher salinities, one could isolate core complexes of interactors associated with DNA or even RNA carryover.

We apologize for this omission and have now added the buffer composition used to wash the beads. This section now reads "To perform the co-immunoprecipitation we followed the manufacturer protocol (ChromoTek, gta-20). Samples were lysed in modified RIPA buffer (50 mM Tris, pH 7.5, 150 mM NaCl, 0.5% sodium deoxycholate, 1% Nonidet P-40, 10 µg/ml aprotinin, 10 µg/ml leupeptin, 10 µg/ml, 1 mM phenylmethylsulfonyl fluoride, benzamidine) for 30 min on ice. The lysate was precleared with 50 µl of protein A/G-Agarose beads at 4°C for 1 h and clarified by centrifugation at 10,000 × g for 10 min. The precleared lysate was incubated overnight with an anti-GFP antibody using anti-GFP-Trap-A beads (ChromoTek, gta-20). The magnetic beads were then pelleted using a magnet (Invitrogen) and washed 3 times with wash buffer (10 mM Tris/Cl pH 7.5, 150 mM NaCl, 0.05 % Nonidet™ P40 Substitute, 0.5 mM EDTA)."

We used the same salt concentration for immunoprecipitation as was used in the lysis buffer to minimize the binding of non-specific proteins. The wash buffer composition is updated in the revised manuscript. The immunoprecipitations were done in biological triplicates to ensure reproducibility and statistical support. A number of proteins are common across all three replicates. We also used wild-type parasites (non-GFP) as a negative control to eliminate non-specific hits, and we used a log2-fold change ≥1.5 relative to wild type parasites as our cutoff between the comparison groups.

We believe that these conditions provide the stringency required to identify high confidence PfMORC interacting proteins, although this still leaves a possibility for additional lower affinity interactions. Future studies will certainly follow up candidate interaction partners to better define this complex. However, the complexity of the complex resembles that reported previously in Toxoplasma gondii (Farhat et al. 2020, Nat Microbiol) as well another report on the PfMORC complexes: https://elifesciences.org/reviewed-prepri nts/92499

• The authors demonstrate that PfMORC creates distinct peaks in and around HP1-bound areas (Figure 2F), hinting at a specific role for PfMORC in heterochromatin compaction, boundary definition, and gene silencing. This pattern is clearly depicted in an example in Figure 2F. It would be beneficial to know if this enrichment profile is replicated elsewhere and, if so, it would be worthwhile to quantify it.

This is an excellent point. Yes, this pattern is seen across the entire genome, where PfMORC is apposed to PfHP1-bound heterochromatic regions. As indicated in the manuscript, we have quantified this effect genome-wide; however, since we already display compiled data for Chromosome 2 (at both chromosome ends) pertaining to the position of PfMORC relative to PfHP1 we do not feel it is essential to provide such a figure for the entire genome as it does not alter the central message of our manuscript. Figure 2F is representative of the genome-wide distribution of PfMORC relative to PfHP1. The raw genome-wide data are available in Supplementary Information for further inspection of specific loci on other chromosomes.

Recommendations for improving the writing and presentation.

MAIN TEXT

Panel e, referenced both in the main text and legend, is missing from Figure 4. This missing panel represents a significant finding of the study, highlighting according to the authors a low correlation between ChIP-seq gene targets and RNA-seq DEGs. This observation implies that PfMORC's global occupancy is more aligned with shaping chromatin architecture than directly regulating specific gene targets. In light of this, the authors should rephrase parts of their manuscript (including abstract and title) to avoid suggesting that PfMORC acts primarily (directly) as a gene regulator, emphasizing instead its role in influencing the topological structure of chromosomes.

We have modified the title as suggested by the reviewer to more accurately reflect that PfMORC modulates chromatin architecture rather than acting as a direct regulator of specific genes. Our new title is: A Plasmodium falciparum MORC protein complex modulates epigenetic control of gene expression through interaction with heterochromatin

We apologize for the omission of Figure 4e, which is now included in the revised manuscript. We found PfMORC occupancy on all chromosomes at subtelomeric regions, which are known to harbor genes related to immune evasion and antigenic variation (including most of the var genes). This study is also in agreement with Bryant et al. (PMID 32816370) which reported PfMORC occupancy along with PfISW1 at var gene promoters. PfMORC has also been identified in complexes with various ApiAP2 proteins in a proteome-wide study (Hillier et al. Cell Rep, PMID 31390575), as well as in immunoprecipitations of PfAP2-G2 (Singh et al., Mol Micro, PMID 33368818) and PfAP2-P (Subudhi et al., Nat Microbiol, PMID 37884813). The recent study by Subudhi et al. reports that PfAP2-P is involved in the regulation of var gene expression, antigenic variation, trophozoite development and parasite egress. It is therefore possible that PfMORC may have different effects on transcriptional regulation through interactions with different ApiAP2 transcription factors. Our comparison of PfMORC with known ApiAP2 protein occupancy reveals a high level of overlap, indicating that PfMORC may affect gene expression in various ways throughout the asexual cycle. Additionally, Hillier et al. show that PfMORC interaction is not limited to ApiAP2 but also implicates several other chromatin remodellers, which is consistent with our own results. We do not imply direct regulation of transcription via PfMORC in our manuscript. To the contrary, we suggest that it interacts with heterochromatin and thereby plays a role in the epigenetic control of asexual blood stage transcriptional regulation which is also clarified in the revised abstract.

Another limitation of differential gene expression was use of the glmS ribozyme system, which resulted in only 50% depletion of the PfMORC transcript. There may still be enough PfMORC to rescue the gene expression we could not detect correctly. Therefore, it is challenging to interpret the function of PfMORC in only chromatin architecture but not in gene expression.

If we believe that PfMORC in Plasmodium isn't mainly adjusting gene expression, the authors' suggestion that MORC is targeted by some AP2s becomes puzzling. How do we make sense of these different ideas? The authors need to clarify this to maintain consistency in their findings.

Based on our data, we hypothesize that PfMORC acts as an accessory protein for ApiAP2 transcription factors. In a number of studies, including ours and the concurrent publication in eLife (https://elifesciences.org/reviewed-preprints/92499), PfMORC co-IPed with several ApiAP2 proteins, suggest it has multiple functions. In our previous study we showed that PfMORC expression is highest in mid and late asexual stages. A comparison of the PfMORC occupancy with 6 ApiAP2 (having different expression profile) suggest plasticity in PfMORC function. We have revised our discussion to make this hypothesis more transparent for the readers.

The authors should cite Farhat et al. 2020 (Extended Data Fig. 1a), as it similarly identified 3 different ELM2-containing proteins in Toxoplasma MORC-associated complexes. This previous work provides context and supports the observations made with PfMORC in this study.

Thank you for the suggestion and pointing out this omission. We have indeed cited the work of the Farhat group in the original manuscript and have now included this additional reference to corroborate the text and provide further support to our conclusions.

Minor corrections to the text and figures.

• Panel e is missing from Figure 4.

As mentioned above Panel e is now included in Figure 4.

• The captions are very minimally detailed. An effort must be made to better describe the panels as well as which statistical tests were used. As it stands, this is not really up to standard.

We have elaborated the captions with more detailed descriptions, and we now provide additional information where further clarification was necessary.

Reviewer #2 (Recommendations For The Authors):

• The study lacks a direct correlation between the inferred function of PfMORC and the heterochromatin state of the genome after its depletion. It would be interesting to perform chip-seq on known heterochromatin markers such as H3K9me3, HP1 or H3K36me2/3 to measure the consequences of PfMORC depletion on global heterochromatin and its boundaries.

While the proposed experiments are certainly interesting, they are beyond the scope of this study. The current manuscript is focused on PfMORC occupancy, its interacting partners, and its impact on differential gene regulation after PfMORC depletion in asexual parasites. Nonetheless, we did in fact compared the PfMORC occupancy with that of various heterochromatin markers (H2A.Z, H3K9ac, H3K4me3, H3K27ac, H3K18ac, H3K9me3, H3K36me2/3, H4K20me3, and H3K4me1) at 30hpi and 4hpi time points. These data are presented in Supplemental Figure 9. We did not find any significant colocalization, but documented the presence of PMORC in H3K36me2 depleted regions.

• The PfMORC depletion was performed using a glms-based genetic system and the reviewer did not find any quantification of the depletion level at 24h or 36h. This is particularly important as the authors present RNA-seq data at these time points.

We would like to clarify that RNA-seq was performed on 32hpi parasites after approximately 48 h treatment with 2.5 mM GlcN. At the trophozoite and schizont stage, PfMORC expression is high, which is why we selected these time points for RNA-seq (32hpi) and ChIP-seq (30hpi and 40hpi). PfMORC protein expression after GlcN treatment is analyzed in our previous paper (Singh et al., Sci Rep, PMID 33479315), where treatment with 2.5 mM GlcN leads to 50% reduction in PfMORC transcript at 32hpi. This is referenced in the Results section; we decided not to repeat the same experiment in the current manuscript.

• The authors performed a thorough analysis of the correlations between ApiAP2 binding, histone modification and genomic localization of PfMORC (their chip-seq data). However, they found an inverse relationship between H3K36me2, a known histone repressive mark, and PfMORC genomic localization. This is particularly surprising when PfMORC itself is presented as a heterochromatin marker. The wording of this data is confusing in the results section (lines 257-258) and never discussed further. This important data should at least be discussed to make sense of this apparent contradiction.

H3K36me2 indeed acts as a global repressive mark in P. falciparum. However, our hypothesis implies that PfMORC not only overlaps with H3K36me2 depleted region, but also interacts with other epigenetic regulators. Therefore, we propose that PfMORC is part of chromatin remodeling complexes involved in heterochromatin dynamics. Moreover, we did not see any overlap between several other heterochromatin markers, suggesting it has a unique binding preference not shared with other heterochromatin markers. Based on this study and parallel work submitted by Chahine et al. (https://elifesciences.org/reviewed-preprints/92499#abstract), it is evident that PfMORC is crucial for gene regulation and chromatin structure maintenance as shown in other organisms. Currently, we do not know what the apparent mutual exclusion between H3K36me2 and PfMORC implies mechanistically or how PfMORC interaction with heterochromatin aids in chromatin integrity. In Arabidopsis thaliana, MORC binding leads to chromatin compaction and reduces DNA accessibility to transcription factors, thereby repressing gene expression. In P. falciparum, overlap in the binding region of PfMORC with different transcription factors suggests several possibilities that require further investigation. Since there is only one gene encoding a PfMORC protein in P. falciparum, it is possible that PfMORC function is not limited to chromatin integrity, but it may also function to modulate gene expression at different stages. To fully explore the function of PfMORC will require investigating the functional role of the other interacting partners we and others have identified.

We have modified the result section per the reviewer's suggestion, and we now also discuss this finding in more detail in the discussion section.

• The ChIP-seq data are central to this manuscript. However, the presentation of this data in Figure 2A suggests that it is very noisy (particularly for Chr1). It would be of interest to present the called peaks together with the normalized data so that the reader can assess the quality of the ChIP-seq data.

Our results clearly demonstrate the enrichment of PfMORC in sub-telomeric regions and internal heterochromatic islands. These results are consistent across all of our replicates taken at two independent time points of parasite asexual blood stage development and correlate well with the results of Le Roch: https://elifesciences.org/reviewed-preprints/92499. The raw data files have been provided and can be re-analyzed by any user.

• The RNA-seq data showed that only a few genes are affected after 24 h of PfMORC depletion. Furthermore, there is an equal number of up- and down-regulated genes. It is not clear why depletion of a heterochromatin marker would induce down-regulation of genes. How these data relate to the partial depletion of PfMORC is not discussed.

We would like to clarify that RNA-seq experiment was performed at 32hpi after GlcN following knockdown as previously described (Singh et al., Sci Rep, PMID 33479315). Briefly, synchronous, early trophozoites stage (24hpi) PfMORCglmS-HA parasites were treated with 2.5 mM GlcN until they reached the trophozoite stage (32 hpi) in the next cycle. These parasites were then collected for analysis by RNA-seq. We did not detect a substantial log-fold change at this point because only 50% of the transcripts were depleted in the glmS-based PfMORC knockdown system. However, we have seen a distinctive pattern of up (60) and down (103) regulated DEGs that are comprised of egress-related genes or surface antigens. We believe that PfMORC interacts with different ApiAP2 proteins, as shown in Figure 3A, and consequently exhibits multiple functions. This finding has now been corroborated in several other recent studies (See response to Reviewer 1 above).

Author response:

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

Thank you for all your recommendations to improve the manuscript. We took them into account and tried to integrate them as much as possible in the paper. I understand that the main issue is the lack of genetic lineage tracing. Unfortunately, I am no longer in a position to perform experiments and as a consequence, we cannot bring these data. However, we previously performed several experiments that attest the ductal origin of the beta cells. As a reminder, we used experiment setting where beta cell regeneration occur from the ducts in the pancreatic tail; we used a genetic approach to over-express CaN specifically in the ducts at the level of the pancreas ; and we investigate the function of CaN under Notch repression, known to trigger beta cell formation from the ducts. Altogether, our data underline the contribution of the ductal cells. In addition, as recommended by the editors, we showed that while the proportion of ductal cells EdU+ increase Figure 5 C-D, the number of ductal cells remain constant  Figure 5A supplemental. We integrate a paragraph in the discussion to remind all these points in the manuscript.  

We thank you greatly for your time and consideration for this work.

Author response:

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

Reviewer #1:

(1) The authors claimed that they examined the arterial and venous identity of the hyperbranched vessels via live imaging analysis of the high glucose-treated Tg(flt1:YFP::kdrl:ras-mCherry) line, and revealed that the hyperbranched ectopic vessels comprised arteries and veins. That's good, of course. However, there are no relevant results in Figure 2. Please revise it.

Thank you very much for the suggestion. We’ve added this part of the results in Figure 2i and j.

(2) In Figures 3f and 3g, some of the ECs protruded long and intricate sprouts, and nearly all the ECs within an ISV underwent the outgrowth of filopodia in some extreme cases (Figure 3g), suggesting that the high glucose treatment induced the endothelial differentiation into tip cell-like cells. The findings are surprising and interesting. In order to further confirm the author's conclusion, in situ hybridization experiments are more appropriate to show the expression changes of tip cell-like cell marker genes in the high glucose-treated embryos.

Thank you very much for your constructive suggestions. We have performed the analysis of single-cell RNA-seq data, and the results showed that the tip cell marker genes such as esm1, apln, and cxcr4a were significantly up-regulated in arterial and capillary ECs after high glucose treatment. The results were integrated into Figure 3 of the revised manuscript.

(3) Embryos treated with AS1842856 or injected with foxo1a-MO exhibited excessive angiogenesis (Figure 5g-i), suggesting the transcription activity of foxo1 is required to maintain the quiescent state of endothelial cells. Did the downregulation of foxo1a lead to the differentiation of endothelial cells into tip-cell-like cells?

Thank you very much for the question. We examined our results carefully and marked these tip cell-like cells with arrow heads in Figure 5h of the revised manuscript.

(4) Foxo1a was significantly downregulated in arterial and capillary ECs after high glucose treatment (Figure 5c-e). More importantly, whether overexpression of foxo1a in the high glucose-treated embryos could eliminate the hyperangiogenic characteristics?

Thank you for the great questions. We performed rescue experiments, and the results suggested that the overexpression of foxo1a partially mitigated the excessive angiogenesis induced by high glucose treatment. These results were integrated into Figure 6 of the revised manuscript.

(5) The authors' results found that foxo1a was enriched in both the predicted binding sites of marcksl1a by ChIP-PCR experiments (Figure 7d). This result is reliable. However, whether these two sites are important for marcksl1a gene transcription needs to be confirmed by relevant experiments, such as luciferase reporter assays.

We’ve performed the luciferase reporter assays and added these data to Figure 8f and g.

Reviewer #2:

Suggested major experiments:

(1) A previous study (Jorgens et al., Diabetes 64, 2015) reported that high tissue glucose levels increased reactive dicarbonyl methylglyoxal (MG) concentrations in zebrafish embryos and triggered the formation of hyperbranched ISVs. Additionally, they illustrated that MG induced the vascular hyperbranching phenotype via enhancing phosphorylated VEGFR and pAKT signaling cascade. The authors must examine whether both pVEGFR and pAKT are increased in noncaloric monosaccharide (NMS)-treated embryos. The authors need also to test the crosstalks between VEGFR/AKT signaling and foxo1a-Marcksl1a pathway in glucose or NMS-treated embryos.

Thank you very much for your suggestion. We treated the embryos with AS1842856 (foxo1 inhibitor) and Lenvatinib (VEGFR inhibitor), and the results showed that Lenvatinib treatment attenuated the excessive angiogenesis induced by foxo1 inhibition. We also examined the expression level of vegfaa after AS1842856 treatment; the results suggested that foxo1 inhibition did not affect the expression of vegfaa.

Author response image 1.

(2) In this manuscript, the authors performed single endothelial cell sequencing in glucose-treated embryos, and found reduced foxo1a expression and upregulated marcksl1a . Based on these data, the authors demonstrated that glucose and NMS-induced excessive angiogenesis through the foxo1a-marcksl1a pathway. The authors must conduct endothelial scRNA-seq in NMS-treated embryos, and analyze and compare the datasets with scRNA-seq datasets from glucose-treated endothelial cells, considering the focus of the paper. In addition, ASBs have been suggested as healthy alternatives to sugar-sweetened beverages. The authors also need to examine carefully whether metabolic gene programs are altered in glucose-treated endothelial cells, which was mentioned in Jorgens et al paper.

Thank you very much for your constructive suggestions. We have performed the whole embryo transcriptome sequencing after high D-glucose and L-glucose treatment. We analyzed and compared the differentially expressed genes of control, high D-glucose-treated, and high L-glucose-treated embryos. The results revealed that 1259 and 1074 genes were up-regulated significantly in high D-glucose and high L-glucose treated embryos, respectively, compared with control.

We also analyzed some metabolic-related genes and found that some genes involved in gluconeogenesis, glycolysis, and oxidative phosphorylation were significantly changed. The results were integrated into supplementary Figure12 and 13 of the revised manuscript.

(3) Glucose or NMS treatments induce the hyperbranched endothelial vessels from the dorsal aorta and ISVs but not cardinal veins. In Figure 4i, the arterial and capillary cell population is increased in glucose-treated embryos, but the venous cell population seems to be reduced. The authors need to check whether arterial/venous differentiation and proliferation are affected in glucose- and NMS-treated embryos.

Thank you for your suggestions. We examined arterial/venous differentiation based on Tg(flt1BAC:YFP::kdrl:ras-mCherry) zebrafish line, in which the YFP is mainly expressed in arterial Endothelial cells. We found the endothelial cells of excessively formed blood vessels induced by high glucose treatment are mainly arterial (Figure 2j). This might explain why the arterial and capillary cell population was increased in glucose-treated embryos.

(4) The manuscript proposes that excessively branched vessels within ISVs arise from the ectopic activation of quiescent endothelial cells (ECs) into tip cells. To confirm this process, the authors need to detect some specific tip cell markers to demonstrate their ectopic activation.

Thank you for your constructive suggestions. We have performed the analysis of single-cell RNA-seq data, and the results showed that the tip cell marker genes such as esm1, apln, and cxcr4a were significantly up-regulated in arterial and capillary ECs after high glucose treatment. The results were integrated into Figure 3 of the revised manuscript.

(5) Disaccharides such as lactose, maltose, and sucrose did not exhibit a notable induction of excessive angiogenic phenotype. However, the specific treatment concentrations utilized in the study were not delineated. Therefore, further investigation is warranted to determine whether increased disaccharide concentrations can cause vascular hyperbranching phenotype.

Thank you very much for the suggestions. We’ve described the concentrations of monosaccharides and disaccharides in the materials and methods section of the revised manuscript. Following the suggestion, we treated zebrafish embryos with a higher concentration of the disaccharide. The results showed that higher concentrations of disaccharide treatment also caused excessive angiogenesis in zebrafish embryos. These results were integrated into supplementary Figure 8 of the revised manuscript.

(6) The authors claim that glucose and NMS (such as L-glucose) induce excessive angiogenesis through the foxo1a-marcksl1a pathway. Following exposure to elevated glucose levels, a substantial down-regulation of foxo1a was observed in arterial and capillary endothelial cells. This down-regulation led to the release of foxo1a inhibition on marccksl1a, subsequently resulting in an augmented expression of marccksl1a and the manifestation of a vascular phenotype. Consequently, it is imperative to investigate whether the foxo1a overexpression can attenuate marccksl1a expression and mitigate the vascular phenotype induced by monosaccharides. Sufficient data support is needed for the conclusion that monosaccharides induce angiogenesis via the foxo1a-marcksl1a pathway.

Thank you very much for your constructive suggestions.

We confirmed the expression of marcksl1a in foxo1a-overexpressed embryos. The results indicated that foxo1a overexpression significantly attenuated marcksl1a expression. The results were integrated into Figure 8c. We also performed the rescue experiments, which indicated that overexpression of foxo1a partially mitigated the excessive angiogenesis induced by high glucose treatment. These results were integrated into Figure 6 of the revised manuscript.

Minor corrections:

(1) Figure 2i, j has no corresponding graphs.

We’ve made the change in Figure 2.

(2) Figure 2h has no vertical coordinates.

We’ve made the change in Figure 2.

(3) All Figures should be referenced within the manuscript.

We’ve checked our manuscript carefully and made the corrections.

(4) The concentrations of monosaccharides and disaccharides employed in this study must be distinctly elucidated within the manuscript and annotated using the internationally recognized unit notation.

We’ve checked our manuscript carefully and described the concentrations of monosaccharides and disaccharides in the revised materials and methods section.

Reviewer #3:

(1) A possible limitation of the study is that the mechanism leading to angiogenesis in the retinal circulation and in peripheral vasculature is certainly different as diabetes is associated with excessive angiogenesis in the retina and a defect in angiogenesis in the peripheral circulation as shown by a reduced post-ischemic revascularization (see Silvestre et al.: DOI: 10.1152/physrev.00006.2013).

Thank you very much for your suggestions. As you said, the peripheral blood vessel model in this study does not fully represent individuals with diabetic retinopathy, which is a limitation. However, from a specific view, the phenotype and mechanism of excessive angiogenesis of peripheral blood vessels in the high glucose model may provide a reference for excessive angiogenesis in the retina; they might have similar etiology and regulation mechanisms in excessive angiogenesis.

(2) Another limitation is that angiogenesis in the embryo is not fully representative of the excessive angiogenesis observed in the diabetic retinal circulation. It would be of interest to analyse the retinal vascular tree in adult fish submitted to high glucose and to ASB.

In our future study, we will try to observe the angiogenesis phenotype in the diabetic retina and improve the disease model.

(3) Line 52: "Endothelial cell dysfunction (ECD)" instead of "Endothelial dysfunction (ECD)".

We’ve made the correction in the revised manuscript.

(4) The authors should elaborate more on the observation showing that L-glucose, D-mannose, D-ribose, and L-arabinose, which could not be digested by animals, also induce excessive angiogenesis. Is the effect indirect?

In the current manuscript, we conducted an in vivo live imaging analysis to show the phenotype of excessive angiogenesis caused by those noncaloric monosaccharides. However, we did not find differences in the phenotypes of embryos treated with noncaloric and caloric monosaccharides. Therefore, we supposed that the mechanisms underlying the phenotypes were similar. The effect might be indirect.

Author Response:

The reviewers suggested that we determine whether the functions of TopAI, YjhQ, and/or YjhP are connected to antibiotic susceptibility. 

We fully agree with the reviewers that the function of TopAI/YjhQ/YjhP is an important topic. Our preliminary studies (not included in the paper) failed to identify a function connected to antibiotic susceptibility, although these studies were far from exhaustive. There are many environmental stressors that can stall ribosomes, making it challenging to find the functionally relevant stressor(s). We feel that further work on this topic is outside the scope of this manuscript.

The reviewers suggested that the SHAPE data are inconsistent with our conclusions about translation of toiL.

We believe the SHAPE data are consistent with our model, although we acknowledge that interpretation of base reactivity is somewhat subjective. We will address the reviewers’ comments on this topic in more detail in our full response.

The reviewers suggested that published Ribo-Seq data are inconsistent with our data showing that toiL start codon/Shine-Dalgarno mutations have no effect on expression of luciferase reporters in the absence of antibiotics. 

Our assays with these mutations looked at expression of topAI, not toiL. Our model predicts that mutations that prevent toiL translation will not induce expression of the downstream genes. We did not look at the effect of these mutations on expression of toiL itself.

The reviewers suggested we use RNA-seq to complement the Ribo-seq data for cells grown +/- tetracycline (Figure 5).

In principle, RNA-seq data would allow us to determine whether tetracycline specifically induces translation of topAI, as opposed to only increasing the RNA level. We did not generate RNA-seq data because prior work from other groups suggests that topAI is too weakly expressed to accurately measure translation efficiency in non-inducing conditions. However, the major conclusion from Figure 5 is that tetracycline stalls ribosomes at start codons, including the start codon of toiL.

Author response:

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

Public Reviews: 

Reviewer #1 (Public Review):

Summary:

The question of whether eyespots mimic eyes has certainly been around for a very long time and led to a good deal of debate and contention. This isn't purely an issue of how eyespots work either, but more widely an example of the potential pitfalls of adopting 'just-so-stories' in biology before conducting the appropriate experiments. Recent years have seen a range of studies testing eye mimicry, often purporting to find evidence for or against it, and not always entirely objectively. Thus, the current study is very welcome, rigorously analysing the findings across a suite of papers based on evidence/effect sizes in a meta-analysis.

Strengths:

The work is very well conducted, robust, objective, and makes a range of valuable contributions and conclusions, with an extensive use of literature for the research. I have no issues with the analysis undertaken, just some minor comments on the manuscript. The results and conclusions are compelling. It's probably fair to say that the topic needs more experiments to really reach firm conclusions but the authors do a good job of acknowledging this and highlighting where that future work would be best placed.

Weaknesses:

There are few weaknesses in this work, just some minor amendments to the text for clarity and information.

We greatly appreciate Reviewer 1’s positive comments on our manuscript. We also revised our manuscript text and a figure in accordance with Reviewer 1’s recommendations.

Reviewer #2 (Public Review):

Many prey animals have eyespot-like markings (called eyespots) which have been shown in experiments to hinder predation. However, why eyespots are effective against predation has been debated. The authors attempt to use a meta-analytical approach to address the issue of whether eye-mimicry or conspicuousness makes eyespots effective against predation. They state that their results support the importance of conspicuousness. However, I am not convinced by this.

There have been many experimental studies that have weighed in on the debate. Experiments have included manipulating target eyespot properties to make them more or less conspicuous, or to make them more or less similar to eyes. Each study has used its own set of protocols. Experiments have been done indoors with a single predator species, and outdoors where, presumably, a large number of predator species predated upon targets. The targets (i.e, prey with eyespot-like markings) have varied from simple triangular paper pieces with circles printed on them to real lepidopteran wings. Some studies have suggested that conspicuousness is important and eye-mimicry is ineffective, while other studies have suggested that more eye-like targets are better protected. Therefore, there is no consensus across experiments on the eye-mimicry versus conspicuousness debate.

The authors enter the picture with their meta-analysis. The manuscript is well-written and easy to follow. The meta-analysis appears well-carried out, statistically. Their results suggest that conspicuousness is effective, while eye-mimicry is not. I am not convinced that their meta-analysis provides strong enough evidence for this conclusion. The studies that are part of the meta-analysis are varied in terms of protocols, and no single protocol is necessarily better than another. Support for conspicuousness has come primarily from one research group (as acknowledged by the authors), based on a particular set of protocols.

Furthermore, although conspicuousness is amenable to being quantified, for e.g., using contrast or size of stimuli, assessment of 'similarity to eyes' is inherently subjective. Therefore, manipulation of 'similarity to eyes' in some studies may have been subtle enough that there was no effect.

There are a few experiments that have indeed supported eye-mimicry. The results from experiments so far suggest that both eye-mimicry and conspicuousness are effective, possibly depending on the predator(s). Importantly, conspicuousness can benefit from eye-mimicry, while eye-mimicry can benefit from conspicuousness.

Therefore, I argue that generalizing based on a meta-analysis of a small number of studies that conspicuousness is more important than eye-mimicry is not justified. To summarize, I am not convinced that the current study rules out the importance of eye-mimicry in the evolution of eyespots, although I agree with the authors that conspicuousness is important.

We understand Reviewer 2’s concerns and have addressed them by adding some sentences in the discussion part (L506- 508, L538-L540). In addition, our findings, which were guided by current knowledge, support the conspicuousness hypothesis, but we acknowledge the two hypotheses are not mutually exclusive (L110-112). We also do not reject the eye mimicry hypothesis. As we have demonstrated, there are still several gaps in the current literature and our understanding (L501-553). Our aim is for this research to stimulate further studies on this intriguing topic and to foster more fruitful discussions.

Recommendations for the authors:

Reviewer #1 (Recommendations For The Authors):

Minor comments

Lines 59/60: "it is possible that eyespots do not involve mimicry of eyes..."

The sentence was revised (L59). To enhance readability, we have integrated Reviewer 1's suggestions by simplifying the relevant section instead of using the suggested sentence.

Line 61: not necessarily aposematism. They might work simply through neophobia, unfamiliarity, etc even without unprofitability

We changed the text in line with the comment from Reviewer 1 (L61-63).

Lines 62/63 - this is a little hard to follow because I think you really mean both studies of real lepidopterans as well as artificial targets. Need to explain a bit more clearly.

We provided an additional explanation of our included primary study type (L64-65).

Lines 93/94 - not quite that they have nothing to do with predator avoidance, but more that any subjective resemblance to eyes is coincidental, or simply as a result of those marking properties being more effective through conspicuousness in their own right.

Line 94 - similarly, not just aposematism. You explain the possible reasons above on l92 as also being neophobia, etc.

We agreed with Reviewer 1’s comments and added more explanations about the conspicuousness hypothesis (L96-97). We have also rewritten the sentences that could be misleading to readers (L428).

Line 96 - this is perhaps a bit misleading as it seems to conflate mechanism and function. The eye mimicry vs conspicuousness debate is largely about how the so-called 'intimidation' function of eyespots works. That is, how eyespots prevent predators from attacking. The deflection hypothesis is a second function of eyespots, which might also work via consciousness or eye mimicry (e.g. if predators try to peck at 'eyes') but has been less central to the mimicry debate.

The explanations and suggestions from Reviewer 1 are very helpful. We revised this part of our manuscript (L103-108) and Figure 1 and its legend to make it clearer that the eyespot hypothesis and the conspicuousness hypothesis explain anti-predator functions from a different perspective than the deflection hypothesis.

There is a third function of eyespots too, that being as mate selection traits. Note that Figure 1 should also be altered to reflect these points.

We wanted to focus on explaining why eyespot patterns can contribute to prey survival. Therefore, we did not state that eyespot patterns function as mate selection traits in this paragraph. Alternatively, we have already mentioned this in the Discussion part (L455-L465) and rewrote it more clearly (L456).

Were there enough studies on non-avian predators to analyse in any way? 

We found a few studies on non-avian predators (e.g. fish, invertebrates, or reptiles), but not enough to conduct a meta-analysis.

Line 171/72 - why? Can you explain, please.

The reason we excluded studies that used bright or contrasting patterns as control stimuli in our meta-analysis is to ensure comparability across primary studies. We added an explanation in the text (L180-181).

Line 177 - can you clarify this?

Without control stimuli, it is challenging to accurately assess the effect of eyespots or other conspicuous patterns on predation avoidance. Control stimuli allow for a comparison of the effect of eyespots or patterns. We added a more detailed explanation to clarify here (L186-188).

Line 309 - presumably you mean 33 papers, each of which may have multiple experiments? I might have missed it, but how many individual experiments in total? 

There were 164 individual experiments. We have now added that information in the manuscript (L320).

Line 320 - paper shaped in a triangle mostly?

We cannot say that most artificial prey were triangular. After excluding the caterpillar type, 57.4% were triangular, while the remaining 43.6% were rectangular (Figure 2b).

Line 406: Stevens.

We fixed this name, thank you (L417).

Discussion - nice, balanced and thorough. Much of the work done has been in Northern Europe where eyespot species are less common. Perhaps things may differ in areas where eyespots are more prevalent.

We appreciate Reviewer 1’s kind words and comments. We agree with your comments and reflected them in our manuscript (L542-545).

Line 477 - True, and predators often have forward-facing eyes making it likely both would often be seen, but a pair of eyes may not be absolutely crucial to avoidance since sometimes a prey animal may only see one eye of a predator (e.g. if the other is occluded, or only one side of the head is visible).

We were grateful for Reviewer 1's comment. We added a sentence noting that the eyespots do not necessarily have to be in pairs to resemble eyes (L490-L492).

Author Response:

Public Reviews:

Reviewer #1 (Public Review):

Summary:

In this study, Bonnifet et al. profile the presence of L1 ORF1p in the mouse and human brain. They claim that ORF1p is expressed in the human and mouse brain at a steady state and that there is an age-dependent increase in expression. This is a timely report as two recent papers have extensively documented the presence of full-length L1 transcripts in the mouse and human brain (PMID: 38773348 & PMID: 37910626). Thus, the finding that L1 ORF1p is consistently expressed in the brain is not surprising, but important to document.  

Thank you for recognizing the importance of this study. The two cited papers have indeed reported the presence of full-length transcripts in the mouse and human brain. However, the first (PMID: 38773348) report has shown evidence of flL1 RNA and ORF1 protein expression in the mouse hippocampus (but not elsewhere) and the second (PMID: 37910626) shows full-length LINE-1 RNA expression and H3K4me3-ChIP data in the frontal and temporal lobe of the human brain, but not protein expression.  

Strengths:

Several parts of this manuscript appear to be well done and include the necessary controls. In particular, the evidence for steady-state expression of ORF1p in the mouse brain appears robust. 

Weaknesses: 

Several parts of the manuscript appear to be more preliminary and need further experiments to validate their claims. In particular, the data suggesting expression of L1 ORF1p in the human brain and the data suggesting increased expression in the aged brain need further validation. Detailed comments: 

(1) The expression of ORF1p in the human brain shown in Figure 1j is not convincing. Why are there two strong bands in the WB? How can the authors be sure that this signal represents ORF1p expression and not nonspecific labelling? Additional validations and controls are needed to verify the specificity of this signal. 

We have validated the antibody (Abcam 245249 - https://www.abcam.com/en-us/products/primary-antibodies/line-1-orf1p-antibody-epr22227-6-ab245249), which we use for Western blotting experiments like in Fig1j), by several means. We have done immunoprecipitations (IPs) and co-immunoprecipitations (co-IPs) followed by quantitative mass spectrometry (LC-MS/MS). We efficiently detect ORF1p in IPs (Western blot) and by quantitative mass spectrometry (5 independent samples per IP-ORF1p and IP-IgG: ORF1p/IgG ratio: 40.86; adj p-value 8.7e-07; human neurons in culture). We also did co-IPs followed by Western blot using two different antibodies, the Millipore or the Abcam antibody to immunoprecipitate and the Abcam antibody for Western blotting (the Millipore AB does not work well on WB in our hands) which consistently showed a double band indicating that both bands are ORF1p-derived. We can provide this data to the revised manuscript, although some of it (the MS data) is subject of another study in preparation. Abcam also reports a double band, and they suspect that the lower band is a truncated form (see the link to their website above). ORF1p Western blots done by other labs with different antibodies have detected a second band in human samples

(1) Sato, S. et al. LINE-1 ORF1p as a candidate biomarker in high grade serous ovarian carcinoma. Sci Rep 13, 1537 (2023) in Figure 1D

(2) McKerrow, W. et al. LINE-1 expression in cancer correlates with p53 mutation, copy number alteration, and S phase checkpoint. Proc. Natl. Acad. Sci. U.S.A. 119, e2115999119 (2022)) showing a Western blot of an inducible LINE-1 (ORFeus) detected by the MABC1152 ORF1p antibody from Millipore Sigma in Figure 7 3) in a publication in eLife (Walter et al. eLife 2016;5:e11418. DOI: 10.7554/eLife.11418) in mouse ES cells with an antibody made in-house from another lab (gift) – Figure 2B

The lower band might thus be a truncated form of ORF1p or a degradation product which appears to be shared by mouse and human ORF1p. We will mention this in the revised version of the paper. In addition, we have used the very well characterized antibody from Millipore (https://www.merckmillipore.com/CH/en/product/Anti-LINE-1-ORF1p-Antibody-clone-4H1,MM_NF-MABC1152?ReferrerURL=https%3A%2F%2Fwww.google.com%2F) for immunostainings and detect ORF1p staining in human neurons in the very same brain regions (Fig 2H) including the cerebellum (selectively in Purkinje cells as in mice in Fig1B panel 10: human images not shown). 

Altogether, based on our experimental validations and evidence from the literature, we are very confident that it is ORF1p that we detect on the blots. 

(2) The data shown in Figure 2g are not convincing. How can the authors be sure that this signal represents ORF1p expression and not non-specific labelling? Extensive additional validations and  controls are needed to verify the specificity of this signal.

Figure 2g shows a Western blot using an extensively used and well characterized ORF1p antibody from abcam (mouse ORF1p - (https://www.abcam.com/en-us/products/primary-antibodies/line-1-orf1p-antibody-epr21844108-ab216324; cited in at least 11 publications) after FACS-sorting of neurons (NeuN+) of the mouse brain. We have validated this ORF1p antibody ourselves in IPs (see Fig 6A) and co-IP followed by mass spectrometry (LC/MS-MS; see Fig 6, where we detect ORF1p exclusively in the 5 independent ORF1p-IP samples and not at all in 5 independent IgG-IP control samples, see Suppl Table 2). This together makes us very confident that we are looking at a specific ORF1p signal. Please note that in the IP of ORF1p shown in Fig6A, there is a double band as well, strongly suggesting that the lower band might be a truncated or processed form of ORF1p. As stated above, this double band has been detected in other studies (Walter et al. eLife 2016;5:e11418. DOI: 10.7554/eLife.11418) in mouse ES cells using an in-house generated antibody against mouse ORF1p. Thus, with either commercial or in-house generated antibodies in some mouse and human samples, there is a double band corresponding to full-length ORF1p and a truncated or processed version of it.

We noticed that we have not added the references of the primary antibodies used in Western blot experiments in the manuscript, which will be corrected in the revised version.  

(3) The data showing a reduction in ORF1p expression in the aged mouse brain is confusing and maybe even misleading. Although there is an increase in the intensity of the ORF1p signal in ORF1p+ cells, the data clearly shows that fewer cells express ORF1p in the aged brain. If these changes indicate an overall loss or gain of ORF1p, expression in the aged brain is not resolved. Thus, conclusions should be more carefully phrased in this section. It is important to show the quantification of NeuN+ and NeuN- cells in young vs aged (not only the proportions as shown in Figure 3b) to determine if the difference in the number of ORF1p+ cells is due to loss of neurons or perhaps a sampling issue. More so, it would be essential to perform WB and/or proteomics experiments to complement the IHC data for the aged mouse samples. 

The data presented in Fig3 C-I show a modest but widespread and reproducible increase in expression of ORF1p per cell. What decreases is the proportion of ORF1p+/NeuN+ cells (Fig3A, B), indicating that fewer cells might express ORF1p in the brain. However, the proportion or number/mm2 of ORF1p+ cells overall does not decrease significantly, neither does the proportion or number/mm2 of NeuN+ cells (data will be added to the revision). We show data of the % of NeuN+ and NeuN- cells in the ventral midbrain (Suppl Fig3C, quantified on confocal images)) which indeed indicates that in this region, there are less neurons in the aged mouse brain compared to the young. There might thus be a very regional decrease in neurons with age in the midbrain motor region. We will, however, as suggested, plot the number of NeuN+ and NeuN- cells per mm2 for the whole brain as well as the different regions in young vs aged to compare actual cell numbers per volume. While it is true that we cannot say that there is an overall loss or gain of ORF1p expression in the aged mouse brain, we believe that this is not of the highest importance as what most likely matters biologically in the context of aging is the quantity of ORF1p per cell (and possibly full-length LINE-1 RNA and ORF2p) and not “per brain”. 

We also plan on doing Western blots on mouse brain tissues from young and aged individuals, however, we might run into limits regarding tissue availability of aged mice. 

(4) The transcriptomic data presented in Figure 4 and Figure 5 are not convincing. Quantification of transposon expression on short read sequencing has important limitations. Longer reads and complementary approaches are needed to study the expression of evolutionarily young L1s (see PMID: 38773348 & PMID: 37910626 for examples of the current state of the art). Given the read length and the unstranded sequencing approach, I would at least ask the authors to add genome browser tracks of the upregulated loci so that we can properly assess the clarity of the results. I would also suggest adding the mappability profile of the elements in question. In addition, since this manuscript focuses on ORF1p, it would be essential to document changes in protein levels (and not just transcripts) in the ageing human brain. 

We agree that there are limitations to the analysis of TEs with short read sequencing and we will add more text on this aspect in a revised version. The approaches shown in PMID: 38773348 & PMID: 37910626 or even a combination of them, would be ideal of course. However, here we reanalyzed a unique existing dataset (Dong et al, Nature Neuroscience, 2018; http://dx.doi.org/10.1038/s41593-018-0223-0), which contains RNA-seq data of human post-mortem dopaminergic neurons in a relatively high number of brain-healthy individuals of a wide age range including some “young” individuals which is rare in post-mortem studies. Such data is unfortunately not available with long read sequencing or any other more appropriate approach yet. Limitations are evident, but all limitations will apply equally to both groups of individuals that we compare. We will add genome browser tracks of the differentially expressed elements. The general mappability profile of the full-length LINE-1 “UIDs” is shown in Suppl Fig 6A. We will color-highlight the specific elements in this graph and will add genome browser data for these elements in a revised version. 

We will not be able to document changes in protein levels in aged human dopaminergic neurons as we do not have access to this material. We have tried to obtain human substantia nigra tissues but were not able to get sufficient amounts to do laser-capture microdissection or FACS analyses, especially of young individuals. There are still important limitations to tissue availability, especially of regions of interest like the substantia nigra pars compacta affected in Parkinson’s disease.

(5) More information is needed on RNAseq of microdissections of dopaminergic neurons from 'healthy' postmortem samples of different ages. No further information on these samples is provided. I would suggest adding a table with the clinical information of these samples (especially age, sex, and cause of death). The authors should also discuss whether this experiment has sufficient power. The human ageing cohort seems very small to me. 

This is a re-analysis of a published dataset (Dong et al, Nat Neurosci, 2018; doi:10.1038/s41593-018-0223-0), available through dbgap (phs001556.v1.p1). In this original article, the criteria for inclusion as a brain-healthy control were as follows:

“…Subjects… were without clinicopathological diagnosis of a neurodegenerative disease meeting the following stringent inclusion and exclusion criteria. Inclusion criteria: (i) absence of clinical or neuropathological diagnosis of a neurodegenerative disease, for example, PD according to the UKPDBB criteria47, Alzheimer’s disease according to NIA-Reagan criteria48, or dementia with Lewy bodies by revised consensus criteria49; for the purpose of this analysis incidental Lewy body cases (not meeting clinicopathological diagnostic criteria for PD or other neurodegenerative disease) were accepted for inclusion; (ii) PMI ≤ 48 h; (iii) RIN50 ≥ 6.0 by Agilent Bioanalyzer (good RNA integrity); and (iv) visible ribosomal peaks on the electropherogram. Exclusion criteria were: (i) a primary intracerebral event as the cause of death; (2) brain tumor (except incidental meningiomas); (3) systemic disorders likely to cause chronic brain damage.”

We do not have access to the cause of death, but we will add available metadata to the manuscript.

We will perform a post-hoc power analysis and add the result to the revision. 

(6) The findings in this manuscript apply to both human and mouse brains. However, the landscape of the evolutionarily young L1 subfamilies between these two species is very different and should be part of the discussion. For example, the regulatory sequences that drive L1 expression are quite different in human and mouse L1s. This should be discussed. 

Indeed, they are very different. We will add this to the discussion.  

(7) On page 3 the authors write: "generally accepted that TE activation can be both, a cause and consequence of aging". This statement does not reflect the current state of the field. On the contrary, this is still an area of extensive investigation and many of the findings supporting this hypothesis need to be confirmed in independent studies. This statement should be revised to reflect this reality. 

We agree, this is overstated, we will change this sentence accordingly.  

Reviewer #2 (Public Review):

Summary: 

Bonnifet et al. sought to characterize the expression pattern of L1 ORF1p expression across the entire mouse brain, in young and aged animals, and to corroborate their characterization with Western blotting for L1 ORF1p and L1 RNA expression data from human samples. They also queried L1 ORF1p interacting partners in the mouse brain by IP-MS. 

Strengths: 

A major strength of the study is the use of two approaches: a deep-learning detection method to distinguish neuronal vs. non-neuronal cells and ORF1p+ cells vs. ORF1p- cells across large-scale images encompassing multiple brain regions mapped by comparison to the Allen Brain Atlas, and confocal imaging to give higher resolution on specific brain regions. These results are also corroborated by Western blotting on six mouse brain regions. Extension of their analysis to post-mortem human samples, to the extent possible, is another strength of the paper. The identification of novel ORF1p interactors in the brain is also a strength in that it provides a novel dataset for future studies. 

Thank you for highlighting the strength of our study. 

Weaknesses: 

The main weakness of the study is that cell type specificity of ORF1p expression was not examined beyond neuron (NeuN+) vs non-neuron (NeuN-). Indeed, a recent study (Bodea et al. 2024, Nature Neuroscience) found that ORF1p expression is characteristic of parvalbumin-positive interneurons, and it would be very interesting to query whether other neuronal subtypes in different brain regions are distinguished by ORF1p expression. 

We agree that this point is important to address. We do provide indications for this in the manuscript. For instance, we detect staining in mouse and human Purkinje cells of the cerebellum in accordance with data from Takahashi et al, Neuron, 2022; DOI: 10.1016/j.neuron.2022.08.011. We also know from previous work, that in the mouse ventral midbrain, dopaminergic neurons (TH+/NeuN+) express ORF1p and that these neurons express higher levels of ORF1p than adjacent non-dopaminergic neurons (TH-/NeuN+; Blaudin de Thé et al, EMBO J, 2018). Others have shown evidence of full-length L1 RNA expression in both excitatory and inhibitory neurons but much less expression in non-neuronal cells (Garza et al, SciAdv, 2023). In sum, although this has not been investigated systematically brain-wide, it does not seem as if ORF1p expression is restricted to PV cells overall. We will deepen the discussion of this aspect in the revised manuscript. To address this question experimentally, we will try to perform ORF1p stainings on different brain regions together with PV stainings and add this data to a revised version, if possible.  

The data suggesting that ORF1p expression is increased in aged mouse brains is intriguing, although it seems to be based upon modestly (up to 27%, dependent on brain region) higher intensity of ORF1p staining rather than a higher proportion of ORF1+ neurons. Indeed, the proportion of NeuN+/Orf1p+ cells actually decreased in aged animals. It is difficult to interpret the significance and validity of the increase in intensity, as Hoechst staining of DNA, rather than immunostaining for a protein known to be stably expressed in young and aged neurons, was used as a control for staining intensity. 

It would have been indeed interesting to have another marker than DNA as a control. However, this requires a protein that is indeed stably expressed throughout the brain and throughout age. We are not aware of a protein for which this has been established. DNA staining with Hoechst does control for technical artefacts. We have whole-brain imaging data for the protein Rbfox3 (NeuN) which we used as a marker for cell identity. If this protein turns out to be stable, we could add this data to a revised version. 

The main weakness of the IP-MS portion of the study is that none of the interactors were individually validated or subjected to follow-up analyses. The list of interactors was compared to previously published datasets, but not to ORF1p interactors in any other mouse tissue. 

As stated in the manuscript, the list of previously published datasets does include a mouse dataset with ORF1p interacting proteins in mouse spermatocytes (please see line 434-435: “ORF1p interactors found in mouse spermatocytes were also present in our analysis including CNOT10, CNOT11, PRKRA and FXR2 among others (Suppl_Table4).”) -> De Luca, C., Gupta, A. & Bortvin, A. Retrotransposon LINE-1 bodies in the cytoplasm of piRNA-deficient mouse spermatocytes: Ribonucleoproteins overcoming the integrated stress response. PLoS Genet 19, e1010797 (2023)). We indeed did not validate any interactors for several reasons (economic reasons and time constraints (post-doc leaving)). However, we feel that the significant overlap with previously published interactors highlights the validity of our data and we anticipate that this list of ORF1p protein interactors in the mouse brain will be of further use for the community.  

The authors achieved the goals of broadly characterizing ORF1p expression across different regions of the mouse brain, and identifying putative ORF1p interactors in the mouse brain. However, findings from both parts of the study are somewhat superficial in depth. 

This provides a useful dataset to the field, which likely will be used to justify and support numerous future studies into L1 activity in the aging mammalian brain and in neurodegenerative disease. Similarly, the list of ORF1p interacting proteins in the brain will likely be taken up and studied in greater depth. 

Reviewer #3 (Public Review):

The question about whether L1 exhibits normal/homeostatic expression in the brain (and in general) is interesting and important. L1 is thought to be repressed in most somatic cells (with the exception of some stem/progenitor compartments). However, to our knowledge, this has not been authoritatively / systematically examined and the literature is still developing with respect to this topic. The full gamut of biological and pathobiological roles of L1 remains to be shown and elucidated and this area has garnered rapidly increasing interest, year-by-year. With respect to the brain, L1 (and repeat sequences in general) have been linked with neurodegeneration, and this is thought to be an aging-related consequence or contributor (or both) of inflammation. This study provides an impressive and apparently comprehensive imaging analysis of differential L1 ORF1p expression in mouse brain (with some supporting analysis of the human brain), compatible with a narrative of non-pathological expression of retrotransposition-competent L1 sequences. We believe this will encourage and support further research into the functional roles of L1 in normal brain function and how this may give way to pathological consequences in concert with aging. However, we have concerns with conclusions drawn, in some cases regardless of the lack of statistical support from the data. We note a lack of clarity about how the 3rd party pre-trained machine learning models perform on the authors' imaging data (validation/monitoring tests are not reported), as well as issues (among others) with the particular implementation of co-immunoprecipitation (ORF1p is not among the highly enriched proteins and apparently does not reach statistical significance for the comparison) - neither of which may be sufficiently rigorous.  

Thank you for your comments on our manuscript. 

In a revised version and a more in-depth response, we will address the concerns about the machine learning paradigm. Concerning the co-IP-MS, we can confirm that ORF1p is among the highly enriched proteins as it was not found in the IgG control (in 5 independent samples), only in the ORF1p-IP (in 5 out of 5 independent samples). This is what the infinite sign in Suppl Table 2 indicates and this is why there is no p-value assigned as infinite/0 doesn’t allow to calculate a p-value. We will make this clearer in a revised version of the manuscript.

Author response:

Thank you for the reviewers’ thoughtful comments and suggestions! We greatly appreciate the feedback and are committed to address all the points raised by the reviewers to strengthen our manuscript.

We plan to conduct additional local structural analyses to better demonstrate our observations of PROTAC-induced LYS-GLY interactions and lysine associability. Specifically, we will add more in-depth analysis such as computing dihedral entropies and Root Mean Square Fluctuation (RMSF) of nearby side chains and integrating various structural alignments to provide better visualization and understanding of the local structural arrangements. We plan to extend and add simulations when needed. We will review the latest available crystal and cryo-EM structures. If new structures are available, we will incorporate them into our revised analysis and discussion.

In the revision, additional figures will be included to offer a more comprehensive assessment of local conformational changes. We will also ensure that explanations of technical terminology are clear to non-expert readers and will address the grammatical and terminology errors highlighted by the reviewers. We will refine our language to more accurately describe the focus on structural dynamics in our study.

Author response:

Public Reviews:

Reviewer #1 (Public review):

Summary:

This work made a lot of efforts to explore the multifaceted roles of the inferior colliculus (IC) in auditory processing, extending beyond traditional sensory encoding. The authors recorded neuronal activitity from the IC at single unit level when monkeys were passively exposed or actively engaged in behavioral task. They concluded that 1）IC neurons showed sustained firing patterns related to sound duration, indicating their roles in temporal perception, 2) IC neuronal firing rates increased as sound sequences progress, reflecting modulation by behavioral context rather than reward anticipation, 3) IC neurons encode reward prediction error and their capability of adjusting responses based on reward predictability, 4) IC neural activity correlates with decision-making. In summary, this study tried to provide a new perspective on IC functions by exploring its roles in sensory prediction and reward processing, which are not traditionally associated with this structure.

Strengths:

The major strength of this work is that the authors performed electrophysiological recordings from the IC of behaving monkeys. Compared with the auditory cortex and thalamus, the IC in monkeys has not been adequately explored.

We appreciate the reviewer’s acknowledgment of the efforts and strengths of our study. Indeed, our goal was to provide a comprehensive exploration of the multifaceted roles of the inferior colliculus (IC) in auditory processing and beyond, particularly in sensory prediction and reward processing. The use of electrophysiological recordings in behaving monkeys was central to our approach, as we sought to uncover the underexplored aspects of IC function in these complex cognitive domains. We are pleased that the reviewer recognizes the value of investigating the IC, a structure that has not been adequately explored in primates compared to other auditory regions like the cortex and thalamus. This feedback reinforces our belief that our work contributes significantly to advancing the understanding of the IC's roles in cognitive processing.

We look forward to addressing any further points the reviewers may have and refining our manuscript accordingly. Thank you for your constructive feedback and for recognizing the strengths of our research approach.

Weaknesses:

(1) The authors cited several papers focusing on dopaminergic inputs in the IC to suggest the involvement of this brain region in cognitive functions. However, all those cited work were done in rodents. Whether monkey's IC shares similar inputs is not clear.

We appreciate the reviewer's insightful comment on the limitations of extrapolating findings from rodent models to monkeys, particularly concerning dopaminergic inputs to the Inferior Colliculus (IC). While it is true that most studies on dopaminergic inputs to the IC have been conducted in rodents, to our knowledge, no studies have been conducted specifically in primates. To address the reviewer's concern, we have added a statement in both the introduction and discussion sections of our manuscript:

- Introduction: " However, these studies were conducted in rodents, and the existence and role of dopaminergic inputs in the primate IC remain underexplored."

- Discussion: " However, the exact mechanisms and functions of dopamine modulation in the inferior colliculus are still not fully understood, particularly in primates. "

(2) The authors confused the two terms, novelty and deviation. According to their behavioral paradigm, deviation rather than novelty should be used in the paper because all the stimuli have been presented to the monkeys during training. Therefore, there is actually no novel stimuli but only deviant stimuli. This reflects that the author has misunderstood the basic concept.

We appreciate the reviewer's clarification regarding the distinction between "novelty" and "deviation" in the context of our behavioral paradigm. We agree that, given the nature of our experimental design where all stimuli were familiar to the monkeys during training, the term "deviation" more accurately describes the stimuli used in our study rather than "novelty."

To address this, we have revised the manuscript to replace the term "novelty" with "deviation" wherever applicable. This change has been made to ensure accurate terminology is used throughout the paper, thereby eliminating any potential misunderstanding of the concepts involved in our study.

We thank the reviewer for pointing out this important distinction, which has improved the clarity and precision of our manuscript.

(3) Most of the conclusions were made based on correlational analysis or speculation without providing causal evidences.

We appreciate the reviewer’s concern regarding the reliance on correlational analyses in our study. Indeed, we acknowledge that the conclusions drawn primarily reflect correlations between neuronal activity and behavioral outcomes, rather than direct causal evidence. This limitation is inherent to many electrophysiological studies, particularly those conducted in behaving primates, where direct manipulation of specific neural circuits to establish causality is often challenging.

This limitation becomes even more complex when considering the IC’s role as a key lower-level relay station in the auditory pathway. Manipulating IC activity could potentially affect auditory responses in downstream pathways, which, in turn, may influence sensory prediction and decision-making processes. Moreover, we hypothesize that the sensory prediction and reward signals observed in the IC may not have direct causal effects but may instead be driven by top-down projections from higher cognitive regions. However, it is important to emphasize that our study provides novel evidence that the IC may exhibit multiple facets of cognitive signaling, which could inspire future research into the underlying mechanisms and broader functional implications of these signals.

To address this, we have taken the following steps in our revised manuscript:

(1) Clarified the Scope of Conclusions: We have revised the language in the Results and Discussion sections to explicitly state that our findings represent correlational relationships rather than causal mechanisms. For example, we now refer to the associations observed between IC activity and behavioral outcomes as "correlational" and have refrained from making definitive causal claims without supporting experimental evidence.

(2) Proposed Future Directions: In the Discussion section, we have included suggestions for future studies to directly test the causality of the observed relationships. We acknowledge the need for further investigation to substantiate the causal links between IC activity and cognitive functions such as sensory prediction, decision-making, and reward processing.

We believe these revisions provide a more balanced interpretation of our findings while emphasizing the importance of future research to build on our results and establish causal relationships. Thank you for raising this critical point, which has led to a more rigorous and transparent presentation of our study.

(4) Results are presented in a very "straightforward" manner with too many detailed descriptions of phenomena but lack of summary and information synthesis. For example, the first section of Results is very long but did not convey clear information.

We appreciate the reviewer’s feedback regarding the presentation of our results. We understand that the detailed descriptions of phenomena may have made it difficult to discern the key findings and overarching themes in the study. We recognize the importance of balancing detailed reporting with clear summaries and synthesis to effectively communicate our findings.

To address this concern, we have made the following revisions to the manuscript:

(1) Condensed and Synthesized Key Findings: We have streamlined the presentation of the Results section by condensing overly detailed descriptions and focusing on the most critical aspects of the data. Key findings are now summarized at the end of each subsection to ensure that the main points are clearly conveyed.

(2) Enhanced Section Summaries: We have added summary statements at the end of each major results section to synthesize the findings and highlight their significance. This should help guide the reader through the narrative and emphasize the key takeaways from each part of the study.

(3) Improved Flow and Clarity: We have revised the structure and organization of the Results section to improve the flow of information. By rearranging certain paragraphs and refining the language, we aim to present the results in a more cohesive and coherent manner.

We believe these changes will make the Results section more accessible and informative, allowing readers to more easily grasp the significance of our findings. Thank you for your valuable suggestion, which has significantly improved the clarity and impact of our manuscript.

(5) The logic between different sections of Results is not clear.

We appreciate the reviewer’s observation regarding the lack of clear logical connections between different sections of the Results. We acknowledge that a coherent flow is essential for effectively communicating the progression of findings and their implications.

To address this concern, we have made the following revisions:

(1) Enhanced Transitions Between Sections: We have introduced clearer transitional statements between sections of the Results. These transitions explicitly state how each new section builds upon or relates to the previous findings, creating a more cohesive narrative.

(2) Integration of Findings: In several places within the Results, we have added brief synthesis paragraphs that integrate findings across sections. These integrative summaries help to tie together the different aspects of our study, demonstrating how they collectively contribute to our understanding of the Inferior Colliculus’s (IC) role in sensory prediction, decision-making, and reward processing.

(3) Clarified Rationale: At the beginning of each major section, we have clarified the rationale behind why certain experiments were conducted, connecting them more clearly to the overarching goals of the study. This should help the reader understand the purpose of each set of results in the context of the broader research objectives.

We believe these changes improve the overall coherence and readability of the Results section, allowing readers to better follow the logical progression of our study. We are grateful for this constructive feedback and believe it has significantly enhanced the manuscript.

(6) In the Discussion, there is excessive repetition of results, and further comparison with and discussion of potentially related work are very insufficient. For example, Metzger, R.R., et al. (J Neurosc, 2006) have shown similar firing patterns of IC neurons and correlated their findings with reward.

We appreciate the reviewer's insightful critique regarding the excessive repetition in the Discussion and the lack of sufficient comparison with related work. We acknowledge that a well-balanced Discussion should not only interpret findings but also place them in the context of existing literature to highlight the novelty and significance of the study.

To address these concerns, we have made the following revisions:

(1) Reduction of Repetition: We have carefully revised the Discussion to minimize redundant repetition of the Results. Instead of restating the findings, we now focus more on their implications, limitations, and how they advance the current understanding of the Inferior Colliculus (IC) and its broader cognitive roles.

(2) Incorporation of Related Work: We have expanded the Discussion to include a more comprehensive comparison with existing literature, specifically highlighting studies that have reported similar findings. For example, we now discuss the work by Metzger et al. (2006), which demonstrated similar firing patterns of IC neurons and correlated these with reward-related processes. This comparison helps contextualize our results and emphasizes the novel contributions our study makes to the field.

We believe these revisions have significantly improved the quality of the Discussion by reducing unnecessary repetition and providing a more thorough engagement with the relevant literature. We are grateful for the reviewer's valuable feedback, which has helped us refine and strengthen the manuscript.

Reviewer #2 (Public review):

Summary:

The inferior colliculus (IC) has been explored for its possible functions in behavioral tasks and has been suggested to play more important roles rather than simple sensory transmission. The authors revealed the climbing effect of neurons in IC during decision-making tasks, and tried to explore the reward effect in this condition.

Strengths:

Complex cognitive behaviors can be regarded as simple ideals of generating output based on information input, which depends on all kinds of input from sensory systems. The auditory system has hierarchic structures no less complex than those areas in charge of complex functions. Meanwhile, IC receives projections from higher areas, such as auditory cortex, which implies IC is involved in complex behaviors. Experiments in behavioral monkeys are always time-consuming works with hardship, and this will offer more approximate knowledge of how the human brain works.

We greatly appreciate the reviewer's positive summary of our work and recognition of the effort involved in conducting experiments on behaving monkeys. We agree with the reviewer that the inferior colliculus (IC) plays a significant role beyond mere sensory transmission, particularly in integrating sensory inputs with higher cognitive functions. Our study aims to shed light on these complex functions by revealing the climbing effect of IC neurons during decision-making tasks and exploring how reward influences this dynamic.

We are encouraged that the reviewer acknowledges the importance of investigating the IC's role within the broader framework of complex cognitive behaviors and appreciates the hierarchical nature of the auditory system. The reviewer's comments reinforce the value of our research in contributing to a more nuanced understanding of how the IC might contribute to sensory-cognitive integration.

We thank the reviewer for highlighting the significance of using behavioral monkey models to approximate human brain function. We are hopeful that our findings will serve as a stepping stone for further research exploring the multifaceted roles of the IC in cognition and behavior.

We will now proceed to address the specific concerns and suggestions provided by the reviewer in the following sections.

Weaknesses:

These findings are more about correlation but not causality of IC function in behaviors. And I have a few major concerns.

We appreciate the reviewer’s concern regarding the reliance on correlational analyses in our study. We acknowledge the importance of distinguishing between correlation and causality. As detailed in our response to Question 3 from Reviewer #1, we recognize the limitations of relying on correlational data and the challenges of establishing direct causal links in electrophysiological studies involving behaving primates.

We have taken steps to clarify this distinction throughout our manuscript. Specifically, we have revised the Results and Discussion sections to ensure that the findings are presented as correlational, not causal, and we have proposed future studies utilizing more direct manipulation techniques to assess causality. We hope these revisions adequately address your concerns.

Comparing neurons' spike activities in different tests, a 'climbing effect' was found in the oddball paradigm. The effect is clearly related to training and learning process, but it still requires more exploration to rule out a few explanations. First, repeated white noise bursts with fixed inter-stimulus-interval of 0.6 seconds was presented, so that monkeys might remember the sounds by rhymes, which is some sort of learned auditory response. It is interesting to know monkeys' responses and neurons' activities if the inter-stimuli-interval is variable. Second, the task only asked monkeys to press one button and the reward ratio (the ratio of correct response trials) was around 78% (based on the number from Line 302). so that, in the sessions with reward, monkeys had highly expected reward chances, does this expectation cause the climbing effect?

We thank the reviewer for raising these insightful points regarding the 'climbing effect' observed in the oddball paradigm and its potential relationship with training, learning processes, and reward expectation. Below, we address each of the reviewer's specific concerns:

(1) Inter-Stimulus Interval (ISI) and Rhythmic Auditory Response:

The reviewer suggests that the fixed inter-stimulus interval (ISI) of 0.6 seconds might lead to a rhythmic auditory response, where monkeys could anticipate the sounds. We appreciate this perspective. However, we believe that rhythm is unlikely to play a significant role in the 'climbing effect' for the following reason: The 'climbing effect' starts from the second sound in the block (Fig.2D and Fig.3B), before any rhythm or pattern could be fully established, as a rhythm generally requires at least three repetitions to form. Unfortunately, we did not explore variable ISIs in the current study, so we cannot directly address this concern with the data at hand.

(2) Reward Expectation and Climbing Effect:

The reviewer raises an important concern about whether the 'climbing effect' could be influenced by the monkeys' high reward expectation, especially given the high reward ratio (~78%) in the sessions. While it is plausible that reward expectation could contribute to the observed increase in neuronal firing rates, we believe the results from our reward experiment (Fig. 4) suggest otherwise. In this experiment, even though reward expectation was likely formed due to the consistent pairing of sounds with rewards (100%), we did not observe a climbing effect in the auditory response. The presence of reward prediction error (Fig. 4D) further suggests that while the monkeys may form reward expectations, these expectations do not directly drive the climbing effect.

To clarify this point, we have added sentences in the revised manuscript to explicitly discuss the relationship between reward expectation and the climbing effect, emphasizing that our findings indicate the climbing effect is not primarily due to reward expectation.

We believe these revisions provide a clearer understanding of the factors contributing to the climbing effect and address the reviewer's concerns effectively. Thank you for these valuable suggestions.

