Author response:

The following is the authors’ response to the original reviews

Public Reviews:

Reviewer #1 (Public Review):

Summary:

Sammons, Masserini et al. examine the connectivity of different types of CA3 pyramidal cells ("thorny" and "athorny"), and how their connectivity putatively contributes to their relative timing in sharp-wave-like activity. First, using patch-clamp recordings, they characterize the degree of connectivity within and between athorny and thorny cells. Based upon these experimental results, they compute a synaptic product matrix, and use this to inform a computational model of CA3 activity. This model finds that this differential connectivity between these populations, augmented by two different types of inhibitory neurons, can account for the relative timing of activity observed in sharp waves in vivo.

We thank the reviewer for reading our manuscript, as well as for their nice summary and constructive comments

Strengths:

The patch-clamp experiments are exceptionally thorough and well done. These are very challenging experiments and the authors should be commended for their in-depth characterization of CA3 connectivity.

Thank you for the recognition of our efforts.

Weaknesses:

(1) The computational elements of this study feel underdeveloped. Whereas the authors do a thorough job experimentally characterizing connections between excitatory neurons, the inhibitory neurons used in the model seem to be effectivity "fit neurons" and appear to have been tuned to produce the emergent properties of CA3 sharp wave-like activity. Although I appreciate the goal was to implicate CA3 connectivity contributions to activity timing, a stronger relationship seems like it could be examined. For example, did the authors try to "break" their model? It would be informative if they attempted different synaptic product matrices (say, the juxtaposition of their experimental product matrix) and see whether experimentally-derived sequential activity could not be elicited. It seems as though this spirit of analysis was examined in Figure 4C, but only insofar as individual connectivity parameters were changed in isolation.

Including the two interneuron types (B and C) in the model is, on the one hand, necessary to align our modeling framework to the state-of-the-art model by Evangelista et al. (2020), which assumes that these populations act as switchers between an SPW and a non-SPW state, and on the other hand, less straightforward because the connectivity involving these interneurons is largely unknown.

For B cells, the primary criterion to set their connections to and from excitatory cells was to balance the effect of the strong recurrent excitation and to achieve a mid-range firing rate for each population during sharp wave events. Our new simulations (Figure 5B) show that the initial suppression of population T (resulting in the long delay) indeed depends in equal proportions on the outlined excitatory connections and on how strongly each excitatory population is targeted by the B interneurons. However, these simulations demonstrate that there is a broad, clearly distinct, region of the parameter space that supports a long delay between the peaks, rather than a marginal set of finetuned parameters. In addition, the simulations show that B interneurons optimally contribute to the suppression of T when they primarily target T (Fig. 5B, panels 3,7,11,12,13) rather than A (panels 4,8,9,10,11). On the contrary, as reported in the parameter table, and now also displayed graphically in the new Figure 4A (included above, with arrow sizes proportional to the synaptic product between the parameters determining the total strength of each connection), we assume B to target A less weakly than T (to make up for the higher excitability of population A). Therefore, the long delay between the peaks in our model emerges in spite of the interneuron connectivity, rather than because of it, and it is an effect of the asymmetric connectivity between the two excitatory populations, in particular the extremely low connection from A to T.

(2) Additional explanations of how parameters for interneurons were incorporated in the model would be very helpful. As it stands, it is difficult to understand the degree to which the parameters of these neurons are biologically constrained versus used as fit parameters to produce different time windows of activity in types of CA3 pyramidal cells.

Response included in point (1).

Reviewer #2 (Public Review):

Sharp wave ripples are transient oscillations occurring in the hippocampus that are thought to play an important role in organising temporal sequences during the reactivation of neuronal activity. This study addresses the mechanism by which these temporal sequences are generated in the CA3 region focusing on two different subtypes of pyramidal neurons, thorny and athorny. Using high-quality electrophysiological recordings from up to 8 pyramidal neurons at a time the authors measure the connectivity rates between these pyramidal cell subtypes in a large dataset of 348 cells. This is a significant achievement and provides important data. The most striking finding is how similar connection characteristics are between cell types. There are no differences in synaptic strength or failure rates and some small differences in connectivity rates and short-term plasticity. Using model simulations, the authors explore the implications of the differences in connectivity rates for the temporal specificity of pyramidal cell firing within sharp-wave ripple events. The simulations show that the experimentally observed connectivity rates may contribute to the previously observed temporal sequence of pyramidal cell firing during sharp wave ripples.

Thank you very much for your careful review of our manuscript and the overall positive assessment.

The conclusions drawn from the simulations are not experimentally tested so remain theoretical. In the simple network model, the authors include basket cell and anti-SWR interneurons but the connectivity of these cell types is not measured experimentally and variations in interneuron parameters may also influence temporal specificity of firing.

As variations in some of these parameters can indeed influence the temporal specificity of firing, we have now performed additional simulations, the results of which are in the new Figures 5 and S5. Please also see response to Reviewer 1, point 1.

In addition, the influence of short-term plasticity measured in their experiments is not tested in the model.

We have now included short-term synaptic depression in all the excitatory-to-excitatory synapses and compensated for the weakened recurrent excitation by scaling some of the other parameters. The results of re-running our simulations in this alternative version of the model are reported in Figure S3 and are qualitatively analogous to those in Figure 4.

Interestingly, the experimental data reveal a large variability in many of the measured parameters. This may strongly influence the firing of pyramidal cells during SWRs but it is not represented within the model which uses the averaged data.

We have now incorporated variability in the following simulation parameters: the strength and latency of the four excitatory-to-excitatory connections as well as the reversal potential and leak conductance of both types of pyramidal cells, assuming variabilities similar to those observed experimentally (see Materials and Methods for details). Upon a slight re-balancing of some inhibitory connection strengths, in order to achieve comparable firing rates, we found that this version of the model also supports the generation of sharp waves with two pyramidal components (Figure S4B), and is, thus, fully analogous to our basic model. Varying the excitatory connectivities as in the original simulations (cf. Figure 4C and Figure S4C) reveals that increasing the athorny-toathorny or decreasing the athorny-to-thorny connectivity still increases the delay between the peaks, although for some connectivity values the peak of the athorny population appears more spread out in time.

Reviewer #3 (Public Review):

Summary:

The hippocampal CA3 region is generally considered to be the primary site of initiation of sharp wave ripples-highly synchronous population events involved in learning and memory although the precise mechanism remains elusive. A recent study revealed that CA3 comprises two distinct pyramidal cell populations: thorny cells that receive mossy fiber input from the dentate gyrus, and athorny cells that do not. That study also showed that it is athorny cells in particular that play a key role in sharp wave initiation. In the present work, Sammons, Masserini, and colleagues expand on this by examining the connectivity probabilities among and between thorny and athorny cells. First, using whole-cell patch clamp recordings, they find an asymmetrical connectivity pattern, with athorny cells receiving the most synaptic connections from both athorny and thorny cells, and thorny cells receiving fewer. They then demonstrate in spiking neural network simulations how this asymmetrical connectivity may underlie the preferential role of athorny cells in sharp wave initiation.

Strengths:

The authors provide independent validation of some of the findings by Hunt et al. (2018) concerning the distinction between thorny and athorny pyramidal cells in CA3 and advance our understanding of their differential integration in CA3 microcircuits. The properties of excitatory connections among and between thorny and athorny cells described by the authors will be key in understanding CA3 functions including, but not limited to, sharp wave initiation.

As stated in the paper, the modeling results lend support to the idea that the increased excitatory connectivity towards athorny cells plays a key role in causing them to fire before thorny cells in sharp waves. More generally, the model adds to an expanding pool of models of sharp wave ripples which should prove useful in guiding and interpreting experimental research.

Thank you very much for your careful review of our manuscript and this positive assessment.

Weaknesses:

The mechanism by which athorny cells initiate sharp waves in the model is somewhat confusingly described. As far as I understood, random fluctuations in the activities of A and B neurons provide windows of opportunity for pyramidal cells to fire if they have additionally recovered from adaptive currents. Thorny and athorny pyramidal cells are then set in a winner-takes-all competition which is quickly won by the athorny cells. The main thesis of the paper seems to be that athorny cells win this competition because they receive more inputs both from themselves and from thorny cells, hence, the connectivity "underlies the sequential activation". However, it is also stated that athorny cells activate first due to their lower rheobase and steeper f-I curve, and it is also indicated in the methods that athorny (but not thorny) cells fire in bursts. It seems that it is primarily these features that make them fire first, something which apparently happens even when the A to A connectivity is set to 0albeit with a very small lag. Perhaps the authors could further clarify the differential role of single cell and network parameters in determining the sequential activation of athorny and thorny cells. Is the role of asymmetric excitatory connectivity only to enhance the initial intrinsic advantage of athorny cells? If so, could this advantage also be enhanced in other ways?

Thank you for the time invested in the review of our manuscript. We especially thank you for pointing out that the description of these dynamics was unclear: we have now improved it in the main text and we provide here an additional summary. As correctly highlighted by Reviewer 3, athorny neurons (A) are more excitable than thorny (T) ones due to single-neuron parameters: therefore, if there is a winner-takes-all competition, they are going to win it. Whether there is a competition in the first place, however, depends on the excitatory (and inhibitory) connections. In particular, we should distinguish two questions: does the activity of populations A and B (PV baskets), without adaptation (so at the beginning of the sharp wave) suppress T? And does the activity of populations T and B suppress A?

The four possible combinations can be appreciated, for example, in the new Figure 5A5. If A can suppress T, but T cannot suppress A (low A-to-T, high T-to-A, bottom right corner, like in the data), A “wins” and T fires later, after a long delay. If both A and T can suppress each other (both cross-connections are low, bottom left corner), we still get the same outcome: A wins because of its earlier and sharper onset (due to single-neuron parameters). If neither population can suppress the other (high cross-connections, top right corner), then there is no competition and the populations reach the peak approximately at the same time. Only in the case in which T can suppress A, but A cannot suppress T (low T-to-A, high A-to-T, top left corner, opposite to the data), then A “loses” the competition. However, since A neurons nevertheless display some early activity (again, due to the single neuron parameters), this scenario is not as clean as the reversed one: rather, A cells have an initial, small peak, then T neurons quickly take over and grow to their own peak, and then, depending on how strongly T neurons suppress A neurons, there may or may not be a second peak for the A neurons. This is the reason why, in the top left corner of Figure 5B, the statistics show either a long positive or long negative delay, depending on whether the first (small) or second (absent, for some parameters) peak of A is taken into account. In summary, the experimentally measured connectivity does not only enhance the initial intrinsic advantage of A cells, but sets up the competitive dynamics in the first place, which are crucial for the emergence of two distinct peaks, rather than a single peak involving both populations.

Although a clear effort has been made to constrain the model with biological data, too many degrees of freedom remain that allow the modeler to make arbitrary decisions. This is not a problem in itself, but perhaps the authors could explain more of their reasoning and expand upon the differences between their modeling choices and those of others. For example, what are the conceptual or practical advantages of using adaptation in pyramidal neurons as opposed to short-term synaptic plasticity as in the model by Hunt et al.?

It should be pointed out that the model by Hunt et al. features adaptation in pyramidal neurons as well, as the neuronal units employed are also adaptive-exponential integrate-and-fire. In an early stage of this project, we obtained from Hunt et al. the code for their model, and ascertained that adaptation is the main mechanism governing the alternations between the sharp-wave and the non-sharp-wave states, to the extent that fully removing short-term plasticity from their model does not have any significant impact on the network dynamics. Therefore, our choices are, in this regard, fully consistent with theirs. In order to confirm that synaptic depression does not significantly impact the dynamics also in our model, we now performed additional simulations (Figure S3), addressed in the main text (lines 149-151) and in the response to Reviewer 1, who expressed similar concerns.

Relatedly, what experimental observations could validate or falsify the proposed mechanisms?

As sharp wave generation in this model relies on disinhibitory dynamics (suppression of the anti-sharp-wave interneurons C), the model could be validated/falsified by proving/disproving that a class of interneurons with anti-sharp-wave features exists. In addition, the mechanism we proposed for the long delay between the peaks of the athorny and thorny activity requires at least some connectivity from athorny to basket and from basket to thorny neurons.

In the data by Hunt et al., thorny cells have a higher baseline (non-SPW) firing rate, and it is claimed that it is actually stochastic correlations in their firing that are amplified by athorny cells to initiate sharp waves. However, in the current model, the firing of both types of pyramidal cells outside of ripples appears to be essentially zero. Can the model handle more realistic firing rates as described by Hunt et al., or as produced by e.g., walking around an environment tiled with place cells, or would that trigger SPWs continuously?

When building this model, we aimed at having two clearly distinct states the network could alternate between, so we picked a rather polarized connectivity to and from the anti-sharp wave cells (C), resulting in polarized states. As a result, we obtain a low, although non-zero, activity of pyramidal neurons in non-SPW states (0.4 spikes/s for athorny and 0.2 spikes/s for thorny). These assumptions can be partially relaxed, for example in the original model by Evangelista et al. (2020), where the background firing rate of pyramidal cells is ~2 spikes/s. It should also be noted that, when walking in an environment tiled with place cells, the hippocampus is subject to additional extra-hippocampal inputs (e.g. from the medial septum, resulting in theta oscillations) and to neuromodulation, which can alter the network in various ways that we have not included in our model. However, our results are not in contradiction to transient SPW-like activity states initiated at a certain phase of the theta oscillation, when the inhibition is weakest.

Recommendations for the authors:

Reviewer #1 (Recommendations For The Authors):

(1) The manuscript reads like it was intended as a short-form manuscript for another journal. The introduction and discussion in particular are very brief and would benefit from being expanded and providing a bigger picture for the reader.

We had originally aimed to submit in the eLife “short report” format. However, also thanks to the suggestion of Reviewer 1, we realized that our text would be better supported by extended introduction and discussion sections, as well as additional figures.

(2) Graphs would benefit from including all datapoints, where appropriate.

All datapoints have now been added to boxplots in the main figures and supplement.

(3) The panels of Figure 4 are laid out strangely, it may be worthwhile to adjust.

We thank the reviewer for this suggestion. We have now adjusted the layout of Figure 4 and believe it is now easier to follow.

Reviewer #2 (Recommendations For The Authors):

Useful points to address include:

(1) Explore within the model the effect of altering interneuron connectivity. Are there other factors that can influence temporal specificity within SWRs?

The effects of varying the connectivity to and from B interneurons (the ones which are SPWactive and therefore relevant for temporal specificity) have now been investigated in the new Figure 5B, in which such parameters were varied in pairs or combined with the two most relevant excitatoryto-excitatory connections.

(2) Implement the experimentally observed short-term plasticity in the model to determine how this influences temporal specificity.

All the findings in Figure 4 have now been replicated in the new Figure S3, in which excitatory-to-excitatory synapses feature synaptic depression.

(3) Consider if it is possible to incorporate observed experimental variability in the model and explore the implications.

All the findings in Figure 4 have now been replicated in the new Figure S4, in which heterogeneity has been introduced in multiple neuronal and synaptic parameters of thorny and athorny neurons.

(4) Include the co-connectivity rates in the data. Ie how many of the recorded neurons are reciprocally connected? Does this change the model simulations?

We have now added the rates of reciprocal connections that we observed into the main text (lines 8688). We found 2 pairs of reciprocally connected athorny neurons and 2 pairs of reciprocally connected thorny neurons. These rates of reciprocity were not statistically significant. We did not observe reciprocal connections in other paired neuron combinations (i.e. athorny-thorny or vice-versa). Coconnectivity does not have any effect on the model simulations, as the model includes thousands of neurons grouped in populations without specific sub-structures. It might, however, be more relevant if the excitatory populations were further subdivided in assemblies.

Reviewer #3 (Recommendations For The Authors):

(1) Specify which part of CA3 you are recording from.

We have added this information into our results section - we recorded from 20 cells in CA3a, 274 cells in CA3b and 54 cells in CA3c. This information can now be found in the text on lines 68-69.

(2) Comment on why you might observe a larger fraction of athorny cells than Hunt et al.

Hunt et al. cite a broad range for the fraction of athorny cells in their discussion (10-20%). It is unclear where these estimates originate from. In their study, Hunt et al. use the bursting and nonbursting phenotypes as proxies for athorny and thorny cells respectively, and report here numbers of 32 and 70 equating to 31% athorny and 69% thorny. This fraction of athorny cells is more or less in line with our own findings, albeit slightly lower (34% and 66%). However, we believe this difference falls within the range of experimental variability. One caveat is that our electrophysiological recordings likely represent a biased sample of cells. In particular, with multipatch recordings, placement of later electrodes is often restricted to the borders of the pyramidal layer so as not to disturb already patched cells. Thus, our recorded cells do not represent a fully random sample of CA3 pyramidal cells. We believe that, only once a reliable genetic marker for athorny cells has been established can the size of this cell population be properly estimated. Furthermore, the ratio of thorny and athorny cells varies along the proximal distal axis of the CA3 so differences in ratios seen between our study and Hunt et al. may arise from sampling differences along this axis.

(3) In Figure 3, Aiii (the cell fractions) could also be represented as a vector of two squares stacked one on top of the other, then you could add multiplication signs between Ai, Aii and Aiii, and an equal sign between Aiii and Aiv.

Thank you! We have implemented this very nice suggestion.

(4) In Figure 4A, it would be helpful to display the strength of the connections similar to how it is done in Figure 3B.

We thank the reviewer for this suggestion. We have now updated Fig 4A to include connection strengths.

Author response:

The following is the authors’ response to the original reviews

Public Reviews:

Reviewer #1 (Public Review):

Summary:

Cognitive and brain development during the first two years of life is vast and determinant for later development. However, longitudinal infant studies are complicated and restricted to occidental high-income countries. This study uses fNIRS to investigate the developmental trajectories of functional connectivity networks in infants from a rural community in Gambia. In addition to resting-state data collected from 5 to 24 months, the authors collected growing measures from birth until 24 months and administrated an executive functioning task at 3 or 5 years old.

The results show left and right frontal-middle and right frontal-posterior negative connections at 5 months that increase with age (i.e., become less negative). Interestingly, contrary to previous findings in high-income countries, there was a decrease in frontal interhemispheric connectivity. Restricted growth during the first months of life was associated with stronger frontal interhemispheric connectivity and weaker right frontal-posterior connectivity at 24 months. Additionally, the study describes that some connectivity patterns related to better cognitive flexibility at pre-school age.

Strengths:

- The authors analyze data from 204 infants from a rural area of Gambia, already a big sample for most infant studies. The study might encourage more research on different underrepresented infant populations (i.e., infants not living in occidental high-income countries).

- The study shows that fNIRS is a feasible instrument to investigate cognitive development when access to fMRI is not possible or outside a lab setting.

- The fNIRS data preprocessing and analysis are well-planned, implemented, and carefully described. For example, the authors report how the choices in the parameters for the motion artifacts detection algorithm affect data rejection and show how connectivity stability varies with the length of the data segment to justify the threshold of at least 250 seconds free of artifacts for inclusion.

- The authors use proper statistical methods for analysis, considering the complexity of the dataset.

We thank the reviewer for highlighting the strengths of this work.

Weaknesses:

- No co-registration of the optodes is implemented. The authors checked for correct placement by looking at pictures taken during the testing session. However, head shape and size differences might affect the results, especially considering that the study involves infants from 5 months to 24 months and that the same fNIRS array was used at all ages.

The fNIRS array used in this work was co-registered onto age-appropriate MNI templates at every time point in a previous published work L. H. Collins-Jones, et al., Longitudinal infant fNIRS channel-space analyses are robust to variability parameters at the group-level: An image reconstruction investigation. Neuroimage 237, 118068 (2021). This is reference No. 68 in the manuscript.

As we mentioned in the section fNIRS preprocessing and data-analysis: ‘The sections were established via the 17 channels of each hemisphere which were grouped into front, middle and back (for a total of six regions) based on a previous co-registration of the BRIGHT fNIRS arrays onto age-appropriate templates’. The procedure mentioned by the reviewer, involving the examination of pictures showing the placement of headbands on participants, aimed to exclude infants with excessive cap displacement from further analysis.

- The authors regress the global signal to remove systemic physiological noise. While the authors also report the changes in connectivity without global signal regression, there are some critical differences. In particular, the apparent decrease in frontal inter-hemispheric connections is not present when global signal regression is omitted, even though it is present for deoxy-Hb. The authors use connectivity results obtained after applying global signal regression for further analysis. The choice of regressing the global signal is questionable since it has been shown to introduce anti-correlations in fMRI data (Murphy et al., 2009), and fNIRS in young infants does not seem to be highly affected by physiological noise (Emberson et al., 2016). Systemic physiological noise might change at different ages, which makes its remotion critical to investigate functional network development. However, global signal regression might also affect the data differently. The study would have benefited from having short separation channels to measure the systemic psychological component in the data.

The work of Emberson et. al (2016) mentioned by the reviewer highlights indeed the challenges of removing systemic changes from the infants’ haemodynamic signal with short-channel separation (SSC). In fact, even a SSC of 1 cm detected changes in the blood in the brain, therefore by regressing this signal from the recorded one, the authors removed both systemic changes AND haemodynamic signal. This paper from Emberson et. al (2016) is taken as a reference in the field to suggest that SSC might not be an ideal tool to remove systemic changes when collecting fNIRS data on young infants, as we did in this work.

We agree with the reviewer's observation that systemic physiological noise may vary with age and among infants. Therefore, for each infant at each age, we regressed the mean value calculated across all channels. This ensures that the regressed signal is not biased by averaged calculations at group levels.

We are aware of the criticisms directed towards global signal regression in the fMRI literature, although some other works showed anticorrelations in functional connectivity networks both with and without global signal regression (Chaia, 2012). Furthermore, Murphy himself revised his criticism on the use of global signal regression in functional connectivity analysis in one of his more recent works (Murphy et al, 2017). The fact that the decreased FC is significant in results from data pre-processed without global signal regression gives us confidence that this finding is statistically robust and not solely driven by this preprocessing choice in our pipeline.

An interesting study by Abdalmalak et al. (2022) demonstrated that failing to correct for systemic changes using any method is inappropriate when estimating FC with fNIRS, as it can lead to a high risk of elevated connectivity across the whole brain (see Figure 4 of the mentioned paper). Consequently, we strongly advocate for the implementation of global signal regression in our analysis pipeline as a fundamental step for accurate functional connectivity estimations.

References:

Emberson, L. L., Crosswhite, S. L., Goodwin, J. R., Berger, A. J., & Aslin, R. N. (2016). Isolating the effects of surface vasculature in infant neuroimaging using short-distance optical channels: a combination of local and global effects. Neurophotonics, 3(3), 031406-031406.

Chaia, X. J., Castañóna, A. N., Öngürb, D., & Whitfield-Gabrielia, S. (2012). Anticorrelations in resting state networks without global signal regression. NeuroImage, 59(2), 1420–1428. https://doi.org/10.1515/9783050076010-014

Murphy, K., & Fox, M. D. (2017). Towards a consensus regarding global signal regression for resting state functional connectivity MRI. NeuroImage, 154(November 2016), 169–173. https://doi.org/10.1016/j.neuroimage.2016.11.052

Abdalmalak, A., Novi, S. L., Kazazian, K., Norton, L., Benaglia, T., Slessarev, M., ... & Owen, A. M. (2022). Effects of systemic physiology on mapping resting-state networks using functional near-infrared spectroscopy. Frontiers in neuroscience, 16, 803297.

- I believe the authors bypass a fundamental point in their framing. When discussing the results, the authors compare the developmental trajectories of the infants tested in a rural area of Gambia with the trajectories reported in previous studies on infants growing in occidental high-income countries (likely in urban contexts) and attribute the differences to adverse effects (i.e., nutritional deficits). Differences in developmental trajectories might also derive from other environmental and cultural differences that do not necessarily lead to poor cognitive development.

We agree with the reviewer that other factors differing between low- and poor-resource settings might have an impact on FC trajectories. We therefore specified this in the discussion as follows: “We acknowledge that differences in FC could also be attributed to other environmental and cultural disparities between high-resource and low-resource settings, and future studies are needed to investigate this further” (line 238).

- While the study provides a solid description of the functional connectivity changes in the first two years of life at the group level, the evidence regarding the links between adverse situations, developmental trajectories, and later cognitive capacities is weaker. The authors find that early restricted growth predicts specific connectivity patterns at 24 months and that certain connectivity patterns at specific ages predict cognitive flexibility. However, the link between development trajectories (individual changes in connectivity) with growth and later cognitive capacities is missing. To address this question adequately, the study should have compared infants with different growing profiles or those who suffered or did not from undernutrition. However, as the authors discussed, they lacked statistical power.

We agree with the reviewer, and indeed we highlighted this as one of the main limitation of our work: “Even given the large sample in our study, we were underpowered to test for group comparisons between sets of infants with distinct undernutrition growth profiles, e.g., infants with early poor growth that later resolved and infants with standard growth early that had a poor growth later. We were also underpowered to test the associations between early growth and FC on clinically undernourished infants (defined as having DWLZ two standard deviations below the mean) (line 311, discussion section).

We believe this is an important point to consider for the field, as it addresses the sample size required for studies investigating brain development in clinically malnourished infants. We hope this will serve as a valuable reference for future studies in the field. For example, a new study led by Prof. Sophie Moore and other members of the BRIGHT team (INDiGO) is currently recruiting six-hundreds pregnant women with the aim of obtaining a broader distribution of infants’ growth measures (https://www.kcl.ac.uk/research/sophie-moore-research-group).

Reviewer #2 (Public Review):

Summary and strengths:

The article pertains to a topic of importance, specifically early life growth faltering, a marker of undernutrition, and how it influences brain functional connectivity and cognitive development. In addition, the data collection was laborious, and data preprocessing was quite rigorous to ensure data quality, utilizing cutting-edge preprocessing methods.

We thank the reviewer for highlighting the strengths of this work.

Weaknesses:

However, the subsequent analysis and explanations were not very thorough, which made some results and conclusions less convincing. For example, corrections for multiple tests need to be consistently maintained; if the results do not survive multiple corrections, they should not be discussed as significant results. Additionally, alternative plans for analysis strategies could be worth exploring, e.g., using ΔFC in addition to FC at a certain age. Lastly, some analysis plans lacked a strong theoretical foundation, such as the relationship between functional connectivity (FC) between certain ROIs and the development of cognitive flexibility.

Thus, as much as I admire the advanced analysis of connectivity that was conducted and the uniqueness of longitudinal fNIRS data from these samples (even the sheer effort to collect fNIRS longitudinally in a low-income country at such a scale!), I have reservations about the importance of this paper's contribution to the field in its present form. Major revisions are needed, in my opinion, to enhance the paper's quality. 

We acknowledge the reviewer’s concern regarding the reporting of results that do not survive multiple comparisons. However, considering the uniqueness of our dataset and the novelty of our work, we believe it is crucial to report all significant findings as well as hypothesis-generating findings that may not pass stringent significance thresholds. We have taken great care to transparently distinguish between results that survived multiple comparisons and those that did not in both the Results and Discussion sections, ensuring that readers are not misled. It is possible that future studies may replicate and further strengthen these associations. Therefore, by sharing these results with the research community, we provide valuable insights for future investigations.

The relationship between FC and cognitive flexibility (as well as the relationship between growth and FC) has been explored focusing on those FC that showed a significant change with age, as specified in the results sections: ‘To investigate the impact of early nutritional status on FC at 24 months, we used multiple regression with the infant growth trajectory [...] and FC at 24 months [...]. To maximise power, we considered only those FC that showed a statistically significant change with age’ (line 183) and ‘To investigate whether FC early in life predicted cognitive flexibility at preschool age, we used multiple regression of FC across the first two years of life against later cognitive flexibility in preschoolers at three and five years. As per the analysis above, we focused on only those FC that showed a statistically significant change with age’ (line 198).

We explored the possibility of investigating the relationship between changes in FC and changes in growth. However, the degrees of freedom in these analyses dropped dramatically (~25/30), thereby putting the significance and the meaning of the results at risk. We look forward to future longitudinal studies with less attrition across these time points to maintain the statistical power necessary to run such analyses.

Reviewer #3 (Public Review):

Summary:

This study aimed to investigate whether the development of functional connectivity (FC) is modulated by early physical growth and whether these might impact cognitive development in childhood. This question was investigated by studying a large group of infants (N=204) assessed in Gambia with fNIRS at 5 visits between 5 and 24 months of age. Given the complexity of data acquisition at these ages and following data processing, data could be analyzed for 53 to 97 infants per age group. FC was analyzed considering 6 ensembles of brain regions and thus 21 types of connections. Results suggested that: i) compared to previously studied groups, this group of Gambian infants have different FC trajectory, in particular with a change in frontal inter-hemispheric FC with age from positive to null values; ii) early physical growth, measured through weight-for-length z-scores from birth on, is associated with FC at 24 months. Some relationships were further observed between FC during the first two years and cognitive flexibility at 4-5 years of age, but results did not survive corrections for multiple comparisons.

Strengths:

The question investigated in this article is important for understanding the role of early growth and undernutrition on brain and behavioral development in infants and children. The longitudinal approach considered is highly relevant to investigate neurodevelopmental trajectories. Furthermore, this study targets a little-studied population from a low-/middle-income country, which was made possible by the use of fNIRS outside the lab environment. The collected dataset is thus impressive and it opens up a wide range of analytical possibilities.

We thank the reviewer for highlighting the strengths of this work.

Weaknesses:

- Analyzing such a huge amount of collected data at several ages is not an easy task to test developmental relationships between growth, FC, and behavioral capacities. In its present form, this study and the performed analyses lack clarity, unity and perhaps modeling, as it suggests that all possible associations were tested in an exploratory way without clear mechanistic hypotheses. Would it be possible to specify some hypotheses to reduce the number of tests performed? In particular, considering metrics at specific ages or changes in the metrics with age might allow us to test different hypotheses: the authors might clarify what they expect specifically for growth-FC-behaviour associations. Since some FC measures and changes might be related to one another, would it be reasonable to consider a dimensionality reduction approach (e.g., ICA) to select a few components for further correlation analyses?

We confirm that this work was motivated by a compelling theoretical question: whether neural mechanisms, specifically FC, can be influenced by early adversity, such as growth, and subsequently impact cognitive outcomes, such as cognitive flexibility. This aligns with the overarching goal of the BRIGHT project, established in 2015 (Lloyd-Fox, 2023). We believe this was evident throughout the manuscript in several instances, for example:

- “The goal of the study was to investigate early physical growth in infancy, developmental trajectories of brain FC across the first two years of life, and cognitive outcome at school age in a longitudinal cohort of infants and children from rural Gambia, an environment with high rates of maternal and child undernutrition. Specifically, we aimed to: (i) investigate whether differences in physical growth through the first two years of life are related to FC at 24 months, and (ii) investigate if trajectories of early FC have an impact on cognitive outcome at pre-school age in these children.” (page 4, introduction)

- “This study investigated how early adversity via undernutrition drives longitudinal changes in brain functional connectivity at five time points throughout the first two years of life and how these developmental trajectories are associated with cognitive flexibility at preschool age.” (page 6, discussion)

- We had a clear hypothesis regarding short-range connectivity decreasing with age and long-range connectivity increasing with age, as stated at the end of the introduction: We hypothesized that (i) long-range FC would increase and short-range FC would decrease throughout the first two years of life” (page 4, line 147). However, we were not able to formulate clear hypotheses about the localization of these connections due to the scarcity of previous studies conducted within this age range, particularly in low-resource settings. The ROI approach for analysis was chosen to mitigate this challenge by reducing the number of comparisons while still enabling us to estimate the developmental trajectories of all the connections from which we acquired data.

Regarding the use of dimensionality reduction approach, we have not considered the use of ICA in our analysis. These methods require selecting a fixed number of components to remove from all participants. However, due to the high variability of infant fNIRS data across the five timepoints, we considered it untenable to precisely determine the number of components to remove at the group level. Such a procedure carries the risk of over-cleaning the data for some participants while leaving noise in for others (Di Lorenzo, 2019). We also felt that using PCA in this initial study would be beyond the scope of the brain-region-specific hypotheses and would be more appropriate in a follow-up analysis of these important data.

References:

Lloyd-Fox, S., McCann, S., Milosavljevic, B., Katus, L., Blasi, A., Bulgarelli, C., Crespo-Llado, M., Ghillia, G., Fadera, T., Mbye, E., Mason, L., Njai, F., Njie, O., Perapoch-Amado, M., Rozhko, M., Sosseh, F., Saidykhan, M., Touray, E., Moore, S. E., … Team, and the B. S. (2023). The Brain Imaging for Global Health (BRIGHT) Study: Cohort Study Protocol. Gates Open Research, 7(126).

Di Lorenzo, R., Pirazzoli, L., Blasi, A., Bulgarelli, C., Hakuno, Y., Minagawa, Y., & Brigadoi, S. (2019). Recommendations for motion correction of infant fNIRS data applicable to multiple data sets and acquisition systems. NeuroImage, 200(April), 511–527.

- It seems that neurodevelopmental trajectories over the whole period (5-24 months) are little investigated, and considering more robust statistical analyses would be an important aspect to strengthen the results. The discussion mentions the potential use of structural equation modelling analyses, which would be a relevant way to better describe such complex data.

We appreciate the complexity of the dataset we are working with, which includes multiple measures and time points. Currently, our focus within the outputs from the BRIGHT project is on examining the relationship between selected measures. While this may not involve statistically advanced modelling at the moment, it is worth noting that most of the results presented in this work have survived correction for multiple comparisons, indicating their statistical robustness. We believe that more advanced statistical analyses are beyond the scope of this rich initial study. In the next phase of the project, known as BRIGHT IMPACT, our team is collaborating with statisticians and experts in statistical modelling to apply more sophisticated and advanced statistical techniques to the data.

- Given the number of analyses performed, only describing results that survive correction for multiple comparisons is required. Unifying the correction approach (FDR / Bonferroni) is also recommended. For the association between cognitive flexibility and FC, results are not significant, and one might wonder why FC at specific ages was considered rather than the change in FC with age. One of the relevant questions of such a study would be whether early growth and later cognitive flexibility are related through FC development, but testing this would require a mediation analysis that was not performed.

We acknowledge the reviewer’s concern regarding the reporting of results that do not survive multiple comparisons. However, considering the uniqueness of our dataset and the novelty of our work, we believe it is crucial to report all significant findings. We have taken great care to transparently distinguish between results that survived multiple comparisons and those that did not in both the Results and Discussion sections, ensuring that readers are not misled. It is possible that future studies may replicate and further strengthen these associations. Therefore, by sharing these results with the research community, we provide valuable insights for future investigations.

We did not perform a mediation analysis as i) ΔWLZ between birth and the subsequent time points positively predicted frontal interhemispheric FC at 24 months, ii) frontal interhemispheric FC at 18 months (and right fronto-posterior connectivity at 24 months) predicted cognitive flexibility at preschool age. Considering that the frontal interhemispheric FC at 24 months that was positively predicted by growth, did not significantly predicted cognitive outcome at preschool age, we did not perform mediation models.

The reviewer raised concerns about using different methods to correct for multiple comparisons throughout the work. Results showing changes in FC with age were Bonferroni corrected, while we used FDR correction for the regression analyses investigating the relationship between growth and FC, as well as FC and cognitive flexibility. Both methods have good control over Type I errors (false positives), but Bonferroni is very conservative, increasing the likelihood of Type II errors (false negatives). We considered Bonferroni an appropriate method for correcting results showing changes in FC with age, where we had a large sample with strong statistical power (i.e. linear mixed models with 132 participants who had at least 250 seconds of good data for 2 out of 5 visits). However, Bonferroni was too conservative for the regression analyses, with N between 57 and 78) (Acharya, 2014; Félix & Menezes, 2018; Narkevich et al., 2020; Narum, 2006; Olejnik et al., 1997).

References:

Acharya, A. (2014). A Complete Review of Controlling the FDR in a Multiple Comparison Problem Framework--The Benjamini-Hochberg Algorithm. ArXiv Preprint ArXiv:1406.7117.

Félix, V. B., & Menezes, A. F. B. (2018). Comparisons of ten corrections methods for t-test in multiple comparisons via Monte Carlo study. Electronic Journal of Applied Statistical Analysis, 11(1), 74–91.

Narkevich, A. N., Vinogradov, K. A., & Grjibovski, A. M. (2020). Multiple comparisons in biomedical research: the problem and its solutions. Ekologiya Cheloveka (Human Ecology), 27(10), 55–64.

Narum, S. R. (2006). Beyond Bonferroni: less conservative analyses for conservation genetics. Conservation Genetics, 7, 783–787.

Olejnik, S., Li, J., Supattathum, S., & Huberty, C. J. (1997). Multiple testing and statistical power with modified Bonferroni procedures. Journal of Educational and Behavioral Statistics, 22(4), 389–406.

- Growth is measured at different ages through different metrics. Justifying the use of weight-for-length z-scores would be welcome since weight-for-age z-scores might be a better marker of growth and possible undernutrition (this impacting potentially both weight and length). Showing the distributions of these z-scores at different ages would allow the reader to estimate the growth variability across infants.

We consistently used WLZ as the metric to measure growth throughout. Our analysis investigating the relationship between WLZ and growth included HCZ at 7/14 days to correct for head size at birth. When selecting the best growth measure for this paper, we opted for WLZ over WAZ, given extant evidence that infants in our sample are smaller and shorter compared to the reference WHO standard for the same age group (Nabwera et al., 2017). Therefore, using WLZ allows us to adjust each infant's weight for its own length.

References:

Nabwera, H. M., Fulford, A. J., Moore, S. E., & Prentice, A. M. (2017). Growth faltering in rural Gambian children after four decades of interventions: a retrospective cohort study. The Lancet Global Health, 5(2), e208–e216.

- Regarding FC, clarifications about the long-range vs short-range connections would be welcome, as well as drawing a summary of what is expected in terms of FC "typical" trajectory, for the different brain regions and connections, as a marker of typical development. For instance, the authors suggest that an increase in long-range connectivity vs a decrease in short-range is expected based on previous fNIRS studies. However anatomical studies of white matter growth and maturation would suggest the reverse pattern (short-range connections developing mostly after birth, contrarily to long-range connections prenatally).

We expected an increase in long-range functional connectivity with age, as discussed in the introduction:

- “Based on data from fMRI, current models hypothesize that FC patterns mature throughout early development (23–27), where in typically developing brains, adult-like networks emerge over the first years of life as long-range functional connections between pre-frontal, parietal, temporal, and occipital regions become stronger and more selective (28–31). This maturation in FC has been shown to be related to the cascading maturation of myelination and synaptogenesis (32, 33) - fundamental processes for healthy brain development (34)” (line 93, page 3, introduction);

- “Importantly, normative developmental patterns may be disrupted and even reversed in clinical conditions that impact development; e.g., increased short-range and reduced long-range FC have been observed in preterm infants (36) and in children with autism spectrum disorder (37, 38)” (line 103, page 3, introduction);

- “We hypothesized that (i) long-range FC would increase and short-range FC would decrease throughout the first two years of life” (line 147, page 4, introduction).

Since inferences about FC patterns recorded with fNIRS are highly limited by the number and locations of the optodes, it is challenging to make strong inferences about specific brain regions. Moreover, infant FC fNIRS studies are still limited, which is why we focused our inferences on long-range versus short-range connectivity, without specifically pinpointing particular brain regions.

Additionally, were unable to locate the works mentioned by the reviewer regarding an increase in short-range white matter connectivity immediately after birth. On the contrary, we found several studies documenting an increase in white-matter long-range connectivity after birth, which is consistent with the hypothesised increase in FC long-range connectivity, such as:

Yap, P. T., Fan, Y., Chen, Y., Gilmore, J. H., Lin, W., & Shen, D. (2011). Development trends of white matter connectivity in the first years of life. PloS one, 6(9), e24678.

Dubois, J., Dehaene-Lambertz, G., Kulikova, S., Poupon, C., Hüppi, P. S., & Hertz-Pannier, L. (2014). The early development of brain white matter: a review of imaging studies in fetuses, newborns and infants. Neuroscience, 276, 48-71.

Stephens, R. L., Langworthy, B. W., Short, S. J., Girault, J. B., Styner, M. A., & Gilmore, J. H. (2020). White matter development from birth to 6 years of age: a longitudinal study. Cerebral Cortex, 30(12), 6152-6168.

Hagmann, P., Sporns, O., Madan, N., Cammoun, L., Pienaar, R., Wedeen, V. J., ... & Grant, P. E. (2010). White matter maturation reshapes structural connectivity in the late developing human brain. Proceedings of the National Academy of Sciences, 107(44), 19067-19072.

Collin G, van den Heuvel MP. The ontogeny of the human connectome: development and dynamic changes of brain connectivity across the life span. Neuroscientist. 2013 Dec;19(6):616-28. doi: 10.1177/1073858413503712.

The authors test associations between FC and growth, but making sense of such modulation results is difficult without a clearer view of developmental changes per se (e.g., what does an early negative FC mean? Is it an increase in FC when the value gets close to 0? In particular, at 24m, it seems that most FC values are not significantly different from 0, Figure 2B). Observing positive vs negative association effects depending on age is quite puzzling. It is also questionable, for some correlation analyses with cognitive flexibility, to focus on FC that changes with age but to consider FC at a given age.

We thank the reviewer for bringing up this important point and understand that it requires some additional consideration. The negative FC values decreasing with age indicate that these regions go from being anti-correlated to becoming increasingly correlated. Hence, FC of these ROIs increased with age. The trajectory seems to suggest that this will keep increasing with age but of course further data need to be collected to assess this.

Unfortunately, when considering ΔFC to predict cognitive flexibility, the numbers of participants dropped significantly, with N=~15/20 infants per group of preschoolers, making it very challenging to interpret the results with meaningful statistical power.

- The manuscript uses inappropriate terms "to predict", "prediction" whereas the conducted analyses are not prediction analyses but correlational.

We thank the reviewer for giving us to opportunity to thoroughly revise the manuscript about this matter. In this work, we had clear hypotheses regarding which variables predicted which certain measures (such as growth predicting FC and FC predicting cognitive outcomes). Therefore, we performed regression analyses rather than correlational analyses to investigate these associations. Hence, we believe that using the term ‘predict and ‘prediction’ is appropriate

Recommendations for the authors:

Reviewer #1 (Recommendations For The Authors):

(1) In the introduction and discussion, the authors talk about the link between developmental trajectories and cognitive capacities, and undernutrition. However, they did not compare developmental trajectories but connectivity patterns at different ages with ΔWLZ and cognitive flexibility. I recommend that the authors rephrase the introduction and discussion.

We thank the reviewer for pointing out places requiring better clarity in the text. We made edits through the introduction to better match our investigations. In particular we changed:

- ‘our understanding of the relationships between early undernutrition, developmental trajectories of brain connectivity, and later cognitive outcomes is still very limited,’ to, ‘our understanding of the relationships between early undernutrition, brain connectivity, and later cognitive outcomes is still very limited’ (line 89, introduction);

- ‘(ii) investigate if trajectories of early FC have an impact on cognitive outcome at pre-school age in these children,’ to, ‘(ii) investigate if early FC has an impact on cognitive outcome at pre-school age in these children’ (line 137, introduction);

- ‘This study investigated how early adversity via undernutrition drives longitudinal changes in brain functional connectivity at five time points throughout the first two years of life and how these developmental trajectories are associated with cognitive flexibility at preschool age,’ to, ‘This study investigated how early adversity via undernutrition drives brain functional connectivity throughout the first two years of life and how these early functional connections are associated with cognitive flexibility at preschool age’ (line 215, discussion).

(2) Considering most research is done in occidental high-income countries, and this work is one of the few presenting research in another context, I think the authors should discuss in the manuscript that differences with previous studies might also be due to environmental and cultural differences. Since the study lacks the statistical power to perform a statistical analysis that directly establishes a link between developmental trajectories and restricted growth and cognitive flexibility, the authors cannot disentangle which differences are related to undernutrition and which might result from growing up in a different environment. I recommend that the authors avoid phrases like (lines 57-58): "We observed that early physical growth before the fifth month of life drove optimal developmental trajectories of FC..." or (lines 223-224) "...our cohort of Gambian infants exhibit atypical developmental trajectories of functional connectivity...".

We thank the reviewer for this observation, and we agree with the reviewer that other factors differing between low- and poor-resource settings might have an impact on FC trajectories. We therefore specified this in the discussion as follows: “We acknowledge that differences in FC could also be attributed to other environmental and cultural disparities between high-resource and low-resource settings, and future studies are needed to explore this further” (line 238). We revised the whole manuscript to reflect similar statements.

(3) To better interpret the results, it would be interesting to know if poor early growth predicts late cognitive flexibility in the tested sample and if the ΔWLZ distributions differ compared to a population in a high-income country where undernutrition is less frequent.

We explored the relationship between changes in growth and cognitive flexibility in the two preschooler group, but there were no significant associations.

Mean and SD values of WLZ are reported in Table 3. The values at every age are negative, indicating that the infants' weight-for-length is below the expected norm at all ages. To our knowledge, no other studies have assessed changes in growth in an infant sample with similar closely spaced age time points in high-income countries, making comparisons on growth changes challenging.

(4) It is unclear why WLZ at birth and HCZ at 7-14 days are included in the models. I imagine this is to ensure that differences are not due to growing restrictions before birth. It would be nice if the authors could explain this.

As the reviewer pointed out, HCZ at 7-14 days was included to ensure associations between growth and FC are not due to physical differences at birth. This case be considered as a 'baseline' measure for cerebral development, in the same way that WLZ at birth was used as a baseline for physical development. Therefore, we can more confidently  assume that the associations between growth and FC were specific to the impact of change in WLZ postnatally and not confounded by the size or maturity of the infant at birth. We specified this in the manuscript as follows: “These analyses were adjusted by WLZ at birth and HCZ at 7/14 days, to more confidently assume that the associations between growth and FC were specific to the impact of change in WLZ postnatally and not confounded by the size or maturity of the infant at birth” (line 520, statistical analysis section in the method section).

(5) Right frontal-posterior connections at 24 months negatively correlate with ΔWLZ. Thus, restricted growth results in stronger frontal-posterior connections at 24 months. However, the same connections at 24 months positively correlate with cognitive flexibility (stronger connections predict better cognitive flexibility). Do the authors have any interpretation of this? How could this relate to previous findings of the authors (Bulgarelli et al. 2020), showing first an increase and then a decrease in functional connectivity between frontal and parietal regions?

We acknowledge that interpreting the negative relationship between changes in growth and fronto-posterior FC at 24 months, alongside the positive association between the same connection and later cognitive flexibility, is challenging. We refrain from relating these findings to those published by Bulgarelli in 2020 due to differences in optode locations and because in that work the decrease in fronto-posterior FC was observed after 24 months (up to 36 months), whereas the endpoint in this study is right at 24 months.

(6) With the growth of the head, the frontal channels move to more temporal areas, right? Could this determine the decrease in frontal inter-hemisphere connections?

As shown in Nabwera (2017) head size does not increase that much in Gambian infants, or at least as expected by the WHO standard measures. We have added HCZ mean and SD values per age in Table 3.

Minor points

- HCZ is used in line 184 but not defined.

We thank the reviewer for spotting this, we have now specified HCZ at line 184 as follows: ‘head-circumference z-score (HCZ)’.

- Table SI2: NIRS not undertaken = the participant was assessed but did want or could not perform... I imagine there is a missing "not".

We thank the reviewer for spotting this, we have now modified the legend of Table SI2 as follows: ‘the participant was assessed but did not want or could not perform the NIRS assessments.’

- The authors should explain what weight-for-length is for those who are not familiar with it.

We have added an explanation of weight-for-length in the experimental design section, line 339 as follows: ‘We then tested for relationships between brain FC at age 24 months with measures of early growth, as indexed by changes in weight-for-length z-scores (reflecting body weight in proportion to attained growth in length) at one month of age, and at each of the four subsequent visits (details provided below).’

Reviewer #2 (Recommendations For The Authors):

(1) I am confused about the authors' interpretation that left and right front-middle and right front-back FC increased with age. It appears in Figure 2 that the negative FC among these ROIs should actually decrease with age. This means that as individuals grow older, the FC values between these regions and zero diminished, albeit starting with negative FC (anticorrelation values) in younger age groups.

Yes, the reviewer is correct. The negative values of the left and right front-middle and right front-back FC decreasing with age indicate that these regions go from being anti-correlated to becoming increasingly correlated. Hence, FC of these ROIs increased with age.

(2) Are these negative values mentioned above at 24 months still negative? Have t-tests been run to examine the differences from zero?

As suggested, we performed t-tests against zero for the mentioned FC at 24 months, and only the left and right fronto-middle FC are significantly different than zero (left fronto-middle FC: t(94) = 1.8, p = 0.036; right fronto-middle FC t(94) = 2.7, p = 0.003).

(3) With so many correlation analyses, have multiple comparisons been consistently controlled for? While I assume this was done according to the Methods section, could the authors clarify whether FDR adjustment was applied to all the p-values at once or to a group of p-values each time? I found the following way of reporting FDR-adjusted p-values quite informative, such as PFDR, 24 pairs of ROIs < 0.05.

We thank the reviewer for this insightful comment. P-values of regression analyses were FDR corrected per connection investigated, i.e. 21 possible ΔWLZ values per connection. We have specified this in the method section as follows: “To ensure statistical reliability, results from the regression analyses on each FC were corrected for multiple comparisons using false discovery rate (FDR)(Benjamini & Hochberg, 1995) per each connection investigated, i.e. 21 possible ΔWLZ values per each connection,” (page 12, Statistical Analyses section).

(4) Can early growth trajectories predict changes in FC? Why not use ΔWLZ to predict ΔFC?

Unfortunately, when considering ΔWLZ to predict ΔFC, the numbers of participants dropped significantly, with N=~30 infants, making it very challenging to interpret the results. We believe this emphasizes the importance of recruiting large samples when conducting longitudinal studies involving infants and employing multiple measures.

(5) I might have missed the rationale, but why weren't the growth changes after 5 months studied?

ΔWLZ between all time points were assessed as predictors of FC at 24 months. We have specified this at line 183 as follows: ‘we used multiple regression with the infant growth trajectory (delta weight for length z-score between all time points, DWLZ) and FC at 24 months’. As indicated in Table 2 and 3 the associations between ΔWLZ at all time points and FC at 24 months were tested, but only those with DWLZ calculated between birth and 1 month and the subsequent time points were significant. DWLZ between 5 months and the subsequent time points, DWLZ between 8 months and the subsequent time points, DWLZ between 12 months and the subsequent time points, DWLZ between 18 months and the subsequent time points did not significantly predict FC at 24 months. These are highlighted in Table 2 and Figure 3 in blue and marked as NS (non-significant).

(6) Once more, the advantage of longitudinal data is that it allows us to tap into developmental changes. Analyzing and predicting cognitive development based solely on FC values at a single age stage (i.e., 24 months) would overlook the benefits of a longitudinal design, which is regrettable. I suggest that the authors attempt to use ΔFC for predictions and observe the outcomes.

As mentioned to point (4) raised by the reviewer, unfortunately, when considering ΔWLZ to predict ΔFC, the numbers of participants dropped significantly, with N=~30 infants, making it very challenging to interpret the results. We believe this emphasizes the importance of recruiting large samples when conducting longitudinal studies involving infants and employing various measures.

(7) In the section "Early FC predicts cognitive flexibility at preschool age", the authors pointed out that "...,none of these survived FDR correction for multiple comparisons." However, the paper discussed the association between FC at 24 months of age and cognitive flexibility, as it was supported by the statistical analysis in the following sections. If FDR correction cannot be satisfied, I would rephrase the implication/conclusion of the results to suggest that early FC does not predict cognitive flexibility at preschool age.

We acknowledge the reviewer’s concern regarding the reporting of results that do not survive multiple comparisons. However, considering the uniqueness of our dataset and the novelty of our work, we believe it is crucial to report all significant findings, even those not passing multiple comparisons corrections, as they may motivate hypothesis-generation for future studies. We have taken great care to transparently distinguish between results that survived multiple comparisons and those that did not in both the Results and Discussion sections, ensuring that readers are not misled. It is possible that future studies may replicate and further support these associations. Therefore, by sharing these results with the research community, we provide valuable insights for future investigations.

Following the reviewer’ suggestion, we specified that results from regression analysis are significant but they did not survive multiple comparisons in the discussion as follows: ‘While our results are consistent with previous studies, we acknowledge that the significant association between early FC and later cognitive flexibility does not withstand multiple comparisons. Therefore, we encourage future studies that may replicate these findings with a larger sample. (line 290, discussion section).

(8) Have the authors assessed the impact of growth trajectories on cognitive flexibility?

We explored the relationship between changes in growth and cognitive flexibility in the two preschooler groups, but there were no significant associations.

(9) Are there no other cognitive or behavioural measures available? Cognitive flexibility is just one domain of cognitive development, and would the impact of undernutrition on cognitive development be domain-specific? There is a lack of theoretical support here. Why choose cognitive flexibility, and should the impact of undernutrition be domain-specific or domain-general?

We agree with the reviewer that in this work, we chose to focus on one specific cognitive outcome. While this does not imply that the impact of undernutrition is domain-specific, cognitive flexibility, being a core executive function, has been extensively studied in terms of its neural underpinnings using other neuroimaging modalities, especially fMRI (for example see Dajani, 2015; Uddin, 2021).

Moreover, other studies looking at the effect of adversity on cognitive outcomes focus on specific cognitive skills, such as working memory (Roberts, 2017), reading and arithmetic skills (Soni, 2021).

We did assess infants also with Mullen Scales of Early Learning (MSEL), although the cognitive flexibility task within the Early Years Toolbox has been specifically designed for preschoolers (Howard, 2015), and this set of tasks has recently been validated in our team in The Gambia (Milosavljevic, 2023).Future works from the BRIGHT team will investigate performance at the MSEL in relation to other variable of the project.

References:

D. R. Dajani, L. Q. Uddin, Demystifying cognitive flexibility: Implications for clinical and developmental neuroscience. Trends Neurosci. 38, 571–578 (2015).

L. Q. Uddin, Cognitive and behavioural flexibility: neural mechanisms and clinical considerations. Nat. Rev. Neurosci. 22, 167–179 (2021).

Roberts, S. B., Franceschini, M. A., Krauss, A., Lin, P. Y., de Sa, A. B., Có, R., ... & Muentener, P. (2017). A pilot randomized controlled trial of a new supplementary food designed to enhance cognitive performance during prevention and treatment of malnutrition in childhood. Current developments in nutrition, 1(11), e000885.

Soni, A., Fahey, N., Bhutta, Z. A., Li, W., Frazier, J. A., Moore Simas, T., ... & Allison, J. J. (2021). Early childhood undernutrition, preadolescent physical growth, and cognitive achievement in India: A population-based cohort study. PLoS Medicine, 18(10), e1003838.

Howard, S. J., & Melhuish, E. (2015). An Early Years Toolbox (EYT) for assessing early executive function, language, self-regulation, and social development: Validity, reliability, and preliminary norms. Journal of Psychoeducational Assessment, 35(3), 255-275.

Milosavljevic, B., Cook, C. J., Fadera, T., Ghillia, G., Howard, S. J., Makaula, H., ... & Lloyd‐Fox, S. (2023). Executive functioning skills and their environmental predictors among pre‐school aged children in South Africa and The Gambia. Developmental Science, e13407.

(10) I would review more previous fNIRS studies on infants if they exist (e.g., the work by S Lloyd-Fox, L Emberson, and others). These studies can help identify brain ROIs likely linked to undernutrition and cognitive flexibility. The current analysis methods lean towards exploratory research. This makes the paper more of a proof-of-concept report rather than a strongly theoretically-driven study.

We thank the reviewer for this important point. While we have reviewed existing fNIRS infant studies, there are no extant works that showed whether specific brain regions are related undernutrition. However, several fMRI studies assessed regions that do support cognitive flexibility, and we mentioned these in the manuscript (for example see Dajani, 2015; Uddin, 2021).

Other than the BRIGHT project, we are aware of two other projects that assessed the effect of undernutrition on brain development, assessing cognitive outcomes in poor-resource settings:

- the BEAN project in Bangladesh in which fNIRS data were recorded from the bilateral temporal cortex (i.e. Pirazzoli, 2022);

- a project in India in which fNIRS data were recorded from frontal, temporal and parietal cortex bilaterally (i.e. Delgado Reyes, 2020)

The brain regions recorded in these studies largely overlap with the brain regions we recorded from in this study.

Another aspect to consider is that infants underwent several fNIRS tasks as part of the BRIGHT project, focusing on social processing, deferred imitation, and habituation responses. Therefore, brain regions for data acquisition were chosen to maximize the likelihood of recording meaningful data for all tasks (Lloyd-Fox, 2023). To clarify the text, we specified this information in the methods section (line 383).

References:

D. R. Dajani, L. Q. Uddin, Demystifying cognitive flexibility: Implications for clinical and developmental neuroscience. Trends Neurosci. 38, 571–578 (2015).

Pirazzoli, L., Sullivan, E., Xie, W., Richards, J. E., Bulgarelli, C., Lloyd-Fox, S., ... & Nelson III, C. A. (2022). Association of psychosocial adversity and social information processing in children raised in a low-resource setting: an fNIRS study. Developmental Cognitive Neuroscience, 56, 101125.

Delgado Reyes, L., Wijeakumar, S., Magnotta, V. A., Forbes, S. H., & Spencer, J. P. (2020). The functional brain networks that underlie visual working memory in the first two years of life. NeuroImage, 219, Article 116971.

Lloyd-Fox, S., McCann, S., Milosavljevic, B., Katus, L., Blasi, A., Bulgarelli, C., Crespo-Llado, M., Ghillia, G., Fadera, T., Mbye, E., Mason, L., Njai, F., Njie, O., Perapoch-Amado, M., Rozhko, M., Sosseh, F., Saidykhan, M., Touray, E., Moore, S. E., … Team, and the B. S. (2023). The Brain Imaging for Global Health (BRIGHT) Study: Cohort Study Protocol. Gates Open Research, 7(126).

(11) Last but not least, in the paper, the authors mentioned that fNIRS offers better spatial resolution and anatomical specificity compared to EEG, thereby providing more precise and reliable localization of brain networks. While I partially agree with this perspective, it remains to be explored whether the current fNIRS analysis strategies indeed yield higher spatial resolution. It is hoped that the authors will delve deeper into this discussion in the paper.

The brain regions of focus were selected based on coregistration work previously conducted at each time point on the array used in this project (Collins-Jones, 2019). We deliberately avoided making claims about small brain regions, considering that head size might increase slightly less with age in The Gambia compared to Western countries (Nabwera, 2017) . However, we maintain that the conclusions drawn in this study offer higher brain-region specificity than could have been  identified with current common EEG methods alone.

References:

L. H. Collins-Jones, et al., Longitudinal infant fNIRS channel-space analyses are robust to variability parameters at the group-level: An image reconstruction investigation. Neuroimage 237, 118068 (2021).

Nabwera, H. M., Fulford, A. J., Moore, S. E., & Prentice, A. M. (2017). Growth faltering in rural Gambian children after four decades of interventions: a retrospective cohort study. The Lancet Global Health, 5(2), e208–e216.

Reviewer #3 (Recommendations For The Authors):

Introduction

- Among important developmental mechanisms to mention are the development of exuberant connections and the further selection/stabilization of the relevant ones according to environmental stimulation, vs the pruning of others.

We agree with the reviewer that the development of exuberant connections and subsequent pruning is a universal process of paramount importance during the first years of life. However, after revising our introduction, given the word limit of the journal, we maintained focus on neurodevelopment and early adversity.

Results

- Adding a few more information on the 6 sections and 21 connections would be welcome. In particular for within-section FC: how was this computed?

The 6 sections were created based on the co-registration of the array used in this study at each age in a previous published work L. H. Collins-Jones, et al., Longitudinal infant fNIRS channel-space analyses are robust to variability parameters at the group-level: An image reconstruction investigation. Neuroimage 237, 118068 (2021). This is reference No. 68 in the manuscript.

As we mentioned in the section fNIRS preprocessing and data-analysis: ‘The sections were established via the 17 channels of each hemisphere which were grouped into front, middle and back (for a total of six regions) based on a previous co-registration of the BRIGHT fNIRS arrays onto age-appropriate templates’.

The 21 connections were defined as all the possible links between the 6 regions, specifically: the interhemispheric homotopic connections (in orange in Figure SI1), which connect the same regions between hemispheres (i.e., front left with front right); the intrahemispheric connections (in green in Figure SI1), which correlate channels belonging to the same region; the fronto-posterior connections (in blue in Figure SI1), which link front and middle, middle and back, and front and back regions of the same hemisphere; and the crossing interhemispheric connections (non-homotopic interhemispheric, in yellow in Figure SI1), which link the front, middle, and back areas between left and right hemispheres. We added these specifications also in the legend of Figure SI1 for clarity.

- The denomination intrahemispheric vs fronto-posterior vs crossed connections is not clear. Maybe prefer intra-hemispheric vs inter-hemispheric homotopic vs inter-hemispheric non-homotopic (also in Figure SI1).

We appreciate the reviewer's suggestion regarding terminology. However, we believe that the term 'inter-hemispheric non-homotopic' could potentially refer to both connections within the same brain hemisphere from front to back and connections crossing between hemispheres, leading to increased confusion. Therefore, we have chosen not to include the term 'non-homotopic' and instead added 'homotopic' to 'interhemispheric' throughout the manuscript to emphasize that these functional connections occur between corresponding regions of the two hemispheres.

- with time -> with age.

We replaced “with time” with “with age” as suggested through the manuscript.

- The description of both HbO2 and HHb results overloads the main text: would it be relevant to present one of the two in Supplementary Information if the results are coherent?

We understand the reviewer’s concern regarding overloading the results section with reporting both chromophores. However, reporting results for both HbO and HHb is considered a crucial step for publications in the fNIRS field, as emphasized in recent formal guidance (Yücel et al., 2020). One of the strengths of fNIRS compared to fMRI is its ability to record from both chromophores, enabling a more precise characterization of brain activations and oscillations. Moreover, in FC studies like this one, ensuring that HbO and HHb results overlap is an important check that increases confidence in interpreting the findings.

References:

Yücel, M. A., von Lühmann, A., Scholkmann, F., Gervain, J., Dan, I., Ayaz, H., Boas, D., Cooper, R. J., Culver, J., Elwell, C. E., Eggebrecht, A. ., Franceschini, M. A., Grova, C., Homae, F., Lesage, F., Obrig, H., Tachtsidis, I., Tak, S., Tong, Y., … Wolf, M. (2020). Best Practices for fNIRS publications. Neurophotonics, 1–34. https://doi.org/10.1117/1.NPh.8.1.012101

- HCZ is not defined when first used.

We thank the reviewer for spotting this, we have now specified HCZ at line 184 as follows: ‘head-circumference z-score (HCZ)’.

- Choosing the analyzed measures to "maximize power" could be criticised.

We appreciate the reviewer’s concern. However, correlating all the FC values with all changes in growth would have raised an important issue for multiple comparisons. We therefore we made a priori decision to focus on investigating the relationship between changes in growth and those FC that showed a significant change with age, considering these as the most interesting ones from a developmental perspective in our sample.

Discussion

- I would recommend using the same order to synthesize results and further discuss them.

We agree with the reviewer that the suggested structure is optimal for a clear discussion section. We have indeed followed it, with each paragraph covering specific aspects:

- Recap of the study aims

- Results summary and discussion of developmental changes

- Results summary and discussion of the relationship between changes in growth and FC

- Results summary and discussion of the relationship between FC and cognitive flexibility

- Limitations

- Conclusion

Given the numerous results presented in this paper, we believe that readers will better digest them by first reading a summary of the results followed by their interpretations, rather than condensing all the interpretations together.

- Highlighting how "atypical" developmental trajectories are in Gambian infants would be welcome in the Results section. Other interpretations can be found than "The observed decrease in frontal inter-hemispheric FC with increasing age may be due to the exposure to early life undernutrition adversity".

We agree with the reviewer that other factors that differ between low- and high-resource settings might have an impact on FC trajectories. We therefore specified this in the discussion as follows: “We acknowledge that differences in FC could also be attributed to other environmental and cultural disparities between high-resource and low-resource settings, and future studies are needed to further investigate cultural, environmental, and genetic effects on brain FC” (line 238).

- Focusing on FC at 24m for the relationship with growth is questionable.

Correlating the FC values at 5 time points with all changes in growth would have raised an important issue for multiple comparisons. We therefore we made a decision a priori to focus on investigating the relationship between changes in growth and FC at 24 months as our final time point of data collection. We added this information in the methods section as follows: “To investigate the impact of undernutrition on FC development, we used DWLZ as independent variables in regression analyses on HbO2 (as the chromophore with the highest signal-to-noise ratio) FC at 24 months, our final time point of data collection” (line 517, method section).

- There is too much emphasis on the correlation between FC and cognitive flexibility, whereas results are not significant after correction for multiple comparisons.

Following the reviewer’ suggestion, we specified that results from regression analysis are significant but they did not survive multiple comparisons in the discussion as follows: While our results are consistent with previous studies, we acknowledge that the significant association between early FC and later cognitive flexibility does not withstand multiple comparisons. Therefore, we encourage future studies that may replicate these findings with a larger sample. (line 290, discussion section).

Methods

- I would recommend detailing how z-scores were computed in the paragraph "Anthropometric measures".

We specified how z-scores were computed in the statistical analysis section as follows: “Anthropometric measures were converted to age and sex adjusted z‐scores that are based on World Health Organization Child Growth Standards (93). Weight‐for‐Length (WLZ) and Head Circumference (HCZ) z-scores were computed” (line 509, method section). As transforming data is the first step of statistical analysis and is not directly related to data collection, we believe it is more appropriate to retain this description in the statistical analysis section.

- FC computation: the mention of "correlating the first and the last 250s" is not clear.

We specified this more clearly in the text as follows: We found that correlating the first and the last 250 seconds of valid data after pre-processing provided the highest percentage of infants with strong correlation between the first and the last portion of data (line 467).

- The manuscript mentions "age 3 years" for the younger preschoolers but ~48months rather corresponds to 4 years.

We revised the entire manuscript and the supplementary materials, but we could not find any instance in which preschoolers are referred with age in months rather than in years.

- Specify the number of children evaluated at 4 and 5 years. Is the test of cognitive flexibility normalized for age? If not, how were the 2 groups considered in the analyses? (age as a confounding factor).

We have added the number of children in the two preschooler groups as follows: younger preschoolers (age mean ± SD=47.96 ± 2.77 months, N=77) and older preschoolers (age mean ± SD=57.58 ± 2.11 months, N=84). (line 484).

The cognitive flexibility test was not normalized for age, as this task was specifically developed for preschoolers (Howard, 2015). As mentioned in ‘Cognitive flexibility at preschool age’ of the methods section, “data were collected in two ranges of preschool ages”, which guided our decision to perform regression analysis on the impact of FC on cognitive flexibility separately within these two age groups, rather than treating them as a single group of preschoolers.

References:

Howard, S. J., & Melhuish, E. (2015). An Early Years Toolbox (EYT) for assessing early executive function, language, self-regulation, and social development: Validity, reliability, and preliminary norms. Journal of Psychoeducational Assessment, 35(3), 255-275.

Figures and Tables

- Table 1 could highlight the significant results. It is not clear what the "baseline" results correspond to.

We have marked in bold the results that are statistically significant in Table 1. In the linear mixed model we performed, the first time point (i.e. 5 months) is chosen as ‘baseline’, i.e. the reference against which the other time points are compared to, and its statistical values refer to its significance against 0 (as it has been performed in Bulgarelli 2020).

- Figures 2 B and C seem redundant? What is SE vs SD?

We believe that both figures 2B and 2C are useful for the readers. While the first one shows the mean FC values at the group level, the second one highlights the individual variability of FC values (typical of infant neuroimaging data), which also why it is interesting to relate these measures to other variables of our dataset (i.e. growth and cognitive flexibility). Figure 2C also reports mean FC values per age, but these might be less visible considering that also one dot per infant is also plotted.

SE stands for standard error, and in the legend of the figure we specified this as follows: ‘Mean and standard error of the mean (SE)’. SD stands for standard deviation, and we have now specified this as follows: ‘mean ± standard deviation (SD)’ .

- Table 2: I would recommend removing results that don't survive corrections for multiple comparisons.

We acknowledge the reviewer’s concern regarding the reporting of results that do not survive multiple comparisons. However, considering the uniqueness of our dataset and the novelty of our work, we believe it is crucial to report all significant findings. We have taken great care to transparently distinguish between results that survived multiple comparisons and those that did not in both the Results and Discussion sections, ensuring that readers are not misled. It is possible that future studies may replicate and further strengthen these associations. Therefore, by sharing these results with the research community, we provide valuable insights for future investigations.

- Figure 3: the top is redundant with Table 2: to be merged? B: the statistical results might be shown in a Table.

We agree with the reviewer that the top part of Figure 3 and Table 2 report the same results. However, given the richness of these findings, we believe that the top part of Figure 3 serves as a useful summary for readers. Additionally, examining both the top and bottom parts of Figure 3 provides a comprehensive overview of the regression analysis conducted in this study.

- Figure SI6: Is it really a % in x-axis?

We thank the reviewer for spotting this typo, the percentage is relevant for the y-axis only. We removed the % symbol from ticks of the x-axis.

- Table SI1: the presented p-values don't seem to survive Bonferroni correction, contrary to what is written.

We thank the reviewer for spotting this mistake, we removed the reference to the Bonferroni correction for the p-values.

- Table SI2: For the proportion of children included in the analysis, maybe be precise that the proportion was computed based on the ones with acquired data. Maybe also add the proportion according to all children, to better show the high drop-out rate at certain ages?

We thank the reviewer for these useful suggestions. We have specified in the legend of the table how we calculated the proportion of infants included as follows: ‘The proportion of children included in the analysis was computed based on the infants with FC data’. We have also added a column in the table called ‘Inclusion rate (from the 204 infants recruited)’, following the reviewer’s suggestion. This will be a useful reference for future studies.

- A few typos should be corrected throughout the manuscript.

We thoroughly revised the main manuscript and the supplementary materials for typos.

Author response:

The following is the authors’ response to the original reviews

Reviewer #1 (Public review):

Summary:

Building on previous in vitro synaptic circuit work (Yamawaki et al., eLife 10, 2021), Piña Novo et al. utilize an in vivo optogenetic-electrophysiological approach to characterize sensory-evoked spiking activity in the mouse's forelimb primary somatosensory (S1) and motor (M1) areas. Using a combination of a novel "phototactile" somatosensory stimuli to the mouse's hand and simultaneous high-density linear array recordings in both S1 and M1, the authors report in awake mice that evoked cortical responses follow a triphasic peak-suppression-rebound pattern response. They also find that M1 responses are delayed and attenuated relative to S1. Further analysis revealed a 20-fold difference in subcortical versus corticocortical propagation speeds.

They also report that PV interneurons in S1 are strongly recruited by hand stimulation. Furthermore, they report that selective activation of PV cells can produce a suppression and rebound response similar to "phototactile" stimuli. Lastly, the authors demonstrate that silencing S1 through local PV cell activation reduces M1 response to hand stimulation, suggesting S1 may directly drive M1 responses.

Strengths:

The study was technically well done, with convincing results. The data presented are appropriately analyzed. The author's findings build on a growing body of both in vitro and in vivo work examining the synaptic circuits underlying the interactions between S1 and M1. The paper is well-written and illustrated. Overall, the study will be useful to those interested in forelimb S1-M1 interactions.

Weaknesses:

Although the results are clear and convincing, one weakness is that many results are consistent with previous studies in other sensorimotor systems, and thus not all that surprising. For example, the findings that sensory stimulation results in delayed and attenuated responses in M1 relative to S1 and that PV inhibitory cells in S1 are strongly recruited by sensory stimulation are not novel (e.g., Bruno et al., J Neurosci 22, 10966-10975, 2002; Swadlow, Philos Trans R Soc Lond B Biol Sci 357, 1717-1727, 2002; Gabernet et al., Neuron 48, 315-327, 2005; Cruikshank et al., Nat Neurosci 10, 462-468, 2007; Ferezou et al., Neuron 56, 907-923, 2007; Sreenivasan et al., Neuron 92, 1368-1382, 2016; Yu et al., Neuron 104, 412-427 e414, 2019). Furthermore, the observation that sensory processing in M1 depends upon activity in S1 is also not novel (e.g., Ferezou et al., Neuron 56, 907-923, 2007; Sreenivasan et al., Neuron 92, 1368-1382, 2016). The authors do a good job highlighting how their results are consistent with these previous studies.

We thank the reviewer for the close reading of the manuscript and the many constructive comments and critiques. As the reviewer notes, there have been many prior studies of related circuits in other sensorimotor systems forming an important context for our study and findings, as we have tried to highlight. We appreciate the suggestions for additional relevant articles to cite.