"Reward effect" on IC neurons' responses were showed in Fig. 4. Is this auditory response caused by physical reward action or not? In reward sessions, IC neurons have obvious response related to the onset of water reward. The electromagnetic valve is often used in water-rewarding system and will give out a loud click sound every time when the reward is triggered. IC neurons' responses may be simply caused by the click sound if the electromagnetic valve is used. It is important to find a way to rule out this simple possibility.

We appreciate the reviewer’s concern regarding the potential confounding factor introduced by the electromagnetic valve’s click sound during water reward delivery, which could be misinterpreted as an auditory response rather than a response to the reward itself. Anticipating this possibility, we took measures to eliminate it by placing the electromagnetic valve outside the soundproof room where the neuronal recordings were performed.

To address your concern more explicitly, we have added sentences in the Methods section of the revised manuscript detailing this setup, ensuring that readers are aware of the steps we took to eliminate this potential confound. By doing so, we believe that the observed reward-related neural activity in the IC is attributable to the reward processing itself rather than an auditory response to the valve click. We appreciate you bringing this important aspect to our attention, and we hope our clarification strengthens the interpretation of our findings.

Reviewer #3 (Public review):

Summary:

The authors aimed to investigate the multifaceted roles of the Inferior Colliculus (IC) in auditory and cognitive processes in monkeys. Through extracellular recordings during a sound duration-based novelty detection task, the authors observed a "climbing effect" in neuronal firing rates, suggesting an enhanced response during sensory prediction. Observations of reward prediction errors within the IC further highlight its complex integration in both auditory and reward processing. Additionally, the study indicated IC neuronal activities could be involved in decision-making processes.

Strengths:

This study has the potential to significantly impact the field by challenging the traditional view of the IC as merely an auditory relay station and proposing a more integrative role in cognitive processing. The results provide valuable insights into the complex roles of the IC, particularly in sensory and cognitive integration, and could inspire further research into the cognitive functions of the IC.

We appreciate the reviewer’s positive summary of our work and recognition of its potential impact on the field. We are pleased that the reviewer acknowledges the significance of our findings in challenging the traditional view of the Inferior Colliculus (IC) as merely an auditory relay station and in proposing its integrative role in cognitive processing.

Our study indeed aims to provide new insights into the multifaceted roles of the IC, particularly in the context of sensory and cognitive integration. We believe that this research could pave the way for future studies that further explore the cognitive functions of the IC and its involvement in complex behavioral processes.

We are encouraged by the reviewer’s positive assessment and are committed to continuing to refine our work in response to the constructive feedback provided. We hope that our findings will contribute to advancing the understanding of the IC’s role in the broader context of neuroscience.

We will now proceed to address the specific concerns and suggestions provided by the reviewer in the following sections.

Weaknesses:

Major Comments:

(1) Structural Clarity and Logic Flow:

The manuscript investigates three intriguing functions of IC neurons: sensory prediction, reward prediction, and cognitive decision-making, each of which is a compelling topic. However, the logical flow of the manuscript is not clearly presented and needs to be well recognized. For instance, Figure 3 should be merged into Figure 2 to present population responses to the order of sounds, thereby focusing on sensory prediction. Given the current arrangement of results and figures, the title could be more aptly phrased as "Beyond Auditory Relay: Dissecting the Inferior Colliculus's Role in Sensory Prediction, Reward Prediction, and Cognitive Decision-Making."

We appreciate the reviewer’s detailed feedback on the structural clarity and logical flow of the manuscript. We understand the importance of presenting our findings in a clear and cohesive manner, especially when addressing multiple complex topics such as sensory prediction, reward prediction, and cognitive decision-making.

To address the reviewer's concerns, we have made the following revisions:

(1) Reorganization of Figures and Results:

We agree with the suggestion to merge Figure 3 into Figure 2. By doing so, we can present the population responses to the order of sounds more effectively, thereby streamlining the focus on sensory prediction. This will allow readers to more easily follow the progression of the results related to this key function of the IC.

We have reorganized the Results section to ensure a smoother transition between the different aspects of IC function that we are investigating. The new structure will better guide the reader through the narrative, aligning with the themes of sensory prediction, reward prediction, and cognitive decision-making.

(2) Revised Title:

In line with the reviewer's suggestion, we have revised the title to "Beyond Auditory Relay: Dissecting the Inferior Colliculus's Role in Sensory Prediction, Reward Prediction, and Cognitive Decision-Making." We believe this title more accurately reflects the scope and focus of our study, as it highlights the three core functions of the IC that we are investigating.

(3) Improved Logic Flow:

We have added introductory statements at the beginning of each section within the Results to clarify the rationale behind the experiments and the logical connections between them. This should help to improve the overall flow of the manuscript and make the progression of our findings more intuitive for readers.

We believe these changes significantly enhance the clarity and logical structure of the manuscript, making it easier for readers to understand the sequence and importance of our findings. Thank you for your valuable suggestion, which has led to a more coherent and focused presentation of our work.

(2) Clarification of Data Analysis:

Key information regarding data analysis is dispersed throughout the results section, which can lead to confusion. Providing a more detailed and cohesive explanation of the experimental design would significantly enhance the interpretation of the findings. For instance, including a detailed timeline and reward information for the behavioral paradigms shown in Figures 1C and D would offer crucial context for the study. More importantly, clearly presenting the analysis temporal windows and providing comprehensive statistical analysis details would greatly improve reader comprehension.

We appreciate the reviewer’s insightful comment regarding the need for clearer and more cohesive explanations of the data analysis and experimental design. We recognize that a well-structured presentation of this information is essential for the reader to fully understand and interpret our findings. To address this, we have made the following revisions:

(1) Detailed Explanation of Experimental Design:

We have included a more detailed explanation of the experimental design, particularly for the behavioral paradigms shown in Figures 1C and 1D. This includes a comprehensive timeline of the experiments, along with explicit information about the reward structure and timing. By providing this context upfront, we aim to give readers a clearer understanding of the conditions under which the neuronal recordings were obtained.

(2) Cohesive Presentation of Data Analysis:

Key information regarding data analysis, which was previously dispersed throughout the Results section, has been consolidated and moved to a dedicated subsection within the Methods. This subsection now provides a step-by-step description of the analysis process, including the temporal windows used for examining neuronal activity, as well as the specific statistical methods employed.

We have also ensured that the temporal windows used for different analyses (e.g., onset window, late window, etc.) are clearly defined and consistently referenced throughout the manuscript. This will help readers track the use of these windows across different figures and analyses.

(3) Enhanced Statistical Analysis Details:

We have expanded the description of the statistical analyses performed in the study, including the rationale behind the choice of tests, the criteria for significance, and any corrections for multiple comparisons. These details are now presented in a clear and accessible format within the Methods section, with relevant information also highlighted in the Result section or the figure legends to facilitate understanding.

We believe these changes will significantly improve the clarity and comprehensibility of the manuscript, allowing readers to better follow the experimental design, data analysis, and the conclusions drawn from our findings. Thank you for this valuable feedback, which has helped us to enhance the rigor and transparency of our presentation.

(3) Reward Prediction Analysis:

The conclusion regarding the IC's role in reward prediction is underdeveloped. While the manuscript presents evidence that IC neurons can encode reward prediction, this is only demonstrated with two example neurons in Figure 6. A more comprehensive analysis of the relationship between IC neuronal activity and reward prediction is necessary. Providing population-level data would significantly strengthen the findings concerning the IC's complex functionalities. Additionally, the discussion of reward prediction in lines 437-445, which describes IC neuron responses in control experiments, does not sufficiently demonstrate that IC neurons can encode reward expectations. It would be valuable to include the responses of IC neurons during trials with incorrect key presses or no key presses to better illustrate this point.

We deeply appreciate the detailed feedback provided regarding the conclusions on the inferior colliculus (IC)'s role in reward prediction within our manuscript. We acknowledge the importance of a robust and comprehensive presentation of our findings, particularly when discussing complex neural functionalities.

In response to the reviewers' concerns, we have made the following revisions to strengthen our manuscript:

(1) Inclusion of Population-Level Data for IC Neurons:

In the revised manuscript, we have included population-level results for IC neurons in a supplementary figure. Initially, we focused on two example neurons that did not exhibit motor-related responses to key presses to isolate reward-related signals. However, most IC neurons exhibit motor responses during key presses (as indicated in Fig.7), which can complicate distinguishing between reward-related activity and motor responses. This complexity is why we initially presented neurons without motor responses. To clarify this point, we have added sentences in the Results section to explain the rationale behind our selection of neurons and to address the potential overlap between motor and reward responses in the IC.

(2) Addition of Data on Key Press Errors and No-Response Trials:

In response to the reviewer’s suggestion, we have demonstrated Peri-Stimulus Time Histograms (PSTHs) for two example neurons during error trials as below, including incorrect key presses and no-response trials. Given that the monkeys performed the task with high accuracy, the number of error trials is relatively small, especially for the control condition (as shown in the top row of the figure). While we remain cautious in drawing definitive conclusions from this limited trials, we observed that no clear reward signals were detected during the corresponding window (typically centered around 150 ms after the end of the sound). It is important to note that the experiment was initially designed to explore decision-making signals in the IC, rather than focusing specifically on reward processing. However, the data in Fig. 6 demonstrated intriguing signals of reward prediction error, which is why we believe it is important to present them.

When combined with the results from our reward experiment (Fig. 5), we believe these findings provide compelling evidence of reward prediction errors being processed by IC neurons. Additionally, we observed that the reward prediction error in the IC appears to be signed, meaning that IC neurons showed robust responses to unexpected rewards but not to unexpected no-reward scenarios. However, the sign of the reward prediction error should be explored in greater depth with specifically designed experiments in future studies.

Author response image 1.

(A) PSTH of the neuron from Figure 6a during a key press trial under control condition. The number in the parentheses in the legend represents the number of trials for control condition. (B) PSTHs of the neuron from Figure 6a during non-key press trials under experimental conditions. The numbers in the parentheses in the legend represent the number of trials for experimental conditions. (C-D) Equivalent PSTHs as in A-B but from the neuron in Figure 6b.

We are grateful for the reviewer's insightful suggestions, which have allowed us to improve the depth and rigor of our analysis. We believe these revisions significantly enhance our manuscript's conclusions regarding the complex functionalities of IC.

Author Response:

We would like to thank the reviewers for their constructive feedback and for acknowledging that our approach offers a simple yet powerful framework with the potential to serve as a comprehensive and intuitive tool for analyzing functional activity and connectivity.

In response to the reviewers’ recommendations, we will aim to improve and clarify the following aspects of our work in an upcoming revision:

Scope and limitations of the “fcHNN projection” (R#1 and R#2):

Both reviewers have correctly noted that the interpretability and explanatory power of the simplistic, two-dimensional fcHNN-based projection is limited. In the revised manuscript, we will clarify that, indeed, attractors are in a close mathematical relationship with the principal components of the raw data (i.e., the eigenvectors of the connectome) within our framework. The fcHNN-projection was introduced solely to establish a link between the proposed framework and concepts with which the reader may be more familiar.

We will enhance the presentation and discussion of our results to emphasize that – as the reviewers also kindly pointed out - the value of our approach lies in modelling how different facets of brain activity dynamically emerge from a common space of functional (ghost) attractors, rather than studying in the static attractor patterns themselves.

Motivations and Rationale for Using the Functional Connectome (R#2):

We agree with Reviewer #2 that a deeper mechanistic explanatory power could be achieved by modeling structure-function coupling, and that our framework is well-suited for this challenge. In our revision, we will highlight this as one of the promising future applications of our framework. We will, furthermore, clarify that the scope of the present work was deliberately restricted to functional connectivity to demonstrate that our framework also allows us to “bypass” the significant challenge of structure-function coupling. This enables us to focus on understanding the origins of canonical resting-state networks, the dynamic responses of the system to perturbations and the complex relationship between task-induced activity and resting-state connectivity, without first solving the structure-function coupling problem.

Additionally, we will mathematically justify the use of linear measures of the functional connectome to reconstruct the underlying non-linear dynamic system, thereby clearly delineating which results can and cannot be considered circular when starting from the functional connectome.

Improvements in Overall Clarity of Presentation (R#1):

In line with the above points and in general, we will strive to enhance the overall clarity of the presentation of our results, including figures, wording, and statistical analysis.

Author response:

Reviewer #2 (Public Review):

In this manuscript, Kafri and colleagues assess the contribution of protein degradation to the cell size-dependent accumulation of total protein. This is an interesting line of research that has not previously been explored. Most of the focus on the size-dependence of protein accumulation has been on the synthesis part of the equation. As cells get too big, the efficiency of cell growth (mass accumulation per unit mass) decreases. It is argued that this is not due to the loss of the efficiency in protein synthesis, but rather is due to the increased protein degradation in larger cells. It is an interesting hypothesis, that might well be true, but there are some issues with key aspects of the data and other supporting data are quite indirect. More work needs to be done to support the central claims.

We thank the reviewer for appreciating the work is interesting and previously unexplored.

The major issue is that the data supporting the proportional increase in protein synthesis with cell size need to be strengthened. Protein synthesis is measured by the amount of a methionine analog that is incorporated in a fixed amount of time. Fig. 2 then plots this amount as a function of cell size, which is presumably measured using a total protein dye (this information is not included; incidentally the axis labels should note what the measurement is 'total protein' or 'forward scatter' rather than the more ambiguous 'cell size'). In any case, something is wrong with the cell size measurements in Figure 2 because many cells basically have almost negligible size (near 0) while others have sizes up to 5 or 6 arbitrary units. It makes no sense that there should be a 10-fold or even 100-fold range in cell sizes. For this reason, I can't interpret the data in Figure 2, which is unfortunate since that is a crucial figure for the authors' argument.

The data supporting higher rates of protein degradation per unit mass in large cells suffers from a similar problem as Figure 3E has the same issue as Figure 2 with too many tiny 'cells'.

Yes, the reviewer is correct that we are using a total protein dye (Alexa fluorophore-conjugated succinimidyl ester, abbreviated as SE) to measure cell size. We have included details regarding the methods of cell size (total protein content) measurement in both the Methods (line 463-466) and Results (line 100-102) sections.

Regarding the reviewer’s concern on the cell size range, we apologize for the confusion the figures may have caused. These cell size measurements are within reasonable range and not 10-fold or 100-fold. Please refer to our detailed response above to essential point #1.

Moreover, the reliance on cycloheximide to treat cells and measure reduction in mass isn't ideal since shutting off all protein synthesis is a pretty drastic perturbation. It would have been better to shut off synthesis of a specific protein and measure its degradation in large and small cells while keeping the cells otherwise intact.

We acknowledge that relying on cycloheximide to measure changes in mass has limitations, as acute inhibition in protein synthesis is a significant perturbation. Ideally, we would measure the degradation of specific proteins in large and small cells while keeping the rest of the cellular processes intact. However, this presents considerable technological challenges. While our evidence clearly shows increased protein degradation and compensatory growth slowdown in large cells, we have not yet identified the specific proteins/genes being targeted. Implementing the reviewer's suggestion would require first screening for a suitable protein/gene to serve as a reporter for compensatory degradation. A significant proteomics screen may allow identification of potential targets, but further validation would necessitate substantial effort, including the generation and validation of a reporter system. We agree that this is a valuable experiment to pursue, but it will likely be part of a follow-up study focused on characterizing the specific protein targets and E3 ligases involved in these processes. In the revised manuscript, we discuss these open questions and future directions in line 380-410.

Reviewer #3 (Public Review):

The authors report a previously undocumented role for UPS-mediated protein turnover in size control in human cells. The study builds on previous observations made by the Kafri group that large cells undergo size compensation by reducing their rate of growth. In particular, recent published work by Ginzberg et al showed that CDK2 inhibition is accompanied by long term size compensation in the form of reduced cell growth whereas CDK6 inhibition is not. The authors investigate the basis for this effect and demonstrate in both unperturbed and perturbed growth/division contexts, using both fixed cells and time lapse microscopy, that the rate of protein synthesis increases proportionately in large cells that undergo size compensation even though mass accumulation is attenuated. The authors show that this effect appears to be mediated by increased proteasomal activity, as demonstrated by proteasome-dependent K48-ubiquitin chain turnover. Intriguingly, this degradation-mediated size compensation mechanism appears to be most active at the G1/S transition, the primary point at which size control operates. The experiments are well controlled, and the conclusions of the study are in general well supported by the data. The authors present an interesting set of discussion points that relate their observations to size control mechanisms in dividing and non-dividing cells. While specific mechanisms are not pursued, this study nevertheless adds an important new insight into the still unsolved problem of size control.

We thank the reviewer for appreciating the novelty of the work.

Author response:

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

Public Reviews:

Reviewer #1 (Public Review):

Summary:

This paper conducted a GWAS meta-analysis for COVID-19 hospitalization among admixed American populations. The authors identified four genome-wide significant associations, including two novel loci (BAZ2B and DDIAS), and an additional risk locus near CREBBP using cross-ancestry meta-analysis. They utilized multiple strategies to prioritize risk variants and target genes. Finally, they constructed and assessed a polygenic risk score model with 49 variants associated with critical COVID-19 conditions.

Strengths:

Given that most of the previous studies were done in European ancestries, this study provides unique findings about the genetics of COVID-19 in admixed American populations. The GWAS data would be a valuable resource for the community. The authors conducted comprehensive analyses using multiple different strategies, including Bayesian fine mapping, colocalization, TWAS, etc., to prioritize risk variants and target genes. The polygenic risk score (PGS) result demonstrated the ability of the cross-population

PGS model for COVID-19 risk stratification.

Thank you very much for the positive comments and the willingness to revise this manuscript.

Weaknesses:

(1) One of the major limitations of this study is that the GWAS sample size is relatively small, which limits its power.

(2) The fine mapping section is unclear and there is a lack of information. The authors assumed one causal signal per locus, and only provided credible sets, but did not provide posterior inclusion probabilities (PIP) for the variants to be causal.

(3) Colocalization and TWAS used eQTL data from GTEx data, which are mainly from European ancestries. It is unclear how much impact the ancestry mismatch would have on the result. The readers should be cautious when interpreting the results and designing follow-up studies.

We agree with that the sample size is relatively small. Despite that, it was sufficient to reveal novel risk loci supporting the robustness of the main findings. We have indicated this limitation at the end of the discussion section.

Thank you for rising this point. As suggested, we have also used SuSIE, which allows to assume more than one causal signal per locus. However, in this case the results were not different from those obtained with the original Bayesian colocalization performed with corrcoverage. Regarding the PIP, at the fine mapping stage we are inclined to put more weight on the functional annotations of the variants in the credible set than on the statistical contributions to the signal. This is the reason why we prefer not to put weight on the PIP of the variants but prioritize variants that were enriched functional annotations.

This is a good point regarding the lack of diversity in GTEx data. We have also used data from AMR populations (GALA II-SAGE models), although it was only available for blood tissue. Regarding the ancestry mismatch between datasets, several studies have attempted to explore the impact. Gay et al. (PMID: 32912333) studied local ancestry effects on eQTLs from the GTEx consortium and concluded that adjustment of eQTLs by local ancestry only yields modest improvement over using global ancestry (as done in GTEx). Moreover, the colocalization results between adjusting by Local Ancestry and Global Ancestry were not significantly different. Besides, Mogil et al. (PMID: 30096133) observed that genes with higher heritability share genetic architecture between populations. Nevertheless, both studies have evidenced decreased power and poorer predictive performances regarding gene expression because of reduced diversity in eQTL analyses. As consequence of the ancestry mismatch, we now warn the readers that this may compromise signal detection (Discussion, lines 531-533). 

Reviewer #2 (Public Review):

This is a genome-wide association study of COVID-19 in individuals of admixed American ancestry (AMR) recruited from Brazil, Colombia, Ecuador, Mexico, Paraguay, and Spain. After quality control and admixture analysis, a total of 3,512 individuals were interrogated for 10,671,028 genetic variants (genotyped + imputed). The genetic association results for these cohorts were meta-analyzed with the results from The Host Genetics Initiative (HGI), involving 3,077 cases and 66,686 controls. The authors found two novel genetic loci associated with COVID-19 at 2q24.2 (rs13003835) and 11q14.1 (rs77599934), and other two independent signals at 3p21.31 (rs35731912) and 6p21.1 (rs2477820) already reported as associated with COVID-19 in previous GWASs. Additional meta-analysis with other HGI studies also suggested risk variants near CREBBP, ZBTB7A, and CASC20 genes.

Strengths:

These findings rely on state-of-the-art methods in the field of Statistical Genomics and help to address the issue of a low number of GWASs in non-European populations, ultimately contributing to reducing health inequalities across the globe.

Thank you very much for the positive comments and the willingness to revise this manuscript.

Weaknesses:

There is no replication cohort, as acknowledged by the authors (page 29, line 587), and no experimental validation to assess the biological effect of putative causal variants/genes. Thus, the study provides good evidence of association, rather than causation, between the genetic variants and COVID-19. Lastly, I consider it crucial to report the results for the SCOURGE Latin American GWAS, in addition to its meta-analysis with HGI results, since HGI data has a different phenotype scheme (Hospitalized COVID vs Population) compared to SCOURGE (Hospitalized COVID vs Non-hospitalized COVID).

We essentially agree with the reviewer in that one of the main limitations of the study is the lack of a replication stage because of the use of all available datasets on a one-stage analysis. To contribute to the interpretation of the findings in the absence of a replication stage, we now assessed the replicability of the novel loci using the Meta-Analysis Model-based Assessment of replicability (MAMBA) approach (PMID: 33785739) and included the posterior probabilities of replication in Table 2. We also explored further the potential replicability of signals in other populations. We agree that the results should be interpreted in terms of associations given the lack of functional validation of main findings, so we have slightly modified the discussion.

As suggested, the SCOURGE Latin American GWAS summary is now accessible by direct request to the Consortium GitHub repository (https://github.com/CIBERER/Scourge-COVID19) (lines 797-799). We have also included the results from the SCOURGE GWAS analysis for the replication of the 40 lead variants in the Supplementary Table 12. Results from the SCOURGE GWAS for the lead variants in the AMR meta-analysis with HGI were already included in the Supplementary Table 2. As note, we have not been able to conduct the meta-analysis with the same hospitalization scheme as in the HGI study since the population-specific results for those analyses were not publicly released. However, sensitivity analyses included within the supplementary material from the COVID-19 Host Genetics Initiative (2021) stated that there were no significant differences in effects (Odds Ratios) between analyses using population controls or just non-hospitalized COVID-19 patients.

Reviewer #3 (Public Review):

Summary:

In the context of the SCOURGE consortium's research, the authors conduct a GWAS meta-analysis on 4,702 hospitalized individuals of admixed American descent suffering from COVID-19. This study identified four significant genetic associations, including two loci initially discovered in Latin American cohorts. Furthermore, a trans-ethnic meta-analysis highlighted an additional novel risk locus in the CREBBP gene, underscoring the critical role of genetic diversity in understanding the pathogenesis of COVID-19.

Strengths:

(1) The study identified two novel severe COVID-19 loci (BAZ2B and DDIAS) by the largest GWAS meta-analysis for COVID-19 hospitalization in admixed Americans.

(2) With a trans-ethnic meta-analysis, an additional risk locus near CREBBP was identified.

Thank you very much for the positive comments and the willingness to revise this manuscript.

Weaknesses:

(1) The GWAS power is limited due to the relatively small number of cases.

(2) There is no replication study for the novel severe COVID-19 loci, which may lead to false positive findings.

We agree with that the sample size is relatively small. Despite that, it was sufficient to reveal novel risk loci supporting the robustness of the main findings. We have indicated this limitation at the end of the discussion section.

Regarding the lack of a replication study, we now assessed the replicability of the novel loci using the Meta-Analysis Model-based Assessment of replicability (MAMBA) approach (PMID: 33785739). We have included the posterior probabilities of replication in Table 2.

(3) Significant differences exist in the ages between cases and controls, which could potentially introduce biased confounders. I'm curious about how the authors treated age as a covariate. For instance, did they use ten-year intervals? This needs clarification for reproducibility.

Thank you for rising this point. Age was included as a continuous variable. This has been now indicated in line 667 (within Material and Methods).

(4)"Those in the top PGS decile exhibited a 5.90-fold (95% CI=3.29-10.60, p=2.79x10-9) greater risk compared to individuals in the lowest decile". I would recommend comparing with the 40-60% PGS decile rather than the lowest decile, as the lowest PGS decile does not represent 'normal controls'.

Thank you. In the revised version, the PGS categories was compared following the recommendation (lines 461-463).

(5) In the field of PGS, it's common to require an independent dataset for training and testing the PGS model. Here, there seems to be an overfitting issue due to using the same subjects for both training and testing the variants.

We are sorry for the misunderstanding. In fact, we have followed the standard to avoid overfitting of the PGS model and have used different training and testing datasets. The training data (GWAS) was the HGI-B2 ALL meta-analysis, in which our AMR GWAS was not included. The PRS model was then tested in the SCOURGE AMR cohort. However, it is true that we did test the combination of the PRS adding the new discovered variants in the SCOURGE cohort. To avoid potential overfitting by adding the new loci, we have excluded from the manuscript the results on which we included the newly discovered variants.

(6) The variants selected for the PGS appear arbitrary and may not leverage the GWAS findings without an independent training dataset.

Again, we are sorry for the misunderstanding. The PGS model was built with 43 variants associated with hospitalization or severity within the HGI v7 results and 7 which were discovered by the GenOMICC consortium in their latest study and were not in the latest HGI release. The variants are included within the Supplementary Table 14, but we have now annotated the discovery GWAS.

(7) The TWAS models were predominantly trained on European samples, and there is no replication study for the findings as well.

This is a good point regarding the lack of diversity in GTEx data. We have also used data from AMR populations (GALA II-SAGE models), although it was only available for blood tissue. Regarding the ancestry mismatch between datasets, several studies have attempted to explore the impact. Gay et al. (PMID: 32912333) studied local ancestry effects on eQTLs from the GTEx consortium and concluded that adjustment of eQTLs by local ancestry only yields modest improvement over using global ancestry (as done in GTEx). Moreover, the colocalization results between adjusting by Local Ancestry and Global Ancestry were not significantly different. Besides, Mogil et al. (PMID: 30096133) observed that genes with higher heritability share genetic architecture between populations. Nevertheless, both studies have evidenced decreased power and poorer predictive performances regarding gene expression because of reduced diversity in eQTL analyses. As consequence of the ancestry mismatch, we now warn the readers that this may compromise signal detection (Discussion, lines 531-533). 

Recommendations for the authors:

Reviewer #1 (Recommendations For The Authors):

(1) The authors mentioned the fine mapping method did not converge for the locus in chr 11. I would consider trying a different fine-mapping method (such as SuSiE or FINEMAP). It would be helpful to provide posterior inclusion probabilities (PIP) for the variants in fine mapping results and plot the PIP values in the regional association plots.

As suggested, we have also used SuSIE, which allows to assume more than one causal signal per locus. However, in this case the results were not different from those obtained with the original Bayesian colocalization performed with corrcoverage. SuSIE’s fine-mapping for chromosome 11 prioritized a single variant, which is likely due to the rare frequency. Thus, we have maintained the fine-mapping as it was originally indicated in the previous version of the manuscript but have now included the credible set in Supplementary Table 6.

Regarding the PIP, at the fine mapping stage we are inclined to put more weight on the functional annotations of the variants in the credible set than on the statistical contributions to the signal. This is the reason why we prefer not to put weight on the PIP of the variants but prioritize variants that were enriched functional annotations.

(2) Please provide more detailed information about the VEP and V2G analysis and how to interpret those results. My understanding of V2G is that it includes different sources of information (such as molecular QTLs and chromatin interactions from different tissues/cell types, etc.). It is unclear what sources of information and weight settings were used in the V2G model.

Thank you for rising this point. As suggested, we have clarified the basis for VEP and V2G and the interpretation (lines 732-743).

(3) The authors identified multiple genes with different strategies, e.g. FUMA, V2G, COLOC, TWAS, etc. How many genes were found/supported by evidence provided by multiple methods? It could be helpful to have a table summarizing the risk genes found by different strategies, and the evidence supporting the genes. e.g. which genes are found by which methods, and the biological functions of the genes, etc.

Thank you for rising this point. As suggested, we now added a new figure (Figure 5) to summarize the findings with the multiple methods used.

(4) It would be helpful to make the code/scripts available for reproducibility.

As suggested, the SCOURGE Latin American GWAS summary and the analysis scripts (https://github.com/CIBERER/Scourge-COVID19/tree/main/scripts/novel-risk-hosp-AMR-2024) are now accessible in the Consortium GitHub repository (https://github.com/CIBERER/Scourge-COVID19) (lines 806-807).

(5) The fonts in some of the figures (e.g. Figure 2) are hard to read.

Thank you. We have now included the figures as SVG files.

Reviewer #2 (Recommendations For The Authors):

- The abstract lacks a conclusion sentence.

Thank you. As suggested, we have included two additional sentences with broad conclusions from the study. We preferred to avoid relying on conclusions related to known or new biological links of the prioritized genes given the lack of functional validation of main findings.

- Regarding the association analysis (page 27, line 677), I wonder if some of the 10 principal components (PCs) are capturing information about the recruitment areas (countries). It may be relevant to test for multicollinearity among these variables.

Since we acknowledge that some of the categories might be correlated with a certain PC but not all of them do, we have calculated GVIF values for the main variables to assess the categorical variable as a single entity. The scaled GVIF^1(1/2*Df)) value for the categorical variable is 1.52. Thus, if we square this value, we obtain 2.31, which can be then used for applying usual rule-of-thumb for VIF values.

- Still on the topic of association analysis, did the authors adjust the logistic model for comorbidities variables from Table 1? Given these comorbidities also have a genetic component and their distribution differs between non-hospitalized vs hospitalized, I am concerned that comorbidities might be confounding the association between genetic variants and COVID.

We did not adjust by comorbidities since HGI studies were not adjusted either and we aimed to be as aligned as possible with HGI. However, as suggested, we have now tested the association between each of the comorbidities in Table 1 and each of the variants in Table 2, using the comorbidities as dependent variables and adjusting for the main covariables (age, sex, PCs and country of recruitment). None of the variants were significantly associated to the comorbidities (line 333).

- If I understood correctly, the 49 genetic variants used to develop the polygenic risk score model (PRS) were based on the HGI total sample size (data release 7), which is predominantly of European ancestry. I am concerned about the prediction accuracy in the AMR population (PRS transferability issue).

We have explored literature in search of other PRS to compare the associated OR in our cohort with ORs calculated in European populations. Horowitz et al. (2022) reported an OR of 1.38 for the top 10% with respect to hospitalization risk in European individuals using a GRS with 12 variants.

We acknowledge that this might be an issue and is now explained in discussion of the revised version (lines 561-568). However, as this is the first time a PRS for COVID-19 is applied to a relatively large AMR cohort, we believe that this analysis will be of value for further analyses regarding PRS transferability, providing a source for comparison in further studies.    

- On page 23, line 579, the authors acknowledge their "GWAS is underpowered". This sentence requires a sample/power calculation, otherwise, I suggest using "is likely underpowered".

Thanks for the input. We have modified the sentence as suggested.

Reviewer #3 (Recommendations For The Authors):

I wonder if the authors have an approximate date when the GWAS summary statistic will be available. I reviewed some manuscripts in the past, and the authors claimed they would deposit the data soon, but in fact it would not happen until 2 years later.

The summary statistics are already available from the SCOURGE Consortium repository https://github.com/CIBERER/Scourge-COVID19 (lines 806-807).

Author response:

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

Reviewer #1 (Recommendations for the Authors):

Major 

(a) In the study the authors focus on the RALF1 peptide. But according to expression data and the study from Abarca et al., 2021, RALF1 is not the only peptide expressed in the root and also having an impact in root growth effect. Similarly, looking at the primary sequence from RALF1 it does not differ much chemically from other RALFs such as RALF33, RALF23, RALF22, etc. So, does the cell wall pectin methylation status also have an impact on the effect of other RALFs on root growth or is that specific of RALF1? 

(b) In addition, is the internalization of FER depending only on RALF1 upon the methylation status of cell wall pectins? Or can other RALFs cause a similar effect potentially?

(c) The authors propose that RALF1 associates with deesterifed pectin, through electrostatic interactions. To do that they perform Biolayer interferometry assays using a buffer with pH 7.4. Is that a relevant pH at the cell wall? Is possible that the authors thought that this may not change the charges of R and K residues, however, it will affect the overall charge of the peptide given the fact that it contains quite some N and Q in the exposed surface. The authors may want to consider that.

(d) Moreover, the authors do not use their peptide RALF1KR, suggested as a peptide not binding OGs, as a control in their OG binding assays. That biochemical experiment should also be included to validate their results and conclusions.

We thank reviewer #1 for these comments. In this work, we focused on RALF1 but the majority of AtRALF peptides, when applied exogenously as synthetic peptides, induce RALF1like effects in Arabidopsis (Abarca et al., 2021; PMID: 34608971). Moreover, all RALF peptides display clusters of R and K residues and are negatively charged (Abarca et al., 2021; PMID: 34608971). In comparison to RALF1, we now also use RALF34 because it was suggested to interact also via the Catharanthus roseus receptor-like kinase 1-like (CrRLK1L) THESEUS1 (THE1). Notably, RALF34 also induced the internalization of FER-GFP. Moreover, the interference with PME also disrupted this activity of RALF34. Therefore, we assume that other RALF peptides display the same or similar signalling modalities. Nevertheless, it remains to be addressed if all RALF family members require PME activity. 

We appreciated these comments and incorporated this aspect in the revised version of the manuscript. The pH was chosen for technical reasons associated with the used BLI buffer. As requested, we also included the RALF1-KR peptide in our OG binding assays. Under these conditions, the mutated peptides were not able to interact with the OGs anymore. Accordingly, we conclude that the K and R residues in RALF1 are crucial for its binding to demethylesterified OGs.  

(e) Another important aspect is regarding their design RALF1KR mutant and its effect in planta. The authors report the following: "RALF1-KR peptides are not bioactive, because they did neither affect root growth, nor cell wall integrity, nor did they induce the ligand-induced endocytosis of FER in epidermal root cells (Figure 5D-I). These findings suggest that the positively charged residues in RALF1 are essential for its activity in roots." According to the structure published by Xiao at el. 2019, the R in the alpha helix from RALF peptides (YISYQSLKR... in RALF1 seq) is directly involved in the interaction with LLGs. So, a mutation in that R may impair the interaction of RALF1 with LLG and therefore the complex formation with FER. So, it is well possible that the effect that the authors are seeing on FER signaling and endocytosis, using this peptide variant, may not be due to the impaired capacity of the peptide to bind deesterified pectin but to not be able to be sensed by the membrane complex directly. To verify that the authors should test, either biochemically or by CoIP in planta, that their RALF1KR variant can still be perceived by the LLG-FER complex. 

We agree with reviewer #1 and do not doubt that the positive charges in RALF1 likely interact with several entities. The respective sites were also covered in Liu et al., 2024 (Cell). It would be interesting to understand how the charge-dependent interaction with pectin modulates the RALF binding to the LLG-FER complex, but these experiments are beyond the scope of this manuscript. We confirmed that the negative charges in RALF1 are essential for OG binding as well as for its bioactivity. We however do not rule out that they bear additional structural functions beyond pectin binding. We clarified this aspect in the revised version. It is conceivable that the pectin and receptor complex binding of RALF1 is molecularly and mechanistically related. 

(f) The authors propose in this study that this effect of RALF1-pectin mode of action on FER is independent from LRXs. That is a very interesting observation which also aligns with similar observations from other independent studies (Moussu et al., 2020; Schoenaers et al. Nat Plants, 2024; Franck et al., 2018). However, that seems to be in conflict with the previous mode of action that the authors had described in Dunser et al., 2019. In that last study the authors had described that FER constitutively interacts with LRX proteins in a direct way to sense cell wall changes. In my view the authors do not critically elaborate to explain these two contradicting results, which are key to understand the mode of action they are describing. This relevant aspect should be addressed more in depth by the authors in their discussion.

Thank you for the comment. We do not see that our findings contradict our previous work (from Dünser et al., 2019). There we concluded that LRX and FER directly interact to sense cell wall characteristics. However, the loss of LRX function abolished the cell wall sensing mechanism, but the respective loss-of-function and dominant negative lines were still able to detect RALF peptides. We hence proposed that the LRX/FER function is at least partially independent of the FER function in RALF perception. This is in agreement with our current study where we conclude again that FER shows LRX-dependent but also -independent modes of action. 

Minor

(g) In the introduction (first page), the authors write the following sentence: "RALF peptides are involved in multiple physiological and developmental processes, ranging from organ growth and pollen tube guidance to modulation of immune responses (Stegmann et al., 2017; Abarca et al., 2021)". RALFs are not involved in pollen tube guidance but pollen tube growth.

So, that should be changed in the Introduction sentence. Also, in addition, the authors could cite additional references here to support the sentence such as Mecchia et al., 2017 or Ge et al. , 2017, in addition. 

Thank you for pointing this out and we apologize for our flaw. We corrected the statement in the revised version of the manuscript and added the citations as requested.

(h) The new study of Schoenaers et al. Nat Plants, 2024 should now be included in the revised version.

Thank you. We implemented this reference in the revised manuscript.

Reviewer #2 (Public Review):

The genetic material used by the authors to strengthen the connection of RALF signalling and

PME activity might not be as suitable as an acute inhibition of PME activity.  The PMEI3ox line generated by Peaucelle et al., 2008 is alcohol-inducible. Was expression of the PMEI induced during the experiments? As ethanol inducible systems can be rather leaky, it would not be surprising if PME activity would be reduced even without induction, but maybe this would warrant testing whether PMEI3 is actually overexpressed and/or whether PME activity is decreased. On a similar note, the PMEI5ox plants do not appear to show the typical phenotype described for this line. I personally don't think these lines are necessary to support the study. Short-term interference with PME activity (such as with EGCG) might be more meaningful than life-long PMEI overexpression, in light of the numerous feedback pathways and their associated potential secondary effects. This might also explain why EGCG leads to an increase in pH, as one would expect from decreased PME activity, while PMEI expression (caveats from above apply) apparently does not (Fig 3A-D).

We agree with reviewer #2. The PMEI3ox line from Peaucelle et al., 2008 is ethanolinducible, but we observed a strong phenotype (at seedling and adult stage) without ethanol induction. We performed all experiments (root growth assays and confocal observations) with as well as without induction using ethanol, leading to similar results. We concluded from that, that the line is either leaky or that overexpression of PMEI3 is already induced upon seed sterilisation with ethanol. Accordingly, we did not intend to use the lines as acute inhibition of PME but rather used the lines to genetically confirm our data derived from acute pharmacological inhibition. We do show in Figure 1G that the levels of de-methylesterified pectin is decreased in the PMEI3ox mutant compared to WT seedlings. It is exactly this alteration that we are exploiting to assess the necessity of charged pectin for RALF1 signalling. Since the apoplastic pH in the PMEI3ox line is not altered compared to WT, we can conclude that the observed effect on RALF1 signalling is entirely due to the altered pectin charge.

We would like to note that the PMEI5ox line indeed shows the reported root-bending phenotype when grown on plates. We started to perform RALF application assays in liquid medium, because EGCG does not show activity on MS plates. Moreover, it allows us to perform the assays with low amounts of synthetic peptides. The seedling images in our root growth assay might be hence misleading since the assay was done in liquid MS medium and the seedlings were carefully straightened on MS plates before imaging. This transfer makes it difficult to observe the root-bending or -curling phenotype, which is typical for PMEI5ox. 

At least at first sight, the observation that OGs are able to titrate RALF from pectin binding seems at odds with the idea of cooperative binding with low affinity, leading to high avidity oligomers. Perhaps the can provide a speculative conceptual model of these interactions?

We added a high concentration of OGs in the media and observed a strong repression of RALF1 activity at the root surface. We assume the OGs form oligomers with RALF peptides in the media, preventing them from penetrating the roots.

I could not find a description of the OG treatment/titration experiments, but I think it would be important to understand how these were performed with respect to OG concentration, timing of the application, etc.

Thank you for pointing this out. The description of the OG RALF titration is added in the methods section.

Reviewer #2 (Recommendations for the Authors):

Page 3: „and can bind to extracellular pectin" Liu et al, 2024 should maybe also be cited here. 

Amended.

I am not so sure about the use of "conceptualizing" in the last sentence of the abstract and elsewhere in the manuscript.

I would suggest adding a few sentences that describe and differentiate what this study and other recently published works (e.g. Dünser, Liu, Mossou, Lin) have revealed about the pectin association of RALFs, LRXs, and FER to help the non-expert reader to navigate this increasingly complex area. May also be worth mentioning that the previously described pectin sensing function of FER is physically separated from the RALF binding domain (Gronnier et al., 2022)

Thank you for your constructive comments. We followed your suggestions and further improved the discussion in the revised version of our manuscript.

Reviewer #3 (Recommendations for the Authors): 

(1) The authors claim that pectin is something like an extracellular signaling scaffold. In other fields, signalling scaffold refers to proteins that tether the signalling components and regulate/are involved in the signal transduction. Here, pectin is a cell wall structural component whose molecular status is sensed and perceived rather than a functional signaling component. To me, it is FERONIA to be called a signalling scaffold in this case. However, this is my view, and the authors may present their concept. 

RALF peptides as well as FERONIA bind to de-methylesterified pectin, which is essential for its signalling output. Albeit not being a protein, we propose that pectin functions like a scaffold tethering both signalling components and thereby enabling signalling. FERONIA has been indeed also proposed to function as a scaffold when tethering other signalling components.

(2) I have no problem with authors using the more general term pectin instead of homogalacturonan throughout the text. Still, authors should, at some point in the text, specify that by pectin, they mean homogalacturonan; the authors did not analyze other pectic types on binding. 

We followed your suggestion.

(3) The authors show that RALF1 binds to OGs with a high avidity. Given the fact that OGs released from homogalacturonan upon pathogen infection are Damage-Associated Molecular Patterns (DAMPs), this opens the possibility that this particular activity of RALF1 might actually function in modulation of immune response. I suggest that authors should not exclude this possibility. 

We fully agree to this possibility for FER-dependent signalling.

(4) Are there any indications that a similar mechanism can be extrapolated to other FERONIA homologs, such as THESEUS or HERCULES? Although it is not essential to comment, I think this could enrich the discussion.

This is a highly interesting research question, which we may follow up in our upcoming studies. RALF34, which is considered a ligand for THESEUS, also induced FER internalization, which was also sensitive to PME inhibition. While this requires further investigation, this finding hints at a common mechanism for FER- and THE-dependent RALF peptides.

(5) I suggest using the model scheme currently in the supplement as a main figure to provide an immediate accessible summary of the findings.

Thank you for the suggestion to add the summary scheme in the main figures. We followed your suggestion.

Author response:

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

Review #1:

(1) It would be helpful to explain the criteria for choosing a given number of clusters and for accepting the final clustering solution more clearly. The quantitative results (silhouette plots, Rand index) in Supplementary Figure 2 should perhaps be included in the main figure to justify the parameter choices and acceptance of specific clustering solutions.

We revised the text and added labels to the original Supplementary Figure 2 (now main Figure 4) to clarify how we arrived at the best settings for random-seed clustering. 

(2) It would be helpful to show how the activity profiles in Figure 3 would look like for 3 or 5 (or 6) clusters, to give the reader an impression of how activity profiles recovered using different numbers of clusters would differ.

We added a new figure (Supplementary Figure 4) that shows 5- and 6-cluster results. Note that the same three subpopulations in Figure 3 were reliably identified as distinct clusters even with alternative settings, corroborating the results in the tSNE space (Supplementary Figure 3). 

(3) The authors attempt to link the microstimulation effects to the presence of functional neuron clusters at the stimulation site. How can you rule out that there were other, session-specific factors (e.g., related to the animal's motivation) that affected both neuronal activity and behavior? For example, could you incorporate aspects of the monkey's baseline performance (mean reaction time, fixation breaks, error trials) into the analysis?

We tested the potential influences of monkeys’ motivational states on our observations using two sets of analysis. First, we examined whether motivational state modulated the likelihood of observing a specific type of neural activity in STN. We focused on three measurements of motivational states: the rate of fixation break, the overall error rate, and mean RT. We found that none of these measurements differed significantly among sessions when we encountered different subpopulations (new Supplemental Figure 7), suggesting that motivational state alone cannot explain the differences in activity patterns of the four subpopulations. 

Second, we examined how motivational state may be reflected in the microstimulation results. To clarify, because we interleaved trials with and without microstimulation, the microstimulation effects cannot be solely explained by session-specific factors. However, it is possible that motivational state can modulate the magnitude of microstimulation effects. We performed correlation analysis between microstimulation effects (difference in each fitted DDM parameter between trials with and without microstimulation) and motivational state (fixation break, error rate, mean RT on trials without microstimulation). We did not find significant correlation for any combination (Supplemental Table 1). These results suggest that the motivational state of the monkey had little influence on our recording and microstimulation results. However, because our monkeys operated within a narrow range of strong engagement on the task, we cannot rule out the possibility that STN activity or microstimulation effects could change significantly if the monkeys were not as engaged. We have added these results in a new section titled “Heterogeneous activity patterns and microstimulation effects cannot be explained by variations in motivational state”. 

(4) Line 84: What was the rationale for not including both coherence and reaction time in one multiple regression model?

On the task we used, RT depends strongly on coherence in a nonlinear fashion (e.g., example behavior in now Figure 5). We thus performed regressions using coherence and RT separately. We revised the text in Methods to clarify our rationale (lines 470-473):

“To quantitatively measure each neuron’s task-related modulation, we performed two multiple linear regressions for each running window, separately for coherence and RT because monkeys’ RT strongly depends on coherence on our task:”

Review #2:

The interpretation of the results, and specifically, the degree to which the identified clusters support each model, is largely dependent on whether the artificial vectors used as model-based clustering seeds adequately capture the expected behavior under each theoretical model. The manuscript would benefit from providing further justification for the specific model predictions summarized in Figure 1B.

We added information on the original figure/equations that were the basis of the artificial vectors we constructed for clustering analysis and their abbreviated summary in Figure 1B (first paragraph in section “STN subpopulations can support previously theorized functions”). These vectors were meant to capture prominent features of the predicted activity patterns, in the forms of choice, time, and motion strength dependencies. We also emphasize that we obtained very similar results using random clustering seeds.

Further, although each cluster's activity can be described in the context of the discussed models, these same neural dynamics could also reflect other processes not specific to the models. That is, while a model attributing the STN's role to assessing evidence accumulation may predict a ramping up of neural activity, activity ramping is not a selective correlate of evidence accumulation and could be indicative of a number of processes, e.g., uncertainty, the passage of time, etc. This lack of specificity makes it challenging to infer the functional relevance of cluster activity and should be acknowledged in the discussion.

We thank the reviewer for pointing out the alternative interpretation of these modulation patterns. We have added this caveat in the Discussion (lines 398-401): “It is also possible that the ramping activity reflects alternative roles for the STN in the evaluation of the decision process, the tracking of elapsed time, or both. How these possible roles relate to those of caudate neurons awaits further investigation (Fan et al., 2024)”. 

Additionally, although the effects of STN microstimulation on behavior provide important causal evidence linking the STN to decision processes, the stimulation results are highly variable and difficult to interpret. The authors provide a reasonable explanation for the variability, showing that neurons from unique clusters are anatomically intermingled such that stimulation likely affects neurons across several clusters. It is worth noting, however, that a substantial body of literature suggests that neural populations in the STN are topographically organized in a manner that is crucial for its role in action selection, providing "channels" that guide action execution. The authors should comment on how the current results, indicative of little anatomical clustering amongst the functional clusters, relate to other reports showing topographical organization.

We thank the reviewer for raising this important point. We have added the following text in the Discussion:

“The intermingled subpopulations may appear at odds with the conventional idea of topography in how the STN is organized. For example, the “tripartite model” suggests that STN is segregated by motor, associative, and limbic functions (Parent and Hazrati, 1995); afferents from motor cortices and neurons related to different types of movements are largely somatotopically organized in the STN (DeLong et al., 1985; Nambu et al., 1996); and certain molecular markers are expressed in an orderly pattern in the STN (reviewed in Prasad and Wallén-Mackenzie, 2024). Because we focused on STN neurons that were responsive on a single oculomotor decision task, our sampling was likely biased toward STN subdivisions related to associative function and oculomotor movements. As such, our results do not preclude the presence of topography at a larger scale. Rather, our results underscore the importance of activity patternbased analysis, in addition to anatomy-based analysis, for understanding the functional organization of the STN.”

Figure 3 is referenced when describing which cluster activity is choice/coherence dependent, yet it is unclear what specific criteria and measures are being used to determine whether activity is choice/coherence "dependent." Visually, coherence activity seems to largely overlap in panel B (top row). Is there a statistically significant distinction between low and high coherence in this plot? The interpretation of these plots and the methods used to determine choice/coherence "dependence" needs further explanation.

We added a new figure (Sup Figure 3) that shows the summary of choice and coherence modulation, based on multiple linear regression analysis, for each subpopulation separately. We also updated the description of these activity patterns in Results (lines 122-130):

In general, the association between cluster activity and each model could be more directly tested. At least two of the models assume coordination with other brain regions. Does the current dataset include recordings from any of these regions (e.g., mPFC or GPe) that could be used to bolster claims about the functional relevance of specific subpopulations? For example, one would expect coordinated activity between neural activity in mPFC and Cluster 2 according to the Ratcliff and Frank model.

We agree completely that simultaneous recordings of STN and its afferent/efferent regions (such as mPFC, GPe, SNr, and GPi) would provide valuable insights into the specific roles of STN and the basal ganglia as a whole. Such recordings are outside the scope of the current study but are in our future plans. 

Additionally, the reported drift-diffusion model (DDM) results are difficult to interpret as microstimulation appears to have broad and varied effects across almost all the DDM model parameters. The DDM framework could, however, be used to more specifically test the relationships between each neural cluster and specific decision functions described in each model. Several studies have successfully shown that neural activity tracks specific latent decision parameters estimated by the DDM by including neural activity as a predictor in the model. Using this approach, the current study could examine whether each cluster's activity is predictive of specific decision parameters (e.g., evidence accumulation, decision thresholds, etc.). For example, according to the Ratcliff and Frank model, activity in cluster 2 might track decision thresholds.

We thank the reviewer for the suggested analysis. Because including the neural activity in the model substantially increases model fitting time, we performed a preliminary round of model fitting for 15 neurons (5 neurons closest to each of the cluster centroids). For each neuron, we measured the average firing rates in three windows: 1) a 350 ms window starting from dots onset (“Dots”), 2) a 350 ms window ending at saccade onset (“Presac”), and 3) a variable window starting from dots onset and ending at 100 ms before saccade onset (“Fullview”). For each window, the firing rates were z-scored across trials.  We incorporated the firing rates into two model types. In the “DV” type, the firing rates were assumed to influence three DDM parameters related to evidence accumulation: k, me, and z. In the “Bound” type, the firing rates were assumed to influence three DDM parameters related to decision bound: a, B_alpha, and B_d. In total, we fitted six combinations of firing rates and model types to each neuron. For comparison, we also fitted the standard model without incorporating firing rates. 

As shown in Author response image 1, firing rates of single STN neurons had minimal contributions to the fits. With the exception of one neuron, AIC values were greater for model variants including firing rates than the standard model (Author response image 1A), indicating that including firing rate did not improve the fits. For all neurons, the actual fitted coefficients for firing rates were several degrees of magnitude smaller than the corresponding DDM parameter (Author response image 1B; note the range of y axis), indicating that the trial-by-trial variation in firing rate had little influence on the evidence accumulation- or decision bound-related parameters. Based on these preliminary fitting results, we believe that a single STN neuron does not have strong enough influence on the overall evidence accumulation or decision bound to be detected with the model fitting method.  We therefore did not expand the fitting analysis to all neurons. 

Author response image 1.

Firing rates of a single STN neuron did not substantially influence decision-related DDM parameters. A, Differences in AIC between DDM variants that included firing rate-dependent terms and the standard DDM. Red dahsed line: difference = -3. Each column represents results from one unit. B, Fitted coefficients for firing rate-related terms were near zero. Note the range of y axis. Values for the top and bottomw panels were obtained from "DV"- and "Bound"-type models, respectively. See text for more details.

We emphasize, however, that the apparent negative results do not necessarily argue against a causal role of the STN in decision making, rather, these results more likely reflect the methodological limitation: because we used a single task context, the monkeys’ natural trial-by- trial variations in the DDM components may be too small. A better design would be to manipulate task contexts to induce larger changes in evidence accumulation or decision bounds and then test for a correlation between single-neuron firing rates and these changes. We are currently using such a design in a follow-up study. 

The table in Figure 1B nicely outlines the specific neural predictions for each theoretical model but it would help guide the reader if the heading for each column also included a few summary words to remind the reader of the crux of each theory, e.g. "Ratcliff+Frank 2012 (adjusted decision-bounds)"

We thank the reviewer for this suggestion. We considered implementing this but eventually decided not to add more headings to the column, because the predicted STN functions of the three models cannot all be succinctly summarized. We thus prefer to include more detailed descriptions in the main text, instead of in the figure. 

The authors frequently refer to contralateral vs. ipsilateral decisions but never explicitly state what this refers to, i.e. contralateral relative to what (visual field, target direction, recording site, etc.)? The reader can eventually deduce that this means contralateral to the recording site but this should be explicitly stated for clarity.

We added in Methods: 

Line 483: “Contralateral/ipsilateral choices refer to saccades toward the targets contralateral/ipsilateral to the recording sites, respectively.” 

Line 535: Contralateral/ipsilateral choices refer to saccades toward the targets contralateral/ipsilateral to the microstimulation sites, respectively.”

Again, for clarity, it would be helpful to explicitly define what the authors mean by "sensitive to choice" when referring to Figure 1B as this could be interpreted to mean left/right or ipsilateral/contralateral.

In the context of Figure 1B, “sensitive to choice” means showing different responses for the two choices in our 2AFC task, regardless of the task geometry. We added explanation in the figure caption.

Color bar labels would be helpful to include in all figures that include plots with color bars.

We apologize for omitting the labels. They are added to Figure 2B and C, Supplemental Fig. 1.  

The authors should briefly note what a "lapse term" is when describing the logistic function results.

We revised the text in Results (lines 184-186) and Methods (line 527) to clarify that lapse terms were used to capture errors independent of motion strength.

Are the 3 example sessions in Figure 4 stimulating the same STN site and/or the same monkey? This information should be noted in the caption or main text.

We revised the caption: “A-C, Monkey’s choice (top) and RT (bottom) performance for trials with (red) and without (black) microstimulation for three example sessions (A,B: two sites in monkey C; C: monkey F).”

Figure 3B the authors note that "the last cluster shows little task-related modulation" - what criteria are they using to make this conclusion? By eye, the last cluster and cluster 1 seem to show a similar degree of modulation when locked to motion onset.

We added a new figure (Suppl Figure 2) that shows the summary of choice and coherence modulation, based on multiple linear regression analysis, for each subpopulation separately. 

Reviewer #3:

We have grouped the reviewer’s public and specific comments by content. 

First, the interpretation of the neural subpopulations' activity patterns in relation to the computational models should be clarified, as the observed patterns may not directly correspond to the specific signals predicted by the models. The authors claim that the first subpopulation of STN neurons reflects the normalization signal predicted by the model of Bogacz and Gurney (2007). However, the observed activity patterns only show choice- and coherence-dependent activity, which may represent the input to the normalization computation rather than its output. The authors should clarify this point and discuss the limitations of their interpretation. 

We agree with the reviewer that the choice- and coherence-dependent activity pattern does not sufficiently indicate a normalization computation. We interpreted such activity as satisfying a necessary condition for, and therefore consistent with, the theoretical model proposed by Bogacz and Gurney. We have reviewed the text to ensure that we never made the claim that the first subpopulation mediates the normalization.   

Second, the authors could consider using a supervised learning method to more explicitly model the pattern correlations between the three profiles. The authors used k-means clustering to identify STN subpopulations. Given the clear distinction between the three types of neural firing patterns, a supervised learning method (e.g., a generalized linear model) could be used as a more explicit encoding model to account for the pattern correlations between the three profiles.

We used two approaches to examine the different response profiles. The “random-seed” approach used non-supervised clustering to probe the functional organization of STN neurons, with no a priori assumption about how many subpopulations may be present. The “model-seed” approach is similar in spirit to what the reviewer suggested: we defined artificial vectors, akin to regressors in a generalized linear model, that showed key modulation features as predicted by previous theoretical models. We then projected the neurons’ activity profiles onto these vectors, akin to performing a regression analysis.   

Third, a neural population model could be employed to better understand how the STN population jointly contributes to decision-making dynamics. The single-neuron encoding analysis reveals mixed effects from multiple decision-related functions. To better understand how the STN population jointly contributes to the decision-making process, the authors could consider using a neural population model (e.g., Wang et al., 2023) to quantify the population dynamics.

We agree with the reviewer that a neural population model would be helpful for testing our understanding of the roles of STN. However, we believe that this is premature at the moment because we have no knowledge about how these different subpopulations interact with each other within STN, nor how they interact with other basal ganglia nuclei. We hope our results provide a foundation for future experiments that can provide more specific insights in the roles of each subpopulation, which can then be tested in a neural population model as the reviewer suggested.  

Finally, the added value of the microstimulation experiments should be more directly addressed in the Results section, as the changes in firing patterns compared to the original patterns are not clearly evident. The microstimulation results (Figure 7A) do not show significant changes in firing patterns compared to the original patterns (Figure 3B). As microstimulation is used to identify the hypothetical role of the STN beyond the correlational analysis, the authors should more directly address the added value of these experiments in the Results section.

We apologize for the confusion. The average firing rates at the top of original Figure 7A (now Figure 8A) were obtained in recordings just before microstimulation, to document which neuron subpopulation was near the stimulation electrode. We were not able to obtain recordings from the same neurons during microstimulation.  

The ordering of the three hypotheses in the Introduction (1) adjusting decision bounds, (2) computing a normalization signal, (3) implementing a nonlinear computation to improve decision bound adjustment, is inconsistent with the order in which they are addressed in the Results section (2, 1, 3). To improve clarity and readability, the authors should consider presenting the hypotheses and their corresponding results in a consistent order throughout the manuscript.

We thank the reviewer for this suggestion. We have reordered the text in Introduction to be consistent.

Author response:

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

Public Reviews: 

Reviewer #1 (Public Review): 

In this manuscript by Wu et al., the authors present the high resolution cryoEM structures of the WT Kv1.2 voltagegated potassium channel. Along with this structure the authors have solved several structures of mutants or experimental conditions relevant to the slow inactivation process that these channels undergo and which is not yet completely understood. 

One of the main findings is the determination of the structure of a mutant (W366F) that is thought to correspond to the slow inactivated state. These experiments confirm results in similar mutants in different channels from Kv1.2 that indicate that inactivation is associated with an enlarged selectivity filter. 

Another interesting structure is the complex of Kv1.2 with the pore blocking toxin Dendrotoxin 1. The results shown in the revised version indicate that the mechanism of block is similar to that of related blocking-toxins, in which a lysine residue penetrates in the pore. Surprisingly, in these new structures, the bound toxin results in a pore with empty external potassium binding sites. 

The quality of the structural data presented in this revised manuscript is very high and allows for unambiguous assignment of side chains. The conclusions are supported by the data. This is an important contribution that should further our understanding of voltage-dependent potassium channel gating. In the revised version, the authors have addressed my previous specific comments, which are appended below. 

(1) In the main text's reference to Figure 2d residues W18' and S22' are mentioned but are not labeled in the insets. 

This has been fixed: line 229, p. 9.

(2) On page 8 there is a discussion of how the two remaining K+ ions in binding sites S3 and S4 prevent permeation K+ in molecular dynamics. However, in Shaker, inactivated W434F channels can sporadically allow K+ permeation with normal single-channel conductance but very reduced open times and open probability at not very high voltages. 

This is noted in the discussion Lines 497-500, p. 18

(3) The structures of WT in the absence of K+ shows a narrower selectivity filter, however Figure 4 does not convey this finding. In fact, the structure in Figure 4B is constructed in such an angle that it looks as if the carbonyl distances are increased, perhaps this should be fixed. Also, it is not clear how the distances between carbonyls given in the text on page 12 are measured. Is it between adjacent or kitty-corner subunits? 

We have changed Fig. 4B to show the same view as in Fig. 4A. In the legend we explain that opposing subunits are shown. We no longer give distances, in view of the lack of detectable carbonyl densities.

(4) It would be really interesting to know the authors opinion on the driving forces behind slow inactivation. For example, potassium flux seems to be necessary for channels to inactivate, which might indicate a local conformational change is the trigger for the main twisting events proposed here. 

We address this in the Discussion, line 506-523, pp. 18-19.

Reviewer #2 (Public Review): 

Cryo_EM structures of the Kv1.2 channel in the open, inactivated, toxin complex and in Na+ are reported. The structures of the open and inactivated channels are merely confirmatory of previous reports. The structures of the dendrotoxin bound Kv1.2 and the channel in Na+ are new findings that will of interest to the general channel community. 

Review of the resubmission: 

I thank the authors for making the changes in their manuscript as suggested in the previous review. The changes in the figures and the additions to the text do improve the manuscript. The new findings from a further analysis of the toxin channel complex are welcome information on the mode of the binding of dendrotoxin. 

A few minor concerns: 

(1) Line 93-96, 352: I am not sure as to what is it the authors are referring to when they say NaK2P. It is either NaK or NaK2K. I don't think that it has been shown in the reference suggested that either of these channels change conformation based on the K+ concentration. Please check if there is a mistake and that the Nichols et. al. reference is what is being referred to. 

Thank you for noticing the error. We meant NaK2K and we have changed this throughout.

(2) Line 365: In the study by Cabral et. al., Rb+ ions were observed by crystallography in the S1, S3 and S4 site, not the S2 site. Please correct. 

Thank you. We have re-written this section, lines 364-381, pp. 13-14.

Reviewer #3 (Public Review): 

Wu et al. present cryo-EM structures of the potassium channel Kv1.2 in open, C-type inactivated, toxin-blocked and presumably sodium-bound states at 3.2 Å, 2.5 Å, 2.8 Å, and 2.9 Å. The work builds on a large body of structural work on Kv1.2 and related voltage-gated potassium channels. The manuscript presents a plethora of structural work, and the authors are commended on the breadth of the studies. The structural studies are well-executed. Although the findings are mostly confirmatory, they do add to the body of work on this and related channels. Notably, the authors present structures of DTx-bound Kv1.2 and of Kv1.2 in a low concentration of potassium (which may contain sodium ions bound within the selectivity filter). These two structures add considerable new information. The DTx structure has been markedly improved in the revised version and the authors arrive at well-founded conclusions regarding its mechanism of block. Regarding the Na+ structure, the authors claim that the structure with sodium has "zero" potassium - I caution them to make this claim. It is likely that some K+ persists in their sample and that some of the density in the "zero potassium" structure may be due to K+ rather than Na+. This can be clarified by revisions to the text and discussion. I do not think that any additional experiments are needed. Overall, the manuscript is well-written, a nice addition to the field, and a crowning achievement for the Sigworth lab. 

Most of this reviewer's initial comments have been addressed in the revised manuscript. Some comments remain that could be addressed by revisions of the text. 

Specific comments on the revised version: 

Quotations indicate text in the manuscript. 

(1) "While the VSD helices in Kv1.2s and the inactivated Kv1.2s-W17'F superimpose very well at the top (including the S4-S5 interface described above), there is a general twist of the helix bundle that yields an overall rotation of about 3o at the bottom of the VSD." 

Comment: This seemed a bit confusing. I assume the authors aligned the complete structures - the differences they indicate seem to be slight VSD repositioning relative to the pore rather than differences between the VSD conformations themselves. The authors may wish to clarify. As they point out in the subsequent paragraph, the VSDs are known to be loosely associated with the pore. 

We aligned the VSDs alone, and it is a twist of the VSD helix bundle.

This is now clarified in lines 269-273, p. 10.

(2) Comment: The modeling of DTx into the density is a major improvement in the revision. Figure 3 displays some interactions between the toxin and Kv1.2 - additional side views of the toxin and the channel might allow the reader to appreciate the interactions more fully. The overall fit of the toxin structure into the density is somewhat difficult to assess from the figure. (The authors might consider using ChimeraX to display density and model in this figure.) 

We have added new panels, and stereo pairs, to Figure 3.

(3) "We obtained the structure of Kv1.2s in a zero K+ solution, with all potassium replaced with sodium, and were surprised to find that it is little changed from the K+ bound structure, with an essentially identical selectivity filter conformation (Figure 4B and Figure 4-figure supplement 1)." 

Comment: It should be noted in the manuscript that K+ and Na+ ions cannot be distinguished by the cryo-EM studies - the densities are indistinguishable. The authors are inferring that the observed density corresponds to Na+ because the protein was exchanged from K+ into Na+ on a gel filtration (SEC) column. It is likely that a small amount of K+ remains in the protein sample following SEC. I caution the authors to claim that there is zero K+ in solution without measuring the K+ content of the protein sample. Additionally, it should be considered that K+ may be present in the blotting paper used for cryo-EM grid preparation (our laboratory has noted, for example, a substantial amount of Ca2+ in blotting paper). The affinity of Kv1.2 for K+ has not been determined, to my knowledge - the authors note in the Discussion that the Shaker channel has "tight" binding for K+. It seems possible that some portion of the density in the selectivity filter could be due to residual K+. This caveat should be clearly stated in the main text and discussion. More extensive exchange into Na+, such as performing the entire protein purification in NaCl, or by dialysis (as performed for obtaining the structure of KcsA in low K+ by Y. Zhou et al. & Mackinnon 2001), would provide more convincing removal of K+, but I suspect that the Kv1.2 protein would not have sufficient biochemical stability without K+ to endure this treatment. One might argue that reduced biochemical stability in NaCl could be an indication that there was a meaningful amount of K+ in the final sample used for cryo-EM (or in the particles that were selected to yield the final high-resolution structure).

We now explain in the Methods section, in more detail the steps taken to avoid any residual Na+ contamination during purification, lines 683-687, pp. 24-25. We have changed the text to point out that the ion species cannot be distinguished in the maps, and note results in NaK2K and KcsA (lines 368-381, pp. 13-14).

We note that the same procedures to remove K+ were used for the Kv1.2sW17’F structure (line 385, p. 14). We qualify the ion replacement to say that Na+ replaces “essentially” all K+ (line 607, p. 21).

(4) Referring to the structure obtained in NaCl: "The ion occupancy is also similar, and we presume that Kv1.2 is a conducting channel in sodium solution." 

Comment: Stating that "Kv1.2 is a conducting channel in sodium solution" and implying that conduction of Na+ is achieved by an analogous distribution of ion binding sites as observed for K+ are strong statements to make - and not justified by the experiments provided. Electrophysiology would be required to demonstrate that the channel conducts sodium in the absence of K+. More complete ionic exchange, better control of the ionic conditions (Na+ vs K+), and affinity measurements for K+ would be needed to determine the distribution of Na+ in the filter (as mentioned above). At minimum, the authors should revise and clarify what the intended meaning of the statement "we presume that Kv1.2 is a conducting channel in sodium solution". As mentioned above, it seems possible/likely that a portion of the density in the filter may be due to K+. 

We now present a more detailed argument (lines 376 to 381, pp. 13-14.)

Recommendations for the authors: 

Reviewing Editor: 

After consultation, the reviewers agree that, although the authors have answered most of the comments raised in the previous review, there remains a concern about the structure obtained in the presence if Na. Given that Kv1.2 is more reluctant to slow inactivation, the conducting structure in Na+ could be due to this fact or that it really has higher affinity for K+ than Na+. In the presence of even a small contamination by K+, this ion could thus occupy the selectivity filter, resulting in an open conformation. The authors should clearly state the steps taken to ensure no contamination by K+. It is also possible that indeed the open structure occurs even in the presence of Na+ in the selectivity filter. This should be also discussed, given that this has been observed in other potassium channel structures. 

Reviewer #1 (Recommendations For The Authors): 

In this revised version of the manuscript, the authors have adequately addressed my previous points and improved the clarity and readability of the manuscript. This is a compelling work that shows inactivated structures if the Kv1.2 potassium channel, especially interesting is a structure in the absence of extracellular potassium ions, that can help understand how a reduction in the availability of these ions speed up entrance into the inactivated state in these ion channels. 

I would just recommend that in the absence of functional data (current recordings) when potassium is removed, the authors just use caution in ascribing this structure to an inactivated state. Also, it should be mentioned that the observed ion densities observed in the pore cannot unambiguously be identified as sodium ions. 

Reviewer #3 (Recommendations For The Authors): 

(1)  "The nearby Leu9 is also important as its substitution by alanine also decreases affinity 1000-fold, but we observe no contacts between this residue and residues of the Kv1.2s channel." 

Comment: It seems early in the text to mention the potential interaction of Leu9 to the channel structure. The authors may wish to discuss Leu9 later in the manuscript - a figure showing the location of Leu9 would strengthen the statement. Any hypothesis on why mutation of it has such a profound effect? 

Add a figure panel showing Leu9 position.

We have rewritten the text as suggested, and have identified Leu9 in several panels of Fig. 3.

(2)  "The X-ray structure of a-DTX (Figure 3A)" 

Comment: The authors may wish to cite a reference to this X-ray structure. 

We now cite Skarzynski (1992) on line 321, p. 12.

Author response:

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

Public Reviews:

Reviewer #1 (Public Review):

Summary:

The authors assess the accuracy of short variant calling (SNPs and indels) in bacterial genomes using Oxford Nanopore reads generated on R10.4 flow cells from a very similar genome (99.5% ANI), examining the impact of variant caller choice (three traditional variant callers: bcftools, freebayes, and longshot, and three deep learning based variant callers: clair3, deep variant, and nano caller), base calling model (fast, hac and sup) and read depth (using both simplex and duplex reads).

Strengths:

Given the stated goal (analysis of variant calling for reads drawn from genomes very similar to the reference), the analysis is largely complete and results are compelling. The authors make the code and data used in their analysis available for re-use using current best practices (a computational workflow and data archived in INSDC databases or Zenodo as appropriate).

Weaknesses:

While the medaka variant caller is now deprecated for diploid calling, it is still widely used for haploid variant calling and should at least be mentioned (even if the mention is only to explain its exclusion from the analysis). 

We have now added Medaka haploid caller to the benchmark. It performs quite well overall (better than the traditional methods), but not as good as Clair3 or DeepVariant.

Appraisal:

The experiments the authors engaged in are well structured and the results are convincing. I expect that these results will be incorporated into "best practice" bacterial variant calling workflows in the future. 

Thank you for the positive appraisal.

Reviewer #2 (Public Review):

Summary:

Hall et al describe the superiority of ONT sequencing and deep learning-based variant callers to deliver higher SNP and Indel accuracy compared to previous gold-standard Illumina short-read sequencing. Furthermore, they provide recommendations for read sequencing depth and computational requirements when performing variant calling.

Strengths:

The study describes compelling data showing ONT superiority when using deep learning-based variant callers, such as Clair3, compared to Illumina sequencing. This challenges the paradigm that Illumina sequencing is the gold standard for variant calling in bacterial genomes. The authors provide evidence that homopolymeric regions, a systematic and problematic issue with ONT data, are no longer a concern in ONT sequencing.

Weaknesses:

(1) The inclusion of a larger number of reference genomes would have strengthened the study to accommodate larger variability (a limitation mentioned by the authors). 

Our strategic selection of 14 genomes—spanning a variety of bacterial genera and species, diverse GC content, and both gram-negative and gram-positive species (including M. tuberculosis, which is neither)—was designed to robustly address potential variability in our results. Moreover, all our genome assemblies underwent rigorous manual inspection as the quality of the true genome sequences is the foundation this research is built upon. Given this, the fundamental conclusions regarding the accuracy of variant calls would likely remain unchanged with the addition of more genomes.  However, we do acknowledge that a substantially larger sample size, which is beyond the scope of this study, would enable more fine-grained analysis of species differences in error rates.

(2) In Figure 2, there are clearly one or two samples that perform worse than others in all combinations (are always below the box plots). No information about species-specific variant calls is provided by the authors but one would like to know if those are recurrently associated with one or two species. Species-specific recommendations could also help the scientific community to choose the best sequencing/variant calling approaches.

Thank you for highlighting this observation. The precision, recall, and F1 scores for each sample and condition can be found in Supplementary Table S4.

Upon investigation of the outliers in Figure 2 we discovered three things. First, there was a parameter in Longshot we were using that automatically capped coverage and lead to a number of false negatives, leading to its outlier. This has now been rectified and the figure is updated accordingly. Second, the outlier in the simplex sup SNP panel (top left) was the same E. coli sample for most variant callers (though Medaka had no issues). The reasoning for this was a variant dense repetitive region. We have added an in-depth explanation of this, along with figures illustrating the issue in Supplementary Section S2, with a brief statement in the main text. Third, the outlier in the duplex sup SNP panel (top right) is due to a very low (duplex) depth sample. This has also been added briefly to the main text and fully in Section S2.

We have now included a species-segregated version of Figure 2 (Suppl. Figures S5-7) for Clair3 with the sup model (best performer) for a clearer interpretation of how each species performs.

(3) The authors support that a read depth of 10x is sufficient to achieve variant calls that match or exceed Illumina sequencing. However, the standard here should be the optimal discriminatory power for clinical and public health utility (namely outbreak analysis). In such scenarios, the highest discriminatory power is always desirable and as such an F1 score, Recall and Precision that is as close to 100% as possible should be maintained (which changes the minimum read sequencing depth to at least 25x, which is the inflection point).

We agree that the highest discriminatory power is always desirable for clinical or public health applications. In which case, 25x is probably a better minimum recommendation. However, we are also aware that there are resource-limited settings where parity with Illumina is sufficient. In these cases, 10x depth from ONT would provide enough data.

The manuscript previously emphasised the latter scenario, but we have revised the text (Discussion) to clearly recommend 25x depth as a conservative aim in settings where resources are not a constraint, ensuring the highest possible discriminatory power.

(4) The sequencing of the samples was not performed with the same Illumina and ONT method/equipment, which could have introduced specific equipment/preparation artefacts that were not considered in the study. See for example https://academic.oup.com/nargab/article/3/1/lqab019/6193612.

To our knowledge, there is no evidence that sequencing on different ONT machines or barcoding kits leads to a difference in read characteristics or accuracy. To ensure consistency and minimise potential variability, we used the same ONT flowcells for all samples and performed basecalling on the same Nvidia A100 GPU. We have updated the methods to emphasise this.

For Illumina and ONT, the exact machines and kits used for each sample have been added as supplementary table S9 We have also added a short paragraph about possible Illumina error rate differences in the ‘Limitations’ section of the Discussion.

The third limitation is that Illumina sequencing was performed on different models: three samples on the NextSeq 500 and the rest on the NextSeq 2000. While differences in error rates exist between Illumina instruments, no specific assessment has been made between these NextSeq models [42]. However, the absolute differences in error rates are minor and unlikely to impact our study significantly. This is particularly relevant since Illumina's lower F1 score compared to ONT was due to missed calls rather than erroneous ones.

In summary, while there may be specific equipment or preparation artifacts to consider, we took steps to minimise these effects and maintain consistency across our sequencing methods.

Reviewer #3 (Public Review):

Hall et al. benchmarked different variant calling methods on Nanopore reads of bacterial samples and compared the performance of Nanopore to short reads produced with Illumina sequencing. To establish a common ground for comparison, the authors first generated a variant truth set for each sample and then projected this set to the reference sequence of the sample to obtain a mutated reference. Subsequently, Hall et al. called SNPs and small indels using commonly used deep learning and conventional variant callers and compared the precision and accuracy from reads produced with simplex and duplex Nanopore sequencing to Illumina data. The authors did not investigate large structural variation, which is a major limitation of the current manuscript. It will be very interesting to see a follow-up study covering this much more challenging type of variation. 

We fully agree that investigating structural variations (SVs) would be a very interesting and important follow-up. Identifying and generating ground truth SVs is a nontrivial task and we feel it deserves its own space and study. We hope to explore this in the future.

In their comprehensive comparison of SNPs and small indels, the authors observed superior performance of deep learning over conventional variant callers when Nanopore reads were basecalled with the most accurate (but also computationally very expensive) model, even exceeding Illumina in some cases. Not surprisingly, Nanopore underperformed compared to Illumina when basecalled with the fastest (but computationally much less demanding) method with the lowest accuracy. The authors then investigated the surprisingly higher performance of Nanopore data in some cases and identified lower recall with Illumina short read data, particularly from repetitive regions and regions with high variant density, as the driver. Combining the most accurate Nanopore basecalling method with a deep learning variant caller resulted in low error rates in homopolymer regions, similar to Illumina data. This is remarkable, as homopolymer regions are (or, were) traditionally challenging for Nanopore sequencing.

Lastly, Hall et al. provided useful information on the required Nanopore read depth, which is surprisingly low, and the computational resources for variant calling with deep learning callers. With that, the authors established a new state-of-the-art for Nanopore-only variant, calling on bacterial sequencing data. Most likely these findings will be transferred to other organisms as well or at least provide a proof-of-concept that can be built upon.

As the authors mention multiple times throughout the manuscript, Nanopore can provide sequencing data in nearly real-time and in remote regions, therefore opening up a ton of new possibilities, for example for infectious disease surveillance.

However, the high-performing variant calling method as established in this study requires the computationally very expensive sup and/or duplex Nanopore basecalling, whereas the least computationally demanding method underperforms. Here, the manuscript would greatly benefit from extending the last section on computational requirements, as the authors determine the resources for the variant calling but do not cover the entire picture. This could even be misleading for less experienced researchers who want to perform bacterial sequencing at high performance but with low resources. The authors mention it in the discussion but do not make clear enough that the described computational resources are probably largely insufficient to perform the high-accuracy basecalling required. 

We have provided runtime benchmarks for basecalling in Supplementary Figure S23 and detailed these times in Supplementary Table S7. In addition, we state in the Results section (P9 L239-241) “Though we do note that if the person performing the variant calling has received the raw (pod5) ONT data, basecalling also needs to be accounted for, as depending on how much sequencing was done, this step can also be resource-intensive.”

Even with super-accuracy basecalling considered, our analysis shows that variant calling remains the most resource-intensive step for Clair3, DeepVariant, FreeBayes, Medaka, and NanoCaller. Therefore, the statement “the described computational resources are probably largely insufficient to perform the high-accuracy basecalling required”, is incorrect. However, we have made this more prominent in the Results and Discussion.

In the results section we added the underlined section:

“… FreeBayes had the largest runtime variation, with a maximum of 597s/Mbp, equating to 2.75 days for the same genome. In contrast, basecalling with a single GPU using the super-accuracy model required a median runtime of 0.77s/Mbp, or just over 5 minutes for a 4Mbp genome at 100x depth. …”

In the discussion we have added the following statement:

“Basecalling is generally faster than variant calling, assuming GPU access, which is likely considered when acquiring ONT-related equipment.”

Recommendations for the authors:

Reviewer #1 (Recommendations For The Authors):

The colour choices in Figure 3 and Figure 4 c made the illustrations somewhat difficult to read. More substantially, a deeper investigation of the causes of non-homopolymeric-related mistaken indel calls would be useful. 

We have updated Figure 3 so that each line has a different style to aid in discriminating between colours. The colour scheme for Figure 4c has also been updated.

In terms of non-homopolymeric false positive (FP) indel calls, we did an investigation of these for Clair3 and DeepVariant on the simplex sup data as these are the two best performing variant callers and deal the best with homopolymers. For Clair3, there were eight FPs across all samples. Five of these were homopolymers. The remaining three occurred within one or two bases of another insertion which inserted a similar sequence to the FP. For DeepVariant, it was much the same story, with 8/11 FP indels being in homopolymers, and the remaining three being within one or two bases of another insertion with a similar sequence. We have added a couple of sentences to the results explaining this finding.

Reviewer #2 (Recommendations For The Authors):

The paper is well-written and provides evidence for the conclusions. Some issues should be addressed.

Include a section in the Results describing species-specific observations, namely if some samples had recurrently lower SNP and INDEL F1 scores (as observed in Figure 2). 

Please see our response in your second point in the ‘Weaknesses’ section of the public review.

Please provide more details on how the samples were sequenced. Section "Sequencing" in the methods is confusing and not clear enough to be reproduced (provide a supplementary table/figure with the workflow for each sample). Add information about how many samples were multiplexed in each run and what was the output achieved in each.

We have now added a Supplementary Table S9 which outlines which instruments, kits, and multiplexing strategies were used for each sample. In addition, the raw pod5 data that we make available has been segregated by sample, so knowledge of the multiplexing strategy is not necessary for someone attempting to reproduce our results.

The authors acknowledge that structural variation was not evaluated in this manuscript. Since ONT sequencing is often used to reconstruct the sequence of plasmids for outbreak/epidemiology analysis, perhaps they could undertake this analysis on a plasmids dataset (which suffers from constant structural variation).

As noted in our response to Reviewer 3’s public review, we fully agree that investigating structural variations (SVs) would be a very interesting and important follow-up. Identifying and generating ground truth SVs is a nontrivial task and we feel it deserves its own space and study. We hope to explore this in the future.

Reviewer #3 (Recommendations For The Authors):

The manuscript is well organized. However, some sections are a bit long and would benefit from being more concise.

Thank you for your valuable feedback and for acknowledging the organisation of our manuscript. We appreciate your suggestion regarding the length of certain sections. We have gone back through and made the manuscript more concise.

Figure 1: Is the Qscore really the same as identity? Isn't the determination of identity only possible after alignment? 

When we say Qscore we are referring to the Phred-scaled version of the read identity, which is alignment based, not the Qscores of the individual bases in the FASTQ file. We have updated the text and figure legend to make this clearer. “The Qscore is the logarithmic transformation of the read identity,  , where 𝑃 is the read identity.”. We also now explicitly state that read identity is alignment-based.

Abbreviations/terms mentioned but not introduced: <br /> - kmers, P2L57

- ANI, P3L93 

We have updated the text to better introduce these terms.

Author response:

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

Reviewer #1 (Public Review):

My main point of concern is the precision of dissection. The authors distinguish cells isolated from the tailbud and different areas in the PSM. They suggest that the cell-autonomous timer is initiated, as cells exit the tailbud.

This is also relevant for the comparison of single cells isolated from the embryo and cells within the embryo. The dissection will always be less precise and cells within the PSM4 region could contain tailbud cells (as also indicated in Figure 1A), while in the analysis of live imaging data cells can be selected more precisely based on their location. This could therefore contribute to the difference in noise between isolated single cells and cells in the embryo. This could also explain why there are "on average more peaks" in isolated cells (p. 6, l. 7).

This aspect should be considered in the interpretation of the data and mentioned at least in the discussion. (It does not contradict their finding that more anterior cells oscillate less often and differentiate earlier than more posterior ones.)

Reviewer #1 rightly points out that selecting cells in a timelapse is more precise than manual dissection. Manual dissection is inherently variable but we believe in general it is not a major source of noise in our experiments. To control for this, we compared the results of 11 manual dissections of the posterior quarter of the PSM (PSM4) with those of the pooled PSM4 data. In general, we did not see large differences in the distributions of peak number or arrest timing that would markedly increase the variability of the pooled data above that of the individual dissections (Figure 1 – supplement figure 7). We have edited the text in the Results to highlight this control experiment (page 6, lines 13-17).

It is of course possible that we picked up adjacent TB cells when dissecting PSM4, however the reviewer’s assertion that inclusion of TB cells “could also explain why there are "on average more peaks" in isolated cells” is incorrect. Later in the paper we show that cells from the TB have almost identical distributions to PSM4 (mean ± SD, PSM4 4.36 ± 1.44; TB 4.26 ± 1.35; Figure 4 _ supplement 1). Thus, inadvertent inclusion of TB cells while dissecting would in fact not increase the number of peaks.

Here, the authors focus on the question of how cells differentiate. The reverse question is not addressed at all. How do cells maintain their oscillatory state in the tailbud? One possibility is that cells need external signals to maintain that as indicated in Hubaud et al. 2014. In this regard, the definition of tailbud is also very vague. What is the role of neuromesodermal progenitors? The proposal that the timer is started when cells exit the tailbud is at this point a correlation and there is no functional proof, as long as we do not understand how cells maintain the tailbud state. These are points that should be considered in the discussion.

The reviewer asks “How do cells maintain their oscillatory state in the tailbud?”. This is a very interesting question, but as recognized by the reviewer, beyond the scope of our current paper.

We now further emphasize the point “One possibility is that cells need external signals to maintain … as indicated in Hubaud et al. 2014” in the Discussion and added a reference to the review Hubaud and Pourquié 2014 (Signalling dynamics in vertebrate segmentation. Nat Rev Mol Cell Biol 15, 709–721 (2014). https://doi.org/10.1038/nrm3891) (page 18, lines 19-22).

To clarify the definition of the TB, we have stated more clearly in the results (page 15, lines 8-12) that we defined TB cells as all cells posterior to the notochord (minus skin) and analyzed those that survived

>5 hours post-dissociation, did not divide, and showed transient Her1-YFP dynamics.

The reviewer asks: What is the role of neuromesodermal progenitors? In responding to this, we refer to Attardi et al., 2018 (Neuromesodermal progenitors are a conserved source of spinal cord with divergent growth dynamics. Development. 2018 Nov 9;145(21):dev166728. doi: 10.1242/dev.166728).

Around the stage of dissection in zebrafish in our work, there is a small remaining group of cells characterized as NMPs (Sox2 +, Tbxta+ expression) in the dorsal-posterior wall of the TB. These NMPs rarely divide and are not thought to act as a bipotential pool of progenitors for the elongating axis, as is the case in amniotes, rather contributing to the developing spinal cord. How this particular group of cells behaves in culture is unclear as we did not subdivide the TB tissue before culturing. It would be possible to specifically investigate these NMPs regarding a role in TB oscillations, but given the results of Attardi et al., 2018 (small number of cells, low bipotentiality), we argue that it would not be significant for the conclusions of the current work. To indicate this, we included a sentence and a citation of this paper in the results towards the beginning of the section on the tail bud (page 15, lines 8-12).

The authors observe that the number of oscillations in single cells ex vivo is more variable than in the embryo. This is presumably due to synchronization between neighbouring cells via Notch signalling in the embryo. Would it be possible to add low doses of Notch inhibitor to interfere with efficient synchronization, while at the same time keeping single cell oscillations high enough to be able to quantify them?

It is a formal possibility that Delta-Notch signaling may have some impact on the variability in the number of oscillations. However, we argue that the significant amount of cell tracking work required to carry out the suggested experiments would not be justified, considering what has been previously shown in the literature. If Delta-Notch signaling was a major factor controlling the variability of the intrinsic program that we describe, then we would expect that in Delta-Notch mutants the anterior- posterior limits of cyclic gene expression in the PSM would extend beyond those seen in wildtype embryos. Specifically, we might expect to see her1 expression extending more anteriorly in mutants to account for the dramatic increase in the number of cells that have 5, 6, 7 and 8 cycles in culture (Fig. 1E versus Fig. 1I). However, as shown in Holley et al., 2002 (Fig. 5A, B; her1 and the notch pathway function within the oscillator mechanism that regulates zebrafish somitogenesis. Development. 2002 Mar;129(5):1175-83. doi: 10.1242/dev.129.5.1175), the anterior limit of her1 expression in the PSM in DeltaD mutants (aei) is not different to WT. Thus, Delta-Notch signaling may exert a limited control over the number of oscillations, but likely not in excess of one cycle difference.

In the same direction, it would be interesting to test if variation is decreased, when the number of isolated cells is increased, i.e. if cells are cultured in groups of 2, 3 or 4 cells, for instance.

This is a great proposal – however the culture setup used here is a wide-field system that doesn’t allow us to accurately follow more than one cell at a time. Cells that adhere to each other tend to crawl over each other, blurring their identity in Z. This is also why we excluded dividing cells in culture from the analysis. Experiments carried out with a customized optical setup will be needed to investigate this in the future.

It seems that the initiation of Mesp2 expression is rather reproducible and less noisy (+/- 2 oscillation cycles), while the number of oscillations varies considerably (and the number of cells continuing to oscillate after Mesp2 expression is too low to account for that). How can the authors explain this apparent discrepancy?

The observed tight linkage of the Mesp onset and Her1 arrest argue for a single timing mechanism that is upstream of both gene expression events; indeed, this is one of the key implications of the paper. However, the infrequent dissociation of these events observed in FGF-treated cells suggests that more than one timing pathway could be involved, although there are other interpretations. We’ve added more discussion in the text on one vs multi-timers (page 17, lines 19-23 – page 18, line 1 - 8)., see next point.

The observation that some cells continue oscillating despite the upregulation of Mesp2 should be discussed further and potential mechanism described, such as incomplete differentiation.

This is an infrequent (5 out of 54 cells) and interesting feature of PSM4 cells in the presence of FGF. We imagine that this disassociation of clock arrest from mesp on-set timing could be the result of alterations in the thresholds in the sensing mechanisms controlling these two processes. Alternatively - as reviewer 2 argues - it might reflect multiple timing mechanisms at work. We have added a discussion of these alternative interpretations (page 17, lines 19-23 – page 18, line 1 - 8).

Fig. 3 supplement 3 B missing

It’s there in the BioRxiv downloadable PDF and full text – but seems to not be included when previewing the PDF. Thanks for the heads up.

Reviewer #2 (Public Review):

The authors demonstrate convincingly the potential of single mesodermal cells, removed from zebrafish embryos, to show cell-autonomous oscillatory signaling dynamics and differentiation. Their main conclusion is that a cell-autonomous timer operates in these cells and that additional external signals are integrated to tune cellular dynamics. Combined, this is underlying the precision required for proper embryonic segmentation, in vivo. I think this work stands out for its very thorough, quantitative, single-cell real-time imaging approach, both in vitro and also in vivo. A very significant progress and investment in method development, at the level of the imaging setup and also image analysis, was required to achieve this highly demanding task. This work provides new insight into the biology underlying embryo axis segmentation.

The work is very well presented and accessible. I think most of the conclusions are well supported. Here a my comments and suggestions:

The authors state that "We compare their cell-autonomous oscillatory and arrest dynamics to those we observe in the embryo at cellular resolution, finding remarkable agreement."

I think this statement needs to be better placed in context. In absolute terms, the period of oscillations and the timing of differentiation are actually very different in vitro, compared to in vitro. While oscillations have a period of ~30 minutes in vivo, oscillations take twice as long in vitro. Likewise, while the last oscillation is seen after 143 minutes in vivo, the timing of differentiation is very significantly prolonged, i.e.more than doubled, to 373min in vitro (Supplementary Figure 1-9). I understand what the authors mean with 'remarkable agreement', but this statement is at the risk of being misleading. I think the in vitro to in vivo differences (in absolute time scales) needs to be stated more explicitly. In fact, the drastic change in absolute timescales, while preserving the relative ones, i.e. the number of oscillations a cell is showing before onset of differentiation remains relatively invariant, is a remarkable finding that I think merits more consideration (see below).

We have changed the text in the abstract (page 1, line 28) to clarify that the agreement is in the relative slowing, intensity increases and peak numbers.

One timer vs. many timers

The authors show that the oscillation clock slowing down and the timing of differentiation, i.e. the time it takes to activate the gene mesp, are in principle dissociable processes. In physiological conditions, these are however linked. We are hence dealing with several processes, each controlled in time (and thereby space). Rather than suggesting the presence of ‘a timer’, I think the presence of multiple timing mechanisms would reflect the phenomenology better. I would hence suggest separating the questions more consistently, for instance into the following three:

a.  what underlies the slowing down of oscillations?

b.  what controls the timing of onset of differentiation?

c.  and finally, how are these processes linked?

Currently, these are discussed somewhat interchangeably, for instance here: “Other models posit that the slowing of Her oscillations arise due to an increase of time-delays in the negative feedback loop of the core clock circuit (Yabe, Uriu, and Takada 2023; Ay et al. 2014), suggesting that factors influencing the duration of pre-mRNA splicing, translation, or nuclear transport may be relevant. Whatever the identity, our results indicate the timer ought to exert control over differentiation independent of the clock.”(page 14). In the first part, the slowing down of oscillations is discussed and then the authors conclude on 'the timer', which however is the one timing differentiation, not the slowing down. I think this could be somewhat misleading.

To help distinguish the clock’s slowing & arrest from differentiation, we have clarified the text in how we describe our experiments using her1-/-; her7-/- cells (page 10, lines 9-20).

From this and previous studies, we learn/know that without clock oscillations, the onset of differentiation still occurs. For instance in clock mutant embryos (mouse, zebrafish), mesp onset is still occurring, albeit slightly delayed and not in a periodic but smooth progression. This timing of differentiation can occur without a clock and it is this timer the authors refer to "Whatever the identity, our results indicate the timer ought to exert control over differentiation independent of the clock." (page 14). This 'timer' is related to what has been previously termed 'the wavefront' in the classic Clock and Wavefront model from 1976, i.e. a "timing gradient' and smooth progression of cellular change. The experimental evidence showing it is cell-autonomous by the time it has been laid down,, using single cell measurements, is an important finding, and I would suggest to connect it more clearly to the concept of a wavefront, as per model from 1976.

We have been explicit about the connection to the clock & wavefront in the discussion (page 17, line 12-17).

Regarding question a., clearly, the timer for the slowing down of oscillations is operating in single cells, an important finding of this study. It is remarkable to note in this context that while the overall, absolute timescale of slowing down is entirely changed by going from in vivo to in vitro, the relative slowing down of oscillations, per cycle, is very much comparable, both in vivo and in vivo.

We have now pointed out the relative nature of this phenomenon in the abstract, page 1, line 28.

To me, while this study does not address the nature of this timer directly, the findings imply that the cell-autonomous timer that controls slowing down is, in fact, linked to the oscillations themselves. We have previously discussed such a timer, i.e. a 'self-referential oscillator' mechanism (in mouse embryos, see Lauschke et al., 2013) and it seems the new exciting findings shown here in zebrafish provide important additional evidence in this direction. I would suggest commenting on this potential conceptual link, especially for those readers interested to see general patterns.

While we posit that the timer provides positional info to the clock to slow oscillations and instruct its arrest – we do not believe that “the findings imply that the cell-autonomous timer that controls slowing down is, in fact, linked to [i.e., governed by] the oscillations themselves.”. As we show, in her1-/-; her7-/- embryos lacking oscillations, the timing / positional information across the PSM still exists as read-out by Mesp expression. Is this different positional information than that used by the clock? – possibly – but given the tight linkage between Mesp onset and the timing/positioning of clock arrest, both cell-autonomously and in the embryo, we argue that the simplest explanation is that the timing/positional information used by the clock and differentiation are the same. Please see page 10, lines 9-20, as well as the discussion (page 17, lines 19-23; page 18. Lines 1-8 ).

We agree that the timer must communicate to the clock– but this does not mean it is dependent on the clock for positional information.

Regarding question c., i.e. how the two timing mechanisms are functionally linked, I think concluding that "Whatever the identity, our results indicate the timer ought to exert control over differentiation independent of the clock." (page 14), might be a bit of an oversimplification. It is correct that the timer of differentiation is operating without a clock, however, physiologically, the link to the clock (and hence the dependence of the timescale of clock slowing down), is also evident. As the author states, without clock input, the precision of when and where differentiation occurs is impacted. I would hence emphasize the need to answer question c., more clearly, not to give the impression that the timing of differentiation does not integrate the clock, which above statement could be interpreted to say.

As far as we can tell, we do not state that “without clock input, the precision of when and where differentiation occurs is impacted”, and we certainly do not want to give this impression. In contrast, as mentioned above, the her1-/-; her7-/- mutant embryo studies indicate that the lack of a clock signal does not change the differentiation timing, i.e. it does not integrate the clock. Of course, in the formation of a real somite in the embryo, the clock’s input might be expected to cause a given cell to differentiate a little earlier or later so as to be coordinated with its neighbors, for example, along a boundary. But this magnitude of timing difference is within one clock cycle at most, and does not match the large variation seen in the cultured cells that spans over many clock cycles.

A very interesting finding presented here is that in some rare examples, the arrest of oscillations and onset of differentiation (i.e. mesp) can become dissociated. Again, this shows we deal here with interacting, but independent modules. Just as a comment, there is an interesting medaka mutant, called doppelkorn (Elmasri et al. 2004), which shows a reminiscent phenotype "the Medaka dpk mutant shows an expansion of the her7 expression domain, with apparently normal mesp expression levels in the anterior PSM.". The authors might want to refer to this potential in vivo analogue to their single cell phenotype.

Thank you, we had forgotten this result. Although we do not agree that this result necessarily means there are two interacting modules, we have included a citation to the paper, along with a discussion of alternative explanations for the dissociation (page 18, lines 2-14).

One strength of the presented in vitro system is that it enables precise control and experimental perturbations. A very informative set of experiments would be to test the dependence of the cell-autonomous timing mechanisms (plural) seen in isolated cells on ongoing signalling cues, for instance via Fgf and Wnt signaling. The inhibition of these pathways with well-characterised inhibitors, in single cells, would provide important additional insight into the nature of the timing mechanisms, their dependence on signaling and potentially even into how these timers are functionally interdependent.

We agree and in future experiments we are taking advantage of this in vitro system to directly investigate the effect of signaling cues on the intrinsic timing mechanism.

Author Response:

Reviewer #1 (Public Review):

Summary:

The authors aimed to identify potential biomarkers for acute myocardial infarction (AMI) through blood metabolomics and fecal microbiome analysis. They found that long chain fatty acids (LCFAs) could serve as biomarkers for AMI and demonstrated a correlation between LCFAs and the gut microbiome. Additionally, in silico molecular docking and in vitro thrombogenic assays showed that these LCFAs can induce platelet aggregation.

Strengths:

The study utilized a comprehensive approach combining blood metabolomics and fecal microbiome analysis.

The findings suggest a novel use of LCFAs as biomarkers for AMI.

The correlation between LCFAs and the gut microbiome is a significant contribution to understanding the interplay between gut health and heart disease.

The use of in silico and in vitro assays provides mechanistic insights into how LCFAs may influence platelet aggregation.

Weaknesses:

The evidence is incomplete as it does not definitively prove that gut dysbiosis contributes to fatty acid dysmetabolism.

We appreciate this reviewer’s insightful comment regarding the causal relationship between gut dysbiosis and fatty acid dysmetabolism. We acknowledge that our study primarily demonstrates a strong association rather than causation. While establishing causality was beyond the scope of the current study, we recognize the importance of addressing this point. In our revised manuscript, we will emphasize the observational nature of our findings and discuss the need for future research, including longitudinal studies and interventional trials, to explore the causal links between gut dysbiosis and fatty acid dysmetabolism. We believe that this clarification strengthens the interpretation of our results and aligns with the reviewer's concern.

The study primarily shows an association between the gut microbiome and fatty acid metabolism without establishing causation.

We agree with the reviewer that our study presents an association rather than definitive proof of causation between the gut microbiome and fatty acid metabolism. To address this, we plan to expand the discussion section to more clearly outline the limitations of our study in establishing causality. We will also propose future research directions, such as the use of animal models and longitudinal human studies, which could help elucidate the causal pathways. By clarifying this aspect, we aim to provide a more balanced perspective on our findings.

Reviewer #2 (Public Review):

Summary:

Fan et al. investigated the relationship between early acute myocardial infarction (eAMI) and disturbances in the gut microbiome using metabolomics and metagenomics analyses. They studied 30 eAMI patients and 26 healthy controls, finding elevated levels of long-chain fatty acids (LCFA) in the plasma of eAMI patients.

Strengths:

The research attributed a substantial portion of LCFA variance in eAMI to changes in the gut microbiome, as indicated by omics analyses. Computational profiling of gut bacteria suggested structural variations linked to LCFA variance. The authors also conducted molecular docking simulations and platelet assays, revealing that eAMI-associated LCFAs may enhance platelet aggregation.

Weaknesses:

The results should be validated using different assays, and animal models should be considered to explore the mechanisms of action.

We appreciate the reviewer’s suggestion to validate our findings using additional assays and animal models. We agree that further validation is crucial to confirm the robustness of our results and to explore the underlying mechanisms in greater detail. While our current study focused on human subjects and in vitro assays to establish initial findings, we acknowledge that additional experimental approaches are necessary. In the revised manuscript, we plan to include a discussion on the potential use of different assays (e.g., advanced metabolomics techniques, multi-omics integration) and animal models to validate and expand upon our findings. Moreover, we are planning to undertake these experiments in future studies to build upon the foundational work presented here.

We believe that our revised responses and the planned manuscript revisions will address the reviewers’ concerns effectively. We are confident that these changes will enhance the overall contribution of our study to the field. Thank you again for your valuable feedback.

Author response:

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

Public Reviews:

Joint Public Review: 

The molecular mechanisms that mediate the regulated exocytosis of neuropeptides and neurotrophins from neurons via large dense-core vesicles (LDCVs) are still incompletely understood. Motivated by their earlier discovery that the Rab3-RIM1 pathway is essential for neuronal LDCV exocytosis, the authors now examined the role of the Rab3 effector Rabphilin-3A in neuronal LDCV secretion. Based on multiple live and confocal imaging approaches, the authors provide evidence for a synaptic enrichment of Rabphilin-3A and for independent trafficking of Rabphilin-3A and LDCVs. Using an elegant NPY-pHluorin imaging approach, they show that genetic deletion of Rabphilin-3A causes an increase in electrically triggered LDCV fusion events and increased neurite length. Finally, knock-out-replacement studies, involving Rabphilin-3A mutants deficient in either Rab3- or SNAP25-binding, indicate that the synaptic enrichment of Rabphilin-3A depends on its Rab3 binding ability, while its ability to bind to SNAP25 is required for its effects on LDCV secretion and neurite development. The authors conclude that Rabphilin-3A negatively regulates LDCV exocytosis and propose that this mechanism also affects neurite growth, e.g. by limiting neurotrophin secretion. These are important findings that advance our mechanistic understanding of neuronal large dense-core vesicle (LDCV) secretion. 

The major strengths of the present paper are: 

(i) The use of a powerful Rabphilin-3A KO mouse model. 

(ii) Stringent lentiviral expression and rescue approaches as a strong genetic foundation of the study. 

(iii) An elegant FRAP imaging approach. 

(iv) A cutting-edge NPY-pHluorin-based imaging approach to detect LDCV fusion events. 

We thank the reviewers for their positive evaluation of our manuscript.

Weaknesses that somewhat limit the convincingness of the evidence provided and the corresponding conclusions include the following: 

(i) The limited resolution of the various imaging approaches introduces ambiguity to several parameters (e.g. LDCV counts, definition of synaptic localization, Rabphilin-3A-LDCV colocalization, subcellular and subsynaptic localization of expressed proteins, AZ proximity of Rabphilin-3A and LDCVs) and thereby limits the reliability of corresponding conclusions. Super-resolution approaches may be required here. 

We thank the reviewer for their constructive suggestion. We fully agree that super-resolution imaging would produce a more precise localization of RPH3A and co-localization with DCVs. We have now repeated our (co)-localization experiments with STED microscopy. We find that RPH3A colocalized with the pre-synaptic marker Synapsin1 and, to a lesser extent, with the post synaptic marker Homer and DCV marker chromogranin B (new Figure 1). This indicates that RPH3A is highly enriched in synapses, mostly the pre-synapse, and that RPH3A partly co-localizes with DCVs.  

(ii) The description of the experimental approaches lacks detail in several places, thus complicating a stringent assessment. 

We apologize for the lack of detail in explaining the experimental approaches. We have included a more detailed description in the revised manuscript. 

(iii) Further analyses of the LDCV secretion data (e.g. latency, release time course) would be important in order to help pinpoint the secretory step affected by Rabphilin-3A. 

We agree. To address this comment, we have now included the duration of the fusion events (new Figure S2D-F). The start time of the fusion events are shown in the cumulative plots in now Figure 3F and I. The kinetics are normal in the RPH3A KO neurons.

(iv) It remains unclear why a process that affects a general synaptic SNARE fusion protein - SNAP25 - would specifically affect LDCV but not synaptic vesicle fusion. 

We agree that we have not addressed this issue systematically enough in the original manuscript. We have now added a short discussion on this topic in the Discussion of the revised manuscript (p 15, line 380-386). In brief, we do not claim full selectivity for the DCV pathway. Some effects of RPH3A deficiency on the synaptic vesicle cycle have been observed. Furthermore, because DCVs typically do not mix in the synaptic vesicle cluster and fuse outside the active zone (and outside the synapse), DCVs might be more accessible to RPH3A regulation.

(v) The mechanistic links between Rabphilin-3A function, LDCV density in neurites, neurite outgrowth, and the proposed underlying mechanisms involving trophic factor release remain unclear. 

We agree that we have not addressed all these links systematically enough in the original manuscript, although we feel that we have at least postulated the best possible working model to link RPH3A function to DCV exocytosis/neurotrophic factor release and neurite outgrowth (p 15-16, line 396-400). Of course, a single study cannot support all these links with sufficient experimental evidence. We have now added a short text on what we can conclude exactly based on our experiments and how we see the links between RPH3A function, DCV exocytosis/neurotrophic factor release, neurite outgrowth and DCV density in neurites (p 13-14, line 317-325).

Reviewer #1 (Public Review): 

Summary:

The manuscript by Hoogstraaten et al. investigates the effect of constitutive Rabphilin 3A (RPH3A) ko on the exocytosis of dense core vesicles (DCV) in cultured mouse hippocampal neurons. Using mCherry- or pHluorin-tagged NPY expression and EGFP- or mCherry tagged RPHA3, the authors first analyse the colocalization of DCVs and RPH3A. Using FRAP, the authors next analyse the mobility of DCVs and RAB3A in neurites. The authors go on to determine the number of exocytotic events of DCVs in response to high-frequency electrical stimulation and find that RPH3A ko increases the number of exocytotic events by a factor 2-3, but not the fraction of released DCVs in a given cell (8x 50Hz stim). In contrast, the release fraction is also increased in RBP3A KOs when doubling the stimulation number (16x 50Hz). They further observe that RPH3A ko increases dendrite and axon length and the overall number of ChgrB-positive DCVs. However, the overall number of DCVs and dendritic length in ko cells directly correlate, indicating that the number of vesicles per dendritic length remains unaffected in the RPH3A KOs. Lentiviral co-expression of tetanus toxin (TeNT) showed a non-significant trend to reduce axon and dendrite length in RPH3a KOs. Finally, the authors use co-expression of RAB3A and SNAP25 constructs to show that RAB3A but not SNAP25 interaction is required to allow the exocytosis-enhancing effect in RPH3A KOs. 

While the authors' methodology is sound, the microscopy results are performed well and analyzed appropriately, but their results in larger parts do not sufficiently support their conclusions. Moreover, the experiments are not always described in sufficient detail (e.g. FRAP; DCV counts vs. neurite length) to fully understand their claims. 

Overall, I thus feel that the manuscript does not provide a sufficient advance in knowledge. 

Strengths: 

- The authors' methodology is sound, and the microscopy results are performed well and analyzed appropriately. 

- Figure 2: The exocytosis imaging is elegant and potentially very insightful. The effect in the RPH3A KOs is convincing. 

- Figure 4: the logic of this experiment is elegant. It shows that the increased number of DCV fusion events in RPH3A KOs is related to the interaction of RPH3A with RAB3A but not with SNAP25. 

We thank the reviewer for their positive evaluation of our manuscript.

Weaknesses: 

- The results in larger parts do not sufficiently support the conclusions. 

- The experiments are not always described in sufficient detail (e.g. FRAP; DCV counts vs. neurite length) to fully understand their claims. 

- Not of sufficient advance in knowledge for this journal 

- The significance of differences in control experiments WT vs. KO) varies between experiments shown in different figures. 

- Axons and dendrites were not analyzed separately in Figures 1 and 2. 

- The colocalization study in Figure 1 would require super-resolution microscopy. 

To address the reviewers’ comments, we have provided a more detailed explanation of our analysis (p 19-20, line 521-542). In addition, we have repeated our colocalization experiments using STED microscopy, see Joint Public Review item (i).  

Reviewer #2 (Public Review): 

Summary: 

Hoogstraaten et al investigated the involvement of rabphilin-3A RPH3A in DCV fusion in neurons during calcium-triggered exocytosis at the synapse and during neurite elongation. They suggest that RPH3A acts as an inhibitory factor for LDV fusion and this is mediated partially via its interaction with SNAP25 and not Rab3A/Rab27. It is a very elegant study although several questions remain to be clarified. 

Strengths: 

The authors use state-of-the-art techniques like tracking NPY-PHluorin exocytosis and FRAP experiments to quantify these processes providing novel insight into LDCs exocytosis and the involvement of RPH3A. 

We thank the reviewer for their positive evaluation of our manuscript.

Weaknesses: 

At the current state of the manuscript, further supportive experiments are necessary to fully support the authors' conclusions. 

We thank the reviewer for their comments and suggestions. We have performed additional experiments to support our conclusions, see Joint Public Review items (i) – (iv)

Reviewer #3 (Public Review): 

Summary: 

The molecular mechanism of regulated exocytosis has been extensively studied in the context of synaptic transmission. However, in addition to neurotransmitters, neurons also secrete neuropeptides and neurotrophins, which are stored in dense core vesicles (DCVs). These factors play a crucial role in cell survival, growth, and shaping the excitability of neurons. The mechanism of release for DCVs is similar, but not identical, to that used for SV exocytosis. This results in slow kinetic and low release probabilities for DCV compared to SV exocytosis. There is a limited understanding of the molecular mechanisms that underlie these differences. By investigating the role of rabphilin-3A (RPH3A), Hoogstraaten et al. uncovered for the first time a protein that inhibits DCV exocytosis in neurons. 

Strengths: 

In the current work, Hoogstraaten et al. investigate the function of rabphilin-3A (RPH3A) in DVC exocytosis. This RAB3 effector protein has been shown to possess a Ca2+ binding site and an independent SNAP25 binding site. Using colocalization analysis of confocal imaging the authors show that in hippocampal neurons RPH3A is enriched at pre- and post-synaptic sites and associates specifically with immobile DCVs. Using site-specific RPH3A mutants they found that the synaptic location was due to its RAB3 interaction site. They further could show that RPH3A inhibits DCV exocytosis due to its interaction with SNAP25. They came to that conclusion by comparing NPY-pHluorin release in WT and RPH3A KO cells and by performing rescue experiments with RPH3A mutants. Finally, the authors showed that by inhibiting stimulated DCV release, RPH3A controlled the axon and dendrite length possibly through the reduced release of neurotrophins. Thereby, they pinpoint how the proper regulation of DCV exocytosis affects neuron physiology. 

We thank the reviewer for their positive evaluation of our manuscript.

Weaknesses: 

Data context 

One of the findings is that RPH3A accumulates at synapses and is mainly associated with immobile DCVs.

However, Farina et al. (2015) showed that 66% of all DCVs are secreted at synapses and that these DCVs are immobile prior to secretion. To provide additional context to the data, it would be valuable to determine if RPH3A KO specifically enhances secretion at synapses. Additionally, the authors propose that RPH3A decreases DCV exocytosis by sequestering SNAP25 availability. At first glance, this hypothesis appears suitable. However, due to RPH3A synaptic localization, it should also limit SV exocytosis, which it does not. In this context, the only explanation for RPH3A's specific inhibition of DCV exocytosis is that RPH3A is located at a synapse site remote from the active zone, thus protecting the pool of SNAP25 involved in SV exocytosis from binding to RPH3A. This hypothesis could be tested using super-resolution microscopy. 

We thank the reviewer for their suggestion. We have now performed super resolution microscopy, see Joint Public Review item (i). However, these new data do not necessarily explain the stronger effect of RP3A deficiency on DCV exocytosis, relative to SV exocytosis. We have added a short discussion on this topic to the revised manuscript, see Joint Public Review item (iv).

Technical weakness 

One technical weakness of this work consists in the proper counting of labeled DCVs. This is significant since most findings in this manuscript rely on this analysis. Since the data was acquired with epi-fluorescence or confocal microscopy, it doesn't provide the resolution to visualize individual DCVs when they are clumped. The authors use a proxy to count the number of DCVs by measuring the total fluorescence of individual large spots and dividing it by the fluorescence intensity of discrete spots assuming that these correspond to individual DCVs. This is an appropriate method but it heavily depends on the assumption that all DCVs are loaded with the same amount of NPY-pHluorin or chromogranin B (ChgB). Due to the importance of this analysis for this manuscript, I suggest that the authors show that the number of DCVs per µm2 is indeed affected by RPH3A KO using super-resolution techniques such as dSTORM, STED, SIM, or SRRF. 

The reviewer is correct that this is a crucial issue, that we have not addressed optimally until now. We have previously devoted a large part of a previous manuscript to this issue, but have not referred to this previous work clearly enough. We have now clarified this (p 7, line 187-190). In brief, we have previously quantified the ratio between fluorescent intensity of ChgB and NPY-pHluorin in confocal microscopy over the number of dSTORM puncta in sparse areas of WT mouse hippocampal neurons (Persoon et al., 2018). This quantification yielded a unitary fluorescence intensity per vesicle that was very stable of different neurons. Although there might be some underestimation of the total number of DCVs when using confocal microscopy, the study of Persoon et al. (2018) has demonstrated that these parameters correlate well and that the estimations are accurate. Considering that the rF/F0 is similar in RPH3A WT and KO neurons (now Figure S2I), meaning that the intensity of NPY-pHluorin of one fusion event is comparable, we can presume that this correlation also applies for the RPH3A KO neurons.

Recommendations for the authors:

Reviewer #1 (Recommendations For The Authors): 

Major points: 

(1) The authors perform an extensive analysis regarding the colocalization of RPH3A and DCVs (Figure 1 upper part). This analysis is hampered by the fact that the recorded data has in relation to vesicle size limited resolution (> 1 µm) to allow making strong claims here. In my view, super-resolution microscopy would be required for the co-localization studies shown in Figure 1. 

We fully agree and have now performed super-resolution microscopy, see Joint Public Review item (i)

(2) The FRAP experiments (Figure 1 lower part) cannot be sufficiently understood from what is presented. The methods say that both laser channels were activated during bleaching but NPY-pHluorin is not bleached in Fig.1E. Explanation of the bleaching is not very circumspect. In 1D, it is rather EGFP-RPH3A that is entering the bleached area than the NPY vesicles. These experiments require a more careful explanation of methodology, observed results, and their interpretation. Overall, the observed effects in the original kymograph traces require a better explanation. 

We acknowledge that NPY-pHluorin in Figure 1E (now Figure 2C) is not completely bleached. NPY-pHluorin appeared to be more difficult to bleach than NPY-mCherry. However, it is important to clarify that we merely bleached the neurites to remove the stationary puncta and facilitate our analysis of DCV/RPH3A dynamics. This bleaching step does not affect the interpretation of our results. We apologize that this was not clearly stated in the text and have made the necessary adjustments in legend, results- and methods section, (p 6-7, line 162-163; p 5, line 140-142 and p 19, line 508-513). Additionally, we apologize for the accidental switch of the kymographs for NPY-mCherry and EGFP-RPH3A in Figure 1D (now Figure 2B, C). We greatly appreciate identifying this error.  

(3) Figure 1: The authors need to mention whether axons, dendrites, or both were analyzed throughout the different panels and how they were identified. Is it possible that axons were wrapping around dendrites in their cultures (compare e.g. Shimojo et al., 2015)? Given the limited spatial resolution and because of this wrapping, interpretation of results could be affected. 

We completely agree with the reviewer’s assessment and conclusion. We are unable to distinguish axons from dendrites using this experimental design. We have made sure to specify in the text that our observation that RPH3A does not co-travel with DCVs is true for both dendrites and axons, (p 5, line 150).

(4) Figure 2: The exocytosis imaging is elegant and potentially very insightful. The effect in the RPH3A KOs is convincing. However, the authors determine the efficacy of exocytosis from NPY-pHluorin unquenching of DCVs only. This is only one of several possible parameters to read out the efficiency of exocytosis. Kinetics like e.g. delay between stimulation and start of exocytosis events or release time course of NPY after DCV fusion were not determined. Such analysis could give a better insight into what process before or after the fusion of DCVs is affected by RPH3A ko. 

We fully agree with the reviewer. We have now included the duration of the fusion events (new Figure S2D-F). The start time of the fusion events are shown in the cumulative plots in now Figure 3F and I. The kinetics are normal in the RPH3A KO neurons.

Moreover, it needs to be mentioned whether 2C and D are from WT or ko cultures. It would be best to show representative examples from both genotypes. 

We have now adjusted this in the new figure (now Figure 3C, D).

The number of fusion events is much increased but the release fraction is not significantly changed. While this is consistent with results in Figure 4C it is at variance with 4F. This raises questions about the reliability of the effects in RPH3A KOs. 

The release fraction indicates the number of fusion events normalized to the total DCV pool. In Figure 4D, we observed a slightly bigger pool size, which explains the lack of significance when analyzing the released fraction. In Figure 4G, however, DCV pool sizes are similar between KO and WT, leading to a statistically significant effect on release fraction in KO neurons. Furthermore, Figures 4B and E distinctly show a substantial increase in fusion events in RPH3A KO neurons. This variability in pool size observed could potentially be attributed to variation in culture or inherent biological variability.

Given the increased number of ChgrB-positive DCVs in RPH3A KOs (shown in Figure 2) and that only the cumulative number of exocytosis events were analysed, how can the authors exclude that the RPH3A ko only affects vesicle number but not release, if the % change in released vesicles is not different to WT? Kinetics of release don't seem to be affected. Importantly, what was the density of NPY-pHluorin vesicles in WT vs. ko? 

In Figure 2 (now Figure 5) we show that RPH3A KO neurons are larger and contain more endogenous ChgB+ puncta than WT neurons. This increased number of ChgrB+ puncta scales with their size as puncta density is not increased. A previous study (Persoon et al., 2018) has demonstrated a strong correlation between DCV number and neuron size. Our data show that RPH3A deficiency increased DCV exocytosis, but the released fraction of vesicles depends on the total number of DCVs, which we determined during live recording by dequenching NPY-pHluorin using NH4+. Considering that this is an overexpression of a heterologous DCV-fusion reporter, and not endogenous staining of DCVs, as in the case of ChgrB+ puncta, some variability is not unexpected.

Also in these experiments, the question arises of whether the authors analyse axons, dendrites, or both throughout the different panels and how they were identified. 

In our experimental design we record all fusion events per cell, including both axons and dendrites but excluding the cell soma. We have clarified this in the method section, (p 19, line 508 and p 19, line 521-522).

(5) Figure 3: in D the authors show that ChgrB-pos. DCV density is slightly increased in KOs. How does this relate to the density of NPY-pHluorin DCVS in Figure 2? 

We do not observe a difference in NPY-pHluorin density (see Author response image 1). However, it is important to note that we relied on tracing neurites in live recording images to determine the neuronal size. In contrast, the ChgB density was based on dendritic length using MAP2 (post-hoc) staining was limited. In addition, Chgr+ puncta represent an endogenous DCV staining, NPY-pHluorin quantification is based on overexpression of a heterologous DCV-fusion reporter. These two factors likely contribute some variability.

Author response image 1.

The authors show a non-significant trend of TeNT coexpression to reduce axon and dendrite lengths in RPH3A KOs. While this trend is visible, I think one cannot draw conclusions from that when not reaching significance. The argument of the authors that the increased axon and dendrite lengths are created by growth factor peptide release from DCV during culture time is interesting. However, the fact that TeNT expression shows a trend toward reducing this effect on axons/dendrites is not sufficient to prove the release of such growth factors. 

We agree. We have toned down this speculation in the revised manuscript, (p 15-16, line 395-400).

Lastly, the authors don't provide insight into the mechanisms, of how RPH3A ko increases the number of DCVs per µm dendritic length in the neurons. In my view, there are too many loose ends in this story of how RPH3A ko first increases spontaneous release of DCVs and then enhances neurite growth and DCV density. Did the authors e.g. measure the spontaneous release of DCVs in their cultures? 

We measured spontaneous release of DCVs during the 30s baseline recording prior to stimulation. We observed no difference in spontaneous release between WT and KO neurons (now Figure S2H). However, baseline recording lasted only 30 seconds. It is possible that this was too short to detect subtle effects.

Other points: 

(1) Figure 4: the logic of this experiment is elegant. It shows that the increased number of DCV fusion events in RPH3A KOs is related to the interaction of RPH3A with RAB3A but not with SNAP25. As mentioned above, it is irritating that the reduction of fusion events in KOs and on the release fraction is sometimes reaching significance, but sometimes it does not. Likewise, the absence of significant effects on DCV numbers is not consistent with the results shown in Figures 3C and D. 

DCV numbers in Figure 3 (now Figure 5) are determined by staining for endogenous ChgB, whereas in Figure 4D and G DCV numbers are determined by overexpressing NPY-pHluorin and counting the dequenched puncta following a NH4+ puff.

(2) Figure 1B: truncation of the y-axis needs to be clearly indicated. 

We have replaced this figure with new Figure 1 and have indicated truncations of the y-axis when needed (new Figure 1E). 

(3) Page 10: "Given that neuropeptides are key modulators of adult neurogenesis (Mu et al., 2010), and that RPH3A depletion leads to increased DCV exocytosis, it is coherent that we observed longer neurites in RPH3A KO neurons." I cannot follow the argument of the authors here: what has neurogenesis to do with neurite length? 

We apologize for the confusion. We have clarified this in the revised text, (p 16, line 398-400).

Minor point: 

There are some typos in the manuscript. e.g., page 8: "... may partially dependent on regulated secretion...); page 6: "...to dequence all...". 

Thank you for noticing, we have corrected the typos.

Reviewer #2 (Recommendations For The Authors): 

(1) Supplementary Figure S1A, in my opinion, should be in Figure 1A as it illustrates all the constructs used in this study and helps the reader to follow it up. 

We thank the reviewer for their suggestion. However, we feel that with the adjustments we have made in Figure 1, the illustrations of the constructs fit better in Figure S1, since new Figure 1 shows the localization of endogenous RPH3A and not that of the constructs.  

(2) One of the conclusions of the manuscript is the synaptic localization of the different RPH3A mutants. The threshold for defining synaptic localization is not clear either from the images nor from the analysis: for example, the Menders coefficient for VGut1-Syn1 which is used as a positive control, ranges from 0.65-0.95 and that of RPH3A and Syn1 ranges from 0.5-0.95. These values should be compared to all mutants and the conclusions should be based on such comparison. 

We agree. We have now repeated our initial co-localization experiment with all the RPH3A mutants (now Figure S1D-F).  

(3) Strengthening this figure with STED/SIM/dSTORM microscopy can verify and add a new understanding of the subtle changes of RPH3A localization. 

We fully agree and have now added super-resolution microscopy data, see Joint Public Review item (i).

(4) As RAB3A/RAB27A (ΔRAB3A/RAB27A) loses the punctate distribution, please clarify how can it function at the synapse and not act as a KO. Is it sorted to the synapse and how does it is sorted to the synapse? 

We used lentiviral delivery to introduce our constructs, resulting in the overexpression of ΔRAB3A/RAB27A mutant RPH3A. This overexpression likely compensates for the loss of the punctate distribution of RPH3A, thereby maintaining its limiting effect on DCV exocytosis. It is plausible that under physiological conditions, the mislocalization of RPH3A would lead to increased exocytosis, similar to what we observed in the KO. 

(5) Is RPH3A expressed in both excitatory and inhibitory neurons? 

We agree this is an important question. Single cell RNA-seq already suggests the protein is expressed in both, but we nevertheless decided to test expression of RPH3A protein in excitatory and inhibitory neurons, using immunocytochemistry with VGAT and VGLUT as markers in hippocampal and striatal WT neurons. We found that RPH3A is expressed in both VGLUT+ hippocampal neurons and VGAT+ striatal neurons (new Figure S1A, B).  

(6) The differential use of ChgB and NPY as markers for DCVs should be clarified and compared as these are used at different stages of the manuscript. 

We have previously addressed the comparison between ChgB and NPY-pHluorin (Persoon et al., 2018). We made sure to indicate this more clearly throughout the manuscript to clarify the use of the two markers. 

(7) FRAP experiments- A graph describing NPY recovery should be added as a reference to 2H and discussed. 

We agree. We have made the necessary adjustments (new Figure 2G).

(8) Figure 2E shows some degree of "facilitation" between the 2 8x50 pulses RPH3A KO neurons. Can the author comment on that? What was the reason for using this dual stimulation protocol? 

There is indeed some facilitation between the two 8 x 50 pulses in KO neurons and to a lesser extent also in the WT neurons, which we have observed before in WT neurons (Baginska et al., 2023). Baginska et al. (2023) showed recently that different stimulation protocols can influence certain fusion dynamics, like the ratio of persistent and transient events and event duration. We used two different stimulation protocols to thoroughly investigate the effect of RPH3A on exocytosis, and assess the robustness of our findings regarding the number of fusion events. Fusion kinetics was similar in WT an KO neurons for both stimulation protocols (new Figure 2D-F).

(9) Figure 3 quantifies dendrites length and then moves to quantify both axon and dendrites for the Tetanus toxin experiment. What are the effects of KO on axon length? In the main figures, it is not mentioned but in S3 it seems not to be affected. How does it reconcile with the main conclusion on neurite length? 

Figure 3H (now Figure 6C) shows the effect of the KO on axon length: the axon length is increased in RPH3A KO neurons compared to WT, similar to dendrite length. Re-expressing RPH3A in KO neurons rescues axonal length to WT levels. In Figure S3, we observe a similar trend as in main Figure 3 (new Figure 6), yet this effect did not reach significance. Based on this, we concluded that neurite length is increased upon RPH3A depletion.

(10) For lay readers, please explain the total pool and how you measured it. However, see the next comment. 

We agree. We have now defined this better in the revised manuscript, (p 19, line 524-527 and p 20, line 535-539).

(11) It is a bit hard to understand if the total number of DCV was increased in the KO and if the pool size was increased and in which figure it is quantified. Some sentences like: "A trend towards a larger intracellular DCV pool in KO compared to WT neurons was observed" do not fit with "No difference in DCV pool size was observed between WT and KO neurons (Figure S2D)" or with "During stronger stimulation (16 bursts of 50 APs at 50 Hz), the total fusion and released fraction of DCVs were increased in KO neurons compared to WT". They are not directly supported, or not related to specific figures. Please indicate if the total DCVs pool, as measured by NH4, was increased and based on that, the fraction of the releasable DCVs following the long stimulation. From Figure 2H, the conclusion is an increase in fusion events. In general, NH4 is not quantified clearly- is it quantified in Figure S2C? And if it is a trend, how can it become significant in Figure 3? 

We agree there has been some inconsistency in the way we describe the data on the total number of DCVs. We have addressed this in the revised text to ensure better clarity. The total DCV pool measured by NPY-pHluorin was not significantly increased in KO neurons, we see a trend towards a bigger DCV pool in the 2x8 50 Hz stimulation paradigm (now Figure S2C), therefore the released fraction of vesicles is not increased in Figure 1G (now Figure 3G). The number of DCV in Figure 3 (now Figure 5) is based on endogenous ChgB staining and not overexpression like the DCV pool measured by NPY-pHluorin. In Figure 3 (now Figure 5) we show that RPH3A KO neurons have slightly more ChgB+ puncta compared to WT.

(12) In Figure 3, the quantification is not clear, discrete puncta are not visible but rather a smear of chromogranin staining. How was it quantified? An independent method to count DCV number, size, and distribution like EM is necessary to support and add further understanding. 

We acknowledge that discrete ChgB puncta are not completely visible in Figure 3 (now Figure 5). Besides the inherent limitation in resolution with confocal imaging, we believe that this is due to ChgB accumulation in the KO neurons, as shown in now Figure 5D. Nonetheless, to address this concern of the reviewer, we have selected other images that represent our dataset (now Figure 5A). Furthermore, the number of ChgB+ DCVs was calculated using SynD software (Schmitz et al., 2011; van de Bospoort et al., 2012) (see previous reply). EM would offer valuable independent confirmation on the total DCV number, size and distribution. However, with the current method we already know that vesicle numbers are at least similar. Does that justify the (major) investment in a quantitative EM study? Moreover, this issue does not affect the central message of the current study.

(13) Can the author discuss if the source of DCVs that are released at the synapse is similar or different from the source of DCVs fused while neurites elongate? 

With our current experimental design, we are unable to draw conclusions regarding this aspect. We are not sure how experiments to identify this source (probably the Golgi?) would be crucial to sustain the central message of our study.

(14) An interesting and related question: what are the expression levels of RPH3A during development and neuronal growth during the nervous system development? 

While we have not specifically examined the expression levels of RPH3A over development, public databases show that RPH3A expression increases over time in mice, consistent with other synaptic proteins (Blake et al., 2021; Baldarelli et al., 2021; Krupke et al., 2017). We have now added this to the revised manuscript (p 2, line 55-56).

(15) The conclusion from Figure 4 about the contribution of SNAP25 interaction to RPH3A inhibitory effect is not convincing. The data are scattered and in many neurons, high levels of fusion events were detected. Further or independent experiments are needed to support this conclusion. For example, is the interaction with SNAP25 important for its inhibitory activity in other DCV-releasing systems like adrenal medulla chromaffin cells? 

We agree that further studies in other DCV-releasing systems like chromaffin cells would provide valuable insight into the role of SNAP25 interaction in RPH3A’s inhibitory effect on exocytosis. However, we believe that starting new series of experiments in another model system is outside of the scope of our current study.

(16) Furthermore, the number of DCVs in the KO is similar in this experiment, raising some more questions about the quantification of the number of vesicles, that differ, in different sections of the manuscript (points # 10,11). 

The total DCV pool in the fusion experiments is measured by overexpression NPY-pHluorin, this cannot be directly compared to the number of endogenous ChgB+ DCV in Figure 3 (now Figure 5), see also item (11)

(17) The statement - "RPH3A is the only negative regulator of DCV" is not completely accurate as other DCV inhibitors like tomosyn were described before. 

We agree. By this statement, we intend to convey that RPH3A is the only negative regulator of DCVs without substantial impact on synaptic vesicle exocytosis, unlike Tomosyns. We have clarified this in the revised text, (p 15, line 366-367).

(18) The support for the effect of KO on the "clustering of DCVs" is not convincing. 

The intensity of endogenous ChgB puncta was decreased in RPH3A KO neurons (now Figure 5E). However, the peak intensity induced by single NPY-pHluorin labeled DCV fusion events (quanta) was unchanged (now Figure S2I). This indicates that the decrease in ChgB puncta intensity must be due to a reduced number of DCVs (quanta) in this specific location. We have interpreted that as ‘clustering’, or maybe ‘accumulation’. However, we only put forward this possibility. We are now more careful in our speculations within the text, (p 11 line 271-277).

(19) Final sentence: "where RPH3A binds available SNAP25, consequently restricting the assembly of SNARE complexes" should be either demonstrated or rephrased as no effect of trans or general SNARE complex formation is shown. 

We agree. We have made the necessary adjustments in the text, (p 15, line 387-389).   

(20) A scheme summarizing RPH3A's interaction with synaptic proteins and its effects on DCVs release, maybe even versus its effects on SVs release, should be considered as a figure or graphic abstract. 

We have included a working model in Figure 7.  

(21) Figure 4 logically should come after Figure 2 to summarize the fusion-related chapter before moving to neurite elongation. 

We have placed Figure 4 after Figure 2 (now Figure 3).