Perhaps a more significant weakness, in my opinion, was the missing analyses given the rich dataset collected. For example, why lump all responsive units and not break them down based on their depth? Given superficial and deep layers respond at different latencies and have different response magnitudes and durations to sensory stimuli (e.g., L2/3 is much more sparse) (e.g., Constantinople et al., Science 340, 1591-1594, 2013; Manita et al., Neuron 86, 1304-1316, 2015; Petersen, Nat Rev Neurosci 20, 533-546, 2019; Yu et al., Neuron 104, 412-427 e414, 2019), their conclusions could be biased toward more active layers (e.g., L4 and L5). These additional analyses could reveal interesting similarities or important differences, increasing the manuscript's impact. Given the authors use high-density linear arrays, they should have this data.

We have analyzed the activity patterns as a function of cortical depth, and now include these results in the manuscript as suggested. The key new finding is that the M1 responses are strongest in upper layers, consistent with expectations based on the excitatory corticocortical synaptic connectivity characterized previously. Changes to the manuscript include new figures (Figure 5; Figure 5 - figure supplement 1), which we explain (Methods: page 14, lines 618-621), describe (new Results section: pages 4-5, lines 183-189), comment on (Discussion: page 9, lines 378-391), and summarize the significance of (Abstract: page 1, lines 22-24). In addition, we incorporated the new laminar analysis into a summary schematic (Figure 9). We thank the reviewer for suggesting this analysis.

Similarly, why not isolate and compare PV versus non-PV units in M1? They did the photostimulation experiments and presumably have the data. Recent in vitro work suggests PV neurons in the upper layers (L2/3) of M1 are strongly recruited by S1 (e.g., Okoro et al., J Neurosci 42, 8095-8112, 2022; Martinetti et al., Cerebral cortex 32, 1932-1949, 2022). Does the author's data support these in vitro observations?

These experiments were relatively complex and M1 optotagging was not routinely included in the stimulus and acquisition protocol. Therefore, we don’t have sufficient data for this analysis. We plan to address this in future studies.

It would have also been interesting to suppress M1 while stimulating the hand to determine if any part of the S1 triphasic response depends on M1 feedback.

We agree that this is of interest but consider this to be outside the scope of the current study.

I appreciate the control experiment showing that optical hand stimulation did not evoke forelimb movement. However, this appears to be an N=1. How consistent was this result across animals, and how was this monitored in those animals? Can the authors say anything about digit movement?

We have performed additional experiments to address this point. A constraint with EMG is that it is limited to the muscle(s) one chooses to record from, and it is difficult to implant tiny muscles of the hand. Therefore, for this analysis, we used kilohertz videography as a high-sensitivity method for movement surveillance across the hand. Hand stimulation did not evoke any detectable movements. Changes in the manuscript include: revised Figure 1 - figure supplement 1; supplementary Figure 1 - video 1; and associated text edits in the Methods (page 13, line 557; page 14, lines 626-639) and Results sections (page 2, lines 84-85).

A light intensity of 5 mW was used to stimulate the hand, but it is unclear how or why the authors chose this intensity. Did S1 and M1 responses (e.g., amplitude and latency) change with lower or higher intensities? Was the triphasic response dependent on the intensity of the "phototactile" stimuli?

As we now say in the Methods > Optogenetic photostimulation of the hand section (page 13, lines 562-565), “This intensity was chosen based on pilot experiments in which we varied the LED power, which showed that this intensity was reliably above the threshold for evoking robust responses in both S1 and M1 without evoking any visually detectable movements (as subsequently confirmed by videography)”.

Reviewer #2 (Public review):

Summary:

Communication between sensory and motor cortices is likely to be important for many aspects of behavior, and in this study, the authors carefully analyse neuronal spiking activity in S1 and M1 evoked by peripheral paw stimulation finding clear evidence for sensory responses in both cortical regions

Strengths:

The experiments and data analyses appear to have been carefully carried out and clearly represented.

Weaknesses:

(1) Some studies have found evidence for excitatory projection neurons expressing PV and in particular some excitatory pyramidal cells can be labelled in PV-Cre mice. The authors might want to check if this is the case in their study, and if so, whether that might impact any conclusions.

Thank you for pointing this out. The prior studies suggest it is mainly a subset of layer 5B excitatory neurons that may express PV. We checked this in two ways. Anatomically, we did not find double-labeling. An electrophysiology assay showed that, although some evoked excitatory synaptic input could be detected in some neurons, these inputs were very weak. Results from these assays are shown in new Figure 6 - figure supplement 1, with associated text edits in the Methods (page 11, lines 469-471; page 15, lines 657-668) and Results (page 5, lines 198-199) sections.

(2) I think the analysis shown in Figure S1 apparently reporting the absence of movements evoked by the forepaw stimulation could be strengthened. It is unclear what is shown in the various panels. I would imagine that an average of many stimulus repetitions would be needed to indicate whether there is an evoked movement or not. This could also be state-dependent and perhaps more likely to happen early in a recording session. Videography could also be helpful.

As noted above, we have performed additional experiments to address this.

(3) Some similar aspects of the evoked responses, including triphasic dynamics, have been reported in whisker S1 and M1, and the authors might want to cite Sreenivasan et al., 2016.

Thank you for pointing this out; we now cite this article (page 1, line 46; page 10, line 415).

Reviewer #3 (Public review):

Summary:

This is a solid study of stimulus-evoked neural activity dynamics in the feedforward pathway from mouse hand/forelimb mechanoreceptor afferents to S1 and M1 cortex. The conclusions are generally well supported, and match expectations from previous studies of hand/forelimb circuits by this same group (Yamawaki et al., 2021), from the well-studied whisker tactile pathway to whisker S1 and M1, and from the corresponding pathway in primates. The study uses the novel approach of optogenetic stimulation of PV afferents in the periphery, which provides an impulselike volley of peripheral spikes, which is useful for studying feedforward circuit dynamics. These are primarily proprioceptors, so results could differ for specific mechanoreceptor populations, but this is a reasonable tool to probe basic circuit activation. Mice are awake but not engaged in a somatosensory task, which is sufficient for the study goals.

The main results are:

(1) brief peripheral activation drives brief sensory-evoked responses at ~ 15 ms latency in S1 and ~25 ms latency in M1, which is consistent with classical fast propagation on the subcortical pathway to S1, followed by slow propagation on the polysynaptic, non-myelinated pathway from S1 to M1;

(2) each peripheral impulse evokes a triphasic activation-suppression-rebound response in both S1 and M1;

(3) PV interneurons carry the major component of spike modulation for each of these phases; (4) activation of PV neurons in each area (M1 or S1) drives suppression and rebound both in the local area and in the other downstream area;

(5) peripheral-evoked neural activity in M1 is at least partially dependent on transmission through S1.

All conclusions are well-supported and reasonably interpreted. There are no major new findings that were not expected from standard models of somatosensory pathways or from prior work in the whisker system.

Strengths:

This is a well-conducted and analyzed study in which the findings are clearly presented. This will provide important baseline knowledge from which studies of more complex sensorimotor processing can build.

Weaknesses:

A few minor issues should be addressed to improve clarity of presentation and interpretation:

(1) It is critical for interpretation that the stimulus does not evoke a motor response, which could induce reafference-based activity that could drive, or mask, some of the triphasic response. Figure S1 shows that no motor response is evoked for one example session, but this would be stronger if results were analyzed over several mice.

As noted above, we have performed additional experiments to address this point.

(2) The recordings combine single and multi-units, which is fine for measures of response modulation, but not for absolute evoked firing rate, which is only interpretable for single units. For example, evoked firing rate in S1 could be higher than M1, if spike sorting were more difficult in S1, resulting in a higher fraction of multi-units relative to M1. Because of this, if reporting of absolute firing rates is an essential component of the paper, Figs 3D and 4E should be recalculated just for single units.

Thank you for noting this. Although the absolute firing rates are not essential for the main findings or conclusions (which as noted focus on response modulations and relative differences) we agree that analyzing the single-unit response amplitudes is useful. Therefore, changes in the manuscript now include: revised Figure 3, and associated text edits in the Methods (page 12, lines 543-545), Results (page 3, lines 115-119), and Discussion (page 7, lines 305-311) sections.

(3) In Figure 5B, the average light-evoked firing rate of PV neurons seems to come up before time 0, unlike the single-trial rasters above it. Presumably, this reflects binning for firing rate calculation. This should be corrected to avoid confusion.

Yes, this reflects the binning. We agree that this is potentially confusing and have removed these average plots below the raster plots, as the rasters alone suffice to demonstrate the result (i.e., that PV units are strongly activated and thus tagged by optogenetic stimulation). Changes are now reflected in revised Figure 6.

(4) In Figure 6A bottom, please clarify what legends "W. suppression" and "W. rebound" mean.

In the figure plot legends, the “W.” has been removed. Changes are now reflected in revised Figure 7 and Figure 7 – figure supplement 1.

Recommendations for the authors:

Reviewer #1 (Recommendations for the authors):

(1) Did you filter the neural signals during acquisition? If so, please include these details in the results.

Signals were bandpass-filtered (2.5 Hz to 7.6 KHz) at the hardware level at acquisition (with no additional software filtering applied), as now clarified in the Methods Electrophysiological recordings section as requested (page 12, lines: 525-526).

Reviewer #2 (Recommendations for the authors):

(1) Some studies have found evidence for excitatory projection neurons expressing PV and in particular some excitatory pyramidal cells can be labelled in PV-Cre mice. The authors might want to check if this is the case in their study, and if so, whether that might impact any conclusions.

Please see above for our response to this issue.

(2) I think the analysis shown in Figure S1 apparently reporting the absence of movements evoked by the forepaw stimulation could be strengthened. It is unclear what is shown in the various panels. I would imagine that an average of many stimulus repetitions would be needed to indicate whether there is an evoked movement or not. This could also be state-dependent and perhaps more likely to happen early in a recording session. Videography could also be helpful.

Please see above for our response to this issue.

(3) Some similar aspects of the evoked responses, including triphasic dynamics, have been reported in whisker S1 and M1, and the authors might want to cite Sreenivasan et al., 2016.

As noted above, we now cite this study.

Author response:

The following is the authors’ response to the original reviews

Reviewer #1 (Public review):

Summary:

In this manuscript, the authors discovered MYL3 of marine medaka (Oryzias melastigma) as a novel NNV entry receptor, elucidating its facilitation of RGNNV entry into host cells through macropinocytosis, mediated by the IGF1R-Rac1/Cdc42 pathway.

Strengths:

In this manuscript, the authors have performed in vitro and in vivo experiments to prove that MnMYL3 may serve as a receptor for NNV via macropinocytosis pathway. These experiments with different methods include Co-IP, RNAi, pulldown, SPR, flow cytometry, immunofluorescence assays, and so on. In general, the results are clearly presented in the manuscript.

Weaknesses:

For the writing in the introduction and discussion sections, the author Yao et al mainly focus on the viral pathogens and fish in Aquaculture, the meaning and novelty of results provided in this manuscript are limited, and not broad in biology. The authors should improve the likely impact of their work on the viral infection field, maybe also in the evolutionary field with the fish model.

(1) Myosin is a big family, why did authors choose MYL3 as a candidate receptor for NNV?

We appreciate your insightful question. We selected MYL3 as a candidate receptor based on a combination of proteomic screening and literature evidence, and functional validation. Increasing evidence indicated that myosins have been implicated in viral infections. For instance, myosin heavy chain 9 plays a role in multiple viral infections (Li et al., 2018), and non-muscle myosin heavy chain IIA has been identified as an entry receptor for herpes simplex virus-1 (Arii et al., 2010). Furthermore, myosin II light chain activation is essential for influenza A virus entry via macropinocytosis (Banerjee et al., 2014). Our previous studies hinted at a potential interaction between MYL3 and CP (Zhang et al., 2020). Huang et al also reported that Epinephelus coioides MYL3 might interact with native NNV CP by proteomic analysis of immunoprecipitation (IP) assay (Huang et al., 2020). Our Co-IP and SPR analyses confirmed a direct interaction between MYL3 and the RGNNV CP. Based on these studies, we selected MYL3 as a candidate receptor for NNV.

References

Huang PY, Hsiao HC, Wang SW, Lo SF, Lu MW, Chen LL. 2020. Screening for the Proteins That Can Interact with Grouper Nervous Necrosis Virus Capsid Protein. Viruses 12:1–20.

Li L, Xue B, Sun W, Gu G, Hou G, Zhang L, Wu C, Zhao Q, Zhang Y, Zhang G, Hiscox JA, Nan Y, Zhou EM. 2018. Recombinant MYH9 protein C-terminal domain blocks porcine reproductive and respiratory syndrome virus internalization by direct interaction with viral glycoprotein 5. Antiviral Res 156:10–20.

Arii J, Goto H, Suenaga T, Oyama M, Kozuka-Hata H, Imai T, Minowa A, Akashi H, Arase H, Kawaoka Y, Kawaguchi Y. 2010. Non-muscle myosin IIA is a functional entry receptor for herpes simplex virus-1.

Banerjee I, Miyake Y, Philip Nobs S, Schneider C, Horvath P, Kopf M, Matthias P, Helenius A, Yamauchi Y. 2014. Influenza A virus uses the aggresome processing machinery for host cell entry. Science (80- ) 346:473–477.

(2) What is the relationship between MmMYL3 and MmHSP90ab1 and other known NNV receptors? Why does NNV have so many receptors? Which one is supposed to serve as the key entry receptor?

We acknowledge the functional diversity of receptors for NNV. MmHSP90ab1 and MmHSC70 have been identified as receptors involved in NNV entry through clathrin-mediated endocytosis (CME), whereas MYL3 facilitates entry via macropinocytosis. These pathways serve as complementary mechanisms for the virus to enter host cells, potentially enhancing infection efficiency. While HSP90ab1 facilitates CME, MYL3 promotes macropinocytosis, both of which are critical for viral internalization, but through distinct endocytic mechanisms.

NNV likely utilizes multiple receptors to increase its host range and infection efficiency. The diversity of receptors ensures that the virus can infect a wide variety of host species. By employing HSP90ab1, HSC70, and MYL3, NNV can exploit different cellular pathways for entry, making it more adaptable to various host environments.

Regarding the identification of a key entry receptor, we agree this is a critical unresolved question. While HSP90ab1/HSC70 appear essential for CME-mediated entry, our data suggest MYL3 plays a distinct role in macropinocytic uptake. To systematically evaluate receptor hierarchy, we initially proposed comparative knockout studies targeting these candidate genes. However, we must acknowledge that current technical limitations in marine fish models – particularly the extended generation time for stable knockout cell lines and challenges in maintaining viable cell cultures post-editing – have delayed these experiments. Nevertheless, we are actively exploring strategies to overcome these obstacles and will continue to refine our approach to address these questions in future research.

(3) In vivo knockout of MYL3 using CRISPR-Cas9 should be conducted to verify whether the absence of MYL3 really inhibits NNV infection. Although it might be difficult to do it in marine medaka as stated by the authors, the introduction of zebrafish is highly recommended, since it has already been reported that zebrafish could serve as a vertebrate model to study NNV (doi: 10.3389/fimmu.2022.863096).

As noted in our manuscript from line 374 to 384, marine medaka is a relatively new model for studying viral infections and is not yet optimized for CRISPR-Cas9-mediated gene knockout. The technical challenges related to precise embryo microinjection and off-target effects using CRISPR-Cas9 in marine medaka complicate the establishment of knockout lines. These limitations, including the time required for multiple breeding generations and molecular screening, currently make this approach difficult to implement.

We fully agree with your suggestion to consider zebrafish as an alternative model. Zebrafish have been well-established as a vertebrate model for studying NNV, and their genetic tractability and well-developed CRISPR-Cas9 protocols provide a more accessible and efficient platform for generating knockout models. In our future studies, we plan to conduct CRISPR-Cas9-mediated knockout experiments targeting multiple NNV receptors in zebrafish. This will allow us to systematically evaluate the role of different receptors in NNV infection and elucidate their potential interactions. The findings from these studies will be included in a future publication, which will provide a more comprehensive understanding of the molecular mechanisms underlying NNV infection in vertebrate models.

(4) The results shown in Figure 6 are not enough to support the conclusion that "RGNNV triggers macropinocytosis mediated by MmMYL3". Additional electron microscopy of macropinosomes (sizes, morphological characteristics, etc.) will be more direct evidence.

Previous study has reported that dragon grouper nervous necrosis virus (DGNNV) enters SSN-1 cells primarily through micropinocytosis and macropinocytosis pathways. Electron microscopy observations revealed several kinds of membrane ruffling and large disproportionate macropinosomes were observed in DGNNV infected cells, indicating NNV infection could triggers micropinocytosis (Liu et al., 2005). In our study, the data from inhibitor treatments, co-localization of MmMYL3 with RGNNV CP, and dextran uptake assays also provide compelling evidence for the involvement of macropinocytosis in RGNNV entry via MmMYL3. These methods are well-established in the literature and have been used extensively to study viral entry pathways (Lingemann et al., 2019). Specifically, the dextran uptake assay has been widely utilized as a marker for macropinocytosis and has provided clear evidence of RGNNV internalization via this pathway. The use of macropinocytosis inhibitors, such as EIPA and Rottlerin, significantly reduced RGNNV entry, further supporting our conclusion. Nonetheless, we acknowledge the potential value of additional electron microscopy studies and will consider this approach in our future research.

References

Liu W, Hsu CH, Hong YR, Wu SC, Wang CH, Wu YM, Chao CB, Lin CS. 2005. Early endocytosis pathways in SSN-1 cells infected by dragon grouper nervous necrosis virus, J Gen Virol.

Lingemann M, McCarty T, Liu X, Buchholz UJ, Surman S, Martin SE, Collins PL, Munir S. 2019. The alpha-1 subunit of the Na+,K+-ATPase (ATP1A1) is required for macropinocytic entry of respiratory syncytial virus (RSV) in human respiratory epithelial cells, PLoS Pathogens.

(5) MYL3 is "predominantly found in muscle tissues, particularly the heart and skeletal muscles". However, NNV is a virus that mainly causes necrosis of nervous tissues (brain and retina). If MYL3 really acts as a receptor for NNV, how does it balance this difference so that nervous tissues, rather than muscle tissues, have the highest viral titers?

While MYL3 is highly expressed in cardiac and skeletal muscles, studies have shown that MYL3, like other myosin light chains, can also be present in non-muscle tissues. Additionally, proteins involved in viral entry do not always need to be the most highly expressed in the final target tissue, as long as they facilitate the initial infection process. For instance, rabies virus is a rhabdovirus which exhibits a marked neuronotropism in infected animals. Transferrin receptor protein 1 can serve as a receptor for rabies virus through CME pathway, but TfR1 expressed most abundantly in liver tissue not nervous system (Wang et al., 2023).

Viral tropism is often determined not only by the presence of an entry receptor but also by co-receptors, cellular factors, and post-entry mechanisms. While MYL3 may act as a receptor for NNV, other factors, such as cell-specific proteases, signaling molecules, and intracellular trafficking pathways, likely contribute to NNV’s preferential replication in the brain and retina.

Reference

Wang Xinxin, Wen Z, Cao H, Luo J, Shuai L, Wang C, Ge J, Wang Xijun, Bu Z, Wang J. 2023. Transferrin Receptor Protein 1 Is an Entry Factor for Rabies Virus. J Virol 97. doi:10.1128/jvi.01612-22

Reviewer #2 (Public review):

Summary:

The manuscript offers an important contribution to the field of virology, especially concerning NNV entry mechanisms. The major strength of the study lies in the identification of MmMYL3 as a functional receptor for RGNNV and its role in macropinocytosis, mediated by the IGF1R-Rac1/Cdc42 signaling axis. This represents a significant advance in understanding NNV entry mechanisms beyond previously known receptors such as HSP90ab1 and HSC70. The data, supported by comprehensive in vitro and in vivo experiments, strongly justify the authors' claims about MYL3's role in NNV infection in marine medaka.

Strengths:

(1) The identification of MmMYL3 as a functional receptor for RGNNV is a significant contribution to the field. The study fills a crucial gap in understanding the molecular mechanisms governing NNV entry into host cells.

(2) The work highlights the involvement of IGF1R in macropinocytosis-mediated NNV entry and downstream Rac1/Cdc42 activation, thus providing a thorough mechanistic understanding of NNV internalization process. This could pave the way for further exploration of antiviral targets.

Thanks for your review.

Reviewer #3 (Public review):

Summary:

The manuscript presents a detailed study on the role of MmMYL3 in the viral entry of NNV, focusing on its function as a receptor that mediates viral internalization through the macropinocytosis pathway. The use of both in vitro assays (e.g., Co-IP, SPR, and GST pull-down) and in vivo experiments (such as infection assays in marine medaka) adds robustness to the evidence for MmMYL3 as a novel receptor for RGNNV. The findings have important implications for understanding NNV infection mechanisms, which could pave the way for new antiviral strategies in aquaculture.

Strengths:

The authors show that MmMYL3 directly binds the viral capsid protein, facilitates NNV entry via the IGF1R-Rac1/Cdc42 pathway, and can render otherwise resistant cells susceptible to infection. This multifaceted approach effectively demonstrates the central role of MmMYL3 in NNV entry.

Thanks for your review.

Recommendations for the authors:

Reviewer #1 (Recommendations for the authors):

(1) Line94: SPR analysis? The full name should be provided when it first shows.

We have defined SPR when it first appears at line 97 in the revised manuscript.

(2) Moreover, is it too many for a manuscript to have a total of nine figures in the main text? Some of them might be moved to the supplementary file.

We have merged the previous Fig 4 and Fig 5 and combined Fig 8 and Fig 9, reducing the number of figures to seven. For the specific details of the figure adjustments, please refer to the corresponding figure legends.

Reviewer #2 (Recommendations for the authors):

(1) Expand on the potential therapeutic implications of targeting MYL3 or the IGF1R pathway in aquaculture settings. Including a discussion of how inhibitors could be developed or tested in future research would give practical context to the findings.

Thanks for your valuable suggestion to expand on the therapeutic implications of targeting MYL3 and the IGF1R pathway in aquaculture. In response, we have discussed potential strategies for developing inhibitors, such as small molecules, peptides, or monoclonal antibodies targeting MYL3 to block its interaction with the viral capsid, and IGF1R inhibitors to prevent macropinocytosis-mediated viral entry. We propose using virtual screening platforms to identify these inhibitors, followed by in vivo testing in aquaculture models. Additionally, combining MYL3 and IGF1R inhibitors could provide a synergistic approach to enhance antiviral efficacy. The relevant discussions have been supplemented at lines 358 to 368 in the revised manuscript.

(2) It is recommended to include the data regarding the lack of interaction between the CMNV CP and MmMYL3 as a supplementary figure.

We have included supplementary data demonstrating that CMNV CP does not interact with MmMYL3, highlighting the specificity of MYL3 for RGNNV. For detailed information, please refer to Fig. S4.

Reviewer #3 (Recommendations for the authors):

Consider discussing the broader implications of these findings, particularly whether MYL3 might serve as a receptor for other viruses.

We appreciate this suggestion. It is important to note that viral receptors typically exhibit specificity for specific types of viruses. Receptor recognition is typically highly specific, and the binding interactions between viral proteins and host receptors often depend on the structural compatibility between the viral capsid/ viral envelope and the host receptor. Our study demonstrates that MYL3 serves as a receptor for NNV based on its direct interaction with the NNV capsid protein (CP). However, when we tested whether MYL3 interacts with CMNV (Covert Mortality Nodavirus), which is phylogenetically closer to NNV, we found that CMNV CP does not bind to MYL3. Given the lack of interaction between MYL3 and CMNV, it is unlikely that MYL3 serves as a receptor for more distantly related viruses. Since MYL3 does not interact with CMNV, a virus more closely related to NNV, it is less likely to function as a receptor for viruses that are more distantly related to NNV. The relevant discussions have been supplemented at lines 306 to 310 in the revised manuscript.

Author response:

The following is the authors’ response to the original reviews

Public Reviews:

Reviewer #1 (Public review):

Summary:

Diarrheal diseases represent an important public health issue. Among the many pathogens that contribute to this problem, Salmonella enterica serovar Typhimurium is an important one. Due to the rise in antimicrobial resistance and the problems associated with widespread antibiotic use, the discovery and development of new strategies to combat bacterial infections is urgently needed. The microbiome field is constantly providing us with various health-related properties elicited by the commensals that inhabit their mammalian hosts. Harnessing the potential of these commensals for knowledge about host-microbe interactions as well as useful properties with therapeutic implications will likely remain a fruitful field for decades to come. In this manuscript, Wang et al use various methods, encompassing classic microbiology, genomics, chemical biology, and immunology, to identify a potent probiotic strain that protects nematode and murine hosts from S. enterica infection. Additionally, authors identify gut metabolites that are correlated with protection, and show that a single metabolite can recapitulate the effects of probiotic administration.

We gratefully appreciate your positive and professional comments.

Strengths:

The utilization of varied methods by the authors, together with the impressive amount of data generated, to support the claims and conclusions made in the manuscript is a major strength of the work. Also, the ability to move beyond simple identification of the active probiotic, also identifying compounds that are at least partially responsible for the protective effects, is commendable.

We gratefully appreciate your positive and professional comments.

Weaknesses:

Although there is a sizeable amount of data reported in the manuscript, there seems to be a chronic issue of lack of details of how some experiments were performed. This is particularly true in the figure legends, which for the most part lack enough details to allow comprehension without constant return to the text. Additionally, 2 figures are missing. Figure 6 is a repetition of Figure 5, and Figure S4 is an identical replicate of Figure S3.

We gratefully appreciate your professional comments. Additional details to perform the related experiments had been added in Materials and methods section and figure legends (e.g., see Line 478-487, Line 996-1001, Line 1010-1012, Line 1019-1020, Line 1031-1033, Line 1041-1042, Line 1051-1053, Line 1082-1083, Line 1087-1088, Line 1093-1094, Line 1105-1107, Line 1113-1114,). Furthermore, we sincerely apologize for the mistakes and the inconvenience in the evaluating process of your review, and we have added the correct Figure 6 (see Line 1043-1053) and Figure S4 (see Line 1084-1088). We will carefully and thoroughly check the whole submitted manuscript along with supplementary information to avoid such mistakes in the future.

Reviewer #2 (Public review):

In this work, the investigators isolated one Lacticaseibacillus rhamnosus strain (P118), and determined this strain worked well against Salmonella Typhimurium infection. Then, further studies were performed to identify the mechanism of bacterial resistance, and a list of confirmatory assays was carried out to test the hypothesis.

We gratefully appreciate your positive and professional comments.

Strengths:

The authors provided details regarding all assays performed in this work, and this reviewer trusted that the conclusion in this manuscript is solid. I appreciate the efforts of the authors to perform different types of in vivo and in vitro studies to confirm the hypothesis.

We gratefully appreciate your positive and professional comments.

Weaknesses:

I have two main questions about this work.

(1) The authors provided the below information about the sources from which Lacticaseibacillus rhamnosus was isolated. More details are needed. What are the criteria to choose these samples? Where did these samples originate from? How many strains of bacteria were obtained from which types of samples?

Sorry for the ambiguous and limited information, more details had been added in Materials and methods section (see Line 480-496). We gratefully appreciate your professional comments.

Lines 486-488: Lactic acid bacteria (LAB) and Enterococcus strains were isolated from the fermented yoghurts collected from families in multiple cities of China and the intestinal contents from healthy piglets without pathogen infection and diarrhoea by our lab.

Sorry for the ambiguous and limited information, we had carefully revised this section and more details had been added in Materials and methods section (see Line 480-496). We gratefully appreciate your professional comments.

Lines 129-133: A total of 290 bacterial strains were isolated and identified from 32 samples of the fermented yoghurt and piglet rectal contents collected across diverse regions within China using MRS and BHI medium, which consist s of 63 Streptococcus strains, 158 Lactobacillus/ Lacticaseibacillus Limosilactobacillus strains, and 69 Enterococcus strains.

Sorry for the ambiguous information, we had carefully revised this section and more details had been added in this section (see Line 129-132). We gratefully appreciate your professional comments.

(2) As a probiotic, Lacticaseibacillus rhamnosus has been widely studied. In fact, there are many commercially available products, and Lacticaseibacillus rhamnosus is the main bacteria in these products. There are also ATCC type strains such as 53103.

I am sure the authors are also interested to know whether P118 is better as a probiotic candidate than other commercially available strains. Also, would the mechanism described for P118 apply to other Lacticaseibacillus rhamnosus strains?

It would be ideal if the authors could include one or two Lacticaseibacillus rhamnosus which are currently commercially used, or from the ATCC. Then, the authors can compare the efficacy and antibacterial mechanisms of their P118 with other strains. This would open the windows for future work.

We gratefully appreciate your professional comments and valuable suggestions. We deeply agree that it will be better and make more sense to include well-known/recognized/commercial probiotics as a positive control to comprehensively evaluate the isolated P118 strain as a probiotic candidate, particularly in comparison to other well-established probiotics, and also help assess whether the mechanisms described for P118 are applicable to other L. rhamnosus strains or lactic acid bacteria in general. Those issues will be fully taken into consideration and included in the further works.

Recommendations for the authors:

Reviewer #1 (Recommendations for the authors):

(1) Line 28 - The sentence "with great probiotic properties" suggests that this strain was already known to have probiotic properties. Is that the case?

We gratefully appreciate your professional comments. This sentence "with great probiotic properties" in this context was intended as a summary of our findings, emphasizing that L. rhamnosus P118 exerts great probiotic properties after evaluating by traditional and C. elegans-infection screening strategies. We had revised this sentence (see Line27-30).

(2) Line 30 - What exactly do authors mean by "traditional"? They should add a bit more information here as to what these methods would be.

We gratefully appreciate your professional comments. By "traditional" methods, we refer to time-consuming and labor-intensive strategies for screening probiotic candidates with heavy works, which include bacterial isolation, culturing, phenotypic characterization, randomized controlled trials, and various in vitro and in vivo tests to assess probiotic properties (Sun et al., 2022). We had indicated this strategy in Line 91-94.

Reference:

Sun Y, Li HC, Zheng L, Li JZ, Hong Y, Liang PF, Kwok LY, Zuo YC, Zhang WY, Zhang HP. Iprobiotics: A machine learning platform for rapid identification of probiotic properties from whole-genome primary sequences. Briefings in Bioinformatics 2022;23.

(3) Line 37 - I believe "harmful microbes" is not the correct term here. I suggest authors use "potentially harmful".

Done as requested (see Line 36, 209, 212, 217, 381). We gratefully appreciate your valuable suggestions.

(4) Line 75 - What exactly do authors mean by "irregular dietary consumption"?

"irregular dietary consumption" means "irregular dietary habits" or " eating irregularly " or "abnormal eating behaviors". We had change to "irregular dietary habits" (see Line 76). We gratefully appreciate your professional comments.

(5) Line 85 - What exactly do authors mean by "without residues in raw food products"?

Here, "without residues in raw food products" means that probiotics barely remain in food animal products (e.g., meat, eggs, dairy) after dietary with probiotics in feeds by livestock and poultry. We gratefully appreciate your professional comments.

(6) Line 86 - Please, give a specific example of yeast.

Done as requested (see Line 85-86), “yeast (e.g., Saccharomyces boulardii, S. cerevisiae)”. We gratefully appreciate your valuable suggestions.

(7) Line 112 - Lactobacillus reuteri should be written out, since this is the first time the species name appears in the main text.

Done as requested (see Line 112). We gratefully appreciate your valuable suggestions.

(8) Lines 115-118 - Please, rewrite for clarity.

Done as requested (see Line 115-118). We gratefully appreciate your valuable suggestions.

(9) Line 118 -Lacticaseibacillus rhamnosus should be written out, since this is the first time the species name appears in the main text.

Done as requested (see Line 118). We gratefully appreciate your valuable suggestions.

(10) Line 119 - Throughout the text authors make it seem like strain P118 was previously known. Is that the case? If yes, how was it isolated again? This should be briefly mentioned in the introduction.

Sorry for the misunderstand caused by this statement, P118 strain was isolated and its probiotic properties were evaluated by our lab, not previously known, and we have revised this sentence (see Line 118-120). We gratefully appreciate your professional comments.

(11) Line 131 - How were strains identified?

Matrix-Assisted Laser Desorption Ionization-Time of Flight Mass Spectrometry (MALDI-TOF MS) method was employed to identify of bacterial species (He et al., 2022). This information was indicated in Materials and methods section (see Line 485-489). We gratefully appreciate your professional comments.

Reference

He D, Zeng W, Wang Y, Xing Y, Xiong K, Su N, Zhang C, Lu Y, Xing X. Isolation and characterization of novel peptides from fermented products of lactobacillus for ulcerative colitis prevention and treatment. Food Science and Human Wellness 2022;11:1464-74.

(12) Figure 1 - Legend needs a lot more info. Where are legends to panels PQ? Also, some of the text is too small to read.

Sorry for the limited info, we have revised Figure 1 legend and added more info (see Line 1000-1019), and we also provide vector graphic of Figure 1. We gratefully appreciate your professional comments.

(13) Line 136 - All strains were screened and 27 strains were positive, right?

Yes, all strains were screened and 27 strains were positive. We gratefully appreciate your professional comments.

(14) Figure 2 - What do authors mean by "spleen index" and "liver index"? This should be described in more detail. Also, p values for 'a', 'b', 'ab' should be given.

The organ index (spleen index, liver index) were calculated according to the formula: organ index = organ weight (g) / body weight (g) *1000, indicating in Materials and methods section (see Line 587-588). “Different lowercase letters ('a', 'b') indicate a significant difference (P < 0.05)” had been added in Line 1020-1029. We gratefully appreciate your professional comments.

(15) Line 212-214 - Again, I suggest authors use "potentially harmful" and "potentially beneficial".

Done as requested (see Line 36, 210, 213, 218, 383). We gratefully appreciate your valuable suggestions.

(16) Figure 3 - Which groups were tested in panels CD? Is this based on color? Legends should be restated in panels or clearly marked in the legend.

Sorry for this mistake, we have revised and added group info in Figure 3C-D (see Line 1013-1020). We gratefully appreciate your professional comments.

(17) Figure 4 - Lacks details.

Sorry for the mistakes, we have revised and added group info in Figure 4D-E and legend (see Line 1031-1037). We gratefully appreciate your professional comments.

(18) Figure 6 - This is a repetition of Figure 5.

Sorry for the mistakes, we have added the correct Figure 6 (see Line 1060-1070). We gratefully appreciate your professional comments.

(19) Lines 329-330 - C. elegans does not "mimic" animal intestinal physiology.

Sorry for the mistakes, we have revised this statement (see Line 139-142, 324-325). We gratefully appreciate your professional comments.

(20) Lines 358 and 418 - What do authors mean by "metabolic dysfunction" and "metabolic disorder"? I assume they mean changes in fecal metabolites. However, these are terms that may have different interpretations in the field of human metabolism. Therefore, I would suggest that the authors specify that they mean changes in fecal metabolite profiles when using these terms.

Sorry for the mistakes caused by this statement, we have revised this statement in the revised version (see Line 34-35, 122, 353-354, 413). We gratefully appreciate your professional comments.

(21) Line 475 - What do authors mean by "superficial effects"?

Sorry for the mistakes, we had change to “beneficial/protective effects” (see Line 469, Line 1074). We gratefully appreciate your professional comments.

(22) Line 486 - Were all yogurts artisanal? Where were piglets from? How were samples collected? Feces, rectal swabs? Does the ethics statement at the end of the manuscript also cover work with piglets?

Yes, all yogurts were artisanal. The 6 healthy piglet rectal content samples without pathogen infection and diarrhea were from a pig farm of Zhejiang province. Yes, the ethics statement at the end of the manuscript also cover the work with piglets.

(23) Line 490 - Which MALDI platform was used? The database used can have important implications for strain identification. What was the confidence of ID? This should be included.

Matrix-Assisted Laser Desorption Ionization-Time of Flight Mass Spectrometry (MALDI-TOF MS, Bruker Daltonik GmbH, Bremen, Germany) was employed to identify of bacterial species with a confidence level > 90%. This information was indicated in Materials and methods section (see Line 487-489). We gratefully appreciate your professional comments.

(24) Line 501 - Is this a widely used method to characterize probiotics? Please, add a reference.

Done as requested (see Line 498). Many probiotics or microbes can produce milk clotting enzyme to clot milk. It's an important measurement in the dairy industry, especially when making cheese (Zhang et al., 2023; Arbita et al., 2024; Shieh et al., 2009). The milk-clotting activity analysis is usually used for evaluating the potential ability of candidate probiotic isolates in clotting milk into cheeses.

Reference:

Zhang Y, Wang J, He J, Liu X, Sun J, Song X, Wu Y. Characteristics and application in cheese making of newly isolated milk-clotting enzyme from bacillus megaterium ly114. Food Res Int 2023;172:113202.

Arbita AA, Zhao J. Milk clotting enzymes from marine resources and their role in cheese-making: A mini review. Crit Rev Food Sci Nutr. 2024;64(27):10036-10047.

Chwen-Jen Shieh, Lan-Anh Phan Thi, Ing-Lung Shih. Milk-clotting enzymes produced by culture of Bacillus subtilis natto. Biochemical Engineering Journal. 2009;1(43): 85-91.

(25) Line 713 - How were fecal metabolites extracted?

Sorry for the missed information, the fecal metabolites extracted information had been added we have revised and added Materials and methods section (see Line 705-706). We gratefully appreciate your professional comments.

(26) Figure 7 - Please correct "macrophages".

Done as requested (see Figure 7, Line 1072). We gratefully appreciate your valuable suggestions.

(27) Table 1 - Should read "number of strains", not size.

Done as requested (see Line1084). We gratefully appreciate your valuable suggestions.

(28) Figure S1B - Is this data for P118?

Sorry for the mistakes, we have revised Figure S1 legend (see Line 1086-1088). We gratefully appreciate your professional comments.

(29) Figure S3 - Legends C, S, PS, P are not specified.

Sorry for the missed information, we have revised and added group info in Figure S3 legend (see Line 1095-1101). We gratefully appreciate your professional comments.

(30) Figure S3B - What is the "clinical symptom score"? How was this determined?

Sorry for the lack information, and the detailed information had been added in Materials and methods section (see Line 659-661, Table S7). We gratefully appreciate your professional comments.

(31) Figure S4 - This is an identical copy of Figure S3.

Sorry for the mistakes, we have added the correct Figure S4 (see Line 1103-1106). We gratefully appreciate your professional comments.

(32) Figure S5 - Legend lacks details.

Sorry for the missed information, we have revised and added group info in Figure S5 legend (see Line 1107-1112). We gratefully appreciate your professional comments.

(33) Figure S8 - What is "GM"? Since it inhibits growth to a greater extent than the highest metabolite concentration used, I imagine it must be an antibiotic (gentamycin?) as a positive control. This needs to be clearly stated.

Sorry for the missed information, GM: 100 μg/mL gentamicin (see Line 1134). We gratefully appreciate your professional comments.

(34) Figure S9 - Labels for panels are missing.

Sorry for the missed information, labels had been added (see Line 1135-1139). We gratefully appreciate your professional comments.

Reviewer #2 (Recommendations for the authors):

(1) This reviewer appreciates the efforts of the authors to provide the details related to this work. In the meantime, the manuscript shall be written in a way that is easy for the readers to follow.

We had tried our best to revise and make improve the whole manuscript to make it easy for the readers to follow (e.g., see Line 27-30, Line 115-120, Line 129-132, Line 480-496). We gratefully appreciate your valuable suggestions.

(2) For example, under the sections of Materials and Methods, there are 19 sub-titles. The authors could consider combining some sections, and/or citing other references for the standard procedures.

We gratefully appreciate your professional comments and valuable suggestions. Some sections had been combined according to the reviewer’s suggestions (see Line 497-530, Line 637-671).

(3) Another example: the figures have great resolution, but they are way too busy. Figures 1 and 2 have 14-18 panels. Figure 5 has 21 panels. Please consider separating into more figures, or condensing some panels.

We deeply agree with you that some submitted figures are way too busy, but it’s not easy to move some results into supplementary information sections, because all of them are essential for fully supporting our hypothesis and conclusions. Nonetheless, some panels had been combined or condensed according to the reviewer’s suggestions (see Line 1000-1020, Line 1052-1071). We gratefully appreciate your professional comments and valuable suggestions.

(4) Line 30: spell out "C." please.

Done as requested (see Line 31). We gratefully appreciate your valuable suggestions.

Author response:

The following is the authors’ response to the original reviews

eLife Assessment

This valuable work explores how synaptic activity encodes information during memory tasks. All reviewers agree that the quality of the work is high. Although experimental data do support the possibility that phospholipase diacylglycerol signaling and synaptotagmin 7 (Syt7) dynamically regulate the vesicle pool required for presynaptic release, concerns remain that the central finding of paired pulse depression at very short intervals was more likely caused by Ca²⁺ channel inactivation than pool depletion. Overall, this is a solid study with valuable findings, but the results warrant consideration of alternative interpretations.

We greatly appreciate invaluable and constructive comments from Editors and Reviewers. We also thank for their time and patience. We are pleased for our manuscript to have been assessed valuable and solid.

One of the most critical concerns was a possible involvement of Ca²⁺ channel inactivation in the strong paired pulse depression (PPD). Meanwhile, we have measured total (free plus buffered) calcium increments induced by each of first four APs in 40 Hz trains at axonal boutons of prelimbic layer 2/3 pyramidal cells. We found that first four Ca²⁺ increments were not different from one another, arguing against possible contribution of Ca²⁺ channel inactivation to PPD. Please see our reply to the 2nd issue in the Weakness section of Reviewer #3.

The second critical issue was on the definition of ‘vesicular probability’. Previously, vesicular probability (p_v) has been used with reference to the releasable vesicle pool which includes not only tightly docked vesicles but also reluctant vesicles. On the other hand, the meaning of p_v in the present study is the release probability of tightly docked vesicles. We clarified this point in our replies to the 1st issues in the Weakness sections of Reviewer #2 and Reviewer #3.

We below described our point-by-point replies to the Reviewers’ comments.

Public Reviews:

Reviewer #1 (Public review):

Shin et al. conduct extensive electrophysiological and behavioral experiments to study the mechanisms of short-term synaptic plasticity at excitatory synapses in layer 2/3 of the rat medial prefrontal cortex. The authors interestingly find that short-term facilitation is driven by progressive overfilling of the readily releasable pool, and that this process is mediated by phospholipase C/diacylglycerol signaling and synaptotagmin-7 (Syt7). Specifically, knockdown of Syt7 not only abolishes the refilling rate of vesicles with high fusion probability, but it also impairs the acquisition of trace fear memory. Overall, the authors offer novel insight to the field of synaptic plasticity through well-designed experiments that incorporate a range of techniques.

Reviewer #2 (Public review):

Summary:

Shin et al aim to identify in a very extensive piece of work a mechanism that contributes to dynamic regulation of synaptic output in the rat cortex at the second time scale. This mechanism is related to a new powerful model is well versed to test if the pool of SV ready for fusion is dynamically scaled to adjust supply demand aspects. The methods applied are state-of-the-art and both address quantitative aspects with high signal to noise. In addition, the authors examine both excitatory output onto glutamatergic and GABAergic neurons, which provides important information on how general the observed signals are in neural networks, The results are compellingly clear and show that pool regulation may be predominantly responsible. Their results suggests that a regulation of release probability, the alternative contender for regulation, is unlikely to be involved in the observed short term plasticity behavior (but see below). Besides providing a clear analysis pof the underlying physiology, they test two molecular contenders for the observed mechanism by showing that loss of Synaptotagmin7 function and the role of the Ca dependent phospholipase activity seems critical for the short term plasticity behavior. The authors go on to test the in vivo role of the mechanism by modulating Syt7 function and examining working memory tasks as well as overall changes in network activity using immediate early gene activity. Finally, they model their data, providing strong support for their interpretation of TS pool occupancy regulation.

Strengths:

This is a very thorough study, addressing the research question from many different angles and the experimental execution is superb. The impact of the work is high, as it applies recent models of short term plasticity behavior to in vivo circuits further providing insights how synapses provide dynamic control to enable working memory related behavior through nonpermanent changes in synaptic output.

Weaknesses:

(1) While this work is carefully examined and the results are presented and discussed in a detailed manner, the reviewer is still not fully convinced that regulation of release provability is not a putative contributor to the observed behavior. No additional work is needed but in the moment I am not convinced that changes in release probability are not in play. One solution may be to extend the discussion of changes in release probability as an alternative.

Quantal content (m) depends on n * p_v, where n = RRP size and p_v =vesicular release probability. The value for p_v critically depends on the definition of RRP size. Recent studies revealed that docked vesicles have differential priming states: loosely or tightly docked state (LS or TS, respectively). Because the RRP size estimated by hypertonic solution or long presynaptic depolarization is larger than that by back extrapolation of a cumulative EPSC plot (Moulder & Mennerick, 2005; Sakaba, 2006) in glutamatergic synapses, the former RRP (denoted as RRP_hyper) may encompass not only AP-evoked fast-releasing vesicles (TS vesicle) but also reluctant vesicles (LS vesicles). Because we measured p_v based on AP-evoked EPSCs such as strong paired pulse depression (PPD) and associated failure rates, p_v in the present study denotes vesicular fusion probability of TS vesicles, not that of LS plus TS vesicles.

Recent studies suggest that release sites are not fully occupied by TS vesicles in the baseline (Miki et al., 2016; Pulido and Marty, 2018; Malagon et al., 2020; Lin et al., 2022). Instead, the occupancy (p_occ) by TS vesicles is subject to dynamic regulation by reversible rate constants (denoted by k₁ and b₁, respectively). The number of TS vesicles (n) can be factored into the number of release sites (N) and p_occ, among which N is a fixed parameter but p_occ depends on k₁/(k₁+b₁) under the framework of the simple refilling model (see Methods). Because these refilling rate constants are regulated by Ca²⁺ (Hosoi, et al., 2008), p_occ is not a fixed parameter. Therefore, release probability should be re-defined as p_occ * p_v. Given that N is fixed, the increase in release probability is a major player in STF. Our study asserts that STF by 2.3 times can be attributed to an increase in p_occ rather than p_v, because p_v is close to unity (Fig. S8). Moreover, strong PPD was observed not only in the baseline but also at the early and in the middle of a train (Fig. 2 and 7) and during the recovery phase (Fig. 3), arguing against a gradual increase in p_v of reluctant vesicles.

We imagine that the Reviewer meant vesicular release or fusion probability (p_v) by ‘release probability’. If so, p_v (of TS vesicles) cannot be a major player in STF, because the baseline p_v is already higher than 0.8 even if it is most parsimoniously estimated (Fig. 2). Moreover, considering very high refilling rate (23/s), the high double failure rate cannot be explained without assuming that p_v is close to unity (Fig. S8).

Conventional models for facilitation assume a post-AP residual Ca²⁺-dependent step increase in p_v of RRP (Dittman et al., 2000) or reluctant vesicles (Turecek et al., 2016). Given that p_v of TS vesicles is close to one, an increase in p_v of TS vesicles cannot account for facilitation. The possibility for activity-dependent increase in fusion probability of LS vesicles (denoted as p_v,LS) should be considered in two ways depending on whether LS and TS vesicles reside in distinct pools or in the same pool. Notably, strong PPD at short ISI implies that p_v,LS is near zero at the resting state. Whereas LS vesicles do not contribute to baseline transmission, short-term facilitation (STF) may be mediated by cumulative increase in p_{v v,LS} that reside in a distinct pool. Because the increase in p_v,LS during facilitation recruits new release sites (increase in N), the variance of EPSCs should become larger as stimulation frequency increases, resulting in upward deviation from a parabola in the V-M plane, as shown in recent studies (Valera et al., 2012; Kobbersmed et al., 2020). This prediction is not compatible with our results of V-M analysis (Fig. 3), showing that EPSCs during STF fell on the same parabola regardless of stimulation frequencies. Therefore, it is unlikely that an increase in fusion probability of reluctant vesicles residing in a distinct release pool mediates STF in the present study.

For the latter case, in which LS and TS vesicles occupy in the same release sites, it is hard to distinguish a step increase in fusion probability of LS vesicles from a conversion of LS vesicles to TS. Nevertheless, our results do not support the possibility for gradual increase in p_v,LS that occurs in parallel with STF. Strong PPD, indicative of high p_v, was consistently found not only in the baseline (Fig. 2 and Fig. S6) but also during post-tetanic augmentation phase (Fig. 3D) and even during the early development of facilitation (Fig. 2D-E and Fig. 7), arguing against gradual increase in p_v,LS. One may argue that STF may be mediated by a drastic step increase of p_v,LS from zero to one, but it is not distinguishable from conversion of LS to TS vesicles.

To address the reviewer’s concern, we incorporated these perspectives into Discussion and further clarified the reasoning behind our conclusions.

References

Moulder KL, Mennerick S (2005) Reluctant vesicles contribute to the total readily releasable pool in glutamatergic hippocampal neurons. J Neurosci 25:3842–3850.

Sakaba, T (2006) Roles of the fast-releasing and the slowly releasing vesicles in synaptic transmission at the calyx of Held. J Neurosci 26(22): 5863-5871.

Please note that papers cited in the manuscript are not repeated here.

(2) Fig 3 I am confused about the interpretation of the Mean Variance analysis outcome. Since the data points follow the curve during induction of short term plasticity, aren't these suggesting that release probability and not the pool size increases? Related, to measure the absolute release probability and failure rate using the optogenetic stimulation technique is not trivial as the experimental paradigm bias the experiment to a given output strength, and therefore a change in release probability cannot be excluded.

Under the recent definition of release probability, it can be factored into p_v and p_occ, which are fusion probability of TS vesicles and the occupancy of release sites by TS vesicles, respectively. With this regard, our interpretation of the Variance-Mean results is consistent with conventional one: different data points along a parabola represent a change in release probability (= p_occ x p_v). Our novel finding is that the increase in release probability should be attributed to an increase in p_occ, not to that in p_v.

(3) Fig4B interprets the phorbol ester stimulation to be the result of pool overfilling, however, phorbol ester stimulation has also been shown to increase release probability without changing the size of the readily releasable pool. The high frequency of stimulation may occlude an increased paired pulse depression in presence of OAG, which others have interpreted in mammalian synapses as an increase in release probability.

To our experience in the calyx of Held synapses, OAG, a DAG analogue, increased the fast releasing vesicle pool (FRP) size (Lee JS et al., 2013), consistent with our interpretation (pool overfilling). Once the release sites are overfilled in the presence of OAG, it is expected that the maximal STF (ratio of facilitated to baseline EPSCs) becomes lower as long as the number of release sites (N) are limited. As aforementioned, the baseline p_v is already close to one, and thus it cannot be further increased by OAG. Instead, the baseline p_occ seems to be increased by OAG.

Reference

Lee JS, et al., Superpriming of synaptic vesicles after their recruitment to the readily releasable pool. Proc Natl Acad Sci U S A, 2013. 110(37): 15079-84.

(4) The literature on Syt7 function is still quite controversial. An observation in the literature that loss of Syt7 function in the fly synapse leads to an increase of release probability. Thus the observed changes in short term plasticity characteristics in the Syt7 KD experiments may contain a release probability component. Can the authors really exclude this possibility? Figure 5 shows for the Syt7 KD group a very prominent depression of the EPSC/IPSC with the second stimulus, particularly for the short interpulse intervals, usually a strong sign of increased release probability, as lack of pool refilling can unlikely explain the strong drop in synaptic output.

The reviewer raises an interesting point regarding the potential link between Syt7 KD and increased initial p_v, particularly in light of observations in Drosophila synapses (Guan et al., 2020; Fujii et al., 2021), in which Syt7 mutants exhibited elevated initial p_v. However, it is important to note that these findings markedly differ from those in mammalian systems, where the role of Syt7 in regulating initial p_v has been extensively studied. In rodents, consistent evidence indicates that Syt7 does not significantly affect initial p_v, as demonstrated in several studies (Jackman et al., 2016; Chen et al., 2017; Turecek and Regehr, 2018). Furthermore, in our study of excitatory synapses in the mPFC layer 2/3, we observed an initial p_v already near its maximal level, approaching a value of 1. Consequently, it is unlikely that the loss of Syt7 could further elevate the initial p_v. Instead, such effects are more plausibly explained by alternative mechanisms, such as alterations in vesicle replenishment dynamics, rather than a direct influence on p_v.

References

Chen, C., et al., Triple Function of Synaptotagmin 7 Ensures Efficiency of High-Frequency Transmission at Central GABAergic Synapses. Cell Rep, 2017. 21(8): 2082-2089.

Fujii, T., et al., Synaptotagmin 7 switches short-term synaptic plasticity from depression to facilitation by suppressing synaptic transmission. Scientific reports, 2021. 11(1): 4059.

Guan, Z., et al., Drosophila Synaptotagmin 7 negatively regulates synaptic vesicle release and replenishment in a dosage-dependent manner. Elife, 2020. 9: e55443.

Jackman, S.L., et al., The calcium sensor synaptotagmin 7 is required for synaptic facilitation. Nature, 2016. 529(7584): 88-91.

Turecek, J. and W.G. Regehr, Synaptotagmin 7 mediates both facilitation and asynchronous release at granule cell synapses. Journal of Neuroscience, 2018. 38(13): 3240-3251.

Reviewer #3 (Public review):

Summary:

The report by Shin, Lee, Kim, and Lee entitled "Progressive overfilling of readily releasable pool underlies short-term facilitation at recurrent excitatory synapses in layer 2/3 of the rat prefrontal cortex" describes electrophysiological experiments of short-term synaptic plasticity during repetitive presynaptic stimulation at synapses between layer 2/3 pyramidal neurons and nearby target neurons. Manipulations include pharmacological inhibition of PLC and actin polymerization, activation of DAG receptors, and shRNA knockdown of Syt7. The results are interpreted as support for the hypothesis that synaptic vesicle release sites are vacant most of the time at resting synapses (i.e., p_occ is low) and that facilitation (and augmentation) components of short-term enhancement are caused by an increase in occupancy, presumably because of acceleration of the transition from not-occupied to occupied. The report additionally describes behavioural experiments where trace fear conditioning is degraded by knocking down syt7 in the same synapses.

Strengths:

The strength of the study is in the new information about short-term plasticity at local synapses in layer 2/3, and the major disruption of a memory task after eliminating short-term enhancement at only 15% of excitatory synapses in a single layer of a small brain region. The local synapses in layer 2/3 were previously difficult to study, but the authors have overcome a number of challenges by combining channel rhodopsins with in vitro electroporation, which is an impressive technical advance.

Weaknesses:

(1) The question of whether or not short-term enhancement causes an increase in p_occ (i.e., "readily releasable pool overfilling") is important because it cuts to the heart of the ongoing debate about how to model short term synaptic plasticity in general. However, my opinion is that, in their current form, the results do not constitute strong support for an increase in p_occ, even though this is presented as the main conclusion. Instead, there are at least two alternative explanations for the results that both seem more likely. Neither alternative is acknowledged in the present version of the report.

The evidence presented to support overfilling is essentially two-fold. The first is strong paired pulse depression of synaptic strength when the interval between action potentials is 20 or 25 ms, but not when the interval is 50 ms. Subsequent stimuli at frequencies between 5 and 40 Hz then drive enhancement. The second is the observation that a slow component of recovery from depression after trains of action potentials is unveiled after eliminating enhancement by knocking down syt7. Of the two, the second is predicted by essentially all models where enhancement mechanisms operate independently of release site depletion - i.e., transient increases in p_occ, p_v, or even N - so isn't the sort of support that would distinguish the hypothesis from alternatives (Garcia-Perez and Wesseling, 2008, https://doi.org/10.1152/jn.01348.2007).

The apparent discrepancy in interpretation of post-tetanic augmentation between the present and previous papers [Sevens Wesseling (1999), Garcia-Perez and Wesseling (2008)] is an important issue that should be clarified. We noted that different meanings of ‘vesicular release probability’ in these papers are responsible for the discrepancy. We added an explanation to Discussion on the difference in the meaning of ‘vesicular release probability’ between the present study and previous studies [Sevens Wesseling (1999), Garcia-Perez and Wesseling (2008)]. In summary, the p_v in the present study was used for vesicular release probability of TS vesicles, while previous studies used it as vesicular release probability of vesicles in the RRP, which include LS and TS vesicles. Accordingly, p_occ in the present study is the occupancy of release sites by TS vesicles.

Not only double failure rate but also other failure rates upon paired pulse stimulation were best fitted at p_v close to 1 (Fig. S8 and associated text). Moreover, strong PPD, indicating release of vesicles with high p_v, was observed not only at the beginning of a train but also in the middle of a 5 Hz train (Fig. 2D), during the augmentation phase after a 40 Hz train (Fig 3D), and in the recovery phase after three pulse bursts (Fig. 7). Given that p_v is close to 1 throughout the EPSC trains and that N does not increase during a train (Fig. 3), synaptic facilitation can be attained only by the increase in p_occ (occupancy of release sites by TS vesicles). In addition, it should be noted that Fig. 7 demonstrates strong PPD during the recovery phase after depletion of TS vesicles by three pulse bursts, indicating that recovered vesicles after depletion display high p_v too. Knock-down of Syt7 slowed the recovery of TS vesicles after depletion of TS vesicles, highlighting that Syt7 accelerates the recovery of TS vesicles following their depletion.

As addressed in our reply to the first issue raised by Reviewer #2 and the third issue raised by Reviewer #3, our results do not support possibilities for recruitment of new release sites (increase in N) having low p_v or for a gradual increase in p_v of reluctant vesicles during short-term facilitation.  

Following statement was added to Discussion in the revised manuscript

“Previous studies suggested that an increase in p_v is responsible for post-tetanic augmentation (Stevens and Wesseling, 1999; Garcia-Perez and Wesseling, 2008) by observing invariance of the RRP size after tetanic stimulation. In these studies, the RRP size was estimated by hypertonic sucrose solution or as the sum of EPSCs evoked 20 Hz/60 pulses train (denoted as ‘RRP_hyper’). Because reluctant vesicles (called LS vesicles) can be quickly converted to TS vesicles (16/s) and are released during a train (Lee et al., 2012), it is likely that the RRP size measured by these methods encompasses both LS and TS vesicles. In contrast, we assert high p_v based on the observation of strong PPD and failure rates upon paired stimulations at ISI of 20 ms (Fig. 2 and Fig. S8). Given that single AP-induced vesicular release occurs from TS vesicles but not from LS vesicles, p_v in the present study indicates the fusion probability of TS vesicles. From the same reasons, p_occ denotes the occupancy of release sites by TS vesicles. Note that our study does not provide direct clue whether release sites are occupied by LS vesicles that are not tapped by a single AP, although an increase in the LS vesicle number may accelerate the recovery of TS vesicles. As suggested in Neher (2024), even if the number of LS plus TS vesicles are kept constant, an increase in p_occ (occupancy by TS vesicles) would be interpreted as an increase in ‘vesicular release probability’ as in the previous studies (Stevens and Wesseling (1999); Garcia-Perez and Wesseling (2008)) as long as it was measured based on RRP_hyper.”

(2) Regarding the paired pulse depression: The authors ascribe this to depletion of a homogeneous population of release sites, all with similar p_v. However, the details fit better with the alternative hypothesis that the depression is instead caused by quickly reversing inactivation of Ca²⁺ channels near release sites, as proposed by Dobrunz and Stevens to explain a similar phenomenon at a different type of synapse (1997, PNAS, https://doi.org/10.1073/pnas.94.26.14843). The details that fit better with Ca²⁺ channel inactivation include the combination of the sigmoid time course of the recovery from depression (plotted backwards in Fig1G,I) and observations that EGTA (Fig2B) increases the paired-pulse depression seen after 25 ms intervals. That is, the authors ascribe the sigmoid recovery to a delay in the activation of the facilitation mechanism, but the increased paired pulse depression after loading EGTA indicates, instead, that the facilitation mechanism has already caused p_r to double within the first 25 ms (relative to the value if the facilitation mechanism was not active). Meanwhile, Ca²⁺ channel inactivation would be expected to cause a sigmoidal recovery of synaptic strength because of the sigmoidal relationship between Ca²⁺-influx and exocytosis (Dodge and Rahamimoff, 1967, https://doi.org/10.1113/jphysiol.1967.sp008367).

The Ca²⁺-channel inactivation hypothesis could probably be ruled in or out with experiments analogous to the 1997 Dobrunz study, except after lowering extracellular Ca²⁺ to the point where synaptic transmission failures are frequent. However, a possible complication might be a large increase in facilitation in low Ca²⁺ (Fig2B of Stevens and Wesseling, 1999, https://doi.org/10.1016/s0896-6273(00)80685-6).

We appreciate the reviewer's thoughtful comment regarding the potential role of Ca²⁺ channel inactivation in the observed paired-pulse depression (PPD). As noted by the Reviewer, the Dobrunz and Stevens (1997) suggested that the high double failure rate at short ISIs in synapses exhibiting PPD can be attributed to Ca²⁺ channel inactivation. This interpretation seems to be based on a premise that the number of RRP vesicles are not varied trial-by-trial. The number of TS vesicles, however, can be dynamically regulated depending on the parameters k₁ and b₁, as shown in Fig. S8, implying that the high double failure rate at short ISIs cannot be solely attributed to Ca²⁺ channel inactivation. Nevertheless, we acknowledge the possibility that Ca²⁺ channel inactivation may contribute to PPD, and therefore, we have further investigated this possibility. Specifically, we measured action potential (AP)-evoked Ca²⁺ transients at individual axonal boutons of layer 2/3 pyramidal cells in the mPFC using two-dye ratiometry techniques. Our analysis revealed no evidence for Ca²⁺ channel inactivation during a 40 Hz train of APs. This finding indicates that voltage-gated Ca²⁺ channel inactivation is unlikely to contribute to the pronounced PPD.

Figure 2—figure supplement 2 shows how we measured the total Ca²⁺ increments at axonal boutons. First we estimated endogenous Ca²⁺-binding ratio from analyses of single AP-induced Ca²⁺ transients at different concentrations of Ca²⁺ indicator dye (panels A to E). And then, using the Ca²⁺ buffer properties, we converted free [Ca²⁺] amplitudes to total calcium increments for the first four AP-evoked Ca²⁺ transients in a 40 Hz train (panels G-I). We incorporated these results into the revised version of our manuscript to provide evidence against the Ca²⁺ channel inactivation.

(3) On the other hand, even if the paired pulse depression is caused by depletion of release sites rather than Ca²⁺-channel inactivation, there does not seem to be any support for the critical assumption that all of the release sites have similar p_v. And indeed, there seems to be substantial emerging evidence from other studies for multiple types of release sites with 5 to 20-fold differences in p_v at a wide variety of synapse types (Maschi and Klyachko, eLife, 2020, https://doi.org/10.7554/elife.55210; Rodriguez Gotor et al, eLife, 2024, https://doi.org/10.7554/elife.88212 and refs. therein). If so, the paired pulse depression could be caused by depletion of release sites with high p_v, whereas the facilitation could occur at sites with much lower p_v that are still occupied. It might be possible to address this by eliminating assumptions about the distribution of p_v across release sites from the variance-mean analysis, but this seems difficult; simply showing how a few selected distributions wouldn't work - such as in standard multiple probability fluctuation analyses - wouldn't add much.

We appreciate the reviewer’s insightful comments regarding the potential increase in p_fusion of reluctant vesicles. It should be noted, however, that Maschi and Klyachko (2020) showed a distribution of release probability (p_r) within a single active zone rather than a heterogeneity in p_fusion of individual docked vesicles. Therefore both p_occ and p_v of TS vesicles would contribute to the p_r distribution shown in Maschi and Klyachko (2020). 

The Reviewer’s concern aligns closely with the first issue raised by Reviewer #2, to which we addressed in detail. Briefly, new release site may not be recruited during facilitation or post-tetanic augmentation, because variance of EPSCs during and after a train fell on the same parabola (Fig. 3). Secondly, strong PPD was observed not only in the baseline but also during early and late phases of facilitation, indicating that vesicles with very high p_v contribute to EPSC throughout train stimulations (Fig. 2, 3, and 7). These findings argue against the possibilities for recruitment of new release sites harboring low p_v vesicles and for a gradual increase in fusion probability of reluctant vesicles.

To address the reviewers’ concern, we incorporated the perspectives into Discussion and further clarified the reasoning behind our conclusions.

(4) In any case, the large increase - often 10-fold or more - in enhancement seen after lowering Ca²⁺ below 0.25 mM at a broad range of synapses and neuro-muscular junctions noted above is a potent reason to be cautious about the LS/TS model. There is morphological evidence that the transitions from a loose to tight docking state (LS to TS) occur, and even that the timing is accelerated by activity. However, 10-fold enhancement would imply that at least 90 % of vesicles start off in the LS state, and this has not been reported. In addition, my understanding is that the reverse transition (TS to LS) is thought to occur within 10s of ms of the action potential, which is 10-fold too fast to account for the reversal of facilitation seen at the same synapses (Kusick et al, 2020, https://doi.org/10.1038/s41593-020-00716-1).

As the Reviewer suggested, low external Ca²⁺ concentration can lower release probability (p_r). Given that both p_v and p_occ are regulated by [Ca²⁺]_i, low external [Ca²⁺] may affect not only p_v but also p_occ, both of which would contribute to low p_r. Under such conditions, it would be plausible that the baseline p_r becomes much lower than 0.1 due to low p_v and p_occ (for instance, p_v decreases from 1 to 0.5, and p_occ from 0.3 to 0.1, then p_r = 0.05), and then p_r (= p_v x p_occ) has a room for an increase by a factor of ten (0.5, for example) by short-term facilitation as cytosolic [Ca²⁺] accumulates during a train.

If p_v is close to one, p_r depends p_occ, and thus facilitation depends on the number of TS vesicles just before arrival of each AP of a train. Thus, post-train recovery from facilitation would depend on restoration of equilibrium between TS and LS vesicles to the baseline. Even if transition between LS and TS vesicles is very fast (tens of ms), the equilibrium involved in de novo priming (reversible transitions between recycling vesicle pool and partially docked LS vesicles) seems to be much slower (13 s in Fig. 5A of Wu and Borst 1999). Thus, we can consider a two-step priming model (recycling pool -> LS -> TS), which is comprised of a slow 1st step (-> LS) and a fast 2nd step (-> TS). Under the framework of the two-step model, the slow 1st step (de novo priming step) is the rate limiting step regulating the development and recovery kinetics of facilitation. Given that on and off rate for Ca²⁺ binding to Syt7 is slow, it is plausible that Syt7 may contribute to short-term facilitation (STF) by Ca²⁺-dependent acceleration of the 1st step (as shown in Fig. 9). During train stimulation, the number of LS vesicles would slowly accumulate in a Syt7 and Ca²⁺-dependent manner, and this increase in LS vesicles would shift LS/TS equilibrium towards TS, resulting in STF. After tetanic stimulation, the recovery kinetics from facilitation would be limited by slow recovery of LS vesicles.

Reference

Wu, L.-G. and Borst J.G.G. (1999) The reduced release probability of releasable vesicles during recovery from short-term synaptic depression. Neuron, 23(4): 821-832.

Please note that papers cited in the manuscript are not repeated here.

Individual points:

(1) An additional problem with the overfilling hypothesis is that syt7 knockdown increases the estimate of p_occ extracted from the variance-mean analysis, which would imply a faster transition from unoccupied to occupied, and would consequently predict faster recovery from depression. However, recovery from depression seen in experiments was slower, not faster. Meanwhile, the apparent decrease in the estimate of N extracted from the mean-variance analysis is not anticipated by the authors' model, but fits well with alternatives where p_v varies extensively among release sites because release sites with low p_v would essentially be silent in the absence of facilitation.

Slower recovery from depression observed in the Syt7 knockdown (KD) synapses (Fig. 7) may results from a deficiency in activity-dependent acceleration of TS vesicle recovery. Although basal occupancy was higher in the Syt7 KD synapses, this does not indicate a faster activity-dependent recovery.

Higher baseline occupancy does not always imply faster recovery of PPR too. Actually PPR recovery was slower in Syt7 KD synapses than WT one (18.5 vs. 23/s). Under the framework of the simple refilling model (Fig. S8Aa), the baseline occupancy and PPR recovery rate are calculated as k₁ / (k₁ + b₁) and (k₁ + b₁), respectively. The baseline occupancy depends on k₁/b₁, while the PPR recovery on absolute values of k₁ and b₁. Based on p_occ and PPR recovery time constant of WT and KD synapses, we expect higher k₁/b₁ but lower values for (k₁ + b₁) in Syt7 KD synapses compared to WT ones.

Lower release sites (N) in Syt7-KD synapses was not anticipated. As you suggested, such low N might be ascribed to little recruitment of release sites during a train in KD synapses. But our results do not support this model. If silent release sites are recruited during a train, the variance should upwardly deviate from the parabola predicted under a fixed N (Valera et al., 2012; Kobbersmed et al. 2020). Our result was not the case (Fig. 3). In the first version of the manuscript, we have argued against this possibility in line 203-208.

As discussed in both the Results and Discussion sections, the baseline EPSC was unchanged by KD (Fig. S3) because of complementary changes in the number of docking sites and their baseline occupancy (Fig. 6). These findings suggest that Syt7 may be involved in maintaining additional vacant docking sites, which could be overfilled during facilitation. It remains to be determined whether the decrease in docking sites in Syt7 KD synapses is related to its specific localization of Syt7 at the plasma membrane of active zones, as proposed in previous studies (Sugita et al., 2001; Vevea et al., 2021).

(2) Figure S4A: I like the TTX part of this control, but the 4-AP part needs a positive control to be meaningful (e.g., absence of TTX).

The reason why we used 4-AP in the presence of TTX was to increase the length constant of axon fibers and to facilitate the conduction of local depolarization in the illumination area to axon terminals. The lack of EPSC in the presence of 4-AP and TTX indicates that illumination area is distant from axon terminals enough for optic stimulation-induced local depolarization not to evoke synaptic transmission. This methodology has been employed in previous studies including the work of Little and Carter (2013).

Reference

Little JP and Carter AG (2013) Synaptic mechanisms underlying strong reciprocal connectivity between the medial prefrontal cortex and basolateral amygdala. J Neurosci, 33(39): 15333-15342.

(3) Line 251: At least some of the previous studies that concluded these drugs affect vesicle dynamics used logic that was based on some of the same assumptions that are problematic for the present study, so the reasoning is a bit circular.

(4) Line 329 and Line 461: A similar problem with circularity for interpreting earlier syt7 studies.

(Reply to #3 and #4) We selected the target molecules as candidates based on their well-characterized roles in vesicle dynamics, and aimed to investigate what aspects of STP are affected by these molecules in our experimental context. For example, we could find that the baseline p_occ and short-term facilitation (STF) are enhanced by the baseline DAG level and train stimulation-induced PLC activation, respectively. Notably, the effect of dynasore informed us that slow site clearing is responsible for the late depression of 40 Hz train EPSC. The knock-down experiments also provided us with information on the critical role of Syt7 in replenishment of TS vesicles. These approaches do not deviate from standard scientific reasoning but rather builds upon prior knowledge to formulate and test hypotheses.

Importantly, our conclusions do not rely solely on the assumption that altering the target molecule impacts synaptic transmission. Instead, our conclusions are derived from a comprehensive analysis of diverse outcomes obtained through both pharmacological and genetic manipulations. These interpretations align closely with prior literature, further validating our conclusions.

Therefore, the use of established studies to guide candidate selection and the consistency of our findings with existing knowledge do not represent a logical circularity but rather a reinforcement of the proposed mechanism through converging lines of evidence.

Recommendations for the authors:

Reviewer #1 (Recommendations for the authors):

Comments:

(1) While the authors claim that Syt7-mediated facilitation is connected to the behavioral deficits they observed, this link is still somewhat speculative. This manuscript could benefit from further discussions of other alternative mechanisms to consider.

We added following statement to Discussion of the revised manuscript:

“The acquisition of trace fear memory was impaired by inhibition of persistent activity in mPFC during trace period (Gilmartin et al., 2013). The similar deficit observed in Syt7 KD animals is consistent with the hypothesis that STF provides bi-stable ensemble activity in a recurrent network (Mongillo et al., 2012). Nevertheless, alternative mechanisms may be responsible for the behavioral deficit. Not only recurrent network but also long-range loop between the mPFC and the mediodorsal (MD) thalamus play a critical role in maintaining persistent activity within the mPFC especially for a delay period longer than 10 s (Bolkan et al., 2017). Prefrontal L2/3 is heavily innervated by MD thalamus, and L2/3-PCs subsequently relay signals to L5 cortico-thalamic (CT) neurons (Collins et al., 2018). Given that L2/3 is an essential component of the PFC-thalamic loop, loss of STF at recurrent synapses between L2/3 PCs may lead to insufficient L2/3 inputs to L5 CT neurons and failure in the reverberant PFC-MD thalamic feedback loop. Therefore, not only L2/3 recurrent network but also its output to downstream network should be considered as a possible network mechanism underlying behavioral deficit caused by Syt7 KD L2/3.”