Reviewer #3 (Recommendations For The Authors): 

One important finding of this study is that RPH3A downregulates neuron size, possibly by inhibiting DCV release. Additionally, the authors demonstrated that the number of DCVs is directly proportional to the number of DCVs per µm2, and that RPH3A KO reduces DCV clustering. This conclusion was drawn by comparing ChgB with NPY-pHluorin loading of the DCVs. However, this comparison is not valid as ChgB is expressed at an endogenous level and NPY-pHluorin is over-expressed. In the KO situation where DCV exocytosis is enhanced, the available endogenous ChgB may be depleted faster than the overexpressed NPY-pHluorin. Hoogstraaten et al. should either perform a study in which ChgB is overexpressed to test whether the difference in DCV remains or at least provides an alternative interpretation of their data. 

We thank the reviewer for this comment. The reviewer challenges one or two conclusions in our original manuscript (It is not entirely clear to what exactly “This conclusion” refers): (a) “the number of DCVs is directly proportional to the number of DCVs per µm2”, and (b) “that RPH3A KO reduces DCV clustering”. The reviewer probably means that the number of DCVs per neuron is directly proportional to size of the neuron (a) and states this (these) conclusion(s) are “not valid as ChgB is expressed at an endogenous level and NPY-pHluorin is over-expressed” because “endogenous ChgB may be depleted faster than the overexpressed NPY-pHluorin”. We have three arguments to conclude that faster depletion of ChgB cannot affect these two conclusions: (1) DCVs bud off from the Golgi with newly synthesized (fresh) ChgB. Whether or not a larger fraction of DCVs is released does not influence this initial ChgB loading into DCVs (together with over-expressed NPY-pHluorin); (2) in hippocampal neurons merely 1-6% of the total DCV pool undergoes exocytosis (the current study and also extensively demonstrated in Persoon et al., 2018). RPH3A KO neurons release few percent more of the total DCV pool. Hence, “depletion of ChgB” is only marginally different between experimental groups; and (c) the proposed experiment overexpressing ChgB will not help scrutinize our current conclusions as ChgB overexpression is known to affect DCV biogenesis and the total DCV pool, most likely much more than a few percent more release by RPH3A deficiency.

Hoogstraaten et al. conducted a thorough analysis of the impact of RPH3A KO and its rescue using various mutants on dendrite and axon length (see Supplementary Figure 3). However, they did not test the effect of the ΔSNAP25 mutant. The authors demonstrated that this mutant is the least efficient in rescuing DCV exocytosis (Figure 4E). Hence the neurons expressing this mutant should have a similar size to the KO neurons. This finding would strongly support the argument that DCV exocytosis regulates neuron size. Otherwise, it would suggest that RPH3A may have a function in regulating exocytosis at the growth cones that is independent of SNAP25. Since the authors most probably have the data that allows them to measure the neuron size (acquired for Supplementary Figure 2), I suggest that they perform the required analysis. 

We agree this is important and performed new experiments to determine the dendrite length of RPH3A WT, KO and KO neurons expressing the ΔSNAP25 mutant. We observed that the dendrite length of RPH3A KO neurons expressing ΔSNAP25 mutant is indeed similar to KO neurons (new Figure S3C). Although not significant we observe a clear trend towards bigger neurons compared to WT.  This strengthens our conclusion that increased DCV exocytosis contributes to the observed increased neuronal size.

The authors displayed the result of DCV exocytosis in two ways. One is by showing the number of exocytosis events the other is to display the proportion of DCVs that were secreted. They do the latter by dividing the secreted DCV by the total number of DCVs. These are visualized at the end of the experiment through NH4+ application. While this method works well for synaptic secretion as the marker of SV is localized to the SV membrane and remains at the synapse upon SV exocytosis, it cannot be applied in the same manner when it is the DCV content that is labeled as it is released upon secretion. Hence, the total pool of vesicles should be the number of DCV counted upon NH4+ application in addition to those that are secreted. This way of analyzing the total pool of DCV might also explain the difference in this pool size between KO neurons stimulated two times with 8 stimuli instead of one time with 16 stimuli (Sup Fig 2 C and D). This is an important point as it affects the conclusions drawn from Figure 2. 

We thank the reviewed for this comment. We agree, and we have made the necessary adjustments throughout the manuscript. 

The kymogram of DCV exocytic events displayed in Figure 2D shows a majority of persistent (>20s long) events. This is strange as NPY-pHluori corresponds to the released cargo. Previous work using the same labeling and stimulation technique showed that content release occurs in less than 10s (Baginska et al. 2023). The authors should comment on that difference. 

In Baginska et al. (2023), the authors distinguished between persistent and transient events. The transient events are shorter than 10s for the 2x8 and 16x stimulation paradigms, whereas persistent events can last for more than 10s. In our study we did not make this distinction. However, in response to this reviewer, we have now quantified the fusion duration per cell. These new data show that the mean duration is similar between genotypes for both stimulation paradigms. We have added these new data (new Figure S2D-F).

In Figures 1D and E, some puncta in the kymogram appeared to persist after bleaching. This raises questions about the effectiveness of the bleaching procedure for the FRAP experiment. 

The reviewer is correct that NPY-pHluorin in Figure 1E (now Figure 2C) is not fully bleached. NPY-pHluorin was more resistant to bleaching than NPY-mCherry. However, we merely bleached the neurites to facilitate our analysis by reducing fluorescence of the stationary puncta without causing phototoxicity. Some remaining fluorescence after bleaching does not affect our conclusions in any way.

In the discussion, the paragraph titled "RPH3A does not travel with DCVs in hippocampal neurons" is quite confusing and would benefit from a streamlined explanation. 

We thank the reviewed for this comment. We made the necessary adjustments to make this paragraph clearer, (p 14, line 339-351).

First paragraph of page 8 "TeNT expression in KO neurons restored neurite length to WT levels. When compared to KO neurons without TeNT, neurite length was not significantly decreased but displayed a trend towards WT levels (Figure 3G, H)." These two sentences are confusing as they seem contradictory. 

We agree that this conclusion has been too strong. However, we do not see a contradiction. The significant effect between KO and control neurons on both axon and dendrite length is lost upon TeNT expression (which forms the basis for our conclusions cited by the reviewer, now Figure 6B, C). While the difference between KO neurons +/- TeNT did not reach statistical significance. The (strong) trend is clearly in the same direction. We have refined our original conclusion in the revised manuscript, (p 12, line 304-306).

The data availability statement is missing. 

We have added the data availability statement, (p 21, line 571-572).

Author response:

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

Public Reviews:

Reviewer #1 (Public Review):

Summary:

O'Leary and colleagues present data identifying several procedures that alter discrimination between novel and familiar objects, including time, environmental enrichment, Rac-1, context reexposure, and brief reminders of the familiar object. This is complimented with an engram approach to quantify cells that are active during learning to examine how their activation is impacted following each of the above procedures at test. With this behavioral data, authors apply a modeling approach to understand the factors that contribute to good and poor object memory recall.

We thank the Reviewer for summarizing the scope and depth of our manuscript, and indeed for recognizing our efforts. We engage below with the Reviewer’s specific criticisms.

Strengths:

Authors systematically test several factors that contribute to poor discrimination between novel and familiar objects. These results are extremely interesting and outline essential boundaries of incidental, nonaversive memory.<br /> These results are further supported by engram-focused approaches to examine engram cells that are reactivated in states with poor and good object recognition recall.

We thank the Reviewer for these positive comments.

Weaknesses:

For the environmental enrichment, authors seem to suggest objects in the homecage are similar to (or reminiscent of) the familiar object. Thus, the effect of improved memory may not be related to enrichment per se as much as it may be related to the preservation of an object's memory through multiple retrievals, not the enriching experiences of the environment itself. This would be consistent with the brief retrieval figure. Authors should include a more thorough discussion of this.

This is one of the main issues highlighted by the Editor and the Reviewers. We agree that these results dove-tail with the reminder experiments. We have included additional discussion, see line 510-546.

Authors should justify the marginally increased number of engram cells in the non-enrichment group that did not show object discrimination at test, especially relative to other figures. More specific cell counting criteria may be helpful for this. For example, was the DG region counted for engram and cfos cells or only a subsection?

There was a marginal, but non-significant increase in the number of labelled cells within the standard housed mice in Figure 3f. The cell counting criteria was the same across experimental groups and conditions, where the entire dorsal and ventral blade of the dorsal DG was counted for each animal. This non-statistically significant variance may be due to surgical and viral spread difference between mice. We have clarified this in the manuscript, see line 229-232.

It is unclear why the authors chose a reactivation time point of 1hr prior to testing. While this may be outside of the effective time window for pharmacological interference with reconsolidation for most compounds, it is not necessarily outside of the structural and functional neuronal changes accompanied by reconsolidation-related manipulations.

A control experiment was performed to demonstrated that a brief reminder exposure of 5 mins on its own was insufficient to induce new learning that formed a lasting memory (Supplementary Figure S4a). Mice given only a brief acquisition period of 5 mins, exhibited no preference for the novel object when tested 1 hour after training, suggesting the absence of a lasting object memory (Supplementary Figure S4b & c). We therefore used the 1-hour time point for the brief reminder experiment in Figure 4a. We have clarified this within the manuscript and supplementary data see line 258-264.

Figure 5: Levels of exploration at test are inconsistent between manipulations. This is problematic, as context-only reexposures seem to increase exploration for objects overall in a manner that I'm unsure resembles 'forgetting'. Instead, cross-group comparisons would likely reveal increased exploration time for familiar and novel objects. While I understand 'forgetting' may be accompanied by greater exploration towards objects, this is inconsistent across and within the same figure. Further, this effect is within the period of time that rodents should show intact recognition. Instead, context-only exposures may form a competing (empty context) memory for the familiar object in that particular context.

The Reviewer raises an important question, and we agree with the Reviewer that there should be caution and qualification around interpreting these results as “forgetting”. Indeed, for the context-only rexposures, cross-group comparisons show increased exploration time for familiar and novel objects. As the mice exhibit relatively high exploration of both the novel and familiar objects. An alternative explanation would be that the mice have not truly forgotten the familiar object, but rather as the mouse has not seen the familiar object in the last 6 context only sessions, its reappearance makes it somewhat novel again. Therefore, this change in the object’s reappearance triggers the animal’s curiosity, and in turn drives exploration by the animal. In addition, the context-only exposures may form a competing memory for the familiar object in that particular context. We have highlighted this in the results and also included greater discussion. See lines 306-315.

I am concerned at the interpretation that a memory is 'forgotten' across figures, especially considering the brief reminder experiments. Typically, if a reminder session can trigger the original memory or there is rapid reacquisition, then this implies there is some savings for the original content of the memory. For instance, multiple context retrievals in the absence of an object reminder may be more consistent with procedures that create a distinct memory and subsequently recruit a distinct engram.

These findings raise an important question regarding the interpretation of ‘forgetting’. If a reminder trial or experience can trigger the original memory, or there is rapid reacquisition, then this would suggest there is a degree of savings for the original memory content (85, 86). Previous work has emphasized retrieval deficits as a key characteristic of memory impairment, supporting the idea that memory recall or accessibility may be driven by learning feedback from the environment (7, 8, 14–18). Within our behavioral paradigm, a lack of memory expression would still constitute forgetting due to the loss of learned behavioral response in the presence of natural retrieval cues. The changes in memory expression may therefore underlie the adaptive nature of forgetting. This is consistent with the idea that the engram is intact and available, but not accessible. Here we studied natural forgetting, and our data showing memory retrieval following optogenetic reactivation demonstrates that the original engram persists at a cellular level, otherwise activation of those cells would no longer trigger memory recall. We also agree with the reviewer that multiple context retrievals may indeed lead to the formation of a second distinct engram that competes with the original. Recent work suggests that retroactive interference emerges from the interplay of multiple engrams competing for accessibility (18). We have added clarification and included extra discission of this interpretation. See lines 589-598.

Authors state that spine density decreases over time. While that may be generally true, there is no evidence that mature mushroom spines are altered or that this is consistent across figures. Additionally, it's unclear if spine volume is consistently reduced in reactivated and non-reactivated engram cells across groups. This would provide evidence that there is a functionally distinct aspect of engram cells that is altered consistently in procedures resulting in poor recognition memory (e.g. increased spine density relative to spine density of non-reactivated engram cells and non-engram cells)

We thank the Reviewer for their helpful comments on explaining our engram dendritic spine data. We agree with the Reviewer that an analysis of the changes in spine type, as well as the difference between engram and non-engram spines as well and reactivation and non-reactivated engram spines would be interesting and may help to further illuminate the morphological changes of forgetting and memory retrieval. Indeed, future analysis could determine if spine density is reduced in reactivated and non-reactivated engram cells or indeed across engram non-engram cells within different learning conditions. This avenue of investigation could determine if there is a functionally distinct aspect of engram cells that are altered following forgetting (67). However, such analysis is beyond the scope of this study. We have highlighted this limitation and included its discussion. See lines 493-499.

Authors should discuss how the enrichment-neurogenesis results here are compatible with other neurogenesis work that supports forgetting.

We validated the effectiveness of the enrichment paradigm to enhance neural plasticity by measuring adult hippocampal neurogenesis. The hippocampus has been identified as one of the only regions where postnatal neurogenesis continues throughout life (75). Levels of adult hippocampal neurogenesis do not remain constant throughout life and can be altered by experience (41–43, 57).  In addition, adult born neurons have been shown to contribute to the process of forgetting (74, 78, 79). Although the contribution of adult born neurons to cognition and the memory engram is not fully understood (80, 81). Mishra et al, showed that immature neurons were actively recruited into the engram following a hippocampal-dependent task (67). Moreover, increasing the level of neurogenesis rescued memory deficits by restoring engram activity (67). Augmenting neurogenesis further rescued the deficits in spine density in both immature and mature engram neurons in a mouse model of Alzheimer’s disease (67). Whether neurogenesis alters spine density on differentially for reactivated or non-reactivation engrams cells remains to be investigated (67, 68). This avenue of research would help to illuminate the morphological changes following forgetting and provide evidence if there is a functionally distinct aspect of engram cells that is altered in forgetting (67, 68). Our engram labelling strategy which utilized c-fos-tTA transgenic mice combined with an AAV9-TRE-ChR2-eYFP virus does not necessarily label sufficient immature neurons. Future work could utilize a different engram preparation, such as a genetic labelling strategy (TRAP2) or using a different immediate early gene promoter such as Arc to investigate the contribution of new-born neurons to the engram ensemble. We have added additional discussion of how our work fits with previous literature investigating neurogenesis and forgetting. See lines 547-565.

Reviewer #2 (Public Review):

Summary:

The manuscript examines an important question about how an inaccessible, natural forgotten memory can be retrieved through engram ensemble reactivation. It uses a variety of strategies including optogenetics, behavioral and pharmacological interventions to modulate engram accessibility. The data characterize the time course of natural forgetting using an object recognition task, in which animals can retrieve 1 day and 1 week after learning, but not 2 weeks later. Forgetting is correlated with lower levels of cell reactivation (c-fos expression during learning compared to retrieval) and reduction in spine density and volume in the engram cells. Artificial activation of the original engram was sufficient to induce recall of the forgotten object memory while artificial inhibition of the engram cells precluded memory retrieval. Mice housed in an enriched environment had a slower rate of forgetting, and a brief reminder before the retrieval session promoted retrieval of a forgotten memory. Repeated reintroduction to the training context in the absence of objects accelerated forgetting. Additionally, activation of Rac1-mediated plasticity mechanisms enhanced forgetting, while its inhibition prolonged memory retrieval. The authors also reproduce the behavioral findings using a computational model inspired by Rescorla-Wagner model. In essence, the model proposes that forgetting is a form of adaptive learning that can be updated based on prediction error rules in which engram relevancy is altered in response to environmental feedback.

We thank the Reviewer for summarizing the scope and depth of our manuscript, and for recognizing our efforts. We engage below the Reviewer’s specific criticisms of our interpretations.

Strengths:

(1) The data presented in the current paper are consistent with the authors claim that seemingly forgotten engrams sometimes remain accessible. This suggests that retrieval deficits can lead to memory impairments rather than a loss of the original engram (at least in some cases).

We thank the Reviewer for their positive summary.

(2) The experimental procedures and statistics are appropriate, and the behavioral effects appear to be very robust. Several key effects are replicated multiple times in the manuscript.

We thank the Reviewer for their positive comments.

Weaknesses:

(1) My major issue with the paper is the forgetting model proposed in Figure 7. Prior work has shown that neutral stimuli become associated in a manner similar to conditioned and unconditioned stimuli. As a result, the Rescorla-Wagner model can be used to describe this learning (Todd & Homes, 2022). In the current experiments, the neutral context will become associated with the unpredicted objects during training (due to a positive prediction error). Consequently, the context will activate a memory for the objects during the test, which should facilitate performance. Conversely, any manipulation that degrades the association between the context and object should disrupt performance. An example of this can be found in Figure 5A. Exposing the mice to the context in the absence of the objects should violate their expectations and create a negative prediction error. According to the Rescorla-Wagner model, this error will create an inhibitory association between the context and the objects, which should make it harder for the former to activate a memory of the latter (Rescorla & Wagner, 1972). As a result, performance should be impaired, and this is what the authors find. However, if the cells encoding the context and objects were inhibited during the context-alone sessions (Figure 5D) then no prediction error should occur, and inhibitory associations would not be formed. As a result, performance should be intact, which is what the authors observe.

What about forgetting of the objects that occurs over time? Bouton and others have demonstrated that retrieval failure is often due to contextual changes that occur with the passage of time (Bouton, 1993; Rosas & Bouton, 1997, Bouton, Nelson & Rosas, 1999). That is, both internal (e.g. state of the animal) and external (e.g. testing room, chambers, experimenter) contextual cues change over time. This shift makes it difficult for the context to activate memories with which it was once associated (in the current paper, objects). To overcome this deficit, one can simply re-expose animals to the original context, which facilitates memory retrieval (Bouton, 1993). In Figure 2D, the authors do something similar. They activate the engram cells encoding the original context and objects, which enhances retrieval.

Therefore, the forgetting effects presented in the current paper can be explained by changes in the context and the associations it has formed with the objects (excitatory or inhibitory). The results are perfectly predicted by the Rescorla-Wagner model and the context-change findings of Bouton and others. As a result, the authors do not need to propose the existence of a new "forgetting" variable that is driven by negative prediction errors. This does not add anything novel to the paper as it is not necessary to explain the data (Figures 7 and 8).

We thank the reviewer for clearly explaining their concern about our model. We are very sorry that we did not sufficiently explain that our model is, in fact, based on the classic Rescorla-Wagner model. The key equation of the model that updates “engram strength”  is equivalent to the canonical Rescorla-Wagner model that is commonly used in research on reinforcement learning and decision-making (105). One potential minor difference is that we crucially assume different learning rates for positive and negative prediction errors. However, this variant of the Rescorla-Wagner model is common in the computational literature and is generally not regarded as a qualitatively different kind of model. In fact, it allows us to capture that establishing an object-context association (after a positive prediction error) is faster than the forgetting process (through negative errors).

The other equations that are explained in detail in the Methods are necessary to simulate exploration behavior and render the model suitable for model fitting. Concerning exploration behavior, we use the softmax function, which is commonly used in combination with the Rescorla-Wager model, in order to translate the learned quantity (in our case, engram strength) into behavior (here exploration). The other equations are necessary to fit the model to the data (learning rate α and behavioral variability in exploration behavior).

Therefore, we fully agree with the reviewer that the Rescorla-Wagner can explain our empirical results, in particular by assuming that the different manipulations affect the strength of object-context associations, which, in turn, governs forgetting as behaviorally observed. 

In our previous version of the manuscript, we only referred to the Rescorla-Wagner model directly in the Methods. But to make this important point clearer, we now refer to the origin of the model multiple times in the Results section as well. See lines 81, 386-393.

We also agree with the reviewer that the learning/forgetting process can be described in terms of changes in object-context associations (e.g., inhibitory associations after a negative prediction error). Therefore, we now explicitly refer to the relationship between updated object-context associations and forgetting and highlight that we believe that stronger associations signal higher engram “relevancy”. See lines 386-393.

We have extended Figure 7 (new panels a and b), where we illustrate the idea that (a) object-context associations govern forgetting and (b) show the key Rescorla-Wagner equation, including a simple explanation of the main terms (engram strength, prediction error, and learning rate). Finally, we have also extended our discussion of the model, where we now directly state that the Rescorla-Wagner model captures the key results of our experiments. See lines 573-580.

In order to further support a link between our empirical data and computational modeling, we also added extra experiments that showed the modulation of engram cells within the dentate gyrus can regulate these object-context associations. See Supplementary Figure 12a-f and lines 401-404.

To summarize our reply, we agree with the reviewer’s comment and hope that we have clarified the direct relationship to the Rescorla-Wagner model.

(2) I also have an issue with the conclusions drawn from the enriched environment experiment (Figure 3). The authors hypothesize that this manipulation alleviates forgetting because "Experiencing extra toys and objects during environmental enrichment that are reminiscent of the previously learned familiar object might help maintain or nudge mice to infer a higher engram relevancy that is more robust against forgetting.". This statement is completely speculative. A much simpler explanation (based on the existing literature) is that enrichment enhances synaptic plasticity, spine growth, etc., which in turn reduces forgetting. If the authors want to make their claim, then they need to test it experimentally. For example, the enriched environment could be filled with objects that are similar or dissimilar to those used in the memory experiments. If their hypothesis is correct, only the similar condition should prevent forgetting.

We thank the Reviewer for this alternative perspective on our findings. First of all, we agree that this statement is speculative. The effects of enrichment on neural plasticity are well established and it likely contributes to the enhanced memory recall. It is important to emphasize that this process of updating is not necessarily separate from enrichment-induced plasticity at an implementational level, but part of the learning experience within an environment containing multiple objects. The enrichment or, more generally, experience, may therefore enhance memory through the modification of activity of specific engram ensembles. The idea of enrichment facilitating memory updating is consistent with the results obtained by the reminder experiments and further supported by our analysis with the Rescorla-Wagner computational model, where experience updates the accessibility of existing memories, possibly through reactivation of the original engram ensemble.

We would like to further clarify that our explanation concerns the algorithmic level, in contrast to the neural level. Based on the computational analyses using the Rescorla-Wagner model and in line with the reviewer’s previous comment on the model, we believe that forgetting is governed by the strength of object-context associations (or engram relevancy). Our interpretation is that stronger associations signal that the memory or engram representation is important ("relevant") and should not be forgotten. Accordingly, due to a vast majority of experiences with extra cage objects in the enriched environment, mice might generally learn that such objects are common in their environment and potentially relevant in the future (i.e., the object-context association is strong, preventing forgetting). Our speculation of these results is to help unify our empirical data with the computational model.

We believe that the Reviewer's alternative explanation in terms of synaptic plasticity, spine growth is not mutually exclusive with the modelling work. It is possible that the computational mechanisms that we explore based on the Rescorla-Wagner model are neuronally related to the biological mechanisms that the reviewer suggests at the implementational level. Therefore, ultimately, the two perspectives might even complement each other. We have included additional discussion to clarify this point. See lines 510-546.

(3) It is well-known that updating can both weaken or strengthen memory. The authors suggest that memory is updated when animals are exposed to the context in the absence of the objects. If the engram is artificially inhibited (opto) during context-only re-exposures, memory cannot be updated. To further support this updating idea, it would be good to run experiments that investigate whether multiple short re-exposures to the training context (in the presence of the objects or during optogenetic activation of the engram) could prevent forgetting. It would also be good to know the levels of neuronal reactivation during multiple re-exposures to the context in the absence versus context in the presence of the objects.

We thank the Reviewer for their comments. We agree that additional experiments would be helpful to further support the idea of updating. We have performed additional experiments to test the idea that multiple short re-exposures to the training context, in the presence of objects prevents forgetting. In this paradigm, mice were repeatedly exposed to the original object pair (Supplementary Figure S5a). The results indicate that repeated reminder trials facilitate object memory recall (Supplementary Figure 5b&c). These data indicated that subsequent object reminders over time facilitates the transition of a forgotten memory to an accessible memory. See Supplementary Figure S5 and Lines 279-287.

(4) There are a number of studies that show boundary conditions for memory destabilization/reconsolidation. Is there any evidence that similar boundary conditions exist to make an inaccessible engram accessible?

The Reviewer asks an interesting question about boundary conditions and engram accessibility. Boundary conditions could indeed affect the degree of destabilization and reconsolidation, the salience or strength of the memory, as well as the timing of retrieval cues. Future models could focus on understanding the specific boundary conditions in which a memory becomes retrievable and the degree to which it is sufficiently destabilized and liable for updating and forgetting. We have included additional discussion on the potential role of boundary conditions for engram accessibility. See lines 661-666.

(5) More details about how the quantification of immunohistochemistry (c-fos, BrdU, DAPI) was performed should be provided (which software and parameters were used to consider a fos positive neurons, for example).

We have added additional information for the parameters of quantification of immunohistochemistry. See lines 796-809.

(6) Duration of the enrichment environment was not detailed.

We have highlighted the details for the environmental enrichment duration. See lines 756.

Reviewer #3 (Public Review):

Summary:

The manuscript by Ryan and colleagues uses a well-established object recognition task to examine memory retrieval and forgetting. They show that memory retrieval requires activation of the acquisition engram in the dentate gyrus and failure to do so leads to forgetting. Using a variety of clever behavioural methods, the authors show that memories can be maintained and retrieval slowed when animals are reared in environmental enrichment and that normally retrieved memories can be forgotten if exposed to the environment in which the expected objects are no longer presented. Using a series of neural methods, the authors also show that activation or inhibition of the acquisition engram is key to memory expression and that forgetting is due to Rac1.

We thank the Reviewer for summarizing the scope and depth of our manuscript, and indeed for recognizing our efforts. We engage below the Reviewer’s specific criticisms of our interpretations.

Strengths:

This is an exemplary examination of different conditions that affect successful retrieval vs forgetting of object memory. Furthermore, the computational modelling that captures in a formal way how certain parameters may influence memory provides an important and testable approach to understanding forgetting.

The use of the Rescorla-Wagner model in the context of object recognition and the idea of relevance being captured in negative prediction error are novel (but see below).

The use of gain and loss of function approaches are a considerable strength and the dissociable effects on behaviour eliminate the possibility of extraneous variables such as light artifacts as potential explanations for the effects.

We thank the Reviewer for their positive comments.

Weaknesses:

Knowing what process (object retrieval vs familiarity) governed the behavioural effect in the present investigation would have been of even greater significance.

The Reviewer touches on an important issue of the object recognition task. Understanding how experience alters object familiarity versus object retrieval and its impact on learning would help to develop better models of object memory and forgetting. We have added additional discussion. See lines 666-669.

The impact of the paper is somewhat limited by the use of only one sex.

We agree that using only male mice limits the impact of the paper. Indeed, the field of behavioural neuroscience is moving to include sex as a variable. Future experiments should include both male and female mice.

While relevance is an interesting concept that has been operationalized in the paper, it is unclear how distinct it is from extinction. Specifically, in the case where the animals are exposed to the context in the absence of the object, the paper currently expresses this as a process of relevance - the objects are no longer relevant in that context. Another way to think about this is in terms of extinction - the association between the context and the objects is reduced results in a disrupted ability of the context to activate the object engram.

We thank the reviewer for their insightful comment on the connection between engram relevance and memory extinction. Lacagnina et al., demonstrated that extinction training suppressed the reactivation of a fear engram, while activating a second putative extinction ensemble (59). In another study, these extinction engram cells and reward cells were shown to be functionally interchangeable (92). Moreover, in a study conducted by Lay et al., the balance between extinction and acquisition was disrupted by inhibiting the extinction recruited neurons in the BLA and CN (93). These results suggested that decision making after extinction can be governed by a balance between acquisition and extinction specific ensembles (93). Together, this may suggest that in the present study, when mice are repeatedly exposed to the training context, the association between the context and the objects is reduced, resulting in a disrupted ability of the context to activate the object engram. Therefore, memory relevance and extinction may operate similarly to effect engram accessibility, and in essence ‘forgetting’ of object memories may be due to neurobiological mechanisms similar to that of extinction learning (4). We have included additional discussion on the link between our results and the extinction literature. See lines 642-654.

Recommendations for the authors:

Reviewer #1 (Recommendations For The Authors):

Additional measures that may help interpretation of and clarify data are:

A minute-by-minute analysis for training and testing may provide insight about the learning rate and testing temporal dynamics that may shed light substantially on differential levels of exploration. This should be applied across figures and would support conclusions from models in Figures 7-8 as well.

Locomotion/distance travelled measures.

We have included additional analysis for a minute-by-minute analysis of training and testing of the object memory test at 24 hr, 2 weeks as well as under the standard housing and enrichment conditions. The results further support the initial finding that novel object recognition is increased in mice that recall the object at 24 hr. Similarly, mice housed in the enriched housing initially explore the novel object more compared to the familiar object. See Supplementary Figure 1 and 2, as well as lines 103-105 and 211-213.

The appropriate control for the context exposure figure would be to expose to a novel context in one group and the acquisition/testing context for the other.

We agree with the reviewer that an additional control of a novel context would further support our findings. Indeed, this line of investigate may dove-tail with the other reviewer comments on the role of competing engrams and interference. Future work could investigate the degree to which novel contexts and multiple memories can affect the rate of forgetting through engram updating. We have included additional discussion. See lines 643 and 655. However, in our experience it is necessary to pre-expose mice to different contexts before object exposure (e.g. Autore et al ’23), in order to form discriminate object/context associations. Establishing such a paradigm for this study would be at odds with the established paradigms and schedules in this current study. Moreover, the possibility that the effect of object displacement on forgetting requires the familiar context, or not, does not impact the main conclusions of this study. However, we agree that it is a point for expansion in the future.

A control virus+light group vs simply a no-light condition.

For optogenetic experiments. Control mice underwent the same surgery procedure with virus and optic fibre implantation. However, no light was delivered to excite or inhibit the respective opsin. Previous papers have shown laser light delivered to tissue expressing an AAV-TRE-EYFP lacking an light-opsin does cause cellular excitation. We have clarified this in the text. See lines 726-729.

Reviewer #2 (Recommendations For The Authors):

Minor details:

(1) In the pharmacological modification of Rac 1, please specify what percentage of DMSO was used to dissolve Rac1 inhibitor and correct the typo 'DSMO'

Rac1 inhibitor (Ehop016) was reconstituted and prepared in PBS with 1% Tween-80, 1% DMSO and 30% PEG. We have clarified this in the text and corrected the typo, thank you. See lines 767.

(2) In the penultimate paragraph there is a typo 'predication error'

This is now corrected. Thankyou.

Reviewer #3 (Recommendations For The Authors):

I was unable to find information on what the No Light group consisted of. Was there a control virus infused, were the animals implanted with optical fibres (in the presence or absence of a virus), were they surgical controls, etc?

For optogenetic experiments. No Light Control mice underwent the same surgery procedure with virus and optic fibre implantation. However, no light was delivered to excite or inhibit the respective opsin. We have clarified this in the text. See lines 726-729.

The discussion lacked specificity in places. For example, the idea of eluding to 'other variables' is somewhat vague (p. 21, middle paragraph). Some examples of what other variables could be relevant would be helpful in capturing what direction or relevance the model may have going forward.

We have expanded the discussion of other variables which might impact engram relevance and how the model might be developed moving forward. These may include, boundary conditions of destabilization and reconsolidation, the salience or strength of the memory as well as the timing of retrieval cues or updating experience. Future models could focus on understanding the specific boundary conditions in which a memory becomes retrievable and the degree to which it is sufficiently destabilized and liable for updating and forgetting. The role of perceptual learning on memory retrieval and forgetting may also be an avenue of future investigation. Understanding how experience alters object familiarity versus object retrieval and its impact on learning would also help to develop better models of object memory and forgetting. In the current study, only male mice were utilized. Therefore, future work could also include sex as a variable to fully elucidate the impact of experience on the processes of forgetting. See lines 642-669.

In the same paragraph (p. 21, middle paragraph) there is mention of multiple engrams and how they can compete. The authors reference Autore et al (2023), but I thought Lacagina did this really beautifully also in an experimental setting. This idea is also expressed in Lay et al. (2022). So additional references would further strengthen the authors argument here.

We thank the reviewer for the additional references for discussing engram competition. We have included these papers in the discission. See lines 642-654.

Relatedly, environmental enrichment was considered in terms of object relevance. I wonder if the authors may want to consider thinking about their results in terms of effects on perceptual learning.

Indeed, perceptual learning maybe playing a role in environmental enrichment. We have included additional discussion. See lines 666-669.

Author response:

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

Public Reviews:

Reviewer #1 (Public Review):

The authors present a model for multisensory correlation detection that is based on the neurobiologically plausible Hassenstein Reichardt detector. It modifies their previously reported model (Parise & Ernst, 2016) in two ways: a bandpass (rather than lowpass) filter is initially applied and the filtered signals are then squared. The study shows that this model can account for synchrony judgement, temporal order judgement, etc in two new data sets (acquired in this study) and a range of previous data sets.

Strengths:

(1) The model goes beyond descriptive models such as cumulative Gaussians for TOJ and differences in cumulative Gaussians for SJ tasks by providing a mechanism that builds on the neurobiologically plausible Hassenstein-Reichardt detector.

(2) This modified model can account for results from two new experiments that focus on the detection of correlated transients and frequency doubling. The model also accounts for several behavioural results from experiments including stochastic sequences of A/V events and sine wave modulations.

Additional thoughts:

(1) The model introduces two changes: bandpass filtering and squaring of the inputs. The authors emphasize that these changes allow the model to focus selectively on transient rather than sustained channels. But shouldn't the two changes be introduced separately? Transients may also be detected for signed signals.

We updated the original model because our new psychophysical evidence demonstrates the fundamental role of unsigned transient for multisensory perception. While the original model received input from sustained unimodal channels (low-pass filters), the new version receives input from unsigned unimodal transient channels. Transient channels are normally modelled through bandpass filters (to remove the DC and high-frequency signal components) and squaring (to remove the sign). While these may appear as two separate changes in the model, they are, in fact, a single one: the substitution of sustained with unsigned transient channels (for a similar approach, see Stigliani et al. 2017, PNAS). Either change alone would not be sufficient to implement a transient channel that accounts for the present results.

That said, we were also concerned with introducing too many changes in the model at once. Indeed, we simply modelled the unimodal transient channels as a single band-pass filter followed by squaring. This is already a stripped-down version of the unsigned transient detectors proposed by Adelson and Bergen in their classic Motion Energy model. The original model consisted of two biphasic temporal filters 90 degrees out of phase (i.e., quadrature filters), whose output is later combined. While a simpler implementation of the transient channels was sufficient in the present study, the full model may be necessary for other classes of stimuli (including speech, Parise, 2024, BiorXiv). Therefore, for completeness, we now include in the Supplementary Information a formal description of the full model, and validate it by simulating our two novel psychophysical studies. See Supplementary Information “The quadrature MCD model” section and Supplementary Figure S8.

(2) Because the model is applied only to rather simple artificial signals, it remains unclear to what extent it can account for AV correlation detection for naturalistic signals. In particular, speech appears to rely on correlation detection of signed signals. Can this modified model account for SJ or TOJ judgments for naturalistic signals?

It can. In a recent series of studies we have demonstrated that a population of spatially-tuned MCD units can account for audiovisual correlation detection for naturalistic stimuli, including speech (e.g. the McGurk Illusion). Once again, unsigned transients were sufficient to replicate a variety of previous findings. We have now extended the discussion to cover this recent research: Parise, C. V. (2024). Spatiotemporal models for multisensory integration. bioRxiv, 2023-12.

Even Nidiffer et al. (2018) which is explicitly modelled by the authors report a significant difference in performance for correlated and anti-correlated signals. This seems to disagree with the results of study 1 reported in the current paper and the model's predictions. How can these contradicting results be explained? If the brain detects correlation on signed and unsigned signals, is a more complex mechanism needed to arbitrate between those two?

We believe the reviewer here refers to our Experiment 2 (where, like Nidiffer at al. (2018) we used periodic stimuli, not Experiment 1, which consists of step stimuli). We were also puzzled by the difference between our Experiment 2 and Nidiffer et al. (2018): we induced frequency doubling, Nidiffer did not. Based on quantitative simulations, we concluded that this difference could be attributed to the fact that while Nidiffer included on each trial an intensity ramp in their periodic audiovisual stimuli, we did not. As a result, when considering the ramp (unlike in Nidiffer’s analyses), all audiovisual signals used by Nidiffer were positively correlated (irrespective of frequency and phase offset), while our signals in Experiment 2 were sometimes correlated and other times not (depending on the phase offset). This important simulation is included in Supplementary Figure S7; we also have now updated the text to better highlight the role of the pedestal in determining the direction of the correlation.

(3) The number of parameters seems quite comparable for the authors' model and descriptive models (e.g. PSF models). This is because time constants require refitting (at least for some experimental data sets) and the correlation values need to be passed through a response mode (i.e. probit function) to account for behavioural data. It remains unclear how the brain adjusts the time constants to different sensory signals.

This is a deep question. For simplicity, here the temporal constants were fitted to the empirical psychometric functions. To avoid overfitting, whenever possible we fitted such parameters over some training datasets, while trying to predict others. However, in some cases, it was necessary to fit the temporal constants to specific datasets. This may suggest that the temporal tuning of those units is not crystalised to some pre-defined values, but is adjusted based on recent perceptual history (e.g., the sequence of trials and stimuli participants are exposed to during the various experiments).

For transparency, here we show how varying the tuning of the temporal constants of the filters affects the goodness of fit of our new psychophysical experiments (Supplementary Figure S8). As it can be readily appreciated, the relative temporal tuning of the unimodal transient detector was critical, though their absolute values could vary over a range of about 15 to over 100ms. The tuning of the low-pass filters of the correlation detector (not shown here) displayed much lower temporal sensitivity over a range between 0.1s to over 1s.

This simulation shows the impact of temporal tuning in our simulations, however, the question remains as to how such a tuning gets selected in the first place. An appealing explanation relies on natural scene statistics: units are temporally tuned to the most common audiovisual stimuli. Although our current empirical evidence does not allow us to quantitatively address this question, in previous simulations (see Parise & Ernst, 2016, Supplementary Figure 8), by analogy with visual motion adaptation, we show how the temporal constants of our model can dynamically adjust and adapt to recent perceptual history. We hope these new and previous simulations address the question about the nature of the temporal tuning of the MCD units.

(4) Fujisaki and Nishida (2005, 2006) proposed mechanisms for AV correlation detection based on the Hassenstein-Reichardt motion detector (though not formalized as a computational model).

This is correct, Fujisaki and Nishida (2005, 2007) also hypothesized that AV synchrony could be detected using a mechanism analogous to motion detection. Interestingly, however, they ruled out such a hypothesis, as their “data do not support the existence of specialized low-level audio-visual synchrony detectors”. Yet, along with our previous work (Parise & Ernst, 2016, where we explicitly modelled the experiments of Fujisaki and Nishida), the present simulations quantitatively demonstrate that a low-level AV synchrony detector is instead sufficient to account for audiovisual synchrony perception and correlation detection. We now credit Fujusaki and Nishida in the modelling section for proposing that AV synchrony can be detected by a cross-correlator.

Finally, we believe the reviewer is referring to the 2005 and 2007 studies of Fujisaki and Nishida (not 2006); here are the full references of the two articles we are referring to:

Fujisaki, W., & Nishida, S. Y. (2005). Temporal frequency characteristics of synchrony–asynchrony discrimination of audio-visual signals. Experimental Brain Research, 166, 455-464.

Fujisaki, W., & Nishida, S. Y. (2007). Feature-based processing of audio-visual synchrony perception revealed by random pulse trains. Vision Research, 47(8), 1075-1093.

Reviewer #2 (Public Review):

Summary:

This is an interesting and well-written manuscript that seeks to detail the performance of two human psychophysical experiments designed to look at the relative contributions of transient and sustained components of a multisensory (i.e., audiovisual) stimulus to their integration. The work is framed within the context of a model previously developed by the authors and is now somewhat revised to better incorporate the experimental findings. The major takeaway from the paper is that transient signals carry the vast majority of the information related to the integration of auditory and visual cues, and that the Multisensory Correlation Detector (MCD) model not only captures the results of the current study but is also highly effective in capturing the results of prior studies focused on temporal and causal judgments.

Strengths:

Overall the experimental design is sound and the analyses are well performed. The extension of the MCD model to better capture transients makes a great deal of sense in the current context, and it is very nice to see the model applied to a variety of previous studies.

Weaknesses:

My one major issue with the paper revolves around its significance. In the context of a temporal task(s), is it in any way surprising that the important information is carried by stimulus transients? Stated a bit differently, isn't all of the important information needed to solve the task embedded in the temporal dimension? I think the authors need to better address this issue to punch up the significance of their work.

In hindsight, it may appear unsurprising that transient signals carry most information for audiovisual integration. Yet, so somewhat unexpectedly, this has never been investigated using perhaps the most diagnostic psychophysical tools for perceived crossmodal timing; namely temporal order and simultaneity judgments–along with carefully designed experiments with quantitative predictions for the effect of either channel. The fact that the results conform to intuitive expectations further supports the value of the present work: grounding empirically with what is intuitively expected. This offers solid psychophysical evidence that one can build on for future advancements. Importantly, developing a model that builds on our new results and uses the same parameters to predict a variety of classic experiments in the field, further supports the current approach.

If “significance” is intended as shaking previous intuitions or theories, then no: this is not a significant contribution. If instead, by significance we intend to build a solid empirical and theoretical ground for future work, then we believe this study is not significant, it is foundational. We hope that this work's significance is better captured in our discussion.

On a side note, there is an intriguing factor around transient vs. sustained channels: what matters is the amount of change, not the absolute stimulus intensity. Previous studies, for example, have suggested a positive cross modal mapping between auditory loudness and visual lightness or brightness [Odegaard et al., 2004]. This study, conversely, challenges this view and demonstrates that what matters for multisensory integration in time is not the intensity of a stimulus, but changes thereof.

In a more minor comment, I think there also needs to be a bit more effort into articulating the biological plausibility/potential instantiations of this sustained versus transient dichotomy. As written, the paper suggests that these are different "channels" in sensory systems, when in reality many neurons (and neural circuits) carry both on the same lines.

The reviewer is right, in our original manuscript we glossed over this aspect. We have now expanded the introduction to discuss their anatomical basis. However, we are not assuming any strict dichotomy between transient and sustained channels; rather, our results and simulations demonstrate that transient information is sufficient to account for audiovisual temporal integration.

Recommendations for the authors:

Reviewer #1 (Recommendations For The Authors):

(1) Related to point 2 of the public review, can the authors provide additional results showing that the model can also account for naturalistic signals and more complex stochastic signals?

While working on this manuscript, we were also working in parallel on a project related to audiovisual integration of naturalistic signals. A pre-print is available online [Parise, 2024, BiorXiv], and the related study is now discussed in the conclusions.

(2) As noted in the public review, Fujisaki and Nishida (2005, 2006) already proposed mechanisms for AV correlation detection based on the Hassenstein-Reichardt motion detector. Their work should be referenced and discussed.

We have now acknowledged the contribution of Fujisaki and Nishida in the modelling section, when we first introduce the link between our model and the Hassenstein-Reichardt detectors.

(3) Experimental parameters: Was the phase shift manipulated in blocks? If yes, what about temporal recalibration?

To minimise the effect of temporal recalibration, the order of trials in our experiments was randomised. Nonetheless, we can directly assess potential short-term recalibration effects by plotting our psychophysical responses against both the current SOA, and that of the previous trials. The resulting (raw) psychometric surfaces below are averaged across observers (and conditions for Experiment 1). In all our experiments, responses are obviously dependent on the current SOA (x-axis). However, the SOA of the previous trials (y-axis) does not seem to meaningfully affect simultaneity and temporal order judgments. The psychometric curves above the heatmaps represent the average psychometric functions (marginalized over the SOA of the previous trial).

All in all, the present analyses demonstrate negligible temporal recalibration across trials, likely induced by a random sequence of lags or phase shifts. Therefore, when estimating the temporal constants of the model, it seems reasonable to ignore the potential effects of temporal recalibration. To avoid increasing the complexity of the present manuscript, we would prefer not to include the present analyses in the revised version.

Author response image 1.

Effect of previous trial. Psychometric surfaces for Experiments 1 and 2 plotted against the lag in the current vs. the previous trial. While psychophysical responses are strongly modulated by the lag in the last trial (horizontal axis), they are relatively unaffected by the lag in the previous trial (vertical axis).

(4) The model predicts no differences for experiment 1 and this is what is empirically observed. Can the authors support these null results with Bayes factors?

This is a good suggestion: we have now included a Bayesian repeated measures ANOVA to the analyses of Experiment 1. As expected, these analyses provide further, though mild evidence in support for the null hypothesis (See Table S2). For completeness, the new Bayesian analyses are presented alongside the previous frequentist ones in the revised manuscript.

Author response:

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

Reviewer #1 (Public Review):

Summary:

This important work advances our understanding of sperm motility regulation during fertilization by uncovering the midpiece/mitochondria contraction associated with motility cessation and structural changes in the midpiece actin network as its mode of action. The evidence supporting the conclusion is solid, with rigorous live cell imaging using state-of-the-art microscopy, although more functional analysis of the midpiece/mitochondria contraction would have further strengthened the study. The work will be of broad interest to cell biologists working on the cytoskeleton, mitochondria, cell fusion, and fertilization. Strengths: The authors demonstrate that structural changes in the flagellar midpiece F-actin network are concomitant to midpiece/mitochondrial contraction and motility arrest during sperm-egg fusion by rigorous live cell imaging using state-of-art microscopy.

Response P1.1: We thank the reviewer for her/his positive assessment of our manuscript.

Weaknesses:

Many interesting observations are listed as correlated or in time series but do not necessarily demonstrate the causality and it remains to be further tested whether the sperm undergoing midpiece contraction are those that fertilize or those that are not selected. Further elaboration of the function of the midpiece contraction associated with motility cessation (a major key discovery of the manuscript) would benefit from a more mechanistic study.

Response P1.2: We thank the reviewer for this point. We have toned down some of our statements since some of the observations are indeed temporal correlations. We will explore some of these possible connections in future experiments. In addition, we have now incorporated additional experiments and possible explanations about the function of the midpiece contraction.

Reviewer #2 (Public Review): 

(1) The authors used various microscopy techniques, including super-resolution microscopy, to observe the changes that occur in the midpiece of mouse sperm flagella. Previously, it was shown that actin filaments form a double helix in the midpiece. This study reveals that the structure of these actin filaments changes after the acrosome reaction and before sperm-egg fusion, resulting in a thinner midpiece. Furthermore, by combining midpiece structure observation with calcium imaging, the authors show that changes in intracellular calcium concentrations precede structural changes in the midpiece. The cessation of sperm motility by these changes may be important for fusion with the egg. Elucidation of the structural changes in the midpiece could lead to a better understanding of fertilization and the etiology of male infertility. The conclusions of this manuscript are largely supported by the data, but there are several areas for improvement in data analysis and interpretation. Please see the major points below.

Response P2.1: We thank the reviewer for the positive comments.

(2) It is unclear whether an increased FM4-64 signal in the midpiece precedes the arrest of sperm motility. in or This needs to be clarified to argue that structural changes in the midpiece cause sperm motility arrest. The authors should analyze changes in both motility and FM4-64 signal over time for individual sperm.

Response P2.2 : We have conducted single cell experiments tracking both FM4-64 and motility as the reviewer suggested (Supplementary Fig S1). We have observed that in all cases, cells gradually diminished the beating frequency and increased FM4-64 fluorescence in the midpiece until a complete motility arrest is observed. A representative example is shown in this Figure but we will reinforce this concept in the results section.

(3) It is possible that sperm stop moving because they die. Figure 1G shows that the FM464 signal is increased in the midpiece of immotile sperm, but it is necessary to show that the FM4-64 signal is increased in sperm that are not dead and retain plasma membrane integrity by checking sperm viability with propidium iodide or other means.

Response P2.3: This is a very good point. In our experiments, we always considered sperm that were motile to hypothesize about the relevance of this observation. We have two types of experiments: 

(1) Sperm-egg Fusion: In experiments where sperm and eggs were imaged to observe their fusion, sperm were initially moving and after fusion, the midpiece contraction (increase in FM4-64 fluorescence was observed) indicating that the change in the midpiece (that was observed consistently in all fusing cells analyzed), is part of the process. 

(2) Sperm that underwent acrosomal exocytosis (AE): we have observed two behaviours as shown in Figure 1: 

a) Sperm that underwent AE and they remain motile without midpiece contraction (they are alive for sure); 

b) Sperm that underwent AE and stopped moving with an increase in FM464 fluorescence. We propose that this contraction during AE is not desired because it will impede sperm from moving forward to the fertilization site when they are in the female reproductive tract. In this case, we acknowledge that the cessation of sperm motility may be attributed to cellular death, potentially correlating with the increased FM4-64 signal observed in the midpiece of immotile sperm that have undergone AE. To address this hypothesis, we conducted image-based flow cytometry experiments, which are well-suited for assessing cellular heterogeneity within large populations.

Author response image 1 illustrates the relationship between cell death and spontaneous AE in noncapacitated mouse sperm, where intact acrosomes are marked by EGFP. Cell death was evaluated using Sytox Blue staining, a dye that is impermeable to live cells and shows affinity for DNA. AE was assessed by the absence of EGFP in the acrosome. 

Author response image 1a indicates a lack of correlation between Sytox and EGFP fluorescence. Two populations of sperm with EGFP signals were found (EGFP+ and EGFP-), each showing a broad distribution of Sytox signal, enabling the distinction between cells that retain plasma membrane integrity (live sperm: Sytox-) and those with compromised membranes (dead cells: Sytox+). The observed bimodal distribution of EGFP signal, regardless of live versus dead cell populations, indicates that the fenestration of the plasma membrane known to occur during AE is a regulated process that does not necessarily compromise the overall plasma membrane integrity. 

These observations are reinforced by the single-cell examples in Author response image 1b, where we were able to identify sperm in four categories: live sperm with intact acrosome (EGFP+/Sytox-), live sperm with acrosomal exocytosis (EGFP-/Sytox-), dead sperm with intact acrosome (EGFP+/Sytox+), and dead sperm with AE (EGFP-/Sytox+). Note the case of AE (lacking EGFP signal) which bears an intact plasma membrane (lacking Sytox Blue signal). Author response image 2 shows single-cell examples of the four categories observed with confocal microscopy to reinforce the observations from Author response image 1a.

Author response image 1.

Fi. Image based flow cytometry analysis (ImageStream Merk II), of non-capacitated mouse sperm, showing the distribution of EGFP signal (acrosome integrity) against Sytox Blue staining (cell viability).  (A) The quadrants show: Sytox Blue + / EGFP low (17.6%), Sytox Blue + / EGFP high (40.1%), Sytox Blue - / EGFP high (20.2%), and Sytox Blue - / EGFP low (21.7%). Each quadrant indicates the percentage of the total sperm population exhibiting the corresponding staining pattern. Axes are presented in a log10 scale of arbitrary units of fluorescence.  (B) Representative single-cell images corresponding to the four categorized sperm populations from the flow cytometry analysis in panel (A). The top row displays sperm with compromised plasma membrane integrity (Sytox Blue +), showing low (left) and high (right) EGFP signals. The bottom row shows sperm with intact plasma membrane (Sytox Blue -), displaying high (left) and low (right) EGFP signal. It is worth noting that when analyzing the percentages in (A), we observed that the data also encompass a population of headless flagella, which was present in all observed categories. Therefore, the percentages should be interpreted with caution.

Author response image 2.

Confocal Microscopy Examples of AE and cell viability. The top row features sperm with compromised plasma membrane integrity (Sytox Blue +) and high EGFP expression; the second row displays sperm with compromised membrane and low EGFP expression; the third row illustrates sperm with intact membrane (Sytox Blue -) and high EGFP expression; the bottom row shows sperm with intact membrane and low EGFP expression. 

Author response images 3-5 provide insight into the relationship between FM4-64 and Sytox Blue fluorescence intensities in non-capacitated sperm (CTRL, Author response image 3), capacitated sperm and acrosome exocytosis events stimulated with 100 µM progesterone (PG, Author response image 4), and capacitated sperm stimulated with 20 µM ionomycin (IONO, Author response image 5). Two populations of sperm with Sytox Blue signals were clearly distinguished (Sytox+ and Sytox-), enabling the discernment between live and dead sperm. Interestingly, the upper right panels of Author response images 3A, 4A, and 5A (Sytox Blue+ / FM4-64 high) consistently show a positive correlation between FM4-64 and Sytox Blue. This observation aligns with the concern raised by Reviewer 2, suggesting that compromised membranes due to cell death provide more binding sites for FM4-64. 

Nonetheless, the lower panels of Author response images 3A, 4A and 5A (Sytox Blue-) show no correlation with FM4-64 fluorescence, indicating that this population can exhibit either low or high FM4-64 fluorescence. As expected, in stark contrast with the CTRL case, the stimulation of AE with PG or IONO in capacitated sperm increased the population of live sperm with high FM4-64 fluorescence (Sytox Blue+ / FM4-64 high: CTRL: 7.85%, PG: 8.73%, IONO: 13.5%). 

Single-cell examples are shown in Author response images 3B, 4B, and 5B, where the four categories are represented: dead sperm with low FM4-64 fluorescence (Sytox Blue+ / FM4-64 low), dead sperm with high FM4-64 fluorescence (Sytox Blue+ / FM4-64 high), live sperm with low FM4-64 fluorescence (Sytox Blue- / FM4-64 low), and live sperm with high FM4-64 fluorescence (Sytox Blue- / FM4-64 high). 

Author response image 3.

Relationship between cell death and FM4-64 fluorescence in capacitated sperm without inductor of RA. Image-based flow cytometry analysis of non-capacitated mouse sperm loaded with FM464 and Sytox Blue dyes, with one and two minutes of incubation time, respectively. (A) The quadrants show: Sytox Blue+ / FM4-64 low (13.3%), Sytox Blue+ / FM4-64 high (49.8%), Sytox Blue- / FM4-64 low (28.1%), and Sytox Blue- / FM4-64 high (7.85%). Each quadrant indicates the percentage of the total sperm population exhibiting the corresponding staining pattern. Axes are presented on a log10 scale of arbitrary units of fluorescence. (B) Representative single-cell images corresponding to the four categorized sperm populations from the flow cytometry analysis in panel (A).

Author response image 4.

Relationship between cell death and FM4-64 fluorescence capacitated sperm stimulated with progesterone. Image-based flow cytometry analysis of non-capacitated mouse sperm loaded with FM4-64 and Sytox Blue dyes, with one and two minutes of incubation time, respectively. (A) The quadrants show: Sytox Blue+ / FM4-64 low (9.04%), Sytox Blue+ / FM4-64 high (61.6%), Sytox Blue- / FM4-64 low (19.7%), and Sytox Blue- / FM4-64 high (8.73%). Each quadrant indicates the percentage of the total sperm population exhibiting the corresponding staining pattern. Axes are presented on a log10 scale of arbitrary units of fluorescence. (B) Representative single-cell images corresponding to the four categorized sperm populations from the flow cytometry analysis in panel (A)

Author response image 5.

Relationship between cell death and FM4-64 fluorescence capacitated sperm stimulated with ionomycin. Image-based flow cytometry analysis of non-capacitated mouse sperm loaded with FM464 and Sytox Blue dyes, with one and two minutes of incubation time, respectively. (A) The quadrants show: Sytox Blue+ / FM4-64 low (4.52%), Sytox Blue+ / FM4-64 high (60.6%), Sytox Blue- / FM4-64 low (20.5%), and Sytox Blue- / FM4-64 high (13.5%). Each quadrant indicates the percentage of the total sperm population exhibiting the corresponding staining pattern. Axes are presented on a log10 scale of arbitrary units of fluorescence. (B) Representative single-cell images corresponding to the four categorized sperm populations from the flow cytometry analysis in panel (A).

Based on the data presented in Author response images 1 to 6, we derive the following conclusions summarized below:

(1) There is no direct relationship between cell death (Sytox Blue-) and AE (EGFP) (Author response images 1 and 2).

(2) There is bistability in the FM4-64 fluorescent intensity. Before reaching a certain threshold, there is no correlation between FM4-64 and Sytox Blue signals, indicating no cell death. However, after crossing this threshold, the FM4-64 signal becomes correlated with Sytox Blue+ cells, indicating cell death (Author response images 4-6).

(3) The Sytox Blue- population of capacitated sperm is sensitive to AE stimulation with progesterone, leading to the expected increase in FM4-64 fluorescence.

Therefore, while the FM4-64 signal alone is not a definitive marker for either AE or cell death, it is crucial to use additional viability assessments, such as Sytox Blue, to accurately differentiate between live and dead sperm in studies of acrosome exocytosis and sperm motility. In the present work, we did not use a cell viability marker due to the complex multicolor, multidimensional fluorescence experiments. However, cell viability was always considered, as any imaged sperm was chosen based on motility, indicated by a beating flagellum. The determination of whether selected sperm die during or after AE remains to be elucidated. The results presented in Figure 2 and Supplementary S1 show examples of motile sperm that experience an increase in FM4-64 fluorescence.

All this information is added to the manuscript (Supplementary Figure 1D).

(4) It is unclear how the structural change in the midpiece causes the entire sperm flagellum, including the principal piece, to stop moving. It will be easier for readers to understand if the authors discuss possible mechanisms.

Response P2.4: As requested, we have incorporated a possible explanation in the discussion section (see line 644-656). We propose three possible hypotheses for the cessation of sperm motility, which can be attributed to the simultaneous occurrence of various events:

(1) Rapid increase in [Ca2+]i levels: A rapid increase in [Ca2+]i levels may trigger the activation of Ca2+ pumps within the flagellum. This process consumes local ATP levels, disrupting glycolysis and thereby depleting the energy required for motility.

(2) Reorganization of the actin cytoskeleton: Alterations in the actin cytoskeleton can lead to changes in the mechanical properties of the flagellum, impacting its ability to move effectively.

(3) Midpiece contraction: Contraction in the midpiece region can potentially interfere with mitochondrial function, impeding the energy production necessary for sustained motility.

(5) The mitochondrial sheath and cell membrane are very close together when observed by transmission electron microscopy. The image in Figure 9A with the large space between the plasma membrane and mitochondria is misleading and should be corrected. The authors state that the distance between the plasma membrane and mitochondria approaches about 100 nm after the acrosome reaction (Line 330 - Line 333), but this is a very long distance and large structural changes may occur in the midpiece. Was there any change in the mitochondria themselves when they were observed with the DsRed2 signal?

Response P2.5: The authors appreciate the reviewer’s observation regarding the need to correct the image in Figure 9A, as the original depiction conveys a misleading representation of the spatial relationship between the mitochondrial sheath and the plasma membrane. This figure has been corrected to accurately reflect a more realistic proximity, while keeping in mind that it is a cartoonish representation.

Regarding the comments about the distances mentioned between former lines 330 and 333, the measurement was not intended to describe the gap between the plasma membrane and the mitochondria but rather the distance between F-actin and the plasma membrane. 

Author response image 6 shows high-resolution scanning electron microscopy (SEM) of two sperm fixed with a protocol tailored to preserve plasma membranes (ref), where the insets clearly show the flagellate architecture in the midpiece with an intact plasma membrane covering the mitochondrial network. A non-capacitated sperm with an intact acrosome is shown in panel A, and a capacitated sperm that has experienced AE is shown in panel B.

Notably, the results depicted in Author response image 6 demonstrate that, irrespective of the AE status, the distance between the plasma membrane and mitochondria consistently remains less than 20 nm, thus confirming the close proximity of these structures in both physiological states. As Reviewer 2 pointed out, if there is no significant difference in the distance between the plasma membrane and mitochondria, then the observed structural changes in the actin network within the midpiece should somehow alter the actual deposition of mitochondria within the midpiece. Figure 5D-F shows that midpiece contraction is associated with a decrease in the helical pitch of the actin network; the distance between turns of the actin helix decreases from  l = 248  nm to  l = 159  nm. This implies a net change in the number of turns the helix makes per 1 µm, from 4 to 6 µm-1.

Author response image 6.

SEM image showing the proximity between plasma membrane and mitochondria. Scale bar 100 nm.

Additionally, a structural contraction can be observed in Figure 5D-F, where the radius of the helix decreases by about 50 nm. To clarify this point, we sought to measure the deposition of individual DsRed2 mitochondria using computational superresolution microscopy—FF-SRM (SRRF and MSSR), Structured Illumination Microscopy (SIM), or a combination of both (SIM + MSSR), in 2D. Author response image 7 shows that these three approaches allow the observation of individual DsRed mitochondria; however, the complexity of their 3D arrangement, combined with the limited space between mitochondria (as seen in Author response image 6), precludes a reliable estimation of mitochondrial organization within the midpiece. To overcome these challenges, we decided to study the midpiece architecture via SEM experiments on non-capacitated versus capacitated sperm stimulated with ionomycin to undergo the AE.

Author response image 7.

Organization of mitochondria observed via FF-SRM and SIM. Scale bar 2 µm. F.N: Fluorescence normalized. F: Frequency

Author response image 8 presents a single-cell comparison of the midpiece architecture in noncapacitated (NC) and acrosome-intact (AI) versus acrosome-reacted (AR) sperm, along with measurements of the midpiece diameter throughout its length. Notably, the diameter of the midpiece increases from the base of the head to more distal regions, ranging from 0.45 nm to 1.10 µm (as shown in Author response images 7 and 8). A significant correlation between the diameter of the flagellum and its curvature was observed (Author response image 9), suggesting a reorganization of the midpiece due to shearing forces. This is further exemplified in Author response images 8 and 9, which provide individual examples of this phenomenon.

Author response image 8.

Comparison of the midpiece architecture in acrosome-intact and acrosome-reacted sperm using scanning electron microscopy (SEM).

As expected, the overall diameter of the midpiece in AI sperm was larger than in AR sperm, with measurements of 0.731 ± 0.008 µm for AI and 0.694 ± 0.007 µm for AR (p = 0.013, Kruskal-Wallis test n > 100, N = 2), as shown in Author response image 10. Additionally, this Author response image 7 indicates that the reorganization of the midpiece architecture involves a change in the periodicity of the mitochondrial network, with frequencies shifting from fNC to fEA mitochondria per micron.  

Author response image 9.

Comparison of the midpiece architecture in acrosome-intact (A) and acrosome-reacted (B) sperm using scanning electron microscopy (SEM).

Collectively, the structural results presented in Figure 5 and Author response images 6 to 10 demonstrate that the AE involves a comprehensive reorganization of the midpiece, affecting its diameter, pitch, and the organization of both the actin and mitochondrial networks. All this information is now incorporated in the new version of the paper (Figure. 2F)

Author response image 10.

Quantification of the midpiece diameter of the sperm flagellum in acrosome-intact and acrosome-reacted sperm analyzed by scanning electron microscopy (SEM). Data is presented as mean ± SEM. Kruskal-Wallis test was employed,  p = 0.013 (AI n=85 , AR n=72).

(6) In the TG sperm used, the green fluorescence of the acrosome disappears when sperm die. Figure 1C should be analyzed only with live sperm by checking viability with propidium iodide or other means.

Response P2.6: We concur with Reviewer 2 that ideally, any experiment conducted for this study should include an intrinsic cell viability test. However, the current research employs a wide array of multidimensional imaging techniques that are not always compatible with, or might be suboptimal for, simultaneous viability assessments. In agreement with the reviewer's concerns, it is recognized that the data presented in Figure 1C may inherently be biased due to cell death. Nonetheless, Author response image 1 demonstrates that the relationship between AE and cell death is more complex than a straightforward all-or-nothing scenario. Specifically, Author response image 1C illustrates a case where the plasma membrane is compromised (Sytox Blue+) yet maintains acrosomal integrity (EGFP+). This observation contradicts Reviewer 1's assertion that "the green fluorescence of the acrosome disappears when sperm die," as discussed more comprehensively in response P2.3.

In light of these observations, we have meticulously revisited the entire manuscript to address and clarify potential biases in our results due to cell death. Consequently, Author response image 5 and its detailed description have been incorporated into the supplementary material of the manuscript to contribute to the transparency and reliability of our findings.

Reviewer #3 (Public Review):

(1) While progressive and also hyperactivated motility are required for sperm to reach the site of fertilization and to penetrate the oocyte's outer vestments, during fusion with the oocyte's plasma membrane it has been observed that sperm motility ceases. Identifying the underlying molecular mechanisms would provide novel insights into a crucial but mostly overlooked physiological change during the sperm's life cycle. In this publication, the authors aim to provide evidence that the helical actin structure surrounding the sperm mitochondria in the midpiece plays a role in regulating sperm motility, specifically the motility arrest during sperm fusion but also during earlier cessation of motility in a subpopulation of sperm post acrosomal exocytosis. The main observation the authors make is that in a subpopulation of sperm undergoing acrosomal exocytosis and sperm that fuse with the plasma membrane of the oocyte display a decrease in midpiece parameter due to a 200 nm shift of the plasma membrane towards the actin helix. The authors show the decrease in midpiece diameter via various microscopy techniques all based on membrane dyes, bright-field images and other orthogonal approaches like electron microscopy would confirm those observations if true but are missing. The lack of additional experimental evidence and the fact that the authors simultaneously observe an increase in membrane dye fluorescence suggests that the membrane dyes instead might be internalized and are now staining intracellular membranes, creating a false-positive result. The authors also propose that the midpiece diameter decrease is driven by changes in sperm intracellular Ca2+ and structural changes of the actin helix network. Important controls and additional experiments are needed to prove that the events observed by the authors are causally dependent and not simply a result of sperm cells dying.