(2) The authors mention that Syt7 contributes to persistent activity during working memory tasks but focus on using only a trace fear conditioning task. However, it would be interesting to see if their results are generalizable to other working memory tasks (i.e. a delayed alternation task).

We thank to Reviewer for the insightful suggestion. Trace fear conditioning (tFC) shares behavioral properties with working memory (WM) tasks in that tFC is vulnerable to attentional distraction and to the load of WM task. In general WM tasks including delayed alternation tasks such as a T-maze task need persistent activity of ensemble neurons representing target-specific information among multiple choices. Different from such WM tasks, tFC is not appropriate to examine target-specific ensemble activity. Because it is not trivial to examine in vivo recordings in KD animals during delayed alternation tasks, it will be appropriate to study the effect of Syt7 KD in a separate study. 

(3) The figure legend in Figure 6A and 6B mentions dotted lines and broken lines in the figure. However, this is confusing, and it is unclear as to what these lines are referring to in the figure.

To avoid the confusion in the figure legend for Figure 6A and 6B, we corrected “dotted line” to " vertical broken line", and “broken lines” to “dashed parabolas”.

(4) The manuscript can benefit from close reading and editing to catch typos and improve general readability (i.e. line 173: the word "are" is repeated twice).

We corrected typographical errors throughout the manuscript and carefully read the manuscript to improve readability. A revised version reflecting these corrections has been prepared and will be resubmitted for your consideration.

Reviewer #3 (Recommendations for the authors):

The points in this section are all minor.

(1) Line 44: Define release probability (p_r) more clearly. Authors use it to mean p_v*p_occ, but others routinely use it to mean p_v*p_occ*N.

We understand that the Reviewer meant “others routinely use it to mean p_v”. At this statement, we meant conventional definition of release probability, which is release probability among vesicles of RRP. We think that it is not appropriate to re-define release probability as p_v * p_occ in this first paragraph of Introduction. Therefore we clarified this issue in Discussion as we mentioned in our reply to the 1st weakness issue raised by Reviewer #3.   

(2) Line 82: For clarity, define better what recurrent excitatory synapses are. It seems that synapses between L2/3 PCs and local targets may all be recurrent?

Each of L2/3 and L5 of the prefrontal cortical layers harbors intralaminar recurrent excitatory synapses between pyramidal cells, called a recurrent network. Previous theoretical studies have proposed that a single layer recurrent network model can have bi-stable E/I balanced states (up- and down-states) if recurrent excitatory synapses display short-term facilitation (STF), and thus is able to temporally hold an information once external input shifts the network to the up-state. In this theory, synapses to local targets across layers are not considered and specific roles of L2/3 and L5 in working memory tasks are still elusive. For clarity, we added a statement at the beginning of the paragraph (line 82): “Each of layer 2/3 (L2/3) and layer 5 (L5) of neocortex displays intralaminar excitatory synapses between pyramidal cells comprising a recurrent network (Holmgren et al., 2003; Thomson and Lamy, 2007)”

(3) Cite earlier studies of short-term synaptic plasticity at synapses between L2/3 pyramidal neurons and local targets in mPFC. If there are none, take more explicit credit for being first.

As we mentioned in Introduction, previous studies on short-term plasticity (STP) at neocortical excitatory recurrent synapses have focused on synapses between L5 pyramidal cells (PCs) (Hemple et al. 2000; Wang et al. 2006; Morishima et al., 2011; Yoon et al., 2020). The local connectivity between L2/3 PCs in the somatosensory cortex has been elucidated by Homgren et al. (2003) and Ko et al. (2011). Although these study showed STP of EPSPs, it was at a fixed frequency or stimulus pattern at high external [Ca²⁺] (2 mM). There is a study on the frequency-dependence of STP of EPSP between L2/3-PCs (Feldmyer et al., 2006). Different from our study, Feldmyer et al., (2006) observed monotonous STD at all frequencies less than 50 Hz, but this study was done in the somatosensory cortex and at high external [Ca²⁺] (2 mM). To our knowledge, no previous study have investigated STP at recurrent excitatory synapses of L2/3 pyramidal cells of the mPFC especially at physiological external [Ca²⁺]. The present study, therefore, represents the first extensive investigation of STP at recurrent excitatory synapses in L2/3 of the mPFC under physiologically relevant external [Ca²⁺].

References

Feldmeyer D, Lubke J, Silver RA, Sakmann B (2002) Synaptic connections between layer 4 spiny neurone-layer 2/3 pyramidal cell pairs in juvenile rat barrel cortex: physiology and anatomy of interlaminar signalling within a cortical column. J Physiol 538:803-822.

Holmgren C, Harkany T, Svennenfors B, Zilberter Y (2003) Pyramidal cell communication within local networks in layer 2/3 of rat neocortex. J Physiol 551:139-153.

Ko H, Hofer SB, Pichler B, Buchanan KA, Sjöström PJ, Mrsic-Flogel TD (2011) Functional specificity of local synaptic connections in neocortical networks. Nature 473:87-91.

Morishima M, Morita K, Kubota Y, Kawaguchi Y (2011) Highly differentiated projection-specific cortical subnetworks. Journal of Neuroscience 31:10380-10391.

Wang Y, Markram H, Goodman PH, Berger TK, Ma J, Goldman-Rakic PS (2006) Heterogeneity in the pyramidal network of the medial prefrontal cortex. Nat Neurosci 9:534-542.

(4) I couldn't figure out the significance of Figure S3. Perhaps this could be explained better.

Optical minimal stimulation methods have not been previously documented in detail. This figure illustrates what parameters we should carefully examine in order to attain optical minimal stimulation, which hopefully stimulates a single afferent fiber. A single fiber stimulation by optical minimal stimulation is supported by the similarity of our estimate for the number of release sites (N) as the previous morphological estimate (Holler et al., 2021). For minimal stimulation, we used a collimated DMD-coupled LED was employed to restrict 470 nm illumination to a small and well-defined region within layer 2/3 of the prelimbic mPFC, and carefully adjusted the illumination radius such that one step smaller (by 1 μm) illumination results in failure to evoke EPSCs. Our typical illumination area ranged between 3–4 μm, as shown in Figure S3A. Under this minimal illumination area, we confirmed unimodal distributions for the EPSC parameters (amplitude, rise time, decay time and time to peak; Figure 3B-E). Otherwise, we excluded the recordings from analysis. We hope this explanation provides a clearer understanding of the figure's significance.

(5) Note that CTZ seems to alter p_r at some synapses.

We acknowledge that CTZ can increase release probability by blocking presynaptic K⁺ currents. Indeed, Ishikawa and Takahashi (2001) reported that CTZ slowed the repolarizing phase of presynaptic action potentials and the frequency of miniature EPSCs in the calyx synapses. Consistently, we observed a slight increase in the baseline EPSC amplitude, from 33.3 pA to 41.9 pA (p=0.045) following the application of 50 µM CTZ. However, given that vesicular release probability (p_v) is already close to 1 at the synapse of our interest, we believe that the observed effect is more likely attributed to an increase in release sites occupancy (p_occ), which would be reflected as an increase in miniature EPSC frequency in Ishikawa and Takahashi (2001). Given that PPR depends on p_v rather than p_occ, this increase in p_occ would not critically change our conclusion that AMPA receptor desensitization is not responsible for the strong PPD.

Reference

Ishikawa, T., & Takahashi, T. (2001). Mechanisms underlying presynaptic facilitatory effect of cyclothiazide at the calyx of Held of juvenile rats. The Journal of Physiology, 533(2), 423-431.

(6) Figure 8B. The result in Figure 8C seems important, but I couldn't figure out why behaviour was not altered during the acquisition phase summarized in Figure 8B. Perhaps this could be explained more clearly for non-experts.

Little difference in freezing behavior during acquisition has been also observed when prelimbic persistent firing was optogenetically inhibited (Gilmartin, 2013). Not only CS (tone) but also other sensory inputs (visual and olfactory etc.) and the spatial context could be a cue predicting US (shock). Moreover, during the acquisition phase, the presence of the electric shock inherently induces a freezing response as a natural defensive behavior, which may obscure specific behavioral changes related to the associative learning process. Therefore, the freezing behavior during acquisition cannot be regarded as a sign for specific association of CS and US. Instead, on the next day, we specifically evaluated the CS-US association of the conditioned animals by measuring freezing behavior in response to CS in a distinct context. We explicitly documented little difference between WT and KD animals during the acquisition phase in the relevant paragraph (line 397).

Author response:

The following is the authors’ response to the original reviews

Reviewer #1 (Public Review):

Summary:

It seems as if the main point of the paper is about the new data related to rat fish although your title is describing it as extant cartilaginous fishes and you bounce around between the little skate and ratfish. So here's an opportunity for you to adjust the title to emphasize ratfish is given the fact that leader you describe how this is your significant new data contribution. Either way, the organization of the paper can be adjusted so that the reader can follow along the same order for all sections so that it's very clear for comparative purposes of new data and what they mean. My opinion is that I want to read, for each subheading in the results, about the the ratfish first because this is your most interesting novel data. Then I want to know any confirmation about morphology in little skate. And then I want to know about any gaps you fill with the cat shark. (It is ok if you keep the order of "skate, ratfish, then shark, but I think it undersells the new data).

The main points of the paper are 1) to define terms for chondrichthyan skeletal features in order to unify research questions in the field, and 2) add novel data on how these features might be distributed among chondrichthyan clades. However, we agree with the reviewer that many readers might be more interested in the ratfish data, so we have adjusted the order of presentation to emphasize ratfish throughout the manuscript.

Strengths:

The imagery and new data availability for ratfish are valuable and may help to determine new phylogenetically informative characters for understanding the evolution of cartilaginous fishes. You also allude to the fossil record.

Thank you for the nice feedback.

Opportunities:

I am concerned about the statement of ratfish paedomorphism because stage 32 and 33 were not statistically significantly different from one another (figure and prior sentences). So, these ratfish TMDs overlap the range of both 32 and 33. I think you need more specimens and stages to state this definitely based on TMD. What else leads you to think these are paedomorphic? Right now they are different, but it's unclear why. You need more outgroups.

Sorry, but we had reported that the TMD of centra from little skate did significantly increase between stage 32 and 33. Supporting our argument that ratfish had features of little skate embryos, TMD of adult ratfish centra was significantly lower than TMD of adult skate centra (Fig1). Also, it was significantly higher than stage 33 skate centra, but it was statistically indistinguishable from that of stage 33 and juvenile stages of skate centra. While we do agree that more samples from these and additional groups would bolster these data, we feel they are sufficiently powered to support our conclusions for this current paper.

Your headings for the results subsection and figures are nice snapshots of your interpretations of the results and I think they would be better repurposed in your abstract, which needs more depth.

We have included more data summarized in results sub-heading in the abstract as suggested (lines 32-37).

Historical literature is more abundant than what you've listed. Your first sentence describes a long fascination and only goes back to 1990. But there are authors that have had this fascination for centuries and so I think you'll benefit from looking back. Especially because several of them have looked into histology and development of these fishes.

I agree that in the past 15 years or so a lot more work has been done because it can be done using newer technologies and I don't think your list is exhaustive. You need to expand this list and history which will help with your ultimate comparative analysis without you needed to sample too many new data yourself.

We have added additional recent and older references: Kölliker, 1860; Daniel, 1934; Wurmbach, 1932; Liem, 2001; Arratia et al., 2001.

I'd like to see modifications to figure 7 so that you can add more continuity between the characters, illustrated in figure 7 and the body of the text.

We address a similar comment from this reviewer in more detail below, hoping that any concerns about continuity have been addressed with inclusion of a summary of proposed characters in a new Table 1, re-writing of the Discussion, and modified Fig7 and re-written Fig7 legend.

Generally Holocephalans are the outgroup to elasmobranchs - right now they are presented as sister taxa with no ability to indicate derivation. Why isn't the catshark included in this diagram?

While a little unclear exactly what was requested, we restructured the branches to indicate that holocephalans diverged earlier from the ancestors that led to elasmobranchs. Also in response to this comment, we added catshark (S. canicula) and little skate (L. erinacea) specifically to the character matrix.

In the last paragraph of the introduction, you say that "the data argue" and I admit, I am confused. Whose data? Is this a prediction or results or summary of other people's work? Either way, could be clarified to emphasize the contribution you are about to present.

Sorry for this lack of clarity, and we have changed the wording in this revision to hopefully avoid this misunderstanding.

Reviewer #1 (Recommendations For The Authors):

Further Strengths and Opportunities:

Your headings for the results subsection and figures are nice snapshots of your interpretations of the results and I think they would be better repurposed in your abstract, which needs more depth. It's a little unusual to try and state an interpretation of results as the heading title in a results section and the figures so it feels out of place. You could also use the headings as the last statement of each section, after you've presented the results. In order I would change these results subheadings to:

Tissue Mineral Density (TMD)

Tissue Properties of Neural Arches

Trabecular mineralization

Cap zone and Body zone Mineralization Patterns

Areolar mineralization

Developmental Variation

Sorry, but we feel that summary Results sub-headings are the best way to effectively communicate to readers the story that the data tell, and this style has been consistently used in our previous publications. No changes were made.

You allude to the fossil record and that is great. That said historical literature is more abundant than what you've listed. Your first sentence describes a long fascination and only goes back to 1990. But there are authors that have had this fascination for centuries and so I think you'll benefit from looking back. Especially because several of them have looked into histology of these fishes. You even have one sentence citing Coates et al. 2018, Frey et al., 2019 and ørvig 1951 to talk about the potential that fossils displayed trabecular mineralization. That feels like you are burying the lead and may have actually been part of the story for where you came up with your hypothesis in the beginning... or the next step in future research. I feel like this is really worth spending some more time on in the intro and/or the discussion.

We’ve added older REFs as pointed out above. Regarding fossil evidence for trabecular mineralization, no, those studies did not lead to our research question. But after we discovered how widespread trabecular mineralization was in extant samples, we consulted these papers, which did not focus on the mineralization patterns per se, but certainly led us to emphasize how those patterns fit in the context of chondrichthyan evolution, which is how we discussed them.

I agree that in the past 15 years or so a lot more work has been done because it can be done using newer technologies. That said there's a lot more work by Mason Dean's lab starting in 2010 that you should take a look at related to tesserae structure... they're looking at additional taxa than what you did as well. It will be valuable for you to be able to make any sort of phylogenetic inference as part of your discussion and enhance the info your present in figure 7. Go further back in time... For example:

de Beer, G. R. 1932. On the skeleton of the hyoid arch in rays and skates. Quarterly Journal of Microscopical Science. 75: 307-319, pls. 19-21.

de Beer, G. R. 1937. The Development of the Vertebrate Skull. The University Press, Oxford.

Indeed, we have read all of Mason’s work, citing 9 of his papers, and where possible, we have incorporated their data on different species into our Discussion and Fig7. Thanks for the de Beer REFs. While they contain histology of developing chondrichthyan elements, they appear to refer principally to gross anatomical features, so were not included in our Intro/Discussion.

Most sections within the results, read more like a discussion than a presentation of the new data and you jump directly into using an argument of those data too early. Go back in and remove the references or save those paragraphs for the discussion section. Particularly because this journal has you skip the method section until the end, I think it's important to set up this section with a little bit more brevity and conciseness. For instance, in the first section about tissue mineral density, change that subheading to just say tissue mineral density. Then you can go into the presentation of what you see in the ratfish, and then what you see in the little skate, and then that's it. You save the discussion about what other elasmobranch's or mineralizing their neural arches, etc. for another section.

We dramatically reduced background-style writing and citations in each Results section (other than the first section of minor points about general features of the ratfish, compared to catshark and little skate), keeping only a few to briefly remind the general reader of the context of these skeletal features.

I like that your first sentence in the paragraph is describing why you are doing. a particular method and comparison because it shows me (the reader) where you're sampling from. Something else is that maybe as part of the first figure rather than having just each with the graph have a small sketch for little skate and catch shark to show where you sampled from for comparative purposes. That would relate back, then to clarifying other figures as well.

Done (also adding a phylogenetic tree).

Second instance is your section on trabecular mineralization. This has so many references in it. It does not read like results at all. It looks like a discussion. However, the trabecular mineralization is one of the most interesting aspect of this paper, and how you are describing it as a unique feature. I really just want a very clear description of what the definition of this trabecular mineralization is going to be.

In addition to adding Table 1 to define each proposed endoskeletal character state, we have changed the structure of this section and hope it better communicates our novel trabecular mineralization results. We also moved the topic of trabecular mineralization to the first detailed Discussion point (lines 347-363) to better emphasize this specific topic.

Carry this reformatting through for all subsections of the results.

As mentioned above, we significantly reduced background-style writing and citations in each Results section.

I'd like to see modifications to figure 7 so that you can add more continuity between the characters, illustrated in figure 7 and the body of the text. I think you can give the characters a number so that you can actually refer to them in each subsection of the results. They can even be numbered sequentially so that they are presented in a standard character matrix format, that future researchers can add directly to their own character matrices. You could actually turn it into a separate table so it doesn't taking up that entire space of the figure, because there need to be additional taxa referred to on the diagram. Namely, you don't have any out groups in figure 7 so it's hard to describe any state specifically as ancestral and wor derived. Generally Holocephalans are the outgroup to elasmobranchs - right now they are presented as sister taxa with no ability to indicate derivation. Why isn't the catshark included in this diagram?

The character matrix is a fantastic idea, and we should have included it in the first place! We created Table 1 summarizing the traits and terminology at the end of the Introduction, also adding the character matrix in Fig7 as suggested, including specific fossil and extant species. For the Fig7 branching and catshark inclusion, please see above.

You can repurpose the figure captions as narrative body text. Use less narrative in the figure captions. These are your results actually, so move that text to the results section as a way to truncate and get to the point faster.

By figure captions, we assume the reviewer refers to figure legends. We like to explain figures to some degree of sufficiency in the legends, since some people do not read the main text and simply skim a manuscript’s abstract, figures, and figure legends. That said, we did reduce the wording, as requested.

More specific comments about semantics are listed here:

The abstract starts negative and doesn't state a question although one is referenced. Potential revision - "Comprehensive examination of mineralized endoskeletal tissues warranted further exploration to understand the diversity of chondrichthyans... Evidence suggests for instance that trabecular structures are not common, however, this may be due to sampling (bring up fossil record.) We expand our understanding by characterizing the skate, cat shark, and ratfish... (Then add your current headings of the results section to the abstract, because those are the relevant takeaways.)"

We re-wrote much of the abstract, hoping that the points come across more effectively. For example, we started with “Specific character traits of mineralized endoskeletal tissues need to be clearly defined and comprehensively examined among extant chondrichthyans (elasmobranchs, such as sharks and skates, and holocephalans, such as chimaeras) to understand their evolution”. We also stated an objective for the experiments presented in the paper: “To clarify the distribution of specific endoskeletal features among extant chondrichthyans”.

In the last paragraph of the introduction, you say that "the data argue" and I admit, I am confused. Whose data? Is this a prediction or results or summary of other people's work? Either way, could be clarified to emphasize the contribution you are about to present.

Sorry for this lack of clarity, and we have changed the wording in this revision to hopefully avoid this misunderstanding.

In the second paragraph of the TMD section, you mention the synarcual comparison. I'm not sure I follow. These are results, not methods. Tell me what you are comparing directly. The non-centrum part of the synarcual separate from the centrum? They both have both parts... did you mean the comparison of those both to the cat shark? Just be specific about which taxon, which region, and which density. No need to go into reasons why you chose those regions here.. Put into methods and discussion for interpretation.

We hope that we have now clarified wording of that section.

Label the spokes somehow either in caption or on figure direction. I think I see it as part of figure 4E, I, and J, but maybe I'm misinterpreting.

Based upon histological features (e.g., regions of very low cellularity with Trichrome unstained matrix) and hypermineralization, spokes in Fig4 are labelled with * and segmented in blue. We detailed how spokes were identified in main text (lines 241-243; 252-254) and figure legend (lines 597-603).

Reviewer #2 (Public Review):

General comment:

This is a very valuable and unique comparative study. An excellent combination of scanning and histological data from three different species is presented. Obtaining the material for such a comparative study is never trivial. The study presents new data and thus provides the basis for an in-depth discussion about chondrichthyan mineralised skeletal tissues.

Many thanks for the kind words

I have, however, some comments. Some information is lacking and should be added to the manuscript text. I also suggest changes in the result and the discussion section of the manuscript.

Introduction:

The reader gets the impression almost no research on chondrichthyan skeletal tissues was done before the 2010 ("last 15 years", L45). I suggest to correct that and to cite also previous studies on chondrichthyan skeletal tissues, this includes studies from before 1900.

We have added additional older references, as detailed above.

Material and Methods:

Please complete L473-492: Three different Micro-CT scanners were used for three different species? ScyScan 117 for the skate samples. Catshark different scanner, please provide full details. Chimera Scncrotron Scan? Please provide full details for all scanning protocols.

We clarified exact scanners and settings for each micro-CT experiment in the Methods (lines 476-497).

TMD is established in the same way in all three scanners? Actually not possible. Or, all specimens were scanned with the same scanner to establish TMD? If so please provide the protocol.

Indeed, the same scanner was used for TMD comparisons, and we included exact details on how TMD was established and compared with internal controls in the Methods. (lines 486-488)

Please complete L494 ff: Tissue embedding medium and embedding protocol is missing. Specimens have been decalcified, if yes how? Have specimens been sectioned non-decalcified or decalcified?

Please complete L506 ff: Tissue embedding medium and embedding protocol is missing. Description of controls are missing.

Methods were updated to include these details (lines 500-503).

Results:

L147: It is valuable and interesting to compare the degree of mineralisation in individuals from the three different species. It appears, however, not possible to provide numerical data for Tissue Mineral Density (TMD). First requirement, all specimens must be scanned with the same scanner and the same calibration values. This in not stated in the M&M section. But even if this was the case, all specimens derive from different sample locations and have, been preserved differently. Type of fixation, extension of fixation time in formalin, frozen, unfrozen, conditions of sample storage, age of the samples, and many more parameters, all influence TMD values. Likewise the relative age of the animals (adult is not the same as adult) influences TMD. One must assume different sampling and storage conditions and different types of progression into adulthood. Thus, the observation of different degrees of mineralisation is very interesting but I suggest not to link this observation to numerical values.

These are very good points, but for the following reasons we feel that they were not sufficiently relevant to our study, so the quantitative data for TMD remain scientifically valid and critical for the field moving forward. Critically, 1) all of the samples used for TMD calculations underwent the same fixation protocols, and 2) most importantly, all samples for TMD were scanned on the same micro-CT scanner using the same calibration phantoms for each scanning session. Finally, while the exact age of each adult was not specified, we note for Fig1 that clear statistically significant differences in TMD were observed among various skeletal elements from ratfish, shark, and skate. Indeed, ratfish TMD was considerably lower than TMD reported for a variety of fishes and tetrapods (summarized in our paper about icefish skeletons, who actually have similar TMD to ratfish: https://doi.org/10.1111/joa.13537).

In response, however, we added a caveat to the paper’s Methods (lines 466-469), stating that adult ratfish were frozen within 1 or 2 hours of collection from the wild, staying frozen for several years prior to thawing and immediate fixation.

Parts of the results are mixed with discussion. Sometimes, a result chapter also needs a few references but this result chapter is full of references.

As mentioned above, we reduced background-style writing and citations in each Results section.

Based on different protocols, the staining characteristics of the tissue are analysed. This is very good and provides valuable additional data. The authors should inform the not only about the staining (positive of negative) abut also about the histochemical characters of the staining. L218: "fast green positive" means what? L234: "marked by Trichrome acid fuchsin" means what? And so on, see also L237, L289, L291

We included more details throughout the Results upon each dye’s first description on what is generally reflected by the specific dyes of the staining protocols. (lines 178, 180, 184, 223, 227, and 243-244)

Discussion

Please completely remove figure 7, please adjust and severely downsize the discussion related to figure 7. It is very interesting and valuable to compare three species from three different groups of elasmobranchs. Results of this comparison also validate an interesting discussion about possible phylogenetic aspects. This is, however, not the basis for claims about the skeletal tissue organisation of all extinct and extant members of the groups to which the three species belong. The discussion refers to "selected representatives" (L364), but how representative are the selected species? Can there be a extant species that represents the entire large group, all sharks, rays or chimeras? Are the three selected species basal representatives with a generalist life style?

These are good points, and yes, we certainly appreciate that the limited sampling in our data might lead to faulty general conclusions about these clades. In fact, we stated this limitation clearly in the Introduction (lines 126-128), and we removed “representative” from this revision. We also replaced general reference to chondrichthyans in the Title by listing the specific species sampled. However, in the Discussion, we also compare our data with previously published additional species evaluated with similar assays, which confirms the trend that we are concluding. We look forward to future papers specifically testing the hypotheses generated by our conclusions in this paper, which serves as a benchmark for identifying shared and derived features of the chondrichthyan endoskeleton.

Please completely remove the discussion about paedomorphosis in chimeras (already in the result section). This discussion is based on a wrong idea about the definition of paedomorphosis. Paedomorphosis can occur in members of the same group. Humans have paedormorphic characters within the primates, Ambystoma mexicanum is paedormorphic within the urodeals. Paedomorphosis does not extend to members of different vertebrate branches. That elasmobranchs have a developmental stage that resembles chimera vertebra mineralisation does not define chimera vertebra centra as paedomorphic. Teleost have a herocercal caudal fin anlage during development, that does not mean the heterocercal fins in sturgeons or elasmobranchs are paedomorphic characters.

We agree with the reviewer that discussion of paedomorphosis should apply to members of the same group. In our paper, we are examining paedomorphosis in a holocephalan, relative to elasmobranch fishes in the same group (Chrondrichthyes), so this is an appropriate application of paedomorphosis. In response to this comment, we clarified that our statement of paedomorphosis in ratfish was made with respect to elasmobranchs (lines 37-39; 418-420).

L432-435: In times of Gadow & Abott (1895) science had completely wrong ideas bout the phylogenic position of chondrichthyans within the gnathostomes. It is curious that Gadow & Abott (1895) are being cited in support of the paedomorphosis claim.

If paedomorphosis is being examined within Chondrichthyes, such as in our paper and in the Gadow and Abbott paper, then it is an appropriate reference, even if Gadow and Abbott (and many others) got the relative position of Chondrichthyes among other vertebrates incorrect.

The SCPP part of the discussion is unrelated to the data obtained by this study. Kawaki & WEISS (2003) describe a gene family (called SCPP) that control Ca-binding extracellular phosphoproteins in enamel, in bone and dentine, in saliva and in milk. It evolved by gene duplication and differentiation. They date it back to a first enamel matrix protein in conodonts (Reif 2006). Conodonts, a group of enigmatic invertebrates have mineralised structures but these structure are neither bone nor mineralised cartilage. Cat fish (6 % of all vertebrate species) on the other hand, have bone but do not have SCPP genes (Lui et al. 206). Other calcium binding proteins, such as osteocalcin, were initially believed to be required for mineralisation. It turned out that osteocalcin is rather a mineralisation inhibitor, at best it regulates the arrangement collagen fiber bundles. The osteocalcin -/- mouse has fully mineralised bone. As the function of the SCPP gene product for bone formation is unknown, there is no need to discuss SCPP genes. It would perhaps be better to finish the manuscript with summery that focuses on the subject and the methodology of this nice study.

We completely agree with the reviewer that many papers claim to associate the functions of SCPP genes with bone formation, or even mineralization generally. The Science paper with the elephant shark genome made it very popular to associate SCPP genes with bone formation, but we feel that this was a false comparison (for many reasons)! In response to the reviewer’s comments, however, we removed the SCPP discussion points, moving the previous general sentence about the genetic basis for reduced skeletal mineralization to the end of the previous paragraph (lines 435-439). We also added another brief Discussion paragraph afterwards, ending as suggested with a summary of our proposed shared and derived chondrichthyan endoskeletal traits (lines 440-453).

Reviewer #2 (Recommendations For The Authors):

Other comments

L40: remove paedomorphism

No change; see above

L53: down tune languish, remove "severely" and "major"

Done (lines 57-59)

L86: provide species and endoskeletal elements that are mineralized

No change; this paragraph was written generally, because the papers cited looked at cap zones of many different skeletal elements and neural arches in many different species

L130: remove TMD, replace by relative, descriptive, values

No change; see above

L135: What are "segmented vertebral neural arches and centra" ?

Changed to “neural arches and centra of segmented vertebrae” (lines 140-141)

L166: L168 "compact" vs. "irregular". Partial mineralisation is not necessarily irregular.

Thanks for pointing out this issue; we changed wording, instead contrasting “non-continuous” and “continuous” mineralization patterns (lines 171-174)

L192: "several endoskeletal regions". Provide all regions

All regions provided (lines 198-199)

L269: "has never been carefully characterized in chimeras". Carefully means what? Here, also only one chimera is analyses, not several species.

Sentence removed

302: Can't believe there is no better citation for elasmobranch vertebral centra development than Gadow and Abott (1895)

Added Arriata and Kolliker REFs here (lines 293-295)

L318 ff: remove discussion from result chapter

References to paedomorphism were removed from this Results section

L342: refer to the species studied, not to the entire group.

Sorry, the line numbering for the reviewer and our original manuscript have been a little off for some reason, and we were unclear exactly to which line of text this comment referred. Generally in this revision, however, we have tried to restrict our direct analyses to the species analyzed, but in the Discussion we do extrapolate a bit from our data when considering relevant published papers of other species.

346: "selected representative". Selection criteria are missing

“selected representative” removed

L348: down tune, remove "critical"

Done

L351: down tune, remove "critical"

Done

L 364: "Since stem chondrichthyans did not typically mineralize their centra". Means there are fossil stem chondrichthyans with full mineralised centra?

Re-worded to “Stem chondrichthyans did not appear to mineralize their centra” (lines 379)

L379: down tune and change to: "we propose the term "non-tesseral trabecular mineralization. Possibly a plesiomorphic (ancestral) character of chondrichthyans"

No change; sorry, but we feel this character state needs to be emphasized as we wrote in this paper, so that its evolutionary relationship to other chondrichthyan endoskeletal features, such as tesserae, can be clarified.

L407: suggests so far palaeontologist have not been "careful" enough?

Apologies; sentence re-worded, emphasizing that synchrotron imaging might increase details of these descriptions (lines 406-408)

414: down tune, remove "we propose". Replace by "possibly" or "it can be discussed if"

Sentence re-worded and “we propose” removed (lines 412-415)

L420: remove paragraph

No action; see above

L436: remove paragraph

No action; see above

L450: perhaps add summery of the discussion. A summery that focuses on the subject and the methodology of this nice study.

Yes, in response to the reviewer’s comment, we finished the discussion with a summary of the current study. (lines 440-453)

Author response:

The following is the authors’ response to the previous reviews

Public Reviews:

Reviewer #1 (Public review):

The manuscript investigates the role of the membrane-deforming cytoskeletal regulator protein Abba in cortical development and its potential implications for microcephaly. It is a valuable contribution to the understanding of Abba's role in cortical development. The strengths and weaknesses identified in the manuscript are outlined below:

Clinical Relevance:

The authors identified a patient with microcephaly and intellectual disability patient harboring a mutation in the Abba variant (R671W), adding a clinically relevant dimension to the study.

Mechanistic Insights:

The study offers valuable mechanistic insights into the development of microcephaly by elucidating the role of Abba in radial glial cell proliferation, radial fiber organization, and the migration of neuronal progenitors. The identification of Abba's involvement in the cleavage furrow during cell division, along with its interaction with Nedd9 and positive influence on RhoA activity, adds depth to our understanding of the molecular processes governing cortical development.

In Vivo Validation:

The overexpression of mutant Abba protein (R671W), which results in phenotypic similarities to Abba knockdown effects, supports the significance of Abba in cortical development.

Weaknesses:

The findings in the study suggest that heterozygous expression of the R671W variant may exert a dominant-negative effect on ABBA's role, disrupting normal brain development and leading to microcephaly and cognitive delay. However, evidence also points to a possible gain-of-function effect, as the mutation does not decrease RhoA activity or PH3 expression in vivo. Additionally, the impact of ABBA depletion on cell fate is not fully addressed. While abnormal progenitor accumulation in the ventricular and subventricular zones is observed, the transition of progenitors to neuroblasts and their ability to support neuroblast migration remains unclear. Impaired cleavage furrow ingression and disrupted Nedd9 and RhoA signaling could lead to structural abnormalities in radial glial progenitors, affecting their scaffold function and neuroblast progression.  The manuscript lacks an exploration of the loss or decrease in interaction between Abba and NEDD9 in the case of the pathogenic patient-derived mutation in Abba. Furthermore, addressing the changes in localization and ineraction in for NEDD9 following over-expression of the mutant are important to further mehcanistically characterizxe this interaction in future studies. These gaps suggest the need for further exploration of ABBA's role in progenitor cell fate and neuroblast migration to clarify its mechanistic contributions to cortical development.

(1) Response to statement on dominant-negative vs. gain-of-function effect of R671W variant:

We appreciate the reviewer’s thoughtful analysis of the potential mechanisms underlying the R671W variant. We agree that the heterozygous expression of the human R671W mutation may initially suggest a dominant-negative effect. However, our data indicate that this variant may instead exert a gain-of-function effect. As highlighted in the discussion section, overexpression of ABBA-R671W in cells that also express wild-type ABBA did not result in a dominant-negative decrease in RhoA activation nor affect PH3 expression in vivo. These findings suggest that the R671W mutation does not impair the canonical ABBA-mediated activation of RhoA, and instead, the resulting phenotype may involve post-mitotic processes, such as altered cell migration. This interpretation is further supported by previous clinical studies reporting additional patients with the same mutation and phenotypic outcomes.

(2) Response to statement on ABBA depletion and progenitor-to-neuroblast transition:

We agree that the question of how ABBA depletion affects cell fate and the progression of radial glial progenitors (RGPs) to neuroblasts is of significant importance. Our findings suggest that ABBA knockdown disrupts cleavage furrow ingression, which may block radial glial cells prior to abscission. This likely contributes to the observed accumulation of cells in the ventricular and subventricular zones, as seen in Figures 2A and 4D. Additionally, disrupted Nedd9 expression and impaired RhoA signaling appear to alter the structural integrity of RGPs, leading to detachment of apical and basal endfeet (Supplementary Figure 3). These structural abnormalities compromise the ability of RGPs to function as scaffolds for neuroblast migration. Although direct live imaging of neuroblast migration was beyond the scope of the current dataset, we believe our evidence strongly supports a model in which ABBA depletion disrupts progenitor structure and migration. Future studies will address these transitions more directly using live imaging and fate-mapping strategies

(3) Response to statement on loss of interaction between ABBA and NEDD9 with the R671W mutation:

We fully agree with the importance of investigating whether the R671W mutation alters ABBA’s interaction with NEDD9. While our study provides evidence for a role of NEDD9 in mediating ABBA function, we acknowledge that we did not directly test whether the R671W mutation disrupts this interaction. We apologize if our manuscript conveyed the impression that this point had been fully addressed. Due to technical limitations, particularly the poor performance of anti-NEDD9 antibodies in slice immunohistochemistry, we were unable to reliably assess the interaction or localization changes in vivo. Nevertheless, this remains a priority for future studies aimed at better understanding the mechanistic underpinnings of the R671W mutation.

(4) Response to statement on future directions for mechanistic characterization of NEDD9 localization and interaction:

We agree with the reviewer that further investigation into NEDD9 localization and its interaction with the ABBA R671W mutant is essential to better define the molecular consequences of this mutation. Unfortunately, as mentioned above, the current tools available to us did not permit reliable immunohistochemical detection of NEDD9 in tissue. We fully intend to pursue alternative approaches, such as tagging strategies or the use of more sensitive detection platforms, to determine whether the R671W mutation affects the subcellular localization or stability of the ABBA-NEDD9 interaction. These experiments will be critical to elucidate the pathway through which ABBA regulates progenitor cell behavior and cortical development.