Response P3.1: We appreciate the reviewer's observations and critiques. In response, we have expanded our experimental approach to include alternative methodologies such as mathematical modeling and electron microscopy, alongside further fluorescence microscopy studies. This diversified approach aims to mitigate potential interpretation artifacts and substantiate the validity of our observations regarding the contraction of the sperm midpiece. Additionally, we have implemented further control experiments to fortify the credibility and robustness of our findings, ensuring a more comprehensive and reliable set of results.

First, we acknowledge the concerns raised by Reviewer 2 regarding the interpretation of the magnitude of the observed contraction of the sperm flagellum's midpiece (see response P2.5). Specifically, we believe that the assertion that "... there is a decrease in midpiece parameter due to a 200 nm shift of the plasma membrane towards the actin helix" stated by reviewer 3 needs careful examination. We recognize that the fluorescence microscopy data provided might not conclusively support such a substantial shift. Our live cell imaging and superresolution microscopy experiments indicate that there is a significant decrease in the diameter of the sperm flagellum associated with AE. This is supported by colocalization experiments where FM4-64-stained structures (fluorescing upon binding to membranes) are observed moving closer to Sir-Actinlabeled structures (binding to F-actin). Quantitatively, Figure S5 describes the spatial shift between FM4-64 and Sir-Actin signals, narrowing from a range of 140-210 nm to 50-110 nm (considering the 2nd and 3rd quartiles of the distributions). The mean separation distance between both signals changes from 180 nm in AI cells to 70 nm in AR cells, a net shift of 110 nm. This observation suggests caution regarding the claim of a "200 nm shift of the plasma membrane towards the actin cortex." 

Moreover, the concerns raised by Reviewer #3 about the potential internalization of membrane dyes, which might create a false-positive result by staining intracellular membranes, offer an alternative mechanism to explain a shift of up to 100 nm. This perspective is also supported by the critique from Reviewer #2 regarding the substantial distance (about 100 nm) between the plasma membrane and mitochondria post-acrosome reaction:  “The authors state that the distance between the plasma membrane and mitochondria approaches about 100 nm after the acrosome reaction (…), but this is a very long distance and large structural changes may occur in the midpiece”. These insights have prompted us to refine our methodology and interpretation of the data to ensure a more accurate representation of the underlying biological processes.

Author response image 11 shows a first principles approach in two spatial dimensions to explore three scenarios where a membrane dye, such as FM4-64, stains structures at and within the midpiece of a sperm flagellum, but yet does not result in a net change of diameter. Author response image 11A-C illustrates three theoretical arrangements of fluorescent dyes: Model 1 features two rigid, parallel structures that mimic the plasma membrane surrounding the midpiece of the flagellum. Model 2 builds on Model 1 by incorporating the possibility of dye internalization into structures located near the membrane, suggesting a slightly more complex interaction with nearby membranous intracellular structures. Model 3 represents an extreme scenario where the fluorescent dyes stain both the plasma membrane and internal structures, such as mitochondrial membranes, indicating extensive dye penetration and binding. Author response image 11D-F displays the convolution of the theoretical fluorescent signals from Models 1 to 3 with the theoretical point spread function (PSF) of a fluorescent microscope, represented by a Gaussian-like PSF with a sigma of 19 pixels (approximately 300 nm). This process simulates how each model's fluorescence would manifest under microscopic observation, showing subtle differences in the spatial distribution of fluorescence among the models. Author response image 11G-I reveals the superresolution images obtained through Mean Shift Super Resolution (MSSR) processing of the models depicted in Author response image 11D-F.

By analyzing the three scenarios, it becomes clear that the signals from Models 2 and 3 shift towards the center compared to Model 1, as depicted in Author response image 11J. This shift in fluorescence suggests that the internalization of the dye and its interaction with internal structures might significantly influence the perceived spatial distribution and intensity of fluorescence, thereby impacting the interpretation of structural changes within the midpiece. Consequently, the experimentally observed contraction of up to 100 nm in  could represent an actual contraction of the sperm flagellum's midpiece, a relocalization of the FM4-64 membrane dyes to internal structures, or a combination of both scenarios.

To discern between these possibilities, we implemented a scanning electron microscopy (SEM) approach. The findings presented in Figure 5 and Author response images 7 to 9 conclusively demonstrate that the AE involves a comprehensive reorganization of the midpiece. This reorganization affects its diameter, which changes by approximately 50 nm, as well as the pitch and the organization of both the actin and mitochondrial networks. This data corroborates the structural alterations observed and supports the validity of our interpretations regarding midpiece dynamics during the AE.

Author response image 11.

Modeling three scenarios of midpiece staining with membrane fluorescent dyes.

Secondly, we wish to clarify that in some of our experiments, we have utilized changes in the intensity of FM4-64 fluorescence as an indirect measure of midpiece contraction. This approach is supported by a linear inverse correlation between these variables, as illustrated in Figure S2D. It is important to note that this observation is correlative and indirect; therefore, our data does not directly substantiate the claim that "in a subpopulation of sperm undergoing AE and sperm that fuse with the plasma membrane of the oocyte, there is a decrease in midpiece parameter due to a 200 nm shift of the plasma membrane towards the actin helix". Specifically, we have not directly measured the distance between the plasma membrane and actin cortex in experiments involving gamete fusion.

All the concerns highlighted in this Response P1.1 have been addressed and incorporated into the manuscript. This addition aims to provide comprehensive insight into the experimental observations and methodologies used, ensuring that the data is transparent and accessible for thorough review and replication.

Editor Comment:

As the authors can see from the reviews, the reviewers had quite different degrees of enthusiasm, thus discussed extensively. The major points in consensus are summarized below and it is highly recommended that the authors consider their revisions.

(1) Causality of midpiece contraction with motility arrest is not conclusively supported by the current evidence. Time-resolved imaging of FM4-64 and motility is needed and the working model needs to be revised with two scenarios - whether the sperm contracting indicates a fertilizing sperm or sperm to be degenerated.

(2) The rationale for using FM4-64 as a plasma membrane marker is not clear as it is typically used as an endo-membrane marker, which is also related to the discrepancy of Fluo-4 signal diameter vs. FM4-64 (Figure 4E). The viability of sperm with increased FM4-64 needs to be demonstrated.

(3) The mechanism of midpiece contraction in motility cessation along the whole flagellum is not discussed.

(4) The use of an independent method to support the changes in midpiece diameter/structural changes such as DsRed (transgenic) or TEM.

(5) The claim of Ca2+ change needs to be toned down.

Response Editor: We thank the editor and the reviewers for their thorough and positive assessment of our work and the constructive feedback to further improve our manuscript. Please find below our responses to the reviewers’ comments. We have addressed all these points in the current version. Briefly,

(1) Time resolved images to show the correlation between FM4-64 fluorescence increase and the motility was incorporated

(2) The rationale for using FM4-64 was added.

(3) The mechanism of midpiece contraction was discussed in the paper

(4) An independent method was included to support our conclusions (SEM and other markers not based on membrane dyes)

(5) The results related to the calcium increase were toned down.

Recommendations for the authors:

Reviewer #1 (Recommendations For The Authors):

(1) To claim midpiece actin polymerization/re-organization is required for AE, demonstrating that AE does not occur in the presence of actin depolymerizing drugs (e.g., Latrunculin A, Cytochalasin D) would be necessary since the current data only shows the association/correlation. Was the block of AE by actin depolymerization observed?

Response R1.1: We agree with the reviewer but unfortunately, since actin polymerization and or depolymerization in the head are important for exocytosis, we cannot use this experimental approach to dissect both events. Addition of these inhibitors block the occurrence of AE (PMID: 12604633).

(2) Please provide the rationale for using FM4-64 to visualize the plasma membrane since it has been reported to selectively stain membranes of vacuolar organelles. What is the principle of increase of FM4-64 dye intensity, other than the correlation with midpiece contraction? For example, in lines 400-402: the authors mentioned that 'some acrosomereacted moving sperm within the perivitelline space had low FM4-64 fluorescence in the midpiece (Figure 6C). After 20 minutes, these sperm stopped moving and exhibited increased FM4-64 fluorescence, indicating midpiece contraction (Figure 6D).' While recognizing the increase of FM4-64 dye intensity can be an indicator of midpiece contraction, without knowing how and when the intensity of FM4-64 dye changes, it is hard to understand this observation. Please discuss.

Response R1.2: FM4-64 is an amphiphilic styryl fluorescent dye that preferentially binds to the phospholipid components of cell membranes, embedding itself in the lipid bilayer where it interacts with phospholipid head groups. Due to its amphiphilic nature, FM dyes primarily anchor to the outer leaflet of the bilayer, which restricts their internalization. It has been demonstrated that FM4-64 enters cells through endocytic pathways, making these dyes valuable tools for studying endocytosis.

Upon binding, FM4-64's fluorescence intensifies in a more hydrophobic environment that restricts molecular rotation, thus reducing non-radiative energy loss and enhancing fluorescence. These photophysical properties render FM dyes useful for observing membrane fusion events. When present in the extracellular medium, FM dyes rapidly reach a chemical equilibrium and label the plasma membrane in proportion to the availability of binding sites.

In wound healing studies, for instance, the fluorescence of FM4-64 is known to increase at the wound site. This increase is attributed to the repair mechanisms that promote the fusion of intracellular membranes at the site of the wound, leading to a rise in FM4-64 fluorescence. Similarly, an increase in FM4-64 fluorescence has been reported in the heads of both human and mouse sperm, coinciding with AE. In this scenario, the fusion between the plasma membrane and the acrosomal vesicle provides additional binding sites for FM4-64, thus increasing the total fluorescence observed in the head. This dynamic response of FM4-64 makes it an excellent marker for studying these cellular processes in real-time.

This study is the first to report an increase in FM4-64 fluorescence in the midpiece of the sperm flagellum. Figures 5 and Author response images 6 to 9 demonstrate that during the contraction of the sperm flagellum, structural rearrangements occur, including the compaction of the mitochondrial sheath and other membranous structures. Such contraction likely increases the local density of membrane lipids, thereby elevating the local concentration of FM4-64 and enhancing the probability of fluorescence emission. Additionally, changes in the microenvironment such as pH or ionic strength during contraction might further influence FM4-64’s fluorescence properties, as detailed by Smith et al. in the Journal of Membrane Biology (2010). The photophysical behavior of FM4-64, including changes in quantum yield due to tighter membrane packing or alterations in curvature or tension, may also contribute to the increased fluorescence observed. Notably, Figure S2 indicates that other fluorescent dyes like Memglow 700, Bodipy-GM, and FM1-43 also show a dramatic increase in their fluorescence during the midpiece contraction. Investigating whether the compaction of the plasma membrane or other mesoscale processes occur in the midpiece of the sperm flagellum could be a valuable area for future research. The use of fluorescent dyes such as LAURDAN or Nile Red might provide further insights into these membrane dynamics, offering a more comprehensive understanding of the biochemical and structural changes during sperm motility and gamete fusion events.

(3) As the volume of the whole midpiece stays the same while the diameter decreases along the whole midpiece (midpiece contraction), the authors need to describe what changes in the midpiece length they observe during the contraction. Was the length of the midpiece during the contraction measured and compared before and after contraction?

Response R1.3: As requested, we have measured the length of the midpiece in AI and AR sperm. As shown in Author response image 12 (For review purposes only), no statistically significant differences were observed. 

Author response image 12.

Midpiece length measured by the length of mitochondrial DsRed2 fluorescence in EGFP-DsRed2 sperm. Measurements were done before (acrosome-intact) and after (acrosome-reacted) acrosome exocytosis and midpiece contraction. Data is presented as the mean ± sem of 14 cells induced by 10 µM ionomycin. Paired t-test was performed, resulting in no statistical significance. 

(4) Most of all, it is not clear what the midpiece, thus mitochondria, contraction means in terms of sperm bioenergetics and motility cessation. Would the contraction induce mitochondrial depolarization or hyperpolarization, increase or decrease of ATP production/consumption? It will be great if this point is discussed. For example, an increase in mitochondrial Ca2+ is a good indicator of mitochondrial activity (ATP production).

Response R1.4: That is an excellent point. We have discussed this idea in the discussion (line 620-624). We are currently exploring this idea using different approaches because we also think that these changes in the midpiece may have an impact in the function of the mitochondria and perhaps, in their fate once they are incorporated in the egg after fertilization. 

(5) The authors claimed that Ca2+ signal propagates from head to tail, which is the opposite of the previous study (PMID: 17554080). Please clarify if it is a speculation. Otherwise, please support this claim with direct experimental evidence (e.g., high-speed calcium imaging of single cells).

Response R1.5: In that study, it was claimed that a [Ca2+]i  increase that propagates from the tail to the head occurs when CatSper is stimulated. They did not evaluate the occurrence of AE when monitoring calcium.

Our data is in agreement with our previous results (PMID: 26819478) that consistently indicated that only the[Ca2+]i  rise originating in the sperm head is able to promote AE. 

(6) Figure 4E: Please explain how come Fluo4 signal diameter can be smaller than FM4-64 dye if it stains plasma membrane (at 4' and 7').

Response R1.6: When colocalizing a diffraction-limited image (Fluo4) with a super-resolution image (FM4-64), discrepancies in signal sizes and locations can become apparent due to differences in resolution. The Fluo4 signal, being diffraction-limited, adheres to a resolution limit of approximately 200-300 nanometers under conventional light microscopy. This limitation causes the fluorescence signal to appear broader and less defined. Conversely, super-resolution microscopy techniques, such as SRRF (Super-Resolution Radial Fluctuations), achieve resolutions down to tens of nanometers, allowing FM4-64 to reveal finer details at the plasma membrane and display potentially smaller apparent sizes of stained structures. Although both dyes might localize to the same cellular regions, the higher resolution of the FM4-64 image allows it to show a more precise and smaller diameter of the midpiece of the flagellum compared to the broader, less defined signal of Fluo4. To address this, the legend of Figure 4E has been slightly modified to clarify that the FM4-64 image possesses greater resolution. 

(7) Figure 5D-G: the midpiece diameter of AR intact cells was shown ~ 0.8 um or more in Figure 2, while now the radius in Figure 5 is only 300 nm. Since the diameter of the whole midpiece is nearly uniform when the acrosome is intact, clarify how and what brings this difference and where the diameter/radius measurement is done in each figure.

Response R1.7: The difference resides in what is being measured. In Figure 2, the total diameter of the cell is measured, through the maximum peaks of FM4-64 fluorescence which is a probe against plasma membrane. As for Figure 5, the radius shown makes reference to the radius of the actin double helix within the midpiece. To that end, cells were fixed and stained with phalloidin, a F-actin probe.

Minor points

(8) Figure S1 title needs to be changed. The "Midpiece contraction" concept is not introduced when Figure S1 is referred to.

Response R1.8: This was corrected in the new version.

(9) Reference #19: the authors are duplicated.

Response R1.9: This was corrected in the new version.

(10) Line 315-318: sperm undergoing contraction -> sperm undergoing AR/AE?

Response R1.10: This was corrected in the new version.

(11) Line 3632 -> punctuation missing.

Response R1.11: Modified as requested.

(12) Movie S7: please add an arrow to indicate the spermatozoon of interest.

Response R1.12:  The arrow was added as suggested.

(13) Line 515: One result of this study was that the sperm flagellum folds back during fusion coincident with the decrease in the midpiece diameter. The authors did not provide an explanation for this observation. Please speculate the function of this folding for the fertilization process.

Response R1.13: As requested, this is now incorporated in the discussion. We speculate that the folding of the flagellum during fusion further facilitates sperm immobilization because it makes it more difficult for the flagellum to beat. Such processes can enhance stability and increase the probability of fusion success. Mechanistically, the folding may occur as a consequence of the deformation-induced stress that develops during the decrease of midpiece diameter. 

Reviewer #2 (Recommendations For The Authors):

(1) Figure 2C, D, E. Does "-1" on the X-axis mean one minute before induction? If so, the diameter is already smaller and FM4-64 fluorescence intensity is higher before the induction in the spontaneous group. Does the acrosome reaction already occur at "-1" in this group?

Response R2.1: Yes, “-1” means that the measurements of the diameter/FM4-64 fluorescence was done one minute before the induction. And it is correct that the diameter is smaller and FM464 fluorescence higher in the spontaneous group because these sperm underwent acrosome exocytosis before the induction, that is, spontaneously.

(2) Figure 3D. Purple dots are not shown in the graph on the right side.

Response R2.2: Modified as requested.

(3) Lines 404-406. "These results suggest that midpiece contraction and motility cessation occur only after acrosome-reacted sperm penetrate the zona pellucida". Since midpiece contraction and motility cessation also occur before the passage through the zona pellucida (Figure 9B), "only" should be deleted.

Response R2.3: Modified as requested.

Reviewer #3 (Recommendations For The Authors):

(1) Do the authors have a hypothesis as to why the observed decrease in midpiece parameter results in cessation of sperm motility? It would be beneficial for the manuscript to include a paragraph about potential mechanisms in the discussion.

Response R3.1: As requested, a potential mechanism has been proposed in the discussion section (line 644-656).

(2) Since the authors propose in Gervasi et al. 2018 that the actin helix might be responsible for the integrity of the mitochondrial sheath and the localization of the mitochondria, is it possible that the proposed change in plasma membrane diameter and actin helix remodeling for example alters the localization of the mitochondria? TEM should be able to reveal any associated structural changes. In its current state, the manuscript lacks experimental evidence supporting the author's claim that the "helical actin structure plays a role in the final stages of motility regulation". The authors should either include additional evidence supporting their hypothesis or tone down their conclusions in the introduction and discussion.

Response  R3.3: We agree with the reviewer. This is an excellent point. As suggested by this reviewer as well as the other reviewers, we have performed SEM to observe the changes in the midpiece observed after its contraction for two main reasons. First, to confirm this observation using a different approach that does not involve the use of membrane dyes. As shown in Author response image 6-10, we have observed that in addition to the midpiece diameter, there is a reorganization of the mitochondria sheet that is also suggested by the SIM experiments. These observations will be explored with more experiments to confirm the structural and functional changes that mitochondria undergo during the contraction. We are currently investigating this phenomenon, These results are now included in the new Figure  2F.

(3) In line 134: The authors write: 'Some of the acrosome reacted sperm moved normally, whereas the majority remained immotile". Do the authors mean that a proportion of the sperm was motile prior to acrosomal exocytosis and became immotile after, or were the sperm immotile to begin with? Please clarify.

Response R3.4: This statement is based on the quantification of the motile sperm after induction of AE within the AR population (Fig. 1C). 

(4) The authors do not provide any experimental evidence supporting the first scenario. In video 1 a lot of sperm do not seem to be moving to begin with, only a few sperm show clear beating in and out of the focal plane. The highlighted sperm that acrosome-reacted upon exposure to progesterone don't seem to be moving prior to the addition of progesterone. In contrast, the sperm that spontaneously acrosome react move the whole time. In video 1 this reviewer was not able to identify one sperm that stopped moving upon acrosomal exocytosis. Similarly in video 3, although the resolution of the video makes it difficult to distinguish motile from non-motile sperm. In video 2 the authors only show sperm that are already acrosome reacted. Please explain and provide additional evidence and statistical analysis supporting that sperm stop moving upon acrosomal exocytosis.

Response R3.5: In videos 1 and 3, the cells are attached to the glass with concanavalin-A, this lectin makes sperm immotile (if well attached) because both the head and tail stick to the glass. The observed motility of sperm in these videos is likely due to them not being properly attached to the glass, which is completely normal. On the contrary, in videos 2 and 4, sperm are attached to the glass with laminin. This is a glycoprotein that only binds the sperm to the glass through its head, that is why they move freely.

(5) Could the authors provide additional information about the FM4-64 fluorescent dye?

What is the mechanism, and how does it visualize structural changes at the flagellum? Since the whole head lights up, does that mean that the dye is internalized and now stains additional membranes, similar to during wound healing assays (PMID 20442251, 33667528). Or is that an imaging artifact? How do the authors explain the correlation between FM4-64 fluorescence increase in the midpiece and the observed change in diameter? Does FM4-64 have solvatochromatic properties?

Response R3.6: We appreciate the insightful queries posed by Reviewer 3, which echo the concerns initially brought forward by Reviewer 1. For a detailed explanation of the mechanism of FM4-64 dye, how we interpret  it, visualizes structural changes in the flagellum, and its behavior during cellular processes, please refer to our detailed response in Response R1.2. In brief, FM464 is a lipophilic styryl dye that preferentially binds to the outer leaflets of cellular membranes due to its amphiphilic nature. Upon binding, the dye becomes fluorescent, allowing for the visualization of membrane dynamics. The increase in fluorescence in the sperm head or midpiece likely results from the dye’s accumulation in areas where membrane restructuring occurs, such as during AE or in response to changes in the flagellum structure.

Regarding the specific questions about internalization and whether FM4-64 stains additional membranes similarly to what is observed in wound healing assays, it's important to note that FM4-64 can indeed be internalized through endocytosis and subsequently label internal vesicular structures. Additionally, FM4-64 may experience changes in its fluorescence as a result of fusion events that increase the lipid content of the plasma membrane, as observed in studies cited (PMID 20442251, 33667528). This characteristic makes FM4-64 valuable not only for outlining cell membranes but also for tracking the dynamics of both internal and external membrane systems, particularly during cellular events that involve significant membrane remodeling, such as wound healing or AE.

Concerning whether the increased fluorescence and observed changes in diameter are artifacts or reflect real biological processes, the correlation observed likely indicates actual changes in the midpiece architecture through molecular mechanisms that remain to be further elucidated. The data presented in Figures 5 and Author response images 6-10 support that this increase in fluorescence is not merely an artifact but a feature of how FM4-64 interacts with its environment. 

Finally, regarding the solvatochromatic properties of FM4-64, while the dye does show changes in its fluorescence intensity in different environments, its solvatochromatic properties are generally less pronounced than those of dyes specifically designed to be solvatochromatic. FM464's fluorescence changes are more a result of membrane interaction dynamics and dye concentration than of solvatochromatic shifts. 

(6) For the experiment summarized in Figure S1, did the authors detect sperm that acrosome-reacted upon exposure to progesterone and kept moving? This reviewer is wondering how the authors reliably measure FM4-64 fluorescence if the flagellum moves in and out of the focal plane. If the authors observe sperm that keep moving, what was the percentage within a sperm population and how did FM4-64 fluorescence change?

Response R3.6: We did identify sperm that underwent acrosome reaction upon exposure to progesterone and continued to exhibit movement. However, due to the issue raised by the reviewer regarding the flagellum going out of focus, we opted to quantify the percentage of sperm that were adhered to the slide (using laminin). This approach allows for the observation of flagellar position over time, facilitating an easy assessment of fluorescence changes. The percentage of sperm that maintained movement after AE is depicted in Figure 1C.

(7) In Figure S1B it doesn't look like the same sperm is shown in all channels or time points, the hook shown in the EGFP channel is not always pointing in the same direction. If FM4-64 is staining the plasma membrane, how do the authors explain that the flagellum seems to be more narrow in the FM4-64 channel than in the brightfield and DsRed2 channel?

Response 3.7: It is the same sperm, but due to technical limitations images were sequentially acquired. For example, for time 5 minutes after progesterone, all images in DIC were taken, then all images in the EGFP channel, then DsRed2* and finally FM4-64. The reason for this was to acquire images as fast as possible, particularly in DIC images which were then processed to get the beat frequency.

Regarding the flagellum that seems to be more narrow in the FM4-64 channel compared to the BF or DsRed2 channel, the explanation is related to the fact that intensity of the DsRed2 signal is stronger than the other two. This higher signal may have increased the amount of photons captured by the detector.

(8) Overall, it would be beneficial to include statistics on how many sperm within a population did change FM4-64 fluorescence during AE and how many did not, in addition to information about motility changes and viability. Did the authors exclude that the addition of FM4-64 causes cell death which could result in immotile sperm or that only dying sperm show an increase in FM4-64 fluorescence?

Response 3.8: The relationship between cell death and the increase in FM4-64 fluorescence is widely discussed in Response P2.3. In our experiments, we always considered sperm that were motile to hypothesize about the relevance of this observation. We have two types of experiments: 

(1) Sperm-egg Fusion: In experiments where sperm and eggs were imaged to observe their fusion, sperm were initially moving and after fusion, the midpiece contraction (increase in FM4-64 fluorescence was observed) indicating that the change in the midpiece (that was observed consistently in all fusing cells analyzed), is part of the process. 

(2) Sperm that underwent AE: we have observed two behaviours as shown in Figure 1: 

a) Sperm that underwent AE and they remain motile without midpiece contraction (they are alive for sure); 

b) Sperm that underwent AE and stopped moving with an increase in FM464 fluorescence. We propose that this contraction during AE is not desired because it will impede sperm from moving forward to the fertilization site when they are in the female reproductive tract. In this case, we acknowledge that the cessation of sperm motility may be attributed to cellular death, potentially correlating with the increased FM4-64 signal observed in the midpiece of immotile sperm that have undergone AE. To address this hypothesis, we conducted image-based flow cytometry experiments, which are well-suited for assessing cellular heterogeneity within large populations.

Regarding the relationship between the increase in FM4-64 and AE, we have always observed that AE is followed by an increase in FM4-64 in the head in mice (PMID: 26819478) as well as in human (PMID: 25100708) sperm. This was originally corroborated with the EGFP sperm. However, not all the cells that undergo AE increase the FM4-64 fluorescence in the midpiece.

(9) The authors report that a fraction of sperm undergoes AE without a change in FM4-64 fluorescence (Figure 1F). How does the [Ca2+]i change in those cells? Again statistics on the distribution of a certain pattern within a population in addition to showing individual examples would be very helpful.

Response 3.9: A recent work shows that an initial increase in [Ca2+]i  is required to induce changes in flagellar beating necessary for hyperactivation (Sánchez-Cárdenas et al., 2018). However, when [Ca2+]i  increases beyond a certain threshold, flagellar motility ceases. These conclusions are based on single-cell experiments in murine sperm with different concentrations of the Ca2+ ionophore, A23187. The authors reported that complete loss of motility was observed when using ionophore concentrations higher than 1 μM. In contrast, spermatozoa incubated with 0.5 μM A23187 remained motile throughout the experiment. Once the Ca2+ ionophore is removed, the sperm would reduce the concentration of this ion to levels compatible with motility and hyperactivation (Navarrete et al., 2016). However, some of the washed cells did not recover mobility in the recorded time window (Sánchez-Cárdenas et al., 2018). These results would indicate that due to the increase in [Ca2+]i  induced by the ionophore, irreversible changes occurred in the sperm flagellum that prevented recovery of mobility, even when the ionophore was not present in the recording medium. 

Taking into account our results, one possible scenario to explain this irreversible change would be the contraction of the midpiece. Our results demonstrate that the increase in [Ca2+]i observed in the midpiece (whether by induction with progesterone, ionomycin or occurring spontaneously) causes the contraction of this section of the flagellum and its subsequent immobilization. 

(10) While the authors results show that changes in [Ca2+]i correlate with the observed reduction of the midpiece diameter, they do not provide evidence that the structural changes are triggered by Ca2+i influx. It could just be a coincidence that both events spatially overlap and that they temporarily follow each other. The authors should either provide additional evidence or tone down their conclusion.

Response 3.10: We agree with the reviewer. As suggested, we have toned down our conclusion.

(11) Are the authors able to detect the changes in the midpiece diameter independent from FM4-64 or other plasma membrane dyes? An alternative explanation could be that the dyes are internalized due to cell death and instead of staining the plasma membrane they are now staining intracellular membranes, resulting in increased fluorescence and giving the illusion that the midpiece diameter decreased. How do the authors explain that the Bodipy-GM1 Signal directly overlaps with DsRed2 and SIR-actin, shouldn't there be some gap? Since the rest of the manuscript is based on that proposed decrease in midpiece diameter the authors should perform orthogonal experiments to confirm their observation.

Response 3.11: As requested by the reviewer, we have not used new methods to visualize the change in sperm diameter in the midpiece. In neither of them, a membrane dye was used. First, we have performed immunofluorescence to detect a membrane protein (GLUT3). Second, we have used scanning electron microscopy. The results are now incorporated in the new Figure 2FG. In both experiments, a change in the midpiece diameter was observed. Please, also visit responses P2.5 and Author response images 8 to 10.  

Regarding the overlap between the signal of Bodipy GM1 (membrane) and the fluorescence of DsRed2 (mitochondria) and Sir-Actin (F-actin), it is only observed in acrosomereacted sperm, not in acrosome-intact sperm (Figure S4). In our view, these structures become closed after midpiece contraction, and the resolution of the images is insufficient to distinguish them clearly. This issue is also evident in Figure 5B. Therefore, we conducted additional experiments using more powerful super-resolution techniques such as STORM (Figures 5D-F).

(12) The proposed gap of 200 nM between the actin helix and the plasma membrane, has been observed by TEM? Considering that the diameter of the mouse sperm midpiece is about 1 um, that is a lot of empty space which leaves only about 600 nm for the rest of the flagellum. The axoneme is 300 nm and there needs to be room for the ODFs and the mitochondria. Please explain.

Response 3.12: Unfortunately, the filament of polymerized actin cannot be observed by TEM. Furthermore, we were discouraged from trying other approaches, such as utilizing phalloidin gold, because for some reason, it does not work properly.

In our view, the 200 nm gap between the actin cytoskeleton and the plasma membrane is occupied by the mitochondria (that is the size that it is frequently reported based on TEM; see https://doi.org/10.1172/jci.insight.166869).

(13) The results provided by the authors do not convince this reviewer that the actin helix moves, either closer to the plasma membrane or toward the mitochondria, the observed differences are minor and not confirmed by statistical analysis.

Response 3.13: As requested, the title of that section was changed. Moreover, our conclusion is exactly as the reviewer is suggesting: “Since the results of the analysis of SiR-actin slopes were not conclusive, we studied the actin cytoskeleton structure in more detail”. This conclusion is based on the statistical analysis shown in Figure S5D-E.

(14) The fluorescence intensity of all plasma membrane dyes increases in all cells chosen by the authors for further analysis. Could the increase in SiR-Actin fluorescence be explained by a microscopy artifact instead of actin helix remodeling? Alternatively, can the authors exclude that the observed increase in SIR-Actin might be an artifact caused by the increase in FM4-64 fluorescence? Since the brightness in the head similarly increases to the fluorescence in the flagellum the staining pattern looks suspiciously similar. Did the authors perform single-stain controls?

Response 3.14: We had similar concerns when we were doing the experiments using SiR-actin. Although we have performed single stain controls to make sure that the actin helix remodelling occurs during the midpiece contraction, we have performed experiments using higher resolution techniques such as STORM using a different probe to stain actin (Phalloidin).

(15) Should actin cytoskeleton remodeling indeed result in a decrease of actin helix diameter, what do the authors propose is the underlying mechanism? Shouldn't that result in changes in mitochondrial structure or location and be visible by TEM? This reviewer is also wondering why the authors focus so much on the actin helix, while the plasma membrane based on the author's results is moving way more dramatically.

Response 3.15: This raises an intriguing point. Currently, we lack an understanding of the underlying mechanism driving actin remodeling, and we are eager to conduct further experiments to explore this aspect. For instance, we are investigating the potential role of Cofilin in remodeling the F-actin network. Initial experiments utilizing STORM imaging have revealed the localization of Cofilin in the midpiece region, where the actin helix is situated.

Regarding mitochondria, thus far, we have not uncovered any evidence suggesting that acrosome reaction or fusion with the egg induces a rearrangement of these organelles within the structure. The rationale for investigating polymerized actin in depth stems from the fact that, alongside the axoneme and other flagellar structures such as the outer dense fibers and fibrous sheet, these are the sole cytoskeletal components present in that particular tail region.

(14) The fact that the authors observe that most sperm passing through the zona pellucida, which requires motility, display high FM4-64 fluorescence, doesn't that contradict the authors' hypothesis that midpiece contraction and motility cessation are connected? Videos confirming sperm motility and information about pattern distribution within the observed sperm population in the perivitelline space should be provided.

Response 3.14: We believe it is a matter of time, as depicted in Figure 1D, our model shows that first the cells lose the acrosome, present motility and low FM4-64 fluorescence in the midpiece (pattern II) and after that, they lose motility and increase FM4-64 fluorescence in the midpiece (pattern III). That is why, we think that when sperm pass the zona pellucida they present pattern II and after some time they evolve into pattern III. 

(15) In the experiments summarized in Figure 8, did all sperm stop moving? Considering that 74 % of the observed sperm did not display midpiece contraction upon fusion, again doesn't that contradict the authors' hypothesis that the two events are interdependent? Similarly, in earlier experiments, not all acrosome-reacted sperm display a decrease in midpiece diameter or stop moving, questioning the significance of the event. If some sperm display a decrease in midpiece diameter and some don't, or undergo that change earlier or later, what is the underlying mechanism of regulation? The observed events could similarly be explained by sperm death: Sperm are dying × plasma membrane integrity changes and plasma membrane dyes get internalized × [Ca2+]i simultaneously increases due to cell death × sperm stop moving.

Response 3.15: The percentage of sperm that did not exhibit midpiece contraction in Fig.8B is 26%, not 74%, indicating that it does not contradict our hypothesis. However, this still represents a significant portion of sperm that remain unchanged in the midpiece, leaving room for various explanations. For instance, it's possible that: i) the change in fluorescence was not detected due to the event occurring after the recording concluded, or ii) in some instances, this alteration simply does not occur. Nevertheless, we did not track subsequent events in the oocyte, such as egg activation, to definitively ascertain the success of fusion. Incorporation of the dye only manifests the initiation of the process.

(16) The authors propose changes in Ca2+ as one potential mechanism to regulate midpiece contraction, however, the Ca2+ measurements during fusion are flawed, as the authors write in the discussion, by potential Ca2+ fluorophore dilution. Considering that the authors observe high Ca2+ in all sperm prior to fusion, could that be a measuring artifact? Were acrosome-intact sperm imaged with the same settings to confirm that sperm with low and high Ca2+ can be distinguished? Should [Ca2+]i changes indeed be involved in the regulation of motility cessation during fusion, could the authors speculate on how [Ca2+]i changes can simultaneously be involved in the regulation of sperm hyperactivation?

Response 3.16: We agree with the reviewer that our experiments using calcium probes are not conclusive for many technical problems. We have toned down our conclusions in the new version of the manuscript.

(17) 74: AE takes place for most cells in the upper segment of the oviduct, not all of them.

Please correct.

Response 3.17: Corrected in the new version.

(18) 88: Achieved through, or achieved by, please correct.

Response 3.18: Corrected in the new version.

(19) 243: Acrosomal exocytosis initiation by progesterone, please specify.

Response 3.19: Modified in the new version.

(20) 277: "The actin cytoskeleton approaches the plasma membrane during the contraction of the midpiece" is misleading. The author's results show the opposite.

Response 3.20: As suggested, this statement was modified.

(21) 298: Why do the authors find it surprising that the F-actin network was unchanged in acrosome-intact sperm that do not present a change in midpiece diameter?

Response 3.21: The reviewer is right. The sentence was modified.

(22) Figures 5D,F: The provided images do not support a shift in the actin helix diameter.

Response 3.22: The shift in the actin helix diameter is provided in Figure 5E and 5G.

(23) Figure S5C: The authors should show representative histograms of spontaneously-, progesterone induced-, and ionomycin-induced AE. Based on the quantification the SiRactin peaks don't seem to move when the AR is induced by progesterone.

Response 3.23: As requested, an ionomycin induced sperm is incorporated.

(24) 392: Which experimental evidence supports that statement?

Response 3.24: A reference was incorporated. 

Reference 13 is published, please update. Response 3.25: updated as requested.

Author response:

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

Reviewer #1 (Public Review):

Using the UK Biobank, this study assessed the value of nuclear magnetic resonance measured metabolites as predictors of progression to diabetes. The authors identified a panel of 9 circulating metabolites that improved the ability in risk prediction of progression from prediabetes to diabetes. In general, this is a well-performed study, and the findings may provide a new approach to identifying those at high risk of developing diabetes. I have some comments that may improve the importance of this study.

We deeply appreciate the reviewer's invaluable time dedicated to the review of this manuscript and the insightful comments to enhance its overall quality.

(1) It is unclear why the authors only considered the top 20 variables in the metabolite selection and why they did not set a wider threshold.

Thank you for the comment. We set the top 20 variables in the metabolite selection balancing the performance of the final diabetes risk prediction model and the clinical applicability due to measurement costs. We have added this explanation in the “Methods” section.

“We chose the intersection set of the top 20 most important variables selected by the three machine learning models, after balancing the performance of the final diabetes risk prediction model and the clinical applicability associated with measurement costs of metabolites.”

(2) The methods section would benefit from a more detailed exposition of how parameter tuning was conducted and the range of parameters explored during the training of the RSF model.

According to the reviewer’s suggestion, we have added a more detailed description of parameters tunning and the range of parameters explored during the training of the RSF model in the “Method S3” section in the Supplementary material.

“The RSF model was fitted using the “randomForestSRC” package and the grid search method was used for hyperparameter tuning. Specifically, the grid search method was used to tune hyperparameters among the RSF model, through minimizing out-of-sample or out-of-bag error1. Each tree in the RSF is constructed from a random sample of the data, typically a bootstrap sample or 63.2% of the sample size (as in the present study). Consequently, not all observations are used to construct each tree. The observations that are not used in the construction of a tree are referred to as out-of-bag observations. In an RSF model, each tree is built from a different sample of the original data, so each observation is “out-of-bag” for some of the trees. The prediction for an observation can then be obtained using only those trees for which the observation was not used for the construction. A classification for each observation is obtained in this way and the error rate can be estimated from these predictions. The resulting error rate is referred to as the out-of-bag error. Through calculating the out-of-bag error in each iteration, the best hyperparameters were finally determined.

The hyperparameters to be tuned and range of grid search in the present study were below: number of trees (50-1000, by 50), number of variables to possibly split at each node (3-6, by 1), and minimum size of terminal node (1-20, by 1)2.”

(3) It is hard to understand the meaning of the decision curve analysis and the clinical implications behind the net benefit, which are required to clarify the application values of models.

Thank you for the comment. We have added more description and discussion about the decision curve analysis in the “Methods” and “Discussion” sections.

“Furthermore, we used decision curve analysis (DCA) to assess the clinical usefulness of prediction model-based guidance for prediabetes management, which calculates a clinical “net benefit” for one or more prediction models in comparison to default strategies of treating all or no patients3.”

“Most importantly, a model with good discrimination does not necessarily have high clinical value. Hence, DCA was used to compare the clinical utility of the model before and after adding the metabolites, and this showed a higher net benefit for the latter than the basic model, suggesting the addition of the metabolites increased the clinical value of prediction, i.e., the potential benefit of guiding management in individuals with prediabetes3,4. These results provided novel evidence supporting the value of metabolic biomarkers in risk prediction and stratification for the progression from prediabetes to diabetes.”

(4) Notably, the NMR platform utilized within the UK Biobank primarily focused on lipid species. This limitation should be discussed in the manuscript to provide context for interpreting the results and acknowledge the potential bias from the measuring platform.

Thank you for the comment. We acknowledged this limitation that NMR platform within the UK Biobank primarily focused on lipid species and the potential bias from the measuring platform and have added this in “Discussion” section.

“Third, the Nightingale metabolomics platform primarily focused on lipids and lipoprotein sub-fractions, and thus the predictive value of other metabolites in the progression from prediabetes to diabetes warranted further research using an untargeted metabolomics approach.”

(5) The manuscript should explain the potential influence of non-fasting status on the findings, particularly concerning lipoprotein particles and composition. There should be a detailed discussion of how non-fasting status may impact the measurement and the findings.

According to the reviewer’s suggestion, we have added more details to explain the potential influence of non-fasting status on our findings in the “Discussion” section.

“Additionally, the use of non-fasting blood samples might increase inter-individual variation in metabolic biomarker concentrations, however, fasting duration has been reported to account for only a small proportion of variation in plasma metabolic biomarker concentrations5. Therefore, we believe the impact of non-fasting samples on our findings would be minor.”

(6) Cross-platform standardization is an issue in metabolism, and further descriptions of quality control are recommended.

Thank you for the comment. We have added more description of quality control in the “Method S1” section in the Supplementary material.

“Metabolic biomarker profiling by Nightingale Health’s NMR platform provides consistent results over time and across spectrometers. Furthermore, the sample preparation is minimal in the Nightingale Health’s metabolic biomarker platform, circumventing all extraction steps. These aspects result in highly repeatable biomarker measurements. Pre-specified quality metrics were agreed between UK Biobank and Nightingale Health to ensure consistent results across the samples, and pilot measurements were conducted. Nightingale Health performed real-time monitoring of the measurement consistency within and between spectrometers throughout the UK Biobank samples. Two control samples provided by Nightingale Health were included in each 96-well plate for tracking the consistency across multiple spectrometers. Furthermore, two blind duplicate samples provided by the UK Biobank were included in each well plate, with the position information unlocked only after results delivery. Coefficient of variation (CV) targets across the metabolic biomarker profile were pre-specified for both Nightingale Health’s internal control samples and UK Biobank’s blind duplicates. The targets were met for each consecutively measured batch of ~25,000 samples. For the majority of the metabolic biomarkers, the CVs were below 5% (https://biobank.ndph.ox.ac.uk/showcase/refer.cgi?id=3000). Further, the distributions of measured biomarkers from 5 sample batches indicated absence of batch effects (https://biobank.ctsu.ox.ac.uk/ukb/ukb/docs/nmrm_app1).”

Reviewer #2 (Public Review):<br /> Deciphering the metabolic alterations characterizing the prediabetes-diabetes spectrum could provide early time windows for targeted preventive measures to extend precision medicine while avoiding disproportionate healthcare costs. The authors identified a panel of 9 circulating metabolites combined with basic clinical variables that significantly improved the prediction from prediabetes to diabetes. These findings provided insights into the integration of these metabolites into clinical and public health practice. However, the interpretation of these findings should take account of the following limitations.

We appreciate the reviewer’s positive comments and encouragement.

(1) First, the causal relationship between identified metabolites and diabetes or prediabetes deserves to be further examined particularly when the prediabetic status was partially defined. Some metabolites might be the results of prediabetes rather than the casual factors for progression to diabetes.

Thank you for your insightful comments. We agree with you that the panel of metabolites in this study might not be the causal factor for progression from prediabetes to diabetes, which needs further validation in experimental studies. We have added this limitation in the “Discussion” section.

“Fifth, we could not draw any conclusion about the causality between the identified metabolites and the risk for progression to diabetes due to the observational nature, which remained to be validated in further experimental studies.”

(2) The blood samples were taken at random (not all in a non-fasting state) and so the findings were subjected to greater variability. This should be discussed in the limitations.

According to the reviewer’s suggestion, we have added more details to explain the potential influence of non-fasting status on our findings in the “Discussion” section.

“Additionally, the use of non-fasting blood samples might increase inter-individual variation in metabolic biomarker concentrations, however, fasting duration has been reported to account for only a small proportion of variation in plasma metabolic biomarker concentrations5. Therefore, we believe the impact of non-fasting samples on our findings would be minor.”

(3) The strength of NMR in metabolic profiling compared to other techniques (i.e., mass spectrometry [MS], another commonly used metabolic profiling method) could be added in the Discussion section.

According to the reviewer’s suggestion, we have added the strength of NMR in metabolic profiling compared to other techniques in the “Discussion” section.

“Circulating metabolites were quantified via NMR-based metabolome profiling within the UK Biobank, which offers metabolite qualification with relatively lower costs and better reproducibility6.”

(4) Fourth, the applied platform focuses mostly on lipid species which may be a limitation as well.

Thank you for the comment. We acknowledged this limitation that NMR platform within the UK Biobank primarily focused on lipid species and the potential bias from the measuring platform and have added this in the “Discussion” section.

“Third, the Nightingale metabolomics platform primarily focused on lipids and lipoprotein sub-fractions, and thus the predictive value of other metabolites in the progression from prediabetes to diabetes warranted further research using an untargeted metabolomics approach.”

(5) It is a very large group with pre-diabetes, but the results only apply to prediabetes and not to the general population. This should be clear, although the authors have also validated the predictive value of these metabolites in the general population.

Thank you for the comment. We agree with you that the results only apply to prediabetes and not to the general population, though they also showed potential predictive value among participants with normoglycemia. We have accordingly modified the relevant expressions in the “Conclusion” section to restrict these findings to participants with prediabetes.

“In this large prospective study among individuals with prediabetes, we detected a panel of circulating metabolites that were associated with an increased risk of progressing to diabetes.”

Recommendations for the Authors:

Thank you for providing the valuable feedback and the time you have dedicated to our work.

(1) In the first paragraph of the Discussion section, please include the specific names of the metabolites selected from machine learning methods.

Thank you for your comment and we have added accordingly in the first paragraph of the “Discussion” section.

“More importantly, our findings suggested that adding the selected metabolites (i.e., cholesteryl esters in large HDL, cholesteryl esters in medium VLDL, triglycerides in very large VLDL, average diameter for LDL particles, triglycerides in IDL, glycine, tyrosine, glucose, and docosahexaenoic acid) could significantly improve the risk prediction of progression from prediabetes to diabetes beyond the conventional clinical variables.”

(2) To enhance the readability and simplicity of the paper, the description of covariate collection in the methods section should be streamlined, with detailed information provided in the supplementary materials.

Thank you for your suggestion and we have moved details about covariates collection to the “Supplementary method S2” to enhance the readability and simplicity of the paper.

“Information on covariates was collected through a self-completed touchscreen questionnaire or verbal interview at baseline, including age, sex, ethnicity, Townsend deprivation index, household income, education, employment status, smoking status, moderate alcohol, physical activity, healthy diet score, healthy sleep score, family history of diabetes, history of cardiovascular disease (CVD), history of hypertension, history of dyslipidemia, history of chronic lung diseases (CLD), and history of cancer.

Physical measurements included systolic (SBP) and diastolic blood pressure (DBP), height, weight, waist circumference (WC), and hip circumference (HC). Body mass index (BMI) was calculated as weight in kilograms divided by the square of height in meters (kg/m²). Missing covariates were imputed by the median value for continuous variables and a missing indicator for categorical variables. More details about covariates collection can be found in Method S2.”

3. Title for Table 2, using Cox proportional hazards prediction models is not common. You may consider the title "Performance of Cox proportional hazards regression models in prediction of progression of prediabetes to diabetes".

Thank you for your suggestion and we have revised it accordingly.

4. Figure 3, did the authors consider competing risk to compute cumulative incidence function?

Thank you for your comment. We did not consider competing risk from death when plotting the cumulative hazard curves. However, following your suggestion, we have included an additional cumulative hazard plot after considering the competing

References

(1) Janitza S, Hornung R. On the overestimation of random forest's out-of-bag error. PLoS One. 2018;13(8):e0201904.

(2) Tian D, Yan HJ, Huang H, et al. Machine Learning-Based Prognostic Model for Patients After Lung Transplantation. JAMA Netw Open. 2023;6(5):e2312022.

(3) Vickers AJ, van Calster B, Steyerberg EW. A simple, step-by-step guide to interpreting decision curve analysis. Diagn Progn Res. 2019;3:18.

(4) Li J, Xi F, Yu W, Sun C, Wang X. Real-Time Prediction of Sepsis in Critical Trauma Patients: Machine Learning-Based Modeling Study. JMIR Form Res. 2023;7:e42452.

(5) Li-Gao R, Hughes DA, le Cessie S, et al. Assessment of reproducibility and biological variability of fasting and postprandial plasma metabolite concentrations using 1H NMR spectroscopy. PLoS One. 2019;14(6):e0218549.

(6) Geng T-T, Chen J-X, Lu Q, et al. Nuclear Magnetic Resonance–Based Metabolomics and Risk of CKD. American Journal of Kidney Diseases. 2023.

Author response:

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

Reviewer #1 (Public Review):

Summary:

The study of human intelligence has been the focus of cognitive neuroscience research, and finding some objective behavioral or neural indicators of intelligence has been an ongoing problem for scientists for many years. Melnick et al, 2013 found for the first time that the phenomenon of spatial suppression in motion perception predicts an individual's IQ score. This is because IQ is likely associated with the ability to suppress irrelevant information. In this study, a high-resolution MRS approach was used to test this theory. In this paper, the phenomenon of spatial suppression in motion perception was found to be correlated with the visuo-spatial subtest of gF, while both variables were also correlated with the GABA concentration of MT+ in the human brain. In addition, there was no significant relationship with the excitatory transmitter Glu. At the same time, SI was also associated with MT+ and several frontal cortex FCs.

Strengths:

(1) 7T high-resolution MRS is used.

(2) This study combines the behavioral tests, MRS, and fMRI.

Weaknesses:

Major:

In Melnick (2013) IQ scores were measured by the full set of WAIS-III, including all subtests. However, this study only used visual spatial domain of gF. I wonder why only the visuo-spatial subtest was used not the full WAIS-III? I am wondering whether other subtests were conducted and, if so, please include the results as well to have comprehensive comparisons with Melnick (2013).

We thank the reviewer for pointing this out. The decision was informed by Melnick’s findings which indicated high correlations between Surround suppression (SI) and the Verbal Comprehension, Perceptual Reasoning, Working Memory, and Processing Speed Indexes, with correlation coefficients of 0.69, 0.47, 0.49, and 0.50, respectively. It is well-established that the hMT+ region of the brain is a sensory cortex involved in visual perception processing (3D perception). Furthermore, motion surround suppression (SI), a specific function of hMT+, aligns closely with this region's activities. Given this context, the Perception Reasoning sub-ability was deemed to have the clearest mechanism for further exploration. Consequently, we selected the most representative subtest of Perception Reasoning—the Block Design Test—which primarily assesses 3D visual intelligence.” For further clarification, due to these reasons, we conducted only the visuo-spatial subtest.

Minor:

Comments:

In the first revised version, we addressed the following recommendations in the 'Author response' file titled 'Recommendation for the authors.' It seems our response may not have reached you successfully. We would like to share and expand upon our response here:

(1) Table 1 and Table supplementary 1-3 contain many correlation results. But what are the main points of these values? Which values do the authors want to highlight? Why are only p-values shown with significance symbols in Table supplementary 2??

(1.1) What are the main points of these values?

Thank reviewer for pointing this out. These correlations represent the relationship between behavior task (SI/BDT) and resting-state functional connectivity. It indicates that left hMT+ is involved in the efficient information integration network when it comes to BDT task. In addition, left hMT+’s surround suppression is involved in several hMT+ - frontal connectivity. Furthermore, the overlap regions between two task indicates the underlying mechanism.

(1.2) Which values do the authors want to highlight?

Table 1 and Table Supplementary 1-3 present the preliminary analysis results for Table 2 and Table Supplementary 4-6. So, we generally report all value. Conversely, in the Table 2 and Table Supplementary 4-6, we highlight the value which support our main conclusion.

(1.3) Why are only p-values shown with significance symbols in Table Supplementary 2?

Thank you for pointing this out, it is a mistake. We have revised it and delete the significance symbols.

(2) Line 27, it is unclear to me what is "the canonical theory".

We thank reviewer for pointing this out. We have revised “the canonical theory" to “the prevailing opinion” (line 27)

(3) Throughout the paper, the authors use "MT+", I would suggest using "hMT+" to indicate the human MT complex, and to be consistent with the human fMRI literature.

We thank reviewer for pointing this out. We have revised them.

(4) At the beginning of the results section, I suggest including the total number of subjects. It is confusing what "31/36 in MT+, and 28/36 in V1" means.

We thank reviewer for pointing this out. We have included the total number of subjects in the beginning of result section. (line 110, line 128)

(5) Line 138, "This finding supports the hypothesis that motion perception is associated with neural activity in MT+ area". This sentence is strange because it is a well-established finding in numerous human fMRI papers. I think the authors should be more specific about what this finding implies.

We thank reviewer for pointing this out. We have revised it to:” This finding is in line with prior results, which indicates that motion perception is associated with neural activity in hMT+ area, but not in EVC (primarily in V1)” (lines 156-158)

(6) There are no unit labels for all x- and y-axies in Figure 1. I only see the unit for Conc is mmol per kg wet weight.

We thank reviewer for pointing this out. Figure 1 is a schematic and workflow chart, so labels for x- and y-axes are not needed. I believe this confusion might pertain to Figure 3. In Figures 3a and 3b, the MRS spectrum does not have a standard y-axis unit as it varies based on the individual physical conditions of the scanner; it is widely accepted that no y-axis unit is used. While the x-axis unit is ppm, which indicate the chemical shift of different metabolites. In Figure 3c, the BDT represents IQ scores, which do not have a standard unit. Similarly, in Figures 3d and 3e, the Suppression Index does not have a standard unit.

(7) Although the correlations are not significant in Figure Supplement 2&3, please also include the correlation line, 95% confidence interval, and report the r values and p values (i.e., similar format as in Figure 1C).

We thank reviewer for pointing this out. We have revised them and include the correlation line, 95% confidence interval, r values and p values.

(8) There is no need to separate different correlation figures into Figure Supplementary 1-4. They can be combined into the same figure.

We thank reviewer for the suggestion. However, each correlation figure in the supplementary figures has its own specific topic and conclusion. Please notes that in the revised version, we have added a figure showing the EVC (primarily in V1) MRS scanning ROI as Supplementary Figure 1. Therefore, the figures the reviewer is concerned about are Supplementary Figure 2-5. The correlation figures in Supplementary Figure 2 indicate that GABA in EVC (primarily in V1) does not show any correlation with BDT and SI, illustrating that inhibition in EVC (primarily in V1) is unrelated to both 3D visuo-spatial intelligence and motion suppression processing. The correlations in Supplementary Figure 3 indicate that the excitation mechanism, represented by Glutamate concentration, does not contribute to 3D visuo-spatial intelligence in either hMT+ or EVC (primarily in V1). Supplementary Figure 4 validates our MRS measurements. Supplementary Figure 5 addresses potential concerns regarding the impact of outliers on correlation significance. Even after excluding two “outliers” from Figures 3d and 3e, the correlation results remain stable.

(9) Line 213, as far as I know, the study (Melnick et al., 2013) is a psychophysical study and did not provide evidence that the spatial suppression effect is associated with MT+.

We thank reviewer for pointing this out. It was a mistake to use this reference, and we have revised it accordingly. (line 242)

(10) At the beginning of the results, I suggest providing more details about the motion discrimination tasks and the measurement of the BDT.

We thank reviewer for pointing this out. We have included some brief description of task in the beginning of result section. (lines 116-120)

(11) Please include the absolute duration thresholds of the small and large sizes of all subjects in Figure 1.

We thank reviewer for the suggestion. We have included these results in Figure 3.

(12) Figure 5 is too small. The items in plot a and b can be barely visible.

We thank reviewer for pointing this out. We increase the size and resolution of the Figure.

Reviewer #3 (Public Review):

(1) Throughout the manuscript, hMT+ connectivity with the frontal cortex has been treated as an a priori hypothesis/space. However, there is no such motivation or background literature mentioned in the Introduction. Can the authors clarify the necessity of functional connectivity? In other words, can BOLD activity of hMT+ in the localizer task substitute for functional connectivity between hMT+ and the frontal cortex?

(1.1) Throughout the manuscript, hMT+ connectivity with the frontal cortex has been treated as an a priori hypothesis/space. However, there is no such motivation or background literature mentioned in the Introduction. Can the authors clarify the necessity of functional connectivity?

We thank reviewer for pointing this out. We offered additional motivation and background literature in the introduction: “Frontal cortex is usually recognized as the cognitive core region (Duncan et al., 2000; Gray et al., 2003). Strong connectivity between the cognitive regions suggests a mechanism for large-scale information exchange and integration in the brain (Barbey, 2018; Cole et al., 2012).  Therefore, the potential conjunctive coding may overlap with the inhibition and/or excitation mechanism of hMT+. Taken together, we hypothesized that 3D visuo-spatial intelligence (as measured by BDT) might be predicted by the inhibitory and/or excitation mechanisms in hMT+ and the integrative functions connecting hMT+ with frontal cortex (Figure 1a).” (lines 67-74). Additionally, we have included a whole-brain analysis for validation. Functional connectivity reveals the information exchange relationships across regions, enhancing our understanding of how hMT+ and the frontal cortex collaborate when solving visual-spatial intelligence tasks.

(1.2) In other words, can BOLD activity of hMT+ in the localizer task substitute for functional connectivity between hMT+ and the frontal cortex?

We thank the reviewer for this question. The localizer task was used solely for defining the hMT+ MRS scanning area. Functional connectivity was measured using resting-state fMRI. Research has shown that resting-state functional connectivity between the frontal cortex and other ROIs can further reveal the neural mechanisms underlying intelligence tasks (Song et al., 2008).

(2) There is an obvious mismatch between the in-text description and the content of the figure:<br /> "In contrast, there was no correlation between BDT and GABA levels in V1 voxels (figure supplement 1a). Further, we show that SI significantly correlates with GABA levels in hMT+ voxels (r = 0.44, P = 0.01, n = 31, Figure 3d). In contrast, no significant correlation between SI and GABA concentrations in V1 voxels was observed (figure supplement 1b)."

We thank reviewer for pointing this out. We have revised it. The revised version is :” In contrast, there was no correlation between BDT and GABA levels in V1 voxels (figure supplement 2a). Further, we show that SI significantly correlates with GABA levels in hMT+ voxels (r = 0.44, P = 0.01, n = 31, Figure 3d). In contrast, no significant correlation between SI and GABA concentrations in V1 voxels was observed (figure supplement 2b).” (lines 151-156)

(3) The authors' response to my previous round of review indicated that the "V1 ROIs" covered a substantial amount of V3 (32%). Therefore, it would no longer be appropriate to call these "V1 ROIs". I'd suggest renaming them as "Early Visual Cortex (EVC) ROIs" to be more accurate. Can the authors justify why choosing the left hemisphere for visual intelligence task, which is typically believed to be right lateralized?

(3.1) The authors' response to my previous round of review indicated that the "V1 ROIs" covered a substantial amount of V3 (32%). Therefore, it would no longer be appropriate to call these "V1 ROIs". I'd suggest renaming them as "Early Visual Cortex (EVC) ROIs" to be more accurate.

We thank the reviewer for pointing this out. We have revised our description of the MRS scanning ROIs to Early Visual Cortex (EVC). Since the majority of our EVC ROIs are in V1 (around 70%) and almost no V2 was included, we decided to mark the EVC ROIs with the explanation "primarily in V1" for better clarification. This terminology has been widely used to better emphasize the V1-based experimental design.

(3.2) Can the authors justify why choosing the left hemisphere for visual intelligence task, which is typically believed to be right lateralized?

We thank the reviewer for pointing this out. The use of the left MT/V5 as a target was motivated by studies demonstrating that left MT+/V5 TMS is more effective at causing perceptual effects (Tadin et al., 2011). Therefore, we chose to use the left hMT+ as our MRS ROI and maintain consistency across different models' ROIs. Additionally, our results support the notion that the visual intelligence task is right lateralized in the frontal cortex. At the resting-fMRI level, we found that significant ROIs, where functional connectivity is highly correlated with BDT scores, are in the right frontal cortex (Figure 5a, b).

(4) "Small threshold" and "large threshold" are neither standard descriptions, and it is unclear what "small threshold" refers to in the following figure caption. Additionally, the unit (ms) is confusing. Does it refer to timing?<br /> "(f) Peason's correlation showing significant negative correlations between BDT and small threshold."

Thank you for pointing this out; we agree with your suggestion. We have revised the terms “small threshold” and “large threshold” to “duration threshold of small grating” and “duration threshold of large grating”, respectively. The unit (ms) refers to timing. The details are described in the methods section: “The duration was adaptively adjusted in each trial, and duration thresholds were estimated using a staircase procedure. Thresholds for large and small gratings were obtained from a 160-trial block that contained four interleaved 3-down/1-up staircases. For each participant, we computed the correct rate for different stimulus durations separately for each stimulus size. These values were then fitted to a cumulative Gaussian function, and the duration threshold corresponding to the 75% correct point on the psychometric function was estimated for each stimulus size”.

(5) In the response letter, the authors mentioned incorporating the neural efficiency hypothesis in the Introduction, but the revised Introduction does not contain such information.

We thank the reviewer for pointing this out. In our revised version, the second paragraph of the introduction addresses the neural efficiency hypothesis: “The “neuro-efficiency” hypothesis is one explanation for individual differences in gF (Haier et al., 1988). This hypothesis puts forward that the human brain’s ability to suppress irrelevant information leads to more efficient cognitive processing. Correspondingly, using a well-known visual motion paradigm (center-surround antagonism) (Liu et al., 2016; Tadin et al., 2003), Melnick et al found a strong link between suppression index (SI) of motion perception and the scores of the block design test (BDT, a subtest of the Wechsler Adult Intelligence Scale (WAIS), which measures the visuo-spatial component (3D domain) of gF (Melnick et al., 2013). Motion surround suppression (SI), a specific function of human extrastriate cortical region, middle temporal complex (hMT+), aligns closely with this region's activities (Gautama & Van Hulle, 2001). Furthermore, hMT+ is a sensory cortex involved in visual perception processing (3D domain) (Cumming & DeAngelis, 2001). These findings suggest that hMT+ potentially plays a significant role in 3D visuo-spatial intelligence by facilitating the efficient processing of 3D visual information and suppressing irrelevant information. However, more evidence is needed to uncover how the hMT+ functions as a core region for 3D visuo-spatial intelligence.” (lines 51-66)

Recommendations for the authors:

Reviewer #1 (Recommendations for The Authors):

In the Code availability, it states that "this paper does not report original code". It seems weird because at least the code to reproduce the figures from the data should be provided.

Thank you for pointing this out. Almost all figures were created using software such as DPABI, BrainNet, and GraphPad Prism 9.5, which are manually operated and do not require code adjustments. However, for the MRS fitting curve, we can provide our MATLAB code for redrawing the MRS fitting. The code has been uploaded to GitHub.

Author response:

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

Public Reviews:

Reviewer #1 (Public Review):

Summary:

In this work, Qiu and colleagues examined the effects of preovulatory (i.e., proestrous or late follicular phase) levels of circulating estradiol on multiple calcium and potassium channel conductances in arcuate nucleus kisspeptin neurons. Although these cells are strongly linked to a role as the "GnRH pulse generator," the goal here was to examine the physiological properties of these cells in a hormonal milieu mimicking late proestrus, the time of the preovulatory GnRH-LH surge. Computational modeling is used to manipulate multiple conductances simultaneously and support a role for certain calcium channels in facilitating a switch in firing mode from tonic to bursting. CRISPR knockdown of the TRPC5 channel reduced overall excitability, but this was only examined in cells from ovariectomized mice without estradiol treatment. The patch clamp experiments are comprehensive and overall solid but a direct demonstration of the role of these conductances in being necessary for surge generation (or at least having a direct physiological consequence on surge properties) is lacking, substantially reducing the impact of the findings.

Strengths:

(1) Examination of multiple types of calcium and potassium currents, both through electrophysiology and molecular biology.

(2) Focus on arcuate kisspeptin neurons during the surge is relatively conceptually novel as the anteroventral periventricular nucleus (AVPV) kisspeptin neurons have received much more attention as the "surge generator" population.

(3) The modeling studies allow for direct examination of manipulation of single and multiple conductances, whereas the electrophysiology studies necessarily require examination of each current in isolation. The construction of an arcuate kisspeptin neuron model promises to be of value to the reproductive neuroendocrinology field.

We thank the reviewer for recognizing our comprehensive examination of Kiss-ARH neurons through electrophysiological, molecular and computational modeling of their activity during the preovulatory surge, which as the reviewer pointed out is “conceptually novel.”  We  have bolstered our argument that Kiss1-ARH neurons transition from synchronized firing to burst firing with the E2-mediated regulation of channel expression with the addition of new experiments. We have addressed the recommendations as follows:

Weaknesses:

(1) The novelty of some of the experiments needs to be clarified. This reviewer's understanding is that prior experiments largely used a different OVX+E2 treatment paradigm mimicking periods of low estradiol levels, whereas the present work used a "high E2" treatment model. However, Figures 10C and D are repeated from a previous publication by the same group, according to the figure legend. Findings from "high" vs. "low" E2 treatment regimens should be labeled and clearly separated in the text. It would also help to have direct comparisons between results from low E2 and high E2 treatment conditions.

We have revised Figures 10C and 10D to include new findings (only) on Tac2 and Vglut2 expression in OVX and E2-treated Kiss1ARH.  Most importantly, our E2 treatment regime is clearly stated in the Methods and is exactly the same that was used previously (Qiu, eLife 2016 and Qiu, eLife 2018) for the induction of the LH surge in OVX mice (Bosch, Molecular and Cellular Endocrinology 2013) .

(2) In multiple places, links are made between the changes in conductances and the transition from peptidergic to glutamatergic neurotransmission. However, this relationship is never directly assessed. The data that come closest are the qPCR results showing reduced Tac2 and increased Vglut2 mRNA, but in the figure legend, it appears that these results are from a prior publication using a different E2 treatment regimen.

In the revised Figure 1, we have now included a clear depiction of the transition from synchronized firing driven by NKB signaling in OVX females to burst firing driven by glutamate in E2-treated females. All of the qPCR results in the revised manuscript are new.  We have used the same E2 treatment paradigm as previously published (Qiu, eLife 2018).

(3) Similarly, no recordings of arcuate-AVPV glutamatergic transmission are made so the statements that Kiss1ARH neurons facilitate the GnRH surge via this connection are still only conjecture and not supported by the present experiments.

Using a horizontal hypothalamic slice preparation, we have shown that Kiss1-ARH neurons excite GnRH neurons via Kiss1ARH glutaminergic input to Kiss1AvPV/Pen neurons (summarized in Fig. 12, Qiu, eLife 2016). We did not think that it was necessary to repeat these experiments for the current manuscript.

(4) Figure 1 is not described in the Results section and is only tenuously connected to the statement in the introduction in which it is cited. The relevance of panels C and D is not clear. In this regard, much is made of the burst firing pattern that arises after E2 treatment in the model, but this burst firing pattern is not demonstrated directly in the slice electrophysiology examples.

We have extensively revised Figure 1 to include new whole-cell, current clamp recordings that document burst firing  in  E2-treated, OVX females, which is now cited in the Results.

(5) In Figure 3, it would be preferable to see the raw values for R1 and R2 in each cell, to confirm that all cells were starting from a similar baseline. In addition, it is unclear why the data for TTA-P2 is not shown, or how many cells were recorded to provide this finding.

Before initiating photo-stimulation for each Kiss1-ARH neuron, we adjust the resting membrane potential to -70 mV, as noted  in each panel in Figure 3, through current injections. We have now included new findings on the effects of the T-channel blocker TTA-P2 on slow EPSP in the revised Figure 3. The number of cells tested with each calcium channel blocker is depicted in each of the bar graphs summarizing the effects of the blockers (Figure 3E).

(6) In Figure 5, panel C lists 11 cells in the E2 condition but panel E lists data from 37 cells. The reason for this discrepancy is not clear.

In Figure 5D, we measured the L-, N-, P/Q and R channel currents after pretreatment with TTA-P2 to block the T-type current, whereas in Figure 5C, we measured the total current without TTA-P2.

(7) In all histogram figures, it would be preferable to have the data for individual cells superimposed on the mean and SEM.

In the revised Figures we have included the individual data points for the individual neurons and animals (qPCR). 

(8) The CRISPR experiments were only performed in OVX mice, substantially limiting interpretation with respect to potential roles for TRPC5 in shaping arcuate kisspeptin neuron function during the preovulatory surge.

The TRPC5 channels are most  important for generating slow EPSPs when expression of NKB is high in the OVX state. Conversely, the glutamatergic response becomes more significant when the expression of NKB and TRPC5 channel are muted in the E2-treated state. Therefore, the CRISPR experiments were specifically conducted in OVX mice to maximize the effects.

(9) Furthermore, there are no demonstrations that the CRISPR manipulations impair or alter the LH surge.

In this manuscript, our focus is on the cellular electrophysiological activity of the Kiss1ARH neurons in OVX and E2-treated OVX females. Exploration of CRISPR manipulations related to the LH surge is certainly slated for future  experiments, but these in vivo experiments are  beyond the scope of these comprehensive cellular electrophysiological and molecular studies.

(10) The time of day of slice preparation and recording needs to be specified in the Methods.

We have provided the times of slice preparation and recordings in the revised Methods and Materials.

Reviewer #2 (Public Review):

Summary:

Kisspeptin neurons of the arcuate nucleus (ARC) are thought to be responsible for the pulsatile GnRH secretory pattern and to mediate feedback regulation of GnRH secretion by estradiol (E2). Evidence in the literature, including the work of the authors, indicates that ARC kisspeptin coordinate their activity through reciprocal synaptic interactions and the release of glutamate and of neuropeptide neurokinin B (NKB), which they co-express. The authors show here that E2 regulates the expression of genes encoding different voltage-dependent calcium channels, calcium-dependent potassium channels, and canonical transient receptor potential (TRPC5) channels and of the corresponding ionic currents in ARC kisspeptin neurons. Using computer simulations of the electrical activity of ARC kisspeptin neurons, the authors also provide evidence of what these changes translate into in terms of these cells' firing patterns. The experiments reveal that E2 upregulates various voltage-gated calcium currents as well as 2 subtypes of calcium-dependent potassium currents while decreasing TRPC5 expression (an ion channel downstream of NKB receptor activation), the slow excitatory synaptic potentials (slow EPSP) elicited in ARC kisspeptin neurons by NKB release and expression of the G protein-associated inward-rectifying potassium channel (GIRK). Based on these results, and on those of computer simulations, the authors propose that E2 promotes a functional transition of ARC kisspeptin neurons from neuropeptide-mediated sustained firing that supports coordinated activity for pulsatile GnRH secretion to a less intense firing in glutamatergic burst-like firing pattern that could favor glutamate release from ARC kisspeptin. The authors suggest that the latter might be important for the generation of the preovulatory surge in females.

Strengths:

The authors combined multiple approaches in vitro and in silico to gain insights into the impact of E2 on the electrical activity of ARC kisspeptin neurons. These include patch-clamp electrophysiology combined with selective optogenetic stimulation of ARC kisspeptin neurons, reverse transcriptase quantitative PCR, pharmacology, and CRIPR-Cas9-mediated knockdown of the Trpc5 gene. The addition of computer simulations for understanding the impact of E2 on the electrical activity of ARC kisspeptin cells is also a strength.

The authors add interesting information on the complement of ionic currents in ARC kisspeptin neurons and on their regulation by E2 to what was already known in the literature. Pharmacological and electrophysiological experiments appear of the highest standards. Robust statistical analyses are provided throughout, although some experiments (illustrated in Figures 7 and 8) do have rather low sample numbers.

The impact of E2 on calcium and potassium currents is compelling. Likewise, the results of Trpc5 gene knockdown do provide good evidence that the TRPC5 channel plays a key role in mediating the NKB-mediated slow EPSP. Surprisingly, this also revealed an unsuspected role for this channel in regulating the membrane potential and excitability of ARC kisspeptin neurons.

We thank the reviewer for recognizing that the “pharmacological and electrophysiological experiments appear of the highest standards” and “the addition of the computer modeling for understanding the impact of E2 on the electrical activity of ARC kisspeptin cells is also a strength.  However, we agree with the reviewer that we needed to provide a direct demonstration of “burst-like” firing of Kiss1-ARH neurons, which we have provided in Figure 1. We have addressed the other recommendations as follows:

Weaknesses:

The manuscript also has weaknesses that obscure some of the conclusions drawn by the authors.

One has to do with the fact that "burst-like" firing that the authors postulate ARC kisspeptin neurons transition to after E2 replacement is only seen in computer simulations, and not in slice patch-clamp recordings. A more direct demonstration of the existence of this firing pattern, and of its prominence over neuropeptide-dependent sustained firing under conditions of high E2 would make a more convincing case for the authors' hypothesis.

We have provided  a more direct demonstration of the existence of this firing pattern in the whole-cell current clamp experiments in the revised Figure 1.

In addition, and quite importantly, the authors compare here two conditions, OVX versus OVX replaced with high E2, that may not reflect the physiological conditions (the diestrous [low E2] and proestrous [high E2] stages of the estrous cycle) under which the proposed transition between neuropeptide-dependent sustained firing and less intense burst firing might take place. This is an important caveat to keep in mind when interpreting the authors' findings. Indeed, that E2 alters certain ionic currents when added back to OVX females, does not mean that the magnitude of these ionic currents will vary during the estrous cycle.

We have published that the magnitude of the slow EPSP, which is TRPC5 channel mediated, varies throughout the estrous cycle with the slow EPSP reaching a maximal amplitude during diestrus, which was significantly reduced during proestrus,  similar to that found in OVX compared to E2-treated, OVX females (Figure 2, Qiu, eLife 2016).  Moreover, TRPC5 channel mRNA expression,  similar to the peptides, is downregulated by an E2 treatment (Figure 10 this manuscript) that mimics proestrus levels of the steroid (Bosch et al., Mol Cell Endocrinology 2013). Furthermore, the magnitude of ionic currents is directly proportional to the number of ion channels expressed in the plasma membrane, which we have found correlates with mRNA expression. Therefore, it is likely that the magnitude of these ionic currents will vary during the estrous cycle.

Lastly, the results of some of the pharmacological and genetic experiments may be difficult to interpret as presented. For example, in Figure 3, although it is possible that blockade of individual calcium channel subtypes suppresses the slow EPSP through decreased calcium entry at the somato-dendritic compartment to sustain TRPC5 activation and the slow depolarization (as the authors imply), a reasonable alternative interpretation would be that at least some of the effects on the amplitude of the slow EPSP result from suppression of presynaptic calcium influx and, thus, decreased neurotransmitter and neuropeptide secretion. Along the same lines, in Figure 12, one possible interpretation of the observed smaller slow EPSPs seen in mice with mutant TRPC5 could be that at least some of the effect is due to decreased neurotransmitter and neuropeptide release due to the decreased excitability associated with TRPC5 knockdown.

The reviewer raises a good point, but our previous findings clearly demonstrated that chelating intracellular calcium with BAPTA in whole-cell current clamp recordings abolishes the slow EPSP and persistent firing (Qiu et al., J. Neurosci 2021), which we have noted is the  rationale for dissecting out the contribution of T, R, N, L and P/Q calcium channels to the slow EPSP in our current studies.  The revised Figure 3 also includes the effects of T-channel blocker.

However, to further bolster the argument for the post-synaptic contribution of the calcium channels to the slow EPSP  and eliminate the potential presynaptic effects of the calcium channel blockers on the postsynaptic slow EPSP amplitude, which may result from reduced presynaptic calcium influx and subsequently decreased neurotransmitter release, we have utilized an additional strategy. Specifically, we have measured the response to the externally administered TACR3 agonist senktide under conditions in which the extracellular calcium influx, as well as neurotransmitter and neuropeptide release, are blocked (revised Figure 3).

Recommendations for the authors:

Reviewer #1 (Recommendations For The Authors):

(1) The use of optogenetics in Figure 3 to trigger the slow EPSP could be better clarified in the text.

We have clarified in the Methods the optogenetic protocol for generating the slow EPSP, which we have published previously (Qiu et al., eLife 2016; eLife 2018, J. Neurosci 2021).

(2) The citation for Figure 4C in the text does not match what is shown in the figure.

Figure 4C has been removed in the revised manuscript.

(3) Figure 5 - it would be clearer to have panel D labeled as "model results" or similar to distinguish it from the slice recording data.

Panel D has been labeled as "Model results”.

(4) The text in lines 191-197 in the Results may be better suited to the Discussion.

We have modified the text in order to present the new findings without the discussion points.

(5) It is somewhat confusing to have figure panels cited out of order in the main text (e.g., 7H before 7G and 8H before 8G).

We have edited the text to report the findings in the proper order of the panels in Figures 7 and 8.

Reviewer #2 (Recommendations For The Authors):

- The observations that E2 treatment of OVX mice has an effect on the magnitude of a number of ionic currents does not necessarily mean that these changes will be seen during the estrous cycle, in response to fluctuations in circulating E2 concentrations. Experiments comparing either different estrous cycle stages or OVX mice treated with low or high E2 would be required to gain insight into this question. As such, the relevance of the authors' findings (however interesting these are as they stand) to any potential physiological endocrine/reproductive state transition is questionable, in the reviewer's opinion. The authors should acknowledge this important caveat and moderate the interpretations of their findings and the conclusions of their manuscript accordingly.

We have published that the magnitude of the slow EPSP, which is TRPC5 channel mediated, varies throughout the estrous cycle with the slow EPSP being large during diestrus and significantly reduced during proestrus,  similar to that found in OVX compared to E2-treated, OVX females (Figure 2, Qiu, eLife 2016).  Moreover, TRPC5 channel mRNA expression,  similar to the peptides, is downregulated by an E2 treatment (Figure 10 this manuscript) that mimics proestrus levels of the steroid (Bosch et al., Mol Cell Endocrinology 2013). Furthermore, the magnitude of ionic currents is directly proportional to the number of ion channels expressed in the plasma membrane, which we have found correlates with mRNA expression. Therefore, it is likely that the magnitude of these ionic currents will vary during the estrous cycle.

- The bursting firing pattern that the authors refer to and postulate will favor glutamate release under high E2 conditions is only seen in the computer simulations, not in patch-clamp recordings in brain slices (see also comment below). This substantially weakens some of the conclusions of the manuscript. Unless the authors can convincingly demonstrate a change in ARC kisspeptin firing pattern in response to increasing E2 using electrophysiology, these conclusions should be moderated.

We now include examples of burst firing activity under E2-treatment conditions in Figure 1 and have included summary figure (pie chart) documenting that a significant percentage of cells exhibit this activity with E2 treatment.  

Other comments:

- Title: "E2 elicits distinct firing patterns" is not shown in this work. As such, the title needs to be revised.

We now show these distinct firing patterns in Figure 1, so we think the wording in the title is an accurate reflection of our findings. 

- Abstract: some of the interpretations are overstated, in the reviewer's opinion.

Line 23, "... elevating the whole-cell calcium current and contributing to high-frequency firing" should be moderated, as what is shown by the authors is that blockade of calcium channel subtypes suppresses the slow EPSP and associated firing, the frequency of which is not reported (see also a later comment).

We now include examples of burst firing activity under E2-treatment conditions in Figure 1 and have modified the abstract to state “high frequency burst firing.”

Lines 26-28, that "mathematical modeling confirmed the importance of TRPC5 channels for initiating and sustaining synchronous firing, while GIRK channels, activated by Dyn binding to kappa opioid receptors, were responsible for repolarization" is simply not what the simulations show, in the reviewer's opinion. Indeed, there is no consideration of synchronous activity in the model, which simulates the firing of a single ARC kisspeptin neuron. Further, the model shows that TRPC5 can contribute to overall excitability (firing in response to current injection, Figure 12G) and that increasing TRPC5 conductance increases firing in response to NKB while this is decreased by adding GIRK conductance to the model (Figure 13A). Therefore, considerations of the importance of TRPC5 channels in initiating synchronous firing and the role of Dyn A-induced GIRK activity should not be included in the interpretations of the mathematical simulations.

The significance of synchronization lies in the fact that when neuronal networks synchronize, the behavior of each neuron within the network becomes identical. In such scenarios, the firing of a single neuron mirrors the activity of the entire neuronal network. Consequently, our model simulations, based on a single-cell neuronal model, can be utilized to make reliable inferences about synchronized neuronal activity.

Lines 31-33 (also lines 92-95), that "the transition to burst firing with high, preovulatory levels of E2 facilitates the GnRH surge through its glutamatergic synaptic connection to preoptic Kiss1 neurons" is not supported by the experiments (physiologic or computational) described in the manuscript, and is, therefore, only speculative. These statements should be removed throughout the manuscript.

Previously, we (Qiu et al., (eLife 2016) documented a direct glutamatergic projection from Kiss1-ARH neurons to Kiss1-AVPV/PeN neurons.  Moreover, Lin et al. (Frontiers Endocrinology 2021) demonstrated that low frequency stimulation of Kiss1-ARH:ChR2 neurons, that is known to only release glutamate, boosts the LH surge, and in a follow-up paper the O’Byrne lab blocked this stimulation with ionotropic glutamate antagonists (Shen et al., Frontiers in Endocrinology 2022).  We have included these references in the Introduction and Discussion, but we did not think that it was necessary to cite these papers in the Abstract.  However, we have re-worded this final statement in the Abstract to: “the transition to burst firing with high, preovulatory levels of E2 would facilitate the GnRH surge….” 

- Introduction: the usefulness of Figure 1 is questionable. From reading the figure legend, it is the reviewer's understanding that panels A and B are published elsewhere (there is no description of methods or results in the manuscript). Further, panels C and D are meant to illustrate that ARC kisspeptin neurons display different types of firing in OVX vs E2-treated OVX mice. The legend to C indicates that the trace illustrates "synchronous firing" but shows one cell (how can this be claimed as synchronous?) - the legend to D indicates that the trace "demonstrates" burst firing in ARC kisspeptin neurons. This part of the figure is, in the reviewer's opinion, misleading because these are only two examples (no quantifications or replicates are provided) obtained by stimulating firing in two different endocrine conditions by two different agonists. The "demonstration" of differential firing patterns would require a thorough examination of firing patterns in response to current injections (as in Figure 12 E-F) or in response to the two agonists, under the different hormonal conditions.

Figure 1 has now been completely revised to include new data documenting the different firing patterns.  The methods detailing these experiments can be found in the Material and Methods section.

The introduction presents a rather incomplete picture of what is known regarding how ARC kisspeptin neurons might coordinate their activity to drive episodic GnRH secretion, and it omits published work showing that blockade of glutamate receptors (in particular AMPA receptors) decreases ARC kisspeptin neuron coordinated activity in the brain slices and in vivo and suppresses pulsatile GnRH/LH secretion in mice.

If we are not mistaken, the reviewer is referring to fiber photometry recordings of GCaMP activity, which we cite in the Discussion.  However, for the Introduction we tried to “set the stage” for our studies on measuring the individual channels underlying the different firing patterns and how they are regulated by E2.

The introduction is also quite long with extensive descriptions of previous work by the authors and in other brain areas that would be better suited for the discussion.

Again, we are trying to rationalize why we focused on particular ion channels based on the literature.

- Results: lines 129-132 should be moderated, as whether calcium channels increase excitability or facilitate TRPC5 channel opening has not been directly assessed here.

High frequency optogenetic stimulation of Kiss1-ARH neurons and NKB through its cognate receptor (TACR3) activates TRPC 5 channels (Qiu et al., eLife 2016; J. Neurosci 2021). BAPTA prevents the opening of TRPC5 channels and abrogates the slow EPSP following high frequency stimulation.  Figure 3 documents that inhibition of voltage-activated calcium channels attenuates the slow EPSP, which results in a decrease in excitability.

Lines 145-146, one limitation of this experiment is that blockade of calcium channel subtypes will not only affect calcium entry and subsequent actions of calcium on TRPC5 channels but also impair the release of neurotransmitters and neuropeptides from kisspeptin neurons. The interpretation that "calcium channels contribute to maintaining the sustained depolarization underlying the slow EPSP" needs, therefore, to be moderated as it is not possible to extract the direct contribution of calcium channels to the activation of TRPC5 channels from these experiments.

We cited our previous findings documenting that chelating intracellular calcium with BAPTA abolishes the slow EPSP and persistent firing (Qiu et al., J Neurosci 2021).  However, to eliminate the potential effects of calcium channel blockers on the slow EPSP amplitude, which may result from reduced presynaptic calcium influx and subsequently decreased neurotransmitter and neuropeptide secretion, we adopted a different strategy by comparing responses between Senktide and Cd2+ plus Senktide. Our findings revealed that the non-selective Ca2+ channel blocker Cd2+ significantly inhibited Senk-induced inward current (Figures 3F-H).

Panel C should be removed from Figure 4, as it is published elsewhere.

Figure 4C has been removed.

Lines 168-169, "...E2 treatment led to a significant increase in the peak calcium current density in Kiss1ARH neurons, which was recapitulated as predicted by our computational modeling..." How did the model "predict" this increase in calcium current density? As no information is provided in the methods or supplementary information as to how the effect of E2 was integrated into the model, the authors will need to provide additional narration in the text to explain this statement. The "T-channel inflection" referred to in the figure legend will also need to be explained. Lastly, in Figure 5C, the current density unit should be pA/pF. 

We have added text in the supplementary information to explain how we used the qPCR and electrophysiological data to inform the model regarding the effect that E2 has on the various ionic currents and noted in the Figure 13 legend that the increase/decrease in the conductances is physiologically mediated by E2. We have eliminated the T-channel inflection point (Figure 5D) and corrected the current density label (Figure 5C).

Lines 198-199, please clarify "E2 does not modulate calcium channel kinetics directly but rather alters the mRNA expression to increase the conductance".

We have clarified that “that long-term E2 treatment does not modulate calcium channel kinetics but rather alters the mRNA expression to increase the calcium channel conductance” by referring to the specific figures (i.e., Figures 4, 6) in a previous sentence.

Figures 7 and 8 titles do not accurately reflect the contents: there is nothing about repolarization in the experiments illustrated in Figure 7 or Figure 8. The sample sizes (3 to 4 cells) are also quite small for these experiments.

We have modified the Figure titles per the reviewer’s comments and increased the cell numbers.

The title of Figure 9 also does not fully reflect the figure's contents. Although panel G does suggest that the M current contributes to regulating the membrane potential, the reviewer's reading of this figure panel is that the fractional contribution of the M current does not vary during a short burst of action potentials. The suggestion that "KCNQ channels play a key role in repolarizing Kiss1ARH neurons following burst firing" (line 272) and the statement that "our modeling predicted that M-current contributed to the repolarization following burst firing" (line 273) should be revised accordingly.

The point is that the M-current contributes, albeit a small fraction, to the repolarization during burst firing.

Line 288, please indicate what figure informs this statement.

We have revised the statement since the modeling (Figure 13) comes later in the Results.

Line 311-313, this sentence only superficially describes the simulation, in the reviewer's opinion. Does the model inform on how TRPC5 channels/currents do that? The supplementary information indicates that there is a tone of extracellular neurokinin B embedded in the model. This is important information that should be clearly stated in the manuscript. The authors should also consider discussing the influence of this neurokinin B tone on the contribution of TRPC5 to cell excitability. As a neurokinin B tone in the extracellular space will likely alter the firing of kisspeptin neurons in the model, readers will likely need more information about all this.

In our current ramp simulations of the model (Fig 12 G&H) there is no involvement of neurokinin B (i.e., the NKB parameter  is set to zero), and the effect on the rheobase is solely due to the decrease of the TRPC5 conductance.  In the model, TRPC5 channels are activated by intracellular calcium levels and are therefore contributing to cell excitability even in the absence of extracellular NKB. The NKB tone is used for the simulations presented in Figure 13 where we vary the TRPC5 conductance under saturating levels of extracellular NKB.

Lines 316-318 also read as quite superficial. More explanations of what is illustrated in Figure 13 are needed. In particular, it is unclear from the methods and supplementary information what the different ratios of conductances in OVX+E2 vs in OVX are and how they were varied in the model. Furthermore, it is unclear to the reviewer how the outcome of these simulations matches the authors' postulate that E2 enables a transition to a burst firing pattern that favors glutamate release. Looking at simulated firing in Figure 13B, E2 (by increasing calcium conductances) would tend to enable high-frequency firing within bursts (nearing 50 Hz by eye) and high burst rates (approximately 4 bursts per second), which the reviewer would argue might be expected to cause significant neuropeptide release in addition to that of glutamate.

We have added to the text: “Furthermore, the burst firing of the OVX+E2 parameterized model was supported by elevated h- and Ca 2+-currents (Figure 13B) as well as by the high conductance of Ca2+ channels relative to the conductance of TRPC5 channels (Figure 13C).” We have also provided in the Supplemental Information (Table of Model Parameters) the specific conductances in the OVX and OVX+E2 state and how they are varied to produce the model simulations.

Granted the high frequency firing during a burst could release peptide, but in the E2-treated, OVX females the expression of the peptides are at “rock bottom.”  Therefore, the sustained high frequency firing during the slow EPSP in the OVX state would generate maximum peptide release.

In Figure 13C, the reviewer is unclear on the ranges of TRPC5 conductances shown. The in vitro experiments suggest that E2 suppresses Trpc5 gene expression and might suppress TRPC5 currents. The ratio of gTRPC5(OVX+E2)/gTRPC5(OVX) should, thus, be <1.0. This is not represented in the parameter space provided, making the interpretation of this simulation difficult. Please clarify what the effect of decreasing gTRPC5 will be on firing patterns in the model.

Thank you for pointing this typographical error.  The ratio should be gTRPC5 (OVX)/TRPC5(OVX + E2) for the X-axis.

- Discussion: many statements and conclusions are overreaching and need to be revised; for example lines 320-322, 329-330, 335-338, 369, 371-373, 391-394, 463-464, and 489-494;

We have tempered these statements, so they are not “overreaching.”

Lines 489-494: the authors should integrate published observations that i) ablation of ARC kisspeptin neurons results in increased LH surges in mice and rats and that ii) optogenetic stimulation of ARC kisspeptin fibers in the POA is only effective at increasing LH secretion in a surge-like manner when done at high frequencies (20 Hz), in their discussion of the role of ARC kisspeptin neurons and their firing patterns in the preovulatory surge.

We have included the paper from the O’Byrne lab (Shen et al. Frontiers in Endocrinology 2022) in the Discussion. However, the Mittleman-Smith paper (Endocrinology, 2016) ablating KNDy neurons using NK3-saporin not only targeted KNDy neurons but other arcuate neurons that express NK3 receptors.  Therefore, we have not cited it in the Discussion.

Author response:

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

Public Reviews:

Reviewer #1 (Public Review):

Summary:

An online database called MRAD has been developed to identify the risk or protective factors for AD.

Strengths:

This study is a very intriguing study of great clinical and scientific significance that provided a thorough and comprehensive evaluation with regard to risk or protective factors for AD. It also provided physicians and scientists with a very convenient, free as well as user-friendly tool for further scientific investigation.

We thank the reviewer for the conclusion and positive comments.

Weaknesses:

(1) Comment: The paper mentions that the MRAD database currently contains data only from European populations, with no mention of data from other populations or ethnicities. Given potential differences in Alzheimer's Disease (AD) across different populations, the limitations of the data should be emphasized in the discussion, along with plans to expand the database to include data from more racial and geographic regions.

Thank you for your valuable comment. Further information regarding the limitations of populations is provided in the Conclusions section (page 19).

The newly added text describing the limitations of populations is as follows:

“However, in this study, since the GWAS datasets for both the exposure and the outcome traits (AD) selected were obtained from the public database (MRC IEU OpenGWAS), where the GWAS datasets for AD are only of European population, and since we use the TwoSampleMR, which requires that the populations for the exposure trait and the outcome trait be the same to satisfy the requirement for a control variable, this study currently has certain limitations in terms of population. We initiated a Mendelian randomization study on AD at clinical hospitals in China and are currently in the sample collection stage to address the limitations. In the future, we will integrate data from more populations and keep updating new progresses in AD research to explore its potential differences in different populations.”

(2) Comment: Sufficient information should be provided to clarify the data sources, sample selection, and quality control methods used in the MRAD database. Readers may expect more detailed information about the data to ensure data reliability, representativeness, and research applicability.

Thank you for your helpful suggestion. We appreciate you taking time and making effort in reviewing our manuscript and thank you for your insightful comments. We agree that adding more details is essential to make the manuscript more reliability, representativeness, and research applicability.

The newly added text describing more detailed information about the data is as follows:

(1) Sufficient information about data sources and sample selection (in the Data sources section of Methods section, page 8):

“Exposure traits

Inclusion criteria: datasets of the European population.

Exclusion criteria: (i) eQTL-related datasets; (ii) AD-related datasets.

“In this study, the GWAS datasets selected were derived from 42,335 GWAS datasets in the public database (MRC IEU OpenGWAS, https://gwas.mrcieu.ac.uk/). Based on the above inclusion and exclusion criteria, 19,942 eQTL-related datasets were excluded first, leaving 22,393 GWAS datasets. Next, the datasets with the European population were selected, and 18,117 GWAS datasets were obtained. Finally, 20 AD-related datasets were excluded; 18,097 GWAS datasets were obtained at the end as the exposure traits of this study (See Table S1 for basic information).

Outcome traits

Inclusion criteria: (i) datasets of patients with AD with complete information and clear data sources; (ii) datasets of the European population.

Exclusion criteria: (i) Number of SNPs <1 million; (ii) datasets with unspecified sex; (iii) datasets with a family history of AD; (iv) datasets with dementia.

Based on the above criteria, 16 GWAS datasets of outcome traits were selected from the MRC IEU OpenGWAS database, comprising datasets of AD from Alzheimer Disease Genetics Consortium (ADGC), Cohorts for Heart and Aging Research in Genomic Epidemiology Consortium (CHARGE), The European Alzheimer’s Disease Initiative (EADI), and Genetic and Environmental Risk in AD/Defining Genetic, Polygenic and Environmental Risk for Alzheimer’s Disease Consortium (GERAD/PERADES) 2019 (ieu-b-2); AD from Benjamin Woolf 2022 (ieu-b-5067); AD from International Genomics of Alzheimer's Project (IGAP) 2013 (ieu-a-297) as the datasets of main outcome traits for AD, as well as 13 datasets from FinnGen biobank 2021 corresponding to various AD subtypes, referred to as AD-finn subtypes. (as shown in Figure 2).”

(2) Sufficient information about quality control methods (in the Statistical models for causal effect inference section of Methods section, page 9-10:

“A random-effects IVW model was used in this study as the major analysis method to uncover potential risk or protective factors for AD. The random-effects IVW model as the gold standard for MR studies, its principle is to calculate the inverse of the variance of each IV as its weight, assuming all IVs are valid. The regression does not include an intercept term, and the final result is the weighted average of the effect estimates from all IVs [34]. This model indicates that the true effect values may vary across different studies due to both sampling error and the heterogeneity of the true effect. The weight of each study is jointly determined by its inverse variance and the estimated heterogeneity variance. Thus, as long as there is no pleiotropy, even when there is significant heterogeneity (p < 0.05), this method remains the best MR model.

To assess the robustness of the IVW results, sensitivity analysis was performed using six additional models: (i) MR-Egger: MR-Egger’s biggest difference from IVW is that it considers the intercept term during regression to evaluate bias caused by horizontal pleiotropy. The intercept represents the magnitude of horizontal pleiotropy, with a value close to 0 indicating minimal pleiotropy. The primary purpose is to detect and correct for horizontal pleiotropy. Thus, when significant horizontal pleiotropy is observed (p < 0.05), this method is preferred [35,36]. (ii) Weighted median: The weighted median method is a technique for evaluating causal relationships using a majority of genetic variants (SNPs). If at least 50% of the SNPs are valid IVs, the median of the causal estimates will tend toward the true causal effect. This method provides an unbiased estimate (i.e., the “majority validity” assumption) [37]. (iii) Simple mode: Involves comparing the frequencies or proportions of genotypes or phenotypes between control and experimental groups. Moreover, it can illustrate whether the observed differences in genotypes or phenotypes between the two groups are statistically significant. (iv) Weighted mode: The weighted mode method is a technique for combining multiple Mendelian randomization estimates. This method assigns weights to the causal effect estimates of different genetic variants on the trait and then takes the weighted mode as the final estimate of the causal effect. In genetic variant estimates, the method can decrease bias caused by outliers. (v) Maximum likelihood: This method is used when it is known that a random sample follows a particular probability distribution; however, the specific parameters of that distribution remain unknown, and it involves conducting multiple experiments, observing the results, and using those results to infer the approximate values of the parameters [38]. (vi) Penalized weighted median: An enhanced version of the weighted median estimate that provides a consistent estimate of the causal effect. (vii) Heterogeneity and horizontal pleiotropy assessment use the heterogeneity tests [39] and Egger intercept tests [40], respectively.”

(3) Comment: While the authors mention that the MRAD database offers interactive visualization interfaces, the paper lacks detailed information on how to interpret and understand these visual results. Guidelines on effectively using these visualization tools to help researchers better comprehend the data are essential.

Thank you very much for your feedback, as we believe that our manuscript has been improved substantially as a result of your input.  Owing to space constraints, the MRAD database user guide is included in the Supplementary Material. Meanwhile, for better understanding, the subheading of the relevant content in the Supplementary Material has been revised to “MRAD User Guide” (see Supplementary Material for details, page 11). Furthermore, considering user-friendliness, the user guide has been integrated into the database and can be accessed directly from the homepage by clicking on the “User Guide” module.

(4) Comment: In the conclusion section of the paper, it is advisable to explicitly emphasize the practical applications and potential clinical significance of the MRAD database. The paper should articulate how MRAD can contribute to the early identification, diagnosis, prevention, and treatment of AD and its potential societal and clinical value more clearly.

Thank you for pointing this out. In the Discussion section of the revised manuscript, we have now added how MRAD can contribute to the early identification, diagnosis, prevention, and treatment of AD and its potential societal as well as clinical value. And we reorganized the structure of Discussion section to make the text easier to understand, which could be helpful to further clarify the significance of MRAD. (page 15)

The newly added text describing the practical applications and potential clinical significance of the MRAD database is as follows:

“(i) The current methods for identifying AD mainly rely on assessment scales, cerebrospinal fluid (CSF) examinations, and brain PET/MRI. However, assessment scales can be biased by factors such as the anxiety and nervousness of the subjects. CSF examinations require an invasive lumbar puncture, leading to low patient acceptance. PET/MRI scans are expensive and have limited equipment accessibility. These limitations restrict early AD identification. Thus, there is a pressing clinical need for readily available, time- and cost-effective, and accurate detection methods. In this study, the Medical laboratory science and Molecular trait used could be less expensive, faster to detect, easier to operate, and more accessible for widespread adoption. They hold great value for early AD identification and have the potential to become crucial tools for identifying AD in the future. (ii) Imaging acts as a powerful assistive tool for diagnosing Alzheimer’s disease. Traditional imaging examinations mainly depict changes in the brain’s macroscopic structure, while research on microstructural changes in disease-related areas is relatively limited. Studies have demonstrated that microstructural neurodegenerative processes are extensive and pronounced during AD progression. Our study results cover traditional macroscopic neuroimaging results and reveal numerous potential causal relationships between brain microstructure and AD. The combination of macroscopic and microstructural insights will provide more valuable information for clinical diagnosis. (iii) Clarifying patient’s disease, past history, and family history can aid in preventing AD at an early stage, and prevention of AD could be attained through monitoring anthropometric indicators, improving gut microbiota, and adjusting lifestyle traits. (iv) Currently, the development of new drugs for AD is mainly underscored by Aβ, Tau, and other inhibitors. Since 2000, global pharmaceutical companies have invested hundreds of billions of dollars in the development of new drugs for AD, and these drugs have not yielded successful results. AD drug development has thus been perceived as having the highest failure rate of all drug research, reaching 99.6%. Hence, further research on molecular traits to find new targets and develop new drugs for these targets will provide new pathways for AD treatment.”

(5) Comment: Grammar and Spelling Errors: There are several spelling and grammar errors in the paper. Referring to a scientific editing service is recommended.

We appreciate your comments and suggestions for improving our manuscript. We have now used a professional editing service offered by Taylor and Francis to revise the grammar and language, and we have obtained a certificate of proof, which is attached. Thank you for recognizing our research, we have tried our best to improve the quality of this paper to ensure that it meets the high standards required for publication in of journal elife.

Reviewer #2 (Public Review):

Summary:

This MR study by Zhao et al. provides a comprehensive hypothesis-free approach to identifying risk and protective factors causal to Alzheimer's Disease (AD).

Strengths:

The study employs a comprehensive, hypothesis-free approach, which is novel over traditional hypothesis-driven studies. Also, causal associations between risk/protective factors and AD were addressed using genetic instruments and analysis.

We greatly appreciate the positive feedback regarding the overall quality of our work.

Major comments:

(1) Comment: The authors used the inverse-variance weighted (IVW) model as the primary method and other MR methods (MR-Egger, weighted mean, etc.) for sensitivity analysis. However, each method has its own assumption, and IVW is only robust when pleiotropy and heterogeneity are not severe. Rather than using IVW imprudently across all associations, it would be more appropriate to choose the best MR method for each association based on heterogeneity/Egger intercept tests. This customized approach, based on tests of MR assumption violations, yields more stable and reliable results. For reference, please follow up on work by Milad et al. (EHJ - "Plasma lipids and risk of aortic valve stenosis: a Mendelian randomization study"). This study selected the best MR model for each association based on pleiotropy and heterogeneity tests. Given the large number of tests in this work, I suggest initially screening significant signals using IVW, as done, and then validating the results using multiple MR methods for those signals. It is common for MR estimates from different methods to vary significantly (with some being statistically significant and others not), and in such cases, the MR estimates from the best-fitted model should be trusted and highlighted.

Thank you for your professional comments. We agree that our description of the Statistical models for causal effect inference was not specific enough. Therefore, we have included a new text describing more details about each method’s assumption and supplied a predefined approach to select the best statistical estimation from these methods in the Statistical models for causal effect inference section of Methods section (page 9-10). However, we would like to clarify our analysis method. In this study, the main analysis method used is the IVW random effects model instead of the IVW fixed effects model. The IVW random effects model indicates that the true effect values of different studies may vary, including both sampling error and heterogeneity of the true effect. The weight of each study is jointly determined by its inverse variance and the estimated heterogeneity variance. Thus, as long as there is no pleiotropy, even when there is significant heterogeneity (p < 0.05), this method is still the best MR model. We would like to thank you again for your feedback, as we believe that our manuscript has been improved substantially as a result of your input.

The newly added text describing more details about each method’s assumption and the customized best-fitted model is as follows:

“Statistical models for causal effect inference

A random-effects IVW model was used in this study as the major analysis method to uncover potential risk or protective factors for AD. The random-effects IVW model as the gold standard for MR studies, its principle is to calculate the inverse of the variance of each IV as its weight, assuming all IVs are valid. The regression does not include an intercept term, and the final result is the weighted average of the effect estimates from all IVs [34]. This model indicates that the true effect values may vary across different studies due to both sampling error and the heterogeneity of the true effect. The weight of each study is jointly determined by its inverse variance and the estimated heterogeneity variance. Thus, as long as there is no pleiotropy, even when there is significant heterogeneity (p < 0.05), this method remains the best MR model.

To assess the robustness of the IVW results, sensitivity analysis was performed using six additional models: (i) MR-Egger: MR-Egger’s biggest difference from IVW is that it considers the intercept term during regression to evaluate bias caused by horizontal pleiotropy. The intercept represents the magnitude of horizontal pleiotropy, with a value close to 0 indicating minimal pleiotropy. The primary purpose is to detect and correct for horizontal pleiotropy. Thus, when significant horizontal pleiotropy is observed (p < 0.05), this method is preferred [35,36]. (ii) Weighted median: The weighted median method is a technique for evaluating causal relationships using a majority of genetic variants (SNPs). If at least 50% of the SNPs are valid IVs, the median of the causal estimates will tend toward the true causal effect. This method provides an unbiased estimate (i.e., the “majority validity” assumption) [37]. (iii) Simple mode: Involves comparing the frequencies or proportions of genotypes or phenotypes between control and experimental groups. Moreover, it can illustrate whether the observed differences in genotypes or phenotypes between the two groups are statistically significant. (iv) Weighted mode: The weighted mode method is a technique for combining multiple Mendelian randomization estimates. This method assigns weights to the causal effect estimates of different genetic variants on the trait and then takes the weighted mode as the final estimate of the causal effect. In genetic variant estimates, the method can decrease bias caused by outliers. (v) Maximum likelihood: This method is used when it is known that a random sample follows a particular probability distribution; however, the specific parameters of that distribution remain unknown, and it involves conducting multiple experiments, observing the results, and using those results to infer the approximate values of the parameters [38]. (vi) Penalized weighted median: An enhanced version of the weighted median estimate that provides a consistent estimate of the causal effect. (vii) Heterogeneity and horizontal pleiotropy assessment use the heterogeneity tests [39] and Egger intercept tests [40], respectively.”

(2) Comment: Lines 157-160 mentioned "But to date, AD has been reported as hypothesis-driven MR study based on a single factor, ignoring the potential role of a huge number of other risk factors. Also, due to the high degree of heterogeneity present in AD subtypes, which have different biological and genetic characteristics. Thus, the previous studies cannot offer a systematic and complete viewpoint.". This statement overlooks a similar study published in Molecular Psychiatry ("A Phenome-wide Association and Mendelian Randomization Study for Alzheimer's Disease: A Prospective Cohort Study of 502,493"), which rigorously assessed the effects of 4171 factors spanning 10 different categories on AD using observational analysis and MR. The authors should revise their statement on the novelty of their study type throughout the manuscript and discuss how their work differs from and potentially strengthens previous studies.

Thank you for directing us to this literature. We have read this article carefully. This study shares some similarities with our study but there are significant differences with regards to sample sources and research fields. The study, as mentioned by the reviewer, used the UKB database as its sample source, and analyzed the association between 10 categories (comprising 4,171 factors) and AD, which were sociodemographic, physical measures, lifestyle and environment, health conditions, mental health, medications and operations, cognitive function, sex-specific factors, employment, and early-life factors. However, the study revealed they are restricted by the available variables from the UKB database, which lead to variables such as air pollution, blood glucose measures and so on were not included. Conversely, our study used samples from the MRC IEU OpenGWAS database, the largest open GWAS database globally. Furthermore, our research focus differs, as we primarily investigate the causal relationship between the following 10 categories (comprising 18,097 traits) and AD, which were Disease, Medical laboratory science, Imaging, Anthropometric, Treatment, Molecular trait, Gut microbiota, Past history, Family history, and Lifestyle trait. Most importantly, we have established a database encompassing all MR analysis results, allowing researchers and clinicians worldwide to conveniently and rapidly retrieve AD-associated risk factors via an online open integrated platform (MRAD, https://gwasmrad.com/mrad/).We have now added a new text in the Background section (page 6-7) describing the differences and potential strengthens towards previous studies.

The newly added text describing the differences and novelty towards previous studies is as follows:

“Chen et al. [30] used MR analysis to reveal the causal relationship between AD and factors including sociodemographic and early life status. However, the study revealed they are restricted by the available variables from the UKB database, which lead to variables such as air pollution, blood glucose measures and so on were not included. And also, due to the high degree of heterogeneity present in AD subtypes, which have different biological and genetic characteristics. Thus, the previous studies cannot offer a systematic and complete viewpoint. Our study uses the MRC IEU OpenGWAS database as the sample source for MR analysis to address the aforementioned limitations. The MRC IEU OpenGWAS database, the largest open GWAS database globally, has compiled 42,335 GWAS summary datasets from sources such as the UK Biobank, FinnGen Biobank, and Biobank Japan. Analyzing large-scale datasets will break new ground for MR research on AD.

MR requires a combination of background knowledge in biology, computer science, software studies, and statistics, which often leads to a dilemma where biologists are not well-versed in computer and statistical fields, while computer science experts struggle to adopt a medical biology mindset. Consequently, the vast majority of available GWAS data have not been effectively utilized through MR. Therefore, the construction of a multi-level data platform specifically for AD based on MR analysis of massive GWAS data is of great strategic significance, and it will facilitate researchers and clinicians worldwide to conveniently and rapidly obtain risk factors that are causally associated with AD.”

Reference:

[30] Chen SD, Zhang W, Li YZ, et al. (2023). A Phenome-wide Association and Mendelian Randomization Study for Alzheimer's Disease: A Prospective Cohort Study of 502,493 Participants From the UK Biobank. Biol Psychiatry. 1;93(9):790-801.

(3) Comment: Given the large number of tests, the multiple testing issue is concerning. To mitigate potential false positives, I recommend employing the Bonferroni threshold or FDR. The authors should only interpret exposures that are significant at the Bonferroni threshold.

We sincerely appreciate the reviewer's feedback. Thank you for pointing this out. We have added the results of the Bonferroni correction to the Statistical models for the causal effect inference section of the Methods section (page 10) in response to the reviewer's feedback.

The newly added text describing Bonferroni threshold is as follows:

“The above analyses were performed using the TwoSampleMR[41] package in the R (version 4.1.2) software. Association of exposures with outcomes was assessed using odds ratio (OR) and 95% confidence interval (95% CI), with OR > 1 indicating a positive association (risk factor) and 0 < OR < 1 indicating a negative association (protective factor). Differences with a two-sided p < .05 were considered statistically significant. Furthermore, owing to the relatively large number of exposure and outcome traits included in this study, the multiple testing correction method Bonferroni correction was added to identify significant hits, threshold for Bonferroni-corrected was 0.05 divided by 289,552 tests (p <1.727e-07).”

(4) Comment: In the discussion, the authors should interpret or highlight exposures that remain significant after multiple testing corrections.

Thank you for your valuable comment. In response to reviewer feedback, we have put extra emphasis on the exposures that remained significant after multiple testing corrections in the Discussion section (page 17). We thank you again for your feedback, as we believe that our manuscript has been improved substantially as a result of your input.

Recommendations for the authors:

Reviewer #1 (Recommendations For The Authors):

(1) Comment: In this study, the authors used the inverse-variance weighted (IVW) model as the major analysis method to perform Mendelian randomization analysis to identify various classes of risk or protective factors for AD, early-onset AD, and late-onset AD. An online database called MRAD has been thereby developed with the assistance of Shiny package. This study is a very intriguing study of great clinical and scientific significance that provided a thorough and comprehensive evaluation with regard to risk or protective factors for AD. It also provided physicians and scientists with a very convenient, free as well as user-friendly tool for further scientific investigation.

I believe this manuscript is great research that is worth publishing with all the comments from the Public Review resolved.

We thank the reviewer for taking the time to read and provide valuable feedback on our manuscript, which allowed us to improve the overall quality of our research. All the comments from the Public Review have been rechecked, and appropriate changes have been made in accordance with the reviewers’ suggestions. Point-by-point responses to all the comments from the Public Review can be found in the above. If there are any further issues, please do not hesitate to let us know, so that we can ensure that our manuscript meets the high standards required for publication.

Reviewer #2 (Recommendations For The Authors):

(1) Comment: In the middle lower left section of the graphical abstract, the overlapping positive (N=63) and overlapping negative (N=16) do not sum to the overlapping number (N=80). Could you clarify if any have both positive and negative effects? Additionally, the font size inside the circular elements is too small to read.

We thank you for raising this issue. We have clarified this in the MRAD utility data mining section of Results section (page 12): A total of 63 exposure traits (risk factors) were positively associated with all the three main outcome traits, while 16 exposure traits (protective factors) were negatively associated with the three main outcome traits, with Ulcerative colitis (ebi-a-GCST000964) being negatively associated with the AD outcome traits of ieu-b-2 and ieu-a-297, and positively associated with the AD outcome traits of ieu-b-5067. Additionally, we apologize for the small, unreadable fonts in the graphical abstract figure. In response to reviewer feedback, we have increased the font size within the figure and enhanced the resolution to improve image readability (page 3).

(2) Comment: The x-axis label ("Alzheimer's disease outcome") should be more descriptive. If published GWAS results are used, indicate this as XXX et al. (2022). Also, specify the AD outcome for each category (e.g., AD, early-onset AD, late-onset AD). The y-axis labels should also be clarified; remove identification codes and retain only the exposure names. Apply the same improvements to Figures 2-8.

We appreciate your comments and suggestions for improving our manuscript.

(i) In response to reviewer feedback, information of published GWAS such as authors and year of publication have now been added to the x-axis labels, as demonstrated in Figure 4 (page 31).

(ii) The outcome IDs are unique. We used these IDs to represent the AD information on the x-axis to maintain a clean and clear figure. The corresponding details for each ID are explained in the Outcome traits section of the Methods section (page 8, as shown in Figure 2). AD_EO refers to early-onset AD, and AD_LO refers to late-onset AD, which are also specified in the Abbreviations (page 4).

(iii) We sincerely appreciate the reviewers’ meticulous feedback. While exposure IDs in this study are unique, exposure names are not. A single exposure name may correspond to multiple IDs, each with a potentially different source of information (e.g., author, year, population sample). We believe obtaining consistent results across multiple IDs further strengthens the reliability of our conclusions. Hence, for better clarity of specific exposure information, the exposure IDs have been retained.

(3) Comment: The results across Figures 1-8 are repetitive and not very informative. Consider other visualizations to condense the information into one or two figures. I would recommend using a Manhattan plot or PheWAS plot concept to effectively display many test results at once. Please display the Bonferroni threshold in the plot as a horizontal line to show which exposures are meaningful after adjusting multiple comparisons.

We appreciate this helpful suggestion. We have now condensed Figures 1–8 into a single figure (as shown in Figure 4). Additionally, we have now displayed the Bonferroni correction results in the sensitivity analysis results figures (as shown in Figure 5, Figure S1-S7).

(4) Comment: Consider placing Figure S1 as Figure 1, condensing Figures 1-8 into Figures 2 and 3, and placing the circular diagrams from Figure S6 as Figure 4.

We appreciate this valuable suggestion. The sequence of the figures has been adjusted.

(5) Comment: Create a main table summarizing robust and consistent exposures for AD that are significant at the Bonferroni threshold for readers. For each exposure, please include estimates from IVW, MR-Egger, weighted median, simple mode, weighted mode, maximum likelihood, and penalized weighted median, along with heterogeneity and horizontal pleiotropy tests. I would also highlight or bold estimates from the best-fit model/MR method to help readers identify the most reliable estimates when estimates from multiple methods are heterogeneous.

We appreciate this helpful suggestion. Owing to the excessive amount of information in the table, we have uploaded the table covering the aforementioned information according to the reviewer’s suggestion as supplementary materials (See Table S2). (i) The corresponding id.exposure that pass the Bonferroni threshold are reflected in red font. (ii) Furthermore, according to the customized best-fitted model (as mentioned in the Statistical models for causal effect inference section of Methods section), when there is no pleiotropy or when pleiotropy is not applicable (less than 3 SNPs), random-effects IVW model is the best model. These corresponding id.exposure are shown in red font with a yellow highlight. (iii) Moreover, according to the customized best-fitted model, when there is pleiotropy, MR-Egger is the best model. These corresponding id.exposure are shown in red font with a green highlight.

(6) Comment: Figures S4-S10: These figures are screenshots of web browsers and may not be worth showing. Consider using tools like Adobe AI or R ggplot to create more refined visualizations that are specific to the research question and improve the message of this work.

Thank you very much for your valuable suggestion in reviewing our manuscript. In this study, Figures S4-S10 are screenshots related to the user guide. We sincerely appreciate the reviewer’s feedback and have revised the subheading of this section to MRAD User Guide to clarify its purpose. Demonstrating both text and figures in this section, we aim to help users understand ways to operate MRAD more intuitively and easily.

(7) Comment: Additionally, please show upfront or highlight results from MR analyses based on R packages, as the author mentioned in the method section. Somehow it's difficult to find results from MR-Egger, weighted median, simple mode, weighted mode, maximum likelihood, and penalized weighted median, along with heterogeneity and horizontal pleiotropy tests in the supplementary materials. Apologies if I missed them. Please ensure these results are clearly presented.

We appreciate your comments and suggestions for improving our manuscript. Thank you for pointing this out. We have added the results of the sensitivity analysis based on R packages (as shown in Figure 5, Figure S1-S7, and Table S2).

Author response:

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

Public Reviews:

Reviewer #1 (Public Review):

I am not convinced how this study relates to HIV individual HFpEF, and the study design does not seem to be well thought out. 

This is an important point and we have modified the manuscript as mentioned below in our responses.

The connectivity of the study experiments is loose, and data analysis and conclusions are broadly overstated and misinterpreted.

We have modified the manuscript thoroughly so the data are interpret properly, and the conclusions are stated logically. 

For example the study lacks any measure of diastolic contractile function, and even if performed, the relevance of TNFa treatments to cells in vitro in these immature cell contexts would remain unclear. There is surprisingly no reported molecular analyses of potential mechanisms of the calcium transient changes. The study falls short in molecular detail and instead relies on drug treatments and responses that are hard to interpret with dosages that are not well justified and treatments that are numerous. Unclear what changes in calcium transients mean functionally without a comprehensive assessment of CM biomechanical contraction and relaxation measurements, and this would also require parallel molecular investigations of potential targets of any phenotypes observed.

As mentioned above, we have modified the manuscript so the data are interpret properly, and the conclusions are stated logically. In terms of mechanisms for the observed phenomenon, we agree that this was not the focus of studies, however, we have provided a paragraph in the discussion that covers this topic. Although Decay and downstroke time were utilized as surrogates of cardiomyocyte relaxation, direct biomechanical characterization of contraction was not conducted in this study. While cytosolic calcium concentration is a predominant factor to regulate the cell’s relaxation (Reference 52 in the manuscript), there are several mechanisms to modify the relationship, including the transition of sarcomere protein isoforms to pathogenic ones (Reference 53 in the manuscript) and the stimulation of β-adrenergic receptor on cardiomyocytes (Reference 54 in the manuscript). Since hiPSC-CMs utilized for each study is from iPS cells derived from a single donor, we believe that the patterns of sarcomere protein expression and the regulation of β-adrenergic receptor pathway should be consistent among samples, supporting their effects should be minimum in our system. We also did not elucidate molecular mechanisms underlying prolonged decay time induced by TNF-α and IFN-γ in this study. Lee et al. reported that 25 ng/ml TNFa treatment induced a longer decay portion of the calcium transient and a decreased sarcoplasmic ATPase (SERCA) expression in rabbit cardiomyocytes from pulmonary vein (Reference 55 in the manuscript), suggesting our observation in iPS-CM is also through decreased expression of SERCA though further studies remain conducted.

Calcium transient data need to be better illustrated such as with representative peak tracings. The data overall is with too few samples, particularly given the inherent heterogeneity of iPSCM studies. The iPS-CM system as a model for diastolic dysfunction remains unestablished.

We have now prepared several representative curves of calcium transient and their derivatives in Figure 4 D and E, H and I, and in Figure 1-figure supplement 1B. In terms of the way to collect Ca-transient data, each dot in the bar graphs represents the average of signals obtained from one well of the 96-well plates. About 75K cells were seeded in one well, and we believe that the number of cells integrated in the analyses should be sufficient for the statistical analyses. We modified our manuscript as this system does not quantifying diastolic function directly, but represents Ca measurements that indicate cardiomyocyte relaxation.

There are unclear dose choices for the various ART drugs tested, as well as the other drugs tested such as SGLT2i. Besides the observation that SLC5A2 (SGLT2 target) is not established to be expressed in adult mammalian cardiomyocytes. 

Thank you for the comment. The dose ranges of ART drugs were chosen to extend to 10fold above the IC50 concentrations and reflects the upper range of circulating drug concentration in patients receiving these medications (Reference 36-39 in the manuscript). For SGLT2 inhibitor concentration, we referred to a paper utilizing 1-10 μM dapagliflozin (PMID: 35818731). We conducted a preliminary study to test the effect of 1 and 10 μM of dapagliflozin on the Ca-transient of iPS-CMs, and we found that 1 μM of the drug treatment did not cause changes in Ca-transient. Marfella et al. reported that SLC5A2 (SGLT2) expresses in cardiomyocytes under diabetic condition (PMID 36096423). Since diabetes is associated with low grade systemic inflammation, HIV patients might also express SGLT2 in cardiomyocytes. Taken together, we believe that the dosages of the drugs used in our studies are relevant to the clinical therapeutical usages of the drugs.

HIV plasma samples were not tested for cytokine levels, but this could be done to assess the validity of the final experiments. It is unclear what is being tested with these experiments. 

This is a good point and we agree with the reviewer. However, we had limited amount of the patient serum and could not perform a comprehensive analysis of these samples. Nevertheless, we have added a section in the Discussion section providing some clinical relevance of our findings based on the papers that have assessed cytokine levels in the serum of HIV patients.

The choice of serum controls from a second institution (UCSF) opens up concerns over batch effects unrelated to differences in diastolic dysfunction. However, there were no differences with the Northwestern samples. It is unclear why this data is included as it does not add to the impact of the study. 

In our study, we utilized two sets of HIV patient serum samples from different institutions, supporting that our results can be reproduced. We believe that these results significantly augmented the rigor of our findings.

There are concerns about the quality of the iPS-CMs since there is no cell imaging or molecular analyses. Figure 5 Supplement 1 images are of low quality and low resolution to assess cell quality. Overall the iPS-CM QC data is extremely sparse 

We have now added the representative images of iPS-CMs to Figure 1- figure supplement 1A. Our group has used hiPS-CMs extensively in the past (PMID: 26439715). We also updated Fig 5 Supplement 1 with images with better resolution and added Fig 5 Supplement 2 with magnified images. 

Reviewer2 (Public Review):

However, there are some topics that are not well-connected, and the rationale and hypothesis are not clearly defined beforehand, such as mitochondrial membrane potential, mitochondrial ROS, and angiogenic potential. 

We modified the manuscript so the rationale and hypothesis of the study is clearly stated. 

As the hiPSC cardiomyocytes are treated with various reagents to measure diastolic dysfunction, it is important to confirm whether the treatment time and dose used were sufficient to exert a functional effect. Dose and time-dependent experiments are essential, or at least sufficient citations should be provided for selecting the dose for IFN and TNF. 

We used previous publications for the dosages of the drugs used in our paper (1-4). 

After IFN and TNF treatment, determining the expression levels of molecular markers of DD/HFpEF is crucial. Again, if sufficient evidence is available, it can be cited. 

We have included a section in the discussion to address this issue. Briefly, Lee et al. reported that 25 ng/ml TNFa induces a longer decay of calcium transient and a decrease in sarcoplasmic ATPase (SERCA) expression in rabbit cardiomyocytes from pulmonary vein (PMID 17383682). The prolonged Cadecay time in hiPS-CM with the drug administration may be due to a decrease in SERCA expression and impaired Ca-uptake into sarcoplasmic reticulum.

The Methods section describes TMRE colocalization and immunofluorescence, but no images are provided.

We have performed immunofluorescence of hiPSC-CM with TMRE for the quantification of mitochondrial membrane potential (MMP). 

The concentration of TNF and IFN in patients is critical, which was acknowledged and discussed as a limitation of the study by the authors. Authors should consider this aspect, and if not feasible, clinical reports should be cited to provide a rough estimation of their concentration. 

Thank you for this comment. A new section detailing the points brought up by the Reviewer is now added to discussion.

Recommendation for the authors:

Reviewer #1 (Recommendation for the authors):

I suggest a more comprehensive analysis of diastolic function including biomechanical studies of contraction and diastolic function. I suggest increasing the sample #'s, getting a better characterziation of the cardiomyocytes, their expression profiles, and maturation state. The team should dig more deeply into potential molecular mechanisms of the calcium transient changes. Are there changes in SERCA or other SR factors' phosphorylation state or other molecular explanations for the observed changes? I would remove the serum treatment experiments as they distract since they didn't show differences. These are a few of the suggestions I would have for the team.

Our system for measurement of Ca-transient unfortunately does not allow to obtain data on the cellular biomechanical property. We modified the manuscript so the results are not overstated and that the interpretation is correct. Since each dot in bar-graphs for Ca-transient data represents the average of signals generated from 75 K cells, we believe that the number of cells analyzed was sufficient for the analyses. Although it is not conclusive, previous reports suggested induction of SERCA2A expression by TNF-α treatment in isolated cardiomyocytes, suggesting that the mechanism underlying the prolonged calcium decay time in our model may be due to changes in SERCA levels. We included the data from human serum samples from HIV patients since they provide a platform to assess the effects of HIV patient serum on. We believe that these data convey a significant progress understanding the process of myocardial dysfunction in HIV patients.

References

Amirayan-Chevillard, N., Tissot-Dupont, H., Capo, C., Brunet, C., Dignat-George, F., Obadia, Y., Gallais, H., and Mege, J. L. (2000) Impact of highly active anti-retroviral therapy (HAART) on cytokine production and monocyte subsets in HIV-infected patients. Clinical and experimental immunology 120, 107-112

Fraietta, J. A., Mueller, Y. M., Yang, G., Boesteanu, A. C., Gracias, D. T., Do, D. H., Hope, J. L., Kathuria, N., McGettigan, S. E., Lewis, M. G., Giavedoni, L. D., Jacobson, J. M., and Katsikis, P. D. (2013) Type I interferon upregulates Bak and contributes to T cell loss during human immunodeficiency virus (HIV) infection. PLoS Pathog 9, e1003658

Lau, S. L., Yuen, M. L., Kou, C. Y., Au, K. W., Zhou, J., and Tsui, S. K. (2012) Interferons induce the expression of IFITM1 and IFITM3 and suppress the proliferation of rat neonatal cardiomyocytes. Journal of cellular biochemistry 113, 841-847

Stone, S. F., Price, P., Keane, N. M., Murray, R. J., and French, M. A. (2002) Levels of IL-6 and soluble IL-6 receptor are increased in HIV patients with a history of immune restoration disease after HAART. HIV Med 3, 21-27

Author response:

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

Reviewer #3:

Comments on current version:

As mentioned in my first review, this work is significantly underpowered for the following reasons: 1) n=4 for each treatment group.; 2) no randomization of the surgical sites receiving treatments; 3) implants surgically inserted without precision/guided surgery. The authors have not addressed these concerns.

On a minor note: not sure why the authors present a methodology to evaluate the dynamic bone formation (line 272) but do not present results (i.e. by means of histomorphometrical analyses) utilizing this methodology.

We sincerely appreciate your thorough review and valuable feedback. We have carefully considered your comments and would like to address them as follows:

As mentioned in my first review, this work is significantly underpowered for the following reasons:

(1) n=4 for each treatment group.;

We acknowledge your concern regarding the limited sample size (n=4 per group). While we understand this may affect statistical power, our choice was influenced by ethical considerations in animal experimentation and resource constraints. Increasing the sample size would undoubtedly strengthen the statistical power of our study. However, the logistical and ethical constraints associated with using a larger number of animals in such invasive procedures were significant limiting factors. Specifically, increasing the number of medium to large experimental animals could raise ethical issues, so we used the minimum number possible. Additionally, our study design was reviewed and approved by the animal IRB, which dictated the minimum number of animals we could use. Nevertheless, we conducted power analysis to ensure that our sample size, although limited, was sufficient to detect significant differences given the high variability typically observed in biological responses. The results obtained from our n=4 samples showed consistent trends and significant differences between groups, indicating the robustness of our findings. I will include this point in the limitations section of the discussion. Thank you.

(2) no randomization of the surgical sites receiving treatments;

Thank you for pointing out this issue. We agree that randomization is essential when considering individual differences and the anatomical variations of the jawbone, such as those found in humans. However, this study is an animal experiment where other conditions were controlled, and the interventions were applied after complete bone healing following tooth extraction. Therefore, the impact of randomization of surgical sites was likely minimal, and it is challenging to determine whether it significantly influenced the experimental results. Of course, twelve female OVX beagles were randomly designated into three groups. (Methods section, line 298) However regarding your concern, we would like to present the robustness of histological results from different surgical sites as shown below. Also we will include this point in the limitations section of the discussion.

Histologic analysis of the different surgical sites showed significant differences in bone formation and osseointegration among the three treatment groups: vehicle control, rhPTH(1-34), and dimeric Cys25PTH(1-34). Goldner trichrome staining (Figure A-C) showed enhanced bone formation in both the rhPTH(1-34) and dimeric Cys25PTH(1-34) groups compared to the vehicle control group. The rhPTH(1-34) group showed the most pronounced bone mass gain around the implant. Both treatment groups showed improved bone-to-implant contact compared to the control group, as indicated by the red arrows.

Masson trichrome staining (Figure D-F) further confirmed these results, showing an increase in bone matrix (blue staining) in the rhPTH(1-34) and dimeric Cys25PTH(1-34) groups, with the dimeric rhPTH(1-34) group showing the most extensive and dense bone formation.

TRAP staining (Figure G-I and G'-I') was used to assess osteoclast activity. Interestingly, both the rhPTH(1-34) and dimeric Cys25PTH(1-34) groups showed an increase in TRAP-positive cells compared to the vehicle control, suggesting enhanced bone remodeling activity. The highest number of TRAP-positive cells was observed in the rhPTH(1-34) group and the highest trabecular number, indicating the most active bone remodeling.

To summarize the results, histological analyses revealed that both rhPTH(1-34) and dimeric Cys25PTH(1-34) treatments significantly enhanced osseointegration and bone formation around titanium implants in a postmenopausal osteoporosis model compared to the control. The rhPTH(1-34) group demonstrated superior outcomes, exhibiting the most substantial increase in bone volume, bone-to-implant contact, and osteoclastic activity, indicating its greater efficacy in promoting bone regeneration and implant integration in this experimental context.

Author response image1.

Histological analysis using Goldner trichrome, Masson trichrome, and TRAP staining

(3) implants surgically inserted without precision/guided surgery. The authors have not addressed these concerns.

The primary purpose of precision guides is to prevent damage to various anatomical structures and to ensure perfect placement at the desired location. Even disregarding the potential inaccuracies of precision guides in actual clinical settings, the primary goal of this animal experiment was not to achieve perfect placement or prevent damage to anatomical structures. Instead, the objective was to histologically measure the integrity of the bone surrounding titanium fixture's platform after pharmacological intervention, ensuring it was fully seated in the alveolar bone. To this end, we secured sufficient visibility through periosteal dissection to confirm the perfect placement of the implant and adhered to the principle of maintaining sufficient mesiodistal distance between each fixture. Using such precision guides in this animal experiment, which is not an evaluation of 'implant precision guides,' could potentially introduce inaccuracies and contradict the experimental objectives. Furthermore, since this experiment was conducted on an edentulous ridge where all teeth had been extracted, achieving the same placement as in the presurgical simulation would be impossible, even with the use of precision guides. Thank you once again for your constructive feedback. We will include this point in the limitations section of the discussion.

On a minor note: not sure why the authors present a methodology to evaluate the dynamic bone formation (line 272) but do not present results (i.e. by means of histomorphometrical analyses) utilizing this methodology.

As the reviewer mentioned, we confirmed that the sentence was included in the Methods section despite the analysis not actually being performed. We sincerely apologize for this oversight and will make the necessary corrections immediately. Thank you very much for your keen observation.

Author response:

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

Reviewer #1 (Public Review):

Major comments: 

My main concern about the manuscript is the extent of both clinical and statistical heterogeneity, which complicates the interpretation of the results. I don't understand some of the antibiotic comparisons that are included in the systematic review. For instance the study by Paul et al (50), where vancomycin (as monotherapy) is compared to co-trimoxazole (as combination therapy). Emergence (or selection) of co-trimoxazole in S. aureus is in itself much more common than vancomycin resistance. It is logical and expected to have more resistance in the co-trimoxazole group compared to the vancomycin group, however, this difference is due to the drug itself and not due to co-trimoxazole being a combination therapy. It is therefore unfair to attribute the difference in resistance to combination therapy. Another example is the study by Walsh (71) where rifampin + novobiocin is compared to rifampin + co-trimoxazole. There is more emergence of resistance in the rifampin + co-trimoxazole group but this could be attributed to novobiocin being a different type of antibiotic than co-trimoxazole instead of the difference being attributed to combination therapy. To improve interpretation and reduce heterogeneity my suggestion would be to limit the primary analyses to regimens where the antibiotics compared are the same but in one group one or more antibiotic(s) are added (i.e. A versus A+B). The other analyses are problematic in their interpretation and should be clearly labeled as secondary and their interpretation discussed. 