Reviewer #2 (Public review):

Summary:

Carabalona and colleagues investigated the role of the membrane-deforming cytoskeletal regulator protein Abba (MTSS1L/MTSS2) in cortical development to better understand the mechanisms of abnormal neural stem cell mitosis. The authors used short hairpin RNA targeting Abba20 with a fluorescent reporter coupled with in utero electroporation of E14 mice to show changes to neural progenitors. They performed flow cytometry for in-depth cell cycle analysis of Abba-shRNA impact to neural progenitors and determined an accumulation in S phase. Using culture rat glioma cells and live imaging from cortical organotypic slides from mice in utero electroporated with Abba-shRNA, the authors found Abba played a prominent role in cytokinesis. They then used a yeast-two-hybrid screen to identify three high confidence interactors: Beta-Trcp2, Nedd9, and Otx2. They used immunoprecipitation experiments from E18 cortical tissue coupled with C6 cells to show Abba requirement for Nedd9 localization to the cleavage furrow/cytokinetic bridge. The authors performed an shRNA knockdown of Nedd9 by in utero electroporation of E14 mice and observed similar results as with the Abba-shRNA. They tested a human variant of Abba using in utero electroporation of cDNA and found disorganized radial glial fibers and misplaced, multipolar neurons, but lacked the impact of cell division seen in the shRNA-Abba model.

Strengths:

Fundamental question in biology about the mechanics of neural stem cell division.

Directly connecting effects in Abba protein to downstream regulation of RhoA via Nedd9.

Incorporation of human mutation in ABBA gene.

Use of novel technologies in neurodevelopment and imaging.

Weaknesses:

Unexplored components of the pathway (such as what neurogenic populations are impacted by Abba mutation) and unleveraged aspects of their data (such as the live imaging) limit the scope of their findings and left significant questions about the effect of ABBA on radial glia development.

(1) Claim of disorganized radial glial fibers lacks quantifications.

- On page 11, the authors claim that knockdown of Abba lead to changes in radial glial morphology observed with vimentin staining. Here they claim misoriented apical processes, detached end feet, and decreased number of RGP cells in the VZ. However, they no not provide quantification of process orientation to better support their first claim. Measurements of radial glia fiber morphology (directionality, length) and of angle of division would be metrics that can be applied to data. Some of these analysis could be done in their time-lapse microscopy images, such as to quantify the number of cell division during their period of analysis (though that is short-15 hours).

Response to: Lack of quantification of disorganized radial glial fibers and cell divisions in time-lapse data

We appreciate the reviewer’s insightful comment regarding the need for quantification of radial glial (RG) fiber morphology. In the revised manuscript, we have addressed this by providing new quantification of changes in vimentin staining, specifically measuring the dispersion of the signal as a proxy for fiber disorganization (see Supplementary Figure 1). These data support the observed morphological changes, including misoriented apical processes and detachment of endfeet, following Abba knockdown.

Regarding time-lapse analysis to track cell divisions, we attempted to follow individual cells during the 15-hour imaging window. However, due to the relatively short duration and limited number of cells that could be reliably tracked, the dataset did not allow for statistically meaningful conclusions. As an alternative approach, we performed live-cell imaging using Anillin-GFP, a reliable marker of mitotic progression. The distribution and accumulation of Anillin-GFP were analyzed in ABBA-shRNA3 and control conditions, and the results (now included in Supplementary Figure 3) indicate an increased number of cells arrested in late mitosis upon ABBA knockdown. This supports the notion of disrupted cytokinesis as a consequence of Abba depletion.

(2) Unclear where effect is:

- In RG or neuroblasts? Is it in cell cleavage that results in accumulation of cells at VZ (as sometimes indicated by their data like in Fig 2A or 4D)? Interrogation of cell death (such as by cleaved caspase 3) would also help. Given their time lapse, can they identify what is happening to the RG fiber? The authors describe a change in "migration" but do not show evidence for this for either progenitor or neuroblast populations. Given they have nice time-lapse imaging data, could they visualize progenitor versus young neuron migration? Analysis of neuroblasts (such as with doublecortin expression in the tissue) would also help understand any issues in migration (of neurons v stem cells).

- At cleaveage furrow? In abscission? There is high resolution data that highlights the cleavage furrow as the location of interest (fig 3A), however there is also data (fig 3B) to suggest Abba is expressed elsewhere as well and there is an overall soma decrease. More detail of the localization of Abba during the division process would be helpful-for example, could cleavage furrow proteins, such as Aurora B, co-localization (and potentially co-IP) help delineate subpopulations of Abba protein? Furthermore, the FRET imaging is unique way to connect their mutation with function-could they measure/quantify differences at furrow compared to rest of soma to further corroborate that Abba-associated RhoA effect was furrow-enriched?

- The data highlights nicely that a furrow doesn't clearly form when ABBA expression and subsequent RhoA activity are decreased (in Fig 3 or 5A). Does this lead to cells that can't divide because of poor abscission, especially since "rounding" still occurs? Or abnormal progenitors (with loss of fiber or inability to support neuroblast migration)? Or abnormal progression of progenitors to neuroblasts?

Response to: Unclear location of the effect (RG vs. neuroblasts; cleavage furrow/abscission; migration issues)

We thank the reviewer for this comprehensive and thought-provoking set of questions.

a) Site of the effect – Radial Glia vs. Neuroblasts:

Our data suggest that the primary effect of ABBA depletion occurs in radial glial progenitors (RGPs), specifically prior to abscission. We observed accumulation of electroporated cells in the ventricular zone (VZ), which we interpret as a result of cytokinetic failure (e.g., Figure 2A, 4D). We also documented detachment of apical and basal endfeet (see Supplementary Figure 3), further supporting structural disruption of RG fibers.

b) Cell death analysis:

We considered using cleaved caspase-3 as a marker for apoptosis, but due to its transient and non-specific activation during development, we opted to assess overall survival via quantification of RGP cell numbers and localization. This approach better reflects the developmental impact of ABBA knockdown (Supplementary Figure 3).

c) Migration defects:

We agree that distinguishing between progenitor and neuroblast migration would be highly informative. Although we did not perform doublecortin or similar staining to differentiate these populations in this dataset, the accumulation of electroporated cells in VZ/SVZ strongly suggests a migration deficit. Addressing this in detail will require new experiments using lineage-specific markers and longer time-lapse recordings, which we plan to explore in future studies.

d) Cleavage furrow and abscission:

Our high-resolution imaging of Anillin-GFP and FRET-based RhoA activity shows that ABBA localizes predominantly at the cleavage furrow. New quantifications of RhoA activity (now in Figure 5) show that the reduction in signaling is most pronounced at the furrow in ABBA knockdown cells. These findings align with the hypothesis that ABBA, through Nedd9 and RhoA, is essential for proper furrow formation and abscission.

e) Mechanistic implications:

As the reviewer notes, ABBA knockdown leads to cells that "round" but do not complete division, likely due to poor cleavage furrow ingression. This could generate abnormal progenitors that are structurally compromised (detached fibers) and thus unable to support neuroblast migration or proper differentiation. The cumulative result is disrupted progression from RGPs to neuroblasts, impaired structural scaffolding, and possibly reduced cell viability.

(3) Limited to a singular time point of mouse cortical development

On page 13, the authors outline the results of their Y2H screen with the identification of three high confidence interactors. Notably, they used a E10.5-E12.5 mouse brain embryo library rather than one that includes E14, the age of their in utero electroporation mice. Many of the authors' claims focus on in utero electroporation of shRNA-Abba of E14 mice that are then evaluated at E16-18. Justification for the focus on this age range should be included to support that their findings can then be applied to all of mouse corticogenesis.

Response to: Use of E10.5–E12.5 library for yeast-two-hybrid (Y2H) screen

We appreciate the reviewer’s concern regarding the developmental stage of the Y2H library. We chose the E10.5–E12.5 brain embryo library based on prior work demonstrating that ABBA expression is strongest during early cortical development, particularly in radial glia at these stages (see Saarikangas et al., J Cell Sci 2008). The radial glia-specific expression of ABBA was previously validated using RC2 and Tuj1 markers at E12.5. Thus, the library we used is well-suited for identifying interactors relevant to radial glial function, including Nedd9. We have clarified this rationale in the revised manuscript.

(4) Detail of the effect of the human variant of the ABBA mutation in mouse is lacking.

Their identification of the R671W mutation is interesting and the IUE model warrants more characterization, as they did with their original KD experiments.

- Could they show that Abba protein levels are decreased (in either cell lines or electroporated tissue)?

- While time-lapse morphology might not have been performed, more analysis on cell division phenotype (such as plane of division and radial glia morphology) would be helpful.

Response to: Lack of detail on R671W human variant effects

We thank the reviewer for encouraging further characterization of the R671W variant. In the revised manuscript, we now provide additional data on interkinetic nuclear migration (INM) defects resulting from R671W overexpression (see Supplementary Figure 3). These changes are consistent with disrupted radial glial organization and mirror aspects of the ABBA knockdown phenotype.

a) Protein levels:

We quantified ABBA expression in cells overexpressing the R671W variant (Supplementary Figure 5) and found no significant reduction compared to wild-type. This argues against a loss-of-function mechanism and supports a gain-of-function or dominant-interfering effect.

b) Morphological and division phenotyping:

While time-lapse imaging of R671W-expressing cells was not available in our dataset, we acknowledge that analyses such as division angle or radial glial morphology would be informative. Unfortunately, we were unable to perform these with the current data, but we agree these are important goals for future work.

Reviewer 2 conclusion:

The resubmission has addressed many of the questions raised.

I have a few comments that should be addressed:

(1) The authors maintain a deficit in "migration of immature neurons" which remains unsubstantiated. In their resonse, they state: "we believe that the data showing the accumulation of migrating electroporated cells in the ventricular (V) and subventricular (SV) zones provide compelling evidence of abnormal migration in ABBA-shRNA electroporated cells. "

- Firstly, they do not demonstrate that it's immature neurons, not RGs, that are affected. Secondly, accumulation of cells at the V-SVZ could be due to soley the inability for the RGC to undergo mitosis, therefore remaining stuck"

The commentary of migration, especially of neurons, should be modified.

We appreciate the reviewer’s careful reading and valid concern regarding our use of the term "migration of immature neurons." We fully agree that the current dataset does not definitively distinguish whether the accumulated cells in the ventricular (V) and subventricular (SV) zones are immature neurons or radial glial progenitors (RGPs) arrested in mitosis.

To clarify, our observations indicate that electroporated cells accumulate in the VZ/SVZ following ABBA knockdown (Figures 2A and 4D), and this was interpreted as evidence of impaired migration. However, we now recognize that this accumulation may primarily reflect a block in cell cycle progression—specifically, at the stage of cleavage furrow ingression and abscission—rather than a migratory defect per se. This is supported by our new data using Anillin-GFP (Supplementary Figure 3), which show increased accumulation of cells with persistent Anillin expression, consistent with mitotic arrest. Furthermore, the detachment of apical and basal processes (also shown in Supplementary Figure 3) suggests that ABBA knockdown affects the structural integrity of RGPs, potentially compromising their scaffold function.

In light of these points, we have revised the manuscript to temper our conclusions regarding “migration defects.” Specifically, we now refer to the phenotype as “abnormal accumulation of progenitor cells” and clarify that, while these findings are consistent with impaired cell progression or scaffolding required for migration, we do not directly demonstrate impaired migration of immature neurons. As suggested, addressing this would require additional analyses, such as time-lapse imaging of post-mitotic cells or staining with markers like Doublecortin, which are beyond the scope of the current dataset but will be a focus of future investigations.

We thank the reviewer again for encouraging a more precise interpretation of our findings

Recommendations for the authors:

Reviewer #1 (Recommendations for the authors):

Supplementary Fig 4B - The figure doesn't show an increase in percentage of PH3 positive cells in the NEDD9-shRNA condition. The control images are also missing for comparison. The figure legend needs to be corrected to match with the figure showing no significant changes.

Thank you for this comment. This has been amended in the revised manuscript in the form of a new revised Supplementary Fig 4.

Reviewer #2 (Recommendations for the authors):

Minor annotations for slice culture assay

The authors should make note of ages of slice cultures in text and have better annotations of slice cultures (for example, in Fig 4-where is mitosis?)

We are sorry for the mistake it's not mitosis, it's the cleavage furrow stage.  In addition, a new amended Figure 4 is provided. 

The effects are hard to see in lower mag slice images in Fig. 6. Would recommend focusing on higher mag to highlight RG differences.

Thank you for this comment. This has been amended in the revised manuscript in the form of a new revised Figure 6.

Author response:

The following is the authors’ response to the original reviews

Public Reviews:

Reviewer #1 (Public review):

Summary:

In this study, Xiao et al. classified retroperitoneal liposarcoma (RPLS) patients into two subgroups based on whole transcriptome sequencing of 88 patients. The G1 group was characterized by active metabolism, while the G2 group exhibited high scores in cell cycle regulation and DNA damage repair. The G2 group also displayed more aggressive molecular features and had worse clinical outcomes compared to G1. Using a machine learning model, the authors simplified the classification system, identifying LEP and PTTG1 as the key molecular markers distinguishing the two RPLS subgroups. Finally, they validated these markers in a larger cohort of 241 RPLS patients using immunohistochemistry. Overall, the manuscript is clear and well-organized, with its significance rooted in the large sample size and the development of a classification method.

Thank you for your positive assessment of our study on classifying RPLS patients based on whole transcriptome sequencing. We appreciate your recognition of the distinct characteristics of the G1 and G2 groups, as well as the significance of our simplified classification system and the identification of LEP and PTTG1 as key molecular markers. Your acknowledgment of the clarity and organization of our manuscript, along with the importance of the large sample size, is greatly appreciated. We will continue to refine our work based on your feedback as we prepare for resubmission.

Weakness:

(1) While the authors suggest that LEP and PTTG1 serve as molecular markers for the two RPLS groups, the process through which these genes were selected remains unclear. The authors should provide a detailed explanation of the selection process.

The selection criteria for identifying LEP and PTTG1 as biomarkers involved selecting prognostic genes that were highly expressed in C1 and C2, respectively, and achieved the highest AUC value in distinguishing the two RPLS groups (Page17 lines 288-290).

(2) To ensure the broader applicability of LEP and PTTG1 as classification markers, the authors should validate their findings in one or two external datasets.

We sincerely appreciate your insightful suggestion regarding the external validation of LEP and PTTG1 as classification biomarkers. To address this concern, we performed an independent validation using an external liposarcoma cohort (GSE30929; Page 6, Lines 104-105)), which comprises 140 primary liposarcoma samples with annotated clinicopathological and survival data. This dataset was selected due to its relevance to RPLS (N=63, 45%) and the availability of distant recurrence-free survival (DRFS) outcomes, aligning with the clinical focus of our study. 

Applying our previously established prognostic model (Risk value = 2.182 × PTTG1 - 2.204 × LEP) to this cohort, we stratified patients into high- and low-risk groups using the median risk score as the cutoff. Consistent with our original findings, the high-risk group exhibited significantly worse DRFS compared to the low-risk group. The ROC curves based on the 1-, 3-, 5-year survival status of patients demonstrated that this model can effectively predict patient DRFS (log-rank P < 0.001, Figure S3A-B). Furthermore, the high-risk group demonstrated a higher proportion of high-grade histology (P < 0.001, Fisher’s exact test, Figure S3C-D).

These results validate the robustness and generalizability of our risk stratification model across distinct liposarcoma cohorts. The external dataset’s alignment with our findings underscores the potential of LEP and PTTG1 as reproducible biomarkers for prognosis and therapeutic stratification in liposarcoma. We have incorporated these validation results into the revised manuscript (Page 18, Lines 305-315) to strengthen the clinical applicability of our conclusions.

(3) Since molecular subtyping is often used to guide personalized treatment strategies, it is recommended that the authors evaluate therapeutic responses in the two distinct groups. Additionally, they should validate these predictions using cell lines or primary cells.

We sincerely appreciate your insightful comments and suggestions regarding the evaluation of therapeutic responses and the validation of our predictions using cell lines or primary cells. We would like to address these points in detail below:

(1) Purpose of the PTTG1- and LEP-based RPLS Classification Model

The primary objective of our study was to develop a molecular subtyping model based on PTTG1 and LEP to guide personalized treatment strategies for patients with RPLS, particularly those classified as low-grade by traditional histopathological criteria but exhibiting poor prognosis. This subgroup of patients may benefit from more aggressive surgical resection, which is a potentially curative approach for RPLS. Our model aims to identify these high-risk patients to ensure complete tumor resection, thereby improving their clinical outcomes.

(2) Therapeutic Response Evaluation in Distinct Groups

In both our validation cohort and external validation cohort, surgical resection was the primary treatment modality for RPLS. After stratifying patients using our model, we observed significant differences in surgical outcomes between the two groups: the high-risk group exhibited poor prognosis, while the low-risk group showed favorable outcomes (Figure 5D-E and Figure S3A-B). Importantly, our model successfully identified low-grade histopathological cases with poor prognosis, who might otherwise be undertreated (Figure 5G-I and Figure S3C-D). By advocating for more thorough surgical resection in these high-risk patients, we aim to improve their prognosis. This achievement aligns with the primary goal of our study, which is to provide a molecular tool for personalized treatment guidance.

(3) Future Validation and Functional Exploration of PTTG1 and LEP

Our study has identified PTTG1 and LEP as key biomarkers for RPLS classification, and we recognize the urgent need to elucidate their molecular functions in RPLS pathogenesis. Here, we are pleased to report that we have already initiated cellular and animal experiments to investigate the roles of PTTG1 and LEP in RPLS. These experiments aim to validate our predictions and explore the underlying mechanisms by which these biomarkers contribute to tumor behavior and treatment response. We anticipate that the results of these studies will provide further mechanistic insights and will be submitted for publication in a suitable journal in the near future.

Reviewer #2 (Public review):

Surgical resection remains the most effective treatment for retroperitoneal liposarcoma. However, postoperative recurrence is very common and is considered the main cause of disease-related death. Considering the importance and effectiveness of precision medicine, the identification of molecular characteristics is particularly important for the prognosis assessment and individualized treatment of RPLS. In this work, the authors described the gene expression map of RPLS and illustrated an innovative strategy of molecular classification. Through the pathway enrichment of differentially expressed genes, characteristic abnormal biological processes were identified, and RPLS patients were simply categorized based on the two major abnormal biological processes. Subsequently, the classification strategy was further simplified through nonnegative matrix factorization. The authors finally narrowed the classification indicators to two characteristic molecules LEP and PTTG1, and constructed novel molecular prognosis models that presented obviously a great area under the curve. A relatively interpretable logistic regression model was selected to obtain the risk scoring formula, and its clinical relevance and prognostic evaluation efficiency were verified by immunohistochemistry. Recently, prognostic model construction has been a hot topic in the field of oncology. The interesting point of this study is that it effectively screened characteristic molecules and practically simplified the typing strategy on the basis of ensuring high matching clinical relevance. Overall, the study is well-designed and will serve as a valuable resource for RPLS research.

Thank you for your insightful feedback on our manuscript. We appreciate your recognition of the importance of precision medicine and molecular characteristics in improving prognosis and individualized treatment for RPLS.

We are pleased that you found our gene expression mapping and innovative molecular classification strategy valuable. Your positive remarks on our pathway enrichment analysis and the categorization of RPLS patients based on abnormal biological processes affirm our approach.

We are also grateful for your acknowledgment of our focus on the characteristic molecules LEP and PTTG1, as well as the development of novel molecular prognosis models with significant predictive capability.

Author response:

The following is the authors’ response to the previous reviews

Joint Public Review:

Summary

In this manuscript, Dong et al. study the directed cell migration of tracheal stem cells in Drosophila pupae. The authors study how the directionality of these cells is regulated along the dorsal trunk. They show that inter-organ communication between the tracheal stem cells and the nearby fat body plays a role in posterior migration. They provide compelling evidence that Upd2 production in the fat body and JAK/STAT activation in the tracheal stem cells play a role. Moreover, they show that JAK/STAT signalling might induce the expression of apicobasal and planar cell polarity genes in the tracheal stem cells which appear to be needed to ensure unidirectional migration. Finally, the authors suggest that trafficking and vesicular transport of Upd2 from the fat body towards the tracheal cells might be important.

Strengths

The manuscript is well written and presents extensive and varied experimental data to show a link between Upd2-JAK/STAT signaling from the fat body and tracheal progenitor cell migration. The authors provide convincing evidence that the fat body, located near the trachea, secretes vesicles containing the Upd2 cytokine and that affecting JAK-STAT signaling results in aberrant migration of some of the tracheal stem cells towards the anterior. Using ChIP-seq as well as analysis of GFP-protein trap lines of planar cell polarity genes in combination with RNAi experiments, the authors show that STAT92E likely regulates the transcription of planar cell polarity genes and some apicobasal cell polarity genes in tracheal stem cells which appear to be needed for unidirectional migration. The work presented here provides some novel insights into the mechanism that ensures polarized migration of tracheal stem cells, preventing bidirectional migration. This might have important implications for other types of directed cell migration in invertebrates or vertebrates including cancer cell migration. Overall, the authors have substantially improved their manuscript since the first submission but there are still some weaknesses.

Weaknesses

Overall, the manuscript lacks insights into the potential significance of the observed phenotypes and of the proposed new signaling model. Most of our concerns could be dealt with by adjusting the text (explaining some parts better and toning down some statements).

(1) Directional migration of tracheal progenitors is only partially compromised, with some cells migrating anteriorly and others maintaining their posterior migration, a quite discrete phenotype.

The strongest migration defects quantified in graphs (e.g. 100 μm) are not shown in images, since they would be out of frame, it would be beneficial to see them. In addition, the consequence of defects in polarized migration on tracheal development is not clear and data showing phenotypes on the final trachea morphology in pupae are not explained nor linked to the previous phenotypes.

We agree with you that it is informative to show strong anterior migration (> 100 μm). Accordingly, we have shown examples in Figure 3B and Figure 7R-S. In addition, we have also discuss on the links between migration defects and the consequential phenotypes of the animal at a later developmental stage in the revised manuscript. The undisciplined migration leads to insufficient regeneration and incomplete remodeling of airway and causes pupal lethality.

(2) Some important information is lacking, such as the origin of mutant and UAS-RNAi lines, which are not reported in the material and methods. For instance, mutants for components of the JAK-STAT pathway are used but not described. Are they all viable at the pupal stage? Otherwise, pupae would not be homozygous mutants. From the figure legend, it seems that the Stat92EF allele has been used, which is a point mutation, thus not leading to an absence of protein. If the hopTUM allele has been used, as mentioned in the legend, it is a gain-of-function allele. Thus, the authors should not conclude that "The aberrant anterior migration of tracheal progenitors in the absence of JAK/STAT components led to impairment of tracheal integrity and caused melanization in the trachea (Figure 3-figure supplement 1E-I)".

We apologize for inadequate description of the experimental materials and methods. We have listed the stock number of mutant and RNAi alleles in Key resource table and Materials. The mutant alleles that we chose to examine can survive to pupal stage, which is key to the success of our subsequent characterization of these mutants. According to your suggestion, we modified the statement for accuracy.

(3) The authors observe that tracheal progenitors display a polarized distribution of Fat that is controlled by JAK-STAT signaling. However, this conclusion is made from a single experiment using only 3 individuals with no statistics. This is insufficient to support the claim that "JAK/STAT signaling promotes the expression of genes involved in planar cell polarity leading to asymmetric localization of Fat in progenitor cells", as mentioned in the abstract, or that "the activated tracheal progenitors establish a disciplined migration through the asymmetrical distribution of polarity proteins which is directed by an Upd2-JAK/STAT signaling stemming from the remote organ of fat body."

We performed multiple biological replicates for Ft distribution experiments and observed similar trend, although we only showed three representative samples. In the revised text, we have included n number for statistic representation and statistic test.

(4) The authors demonstrate that Upd2 is transported through vesicles from the fat body to the tracheal progenitors. It remains somewhat unclear in the proposed model how Upd2 activates JAK-STAT signaling. Are vesicles internalized, as it seems to be proposed, and thus how does Upd2 activate JAK-STAT signaling intracellularly? Or is Upd2 released from vesicles to bind Dome extracellularly to activate the JAK-STAT pathway? Moreover, it is not clear nor discussed what would be the advantage of transporting the ligand in vesicles compared to classical ligand diffusion.

We do not know whether the association between Upd2 and Lbm is inside or outside vesicles. The vesicular trafficking of Upd2 is our observation and supported by various genetic and biochemical experiments. Our research does not imply the message that this vesicular trafficking has advantage over diffusion.

Author response:

The following is the authors’ response to the original reviews

Joint Public Review:

Idiopathic scoliosis (IS) is a common spinal deformity. Various studies have linked genes to IS, but underlying mechanisms are unclear such that we still lack understanding of the causes of IS. The current manuscript analyzes IS patient populations and identifies EPHA4 as a novel associated gene, finding three rare variants in EPHA4 from three patients (one disrupting splicing and two missense variants) as well as a large deletion (encompassing EPHA4) in a Waardenburg syndrome patient with scoliosis. EPHA4 is a member of the Eph receptor family. Drawing on data from zebrafish experiments, the authors argue that EPHA4 loss of function disrupts the central pattern generator (CPG) function necessary for motor coordination.

The main strength of this manuscript is the human genetic data, which provides convincing evidence linking EPHA4 variants to IS. The loss of function experiments in zebrafish strongly support the conclusion that EPHA4 variants that reduce function lead to IS.

The conclusion that disruption of CPG function causes spinal curves in the zebrafish model is not well supported. The authors' final model is that a disrupted CPG leads to asymmetric mechanical loading on the spine and, over time, the development of curves. This is a reasonable idea, but currently not strongly backed up by data in the manuscript. Potentially, the impaired larval movements simply coincide with, but do not cause, juvenile-onset scoliosis. Support for the authors' conclusion would require independent methods of disrupting CPG function and determining if this is accompanied by spine curvature. At a minimum, the language of the manuscript could be toned down, with the CPG defects put forward as a potential explanation for scoliosis in the discussion rather than as something this manuscript has "shown". An additional weakness of the manuscript is that the zebrafish genetic tools are not sufficiently validated to provide full confidence in the data and conclusions.

We highly appreciate the reviewer’s insightful comments and the acknowledgment of the main values of our study. We agree with the reviewer that further experiments are needed to fully establish the relationship between CPG and scoliosis. In response, we have revised the conclusion in the manuscript to better reflect this. Additionally, we conducted further analyses on the mutants to provide additional evidence supporting this concept.

Reviewer #1 (Recommendations for the authors):

Epha4a mutant zebrafish exhibited mild spinal curves, mostly laterally and in the tail. This was 75% of homozyous mutants but also, surprisingly, about 20% of heterozygotes. epha4b mutants also developed some mild scoliosis. If the two zebrafish paralogs can compensate for each other (partial redundancy), we might expect more severe scoliosis in double mutants. Did the authors generate and analyze double mutants? I believe it would be very useful for this study to report the zebrafish phenotype of loss of both paralogs together.

We appreciate the reviewer’s insightful comment regarding the potential value of reporting the phenotype of eph4a/eph4b double mutants. While we fully agree that this analysis would be valuable, our attempts to generate double mutants have been unsuccessful. These two genes are closely linked on the chromosome, with less than 100 kb separating them, which makes it challenging to generate double mutants through standard genetic crossing. Establishing a double mutant line would require more than a year due to the technical constraints of the process. Although we are unable to address this question directly at this time, we hypothesize that eph4a/eph4b double mutants may exhibit a higher likelihood of body axis abnormalities based on the phenotypes observed in single mutants and the known functions of these genes.

We hope this perspective will provide some useful context despite the limitations.

In Figure 1F, a pCDK5 western blot is performed as a readout of EPH4A signaling after either WT or C849Y mutant EPH4A is transfected into HEK 293T cells. It would be useful to mention in the text, or at least the figure legend, how this experiment was performed/where the protein samples came from. It is included in the methods, but in the main text, it simply says "we conducted western blotting" without mentioning whether the protein samples were from cell lines, patients, or another source.

Sorry for our ignorance. A detailed description of the western blotting conduction was supplemented at both “results” part (page 8, line 187-190) and the Figure 1 legend.

Was the relative turn angle biased to the left or right side of the fish? (i.e. is a positive angle a rightward or leftward turn?)

We are sorry for our unclear description. In Figure 3D, positive angle means turning left, while negative angle means turning right. In wild-type larvae, the average turning angle over a 4-minute period is approximately 0, whereas in mutants, this value deviates from 0, indicating a directional preference (positive for leftward and negative for rightward turns) in swimming behavior during the recording period. We have also made the necessary supplementation in the text and figure legend.

In Figure 4, morpholinos rather than mutants are used, but it is not clear why. Has it been established that the MO used disrupts gene function specifically? Can the effect of the MO be rescued by expressing a wild-type mRNA of Epha4a? Does MO knockdown induce spinal curves if fish are raised? Indeed, this could be a way to determine whether the spinal curves are caused by early events in development (when MOs are active).

Thanks for the comments. The efficacy of relevant MOs has been well-documented in numerous previous studies (Addison et al., 2018; Cavodeassi et al., 2013; Letelier et al., 2018; Royet et al., 2017). Following this reviewer’s suggestion, we have raised the epha4a morphants into adults, while no scoliosis were observed, suggesting that the spinal curvature formation may be induced by long-term defects in the absence of Epha4a. Additionally, we reconfirmed the abnormal motor neuron activation frequency phenotype in the mutants background. The corresponding data have replaced the original Figure 4 in the manuscript. 

References

(1) Addison, M., Xu, Q., Cayuso, J., and Wilkinson, D.G. (2018). Cell Identity Switching Regulated by Retinoic Acid Signaling Maintains Homogeneous Segments in the Hindbrain. Dev Cell 45, 606-620 e603.

(2) Cavodeassi, F., Ivanovitch, K., and Wilson, S.W. (2013). Eph/Ephrin signalling maintains eye field segregation from adjacent neural plate territories during forebrain morphogenesis. Development 140, 4193-4202.

(3) Letelier, J., Terriente, J., Belzunce, I., Voltes, A., Undurraga, C.A., Polvillo, R., Devos, L., Tena, J.J., Maeso, I., Retaux, S., et al. (2018). Evolutionary emergence of the rac3b/rfng/sgca regulatory cluster refined mechanisms for hindbrain boundaries formation. Proc Natl Acad Sci U S A 115, E3731-E3740.

(4) Royet, A., Broutier, L., Coissieux, M.M., Malleval, C., Gadot, N., Maillet, D., Gratadou-Hupon, L., Bernet, A., Nony, P., Treilleux, I., et al. (2017). Ephrin-B3 supports glioblastoma growth by inhibiting apoptosis induced by the dependence receptor EphA4. Oncotarget 8, 23750-23759.

Reviewer #2 (Recommendations for the authors):

Supplementary Table 3 is missing.

Sorry for any inconvenience caused to the reviewers. Due to the size of the supplementary Table 3, we have separately uploaded an Excel file as supplementary materials. We have also double-checked during the resubmission process of the revised manuscript. Thanks for your thorough review.

The authors report only a single mutant allele for zebrafish epha4a and epha4b. Additionally, they provide no information about how many generations each allele has been outcrossed. The authors should provide some type of validation that the phenotypes they describe result from loss of function of the targeted gene and not from an off-targeting event.

Thanks for the comments. For epha4a and epha4b mutants, each homozygous mutant was initially derived from the self-crossing of first filial generation heterozygotes, and subsequent homozygous generations were maintained for fewer than three rounds of in-crossing. Interestingly, we observed a reduction in the incidence of scoliosis across successive generations. This trend may be attributed to potential genetic compensation mechanisms, which could mitigate the phenotypic severity over time. To address concerns about possible off-target effects, we synthesized and injected epha4a mRNA to test for phenotypic rescue. Our data show that epha4a mRNA injection partially restored swimming coordination in the mutants (Fig. S5). Moreover, similar motor coordination defects have been reported in Epha4-deficient mice, as documented in previous studies (Kullander et al., 2003; Borgius et al., 2014). These findings collectively strengthen the hypothesis that Epha4a plays a critical role in regulating motor coordination.

References

(1) Borgius, L., Nishimaru, H., Caldeira, V., Kunugise, Y., Low, P., Reig, R., Itohara, S., Iwasato, T., and Kiehn, O. (2014). Spinal glutamatergic neurons defined by EphA4 signaling are essential components of normal locomotor circuits. J Neurosci 34, 3841-3853.

(2) Kullander, K., Butt, S.J., Lebret, J.M., Lundfald, L., Restrepo, C.E., Rydstrom, A., Klein, R., and Kiehn, O. (2003). Role of EphA4 and EphrinB3 in local neuronal circuits that control walking. Science 299, 1889-1892.

The authors need to provide allele designations for the mutant alleles following accepted nomenclature guidelines.

Thank you for your careful review! We have reviewed and made revisions to the genes and mutation symbols throughout the entire text.

The three antisense morpholino oligonucleotides need to be validated for efficacy and specificity.

Thanks for the comments. The morpholinos were extensively used and validated in previous studies, and the efficacy of these morpholinos has been thoroughly validated in multiple studies (Addison et al., 2018; Cavodeassi et al., 2013; Letelier et al., 2018; Royet et al., 2017). Furthermore, we also performed swimming behavior analysis in the mutant background, which showed similar results as the morphants. Moreover, we also performed rescue experiments to confirm the specificity of the mutants (Fig. S5). Finally, we reconfirmed the abnormal calcium signaling in the mutants (Fig. 4), which further support our previous knockdown results.

References

(1) Addison, M., Xu, Q., Cayuso, J., and Wilkinson, D.G. (2018). Cell Identity Switching Regulated by Retinoic Acid Signaling Maintains Homogeneous Segments in the Hindbrain. Dev Cell 45, 606-620 e603.

(2) Cavodeassi, F., Ivanovitch, K., and Wilson, S.W. (2013). Eph/Ephrin signalling maintains eye field segregation from adjacent neural plate territories during forebrain morphogenesis. Development 140, 4193-4202.

(3) Letelier, J., Terriente, J., Belzunce, I., Voltes, A., Undurraga, C.A., Polvillo, R., Devos, L., Tena, J.J., Maeso, I., Retaux, S., et al. (2018). Evolutionary emergence of the rac3b/rfng/sgca regulatory cluster refined mechanisms for hindbrain boundaries formation. Proc Natl Acad Sci U S A 115, E3731-E3740.

(4) Royet, A., Broutier, L., Coissieux, M.M., Malleval, C., Gadot, N., Maillet, D., Gratadou-Hupon, L., Bernet, A., Nony, P., Treilleux, I., et al. (2017). Ephrin-B3 supports glioblastoma growth by inhibiting apoptosis induced by the dependence receptor EphA4. Oncotarget 8, 23750-23759.

Line 229. "While in consistent with previous reports, the hindbrain rhombomeric boundaries were found to be defective....". This sentence is not clear. Please describe how it is "inconsistent".

Thanks for the comments and sorry for the unclear description, we have described this more clearly in our revised manuscript (page 9, line 229-230).

Animals frequently are described as "heterozygous mutants" or "mutants". Please make clear that the latter are homozygous mutant animals.

Thanks for the comments. In the manuscript, all references to mutants specifically indicate homozygous mutants. Heterozygous mutants are explicitly identified as such.

The chromatin interaction portion of the Methods does not include any information on how these experiments were conducted or where the data were obtained. This information needs to be provided.

Thanks for your advice. The detailed information of chromatin interaction mapping has been provided in “Methods and Materials” (page 18-19, line 450-455). Information about the interacting regions was derived from Hi-C datasets of 21 tissues and cell types provided by GSE87112. The significance of interactions for Hi-C datasets was computed by Fit-Hi-C, with an FDR ≤ 10-6 considered significant.

The authors present single-cell RNA-seq data in Supplementary Figure 5 for which they cite Cavone et al, 2021. This seems like an odd database to use. Can the authors provide an explanation for choosing it? In any case, the citation should also be made in the Supplementary Figure 5 legend.

Thank you for your rigorous comment, we have cited this literature in the proper place of the revised manuscript. Cavone et al. used the her4.3:GFP line to label ependymo-radial glia (ERG) progenitor cells and performed single-cell RNA-seq on FACS-isolated fluorescent cells. The isolated cells included not only ERG progenitors but also undifferentiated and differentiated neurons and oligodendrocytes. The authors attributed this to the relative stability of the GFP protein, which remained in the progeny of GFP-expressing her4.3+ ERG progenitor cells, thus effectively acting as a short-term cell lineage tracer. Indeed, clustering analysis of this data successfully identifies neural progenitors and other neural clusters. Therefore, we consider that this scRNA-seq data encompasses a comprehensive range of neural cell types and is suitable for analyzing the expression of genes of interest. Furthermore, we downloaded and analyzed the scRNA-seq data of the zebrafish nervous system reported by Scott et al. in 2021 (Fig. S7B) (Scott et al., 2021). Despite differences in the developmental stages of the larvae analyzed (Cavone et al. examined larvae at 4 dpf, whereas Scott et al. analyzed larvae at 24, 36, and 48 hpf), our findings are consistent. Specifically, epha4a and epha4b are expressed in interneurons, whereas efnb3a and efnb3b are enriched in floor plate cells.