Thank you for raising these important points and highlighting the need for clarification. We understand that the reviewer has concerns regarding the following points:

(1) The structure of presenting our analyses, i.e. main analyses and sub-group analyses and their corresponding discussion and interpretation

Our primary interest was whether combining antibiotics has an overarching effect on resistance and to identify factors that explain potential differences of the effect of combining antibiotic across pathogens/drugs. Therefore, pooling all studies, and thereby all combinations of antibiotics, is one of our main analyses. The decision to pool all studies that compare a lower number of antibiotics to a higher number of antibiotics was hence predefined in our previously published study protocol (PROSPERO CRD42020187257).

We indeed, find that heterogeneity is high in our statistical analyses. As planned in our study protocol, we did perform several prespecified sub-group analyses and added additional ones. We now emphasize that several sub-group analyses were performed to investigate heterogeneity (L 119ff): “The overall pooled estimates are based on studies that focus on various clinical conditions/pathogens and compare different antibiotics treatments. To explore the impact of these and other potential sources of heterogeneity on the resistance estimates we performed various sub-group analyses and metaregression.” 

The performed sub-group analyses specifically focused on specific pathogens/clinical conditions (figure 3) or explored heterogeneity due to different antibiotics in comparator arms – as suggested by the reviewer (figure 3B, SI section 6). We find that the heterogeneity remains high even if only resistances to antibiotics common to both arms are considered (SI section 6.1.8). With this analysis we excluded comparisons of different antibiotics (e.g., A vs B+C), such as those between vancomycin and cotrimoxazole named by the reviewer. While we aimed to explore heterogeneity and investigate potential factors affecting the effect of combining antibiotic on resistance, limitations arose due to limited evidence and the nature of data provided by the identified studies. Therefore, interpretability remains also limited for the subgroup analyses, which we highlight in the discussion. (L 186 ff: We accounted for many sources of heterogeneity using stratification and meta-regression, but analyses were limited by missing information and sparse data.) Further, specific subgroup analyses are discussed in more detail in the SI.

(2) Difference in resistance development due to the type of the antibiotics or due to combination therapy?

The reviewer raises an important point, which we also try to make: future studies should be systematically designed to compare antibiotic combination therapy, i.e. identical antibiotics in treatment arms should be used, except for additional antibiotics used in both treatment arms. We already mentioned this point in our discussion but highlight this now by emphasizing how many studies did not have identical antibiotics in their treatment arms. We write in L194ff: “19 (45%) of our included studies compared treatment arms with no antibiotics in common, and 22 studies (52%) had more than one antibiotic not identical in the treatment arms (table 1). To better evaluate the effect of combination therapy, especially more RCTs would be needed where the basic antibiotic treatment is consistent across both treatment arms, i.e. the antibiotics used in both treatment arms should be identical, except for the additional antibiotic added in the comparator arm (table 1).”

Furthermore, we investigated the importance of the type of antibiotics with several subgroup analyses (e.g. SI sections 6.1.8 and 6.1.10). We now further highlight the concern of the type of antibiotics in the result section of the main manuscript, where we discuss the sub-group analysis with no common antibiotics in the treatment arms 131 ff: “Furthermore, a lower number of antibiotics performed better than a higher number if the compared treatment arms had no antibiotics in common (pooled OR 4.73, 95% CI 2.14 – 10.42; I2\=37%, SI table S3), which could be due to different potencies or resistance prevalences of antibiotics as discussed in SI (SI section 6.1.10).” As mentioned above we also perform sub-group analyses, where only resistances of antibiotics common to both arms are considered (SI section 6.1.8). However, as discussed in the corresponding sections, the systematic assessment of antibiotic combination therapy remains challenging as not all resistances against antibiotics used in the arms were systematically measured and reported. Furthermore, the power of these sub-group analyses is naturally a concern, as they include fewer studies. 

Another concern is about the definition of acquisition of resistance, which is unclear to me. If for example meropenem is administered and the follow-up cultures show Enterococcus species (which is intrinsically resistant to meropenem), does this constitute acquisition of resistance? If so, it would be misleading to determine this as an acquisition of resistance, as many people are colonized with Enterococci and selection of Enterococci under therapy is very common. If this is not considered as the acquisition of resistance please include how the acquisition of resistance is defined per included study. Table S1 is not sufficiently clear because it often only contains how susceptibility testing was done but not which antibiotics were tested and how a strain was classified as resistant or susceptible. 

Thank you for pointing out this potential ambiguity. The definition of acquisition of resistance reads now (L 275 ff): “A patient was considered to have acquired resistance if, at the follow-up culture, a resistant bacterium (as defined by the study authors) was detected that was not present in the baseline culture.” We also changed the definition accordingly in the abstract (L 36 ff). We hope that the definition of acquisition is now clearer. Our definition of “acquisition of resistance” is agnostic to bacterial species and hence intrinsically resistant species, as the example raised by the reviewer, can be included if they were only detected during the follow-up culture by the studies. Generally, it was not always clear from the studies, which pathogens were screened for and whether the selection of intrinsically resistant bacteria was reported or not. Therefore, we rely on the studies' specifications of resistant and non-resistant without further distinction from our side, i.e. classifying data into intrinsic and non-intrinsic resistance. Overall, the outcome “acquisition of resistance” can be interpreted as a risk assessment for having any resistant bacterium during or after treatment. In contrast, the outcome “emergence of resistance” is more rigorous, demanding the same species to be detected as more resistant during or after treatment.

The information, which antibiotic susceptibility tests were performed in each individual study can be found in the main text in table 1. However, we agree that this information should be better linked and highlighted again in table S1. We therefore now refer to table 1 in the table description of table S1. L134 ff.: “See table 1 in the main text for which antibiotics the antibiotics tested and reported extractable resistance data”. Furthermore, we added the breakpoints for resistant and susceptible classification if specifically stated in the main text of the study. However, we did not do further research into old guidelines, manufactures manuals or study protocols in case the breakpoints are not specifically stated in the main text as the main goal of this table, in our opinion, is to show a justification, why the studies could be considered for a resistance outcome. We therefore decided against further breakpoint investigations for studies, where the breakpoint is not specifically stated in the main text. 

Line 85: "Even though within-patient antibiotic resistance development is rare, it may contribute to the emergence and spread of resistance." 

Depending on the bug-drug combination, there is great variation in the propensity to develop within-patient antibiotic resistance. For example: within-patient development of ciprofloxacin resistance in Pseudomonas is fairly common while within-patient development of methicillin resistance in S. aureus is rare. Based on these differences, large clinical heterogeneity is expected and it is questionable where these studies should be pooled. 

We agree that our formulation neglects differences in prevalence of within-host resistance emergence depending on bug-drug combinations. We changed our statement in L 86 to: “Within-patient antibiotic resistance development, even if rare, may contribute to the emergence and spread of resistance.”

Line 114: "The overall pooled OR for acquisition of resistance comparing a lower number of antibiotics versus a higher one was 1.23 (95% CI 0.68 - 2.25), with substantial heterogeneity between studies (I2=77.4%)" 

What consequential measures did the authors take after determining this high heterogeneity? Did they explore the source of this large heterogeneity? Considering this large heterogeneity, do the authors consider it appropriate to pool these studies?

Thank you for highlighting this lack of clarity. As mentioned above, we now highlight that we performed several subgroup analyses to investigate heterogeneity. (L 116ff): “The overall pooled estimates are based on studies that focus on various clinical conditions/pathogens and compare different antibiotics treatments. To explore the impact of these and other potential sources of heterogeneity on the resistance estimates we performed various subgroup analyses and meta-regression.” Nevertheless, these analyses faced limitations due to the scarcity of evidence and often still showed a high amount of heterogeneity. Given the lack of appropriate evidence, it is hard to identify the source of heterogeneity. The decision to pool all studies was pre-specified in our previously published study protocol (PROSPERO CRD42020187257) and was motivated by the question whether there is a general effect of combination therapy on resistance development or identify factors that explain potential differences of the effect of combination therapy across bug-drug combinations. Therefore, we think that the presentation of the overall pooled estimate is appropriate, as it was predefined, and potential heterogeneity is furthermore explored in the subgroup analyses. 

Reviewer #1 (Recommendations For The Authors): 

I want to congratulate the investigators for the rigorous approach followed and the - in my opinion - correct interpretation of the data and analysis. The disappointing outcome is independent of the quality of the approach used. Yet, the consequences of that outcome are rather limited, and will not be surprising for - at least - some in the field of antibiotic resistance. 

Thank you for your positive and differentiated feedback.

Reviewer #2 (Recommendations For The Authors): 

Line 93: "The screening of the citations of the 41 studies identified one additional eligible study, for a total of 42 studies". 

Why was this study missed in the search strategy? 

What is the definition of "quasi-RCTs"? Why were these included in the analysis? 

Thank you for pointing out this lack of clarity. The additional study, which was found through screening the references of included studies, was not identified with our search strategy as neither the abstract nor database specific identifiers provided any indications that resistance was measured in this study. We added an explanation in the supplementary materials L 792 ff. and refer to this explanation in the main manuscript (L 95). 

Quasi-randomized trials are trials that use allocation methods, which are not considered truly random. We added this specification in L 95. It now reads: “….two quasi-RCTs, where the allocation method used is not truly random” and in L 252 ff: “Studies were classified as quasi-RCTs if the allocation of participants to study arms was not truly random.” For instance, the study Macnab et al. (1994) assigned patients alternately to the treatment arms. Quasi-randomized controlled trials can lead to biases and especially old studies are more likely to have used quasi-random allocation methods. This can also be seen in our study, where the two quasi-randomized controlled trials were published in 1994 and 1997. The bias is considered in the risk of bias assessment and in our conducted sensitivity analysis regarding the impact of risk of bias on our estimates (supplementary information sections 3.0 and 4.2). Furthermore, one of the two previous conducted meta-analyses comparing beta-lactam monotherapy to beta-lactam and aminoglycoside, which assessed resistance development also included quasi-randomized controlled trials Paul et al 2014. Overall, while designing the study, we decided to include quasi-randomized controlled trials to increase statistical power as we expected that limited statistical power might be a concern and decided to assess potential biases in the risk of bias assessment.  

Line 100: "Consequently, most studies did not have the statistical power to detect a large effect on within-patient resistance development (figure 2 B, SI p 14).". 

Small studies actually have more power to detect large effects while smaller power to detect small effects. Please rephrase. 

Thank you for pointing out this lack of clarity. We rephrased the sentence in order to emphasize our point that the studies are underpowered even if we assume in our power analysis a large effect on resistance development between treatment arms. In this context “the small” studies include too few patients to detect a large difference in resistance development. As resistance development is a rare event, generally studies have to include a larger number of patients to estimate the effect of intervention. We rephrased the sentence in L 101ff to: “Consequently, most studies did not have the statistical power to detect differences in within-patient resistance development even if we assume that the effect on resistance development is large between treatment arms.”

Line 108: "... and prophylaxis for blood cancer patients with four studies (10%) respectively.". 

I would suggest using the medical term hematological malignancy patients. 

Thank you for the suggestion, we changed it as suggested to hematological malignancy patients, also accordingly in the figures, and table 1.

Line 117: "Since the results for the two resistance outcomes are comparable, our focus in the following is on the acquisition of resistance". 

The first OR is 1.23 and the second is 0.74, why do you consider these outcomes as comparable? 

Thank you for pointing out our unprecise formulation. Due to the lack of power the exact estimates need to be interpreted with care. Here, we wanted to make the point that qualitatively the results of both outcomes do not differ in the sense that our analysis shows no substantial difference between a higher and a lower number of antibiotics. We rephrased the sentence to be more precise (L 123ff): “The results for the two resistance outcomes are qualitatively comparable in the sense that individual estimates may differ, but show similar absence of evidence to support either the benefit, harm or equivalence of treating with a higher number of antibiotics. Therefore, our …”. More detailed discussion about differences in estimates can be found in the SI, when the estimates of emergence of resistance are presented (e.g. SI section 2.1).

Line 123: "Furthermore, a lower number of antibiotics performed better than a higher number if the compared treatment arms had no antibiotics in common (pooled OR 4.73, 95% CI 2.14 - 10.42; I 2 =37%, SI p 7).". 

How do you explain this? What does this mean? 

We now added a more detailed explanation in the supplement (L 376ff.): “The result that if the treatment arms had no antibiotics in common a lower number of antibiotics performed better than a higher number of antibiotics could be due to different potencies of antibiotics or resistance prevalences. Further, there could be a bias to combine less potent antibiotics or antibiotics with higher resistance prevalence to ensure treatment efficacy, which couldlead to higher chances to detect resistances in the treatment arm with higher number of antibiotics, e.g. by selecting pre-existing resistance due to antibiotic treatment (see also section 6.1.9).” We furthermore already specifically mention this point in the main manuscript and refer then to the detailed explanation in the SI (L134 ff, “which could be due to different potencies or resistance prevalences of antibiotics as discussed in SI (SI section 6.1.10)”)

Overall, we want to point out that these results need to be interpreted with caution as overall the statistical power is limited to confidently estimate the difference in effect of a higher and lower number of antibiotics.

Line 125: ". In contrast, when restricting the analysis to studies with at least one common antibiotic in the treatment arms are pooled there was little evidence of a difference (pooled OR 0.55, 95% CI 0.28 - 1.07". 

The difference was not statistically significant but there does seem to be an indication of a difference, please rephrase. 

We rephrased the sentence to (L135 ff.): “In contrast, when restricting the analysis to studies with at least one common antibiotic in the treatment arms we found no evidence of a difference, only a weak indication that a higher number of antibiotics performs better (pooled OR 0.55, 95% CI 0.28 – 1.07; I2 \=74%, figure 3B).” 

Line 190: "Similarly, today, relevant cohort studies could be analysed collaboratively using various modern statistical methods to address confounding by indication and other biases (66, 67)". 

However, residual confounding by indication is likely. Please also mention the disadvantages of observational studies compared to RCTs. 

We now highlight that causal inference with observational data comes with its own challenges and stress that randomized controlled trials are still considered the gold standard. L 204ff now reads: “However, even with appropriate causal inference methods, residual confounding cannot be excluded when using observational data (67). Therefore, will remain the gold standard to estimate causal relationships.”

Line 230: "Gram-negative bacteria have an outer membrane, which is absent in grampositive bacteria for instance, therefore intrinsic resistance against antibiotics can be observed in gram-negative bacteria (11)". 

Intrinsic resistance is not unique for Gram-negative bacteria but also exists for Grampositive bacteria. 

We agree with the reviewer that intrinsic resistance is not unique to gram-negative bacteria and refined our writing. We additionally added that differences between gram-negative and gram-positive bacteria are not only to be expected due to differing intrinsic resistances but also due to potential differences in the mechanistic interactions of antibiotics, i.e., synergy or antagonism. The paragraph reads now (SI L289): “The gram status of a bacterium may potentially determine how effective an antibiotic, or an antibiotic combination is. Differences between gram-negative and gram-positive bacteria such as distinct bacterial surface organisation can lead to specific intrinsic resistances of gram-negative and grampositive bacteria against antibiotics (55). These structural differences can lead to varying effects of antibiotic combinations between gram-negative and gram-positive bacteria (56).”

Author response:

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

Recommendations for the authors:

Reviewer #1 (Recommendations For The Authors):

Line 127. Provide a few more words describing the voltage protocol. To the uninitiated, panels A and B will be difficult to understand. "The large negative step is used to first close all channels, then probe the activation function with a series of depolarizing steps to re-open them and obtain the max conductance from the peak tail current at -36 mV. "

We have revised the text as suggested (revision lines 127 to Line 131): “From a holding potential within the gK,L activation range (here –74 mV), the cell is hyperpolarized to –124 mV, negative to EK and the activation range, producing a large inward current through open gK,L channels that rapidly decays as the channels deactivate. We use the large transient inward current as a hallmark of gK,L. The hyperpolarization closes all channels, and then the activation function is probed with a series of depolarizing steps, obtaining the max conductance from the peak tail current at –44 mV (Fig. 1A).”

Incidentally, why does the peak tail current decay? 

We added this text to the figure legend to explain this: “For steps positive to the midpoint voltage, tail currents are very large. As a result, K+ accumulation in the calyceal cleft reduces driving force on K+, causing currents to decay rapidly, as seen in A (Lim et al., 2011).”

The decay of the peak tail current is a feature of gK,L (large K+ conductance) and the large enclosed synaptic cleft (which concentrates K+ that effluxes from the HC). See Govindaraju et al. (2023) and Lim et al. (2011) for modeling and experiments around this phenomenon.

Line 217-218. For some reason, I stumbled over this wording. Perhaps rearrange as "In type II HCs absence of Kv1.8 significantly increased Rin and tauRC. There was no effect on Vrest because the conductances to which Kv1.8 contributes, gA and gDR activate positive to the resting potential. (so which K conductances establish Vrest???). 

We kept our original wording because we wanted to discuss the baseline (Vrest) before describing responses to current injection.

Vrest is presumably maintained by ATP-dependent Na/K exchangers (ATP1a1), HCN, Kir, and mechanotransduction currents. Repolarization is achieved by delayed rectifier and A-type K+ conductances in type II HCs.

Figure 4, panel C - provides absolute membrane potential for voltage responses. Presumably, these were the most 'ringy' responses. Were they obtained at similar Vm in all cells (i.e., comparisons of Q values in lines 229-230). 

We added the absolute membrane potential scale. Type II HC protocols all started with 0 pA current injection at baseline, so they were at their natural Vrest, which did not differ by genotype or zone. Consistent with Q depending on expression of conductances that activate positive to Vrest, Q did not co-vary with Vrest (Pearson’s correlation coefficient = 0.08, p = 0.47, n= 85).

Lines 254. Staining is non-specific? Rather than non-selective? 

Yes, thanks - Corrected (Line 264).

Figure 6. Do you have a negative control image for Kv1.4 immuno? Is it surprising that this label is all over the cell, but Kv1.8 is restricted to the synaptic pole? 

We don’t have a null-animal control because this immunoreactivity was done in rat. While the cuticular plate staining was most likely nonspecific because we see that with many different antibodies, it’s harder to judge the background staining in the hair cell body layer. After feedback from the reviewers, we decided to pull the KV1.4 immunostaining from the paper because of the lack of null control, high background, and inability to reproduce these results in mouse tissue. In our hands, in mouse tissue, both mouse and rabbit anti-KV1.4 antibodies failed to localize to the hair cell membrane. Further optimization or another method could improve that, but for now the single-cell expression data (McInturff et al., 2018) remain the strongest evidence for KV1.4 expression in murine type II hair cells.

Lines 400-404. Whew, this is pretty cryptic. Expand a bit? 

We simplified this paragraph (revision lines 411-413): “We speculate that gA and gDR(KV1.8) have different subunit composition: gA may include heteromers of KV1.8 with other subunits that confer rapid inactivation, while gDR(KV1.8) may comprise homomeric KV1.8 channels, given that they do not have N-type inactivation .”

Line 428. 'importantly different ion channels'. I think I understand what is meant but perhaps say a bit more. 

Revised (Line 438): “biophysically distinct and functionally different ion channels”.

Random thought. In addition to impacting Rin and TauRC, do you think the more negative Vrest might also provide a selective advantage by increasing the driving force on K entry from endolymph? 

When the calyx is perfectly intact, gK,L is predicted to make Vrest less negative than the values we report in our paper, where we have disturbed the calyx to access the hair cell (–80, Govindaraju et al., 2023, vs. –87 mV, here). By enhancing K+ accumulation in the calyceal cleft, the intact calyx shifts EK—and Vrest—positively (Lim et al., 2011), so the effect on driving force may not be as drastic as what you are thinking.

Reviewer #2 (Recommendations For The Authors):

(1) Introduction: wouldn't the small initial paragraph stating the main conclusion of the study fit better at the end of the background section, instead of at the beginning? 

Thank you for this idea, we have tried that and settled on this direct approach to let people know in advance what the goals of the paper are.

(2) Pg.4: The following sentence is rather confusing "Between P5 and P10, we detected no evidence of a non-gK,L KV1.8-dependent.....". Also, Suppl. Fig 1A seems to show that between P5 and P10 hair cells can display a potassium current having either a hyperpolarised or depolarised Vhalf. Thus, I am not sure I understand the above statement. 

Thank you for pointing out unclear wording. We used the more common “delayed rectifier” term in our revision (Lines 144-147): “Between P5 and P10, some type I HCs have not yet acquired the physiologically defined conductance, gK,L.. N effects of KV1.8 deletion were seen in the delayed rectifier currents of immature type I HCs (Suppl. Fig. 1B), showing that they are not immature forms of the Kv1.8-dependent gK,L channels. ”

(3) For the reduced Cm of hair cells from Kv1.8 knockout mice, could another reason be simply the immature state of the hair cells (i.e. lack of normal growth), rather than less channels in the membrane? 

There were no other signs to suggest immaturity or abnormal growth in KV1.8–/– hair cells or mice. Importantly, type II HCs did not show the same Cm effect.

We further discussed the capacitance effect in lines 160-167: “Cm scales with surface area, but soma sizes were unchanged by deletion of KV1.8 (Suppl. Table 2). Instead, Cm may be higher in KV1.8+/+ cells because of gK,L for two reasons. First, highly expressed trans-membrane proteins (see discussion of gK,L channel density in Chen and Eatock, 2000) can affect membrane thickness (Mitra et al., 2004), which is inversely proportional to specific Cm. Second, gK,L could contaminate estimations of capacitive current, which is calculated from the decay time constant of transient current evoked by small voltage steps outside the operating range of any ion channels. gK,L has such a negative operating range that, even for Vm negative to –90 mV, some gK,L channels are voltage-sensitive and could add to capacitive current.”

(4) Methods: The electrophysiological part states that "For most recordings, we used .....". However, it is not clear what has been used for the other recordings.

Thanks for catching this error, a holdover from an earlier ms. version.  We have deleted “For most recordings” (revision line 466).

Also, please provide the sign for the calculated 4 mV liquid junction potential. 

Done (revision line 476).

Reviewer #3 (Recommendations For The Authors): 

(1) Some of the data in panels in Fig. 1 are hard to match up. The voltage protocols shown in A and B show steps from hyperpolarized values to -71mV (A) and -32 mV (B). However, the value from A doesn't seem to correspond with the activation curve in C.

Thank you for catching this.  We accidentally showed the control I-X curve from a different cell than that in A. We now show the G-V relation for the cell in A.

Also the Vhalf in D for -/- animals is ~-38 mV, which is similar to the most positive step shown in the protocol.

The most positive step in Figure 1B is actually –25 mV. The uneven tick labels might have been confusing, so we re-labeled them to be more conventional.

Were type I cells stepped to more positive potentials to test for the presence of voltage-activated currents at greater depolarizations? This is needed to support the statement on lines 147-148. 

We added “no additional K+ conductance activated up to +40 mV” (revision line 149-150).  Our standard voltage-clamp protocol iterates up to ~+40 mV in KV1.8–/– hair cells, but in Figure 1 we only showed steps up to –25 mV because K+ accumulation in the synaptic cleft with the calyx distorts the current waveform even for the small residual conductances of the knockouts. KV1.8–/– hair cells have a main KV conductance with a Vhalf of ~–38 mV, as shown in Figure 1, and we did not see an additional KV conductance that activated with a more positive Vhalf up to +40 mV.

(2) Line 151 states "While the cells of Kv1.8-/- appeared healthy..." how were epithelia assessed for health? Hair cells arise from support cells and it would be interesting to know if Kv1.8 absence influences supporting cells or neurons. 

We added our criteria for cell health to lines 477-479: “KV1.8–/– hair cells appeared healthy in that cells had resting potentials negative to –50 mV, cells lasted a long time (20-30 minutes) in ruptured patch recordings, membranes were not fragile, and extensive blebbing was not seen.”

Supporting cells were not routinely investigated. We characterized calyx electrical activity (passive membrane properties, voltage-gated currents, firing pattern) and didn’t detect differences between +/+, +/–, and –/– recordings (data not shown). KV1.8 was not detected in neural tissue (Lee et al., 2013). 

(3) Several different K+ channel subtypes were found to contribute to inner hair cell K+ conductances (Dierich et al. 2020) but few additional K+ channel subtypes are considered here in vestibular hair cells. Further comments on calcium-activated conductances (lines 310-317) would be helpful since apamin-sensitive SK conductances are reported in type II hair cells (Poppi et al. 2018) and large iberiotoxin-sensitive BK conductances in type I hair cells (Contini et al. 2020). Were iberiotoxin effects studied at a range of voltages and might calcium-dependent conductances contribute to the enhanced resonance responses shown in Fig. 4? 

We refer you to lines 310-317 in the original ms (lines 322-329 in the revised ms), where we explain possible reasons for not observing IK(Ca) in this study.

(4) Similar to GK,L erg (Kv11) channels show significant Cs+-permeability. Were experiments using Cs+ and/or Kv11 antagonists performed to test for Kv11? 

No. Hurley et al. (2006) used Kv11 antagonists to reveal Kv11 currents in rat utricular type I hair cells with perforated patch, which were also detected in rats with single-cell RT-PCR (Hurley et al. 2006) and in mice with single-cell RNAseq (McInturff et al., 2018).  They likely contribute to hair cell currents, alongside Kv7, Kv1.8, HCN1, and Kir. 

(5) Mechanosensitive ("MET") channels in hair cells are mentioned on lines 234 and 472 (towards the end of the Discussion), but a sentence or two describing the sensory function of hair cells in terms of MET channels and K+ fluxes would help in the Introduction too. 

Following this suggestion we have expanded the introduction with the following lines  (78-87): “Hair cells are known for their large outwardly rectifying K+ conductances, which repolarize membrane voltage following a mechanically evoked perturbation and in some cases contribute to sharp electrical tuning of the hair cell membrane.  Because gK,L is unusually large and unusually negatively activated, it strongly attenuates and speeds up the receptor potentials of type I HCs (Correia et al., 1996; Rüsch and Eatock, 1996b). In addition, gK,L augments a novel non-quantal transmission from type I hair cell to afferent calyx by providing open channels for K+ flow into the synaptic cleft (Contini et al., 2012, 2017, 2020; Govindaraju et al., 2023), increasing the speed and linearity of the transmitted signal (Songer and Eatock, 2013).”

(6) Lines 258-260 state that GKL does not inactivate, but previous literature has documented a slow type of inactivation in mouse crista and utricle type I hair cells (Lim et al. 2011, Rusch and Eatock 1996) which should be considered. 

Lim et al. (2011) concluded that K+ accumulation in the synaptic cleft can explain much of the apparent inactivation of gK,L. In our paper, we were referring to fast, N-type inactivation. We changed that line to be more specific; new revision lines 269-271: “KV1.8, like most KV1 subunits, does not show fast inactivation as a heterologously expressed homomer (Lang et al., 2000; Ranjan et al., 2019; Dierich et al., 2020), nor do the KV1.8-dependent channels in type I HCs, as we show, and in cochlear inner hair cells (Dierich et al., 2020).”

(7) Lines 320-321 Zonal differences in inward rectifier conductances were reported previously in bird hair cells (Masetto and Correia 1997) and should be referenced here.

Zonal differences were reported by Masetto and Correia for type II but not type I avian hair cells, which is why we emphasize that we found a zonal difference in I-H in type I hair cells. We added two citations to direct readers to type II hair cell results (lines 333-334): “The gK,L knockout allowed identification of zonal differences in IH and IKir in type I HCs, previously examined in type II HCs (Masetto and Correia, 1997; Levin and Holt, 2012).”

Also, Horwitz et al. (2011) showed HCN channels in utricles are needed for normal balance function, so please include this reference (see line 171). 

Done (line 184).

(8) Fig 6A. Shows Kv1.4 staining in rat utricle but procedures for rat experiments are not described. These should be added. Also, indicate striola or extrastriola regions (if known). 

We removed KV1.4 immunostaining from the paper, see above.

(9) Table 6, ZD7288 is listed -was this reagent used in experiments to block Gh? If not please omit. 

ZD7288 was used to block gH to produce a clean h-infinity curve in Figure 6, which is described in the legend.

(10) In supplementary Fig. 5A make clear if the currents are from XE991 subtraction. Also, is the G-V data for single cell or multiple cells in B? It appears to be from 1 cell but ages P11-505 are given in legend. 

The G-V curve in B is from XE991 subtraction, and average parameters in the figure caption are for all the KV1.8–/–  striolar type I hair cells where we observed this double Boltzmann tail G-V curve. I added detail to the figure caption to explain this better.

(11) Supplementary Fig. 6A claims a fast activation of inward rectifier K+ channels in type II but not type I cells-not clear what exactly is measured here.

We use “fast inward rectifier” to indicate the inward current that increases within the first 20 ms after hyperpolarization from rest (IKir, characterized in Levin & Holt, 2012) in contrast to HCN channels, which open over ~100 ms. We added panel C to show that the activation of IKir is visible in type II hair cells but not in the knockout type I hair cells that lack gK,L. IKir was a reliable cue to distinguish type I and type II hair cells in the knockout.

For our actual measurements in Fig 6B, we quantified the current flowing after 250 ms at –124 mV because we did not pharmacologically separate IKir and IH.

Could the XE991-sensitive current be activated and contributing?

The XE991-sensitive current could decay (rapidly) at the onset of the hyperpolarizing step, but was not contributing to our measurement of IKir­ and IH, made after 250 ms at –124 mV, at which point any low-voltage-activated (LVA) outward rectifiers have deactivated. Additionally, the LVA XE991-sensitive currents were rare (only detected in some striolar type I hair cells) and when present did not compete with fast IKir, which is only found in type II hair cells.

Also, did the inward rectifier conductances sustain any outward conductance at more depolarized voltage steps? 

For the KV1.8-null mice specifically, we cannot answer the question because we did not use specific blocking agents for inward rectifiers.  However, we expect that there would only be sustained outward IR currents at voltages between EK and ~-60 mV: the foot of IKir’s I-V relation according to published data from mouse utricular hair cells – e.g., Holt and Eatock 1995, Rusch and Eatock 1996, Rusch et al. 1998, Horwitz et al., 2011, etc.  Thus, any such current would be unlikely to contaminate the residual outward rectifiers in Kv1.8-null animals, which activate positive to ~-60 mV. 

(I-HCN is also not a problem, because it could only be outward positive to its reversal potential at ~-40 mV, which is significantly positive to its voltage activation range.)

Author response:

(1) Reviewer 1 suggested that we repeat the analyses in additional ROIs in the prefrontal cortex (PFC). We appreciate this suggestion and believe it will contribute to a comprehensive understanding of the current findings. These results will be included in the revision.

(2) Reviewer 1 suggested that we also examine results in motor-related ROIs to rule out influences from response planning. We would like to note that our experimental design makes it unlikely that response planning would have influenced our results, as participants were unable to plan their motor responses in advance due to randomized response mapping on a trial-by-trial basis. Nevertheless, we agree with the reviewer that showing results from motor-related ROIs is important, and will include these results in the revision.

(3) Reviewer 1 raised a question about the effect size of the results across different ROIs. In our manuscript, we tried to avoid direct comparisons of representational strength across ROIs, by focusing on the differences in representational strength between conditions within the same ROI. Nevertheless, we agree that clarifying this issue is important, which we will address in the revision.

(4) Reviewer 2 raised a concern about the similarity between the RNN and fMRI results. We acknowledge that the complexity of our results makes it challenging to replicate all fMRI findings within a single RNN (e.g., simulating three brain regions in a single network with distinct result patterns). Nonetheless, the current RNNs effectively captured our key fMRI findings, including increased stimulus representation in frontal cortex as well as the tradeoff in category representation with varying levels of flexible control. Reviewer 2 also made several suggestions in tweaking the RNN structure and in choosing alternative analysis methods. We are happy to carry out these points as we think they could potentially increase the alignment between the two modalities.

Author response:

We are grateful to the reviewers and editors for their insightful comments. All recognized that, while mutation recurrences have been used for inferring cancer drivers, our approach has the rigor of quantitative analysis. We would like to add that, without rigorously ruling out mutational hotspots, most CDNs have not been accepted as driver mutations.

This paper develops the theory stating that (i) recurrent point mutations are true Cancer Driving Nucleotides (CDNs); and (ii) non-recurrent mutations are unlikely to be CDNs. The reviewers question that, with the theory, we still have not discovered new driving mutations. This is done in the companion paper. Table 3 shows that, averaged across cancer types, the conventional method would identify 45 CDGs while the CDN method tallies 258 CDGs. The power of the CDN method in identifying new driver genes is evident.

The second question is "By this theory, will we be able discover most CDNs when the sample size increases from ~ 1000 to 10,000?" This is a question of forecast and can be partially answered using GENIE data. Fig. 7 of this study shows that, when n increases from ~ 1000 to ~ 9,000, the numbers of discovered CDNs increase by 3 – 5 fold, most of which come from the two-hit class, as expected.

Fig. 7 also addresses the queries whether we have used datasets other than TCGA. We indeed have used all public data, including GENIE, ICGC and other integrated resources such as COSMIC. For the main study, we rely on TCGA because it is unbiased for estimating the probability of CDN occurrences. In many datasets, the numerators are given but the denominators are not (the number of patients with the mutation / the total number of patients surveyed). 

The third question is about mutation recurrences among cancer types. As stated by one reviewer, "different cancer types have unique mutational landscapes". While this is true when the analysis is done at the whole-gene level, one gets a different picture at the nucleotide level where the resolution is much higher. The pan-cancer trend of point mutations is evident in Fig. 4 of the companion paper.

Again, we heartily appreciate the criticisms and suggestions of the reviewers and editors!

Author response:

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

Reviewer #1 (Public Review):

[...] Overall the manuscript is well written, and the successful generation of the new endogenous Cac tags (Td-Tomato, Halo) and CaBeta, stj, and stolid genes with V5 tags will be powerful reagents for the field to enable new studies on calcium channels in synaptic structure, function, and plasticity. There are also some interesting, though not entirely unexpected, findings regarding how Brp and homeostatic plasticity modulate calcium channel abundance. However, a major concern is that the conclusions about how "molecular and organization diversity generate functional synaptic heterogeneity" are not really supported by the data presented in this study. In particular, the key fact that frames this study is that Cac levels are similar at Ib and Is active zones, but that Pr is higher at Is over Ib (which was previously known). While Pr can be influenced by myriad processes, the authors should have first assessed presynaptic calcium influx - if they had, they would have better framed the key questions in this study. As the authors reference from previous studies, calcium influx is at least two-fold higher per active zone at Is over Ib, and the authors likely know that this difference is more than sufficient to explain the difference in Pr at Is over Ib. Hence, there is no reason to invoke differences in "molecular and organization diversity" to explain the difference in Pr, and the authors offer no data to support that the differences in active zone structure at Is vs Ib are necessary for the differences in Pr. Indeed, the real question the authors should have investigated is why there are such differences in presynaptic calcium influx at Is over Ib despite having similar levels/abundance of Cac. This seems the real question, and is all that is needed to explain the Pr differences shown in Fig. 1. The other changes in active zone structure and organization at Is vs Ib may very well contribute to additional differences in Pr, but the authors have not shown this in the present study, and rely on other studies (such as calcium-SV coupling at Is vs Ib) to support an argument that is not necessitated by their data. At the end of this manuscript, the authors have found an interesting possibility that Stj levels are reduced at Is vs Ib, that might perhaps contribute to the difference in calcium influx. However, at present this remains speculative.

Overall, the authors have generated powerful reagents for the field to study calcium channels and how they are regulated, but draw conclusions about active zone structure and organization contributing to functional heterogeneity that are not strongly supported by the data presented.

Reviewer 1 raises an interesting question that we agree will form the basis of important studies. Here, we set out to address a different question, which we will work to better frame. While we and others had previously found a strong correlation between calcium channel abundance and synaptic release probability (Pr (Akbergenova et al., 2018; Gratz et al., 2019; Holderith et al., 2012; Nakamura et al., 2015; Sheng et al., 2012)), more recent studies found that calcium channel abundance does not necessarily predict synaptic strength (Aldahabi et al., 2022; Rebola et al., 2019). Our study explores this paradox and presents findings that provide an explanation: calcium channel abundance predicts Pr among individual synapses of either low-Pr type-Ib or high-Pr type-Is inputs where modulating channel number tunes synaptic strength, but does not predict Pr between the two inputs, indicating an inputspecific role for calcium channel abundance in promoting synaptic strength. Thus, we propose that calcium channel abundance predictably modulates synaptic strength among individual synapses of a single input or synapse subtype, which share similar molecular and spatial organization, but not between distinct inputs where the underlying organization of active zones differs. Consistently, in the mouse, calcium channel abundance correlates strongly with release probability specifically when assessed among homogeneous populations of connections (Aldahabi et al., 2022; Holderith et al., 2012; Nakamura et al., 2015; Rebola et al., 2019; Sheng et al., 2012).

As Reviewer 1 notes, the two-fold difference in calcium influx at type-Is synapses is certainly an important difference underlying three-fold higher Pr. However, growing evidence indicates that calcium influx alone, like calcium channel abundance, does not reliably predict synaptic strength between inputs. For example, Rebola et al. (2019) compared cerebellar synapses formed by granule and stellate cells and found that lower Pr granule synapses exhibit both higher calcium channel abundance and calcium influx. In another example, Aldahabi et al. (2023) demonstrate that even when calcium influx is greater at high-Pr synapses, it does not necessarily explain differences in synaptic strength between inputs. Studying excitatory hippocampal CA1 synapses onto distinct interneuronal targets, they found that raising calcium entry at low-Pr inputs to high-Pr synapse levels is not sufficient to increase synaptic strength to high-Pr synapse levels. Similarly, at the Drosophila NMJ, the finding that type-Ib synapses exhibit loose calcium channel-synaptic vesicle coupling whereas type-Is synapses exhibit tight coupling suggests factors beyond calcium influx also contribute to differences in Pr between the two inputs (He et al., 2023). Consistently, a two-fold increase in external calcium does not induce a three-fold increase in release at low-Pr type-Ib synapses (He et al., 2023). Thus, upon finding that calcium channel abundance is similar at type-Ib and -Is synapses, we focused on identifying differences beyond calcium channel abundance and calcium influx that might contribute their distinct synaptic strengths. We agree that these studies, ours included, cannot definitively determine the contribution of identified organizational differences to distinct release probabilities because it is not currently possible to specifically alter subsynaptic organization, and will ensure that our language is tempered accordingly. However, in addition to the studies cited above and our findings, recent work demonstrating that homeostatic potentiation of neurotransmitter release is accompanied by greater spatial compaction of multiple active zone proteins (Dannhauser et al., 2022; Mrestani et al., 2021) and decreased calcium channel mobility (Ghelani et al., 2023) provide support for the interpretation that subsynaptic organization is a key parameter for modulating Pr.

Reviewer #2 (Public Review):

The authors aim to investigate how voltage-gated calcium channel number, organization, and subunit composition lead to changes in synaptic activity at tonic and phasic motor neuron terminals, or type Is and Ib motor neurons in Drosophila. These neuron subtypes generate widely different physiological outputs, and many investigations have sought to understand the molecular underpinnings responsible for these differences. Additionally, these authors explore not only static differences that exist during the third-instar larval stage of development but also use a pharmacological approach to induce homeostatic plasticity to explore how these neuronal subtypes dynamically change the structural composition and organization of key synaptic proteins contributing to physiological plasticity. The Drosophila neuromuscular junction (NMJ) is glutamatergic, the main excitatory neurotransmitter in the human brain, so these findings not only expand our understanding of the molecular and physiological mechanisms responsible for differences in motor neuron subtype activity but also contribute to our understanding of how the human brain and nervous system functions.

The authors employ state-of-the-art tools and techniques such as single-molecule localization microscopy 3D STORM and create several novel transgenic animals using CRISPR to expand the molecular tools available for exploration of synaptic biology that will be of wide interest to the field. Additionally, the authors use a robust set of experimental approaches from active zone level resolution functional imaging from live preparations to electrophysiology and immunohistochemical analyses to explore and test their hypotheses. All data appear to be robustly acquired and analyzed using appropriate methodology. The authors make important advancements to our understanding of how the different motor neuron subtypes, phasic and tonic-like, exhibit widely varying electrical output despite the neuromuscular junctions having similar ultrastructural composition in the proteins of interest, voltage gated calcium channel cacophony (cac) and the scaffold protein Bruchpilot (brp). The authors reveal the ratio of brp:cac appears to be a critical determinant of release probability (Pr), and in particular, the packing density of VGCCs and availability of brp. Importantly, the authors demonstrate a brp-dependent increase in VGCC density following acute philanthotoxin perfusion (glutamate receptor inhibitor). This VGCC increase appears to be largely responsible for the presynaptic homeostatic plasticity (PHP) observable at the Drosophila NMJ. Lastly, the authors created several novel CRISPRtagged transgenic lines to visualize the spatial localization of VGCC subunits in Drosophila. Two of these lines, CaBV5-C and stjV5-N, express in motor neurons and in the nervous system, localize at the NMJ, and most strikingly, strongly correlate with Pr at tonic and phasic-like terminals.

(1) The few limitations in this study could be addressed with some commentary, a few minor follow-up analyses, or experiments. The authors use a postsynaptically expressed calcium indicator (mhcGal4>UAS -GCaMP) to calculate Pr, yet do not explore the contribution that glutamate receptors, or other postsynaptic contributors (e.g. components of the postsynaptic density, PSD) may contribute. A previous publication exploring tonic vs phasic-like activity at the drosophila NMJ revealed a dynamic role for GluRII (Aponte-Santiago et al, 2020). Could the speed of GluR accumulation account for differences between neuron subtypes?

We did observe that GCaMP signals are higher at type Is synapses, where synapses tend to form later but GluRs accumulate more rapidly upon innervation (Aponte-Santiago et al., 2020). However, because we are using our GCaMP indicator as a plus/minus readout of synaptic vesicle release at mature synapses, we do not expect differences in GluR accumulation to have a significant effect on our measures. Consistently, the difference in Pr we observe between type-Ib and -Is inputs (Fig. 1C) is similar to that previously reported (He et al., 2023; Lu et al., 2016; Newman et al., 2022).

(2) The observation that calcium channel density and brp:cac ratio as a critical determinant of Pr is an important one. However, it is surprising that this was not observed in previous investigations of cac intensity (of which there are many). Is this purely a technical limitation of other investigations, or are other possibilities feasible? Additionally, regarding VGCC-SV coupling, the authors conclude that this packing density increases their proximity to SVs and contributes to the steeper relationship between VGCCs and Pr at phasic type Is. Is it possible that brp or other AZ components could account for these differences. The authors possess the tools to address this directly by labeling vesicles with JanellaFluor646; a stronger signal should be present at Is boutons. Additionally, many different studies have used transmission electron microscopy to explore SVs location to AZs (t-bars) at the Drosophila NMJ.

To date, the molecular underpinnings of heterogeneity in synaptic strength have primarily been investigated among individual type-Ib synapses. However, a recent study investigating differences between type-Ib and -Is synapses also found that the Cac:Brp ratio is higher at type-Is synapses (He et al., 2023).

At this point, we do not know which active zone components are responsible for the organizational (Figs. 1, 2) and coupling (now demonstrated by He et al., 2023) differences between type-Ib and -Is synapses or what establishes the differences in active zone protein levels we observe (Figs. 3,6), although Brp likely plays a local role. We find that Brp is required for dynamically regulating calcium channel levels during homeostatic plasticity and plays distinct roles at type-Ib and -Is synapses (Figs. 3, 4). Brp regulates a number of proteins critical for the distribution of docked synaptic vesicles near T bars of type Ib active zones, including Unc13 (Bohme et al., 2016). Extending these studies to type-Is synapses will be of great interest.

(3) In reference to the contradictory observations that VGCC intensity does not always correlate with, or determine Pr. Previous investigations have also observed other AZ proteins or interactors (e.g. synaptotagmin mutants) critically control release, even when the correlation between cac and release remains constant while Pr dramatically precipitates.

This is an important point as a number of molecular and organizational differences between high- and low-Pr synapses certainly contribute to baseline functional differences. The other proteins we (Figs. 3,6) and others (Dannhauser et al., 2022; Ehmann et al., 2014; He et al., 2023; Jetti et al., 2023; Mrestani et al., 2021; Newman et al., 2022) have investigated are less abundant and/or more densely organized at type-Is synapses. Investigating additional active zone proteins, including synaptic proteins, and determining how these factors combine to yield increased synaptic strength are important next steps.

(4) To confirm the observations that lower brp levels results in a significantly higher cac:brp ratio at phasic-like synapses by organizing VGCCs; this argument could be made stronger by analyzing their existing data. By selecting a population of AZs in Ib boutons that endogenously express normal cac and lower brp levels, the Pr from these should be higher than those from within that population, but comparable to Is Pr. I believe the authors should also be able to correlate the cac:brp ratio with Pr from their data set generally; to determine if a strong correlation exists beyond their observation for cac correlation.

We do not have simultaneous measures of Pr and Cac and Brp abundance. However, our findings suggest that distinct Cac:Brp ratios at type Ib and Is inputs reflect underlying organizational differences that contribute to distinct release probabilities between the two synaptic subtypes. In contrast, within either synaptic subtype, release probability is positively correlated with both Cac and Brp levels. Thus, the mechanisms driving functional differences between synaptic subtypes are distinct from those driving functional heterogeneity within a subtype, so we do not expect Cac:Brp ratio to correlate with Pr among individual type-Ib synapses. We will work to clarify this point in the revised text.

(5) For the philanthotoxin induced changes in cac and brp localization underlying PHP, why do the authors not show cac accumulation after PhTx on live dissected preparations (i.e. in real time)? This also be an excellent opportunity to validate their brp:cac theory. Do the authors observe a dynamic change in brp:cac after 1, or 5 minutes; do Is boutons potentiate stronger due to proportional increases in cac and brp? Also regarding PhTx-induced PHP, their observations that stj and α2δ-3 are more abundant at Is synapses, suggests that they may also play a role in PhTx induced changes in cac. If either/both are overexpressed during PhTx, brp should increase while cac remains constant. These accessory proteins may determine cac incorporation at AZs.

As we have previously followed Cac accumulation in live dissected preparations and found that levels increase proportionally across individual synapses (Gratz et al., 2019), we did not attempt to repeat these challenging experiments at smaller type-Is synapses. We will reanalyze our data to investigate Cac:Brp ratio at individual active zones post PhTx. However, as noted above, we do not expect changes in the Cac:Brp ratio to correlate with Pr among individual synapses of single inputs as this measure reflects organization differences between inputs and PhTx induces an increase in the abundance of both proteins at both inputs.

Determining the effect of PhTx on Stj levels at type-Ib and -Is active zones is an excellent idea and might provide insight into how lower Stj levels correlate with higher Pr at type-Is synapses. While prior studies have demonstrated critical roles for Stj in regulating Cac accumulation during development and in promoting presynaptic homeostatic potentiation (Cunningham et al., 2022; Dickman et al., 2008; Kurshan et al., 2009; Ly et al., 2008; Wang et al., 2016), its regulation during PHP has not been investigated.

Taken together this study generates important data-driven, conceptional, and theoretical advancements in our understanding of the molecular underpinnings of different motor neurons, and our understanding of synaptic biology generally. The data are robust, thoroughly analyzed, appropriately depicted. This study not only generates novel findings but also generated novel molecular tools which will aid future investigations and investigators progress in this field.

References

Akbergenova, Y., K.L. Cunningham, Y.V. Zhang, S. Weiss, and J.T. Littleton. 2018. Characterization of developmental and molecular factors underlying release heterogeneity at Drosophila synapses. eLife. 7.

Aldahabi, M., F. Balint, N. Holderith, A. Lorincz, M. Reva, and Z. Nusser. 2022. Different priming states of synaptic vesicles underlie distinct release probabilities at hippocampal excitatory synapses. Neuron. 110:4144-4161 e4147.

Aponte-Santiago, N.A., K.G. Ormerod, Y. Akbergenova, and J.T. Littleton. 2020. Synaptic Plasticity Induced by Differential Manipulation of Tonic and Phasic Motoneurons in Drosophila. The Journal of neuroscience : the official journal of the Society for Neuroscience. 40:6270-6288.

Bohme, M.A., C. Beis, S. Reddy-Alla, E. Reynolds, M.M. Mampell, A.T. Grasskamp, J. Lutzkendorf, D.D. Bergeron, J.H. Driller, H. Babikir, F. Gottfert, I.M. Robinson, C.J. O'Kane, S.W. Hell, M.C. Wahl, U. Stelzl, B. Loll, A.M. Walter, and S.J. Sigrist. 2016. Active zone scaffolds differentially accumulate Unc13 isoforms to tune Ca(2+) channel-vesicle coupling. Nature neuroscience. 19:1311-1320.

Cunningham, K.L., C.W. Sauvola, S. Tavana, and J.T. Littleton. 2022. Regulation of presynaptic Ca(2+) channel abundance at active zones through a balance of delivery and turnover. Elife. 11.

Dannhauser, S., A. Mrestani, F. Gundelach, M. Pauli, F. Komma, P. Kollmannsberger, M. Sauer, M. Heckmann, and M.M. Paul. 2022. Endogenous tagging of Unc-13 reveals nanoscale reorganization at active zones during presynaptic homeostatic potentiation. Front Cell Neurosci. 16:1074304.

Dickman, D.K., P.T. Kurshan, and T.L. Schwarz. 2008. Mutations in a Drosophila alpha2delta voltage gated calcium channel subunit reveal a crucial synaptic function. The Journal of neuroscience : the official journal of the Society for Neuroscience. 28:31-38.

Ehmann, N., S. Van De Linde, A. Alon, D. Ljaschenko, X.Z. Keung, T. Holm, A. Rings, A. Diantonio, S. Hallermann, U. Ashery, M. Heckmann, M. Sauer, and R.J. Kittel. 2014. Quantitative super-resolution imaging of Bruchpilot distinguishes active zone

states. Nature Communications. 5.

Ghelani, T., M. Escher, U. Thomas, K. Esch, J. Lützkendorf, H. Depner, M. Maglione, P. Parutto, S. Gratz, T. Matkovic-Rachid, S. Ryglewski, A.M. Walter, D. Holcman, K. O‘Connor Giles, M. Heine, and S.J. Sigrist. 2023. Interactive nanocluster compaction of the ELKS scaffold and Cacophony Ca²⁺ channels drives sustained active zone potentiation. Science Advances. 9:eade7804.

Gratz, S.J., P. Goel, J.J. Bruckner, R.X. Hernandez, K. Khateeb, G.T. Macleod, D. Dickman, and K.M. O'Connor-Giles. 2019. Endogenous tagging reveals differential regulation of Ca²⁺ channels at single AZs during presynaptic homeostatic potentiation and depression. The Journal of Neuroscience:3068-3018.

He, K., Y. Han, X. Li, R.X. Hernandez, D.V. Riboul, T. Feghhi, K.A. Justs, O. Mahneva, S. Perry, G.T. Macleod, and D. Dickman. 2023. Physiologic and Nanoscale Distinctions Define Glutamatergic Synapses in Tonic vs Phasic Neurons. The Journal of neuroscience : the official journal of the Society for Neuroscience. 43:4598-4611.

Holderith, N., A. Lorincz, G. Katona, B. Rózsa, A. Kulik, M. Watanabe, and Z. Nusser. 2012. Release probability of hippocampal glutamatergic terminals scales with the size of the active zone. Nature neuroscience. 15:988-997.

Jetti, S.K., A.B. Crane, Y. Akbergenova, N.A. Aponte-Santiago, K.L. Cunningham, C.A. Whittaker, and J.T. Littleton. 2023. Molecular Logic of Synaptic Diversity Between Drosophila Tonic and Phasic Motoneurons. bioRxiv:2023.2001.2017.524447.

Kurshan, P.T., A. Oztan, and T.L. Schwarz. 2009. Presynaptic alpha2delta-3 is required for synaptic morphogenesis independent of its Ca2+-channel functions. Nature neuroscience. 12:1415-1423.

Lu, Z., A.K. Chouhan, J.A. Borycz, Z. Lu, A.J. Rossano, K.L. Brain, Y. Zhou, I.A. Meinertzhagen, and G.T. Macleod. 2016. High-Probability Neurotransmitter Release Sites Represent an Energy-Efficient Design. Current biology : CB. 26:2562-2571.

Ly , C.V., C.-K. Yao , P. Verstreken , T. Ohyama , and H.J. Bellen 2008. straightjacket is required for the synaptic stabilization of cacophony, a voltage-gated calcium channel α1 subunit. Journal of Cell Biology. 181:157-170.

Mrestani, A., M. Pauli, P. Kollmannsberger, F. Repp, R.J. Kittel, J. Eilers, S. Doose, M. Sauer, A.-L. Sirén, M. Heckmann, and M.M. Paul. 2021. Active zone compaction correlates with presynaptic homeostatic potentiation. Cell Reports. 37:109770.

Nakamura, Y., H. Harada, N. Kamasawa, K. Matsui, Jason S. Rothman, R. Shigemoto, R.A. Silver, David A. DiGregorio, and T. Takahashi. 2015. Nanoscale Distribution of Presynaptic Ca2+ Channels and Its Impact on Vesicular Release during Development. Neuron. 85:145-158.

Newman, Z.L., D. Bakshinskaya, R. Schultz, S.J. Kenny, S. Moon, K. Aghi, C. Stanley, N. Marnani, R. Li, J. Bleier, K. Xu, and E.Y. Isacoff. 2022. Determinants of synapse diversity revealed by superresolution quantal transmission and active zone imaging. Nature Communications. 13:229.

Rebola, N., M. Reva, T. Kirizs, M. Szoboszlay, A. Lőrincz, G. Moneron, Z. Nusser, and D.A. Digregorio. 2019. Distinct Nanoscale Calcium Channel and Synaptic Vesicle Topographies Contribute to the Diversity of Synaptic Function. Neuron. 104:693-710.e699.

Sheng, J., L. He, H. Zheng, L. Xue, F. Luo, W. Shin, T. Sun, T. Kuner, D.T. Yue, and L.-G. Wu. 2012. Calcium-channel number critically influences synaptic strength and plasticity at the active zone. Nature neuroscience. 15:998-1006.

Wang, T., R.T. Jones, J.M. Whippen, and G.W. Davis. 2016. alpha2delta-3 Is Required for Rapid Transsynaptic Homeostatic Signaling. Cell Rep. 16:2875-2888.

Reviewer #1 (Recommendations For The Authors): 

Major points: 

(1) A central question regarding VGCC differences at Is vs Ib active zones is why is calcium influx higher at Is active zones compared to Ib. Ideally, the authors would have started this study by showing correlations between Cac abundance, presynaptic calcium influx, and Pr at Is vs Ib active zones. If they had, they would likely find that Cac abundance scales with calcium influx and Pr within Is vs Ib, but that calcium influx is over two-fold enhanced at Is over Ib when normalized to the same Cac abundance. This is more than sufficient to explain the Pr differences, so the rest of the study should have focused on revealing why influx is different at Is over Ib despite an apparently similar level of Cac abundance. Then the examination of CaBeta, Stj, etc could have been used to help explain this conundrum. 

A lesson might be gleaned in how to structure this narrative from the Rebola 2019 study, which the authors cite and discuss at length. Similar to the current study, that paper started with two synapses ("strong" vs "weak") and sought to explain why they were so different in synaptic strength. First, they examined presynaptic calcium influx, and surprisingly found that the strong synapse had reduced calcium influx compared to the weak. Then the rest of the paper sought to explain why synaptic strength (Pr) was higher at the strong synapse despite reduced calcium influx. The authors do not use this logical flow and narrative in the present study, despite the focus being on how Cav2 channels contribute to strong vs weak synapses - and the primary function of Cav2 channels is to pass calcium at active zones to drive vesicle fusion. 

Although the authors did not show that presynaptic calcium influx is higher at Is vs Ib active zones in the current manuscript, other studies have previously established that calcium influx is two-fold higher at Is active zones vs Ib (as the authors cite). Rather than focusing so much on Pr at Is vs Ib active zones, which as the authors know can be influenced by myriad differences, it seems the more relevant parameter to study is simply to address presynaptic calcium influx at Is vs Ib, which is the primary function of Cac. Put more simply, if Cac levels are the same at Is vs Ib active zones, why is calcium influx at least two-fold higher at Is? 

It would therefore seem crucial for the authors to determine presynaptic calcium influx levels (ideally at individual AZs) to really understand how Cac intensity levels correlate with calcium influx. The authors instead map Pr at individual AZs, but as the authors know there are many variables that influence whether a SV releases in addition to calcium influx. There are a number of options for this kind of imaging in Drosophila, including genetically encoded calcium indicators targeted to active zones. But since several studies have previously established that influx is higher at Is active zones over Ib, this may not be necessary. That being said, there is a lot of value in quantitatively analyzing Cac/Stj/CaBeta abundance, calcium influx, and Pr together at individual active zones.

We appreciate the perspective that we could have focused on why Ca2+ influx is 2x greater at type Is active zones, which we agree is an important and interesting question. However, growing evidence indicates that Ca2+ influx alone, like Ca2+ channel abundance, does not reliably predict synaptic strength between inputs. So, here we focused instead on how other differences between synapses influence Pr and contribute to synaptic heterogeneity between and/or among synapses formed by strong and weak inputs. We have changed our title and framing to better reflect this focus. 

As Reviewer 1 notes, Rebola et al. (2019) found that lower Pr granule synapses exhibit higher Ca2+ influx (and Ca2+ channel abundance). In another example, Aldahabi et al. (2022) demonstrated that even when Ca2+ influx is greater at high-Pr synapses, it does not necessarily explain differences in synaptic strength as raising Ca2+ entry at low-Pr synapses to high-Pr synapse levels was not sufficient to increase synaptic strength to high-Pr input levels. Similar findings have been reported at tonic and phasic synapses of the Crayfish NMJ (Msghina, 1999).

Several lines of evidence argue that factors beyond Ca2+ influx also play important roles in establishing distinct release properties at the Drosophila NMJ. A recent study using using a botulinum transgene to isolate type Ib and Is synapses for electrophysiological analysis found that increasing external [Ca2+] from physiological levels (1.8 mM) to 3 mM or even 6 mM does not result in a 3-fold increase in EPSCs or quantal content at type Ib synapses despite the prediction that the increase would be even greater given the power dependence of release on between Ca2+ concentration (He et al., 2023). The authors further found that type Ib synapses are more sensitive than type Is synapses to the slow Ca2+ chelator EGTA, indicating looser Ca2+ channel-SV coupling. 

Consistently, we find that although VGCC levels are similar at the two inputs, their density is greater at type Is active zones (Figs. 1 and 2). Our findings also reveal additional molecular differences that may contribute to the observed differences in neurotransmitter release properties between the two inputs, including lower levels of the active zone protein Brp (Fig 3) and the auxiliary subunit α2δ-3/Stj (Fig. 6) at high Pr type Is inputs. In contrast, levels of each of these proteins positively correlate with synaptic strength among active zones of a single input, whether low- or high-Pr (Figs. 1, 3, 6). Similarly, levels of each of these proteins increase during homeostatic potentiation of neurotransmitter release (Figs. 4 and 7). Thus, we propose that two broad mechanisms contribute to synaptic diversity in the nervous system: (1) spatial organization and relative molecular content establish distinct average basal release probabilities that differ between inputs and (2) among individual synapses of distinct inputs, coordinated modulation of Ca2+ channel and active zone protein abundance independently tunes Pr. These intersecting mechanisms provide a framework for understanding the extensive and dynamic synaptic diversity observed across nervous systems.

(2) In addition to key points made above, it seems the authors should at least consider (if not experimentally test) what other differences might contribute to the higher calcium influx at Is over Ib:  

- Distinct splice isoforms of Cac (and/or Stj/Cabeta): The recent RNAseq analysis of gene expression at Is vs Ib motor neurons from Troy Littleton's group may inform this consideration? 

- Stj reduction at Is: Do channel studies in heterologous systems give any insight into VGCC channel function with and without a2d-3? Do Cav2 channels without a2d pass more calcium? This would then offer an obvious solution to the key conundrum underlying this study. 

These are excellent questions that we are actively pursuing. While there is no evidence of differentially expressed splice isoforms of Stj or Ca-β in the recent RNA-seq data from Jetti et al., 2023, subtle changes in Cac isoform usage were observed that may contribute to differences in Ca2+ influx. In heterologous systems, α2δ expression generally increases Ca2+ channel membrane insertion and  Ca2+ currents. However, in vivo α2δ’s can also mediate extracellular interactions that may modulate channel function. We address these points in greater detail in the revised discussion.  

(3) Assess Stj and CaBeta levels at AZs after PhTx: The successful generation of endogenously tagged Stj and CaBeta enables some relatively easy experiments that would be of interest, similar to what the authors present for Cac. Does Brp similarly control Stj and CaBeta at Is vs Ib compared to what they show for Cac? In addition, does homeostatic plasticity similarly change Stj and CaBeta at Is vs Ib compared to what the authors have shown for Cac? i.e., do they both similarly increase in intensity, by the same amount, as Cac? 

We agree and have included an analysis of α2δ-3/Stj levels following PhTx exposure (Fig. 7A-C). We have also investigated the regulation of Stj during chronic presynaptic homeostatic potentiation (Fig. 7D-F). In both cases, StjV5-N levels significantly increase at type Ib and Is active zones, consistent with our finding that among AZs of either type Ib or Is inputs, Stj levels correlate with Cac abundance and, thus, Pr. Together with our and others’ findings, this suggests that coordinated increases Ca2+ channel, auxiliary subunit,  and active zone protein abundance positively tunes synaptic strength at diverse synaptic subtypes.

Minor points: 

(1) Including line numbers would make reviewing/commenting easier. 

We apologize for this oversight and have added line numbers to the revised manuscript.

(2) Fig. 2I: It is not apparent what the mean cluster density is between Ib vs Is (as it is in Fig. 2F-H graphs). The mean and error bars should be included in 2I as it is in 2G. Same with Fig. 3C. 

Thank you for pointing this out. We have added error bars to the paired analysis in 2I as well as in 3C and 1C.

(3) Fig. 4 - it might make more sense to normalize Brp and Cac intensity as a percentage of baseline (PhTx at Is or Ib) rather than normalizing everything to control Ib. 

We have revised the graphs as suggested in Figure 4 and throughout.

(4) Page 5 bottom - REFS missing after Fig. 1E. 

Thank you for catching this. We have fixed it.

Reviewer #2 (Recommendations For The Authors): 

This reader found differentiating between low Pr sites (deep purple) and cac measurements (black) difficult in Fig 1B. You may consider depicting this differently. 

Thank you for this feedback. We have changed the color scheme to improve readability.

I found it difficult to discern the difference between experiments Fig 1E and Fig 1J. Why are individual dots distributed differently? 

The individual data points are the same as in 1E and 1F, but we have removed the individual NMJ dimensionality to combine all Is and Ib data points together along with best fit lines for comparison of their slopes. We have added text to the revised manuscript to clarify this.

Results section, second paragraph, add references, remove 'REF': We next investigated the correlation between Pr and VGCC levels and found that at type Is inputs, single-AZ Cac intensity positively correlates with Pr (Fig. 1E; REFS). 

Thank you. We have corrected this error.

Author response:

Reviewer #1 (Public Review):

Greter et al. provide an interesting and creative use of lactulose as a "microbial metabolism" inducer, combined with tracking of H2 and other fermentation end products. The topic is timely and will likely be of broad interest to researchers studying nutrition, circadian rhythm, and gut microbiota. However, a couple of moderate to major concerns were noted that may impact the interpretation of the current data:

(1)  Much of the data relies on housing gnotobiotic mice in metabolic cages, but I couldn't find any details of methods to assess contamination during multiple days of housing outside of gnotobiotic isolators/cages. Given the complexity of the metabolic cage system used, sterility would likely be incredibly challenging to achieve. More details needed to be included about how potential contamination of the mice was assessed, ideally with 16S rRNA gene sequencing data of the endpoint samples and/or qPCR for total colonization levels relative to the more targeted data shown.

We thank the reviewer for pointing out that we have not made the experimental setup clear in the text. One of the unique features of our metabolic cage setup is that the mice do not need to be housed outside gnotobiotic isolators, but that the whole system is placed inside an isolator. We have developed and published this system recently (Hoces et al, PLOS Biol 2022), including extensive testing for sterility/gnotobiosis. We will improve clarity in a revised version.

Given that 16S sequencing of germ-free mice will typically produce false positive reads, we used Blautia pseudococcoides as an indicator strain for contaminations. This strain is present in our SPF mouse colony, forms spores that are highly resilient to decontamination measures, and has been the most likely contaminant in our gnotobiotic system. We have checked for presence of this strain in the cecum content of all our animals at the end of each experiment, and only included experiments which had a B. pseudococcoides signal below threshold level.

(2)  The language could be softened to provide a more nuanced discussion of the results. While lactulose does seem to induce microbial metabolism it also could have direct effects on the host due to its osmotic activity or other off-target effects. Thus, it seems more precise to just refer to lactulose specifically in the figure titles and relevant text. Additionally, the degree to which lactulose "disrupts the diurnal rhythm" isn't clear from the data shown, especially given that the markers of circadian rhythm rapidly recover from the perturbation. It is probably more precise to instead state that lactulose transiently induces fermentation during the light phase or something to that effect. The discussion could also be expanded to address what methods are available or could be developed to build upon the concepts here; for example, the use of genetic inducers of metabolism which may avoid the more complex responses to lactulose.

The point about language is well taken. We tried to make the argument that what we call disruption of the diurnal rhythm is acute, meaning that it is not disrupting the rhythm "chronically" (i.e., for longer), but that it recovers rapidly from this transient disruption. Given the confusion this wording is causing we are rephrasing this in a new version of the manuscript.

We also appreciate the mention of concepts from our study that can be built on in future studies, and we will add a paragraph on potential further research.

Despite these concerns, this was still an intriguing and valuable addition to the growing literature on the interface of the microbiome and circadian fields.

We thank the reviewer for all their encouraging and constructive remarks!

Reviewer #2 (Public Review):

Summary:

The authors aimed to investigate how microbial metabolites, such as hydrogen and short-chain fatty acids (SCFAs), influence feeding behavior and circadian gene expression in mice.

Specifically, they sought to understand these effects in different microbial environments, including a reduced community model (EAM), germ-free mice, and SPF mice. The study was designed to explore the broader relationship between the gut microbiome and host circadian rhythms, an area that is not well understood. Through their experiments, the authors hoped to elucidate how microbial metabolism could impact circadian clock genes and feeding patterns, potentially revealing new mechanisms of gut microbiome-host interactions.

Strengths:

The manuscript presents a well-executed investigation into the complex relationship between microbial metabolites and circadian rhythms, with a particular focus on feeding behavior and gene expression in different mouse models. One of the major strengths of the work lies in its innovative use of a reduced community model (EAM) to isolate and examine the effects of specific microbial metabolites, which provides valuable insights into how these metabolites might influence host behavior and circadian regulation. The study also contributes to the broader understanding of the gut microbiome's role in circadian biology, an area that remains poorly understood. The experiments are thoughtfully designed, with a clear rationale that ties together the gut microbiome, metabolic products, and host physiological responses. The authors successfully highlight an intriguing paradox: the significant influence of microbial metabolites in the EAM model versus the lack of effect in germ-free and SPF mice, which adds depth to the ongoing exploration of microbial-host interactions. Despite some methodological concerns, the manuscript offers compelling data and opens up new avenues for research in the field of microbiome and circadian biology.

We thank the reviewer for their encouraging remarks, specifically on the surprising findings that microbial metabolism seems to affect circadian clock gene expression and behavior differently in EAM and SPF mice.

Weaknesses:

The manuscript, while providing valuable insights, has several methodological weaknesses that impact the overall strength of the findings. First, the process for stool collection lacks clarity, raising concerns about potential biases, such as the risk of coprophagia, which could affect the dry-to-wet weight ratio analysis and compromise the validity of these measurements.

We thank the reviewer for pointing out that our description of the specific methods used for collecting feces were presented in a somewhat confusing manner. In short, dry and wet fecal weights were determined based on fecal pellets that were freshly produced and directly collected from restrained mice. To determine total fecal output over time, we collected all fecal pellets produced in a 5 hour window in a cage, determined their dry weight, and then used the water content determined for fresh feces to calculate wet weight. Using this method, we cannot account for potential differences in coprophagia between the groups. However, this is not likely to affect the dry-to-wet ratio of fecal output in our results.

Additionally, the use of the term "circadian" in some contexts appears inaccurate, as "diurnal" might be more appropriate, especially given the uncertainty regarding whether the observed microbiome fluctuations are truly circadian.

Similarly to our answer to reviewer 1 above, we appreciate this remark about imprecise language and have addressed this issue in the text. Indeed, we do not think the microbiota fluctuations are truly circadian, but likely a result of the entrainment through the host's food intake.

Another significant issue is the unexpected absence of an osmotic effect of lactulose in EAM mice, which contradicts the known properties of lactulose as an osmotic laxative. This finding requires further verification, including the use of a positive control, to ensure it is not artifactual.

This is a good point. We have used this lactulose dosage specifically to induce microbial metabolism without causing osmotic diarrhea, and went to some lengths do demonstrate this. In response to this comment (and one by reviewer 3 below about transit time), we are planning an experiment that will use a higher lactulose dose as a positive control.

The presentation of qRT-PCR data as log2-fold changes, with a mean denominator, could introduce bias by artificially reducing variability, potentially leading to spurious findings or increased risk of Type I error. This approach may explain the unexpected activation of both the positive and negative limbs of the circadian clock.

While we agree that our description of the qpcr method used for measuring circadian clock gene expression was lacking detail, we do not see how log2-fold changes (as opposed to, e.g., fold change) would lead to an increased risk of Type 1 error. We did not use a mean denominator for analyzing the data but used the house-keeping data for the same sample as denominator for the respective circadian clock genes. This will be described more clearly in a revised methods section.

Moreover, the lack of detailed information on the primers and housekeeping genes used in the experiments is concerning, particularly given the importance of using non-circadian housekeeping genes for accurate normalization.

We apologize for this omission, it seems like the resource table got lost in the submission, leading to missing information. It will be included in the revised manuscript.

The methods for measuring metabolic hormones, such as GLP-1 and GIP, are also not adequately described. If DPP-IV/protease inhibitor tubes were not used, the data could be unreliable due to the rapid degradation of these hormones by circulating proteases.

We thank the reviewer for spotting this mistake. We will add details of how GLP-1 and GIP were measured to the methods section. While we did not use DPP-IV/protease inhibitor tubes, we added the inhibitors to the syringes when sampling blood, leading to the same effect.

Finally, the manuscript does not address the collection of hormone levels during both fasting and fed phases, a critical aspect for interpreting the metabolic impact of microbial metabolites.

We agree that it will be interesting to measure hormone levels also in the fed phase, and we will include this data in a revised version of the manuscript. Even with that data, a more thorough examination of hormone levels over the diurnal cycle, as suggested by reviewer 3, might be relevant for a full-scale follow-up. Given our data, we of course cannot exclude that there may be time-point-specific differences and therefore have softened the language around this conclusion to state that hormone levels are not acutely changed after a lactulose intervention “at the time-points examined”.

These methodological concerns collectively weaken the robustness of the study's results and warrant careful reconsideration and clarification by the authors.

Because of these weaknesses, the authors have partially achieved their aims by providing novel insights into the relationship between microbial metabolites and host circadian rhythms. The data do suggest that microbial metabolites can significantly influence feeding behavior and circadian gene expression in specific contexts. However, the unexpected absence of an osmotic effect of lactulose, the potential biases introduced by the log2-fold change normalization in qRT- PCR data, and the lack of clarity in critical methodological details weaken the overall conclusions. While the study provides valuable contributions to understanding the gut microbiome's role in circadian biology, the methodological weaknesses prevent a full endorsement of the authors' conclusions. Addressing these issues would be necessary to strengthen the support for their findings and fully achieve the study's aims.

We thank the reviewer again for their careful and critical reading of our work, and for their constructive input. We hope that many of the concerns will be addressed by providing more methodological detail and additional experimental data in the revised version of our manuscript.

Despite the methodological concerns raised, this work has the potential to make a significant impact on the field of circadian biology and microbiome research. The study's exploration of the interaction between microbial metabolites and host circadian rhythms in different microbial environments opens new avenues for understanding the complex interplay between the gut microbiome and host physiology. This research contributes to the growing body of evidence that microbial metabolites play a crucial role in regulating host behaviors and physiological processes, including feeding and circadian gene expression.

We thank the reviewer for their encouraging remarks!

Reviewer #3 (Public Review):

Summary:

In the manuscript by Greter, et al., entitled "Acute targeted induction of gut-microbial metabolism affects host clock genes and nocturnal feeding" the authors are attempting to demonstrate that an acute exposure to a non-nutritive disaccharide (lactulose) promotes microbial metabolism that feeds back onto the host to impact circadian networks. The premise of the study is interesting and the authors have performed several thoughtful experiments to dissect these relationships, providing valuable insights for the field. However, the work presented does not necessarily support some of the conclusions that are drawn. For instance, lactulose is administered during the fasting period to mimic the impact of a feeding bout on the gut microbiota, but it would be important to perform this treatment during the fed state as well to show that the effects on food intake, etc. do not occur.

This is a good point, and we will include an experiment addressing this in a revised version of the manuscript.

To truly draw the conclusion that the current outcomes are directly connected to and mediated via an impact on the host circadian clock, it would be ideal to perform these studies in a circadian gene knock-out animal (i.e., Cry1 or Cry2 KO mice, or perhaps Bmal-VilCre tissue- specific KO mice). If the effects are lost in these animals, this would more concretely connect the current findings to the circadian clock gene network.

We agree that these would be interesting experiments to follow up on the question how the observed effects are actuated by host functions. However, they would require a large amount of preparatory work (including rederiving the KO mice to get them germ-free in our gnotobiotic facility), we argue that they are beyond the scope of this study.

Despite these reservations, the work is promising.

We thank the reviewer for their encouraging assessment.

Strengths:

Attempting to disentangle nutrient acquisition from microbial fermentation and its impact on diurnal dynamics of gut microbes on host circadian rhythms is an important step for providing insights into these host-microbe interactions.

The authors utilize a novel approach in leveraging lactulose coupled with germ-free animals and metabolic cages fitted with detectors that can measure microbial byproducts of fermentation, particularly hydrogen, in real-time.

The authors consider several interesting aspects of lactulose delivery, including how it shifts osmotic balance as well as provides calculations that attempt to explain the caloric contribution of fermentation to the animal in the context of reduced food intake. This provides interesting fundamental insights into the role of microbial outputs on host metabolism.

Thank you!

Weaknesses:

While the authors have done a large amount of work to examine the osmotic vs. metabolic influence of lactulose delivery, the authors have not accounted for the enlarged cecum and increased cecal surface area in germ-free mice. The authors could consider an additional control of cecectomy in germ-free mice.

We thank the reviewer for pointing out the potential effect of the anatomical differences of germ- free and conventionally colonized mice. We agree that when comparing germ-free mice to SPF mice, the enlarged cecum area in germ-free animals could lead to differences in water release or uptake. However, this is not the case in the gnotobiotic mice colonized with our minimal microbiota, which have comparable cecum sizes to germ-free mice, and thus comparing water transport over the cecum wall between those groups can be done without correcting for cecal surface areas. We will add information on cecum sizes in the different experimental groups to a revised version of the manuscript.

The authors have examined GI hormones as one possible mechanism for how food intake is altered by microbial fermentation of lactulose. However, the authors measure PYY and GLP-1 only at a single time point, stating that there are no differences between groups. Given the goal of the studies is to tie these findings back into circadian rhythms, it would be important to show if the diurnal patterns of these GI hormones are altered.

We fully agree that a deeper investigation of the diurnal fluctuations of hormone levels would be an interesting next step in studying whether perturbations in food intake can disturb these rhythms. Doing this for the whole rhythm would really require a full second study. For a revised version of this manuscript, we will add a second time-point of hormone measurements (during the fed phase) to this study. In addition, we will soften the statements made around these data to point out just that hormone level fluctuations could not be detected during specific time points after lactulose treatment, and therefore do not seem to explain the imminent behavioral changes.

Considerations of other factors, such as conjugated vs. deconjugated bile acids, microbial bile salt hydrolase activity, and bile acid resorption, might be an important consideration for how lactulose elicits more influence on ileal circadian clock genes relative to cecum and colon.

We absolutely agree that investigation of microbial bile acid modification and their metabolism by the host would be an interesting topic for a follow-up study.

Measurements of GI transit time (both whole gut and regional) would be an important for consideration for how lactulose might be impacting the ileum vs. cecum vs. colon.

This is also an interesting point, and we will add an assessment of transit time to a revised version of the manuscript.

Author response:

General comment:

"This important study examined neuronal activity in the dentate nucleus of the cerebellum when monkeys performed a difficult perceptual decision-making task. The authors provide convincing evidence that the cerebellum represents sensory, motor, and behavioral outcome signals that are sent to the attentional system, but further analysis focusing on the disparity of performance between animals would improve the quality of the paper. This paper is of great general interest in that it shows the involvement of the cerebellum in cognitive processes at the neuronal level."

We thank you for these general comments, and we agree with all of them. 

Public Reviews (Reviewer #1):

Summary:

Recordings were made from the dentate nucleus of two monkeys during a decision-making task. Correlates of stimulus position and stimulus information were found to varying degrees in the neuronal activities. 

We agree with this summary.

Strengths:

A difficult decision-making task was examined in two monkeys.

We agree with this statement.

Weaknesses:

One of the monkeys did not fully learn the task. The manuscript lacked a coherent hypothesis to be tested, and no attempt was made to consider the possibility that this part of the brain may have little to do with the task that was being studied. 

We understand these comments. It is correct that one of the monkeys did not fully learn the task, but it should be noted that both monkeys learned significantly above chance level, and we therefore find the recordings of both monkeys useful. We tested the hypothesis that neurons of the nucleus dentate can dynamically modulate their activity during a visual attention task, comprising not only sensorimotor but also cognitive attentional components. We agree that this hypothesis should be spelled out more explicitly in the introduction, which we will do in the revised version. We also appreciate the comment of this Reviewer that in our original submission we did not show our attempt to consider the possibility that this part of the brain may have little to do with the task that was being studied. We in fact did consider this possibility in that we applied muscimol to the dentate nucleus in one of the monkeys. The data of this one successful experiment show that the behaviour was reversibly affected in line with our hypothesis. Given that this only concerned one of the monkeys, we preferred not to present these data in the article. However, as the Reviewer correctly points out that this question remains hanging in the air, we will show them in our formal rebuttal letter. Please note that we decided to focus at the end of our research project on the tracing experiments, showing in both monkeys the connections of the dentate nucleus with the regions that are involved in attention. As a result, both monkeys have been sacrificed and we cannot expand upon our muscimol experiments anymore (which would have been useful indeed).

Last but not least, given the comments of the Reviewers, we will also add a Supplementary figure to Figure 2, in which we will present the data for both monkeys separately and provide our interpretation. This may help to strengthen our conclusions. 

Public Reviews (Reviewer #2):

The authors trained monkeys to discriminate peripheral visual cues and associate them with planning future saccades of an indicated direction. At the same time, the authors recorded single-unit neural activity in the cerebellar dentate nucleus. They demonstrated that substantial fractions of DN cells exhibited sustained modulation of spike rates spanning task epochs and carrying information about stimulus, response, and trial outcome. Finally, tracer injections demonstrated this region of the DN projects to a large number of targets including several known to interconnect the visual attention network. The data compellingly demonstrate the authors' central claims, and the analyses are well-suited to support the conclusions. Importantly, the study demonstrates that DN cells convey many motor and nonmotor variables related to task execution, event sequencing, visual attention, and arguably decision-making/working memory.

We thank the Reviewer for this positive and constructive feedback.

Author response:

We would like to thank the reviewers for their time and for their kind comments about our work. We expect that their comments will help us to improve the manuscript and so will plan the following experiments/revisions to address some of their comments:

Reviewer 1 (Public Review):

(1) The cutoffs the authors used to define "conditionally essential" mutants are not reported. The results also lack validation for lethality using a titratable system. It would be ideal to validate several genes in each dataset to determine cutoffs (i.e. 5-fold decrease in insertion mutants) for conditional lethality. It was not done (or described) here.

We will report the cutoffs used when we generate the revised manuscript. Our experiments identified hundreds of lethal combinations and we have six datasets, validation of several genes from each would require generation of at least 20 depletion strains and subsequent testing of each. Validation using a depletion system would therefore be a significant undertaking and is typically not the standard when using these approaches. However, should time permit then we will attempt a subset of these experiments.

(2) Also, two mutations that both make the cells sick could provide an additive effect (i.e. dapF and BamB), which doesn't necessarily mean the pathways are linked. The authors should revise their wording. They have not shown genetic linkage in some cases.

We will revise the text to address this.

(3) Mutations throughout the manuscript are not complemented. It would be ideal to add complementation data to show the gene-phenotype relationship is specific.

We thank the reviewers for highlighting this and will complete the complementation experiments.

(4) Also, I would argue the term "conditionally essential genes" should be replaced with "synthetically lethal". Strains were compared in the same conditions but with different genetic backgrounds.

We take the reviewers point and will revise the text accordingly.

Reviewer 2 (Public Review):

Weaknesses:

(1) An important control in any genetic interaction study is to do complementation tests to demonstrate that the phenotype observed is indeed due to the missing gene under analysis. Although the Keio library was designed to avoid polar effects, it is impossible to predict other undesirable effects of the deletions (hitting of a non-annotated sRNA or RNA stability effects, for example). Thus, before one can safely conclude that a proposed genetic interaction is real, complementation tests should be carried out. This seems particularly important in the case of a new and surprising interaction, such as that between bamB and DNA replication and repair genes.

We thank the reviewers for highlighting this and will complete the complementation experiments.

(2) Why not include the suppressor interactions in the work? There are probably plenty, and in principle, they should be as informative as the conditional essential (or synthetic lethal) ones. The only one highlighted in the paper is that between bamB and diaA, since it nicely fits with the synthetic lethal effects with initiation inhibitors seqA and hda. Even if the authors cannot make sense of the suppressor interactions, their inclusion in the paper should make the dataset richer and more valuable to the community.

These data are available in supplementary table 1. However, we appreciate this is not obvious and so will make a new supplementary table and include a brief description of the data for the revised paper.

(3) The enrichment analysis in Figure 2B deserves some clarification. What is the meaning of gene ratio? How can single genes of a pathway yield an enrichment signal? Why weren´t seqA and hda included in the DNA replication class in 2B?

We apologise for the confusion caused and will include a description of the analysis in the methods section.

(4) The writing puts too much emphasis on demonstrating that bam lipoproteins and chaperones are specialized instead of fully redundant. However, I have the impression this is a long-settled conclusion in the field, as the manuscript itself describes at several points when reviewing the literature.

We will revise the text to reduce this emphasis.

Reviewer #3 (Public Review):

In this work, Bryant, et al. investigate genetic interactions between non-essential members of the outer membrane protein biogenesis pathway and other genes in the genome using a transposon-directed insertion sequencing (TraDIS) approach in E. coli K-12. The authors identify interactions with other components of the envelope including LPS, peptidoglycan, and enterobacterial common antigen biogenesis, and they tie these interactions to specific members of the outer membrane biogenesis pathway. Although many of these interactions are known and have been previously investigated in the field, the study provides several synthetic phenotypes that could be useful for further investigations.

The strengths of the paper include their unbiased, TraDIS approach, and follow up on the interactions they observe. The interactions with genes of unknown function also are of interest as they may suggest experiments to find the functions of these genes. The largest weakness of this paper is the use of a gene deletion allele for bamB that is known to be polar leading to decreased expression of an essential gene. This largely invalidates all results related to DNA replication. In addition, it is a weakness that the paper does not adequately address its place in the field through discussion of existing results on the interactions they investigate.

We appreciate the reviewers’ comments and concerns about the bamB allele, and we will address these concerns by completing complementation experiments for the CRISPRi depletion experiments and the run-out assays. However, despite the statement that it is known to be polar, several previous studies have also used the bamB Keio library strain. Many of these studies transfer the allele to a clean background and use the derivative in which the cassette has been removed as we have done here (Cox et al., 2017, Gunasinghe et al., 2018, Psonis et al., 2019, Storek et al., 2019, Ranava et al. 2021, Steenhuis et al., 2021, Thewasano et al., 2023). Therefore, we feel somewhat justified in our choice of strain.

We are unable to find a reference for the Keio bamB strain causing polar effects and would have appreciated the reviewers’ guidance here. However, we believe the concern about polar effects stems from the observations of Ruiz et al., (2005), in which it was observed that a yfgL::ISE1 allele causes polar effects. This was hypothesised to be due to the ORF contained within the IS being transcribed in the opposite orientation to yfgL and the downstream der gene. They subsequently observed that a strain carrying a Tn5KAN-I-SceI insertion in yfgL (yfgL::kan) did not cause polar effects and this was hypothesised to be due to the kan cassette being co-oriented with yfgL. In addition, Charlson et al., 2006 generated a yfgL deletion by replacing the majority of the gene with a kan cassette in a manner similar to that of the Keio library that was subsequently flipped out. This study also found no evidence of polar effects on der. In theory, the strain used here, and in previous studies by other groups, should provide minimal disruption to transcription through generation of a mini-gene from the original bamB sequence to maintain operon expression. This is in contrast to the disruption caused by the yfgL::ISE1 allele.

While we do appreciate the concern, several pieces of evidence lend themselves to counter the statement that our strain choice largely invalidates the results. The der GTPase is essential, hence the concern about polar effects leading to the bamB phenotypes we see. However, depletion of der leads to cold sensitivity, whereas we find that the bamB strain used here actually performs better in colder temperatures. In addition, the der depletion is sensitive to doxycycline, whereas the bamB mutant has increased fitness in this condition (Fig 1) (Bharat and Brown, 2015, Hwang and Inouye, 2008). Hence, should the mutation lead to decreased expression of der then we would expect the bamB strain to phenocopy the der depletion, which it does not. Regardless of this information, we will still address these concerns by completing complementation experiments.

Author response:

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

Reviewer #1 (Public Review):

Weakness 1. Enhancing Reproducibility and Robustness: To enhance the reproducibility and robustness of the findings, it would be valuable for the authors to provide specific numbers of animals used in each experiment. Explicitly stating the penetrance of the rod-like neurocranial shape in dact1/2-/- animals would provide a clearer understanding of the consistency of this phenotype. 

In Fig. 3 and Fig. 4 animal numbers were added to the figure and figure legend (line 1111). In Fig. 5 animal numbers were added to the figure. We now state that dact1/2-/- animals exhibit the rod-like neurocranial shape that is completely penetrant (Line 260). 

Weakness 2. Strengthening Single-Cell Data Interpretation: To further validate the single-cell data and strengthen the interpretation of the gene expression patterns, I recommend the following: 

-Provide a more thorough explanation of the rationale for comparing dact1/2 double mutants with gpc4 mutants.

-Employ genotyping techniques after embryo collection to ensure the accuracy of animal selection based on phenotype and address the potential for contamination of wild-type "delayed" animals.

-Supplement the single-cell data with secondary validation using RNA in situ or immunohistochemistry techniques. 

An explanation of our rationale was added to the results section (Lines 391403) and a summary schematic was added to Figure 6 (panel A).

Genotyping of the embryos was not possible but quality control analysis by considering the top 2000 most variable genes across the dataset showed good clustering by genotype, indicating the reproducibility of individuals in each group (See Supplemental Fig. 4).

The gene expression profiles obtained in our single-cell data analysis for gpc4, dact1, and dact2 correlate closely with our in situ hybridization analyses. Further, our data is consistent with published zebrafish single-cell data. We validated our finding of increased capn8 expression in dact1/2 mutants by in situ hybridization. Therefore we are confident in the robustness of our single-cell data.  

Weakness 3. Directly Investigating Non-Cell-Autonomous Effects: To directly assess the proposed non-cell-autonomous role of dact1/2, I suggest conducting transplantation experiments to examine the ability of ectodermal/neural crest cells from dact1/2 double mutants to form wild-type-like neurocranium.  

The reviewer’s suggestion is an excellent experiment and something to consider for future work. Cell transplant experiments between animals of specific genotypes are challenging and require large numbers. It is not possible to determine the genotype of the donor and recipient embryos at the early timepoint of 1,000 cell stage where the transplants would have to be done in the zebrafish. So that each transplant will have to be carried out blind to genotype from a dact1+/-; dact2+/- or dact1-/-; dact2+/- intercross and then both animals have to be genotyped at a subsequent time point, and the phenotype of the transplant recipient be analyzed. While possible, this is a monumental undertaking and beyond the scope of the current study.

Weakness 4. Further Elucidating Calpain 8's Role: To strengthen the evidence supporting the critical role of Calpain 8, I recommend conducting overexpression experiments using a sensitized background to enhance the statistical significance of the findings. 

We thank the reviewer for their suggestion and have now performed capn8 overexpression experiments in embryos generated from dact1/2 double heterozygous breeding. We found a statistically significant effect of capn8 overexpression in the dact1+/-,dact2+/- fish (Lines 462-464 and Fig. 8C,D). 

Minor Comments:  

Comment: Creating the manuscript without numbered pages, lines, or figures makes orientation and referencing harder.  

Revised

Comment: Authors are inconsistent in the use of font and adverbs, which requires extra effort from the reader. ("wntIIf2 vs wnt11f2 vs wnt11f2l"; "dact1/2-/- vs dact1/dact2 -/-"; "whole-mount vs wholemount vs whole mount").  

Revised throughout.

Comment: Multiple sentences in the "Results" belong to the "Materials and Methods" or the "Discussion" section. 

We have worked to ensure that sentences are within the appropriate sections of the manuscript.

Comment: Abstract:

"wnt11f2l" should be "wnt11f2"  

Revised (Line 24).

Comment: Main text:

Page 5 - citation Waxman, Hocking et al. 2004 is used 3x without interruption any other citation. 

Revised (Line 112).

Page 9 - "dsh" mutant is mentioned once in the whole manuscript - is this a mistake?

Revised, Rewritten (Line 196).

Page 10 - Fig 2B does not show ISH.

Revised (Line 229).

Page 11 - "kyn" mutant is mentioned here for the first time but defined on page 15.

Revised (Line 245). Now first described on page 4.

Page 14 - "cranial CNN" should be CNCC.

Revised. (Line 334)

Page 16 - dact1/dact2/gpc4: Fig. 5C is used but it should be Fig 5E.

Revised. (Line 381)

Page 18 - dact1/2-/- or dact1-/-, dact2-/-. 

Revised. (Line 428)

Comment: Methods:

Page 24 - ZIRC () "dot" is missing. ChopChop ")" is missing. "located near the 5' end of the gene" - In the Supplementary Figure 1 looks like in the middle of the gene.

Revised. (Lines 600, 609, 611, respectively).

Page 25 - WISH -not used in the main text.

Revised. (Line 346).

Page 26 - 4% (v/v) formaldehyde; at 4C - 4{degree sign}C; 50% (v/v) ethanol; 3% (w/v) methylcellulose.

Revised. (Lines 659, 660, 662).

Page 27 - 0.1% (w/v) BSA. 

Revised. (Line 668).

Comment: Discussion:

The overall discussion requires more references and additional hypotheses. On page 20, when mentioning 'as single mutants develop normally,' does this refer to the entire animals or solely the craniofacial domain? Are these mutants viable? If they are, it's crucial to discuss this phenomenon in relation to prior morpholino studies and genetic compensation.

Observing how the authors interpret previously documented changes in nodal and shh signaling would be beneficial. While Smad1 is discussed, what about other downstream genes? Is shh signaling altered in the dact1/2 double mutants? 

We have revised the Discussion to include more references (Lines 473, 476, 483, 488, 491, 499, 501, 502, 510, 515, 529, 557, 558) and additional hypotheses (Lines 503-505, 511-519, 522-525). We have added more specific information regarding the single mutants (Lines 270-275, 480-493, Fig. S3). We have added discussion of other downstream genes, including smad1 (Lines 561-572) and shh (Lines 572-580).

Comment: Figures:

Appreciating differences between specimens when eyes were or were not removed is quite hard.

Yes this was an unfortunate oversight, however, the key phenotype is the EP shown in the dissections.

Fig 1. - wntIIf2 vs wnt11f2? C - Thisse 2001 - correct is Thisse et al. 2001.

Revised typo in Fig 1. (And Line 1083).

Fig 1E: These plots are hard to understand without previous and detailed knowledge. Authors should include at least some demarcations for the cephalic mesoderm, neural ectoderm, mesenchyme, and muscle. Missing color code.

We have moved this data to supplementary figure S1 and have added labels of the relevant cell types and have added the color code.

Comment:- Fig 2 - In the legend for C - "wildtype and dact2-/- mutant" and "dact1/2 mutant"; in the picture is dact1-/-, dact2-/-.

Revised (Line 1105).

Fig 2 - B - it is a mistake in 6th condition dact1: 2x +/+, heterozygote (+/-) is missing.

Revised Figure 2B.

Fig 4. - Typo in the legend: dact1/"t"2-/- .

Revised. (Line 1127).

Fig 8C - In my view, when the condition gfp mRNA says "0/197, " none of the animals show this phenotype. I assume the authors wanted to say that all the animals show this phenotype; therefore, "197/197" should be used.

We have removed this data from the figure as there were concerns by the reviewers regarding reproducibility. 

Fig S1 - Missing legend for the 28 + 250, 380 + 387 peaks? RT-qPCR - is not mentioned in the Materials and Methods. In D - ratio of 25% (legend), but 35% (graph).

Revised.(Line 1203, Line 625, Line 1213, respectively).

Fig S2 - The word "identified" - 2x in one sentence. 

Revised. (Line 1230).

Reviewer #2 (Public Review):

Weakness(1) While the qualitative data show altered morphologies in each mutant, quantifications of these phenotypes are lacking in several instances, making it difficult to gauge reproducibility and penetrance, as well as to assess the novel ANC forms described in certain mutants.  

In Fig. 3 and Fig. 4 animal numbers were added to the figure legend. In Fig. 5 animal numbers were added to the figure to demonstrate reproducibility. We now state that dact1/2-/- animals exhibit the rod-like neurocranial shape that is completely penetrant (Line 260). As the altered morphologies that we report are qualitatively significant from wildtype we did not find it necessary to make quantitative measurements. For experiments in which it was necessary to in-cross triple heterozygotes (Fig 3, Fig. 5), we dissected and visually analyzed the ANC of at least 3 compound mutant individuals. At least one individual was dissected for the previously published or described genotypes/phenotypes (i.e. wt, wntllf2-/-, dact1/2-/-, gpc4-/-, wls/-). We realize quantitative measurements may identify subtle differences between genotypes. However, the sheer number of embryos needed to generate these relatively rare combinatorial genotypes and the amount of genotyping required prevented quantitative analyses. 

Weakness 2) Germline mutations limit the authors' ability to study a gene's spatiotemporal functional requirement. They therefore cannot concretely attribute nor separate early-stage phenotypes (during gastrulation) to/from late-stage phenotypes (ANC morphological changes). 

We agree that we cannot concretely attribute nor separate early and latestage phenotypes. Conditional mutants to provide temporal or cell-specific analysis are beyond the scope of this work. Here we speculate based on evidence obtained by comparing and contrasting embryos with grossly similar early phenotypes and divergent late-stage phenotypes. We believe our findings contribute to the existing body of literature on zebrafish mutants with both early convergent extension defects and craniofacial abnormalities.   

Weakness (3) Given that dact1/2 can regulate both canonical and non-canonical wnt signaling, this study did not specifically test which of these pathways is altered in the dact1/2 mutants, and it is currently unclear whether disrupted canonical wnt signaling contributes to the craniofacial phenotypes, even though these phenotypes are typical non-canonical wnt phenotypes. 

Previous literature has attributed canonical wnt, non-canonical wnt, and nonwnt functions to dact, and each of these likely contributes to the dact mutant phenotype (Lines 87-89). We performed cursory analyses of tcf/lef:gfp expression in the dact mutants and did not find evidence to support further analysis of canonical wnt signaling in these fish. Single-cell RNAseq did not identify differential expression of any canonical or non-canonical wnt genes in the dact1/2 mutants.

Further research is needed to parse out the intracellular roles of dact1 and dact2 in response to wnt and tgf-beta signaling. Here we find that dact may also have a role in calcium signaling, and further experiments are needed to elaborate this role.      

Weakness (4) The use of single-cell RNA sequencing unveiled genes and processes that are uniquely altered in the dact1/2 mutants, but not in the gpc4 mutants during gastrulation. However, how these changes lead to the manifested ANC phenotype later during craniofacial development remains unclear. The authors showed that calpain 8 is significantly upregulated in the mutant, but the fact that only 1 out of 142 calpainoverexpressing animals phenocopied dact1/2 mutants indicates the complexity of the system. 

To further test whether capn8 overexpression may contribute to the ANC phenotype we performed overexpression experiments in the resultant embryos of dact1/dact2 double het incross. We found the addition of capn8 caused a small but statistically significant occurrence of the mutant phenotype in dact1/2 double heterozygotes (Fig.8D). We agree with the reviewer that our results indicate a complex system of dysregulation that leads to the mutant phenotype. We hypothesize that a combination of gene dysregulation may be required to recapitulate the mutant ANC phenotype. Further, as capn8 activity is regulated by calcium levels, overexpression of the mRNA alone likely has a small effect on the manifestation of the phenotype. 

Weakness (5) Craniofacial phenotypes observed in this study are attributed to convergent extension defects but convergent extension cell movement itself was not directly examined, leaving open if changes in other cellular processes, such as cell differentiation, proliferation, or oriented division, could cause distinct phenotypes between different mutants. 

Although convergent extension cell movements were not directly examined, our phenotypic analyses of the dact1/2 mutant are consistent with previous literature where axis extension anomalies were attributed to defects in convergent extension (Waxman 2004, Xing 2018, Topczewski 2001). We do not attribute the axis defect to differentiation differences as in situ analyses of established cell type markers show the existence of these cells, only displaced relative to wildtype (Figure 1). We agree that we cannot rule out a role for differences in apoptosis or proliferation however, we did not detect transcriptional differences in dact1/2 mutants that would indicate this in the single-cell RNAseq dataset. Defects in directed division are possible, but alone would not explain that dact1/2 mutant phenotype, particularly the widened dorsal axis (Figure 1).

Major comments:  

Comment (1) The author examined and showed convergent extension phenotype (CE) during body axis elongation in dact1/dact2-/- homozygous mutants. Given that dact2-/- single mutants also displayed shortened axis, the authors should either explain why they didn't analyze CE in dact2-/- (perhaps because that has been looked at in previously published dact2 morphants?) or additionally show whether CE phenotypes are present in dact1 and dact2 single mutants.  

The authors should quantify the CE phenotype in both dact2-/- single mutants and dact1/dact2-/- double mutants, and examine whether the CE phenotypes are exacerbated in the double mutants, which may lend support to the authors' idea that dact1 can contribute to CE. The authors stated in the discussion that they "posit that dact1 expression in the mesoderm is required for dorsal CE during gastrulation through its role in noncanonical Wnt/PCP signaling". However, no evidence was presented in the paper to show that dact1 influences CE during body axis elongation.  

Because any axis shortening in shortening in dact2-/- single mutants was overcome during the course of development and at 5 dpf there was no noticeable phenotype, we did not analyze the single mutants further.  

We have added data to demonstrate the resulting phenotype of each combinatorial genotype to provide a more clear and detailed description of the single and compound mutants (Fig. S3). 

Our hypothesis that dact1 may contribute to convergent extension is based on its apparent ability to compensate (either directly or indirectly) for dact2 loss in the dact2-/- single mutant. 

Comment (2) Except in Fig. 2, I could not find n numbers given in other experiments. It is therefore unclear if these mutant phenotypes were fully or partially penetrant. In general, there is also a lack of quantifications to help support the qualitative results. For example, in Fig. 4, n numbers should be given and cell movements and/or contributions to the ANC should be quantified to statistically demonstrate that the second stream of CNCC failed to contribute to the ANC.  

Similarly, while the fan-shaped and the rod-shaped ANCs are very distinct, the various rod-shaped ANCs need to be quantified (e.g. morphometry or measurements of morphological features) in order for the authors to claim that these are "novel ANC forms", such as in the dact1/2-/-, gpc4/dact1/2-/-, and wls/dact1/2-/- mutants (Fig. 5).  

We have added n numbers for each experiment and stated that the rod-like phenotype of the dact1/2-/- mutant was fully penetrant. 

Regarding CNCC experiments, we repeated the analysis on 3 individual controls and mutants and did not find evidence that CNCC migration was directly affected in the dact1/2 mutant. Rather, differences in ANC development are likely secondary to defects in floor plate and eye field morphometry. Therefore we did not do any further analyses of the CNCCs.

Regarding figure 5, we have added n numbers. We dissected and analyzed a minimum of three triple mutants (dact1/2-/-,gpc4-/- and dact1/2-/-,wls-/-) and numerous dact1/s double mutants and found that the triple mutant ANC phenotype was consistent and recognizably different enough from the dact1/2-/-, or gpc4 or wls single mutant that morphometry measurements were not needed. Further, the triple mutant phenotype (narrow and shortened) appears to be a simple combination of dact1/2 (narrow) and gpc4/wls (shortened) phenotypes. As we did not find evidence of genetic epistasis, we did not analyze the novel ANC forms further.

Comment (3): The authors have attributed the ANC phenotypes in dact1/2-/- to CE defects and altered noncanonical wnt signaling. However, no evidence was presented to support either. The authors can perhaps utilize diI labelling, photoconversionmediated lineage tracing, or live imaging to study cell movement in the ANC and compare that with the cell movement change in the gpc4-/- , and gpc4/dact1/2-/- mutants in order to first establish that dact1/2 affect CE and then examine how dact1/2 mutations can modulate the CE phenotypes in gpc4-/- mutants.  

Concurrently, given that dact1 and dact2 can affect (perhaps differentially) both canonical and non-canonical wnt signaling, the authors are encouraged to also test whether canonical wnt signaling is affected in the ANC or surrounding tissues, or at minimum, discuss the potential role/contribution of canonical wnt signaling in this context.  

Given the substantial body of research on the role of noncanonical wnt signaling and planar cell polarity pathway on convergent extension during axis formation (reviewed by Yang and Mlodzik 2015, Roszko et al., 2009) and the resulting phenotypes of various zebrafish mutants (i.e. Xing 2018, Topczewski 2001), including previous research on dact1 and 2 morphants (Waxman 2004), we did not find it necessary to analyze CE cell movements directly.  

Our finding that CNCC migration was not defective in the dact1/2 mutants and the knowledge that various zebrafish mutants with anterior patterning defects (slb, smo, cyc) have a similar craniofacial abnormality led us to conclude that the rod-like ANC in the dact1/2 mutant was secondary to an early patterning defect (abnormal eye field morphology). Therefore, testing dact1/2 and convergent extension or wnt signaling in the ANC itself was not an aim of this paper.  

Comment (4) The authors also have not ruled out other possibilities that could cause the dact1/2-/- ANC phenotype. For example, increased cell death or reduced proliferation in the ANC may result in the phenotype, and changes in cell fate specification or differentiation in the second CNCC stream may also result in their inability to contribute to the ANC. 

We agree that we cannot rule out whether cell death or proliferation is different in the dact1/2 mutant ANC. However, because we do not find the second CNCC stream within the ANC, this is the most likely explanation for the abnormal ANC shape. Because the first stream of CNCC are able to populate the ANC and differentiate normally, it is most likely that the inability of the second stream to populate the ANC is due to steric hindrance imposed by the abnormal cranial/eye field morphology. These hypotheses would need to be tested, ideally with an inducible dact1/2 mutant, however, this is beyond the scope of this paper.     

Comment (5) The last paragraph of the section "Genetic interaction of dact1/2 with Wnt regulators..." misuses terms and conflates phenotypes observed. For instance, the authors wrote "dact2 haploinsuffciency in the context of dact1-/-; gpc4-/- double mutant produced ANC in the opposite phenotypic spectrum of ANC morphology, appearing similar to the gpc4-/- mutant phenotype". However, if heterozygous dact2 is not modulating phenotypes in this genetic background, its function is not "haploinsuffcient". The authors then said, "These results show that dact1 and dact2 do not have redundant function during craniofacial morphogenesis, and that dact2 function is more indispensable than dact1". However this statement should be confined to the context of modulating gpc4 phenotypes, which is not clearly stated. 

Revised (Lines 380, 382).   

Comment (6) For the scRNA-seq analysis, the authors should show the population distribution in the UMAP for the 3 genotypes, even if there are no obvious changes. The authors are encouraged, although not required, to perform pseudotime or RNA velocity analysis to determine if differentiation trajectories are changed in the NC populations, in light of what they found in Fig. 4. The authors can also check the expression of reporter genes downstream of certain pathways, e.g. axin2 in canonical wnt signaling, to query if these signaling activities are changed (also related to point #3 above). 

We have added population distribution data for the 3 genotypes to Supplemental Figure 4. Although RNA velocity analysis would be an interesting additional analysis, we would hypothesize that the NC population is not driving the differences in phenotype. Rather these are likely changes in the anterior neural plate and mesoderm. 

Comment (7) While the phenotypic difference between gpc4-/- and dact1/2-/- are in the ANC at a later stage, ssRNA-seq was performed using younger embryos. The authors should better explain the rationale and discuss how transcriptomic differences in these younger embryos can explain later phenotypes. Importantly, dact1, dact2, and capn8 expression were not shown in and around the ANC during its development and this information is crucial for interpreting some of the results shown in this paper. For example, if dact1 and dact2 are expressed during ANC development, they may have specific functions during that stage. Alternatively, if dact1 and dact2 are not expressed when the second stream CNCCs are found to be outside the ANC, then the ANC phenotype may be due to dact1/2's functions at an earlier time point. The author's statement in the discussion that "embryonic fields determined during gastrulation effect the CNCC ability to contribute to the craniofacial skeleton" is currently speculative. 

We have reworded our rationale and hypothesis to increase clarity (Lines 391-405). We believe that the ANC phenotype of the dact1/2 mutants is secondary to defective CE and anterior axis lengthening, as has been reported for the slb mutant (Heisenberg 1997, 2000). We utilized the gpc4 mutant as a foil to the dact1/2 mutant, as the gpc4 mutant has defective CE and axis extension without the same craniofacial phenotype.

We have added dact1 and dact2 WISH of 24 and 48 hpf (Fig1. D,E) to show expression during ANC development. 

Comment (8) The functional testing of capn8 did not yield a result that would suggest a strong effect, as only 1 in 142 animals phenocopied dact1/2. Therefore, while the result is interesting, the authors should tone down its importance. Alternatively, the authors can try knocking down capn8 in the dact1/2 mutants to test how that affects the CE phenotype during axis elongation, as well as ANC morphogenesis. 

As overexpression of capn8 in wildtype animals did not result in a significant phenotype, we tested capn8 overexpression in compound dact1/2 mutants as these have a sensitized background. We found a small but statistically significant effect of exogenous capn8 in dact1+/-,dact2+/- animals. While the effect is not what one would expect comparing to Mendelian genetic ratios, the rod-like ANC phenotype is an extreme craniofacial dysmorphology not observed in wildtype or mRNA injected embryos hence significant. The experiment is limited by the available technology of over-expressing mRNA broadly without temporal or cell specificity control. It is possible that if capn8 over-expression was restricted to specific cells (floor plate, notochord or mesoderm) and at the optimal time period during gastrulation/segmentation that the aberrant ANC phenotype would be more robust. We agree with the reviewer that although the finding of a new role for capn8 during development is interesting, its importance in the context of dact should be toned down and we have altered the manuscript accordingly (Lines 455-467).  

Comment (9) A difference between the two images in Fig. 8B is hard to distinguish.

Consider showing flat-mount images. 

We have added flat-mount images to Fig. 8B

Minor comments:

Comment (1) wnt11f2 is spelled incorrectly in a couple of places, e.g. "wnt11f2l" in the abstract and "wntllf2" in the discussion. 

Revised throughout.

Comment (2) For Fig. 1D, the white dact1 and yellow dact2 are hard to distinguish in the merged image. Consider changing one of their colors to a different one and only merge dact1 and dact2 without irf6 to better show their complementarity.  

We agree with the reviewer that the expression patterns of dact1 and dact2 are difficult to distinguish in the merged image. We have added outlines of the cartilage elements to the images to facilitate comparisons of dact1 and dact2 expression (Fig 1F). 

Comment (3) For Fig. 1E, please label the clusters mentioned in the text so readers can better compare expressions in these cell populations.  

We have moved this data to supplementary figure S1 and have added labels.

Comment (4) The citing and labelling of certain figures can be more specific. For example, Fig. S1A, B, and Fig. S1C should be used instead of just Fig. S1 (under the section titled dact1 and dact2 contribute to axis extension...". Similarly, Fig. 4 can be better labeled with alphabets and cited at the relevant places in the text.  

We have modified the labeling of the figures according to the reviewer’s suggestion (Fig S2 (previously S1), Fig4) and have added reference to these labels in the text (Lines 202, 204, 212, 328, 334, 336). 

Comment (5) For Fig. 2B, the (+/+,-/-) on x-axis should be (+/-,-/-).  

Revised in Figure 2B.

Comment (6) Several figures are incorrectly cited. Fig. 2C is not cited, and the "Fig. 2C" and "Fig. 2D" cited in the text should be "Fig. 2D" and "Fig. 2E" respectively. Similarly, Fig. 5C and D are not cited in the text and the cited Fig. 5C should be 5E. The VC images in Fig. 5 are not talked about in the text. Finally, Fig. 7C was also not mentioned in the text.  

We have corrected the labeling and have added descriptions of each panel in the Results (Fig.2 Line 231, 237, 242, Fig 5 Line 373, 381, Fig 7 line 431). 

Comment (7) In the main text, it is indicated that zebrafish at 3ss were used for ssRNAseq, but in the figure legend, it says 4ss. 

Revised (Line 682)

Comment (8) No error bars in Fig. S1B and the difference between the black and grey shades in Fig. S1D is not explained.  

Error bars are not included in the graphs of qPCR results (now Fig S2C) as these are results of a pool of 8 embryos performed one time. We have added a legend to explain the gray vs. black bars (now Fig S2E). 

Reviewer #3 (Public Review):  

Weaknesses: The hypotheses are very poorly defined and misinterpret key previous findings surrounding the roles of wnt11 and gpc4, which results in a very confusing manuscript. Many of the results are not novel and focus on secondary defects. The most novel result of overexpressing calpain8 in dact1/2 mutants is preliminary and not convincing.  

We apologize for not presenting the question more clearly. The Introduction was revised with particular attention to distinguish this work using genetic germline mutants from prior morpholino studies. Please refer to pages 4-5, lines 106-121.

Weakness 1) One major problem throughout the paper is that the authors misrepresent the fact that wnt11f2 and gpc4 act in different cell populations at different times. Gastrulation defects in these mutants are not similar: wnt11 is required for anterior mesoderm CE during gastrulation but not during subsequent craniofacial development while gpc4 is required for posterior mesoderm CE and later craniofacial cartilage morphogenesis (LeClair et al., 2009). Overall, the non-overlapping functions of wnt11 and gpc4, both temporally and spatially, suggest that they are not part of the same pathway.  

We have reworded the text to add clarity. While the loss of wnt11 versus the loss of gpc4 may affect different cell populations, the overall effect is a shortened body axis. We stressed that it is this similar impaired axis elongation phenotype but discrepant ANC morphology phenotypes in the opposite ends of the ANC morphologic spectrum that is very interesting and leads us to investigate dact1/2 in the genetic contexts of wnt11f2 and gpc4.  Pls refer to page 4, lines 73-84. Further, the reviewer’s comment that wnt11 and gpc4 are spatially and temporally distinct is untested. We think the reviewer’s claim of gpc4 acting in the posterior mesoderm refers to its requirement in the tailbud (Marlow 2004). However this does not exclude gpc4 from acting elsewhere as well. Further experiments would be necessary. Both wnt11f2 and gpc4 regulate non-canonical wnt signaling and are coexpressed during some points of gastrulation and CF development (Gupta et al., 2013; Sisson 2015). This data supports the possibility of overlapping roles. 

Weakness 2) There are also serious problems surrounding attempts to relate single-cell data with the other data in the manuscript and many claims that lack validation. For example, in Fig 1 it is entirely unclear how the Daniocell scRNA-seq data have been used to compare dact1/2 with wnt11f2 or gpc4. With no labeling in panel 1E of this figure these comparisons are impossible to follow. Similarly, the comparisons between dact1/2 and gpc4 in scRNA-seq data in Fig. 6 as well as the choices of DEGs in dact1/2 or gpc4 mutants in Fig. 7 seem arbitrary and do not make a convincing case for any specific developmental hypothesis. Are dact1 and gpc4 or dact2 and wnt11 coexpressed in individual cells? Eyeballing similarity is not acceptable.  

We have moved the previously published Daniocell data to Figure S1 and have added labeling. These data are meant to complement and support the WISH results and demonstrate the utility of using available public Daniocell data. Please recommend how we can do this better or recommend how we can remediate this work with specific comment. 

Regarding our own scRNA-seq data, we have added rationale (line 391-403) and details of the results to increase clarity (Lines 419-436). We have added a panel to Figure 6 (panel A) to help illustrate or rationale for comparing dact1/2 to gpc4 mutants to wt. The DEGs displayed in Fig.7A are the top 50 most differentially expressed genes between dact1/2 mutants and WT (Figure 7 legend, line 422-424).   

We have looked at our scRNA-seq gene expression results for our clusters of interest (lateral plate mesoderm, paraxial mesoderm, and ectoderm). We find dact1, dact2, and gpc4 co-expression within these clusters. Knowing whether these genes are coexpressed within the same individual cell would require going back and analyzing the raw expression data. We do not find this to be necessary to support our conclusions. The expression pattern of wnt11f2 is irrelevant here.   

Weakness 3) Many of the results in the paper are not novel and either confirm previous findings, particularly Waxman et al (2004), or even contradict them without good evidence. The authors should make sure that dact2 loss-of-function is not compensated for by an increase in dact1 transcription or vice versa. Testing genetic interactions, including investigating the expression of wnt11f2 in dact1/2 mutants, dact1/2 expression in wnt11f2 mutants, or the ability of dact1/2 to rescue wnt11f2 loss of function would give this work a more novel, mechanistic angle.

We clarified here that the prior work carried out by Waxman using morppholinos, while acceptable at the time in 2004, does not meet the rigor of developmental studies today which is to generate germline mutants. The reviewer’s acceptance of the prior work at face value fails to take the limitation of prior work into account. Further, the prior paper from Waxman et al did not analyze craniofacial morphology other than eyeballing the shape of the head and eyes. Please compare the Waxman paper and this work figure for figure and the additional detail of this study should be clear. Again, this is by no means any criticism of prior work as the prior study suffered from the technological limitations of 2004, just as this study also is the best we can do using the tools we have today. Any discrepancies in results are likely due to differences in morpholino versus genetic disruption and most reviewers would favor the phenotype analysis from the germline genetic context. We have addressed these concerns as objectively as we can in the text (Lines 482-493). The fact that dact1/2 double mutants display a craniofacial phenotype while the single mutants do not, suggests compensation (Lines 503-505), but not necessarily at the mRNA expression level (Fig. S2C). 

This paper tests genetic interaction through phenotyping the wntll/dact1/dact2 mutant.

Our results support the previous literature that dact1/2 act downstream of wnt11 signaling. There is no evidence of cross-regulation of gene expression. We do not expect that changes in wnt11 or dact would result in expression changes in the others.

RNA-seq of the dact1/2 mutants did not show changes in wnt11 gene expression. Unless dact1 and/or dact2 mRNA are under expressed in the wnt11 mutant, we would not expect a rescue experiment to be informative. And as wnt11 is not a focus of this paper, we have not performed the experiment.  

Weakness 4) The identification of calpain 8 overexpression in Dact1/2 mutants is interesting, but getting 1/142 phenotypes from mRNA injections does not meet reproducibility standards.

As the occurrence of the mutant phenotype in wildtype animals with exogenous capn8 expression was below what would meet reproducibility standards, we performed an additional experiment where capn8 was overexpressed in embryos resulting from dact1/dact2 double heterozygotes incross (Fig. 8). We reasoned that an effect of capn8 overexpression may be more robust on a sensitized background. We found a statistically significant effect of capn8 in dact1/2 double heterozygotes, though the occurrence was still relatively rare (6/80). These data suggest dysregulation of capn8 contributes to the mutant ANC phenotype, though there are likely other factors involved. 

Comment: The manuscript title is not representative of the findings of this study.  

We revised the title to strictly describe that we generated and carried out genetic analysis in loss of function compound mutants (Genetic requirement) and that we found capn8 was important which modified this requirement.

Introduction: p.4:

Comment: Anterior neurocranium (ANC) - it has to be stated that this refers to the combined ethmoid plate and trabecular cartilages. 

Thank you, we agree that the ANC and ethmoid plate terminology has been confusing in the literature and we should endeavor to more clearly describe that the phenotypes in question are all in the ethmoid plate and the trabeculae are not affected. ANC has been replaced with ethmoid plate (EP) throughout the manuscript and figures. We also describe that all the observed phenotypes affect the ethmoid plate and not the trabeculae, (pages 13, Lines 265-267).

Comment: Transverse dimension is incorrect terminology - replace with medio-lateral.

Revised (Lines 69, 74).

Comment: Improper way of explaining the relationship between mutant and gene..."Another mutant knypek, later identified as gpc4..." a better  way to explain this would be that the knypek mutation was found to be a non-sense mutation in the gpc4 gene.  

Revised (Line 71)

Comment: "...the gpc4 mutant formed an ANC that is wider in the transverse dimension than the wildtype, in the opposite end of the ANC phenotypic spectrum compared to wnt11f2...These observations beg the question how defects in early patterning and convergent extension of the embryo may be associated with later craniofacial morphogenesis."

This statement is broadly representative of the general failure to distinguish primary from secondary defects in this manuscript. Focusing on secondary defects may be useful to understand the etiology of a human disease, but it is misleading to focus on secondary defects when studying gene function. The rod-like ethmoid of slb mutant results from a CE defect of anterior mesoderm during gastrulation(Heisenberg et al. 1997, 2000), while the wide ethmoid plate of kny mutants results from CE defects of cartilage precursors (Rochard et al., 2016). Based on this evidence, wnt11f2 and gpc4 act in different cell populations at different times.  

It is true that the slb mutant craniofacial phenotype has been stated as secondary to the CE defect during gastrulation and the kny phenotype as primary to chondrocyte CE defects in the ethmoid, however the direct experimental evidence to conclude only primary or only secondary effects does not yet exist. There is no experiment to our knowledge where wnt11f2 was found to not affect ethmoid chondrocytes directly. Likewise, there is no experiment having demonstrated that dysregulated CE in gpc4 mutants does not contribute to a secondary abnormality in the ethmoid. 

Here, we are analyzing the CE and craniofacial phenotypes of the dact1/2 mutants without any assumptions about primary or secondary effects and without drawing any conclusions about wnt11f2 or gpc4 cellular mechanisms.     

Comment: "The observation that wnt11f2 and gpc4 mutants share similar gastrulation and axis extension phenotypes but contrasting ANC morphologies supports a hypothesis that convergent extension mechanisms regulated by these Wnt pathway genes are specific to the temporal and spatial context during embryogenesis."

This sentence is quite vague and potentially misleading. The gastrulation defects of these 2 mutants are not similar - wnt11 is required for anterior mesoderm CE during gastrulation and has not been shown to be active during subsequent craniofacial development while gpc4 is required for posterior mesoderm CE and craniofacial cartilage morphogenesis (LeClair et al., 2009). Here again, the non-spatially overlapping functions of wnt11 and gpc4 suggest that are not part of the same pathway.  

Though the cells displaying defective CE in wnt11f2 and gpc4 mutants are different, the effects on the body axis are similar. The dact1/2 showed a similar axis extension defect (grossly) to these mutants. Our aim with the scRNA-seq experiment was to determine which cells and gene programs are disrupted in dact1/2 mutants. We found that some cell types and programs were disrupted similarly in dact1/2 mutants and gpc4 mutants, while other cells and programs were specific to dact1/2 versus gpc4 mutants. We can speculate that these that were specific to dact1/2 versus gpc4 may be attributed to CE in the anterior mesoderm, as is the case for wnt11. 

p.5

Comment: "We examined the connection between convergent extension governing gastrulation, body axis segmentation, and craniofacial morphogenesis." A statement focused on the mechanistic findings of this paper would be welcome here, instead of a claim for a "connection" that is vague and hard to find in the manuscript.  

We have rewritten this statement (Line 125).

p.7 Results:

Comment: It is unclear why Farrel et al., 2018 and Lange et al., 2023 are appropriate references for WISH. Please justify or edit.  

This was a mistake and has been edited (Page 9).

Comment: " Further, dact gene expression was distinct from wnt11f2." This statement is inaccurate in light of the data shown in Fig1A and the following statements - please edit to reflect the partially overlapping expression patterns.  

We have edited to clarify (Lines 142-143).

p.8

Comment: "...we examined dact1 and 2 expression in the developing orofacial tissues. We found that at 72hpf..." - expression at 72hpf is not relevant to craniofacial morphogenesis, which takes place between 48h-60hpf (Kimmel et al., 1998; Rochard et al., 2016; Le Pabic et al., 2014).  

We have included images and discussion of dact1 and dact2 expression at earlier time points that are important to craniofacial development (Lines 160-171)(Fig 1D,E). 

Comment: "This is in line with our prior finding of decreased dact2 expression in irf6 null embryos". - This statement is too vague. How are th.e two observations "in line".  

We have removed this statement from the manuscript.

Comment: Incomplete sentence (no verb) - "The differences in expression pattern between dact1 and dact2...".  

Revised (Line 172).

Comment: "During embryogenesis..." - Please label the named structures in Fig.1E.

Please be more precise with the described expression time. Also, it would be useful to integrate the scRNAseq data with the WISH data to create an overall picture instead of treating each dataset separately.  

We have moved the previously published Daniocell data to supplementary figure S1 and have labeled the key cell types. 

p.9

Comment: "The specificity of the gene disruption was demonstrated by phenotypic rescue with the injection of dact1 or dact2 mRNA (Fig. S1)." - please describe what is considered a phenotypic rescue.

-The body axis reduction of dact mutants needs to be documented in a figure. Head pictures are not sufficient. Is the head alone affected, or both the head and trunk/tail? Fig.2E suggests that both head and trunk/tail are affected - please include a live embryos picture at a later stage.  

We have added a description of how phenotypic rescue was determined (Line 208). We have added a figure with representative images of the whole body of dact1/2 mutants. Measurements of body length found a shortening in dact1/2 double mutants versus wildtype, however differences were not found to be significantly different by ANOVA (Fig. 3C, Fig. S3, Line 270-275).

p. 11

Comment: "These dact1-/-;dact2-/- CE phenotypes were similar to findings in other Wnt mutants, such as slb and kny (Heisenberg, Tada et al., 2000; Topczewski, Sepich et al., 2001)." The similarity between slb and kny phenotypes should be mentioned with caution as CE defects affect different regions in these 2 mutants. It is misleading to combine them into one phenotype category as wnt11 and gpc4 are most likely not acting in the same pathway based on these spatially distinct phenotypes.  

Here we are referring to the grossly similar axis extension defects in slb and kny mutants. We refer to these mutants to illustrate that dact1 and or 2 deficiency could affect axis extension through diverse mechanisms. We have added text for clarity (Lines 249-252).  

Comment: "No craniofacial phenotype was observed in dact1 or dact2 single mutants. However, in-crossing to generate [...] compound homozygotes resulted in dramatic craniofacial deformity."

This result is intriguing in light of (1) the similar craniofacial phenotype previously reported by Waxman et al (2004) using morpholino- based knock-down of dact2, and the phenomenon of genetic compensation demonstrated by Jakutis and Stainier 2001 (https://doi.org/10.1146/annurev-genet-071719-020342). The authors should make sure that dact2 loss-of-function is not compensated for by an increase in dact1 transcription, as such compensation could lead to inaccurate conclusions if ignored.  

We agree with the reviewer that genetic compensation of dact2 by dact1 likely explains the different result found in the dact2 morphant versus CRISPR mutant. We found increased dact1 mRNA expression in the dact2-/- mutant (Fig S2X) however a more thorough examination is required to draw a conclusion. Interestingly, we found that in wildtype embryos dact1 and dact2 expression patterns are distinct though with some overlap. It would be informative to investigate whether the dact1 expression pattern changes in dact2-/- mutants to account for dact2 loss.   

Comment: "Lineage tracing of NCC movements in dact1/2 mutants reveals ANC composition" - the title is misleading - ANC composition was previously investigated by lineage tracing (Eberhardt et al., 2006; Wada et al., 2005).  

This has been reworded (Line 292)

p.13

Comment: There is no frontonasal prominence in zebrafish.  

This is true, texts have been changed to frontal prominence.  (Lines 293,

299, 320)

Comment: The rationale for investigating NC migration in mutants where there is a gastrula-stage failure of head mesoderm convergent extension is unclear. The whole head is deformed even before neural crest cells migrate as the eye field does not get split in two (Heisenberg et al., 1997; 2000), suggesting that the rod-like ethmoid plate is a secondary defect of this gastrula-stage defect. In addition, neural crest migration and cartilage morphogenesis are different processes, with clear temporal and spatial distinctions.  

We carried out the lineage tracing experiment to determine which NC streams contributed to the aberrantly shaped EP, whether the anteromost NC stream frontal prominence, the second NC stream of maxillary prominence, or both.  We found that the anteromost NCC did contribute to the rod-like EP, which is different from when hedgehod signaling is disrupted,  So while it is possible that the gastrula-effect head mesoderm CE caused a secondary effect on NC migration, how the anterior NC stream and second NC stream are affected differently between dact1/2 and shh pathway is interesting.  We added discussion of this observation to the manuscript (page 23, Lines 514-520). 

p. 14-16

Comment: Based on the heavy suspicion that the rod-like ethmoid plate of the dact1/2 mutant results from a gastrulation defect, not a primary defect in later craniofacial morphogenesis, the prospect of crossing dact1/2 mutants with other wnt-pathway mutants for which craniofacial defects result from craniofacial morphogenetic defects is at the very least unlikely to generate any useful mechanistic information, and at most very likely to generate lots of confusion. Both predictions seem to take form here.  

However, the ethmoid plate phenotype observed in the gpc4-/-; dact1+/-; dact2-/- mutants (Fig. 5E) does suggest that gpc4 may interact with dact1/2 during gastrulation, but that is the case only if dact1+/-; dact2-/- mutants do not have an ethmoid cartilage defect, which I could not find in the manuscript. Please clarify.  

The perspective that the rod-like EP of the dact1/2 is due to gastrulation defect is being examined here. Why would other mutants such as wnt11f2 and gpc4 that have gastrulation CE defects have very different EP morphology, whether primary or secondary NCC effect?  Further dact1 and dact2 were reported as modifiers of Wnt signaling, so it is logical to genetically test the relationship between dact1, dact2, wnt11f2, gpc4 and wls. The experiment had to be done to investigate how these genetic combinations impact EP morphology. This study found that combined loss of dact1, dact2 and wls or gpc4 yielded new EP morphology different than those previously observed in either dact1/2, wls, gpc4, or any other mutant is important, suggesting that there are distinct roles for each of these genes contributing to facial morphology, that is not explained by CE defect alone.   

Comment: I encourage the authors to explore ways to test whether the rod-like ethmoid of dact1/2 mutants is more than a secondary effect of the CE failure of the head mesoderm during gastrulation. Without this evidence, the phenotypes of dact1/2 -gpc4 or - wls are not going to convince us that these factors actually interact.  

Actually, we find our results to support the hypothesis that the ethmoid of the dact1/2 mutants is a secondary effect of defective gastrulation and anterior extension of the body axis. However, our findings suggest (by contrasting to another mutant with impaired CE during gastrulation) that this CE defect alone cannot explain the dysmorphic ethmoid plate. Our single-cell RNA seq results and the discovery of dysregulated capn8 expression and proteolytic processes presents new wnt-regulated mechanisms for axis extension.    

p. 20 Discussion

Comment: "Here we show that dact1 and dact2 are required for axis extension during gastrulation and show a new example of CE defects during gastrulation associated with craniofacial defects."

Waxman et al. (2004) previously showed that dact2 is involved in CE during gastrulation.

Heisenberg et al. (1997, 2000), previously showed with the slb mutant how a CE defect during gastrulation causes a craniofacial defect.  

The Waxman paper using morpholino to disrupt dact2 is produced limited analysis of CE and no analysis of craniofacial morphogenesis. We generated genetic mutants here to validate the earlier morpholino results and to analyze the craniofacial phenotype in detail. We have removed the word “new” to make the statement more clear (Line 475).

Comment: "Our data supports the hypothesis that CE gastrulation defects are not causal to the craniofacial defect of medially displaced eyes and midfacial hypoplasia and that an additional morphological process is disrupted."

It is unclear to me how the authors reached this conclusion. I find the view that medially displaced eyes and midfacial hypoplasia are secondary to the CE gastrulation defects unchallenged by the data presented. 

This statement was removed and the discussion was reworded.

Comment: The discussion should include a detailed comparison of this study's findings with those of zebrafish morpholino studies.  

We have added more discussion to compare ours to the previous morpholino findings (Lines 476-484).

Comment: The discussion should try to reconcile the different expression patterns of dact1 and dact2, and the functional redundancy suggested by the absence of phenotype of single mutants. Genetic compensation should be considered (and perhaps tested).  

The different expression patterns of dact1 and dact2 along with our finding that dact1 and dact2 genetic deficiency differently affect the gpc4 mutant phenotype suggest that dact1 and dact2 are not functionally redundant during normal development. This is in line with the previously published data showing different phenotypes of dact1 or dact2 knockdown. However, our results that genetic ablation of both dact1 and dact2 are required for a mutant phenotype suggests that these genes can compensate upon loss of the other. This would suggest then that the expression pattern of dact1 would be changed in the dact2 mutant and visa versa. We find that this line of investigation would be interesting in future studies. We have addressed this in the Discussion (Lines 485498).

Comment: "Based on the data...Conversely, we propose...ascribed to wnt11f2 "

Functional data always prevail overexpression data for inferring functional requirements.  

This is true.

p.21

Comment: "Our results underscore the crucial roles of dact1 and dact2 in embryonic development, specifically in the connection between CE during gastrulation and ultimate craniofacial development."

How is this novel in light of previous studies, especially by Waxman et al. (2004) and Heisenberg et al. (1997, 2000). In this study, the authors fail to present compelling evidence that craniofacial defects are not secondary to the early gastrulation defects resulting from dact1/2 mutations.  p. 22

We have not claimed that the craniofacial defects are not secondary to the gastrulation defects. In fact, we state that there is a “connection”. Further, we do not claim that this is the first or only such finding. We believe our findings have validated the previous dact morpholino experiments and have contributed to the body of literature concerning wnt signaling during embryogenesis. 

Comment: The section on Smad1 discusses a result not reported in the results section. Any data discussed in the discussion section needs to be reported first in the results section.  

We have added a comment on the differential expression of smad1 to the results section (Lines 446-448).