References

(1) Scott, K., O'Rourke, R., Winkler, C.C., Kearns, C.A., and Appel, B. (2021). Temporal single-cell transcriptomes of zebrafish spinal cord pMN progenitors reveal distinct neuronal and glial progenitor populations. Dev Biol 479, 37-50.

In Figure Legend 1, "expressed from the EPHA4-mutant plasmid" is not an accurate description of the experiment.

Sorry for the previous inaccurate description. The description has been revised to accurately reflect the experiment. “Western blot analysis of EPHA4-c.2546G>A variant showing the protein expression levels of EPHA4 and CDK5 and the amount of phosphorylated CDK5 (pCDK5) in HEK293T cells transfected with EPHA4-mutant or EPHA4-WT plasmid”.

Figure 3 panels J and K need more explanation. I don't understand what the different colors represent nor do I understand what are wild type and what are mutant data.

Thank you for your valuable feedback. We apologize for the lack of clarity in the original figure legend. To address this, we have revised the legend of Figure 3 to provide a more detailed explanation. In panels J and K, each color-coded curve represents the response of an individual larva from an independent experimental trial to the stimulus. Specifically, panel J depicts the response data for the wild-type larvae, whereas panel K presents the response data for the homozygous epha4a mutants.

Please provide the genotypes for the images in Figure 5A.

Thanks for the comments and we are sorry for our unclear description, we have described this more clearly in the Figure 5.

Figure legend 6B should also note the heterozygote data with the wild type and homozygous mutant data.

Thanks for the comments, the data are now included in Figure 6B.

Epha4 and Efnb3 have well-established roles in axon guidance. Although this is noted in the Discussion, I think a more extensive description of prior findings would be helpful.

Thanks for your valuable feedback. A more detailed description of the roles of Epha4 and Efnb3 in axon guidance was provided in the “Discussion” (page 16, line 388-396).

The main conclusion of this manuscript is that EPHA4 variants cause IS by disrupting central pattern generator function. I think this is misleading. I think that the more valid conclusion is that EPHA4 loss of function causes axon pathfinding defects that impair locomotion by disrupting CPG activity, thereby leading to IS. I urge the authors to consider this more nuanced interpretation.

Thank you for your insightful comments. We appreciate your suggestion to refine our main conclusion. We agree that the proposed revision more accurately reflects our findings and will revise the manuscript accordingly to state that “EPHA4 loss of function causes axon pathfinding defects, which impair locomotion by disrupting central pattern generator activity, potentially leading to IS.”

Author response:

The following is the authors’ response to the original reviews

Public Reviews:

Reviewer #1 (Public review):

Summary:

In this study, Seidenthal et al. investigated the role of the C. elegans Flower protein, FLWR-1, in synaptic transmission, vesicle recycling, and neuronal excitability. They confirmed that FLWR-1 localizes to synaptic vesicles and the plasma membrane and facilitates synaptic vesicle recycling at neuromuscular junctions, albeit in an unexpected manner. The authors observed that hyperstimulation results in endosome accumulation in flwr-1 mutant synapses, suggesting that FLWR-1 facilitates the breakdown of endocytic endosomes, which differs from earlier studies in flies that suggested the Flower protein promotes the formation of bulk endosomes. This is a valuable finding. Using tissue-specific rescue experiments, the authors showed that expressing FLWR-1 in GABAergic neurons restored the aldicarb-resistant phenotype seen in flwr-1 mutants to wild-type levels. In contrast, FLWR-1 expression in cholinergic neurons in flwr-1 mutants did not restore aldicarb sensitivity, yet muscle expression of FLWR-1 partially but significantly recovered the aldicarb-resistant defects. The study also revealed that removing FLWR-1 leads to increased Ca²⁺ signaling in motor neurons upon photo-stimulation. Further, the authors conclude that FLWR-1 contributes to the maintenance of the excitation/inhibition (E/I) balance by preferentially regulating the excitability of GABAergic neurons. Finally, SNG-1::pHluorin data imply that FLWR-1 removal enhances synaptic transmission, however, the electrophysiological recordings do not corroborate this finding.

Strengths:

This study by Seidenthal et al. offers valuable insights into the role of the Flower protein, FLWR-1, in C. elegans. Their findings suggest that FLWR-1 facilitates the breakdown of endocytic endosomes, which marks a departure from its previously suggested role in forming endosomes through bulk endocytosis. This observation could be important for understanding how Flower proteins function across species. In addition, the study proposes that FLWR-1 plays a role in maintaining the excitation/inhibition balance, which has potential impacts on neuronal activity.

Weaknesses:

One issue is the lack of follow-up tests regarding the relative contributions of muscle and GABAergic FLWR-1 to aldicarb sensitivity. The findings that muscle expression of FLWR-1 can significantly rescue aldicarb sensitivity are intriguing and may influence both experimental design and data interpretation. Have the authors examined aldicarb sensitivity when FLWR-1 is expressed in both muscles and GABAergic neurons, or possibly in muscles and cholinergic neurons? Given that muscles could influence neuronal activity through retrograde signaling, a thorough examination of FLWR-1's role in muscle is necessary, in my opinion.

We thank the reviewer for this suggestion. Indeed, the retrograde inhibition of cholinergic transmission by signals from muscle has been demonstrated by the Kaplan lab in a number of publications. We have now done the experiments that were suggested, see the new Fig. S3B: rescuing FLWR-1 in cholinergic neurons and in muscle did not perform any better in the aldicarb assay, while co-rescue in GABAergic neurons and muscle, like rescue in GABA neurons, led to a complete rescue to wild type levels. Thus, retrograde signaling from muscle to neurons does not contribute to effects on the E/I imbalance caused by the absence of FLWR1. The fact that muscle rescue can partially rescue the flwr-1 phenotype is likely due a cellautonomous effect of FLWR-1 on muscle excitability, facilitating muscle contraction.

Would the results from electrophysiological recordings and GCaMP measurements be altered with muscle expression of FLWR-1? Most experiments presented in the manuscript compare wild-type and flwr-1 mutant animals. However, without tissue-specific knockout, knockdown, or rescue experiments, it is difficult to separate cell-autonomous roles from non-cell-autonomous effects, in particular in the context of aldicarb assay results. Also, relying solely on levamisole paralysis experiments is not sufficient to rule out changes in muscle AChRs, particularly due to the presence of levamisole-resistant receptors.

We repeated the Ca²⁺ imaging in cholinergic neurons, in response to optogenetic activation, with expression of FLWR-1 in muscle, see Fig. 4E. This did not significantly alter the increased excitability of the flwr-1 mutant. Thus, we conclude that, along with the findings in aldicarb assays, the function of FLWR-1 in muscle is cell-autonomous, and does not indirectly affect its roles in the motor neurons. Also, cholinergic expression of FLWR-1 by itself reduced Ca²⁺ levels to those in wild type (Fig. 4E). In addition, we now also assessed the contribution of the N-AChR (ACR-16) to aldicarb-induced paralysis (Fig. S3C), showing that flwr-1 and acr-16 mutations independently mediate aldicarb resistance, and that these effects are additive. Thus, FLWR-1 does not affect the expression level or function of the N-AChR, as otherwise, the flwr1; acr-16 double mutation would not exacerbate the phenotype of the single mutants.

This issue regarding the muscle role of FLWR-1 also complicates the interpretation of results from coelomocyte uptake experiments, where GFP secreted from muscles and coelomocyte fluorescence were used to estimate endocytosis levels. A decrease in coelomocyte GFP could result from either reduced endocytosis in coelomocytes or decreased secretion from muscles. Therefore, coelomocytespecific rescue experiments seem necessary to distinguish between these possibilities.

We have performed a rescue of FLWR-1 in coelomocytes to address this, and found that this fully recovered the CC GFP signals to wild type levels. Therefore, the absence of FLWR-1 in muscles does not affect exocytosis of GFP. The data can be found in Fig. 5A, B.

The manuscript states that GCaMP was used to estimate Ca²⁺ levels at presynaptic sites. However, due to the rapid diffusion of both Ca²⁺ and GCaMP, it is unclear how this assay distinguishes Ca²⁺ levels specifically at presynaptic sites versus those in axons. What are the relative contributions of VGCCs and ER calcium stores here? This raises a question about whether the authors are measuring the local impact of FLWR-1 specifically at presynaptic sites or more general changes in cytoplasmic calcium levels.

We compared Ca²⁺ signals in synaptic puncta versus axon shafts, and did not find any differences. The data previously shown have been replaced by data where the ROIs were restricted to synaptic puncta. The outcome is the same as before. These data are provided in Fig. 4A, B, E, F. We thus conclude that the impact of FLWR-1 is local, in synaptic boutons.

The experiments showing FLWR-1's presynaptic localization need clarification/improvement. For example, data shown in Fig. 3B represent GFP::FLWR-1 is expressed under its own promoter, and TagRFP::ELKS-1 is expressed exclusively in GABAergic neurons. Given that the pflwr-1 drives expression in both cholinergic and GABAergic neurons, and there are more cholinergic synapses outnumbering GABAergic ones in the nerve cord, it would be expected that many green FLWR-1 puncta do not associate with TagRFP::ELKS-1. However, several images in Figure 3B suggest an almost perfect correlation between FLWR-1 and ELKS-1 puncta. It would be helpful for the readers to understand the exact location in the nerve cord where these images were collected to avoid confusion.

Thank you for making us aware that the provided images may be misleading. We have now extended this Figure (Fig. 3A-C) and provided more intensity profiles along the nerve cords in Fig. S4A-C. The quantitative analysis of average R² for the two fluorescent signals in each neuron type did not show any significant difference between the two, also after choosing slightly smaller ROIs for line scan analysis. We also highlighted the puncta corresponding to FLWR-1 in both neurons types, as well as to ELKS-1 in each specific neuron type, to identify FLWR-1 puncta without co-localized ELKS-1 signal. Also, we indicated the region that was imaged, i.e. the DNC posterior of the vulva, halfway to the posterior end of the nerve cord.

The SNG-1::pHluorin data in Figure 5C is significant, as they suggest increased synaptic transmission at flwr-1 mutant synapses. However, to draw conclusions, it is necessary to verify whether the total amount of SNG-1::pHluorin present on synaptic vesicles remains the same between flwr-1 mutant and wild-type synapses. Without this comparison, a conclusion on levels of synaptic vesicle release based on changes in fluorescence might be premature, in particular given the results of electrophysiological recordings.

We appreciate the comment. We now added data and experiments that verify that the basal SNG-1::pHluorin signal in the plasma membrane, measured at synaptic puncta and in adjacent axonal areas, is not different in flwr-1 mutants compared to wild type in the absence of stimulation. This data can be found in Fig. S5A. In addition, we cultured primary neurons from transgenic animals to compare total SNG-1::pHluorin to the vesicular fraction, by adding buffers of defined pH to the external, or buffers that penetrate the cell and fix intracellular pH. These experiments (Fig. S5B, C) showed no difference in the vesicle fraction of the pHluorin signal in wild type vs. flwr-1 mutant cells, demonstrating that flwr-1 mutants do not per se have altered SNG-1::pHluorin in their SV or plasma membranes.

Finally, the interpretation of the E74Q mutation results needs reconsideration. Figure 8B indicates that the E74Q variant of FLWR-1 partially loses its rescuing ability, which suggests that the E74Q mutation adversely affects the function of FLWR-1. Why did the authors expect that the role of FLWR-1 should have been completely abolished by E74Q? Given that FLWR-1 appears to work in multiple tissues, might FLWR-1's function in neurons requires its calcium channel activity, whereas its role in muscles might be independent of this feature? While I understand there is ongoing debate about whether FLWR1 is a calcium channel, the experiments in this study do not definitively resolve local Ca²⁺ dynamics at synapses. Thus, in my opinion, it may be premature to draw firm conclusions about calcium influx through FLWR-1.

Thank you for bringing this up. We did not expect E74Q to necessarily abolish FLWR-1 function, unless it would be a Ca²⁺ channel. Of course the reviewer is right, FLWR-1 might have functions as an ion channel as well as channel-independent functions. Yet, we are quite confident that FLWR-1 is not an ion channel. Instead, we think that E74Q alters stability of the protein (however, in the absence of biochemical data, we removed this conclusion), and that this impairs the function of FLWR-1 as a modulator, or possibly even, accessory subunit of the PMCA MCA-3. This interaction was indicated by a new experiment we added, where we found that FLWR-1 and MCA-3 must be physically very close to each other in the plasma membrane, using bimolecular fluorescence complementation (see new Fig. 9A, B). This provides a reasonable explanation for findings we obtained, i.e. increased Ca²⁺ levels in stimulated neurons of the flwr-1 mutant. If FLWR-1 acts as a stimulatory subunit of MCA-3, then its absence may cause reduced MCA-3 function and thus an accumulation of Ca²⁺ in the synaptic terminals. In Drosophila, hyperstimulation of neurons led to reduced Ca²⁺ levels (Yao et al., 2017, PLoS Biol 15: e2000931), suggesting that Flower is a Ca²⁺ channel. Based on our findings, we suggest an alternative explanation. Based on proteomics, the PMCA is a component of SVs (Takamori et al., 2006, Cell 127: 831-846). Increased insertion of PMCA into the plasma membrane during high stimulation, along with impaired endocytosis in flower mutants, would increase the steadystate levels of PMCA in the PM. This could lead to reduced steady state levels of Ca²⁺. This ‘g.o.f.’ in Flower may also impact on Ca²⁺ microdomains of the P/Q type VGCC required for SV fusion, which could contribute to the rundown of EPSCs we find during synaptic hyperstimulation (Fig. 5G-J). We acknowledge, though, that Yao et al. (2009, Cell 138: 947– 960), showed increased uptake of Ca²⁺ into liposomes reconstituted with purified Flower protein. However, it cannot be ruled out that a protein contaminant could be responsible, as the controls were empty liposomes, not liposomes reconstituted with a mutated Flower protein purified the same way.

We also tested the E74Q mutant in its ability to rescue the reduced PI(4,5)P₂ levels in coelomocytes (CCs), where we observed no positive effect. While we have not measured Ca²⁺ in CCs, we would assume that here a function of FLWR-1 affecting increased PI(4,5)P₂ levels is not linked to a channel function. It was, nevertheless, compromised by E74Q (Fig. 8D).

Also, the aldicarb data presented in Figures 8B and 8D show notable inconsistencies that require clarification. While Figure 8B indicates that the 50% paralysis time for flwr-1 mutant worms occurs at 3.5-4 hours, Figure 8D shows that 50% paralysis takes approximately 2.5 hours for the same flwr-1 mutants. This discrepancy should be addressed. In addition, the manuscript mentions that the E74Q mutation impairs FLWR-1 folding, which could significantly affect its function. Can the authors show empirical data supporting this claim?

We performed the aldicarb assays in a consistent manner, but nonetheless note that some variability from day to day can affect such outcomes. Importantly, we always measured each control (wild type, flwr-1) along with each test strain (FLWR-1 point mutants), to ensure the relevant estimate of a point-mutant’s effect. These assays have been repeated, now including the FLWR-1 wild type rescue strain as a comparison. The data are now combined in Fig. 8B. Regarding the assumed instability of the E74Q mutant, as we, indeed, do not have any experimental data supporting this, we removed this sentence.

Reviewer #2 (Public review):

Summary:

The Flower protein is expressed in various cell types, including neurons. Previous studies in flies have proposed that Flower plays a role in neuronal endocytosis by functioning as a Ca²⁺ channel. However, its precise physiological roles and molecular mechanisms in neurons remain largely unclear. This study employs C. elegans as a model to explore the function and mechanism of FLWR-1, the C. elegans homolog of Flower. This study offers intriguing observations that could potentially challenge or expand our current understanding of the Flower protein. Nevertheless, further clarification or additional experiments are required to substantiate the study's conclusions.

Strengths:

A range of approaches was employed, including the use of a flwr-1 knockout strain, assessment of cholinergic synaptic activity via analyzing aldicarb (a cholinesterase inhibitor) sensitivity, imaging Ca²⁺ dynamics with GCaMP3, analyzing pHluorin fluorescence, examination of presynaptic ultrastructure by EM, and recording postsynaptic currents at the neuromuscular junction. The findings include notable observations on the effects of flwr-1 knockout, such as increased Ca²⁺ levels in motor neurons, changes in endosome numbers in motor neurons, altered aldicarb sensitivity, and potential involvement of a Ca²⁺-ATPase and PIP2 binding in FLWR-1's function.

Weaknesses:

(1) The observation that flwr-1 knockout increases Ca²⁺ levels in motor neurons is notable, especially as it contrasts with prior findings in flies. The authors propose that elevated Ca²⁺ levels in flwr-1 knockout motor neurons may stem from "deregulation of MCA-3" (a Ca²⁺ ATPase in the plasma membrane) due to FLWR-1 loss. However, this conclusion relies on limited and somewhat inconclusive data (Figure 7). Additional experiments could clarify FLWR-1's role in MCA-3 regulation. For instance, it would be informative to investigate whether mutations in other genes that cause elevated cytosolic Ca²⁺ produce similar effects, whether MCA-3 physically interacts with FLWR-1, and whether MCA-3 expression is reduced in the flwr-1 knockout.

We thank the reviewer for bringing up these critical points. As to other mutations that produce elevated cytosolic Ca²⁺: Possible mutations could be g.o.f. mutations of the ryanodine receptor UNC-68, the sarco-endoplasmatic Ca²⁺ ATPase, or mutants affecting VGCCs, like the L-type channel EGL-19 or the P/Q-type channel UNC-2. However, any such mutant would affect muscle contractions (as we have shown for r.o.f. mutations in unc-68, egl-19 and unc-2 in Nagel et al. 2005 Curr Biol 15: 2279-84) and thus would affect aldicarb assays (see aldicarb resistance induced by RNAi of these genes in Sieburth et al., 2005, Nature 436: 510). The same should be expected for g.o.f. mutations of any such gene. In neurons, we would expect increased or decreased Ca²⁺ levels in response to stimulation.

Regarding the physical interaction of MCA-3 and FLWR-1, we performed bimolecular fluorescence complementation, with two fragments of mVenus fused to the two proteins. This assay shows mVenus reconstitution (i.e., fluorescence) if the two proteins are found in close vicinity to each other. Testing MCA-3 and FLWR-1 in muscle indeed showed a robust signal, evenly distributed on the plasma membrane. As a control, FLWR-1 did not interact with another plasma membrane protein, the stomatin UNC-1 interacting with gap junction proteins (Chen et al., 2007, Curr Biol 17: 1334-9). FLWR-1 also interacted with the ER chaperone Nicalin (NRA2 in C. elegans), which helps assembling the TM domains of integral membrane proteins in association with the SEC translocon. However, this signal only occurred in the ER membrane, demonstrating the specificity of the BiFC assay. This data is presented in Fig. 9A, B. Additionally, we show that FLWR-1 expression has a function in stabilizing MCA-3 localization at synapses, which is also in line with the idea of a direct interaction (Fig. 9C, D).

(2) In silico analysis identified residues R27 and K31 as potential PIP2 binding sites in FLWR-1. The authors observed that FLWR-1(R27A/K31A) was less effective than wild-type FLWR-1 in rescuing the aldicarb sensitivity phenotype of the flwr-1 knockout, suggesting that FLWR-1 function may depend on PIP2 binding at these two residues. Given that mutations in various residues can impair protein function non-specifically, additional studies may be needed to confirm the significance of these residues for PIP2 binding and FLWR-1 function. In addition, the authors might consider explicitly discussing how this finding aligns or contrasts with the results of a previous study in flies, where alanine substitutions at K29 and R33 impaired a Flower-related function (Li et al., eLife 2020).

We further investigated the role of these two residues in an in vivo assay for PIP2 binding and membrane association of a reporter. We used the coelomocytes (CCs), in which a previous publication demonstrated that a GFP variant tagged with a PH domain would be recruited to the CC membrane (Bednarek et al., 2007, Traffic 8: 543-53). This assay was performed in wild type, flwr-1 mutants, and flwr-1 mutants rescued with wild type FLWR-1, the FLWR-1(E74Q) mutant, or the FLWR-1(K27A; R31A) double mutant. The data are shown in Fig. 8C, D. While the wild type FLWR-1 rescued PH-GFP levels at the CC membrane to the wild type control, the FLWR-1(K27A; R31A) double mutant did not rescue the reporter binding, indicating that, at least in CCs, reduced PIP2 levels are associated with non-functional FLWR-1. Mechanistically, this is not clear at present, though we noted a possible mechanism as found for synaptotagmin, that recruits the PIP2 kinase to the plasma membrane via a lysine and arginine containing motif (Bolz et al., 2023, Neuron 111: 3765-3774.e3767). We mention this now in the discussion. We also discussed our data with respect to the findings of Li et al., about the analogous residues K27, R31 (K29, R33) in the discussion section, i.e. lines 667-670, and the differences of our findings in electron microscopy compared to the Drosophila work (more rather than less bulk endosomes) were discussed in lines 713-720.

(3) A primary conclusion from the EM data was that FLWR-1 participates in the breakdown, rather than the formation, of bulk endosomes (lines 20-22). However, the reasoning behind this conclusion is somewhat unclear. Adding more explicit explanations in the Results section would help clarify and strengthen this interpretation.

We added a sentence trying to better explain our reasoning. Mainly, the argument is that accumulation of such endosomes of unusually large size is seen in mutants affecting formation of SVs from the endosome (in endophilin and synaptojanin mutants), while mutants affecting mainly endocytosis (dynamin) cause formation of many smaller endocytic structures that stay attached to the plasma membrane (Kittelmann et al., 2013, PNAS 110: E3007-3016). We changed our data analysis in that we collated the data for what we previously termed endosomes and large vesicles. According to the paper by Watanabe, 2013, eLife 2: e00723, endosomes are defined by their location in the synapse, and their size. However, this work used a much shorter stimulus and froze the preparations within a few dozens to hundreds of msec after the stimulus, while we used the protocol of Kittelmann 2013, which uses 30 sec stimulation and freezing after 5 sec. There, endosomes were defined as structures larger than SVs or DCVs, but no larger than 80 nm, with an electron dense lumen, and were very rarely observed. In contrast, large vesicles or ‘100 nm vesicles’, ranged from 50-200 nm diameter, with a clear lumen, were morphologically similar to the bulk endosomes as observed by Li et al., 2021. We thus reordered our data and jointly analyzed these structure as large vesicles / bulk endosomes. The outcome is still the same, i.e. photostimulated flwr-1 mutants showed more LVs than wild type synapses.

(4) The aldicarb assay results in Figure 3 are intriguing, indicating that reduced GABAergic neuron activity alone accounts for the flwr-1 mutant's hyposensitivity to aldicarb. Given that cholinergic motor neurons also showed increased activity in the flwr-1 mutant, one might expect the flwr-1 mutant to display hypersensitivity to aldicarb in the unc-47 knockout background. However, this was not observed. The authors might consider validating their conclusion with an alternative approach or, at the minimum, providing a plausible explanation for the unexpected result. Since aldicarb-induced paralysis can be influenced by factors beyond acetylcholine release from cholinergic motor neurons, interpreting aldicarb assay results with caution may be advisable. This is especially relevant here, as FLWR-1 function in muscle cells also impacts aldicarb sensitivity (Figure S3B). Previous electrophysiological studies have suggested that aldicarb sensitivity assays may sometimes yield misleading conclusions regarding protein roles in acetylcholine release.

We tested the unc-47; flwr-1 animals again at a lower concentration of aldicarb, to see if the high concentration may have leveled the differences between unc-47 animals and the double mutant. This experiment is shown in Fig. S3D, demonstrating that the double mutant is significantly less resistant to aldicarb. This verifies that FLWR-1 acts not only in GABAergic neurons, but also in cholinergic neurons (as we saw by electron microscopy and electrophysiology), and that the increased excitability of cholinergic cells leads to more acetylcholine being released. In the double mutant, where GABA release is defective, this conveys hypersensitivity to aldicarb.

(5) Previous studies have suggested that the Flower protein functions as a Ca²⁺ channel, with a conserved glutamate residue at the putative selectivity filter being essential for this role. However, mutating this conserved residue (E74Q) in C. elegans FLWR-1 altered aldicarb sensitivity in a direction opposite to what would be expected for a Ca²⁺ channel function. Moreover, the authors observed that E74 of FLWR1 is not located near a potential conduction pathway in the FLWR-1 tetramer, as predicted by Alphafold3. These findings raise the possibility that Flower may not function as a Ca²⁺ channel. While this is a potentially significant discovery, further experiments are needed to confirm and expand upon these results.

As above, we do not exclude that FLWR-1 may constitute a channel, however, based on our findings, AF3 structure predictions and data in the literature, we are considering alternative explanations for the observed effect on Ca²⁺ levels of Flower mutants in worms and flies. The observations of increase Ca²⁺ levels in stimulated flwr-1 mutant neurons could result from a reduced stimulation of the PMCA, and this was also observed with low stimulation in Drosophila (Yao et al., 2017). This idea is supported by the indications of a direct physical interaction, or proximity, of the two proteins. The reduced Ca²⁺ levels after hyperstimulation of Drosophila Flower mutants may have to do with increased levels of non-recycling PMCA in the plasma membrane, indicating that PMCA requires Flower for recycling. This could be underlying the rundown of evoked PSCs we find in worm flwr-1 mutants, and would also be in line with a function of FLWR-1 and MCA-3 in coelomocytes, cells that constantly endocytose, and in which both proteins are required for proper function (our data, Figs. 5A, B; 8D, E) and Bednarek et al., 2007 (Traffic 8: 543-553). CCs need to recycle / endocytose membranes and membrane proteins, and such proteins, likely including FLWR-1 and MCA-3, need to be returned to the PM effectively.

We thus refrained from testing a putative FLWR-1 channel function in Xenopus oocytes, in part also because we would not be able to acutely trigger possible FLWR-1 gating. A constitutive Ca²⁺ current, if it were present, would induce large Cl^- conductance in oocytes, that would likely be problematic / killing the cells. The demonstration that FLWR-1(E74Q) does not rescue the PI(4,5)P₂ levels in coelomocytes is also more in line with a non-channel function of FLWR-1.

(6) Phrases like "increased excitability" and "increased Ca²⁺ influx" are used throughout the manuscript. However, there is no direct evidence that motor neurons exhibit increased excitability or Ca²⁺ influx. The authors appear to interpret the elevated Ca²⁺ signal in motor neurons as indicative of both increased excitability and Ca²⁺ influx. However, this elevated Ca²⁺ signal in the flwr-1 mutant could occur independently of changes in excitability or Ca²⁺ influx, such as in cases of reduced MCA-3 activity. The authors may wish to consider alternative terminology that more accurately reflects their findings.

Thank you, we rephrased the imprecise wording. Ca²⁺ influx was meant with respect to the cytosol.

Reviewer #3 (Public review):

Summary:

Seidenthal et al. investigated the role of the Flower protein, FLWR-1, in C. elegans and confirmed its involvement in endocytosis within both synaptic and non-neuronal cells, possibly by contributing to the fission of bulk endosomes. They also uncovered that FLWR-1 has a novel inhibitory effect on neuronal excitability at GABAergic and cholinergic synapses in neuromuscular junctions.

Strengths:

This study not only reinforces the conserved role of the Flower protein in endocytosis across species but also provides valuable ultrastructural data to support its function in the bulk endosome fission process. Additionally, the discovery of FLWR-1's role in modulating neuronal excitability broadens our understanding of its functions and opens new avenues for research into synaptic regulation.

Weaknesses:

The study does not address the ongoing debate about the Flower protein's proposed Ca²⁺ channel activity, leaving an important aspect of its function unexplored. Furthermore, the evidence supporting the mechanism by which FLWR-1 inhibits neuronal excitability is limited. The suggested involvement of MCA-3 as a mediator of this inhibition lacks conclusive evidence, and a more detailed exploration of this pathway would strengthen the findings.

We added new data showing the likely direct interaction of FLWR-1 with the PMCA, possibly upregulating / stimulating its function. This data is shown now in Fig. 9A, B. Also, we show now that FLWR-1 is required to stabilize MCA-3 expression / localization in the pre-synaptic plasma membrane (Fig. 9C, D). These findings are not supporting the putative function of FLWR-1 as an ion channel, but suggest that increased Ca²⁺ levels following neuron stimulation in flwr-1 mutants are due to an impairment of MCA-3 and thus reduced Ca²⁺ extrusion.

Recommendations for the authors:

Reviewer #2 (Recommendations for the authors):

The authors might consider focusing on one or two key findings from this study and providing robust evidence to substantiate their conclusions.

We did substantiate the interactions of FLWR-1 and the PMCA, as well as assessing the function of FLWR-1 in the coelomocytes and the function of FLWR-1 in regulating PIP2 levels in the plasma membrane.

Reviewer #3 (Recommendations for the authors):

(1) Behavioral Analysis of Locomotion

In Figure 1, the authors are encouraged to examine whether flwr-1 mutants show altered locomotion behaviors, such as velocity, in a solid medium.

We performed such an analysis for wild type, comparing to flwr-1 mutants and flwr-1 mutants rescued with FLWR-1 expressed from the endogenous promoter. The data are shown in Fig. S1C. There was no difference. We note that we observed differences in swimming assays also only when we strongly stimulated the cholinergic neurons by optogenetic depolarization, but not during unstimulated, normal swimming.

(2) Validation of FLWR-1 Tagging

In Figure 2A, it is recommended that the authors confirm the functionality of the C-terminal-tagged FLWR-1.

We performed such rescue assays during swimming. The data is shown in Fig. S2S, E. While the GFP::FLWR-1 animals were slightly affected right after the photostimulation, they quickly caught up with the wild type controls, while flwr-1 mutants remained affected even after several minutes.

(3) Explanation of Differential Rescue in GABAergic Neurons and Muscle

The authors should provide a rationale for why restoring FLWR-1 in GABAergic neurons fully rescues the aldicarb resistance phenotype, while its restoration in muscle also partially rescues it.

We think that these effects are independent of each other, i.e. loss of FLWR-1 in muscles increases muscular excitability, which becomes apparent in the behavioral assay that depends on locomotion and muscle contraction. To assess this further, we performed combined GABAergic neuron and muscle rescue assays, as shown in Fig. S3B. The double rescue was not different from wild type, and performed better than the muscle rescue alone.

(4) Rescue Experiments for Swimming Defect in GABAergic Neurons

Consider adding rescue experiments to determine whether expressing FLWR-1 specifically in GABAergic neurons can restore the swimming defect phenotype.

We did not perform this assay as swimming is driven by cholinergic neurons, meaning that we would only indirectly probe GABAergic neuron function and a GABAergic FLWR-1 rescue would likely not improve swimming much. Also, given the importance of the correct E/I balance in the motor neurons, it would likely require achieving expression levels that are very precisely matching endogenous expression levels, which is not possible in a cell-specific manner.

(5) Further Data on GCaMP Assay for mca-3; flwr-1 Additive Effect

The additive effect of the mca-3 and flwr-1 mutations on GCaMP signals requires further data for substantiation. Additional GCaMP recordings or statistical analysis would provide stronger support for the proposed interaction between MCA-3 and FLWR-1 in calcium signaling.

Thank you. We increased the number of observations, and could thus improve the outcome of the assay in that it became more conclusive. Meaning, the double mutation was not exacerbating the effect of either single mutant, demonstrating that FLWR-1 and MCA-3 are acting in the same pathway. The data are in Fig. 7B, C.

(6) Inclusion of Wild-Type FLWR-1 Rescue in Figures 8B and 8D

Figures 8B and 8D would benefit from the inclusion of wild-type FLWR-1 as a rescue control.

We included the FLWR-1 wild type rescue as suggested and summarized the data in Fig. 8B.

Author response:

The following is the authors’ response to the previous reviews

Responses to final minor critiques following initial revision

Reviewer #1 (Recommendations for the authors): 

The authors have generally done an excellent job of addressing my and the other reviewers' concerns. I have a few additional concerns that the authors could consider addressing through changes to the text: 

We thank the Reviewer for this assessment and are glad to have addressed the major points.

- Regarding the gRNA used for NMR studies, I thank the authors for adding additional rationale for their design of the RNA used. However, I still believe that it is misleading to term this RNA as a "gRNA", given that it is mainly composed of a sequence that is arbitrary (the spacer) and the sections of the gRNA that are constant between all gRNAs are truncated in a way that removes secondary structure that is likely essential for specific contacts with the Rec domains. I do not believe the authors need to make alterations to any of their experiments. However, I do think their description of the "gRNA" should be updated to properly reflect that this RNA lacks any of the secondary structure present in a typical gRNA, much of which is necessary to confer specificity of binding between GeoCas9 and the gRNA. As mentioned in my previous review, this may be best achieved by adding a cartoon of the secondary structure of the full-length gRNA and highlighting the region that was used in the truncated "gRNA". 

We understand the Reviewer’s point. For any experiment in which the gRNA was truncated (i.e. NMR or some MST studies), we have clarified the text and no longer call it a “gRNA.” We state initially that it is a portion of the gRNA and then call it simply an “RNA.” 

For experiments using the full-length constructs, we have kept the term “gRNA,” as it remains appropriate.

We have also added a final Supplementary figure (S12) showing the structures of the truncated and full-length RNAs used, based on the _Geo_Cas9 cryo-EM structure and predicted with RNAfold.

- Lines 256-257: "The ~3-fold decrease in Kd...". I believe the authors are discussing the Kd's of the mutants relative to WT, in which case the Kd increased. Also, the fold-change appears closer to 2fold than to 3-fold. 

Yes, the Reviewer makes a good catch. We have corrected this.

- Lines 407-408: "The mutations also diminished the stability of the full-length GeoCas9 RNP complex." This statement seems at odds with the authors' conclusions in the Results section that the full-length GeoCas9 variants had comparable affinities for the gRNAs (lines 376-382) 

We agree that this seems contradictory. In the absence of full-length structures for all variants, we can’t definitively state what causes this. It could be that the mutation has an interesting allosteric effect on structure that does not affect RNA binding but induces the Cas9 protein to simply fall apart at lower temperatures, rendering the binding interaction moot. We have added a statement to this section.

- The authors chose to keep "SpCas9" for consistency with their prior work and the work of many several others, including Doudna et al and Zhang et al. However, I will note that their publications on GeoCas9, the Doudna lab did use SpyCas9 to ensure consistent nomenclature within the publications. 

We have made the change to “_Spy_Cas9”

Reviewer #3 (Recommendations for the authors): 

The authors clearly answered most of my concerns. I still have some technical questions about the analysis of CPMG-RD data but the numbers provided now seem to make sense. While I still think that crystal structures of the point mutant would make the conclusions more "bullet proof", I do appreciate the work associated with this and consider that the manuscript can be published as is. 

We agree that additional magnetic fields could allow for additional models of CPMG data fitting and that additional crystal structures of the mutants could add to the conclusions. We appreciate the Reviewer recognizing the balance of the current results and potential future studies in signing off on publication.

Author response:

Public Reviews:

Reviewer #1 (Public review):

Summary:

In this study, Nakagawa and colleagues report the observation that YAP is differentially localized, and thus differentially transcriptionally active, in spheroid cultures versus monolayer cultures. YAP is known to play a critical role in the survival of drug-tolerant cancer cells, and as such, the higher levels of basally activated YAP in monolayer cultures lead to higher fractions of surviving drug-tolerant cells relative to spheroid culture (or in vivo culture). The findings of this study, revealed through convincing experiments, are elegantly simple and straightforward, yet they add significantly to the literature in this field by revealing that monolayer cultures may actually be a preferential system for studying residual cell biology simply because the abundance of residual cells in this format is much greater than in spheroid or xenograft models. The potential linkage between matrix density and stiffness and YAP activation, while only speculated upon in this manuscript, is intriguing and a rich starting point for future studies.

Although this work, like any important study, inspires many interesting follow-on questions, I am limiting my questions to only a few minor ones, which may potentially be explored either in the context of the current study or in separate, follow-on studies.

We appreciate Reviewer #1's comments that our work is of importance to the field and particularly that it will "...add significantly to the literature in this field by revealing that monolayer cultures may actually be a preferential system for studying residual cell biology..."  We have sought to highlight the importance of how our findings could be applied to study resistance mechanisms at various points in the manuscript.

Strengths:

The major strengths of the work are described above.

Weaknesses:

Rather than considering the following points as weaknesses, I instead prefer to think of them as areas for future study:

(1) Given the field's intense interest in the biology and therapeutic vulnerabilities of residual disease cells, I suspect that one major practical implication of this work could be that it inspires scientists interested in working in the residual disease space to model it in monolayer culture. However, this relies upon the assumption that drug-tolerant cells isolated in monolayer culture are at least reasonably similar in nature to drug-tolerant cells isolated from spheroid or xenograft systems. Is this true? An intriguing experiment that could help answer this question would be to perform gene expression profiling on a cell line model in the following conditions: monolayer growth, drug tolerant cells isolated from monolayer growth conditions, spheroid growth, drug tolerant cells isolated from spheroid growth conditions, xenograft tumors, and drug tolerant cells isolated from xenograft tumors. What are the genes and programs shared between drug-tolerant cells cultured in the three conditions above? Which genes and programs differ between these conditions? Data from this exercise could help provide additional, useful context with which to understand the benefits and pitfalls of modeling residual tumor cell growth in monolayer culture.

We thank the reviewer for suggesting valuable future studies. We agree that the proposed experiments represent important next steps in understanding the role of YAP and other pathways in primary resistance. We believe, however, these experiments are both beyond the scope of the current manuscript and beyond what can reasonably be addressed in a revision. The distinct challenges associated with comparing in vivo and in vitro conditions would require significant optimization of single-cell approaches, especially given the robust cell death driven by afatinib treatment in vivo. Given the complexity of in vivo experimentation, we are concerned that such studies may not guarantee biologically meaningful insights. Nonetheless, we agree that this is a compelling direction for future research. If common gene expression patterns could be identified despite these challenges, such studies could help validate monolayer culture as a relevant model for investigating residual disease.

(2) In relation to the point above, there is an interesting and established connection between mesenchymal gene expression and YAP/TAZ signaling. For example, analyses of gene expression data from human tumors and cell lines demonstrate an extremely strong correlation between these two gene expression programs. Further, residual persister cancer cells have often been characterized as having undergone an EMT-like transition. From the analysis above, is there evidence that residual tumor cells with increased YAP signaling also exhibit increased mesenchymal gene expression?

We agree with the reviewer that a connection between YAP/TAZ activity and EMT is likely, given prior studies exploring correlations between these two gene signatures. We believe, however, exploring EMT represents a distinct research direction from the primary focus of the current manuscript.  We are concerned exploration of EMT, especially in the absence of corresponding preclinical models or mechanistic data directly linking EMT to therapy resistance in our models, could distract from the main conclusions of the manuscript. While we plan to stain for EMT-associated markers in the residual cancer tissue from the in vivo studies, it remains unclear whether such data would meaningfully contribute to the revised manuscript, regardless of the outcome.

Reviewer #2 (Public review):

The manuscript by Nakagawa R, et al describes a mechanism of how NSCLC cells become resistant to EGFR and KRAS G12C inhibition. Here, the authors focus on the initial cellular changes that occur to confer resistance and identify YAP activation as a non-genetic mechanism of acute resistance.

The authors performed an initial xenograft study to identify YAP nuclear localization as a potential mechanism of resistance to EGFRi. The increase in the stromal component of the tumors upon Afatinib treatment leads the authors to explore the response to these inhibitors in both 2D and 3D culture. The authors extend their findings to both KRAS G12C and BRAF inhibitors, suggesting that the mechanism of resistance may be shared along this pathway.

The paper would benefit from additional cell lines to determine the generalizability of the findings they presented. While the change in the localization of YAP upon Afatinib treatment was identified in a xenograft model, the authors do not return to animal models to test their potential mechanism, and the effects of the hyperactivated S127A YAP protein on Afatinib sensitivity in culture are modest. Also, combination studies of YAP inhibitors and EGFR/RAS/RAF inhibitors would have strengthened the studies.

We thank the reviewer for their insightful comments. In this manuscript, we present data from 5 cell lines representing the EGFR/BRAF/KRAS pathway, demonstrating the generalizability of YAP-driven decreased cancer cell sensitivity to targeted inhibitors when cultured in 2D compared to spheroid counterparts. While expanding this analysis to a larger panel of cell lines is beyond the scope of the current study, we believe our findings provide a strong rationale for future investigations, including high-throughput screens conducted by other research groups and pharmaceutical companies, to recognize the value in screening spheroid cell cultures. We hope this work helps shift the field of cancer therapeutics toward screening approaches that better reflect tumor biology into drug discovery pipelines and believe this could be one of the most impactful and enduring contributions of our study.

Reviewer #2 also mentions that "...combination studies of YAP inhibitors and EGFR/RAS/RAF inhibitors would have strengthened the studies..."  The concept that YAP/TAZ inhibitors (i.e. TEAD inhibitors) could be additive or synergistic in 2D culture is one that is being actively tested across several groups and in pharma. Several recent examples include a publication by Hagenbeek, et al., Nat. Cancer, 2023 (PMID: 37277530) showing that a TEAD inhibitor overcomes KRASG12C inhibitor resistance. Additional, recent work by Pfeifer, et al., Comm. Biol., 2024 (PMID: 38658677) suggests a similar effect between EGFR inhibitors and a different TEAD inhibitor. While neither of these studies extensively probes cell death pathways in the way performed in our studies, they nevertheless provide strong evidence that indeed TEAD + targeted EGFR/RAF/RAS inhibition in 2D have additive, if not synergistic, effects. We feel that these recent published studies affirm our findings and repeating such experiments is unlikely to add much new information. We thus feel they are beyond the scope of our present studies.

Author response:

The following is the authors’ response to the original reviews

Reviewer #1 (Public Review):

Summary:

Olfactory sensory neurons (OSNs) in the olfactory epithelium detect myriads of environmental odors that signal essential cues for survival. OSNs are born throughout life and thus represent one of the few neurons that undergo life-long neurogenesis. Until recently, it was assumed that OSN neurogenesis is strictly stochastic with respect to subtype (i.e. the receptor the OSN chooses to express).

However, a recent study showed that olfactory deprivation via naris occlusion selectively reduced birthrates of only a fraction of OSN subtypes and indicated that these subtypes appear to have a special capacity to undergo changes in birthrates in accordance with the level of olfactory stimulation. These previous findings raised the interesting question of what type of stimulation influences neurogenesis, since naris occlusion does not only reduce the exposure to potentially thousands of odors but also to more generalized mechanical stimuli via preventing airflow.

In this study, the authors set out to identify the stimuli that are required to promote the neurogenesis of specific OSN subtypes. Specifically, they aim to test the hypothesis that discrete odorants selectively stimulate the same OSN subtypes whose birthrates are affected. This would imply a highly specific mechanism in which exposure to certain odors can "amplify" OSN subtypes responsive to those odors suggesting that OE neurogenesis serves, in part, an adaptive function.

To address this question, the authors focused on a family of OSN subtypes that had previously been identified to respond to musk-related odors and that exhibit higher transcript levels in the olfactory epithelium of mice exposed to males compared to mice isolated from males. First, the authors confirm via a previously established cell birth dating assay in unilateral naris occluded mice that this increase in transcript levels actually reflects a stimulus-dependent birthrate acceleration of this OSN subtype family. In a series of experiments using the same assay, they show that one specific subtype of this OSN family exhibits increased birthrates in response to juvenile male exposure while a different subtype shows increased birthrates to adult mouse exposure. In the core experiment of the study, they finally exposed naris occluded mice to a discrete odor (muscone) to test if this odor specifically accelerates the birth rates of OSN types that are responsive to this odor. This experiment reveals a complex relationship between birth rate acceleration and odor concentrations showing that some muscone concentrations affect birth rates of some members of this family and do not affect two unrelated OSN subtypes.

In addition to the results nicely summarized by the reviewer, which focus on experiments to examine the effects of odor stimulation on unilateral naris occluded (UNO) mice, an important part of the present study are experiments on non-occluded (i.e., non-UNO-treated) mice. These experiments show: 1) that the exposure of non-occluded mice to odors from adolescent male mice selectively increases quantities of newborn OSNs of the musk-responsive subtype Olfr235 (Figure 3G, H; previously Figure 6), 2) the exposure of non-occluded female mice to 2 different musk odorants (muscone, ambretone) selectively increases quantities of newborn OSNs of 3 musk responsive subtypes: Olfr235, Olfr1440 and Olfr1431 (Figure 4D-F; previously Figure 6), and 3) the exposure of non-occluded adult female mice to a musk odorants selectively increases quantities of newborn OSNs of musk responsive subtypes (Figure 5; previously Fig. S7). We have reorganized the revised manuscript to more prominently and clearly present the experimental design and findings of these experiments. We have also made changes to clarify (via schematics) the experimental conditions used (i.e., UNO, non-UNO, odor exposure) in each experiment.

Strengths:

The scientific question is valid and opens an interesting direction. The previously established cell birth dating assay in naris occluded mice is well performed and accompanied by several control experiments addressing potential other interpretations of the data.

Weaknesses:

(1) The main research question of this study was to test if discrete odors specifically accelerate the birth rate of OSN subtypes they stimulate, i.e. does muscone only accelerate the birth rate of OSNs that express muscone-responsive ORs, or vice versa is the birthrate of muscone-responsive OSNs only accelerated by odors they respond to?

This question is only addressed in Figure 5 of the manuscript and the results only partially support the above claim. The authors test one specific odor (muscone) and find that this odor (only at certain concentrations) accelerates the birth rate of some musk-responsive OSN subtypes, but not two other unrelated control OSN subtypes. This does not at all show that musk-responsive OSN subtypes are only affected by odors that stimulate them and that muscone only affects the birthrate of musk-responsive OSNs, since first, only the odor muscone was tested and second, only two other OSN subtypes were tested as controls, that, importantly, are shown to be generally stimulus-independent OSN subtypes (see Figure 2 and S2).

As a minimum the authors should have a) tested if additional odors that do not activate the three musk-responsive subtypes affect their birthrate b) choose 2-3 additional control subtypes that are known to be stimulus-dependent (from their own 2020 study) and test if muscone affects their birthrates.

We appreciate these suggestions. Within the revised manuscript, we have described and included the results from several new experiments:

(1) As noted by the reviewer, we had previously tested the effects of exposure to only one exogenous musk odorant, muscone, on quantities of newborn OSNs of the musk-responsive subtypes Olfr235, Olfr1440, and Olfr1431. To test whether the effects observed with muscone exposure occur with other musk odorants, we assessed the effects of exposure to ambretone (5-cyclohexadecenone), a musk odorant previously found to robustly activate musk-responsive OSNs (Sato-Akuhara et al., 2016; Shirasu et al., 2014), on quantities of newborn OSNs of 3 musk-responsive subtypes Olfr235, Olfr1440, and Olfr1431, as well as the SBT-responsive subtype Olfr912, in the OEs of non-occluded female mice. Exposure to ambretone was found to significantly increase quantities of newborn OSNs of all 3 musk-responsive subtypes (Figure 4D-F) but not the SBT-responsive subtype (Figure 4–figure supplement 4C-left), indicating that a variety of musk odorants can accelerate the birthrates of musk responsive subtypes.

(2) To verify that exogenous non-musk odors do not increase quantities of newborn OSNs of musk responsive OSN subtypes (point a, above), we quantified newborn OSNs of 3 musk-responsive subtypes, Olfr235, Olfr1440, and Olfr1431, in non-occluded female mice that were exposed to the non-musk odorants SBT or IAA. As expected, neither of these odorants significantly affected the birthrates of the subtypes tested (Figure 4D-F).

(3) To confirm that exogenous musk odors do not accelerate the birthrates of non-musk responsive OSN subtypes that were previously found to undergo stimulation-dependent neurogenesis (point b, above), we quantified newborn OSNs of 2 such subtypes, Olfr827 and Olfr1325, in non-occluded female mice that were exposed to muscone. As expected, exposure to muscone did not significantly affect the birthrates of either of these subtypes (Figure 4–figure supplement 4C-middle, right).

(4) To provide additional confirmation that only some OSN subtypes have a capacity to exhibit increases in newborn OSN quantities in the presence of odors that activate them, we compared quantities of newborn OSNs of the SBT-responsive subtype Olfr912 in non-occluded females that were either exposed to 0.1% SBT versus unexposed controls. As expected, exposure of SBT caused no significant increase in quantities of newborn Olfr912 OSNs (Figure 4–figure supplement 4C-left).

(2) The finding that Olfr1440 expressing OSNs do not show any increase in UNO effect size under any muscone concentration (Figure 5D, no significance in line graph for UNO effect sizes, middle) seems to contradict the main claim of this study that certain odors specifically increase birthrates of OSN subtypes they stimulate. It was shown in several studies that olfr1440 is seemingly the most sensitive OR for muscone, yet, in this study, muscone does not further increase birthrates of OSNs expressing olfr1440. The effect size on birthrate under muscone exposure is the same as without muscone exposure (0%).

In contrast, the supposedly second most sensitive muscone-responsive OR olfr235 shows a significant increase in UNO effect size between no muscone exposure (0%) and 0.1% as well as 1% muscone.

Findings that quantities of newborn Olfr1440 OSNs do not show a significantly greater UNO effect size in the OEs from mice exposed to muscone compared to control mice was also somewhat surprising to us. We think that there are two potential explanations for this result: 1) Unlike subtype Olfr235, subtype Olfr1440 exhibits a significant open-side bias in newborn OSN quantities in UNO-treated adolescent females even in the absence of exposure to muscone. We speculate that this subtype (as well as subtype Olfr1431) is stimulated by odors that are emitted by female mice at the adolescent stage, and/or by another environmental source. This may limit the influence of muscone exposure on the UNO effect size. 2) There is compelling evidence that odors within the environment can enter the closed side of the OE transnasally [via the nasopharyngeal canal (Kelemen, 1947)] and/or retronasally (via the nasopharynx) in UNO-treated mice [reviewed in (Coppola, 2012)]. Thus, it is conceivable that chronic exposure of UNO-treated mice to muscone results in the eventual entry on the closed side of the OE of muscone at concentrations sufficient to promote neurogenesis. If Olfr1440 is more sensitive to muscone than Olfr235 [e.g., (Sato-Akuhara et al., 2016; Shirasu et al., 2014)], OSNs of this subtype may be especially sensitive to small amounts of odors that enter the closed side of the OE transnasally and/or retronasally. These explanations are supported by the following results:

- UNO-treated females exposed to 0.1% muscone show higher quantities of newborn Olfr1440 OSNs on both the open and closed sides of the OE in muscone exposed females compared to their unexposed counterparts (Figure 4–figure supplement 1A-middle). Similar results were also observed for newborn Olfr235 OSNs (Figure 4C-middle), albeit to a lesser extent, perhaps due to the lower sensitivity of this subtype to muscone.

- In non-occluded female mice, exposure to 0.1% muscone was found to significantly increase quantities of newborn Olfr1440 OSNs, as well as newborn Olfr235 and Olfr1431 OSNs (Figure 4D-F in revised manuscript; Figure 6 in original version). Similar results were also observed upon exposure to ambretone, another musk odor (Figure 4D-F). These experiments strongly support the hypothesis that musk odors selectively increase birthrates of OSN subtypes that they stimulate.

We have addressed these points within the results section of the revised manuscript.

(3) The authors introduce their choice to study this particular family of OSN subtypes with first, the previous finding that transcripts for one of these musk-responsive subtypes (olfr235) are downregulated in mice that are deprived of male odors. Second, musk-related odors are found in the urine of different species. This gives the misleading impression that it is known that musk-related odors are indeed excreted into male mouse urine at certain concentrations. This should be stated more clearly in the introduction (or cited, if indeed data exist that show musk-related odors in male mouse urine) because this would be a very important point from an ethological and mechanistic point of view.

In addition, this would also be important information to assess if the chosen muscone concentrations fall at all into the natural range.

These are important points, which have addressed within the revised manuscript:

(1) Within the introduction, we have now stated that the emission of musk odors by mice has not been documented. We have also added extensive discussions of what is known about the emission of musk odors by mice in a new subsection within Results, as well as within the Discussion section. Most prominently, we have cited one study (Sato-Akuhara et al., 2016) that noted unpublished evidence for the emission of Olfr1440-activating compounds from male preputial glands: “Indeed, our preliminary experiments suggest that there are unidentified compounds that activate MOR215-1 in mouse preputial gland extracts.” Another study, which used histomorphology, metabolomic and transcriptomic analyses to compare the mouse preputial glands to muskrat scent glands, found that the two glands are similar in many ways, including molecular composition (Han et al., 2022). However, the study did not identify known musk compounds within mouse preputial glands.

(2) Based on the reviewer’s feedback and our own curiosity, we used GC-MS to analyze both mouse urine and preputial gland extracts for the presence of known musk odorants, particularly those known to activate Olfr235 and Olfr1440 (Sato-Akuhara et al., 2016). Although we were unable to find evidence for known musk odorants in mouse urine extracts (possibly due to insufficient sensitivity of the assay employed), we found that preputial gland extracts contain GC-MS signals that are structurally consistent with known musk odorants. A limitation of this approach, however, is that the conclusive identification of specific musk odorants in extracts derived from mouse urine and tissues requires comparisons to pure standards, many of which we could not readily obtain. For example, we were unable to obtain a pure sample of cycloheptadecanol, a musk molecule with a predicted potential match to a signal identified within preputial gland extracts. Another limitation is that although several known musk odorants have been found to activate Olfr235 and Olfr1440 OSNs, it is conceivable that structurally distinct odorants that have not yet been identified might also activate them. The findings from these experiments have been included in a new figure within the revised manuscript (Appendix 2–figure 1).

Related: If these are male-specific cues, it is interesting that changes in OR transcripts (Figure 1) can already be seen at the age of P28 where other male-specific cues are just starting to get expressed. This should be discussed.

We agree that the observed changes in quantities of newborn OSNs of musk-responsive subtypes in mice exposed to juvenile male odors deserves additional discussion. We have included a more extensive discussion of this observation in both the Results and Discussion sections of the revised manuscript.

(4) Figure 5: Under muscone exposure the number of newborn neurons on the closed sides fluctuates considerably. This doesn't seem to be the case in other experiments and raises some concerns about how reliable the naris occlusion works for strong exposure to monomolecular odors or what other potential mechanisms are at play.

We agree that the variability in quantities of newborn OSNs of musk-responsive subtypes on the closed side of the OE of UNO-treated mice deserves further discussion. As noted above, we suspect that these fluctuations are due, at least in part, to transnasal and/or retronasal odor transfer via the nasopharyngeal canal (Kelemen, 1947) and nasopharynx, respectively [reviewed in (Coppola, 2012)], which would be expected to result in exposure of the closed OE to odor concentrations that rise with increasing environmental concentrations. In support of this, quantities of newborn Olfr235 and Olfr1440 OSNs increase on both the open and closed sides with increasing muscone concentration (except at the highest concentration, 10%, in the case of Olfr1440) (Figure 4C-middle, Figure 4–figure supplement 1A-middle). It is conceivable that reductions in newborn Olfr1440 OSN quantities observed in the presence of 10% muscone reflect overstimulation-dependent reductions in survival. Our findings from UNO-based experiments are consistent with expectations that naris occlusion does not completely block exposure to odorants on the closed side, particularly at high concentrations. However, they also appear consistent with the hypothesis that exposure to musk odors promotes the neurogenesis of musk-responsive OSN subtypes.

Considering the limitations of the UNO procedure, it is important to note that the present study also includes experimental exposure of non-occluded animals to both male odors (Figure 3G, H) and exogenous musk odorants (Figures 4D-F). Findings from the latter experiments provide strong evidence that exposure to multiple musk odorants (muscone, ambretone) causes selective increases in the birthrates of multiple musk-responsive OSN subtypes (Olfr235, Olfr1440, Olfr1431).

We have included within the Results section of the revised manuscript a discussion of how observed effects of muscone exposure of UNO-treated mice may be influenced by transnasal/ retronasal odor transfer to the closed side of the OE.

(5) In contrast to all other musk-responsive OSN types, the number of newborn OSNs expressing olfr1437 increases on the closed side of the OE relative to the open in UNO-treated male mice (Figure 1). This seems to contradict the presented theory and also does not align with the bulk RNAseq data (Figure S1).

Subtype Olfr1437 is indeed an outlier among musk-responsive subtypes that were previously found to be more highly represented in the OSN population in 6-month-old sex-separated males compared to females (Appendix 1–figure 1)(C. van der Linden et al., 2018; Vihani et al., 2020). Somewhat unexpectedly, our findings from scRNA-seq experiments show slightly greater quantities of immature Olfr1437 OSNs on the closed side of the OE in juvenile males (Figure 1D, E of the revised manuscript, which now includes data from a second OE). Perhaps more informatively considering the small number of iOSNs of specific subtypes in the scRNA-seq datasets, EdU birthdating experiments show no difference in newborn Orlfr1437 OSN quantities on the 2 sides of the OE from UNO-treated juvenile males (Figure 2G). It is unclear to us why subtype Olfr1437 does not show open-side biases in newborn OSN quantities in juvenile male mice, but potential explanations include:

- Age: Findings based on bulk RNA-seq that musk responsive OSN subtypes are more highly represented in mice exposed to male odors analyzed mice that were 6 months old (C. van der Linden et al., 2018) or > 9 months old (Vihani et al., 2020) at the time of analysis. By contrast, the present study primarily analyzed mice that were juveniles (PD 28) at the time of scRNA-seq analysis (Figure 1) or EdU labeling (Figure 2G). It is conceivable that different musk-responsive subtypes are selectively responsive to distinct odors that are emitted at different ages. In this scenario, odors that increase the birthrates of Olfr235, Olfr1440, and Olfr1431 OSNs may be emitted starting at the juvenile stage, while those that increase the birthrate of Olfr1437 OSNs may be emitted in adulthood. In potential support of this, juvenile males exposed to their adult parents at the time of EdU labeling showed a slightly greater (although not statistically significantly different) UNO effect size in quantities of newborn Olfr1437 OSNs compared to controls (Figure 3–figure supplement 3).

- Capacity for stimulation-dependent neurogenesis: It is also conceivable that, unlike other musk-responsive OSN subtypes, Olfr1437 OSNs lack the capacity for stimulation-dependent neurogenesis (like the SBT-responsive subtype Olfr912, for example). If so, this would imply that increased representations of Olfr1437 OSNs observed in mice exposed to male odors for long periods (C. van der Linden et al., 2018; Vihani et al., 2020) may be due to male odor-dependent increases in the lifespans of Olfr1437 OSNs.

Within the Discussion section of the revised manuscript, we have discussed the findings concerning Olfr1437.

(6) The authors hypothesize in relation to the accelerated birthrate of musk-responsive OSN subtypes that "the acceleration of the birthrates of specific OSN subtypes could selectively enhance sensitivity to odors detected by those subtypes by increasing their representation within the OE". However, for two other OSN subtypes that detect male-specific odors, they hypothesize the opposite "By contrast, Olfr912 (Or8b48) and Olfr1295 (Or4k45), which detect the male-specific non-musk odors 2-sec-butyl-4,5-dihydrothiazole (SBT) and (methylthio)methanethiol (MTMT), respectively, exhibited lower representation and/or transcript levels in mice exposed to male odors, possibly reflecting reduced survival due to overstimulation."

Without any further explanation, it is hard to comprehend why exposure to male-derived odors should, on one hand, accelerate birthrates in some OSN subtypes to potentially increase sensitivity to male odors, but on the other hand, lower transcript levels and does not accelerate birth rates of other OSN subtypes due to overstimulation.

We agree that this point deserves further explanation. Within the revised manuscript, we have expanded the Introduction and Results to describe evidence from previous studies that exposure to stimulating odors causes two categories of changes to specific OSN subtypes: elevated representations or reduced representations within the OSN population. In one study (C. J. van der Linden et al., 2020), UNO treatment was found to cause a fraction of OSN subtypes to exhibit lower birthrates and representations on the closed side of the OE relative to the open. By contrast, another fraction of OSN subtypes exhibited higher representations on the closed side of the OEs of UNO-treated mice, but no difference in birthrates between the two sides. The latter subtypes were found to be distinguished by their receipt of extremely high levels of odor stimulation, suggesting that reduced odor stimulation via naris occlusion may lengthen their lifespans. In support of the possibility that Olfr912 (and Olfr1295), which detect SBT and MTMT, respectively (Vihani et al., 2020), which are emitted specifically by male mice (Lin et al., 2005; Schwende et al., 1986), UNO treatment was previously found to increase total Olfr912 OSN quantities on the closed side compared to the open side in sex-separated males (C. van der Linden et al., 2018), a finding confirmed in the present study (Figure 3–figure supplement 1H).

Taken together, findings from previous studies as well as the current one indicate that olfactory stimulation can accelerate the birthrates and/or reduced the lifespans of OSNs, depending on the specific subtypes and odors within the environment. As we have now indicated in the Discussion, we do not yet know what distinguishes subtypes that undergo stimulation-dependent neurogenesis, but it is conceivable that they detect odors with a particular salience to mice. Thus, observations that some odorants (e.g., musks) cause stimulation-dependent neurogenesis while others do not (e.g., SBT) might reflect an animal’s specific need to adapt its sensitivity to the former. Alternatively, it is conceivable that stimulation-dependent reductions in representations of subtypes such as Olfr912 and Olfr1295 reflect a fundamentally different mode of plasticity that is also adaptive, as has been hypothesized (C. van der Linden et al., 2018; Vihani et al., 2020).

Reviewer #1 (Recommendations For The Authors):

To support the main claim, several controls are necessary as mentioned under point 1 of the public review.

As outlined in our responses to the public review, new experiments within the revised manuscript indicate the following:

(1) Accelerated birthrates of 3 different musk responsive OSN subtypes (Olfr235, Olfr1440, Olfr1431) are observed in non-occluded mice following exposure to multiple exogenous musk odorants (muscone, ambretone) (Figure 4D-F).

(2) Exposure of non-occluded mice to non-musk odors (SBT, IAA) does not accelerate the birthrates of musk responsive OSN subtypes (Olfr235, Olfr1440, Olfr1431) (Figure 4D-F).

(3) Exposure of mice to exogenous musk odors (muscone, ambretone) does not accelerate the birthrates of non-musk responsive OSN subtypes (e.g., Olfr912), including those previously found to undergo stimulation-dependent neurogenesis (Olfr827, Olfr1325) (Figure 4–figure supplement 4C).

(4) Only a fraction of OSN subtypes have a capacity to undergo accelerated neurogenesis in the presence of odors that activate them (e.g., Olfr912 birthrates are not accelerated by SBT exposure) (Figure 4–figure supplement 4C-left).

In addition, this study could be considerably improved by showing that the proposed mechanism applies beyond a single OSN subtype (olfr235), especially since the most sensitive OR subtype (expressing olfr1440) does not align with the main claim. The introduction states that this is difficult because the ligands for many ORs are unknown including all subtypes previously found to undergo stimulation-dependent neurogenesis referring to your 2020 study. While this reviewer agrees that the lack of deorphanization is a significant hurdle in the field, the 2020 study states that about 4% of all ORs (which should equal >40 ORs) show a stimulus-dependent down-regulation on the closed side, not only the 7 ORs which are closer examined (Figure 1). It would tremendously improve the impact of the current study to show that the proposed effect applies also to one of these other >40 ORs.

We appreciate this question, as it alerted us to some shortcomings in how our findings were presented within the original manuscript. We respectfully disagree that only findings regarding subtype Olfr235 align with the main hypothesis of this study, which is that discrete odors can selectively promote the neurogenesis of sensory neuron subtypes that they stimulate. Specifically, we would like to draw attention to experiments on non-occluded female mice exposed to exogenous musk odorants (muscone, ambretone; revised Figures 4D-F; previously, Figure 6). Findings from these experiments provide compelling evidence that exposure to musk odorants causes selective increases in the birthrates of three different musk-responsive OSN subtypes: Olfr235, Olfr1440, and Olfr1431. Thus, we would suggest that results from the present study already show that the proposed mechanism applies to more than the just Olfr235 subtype. However, we agree with what we think is the essence of the reviewer’s point: that it is important to determine the extent to which this mechanism applies to OSN subtypes that are responsive to other (i.e., non-musk) odorants. While, as noted by the reviewer, our previous study identified several OSN subtypes that undergo stimulation-dependent neurogenesis (as well as many others that predicted to do so)(C. J. van der Linden et al., 2020), we are not aware of ligands that have been identified with high confidence for those subtypes. Although we are in the process of conducting experiments to identify additional odor/subtype pairs to which the mechanism described in this study applies, the early-stage nature of these experiments precludes their inclusion in the present manuscript.

The ethological and mechanistic relevance of the current study could be significantly improved by showing that musk-related odors that activate olfr235 are actually found in male mouse urine (and additionally are not found in female mouse urine). Otherwise, the implicated link between the acceleration of OSN birthrates by exposure to male odors and acceleration by specific monomolecular odors does not hold, raising the question of any natural relevance (e.g. the proposed adaptive function to increase sensitivity to certain odors).

As noted in our responses to the public review, we have addressed this important point within the revised manuscript as follows:

(1) We have included an extensive discussion of what is known about the emission of musk-like odors by mice.

(2) We have used GC-MS to analyze both mouse urine and preputial gland extracts for the presence of known musk compounds. Although inconclusive, we report that preputial glands contain signals that are structurally consistent with known musk compounds. The findings of these experiments have been included in the revised manuscript (new Appendix 2–figure 1), along with a discussion of their limitations.

Reviewer #2 (Public Review):

In their paper entitled "In mice, discrete odors can selectively promote the neurogenesis of sensory neuron subtypes that they stimulate" Hossain et al. address lifelong neurogenesis in the mouse main olfactory epithelium. The authors hypothesize that specific odorants act as neurogenic stimuli that selectively promote biased OR gene choice (and thus olfactory sensory neuron (OSN) identity). Hossain et al. employ RNA-seq and scRNA-seq analyses for subtype-specific OSN birthdating. The authors find that exposure to male and musk odors accelerates the birthrates of the respective responsive OSNs. Therefore, Hossain et al. suggest that odor experience promotes selective neurogenesis and, accordingly, OSN neurogenesis may act as a mechanism for long-term olfactory adaptation.

We appreciate this summary but would like to underscore that a mechanism involving biased OR gene choice is just one of two possibilities proposed in the Discussion section to explain how odorant stimulation of specific subtypes accelerates the birthrates of those subtypes.

The authors follow a clear experimental logic, based on sensory deprivation by unilateral naris occlusion, EdU labeling of newborn neurons, and histological analysis via OR-specific RNA-FISH. The results reveal robust effects of deprivation on newborn OSN identity. However, the major weakness of the approach is that the results could, in (possibly large) parts, depend on "downregulation" of OR subtype-specific neurogenesis, rather than (only) "upregulation" based on odor exposure. While, in Figure 6, the authors show that the observed effects are, in part, mediated by odor stimulation, it remains unclear whether deprivation plays an "active" role as well. Moreover, as shown in Figure 1C, unilateral naris occlusion has both positive and negative effects in a random subtype sample.

In our view, the present study involves two distinct and complementary experimental designs: 1) odor exposure of UNO-treated animals and 2) odor exposure of non-occluded animals. Here we address this comment with respect to each of these designs:

(1) For experiments performed on UNO-treated animals, we agree that observed differences in birthrates on the open and closed sides of the OE reflect, largely, a deceleration (i.e., downregulation) of the birthrates of these subtypes on the closed side relative to the open (as opposed to an acceleration of birthrates on the open side). Our objective in using this design was to test the extent to which specific OSN subtypes undergo stimulation-dependent neurogenesis under various odor exposure conditions. According to the main hypothesis of this study, a lower birthrate of a specific OSN subtype on the closed side of the OE compared to the open is predicted to reflect a lower level of odor stimulation on the closed side received by OSNs of that subtype. However (and as described in our responses to reviewer #1), a limitation of this design is that environmental odorants, especially at high concentrations, are likely to stimulate responsive OSNs on the closed side of the OE in addition to the open side due to transnasal and/or retronasal air flow.

(2) Experiments performed on non-occluded animals were designed to provide critical complementary evidence that specific OSN subtypes undergo accelerated neurogenesis in the presence of specific odors. Using this design, we have found compelling evidence that:

- Exposure of non-occluded mice to male odors causes the selective acceleration of the birthrate of Olfr235 OSNs (Figure 3G, H).

- Exposure of non-occluded female mice to two different musk odorants (muscone and ambretone) selectively accelerates the birthrates three different musk responsive subtypes: Olfr235, Olfr1440, and Olf1431 (Figure 4D-F and Figure 4–figure supplement 4C).

We have reorganized the revised manuscript to more clearly present the most important experimental findings using these two experimental designs. We have also highlighted (via schematics) the experimental conditions (e.g., UNO, non-occlusion, odor exposure) used for each experiment.

Another weakness is that the authors build their model (Figure 8), specifically the concept of selectivity, on a receptor-ligand pair (Olfr912 that has been shown to respond, among other odors, to the male-specific non-musk odors 2-sec-butyl-4,5-dihydrothiazole (SBT)) that would require at least some independent experimental corroboration. At least, a control experiment that uses SBT instead of muscone exposure should be performed.

We agree that this important concern deserves additional control experiments and discussion. We have addressed this concern within the revised manuscript as follows:

- Within the Results section, we have added multiple new control experiments (detailed in response to Reviewer #1), including the one recommended above. As suggested, we quantified newborn OSNs of the SBT-responsive subtype Olfr912 in non-occluded females that were either exposed to 0.1% SBT or unexposed controls. Exposure of SBT was found to cause no significant increase in quantities of newborn Olfr912 OSNs (newly added Figure 4–figure supplement 4C-left). These findings further support the model in Figure 7 (previously Figure 8) that only a fraction of OSN subtypes have a capacity to undergo accelerated neurogenesis in the presence of odors that activate them.

- Also within the Results section, we have made efforts to better highlight relevant control experiments that were included in the original version, particularly those showing that quantities of newborn Olfr912 OSNs are not affected by UNO in mice exposed to male odors (Figure 2H and Figure 3–figure supplement 1G; previously Figure 2F and Figure 3H) or by exposure of non-occluded females to male odors (Figure 3H; previously Figure 6E). Since Olfr235 is responsive to component(s) of male odors (C. van der Linden et al., 2018; Vihani et al., 2020), these results indicate that this subtype does not have the capacity of stimulation-dependent neurogenesis, which is consistent with our previous findings that only a fraction of subtypes have this capacity (C. J. van der Linden et al., 2020).

In this context, it is somewhat concerning that some results, which appear counterintuitive (e.g., lower representation and/or transcript levels of Olfr912 and Olfr1295 in mice exposed to male odors) are brushed off as "reflecting reduced survival due to overstimulation." The notion of "reduced survival" could be tested by, for example, a caspase3 assay.

This is a point that we agree deserves further discussion. Please see the explanation that we have outlined above in response to Reviewer #1.

Within the revised manuscript, we have expanded the Introduction to describe evidence from previous studies that exposure to stimulating odors causes two categories of changes to specific OSN subtypes: elevated representations or reduced representations within the OSN population. We outline evidence from previous studies that Olfr912 and Olfr1295 belong to the latter category, and that the representations of these subtypes are likely reduced by male odor overstimulation-dependent shortening of OSN lifespan.

Important analyses that need to be done to better be able to interpret the findings are to present (i) the OR+/EdU+ population of olfactory sensory neurons not just as a count per hemisection, but rather as the ratio of OR+/EdU+ cells among all EdU+ cells; and (ii) to the ratio of EdU+ cells among all nuclei (UNO versus open naris). This way, data would be normalized to (i) the overall rate of neurogenesis and (ii) any broad deprivation-dependent epithelial degeneration.

We have addressed this concern in two ways within the revised manuscript:

(1) We have noted within the Methods section that the approach of using half-sections for normalization has been used in multiple previous studies for quantifying newborn (OR+/EdU+) and total (OR+) OSN abundances (Hossain et al., 2023; Ibarra-Soria et al., 2017; C. van der Linden et al., 2018; C. J. van der Linden et al., 2020). Additionally, within the figure legends and Methods, we have more thoroughly described the approach used, including that it relies on averaging the quantifications from at least 5 high-quality coronal OE tissue sections that are evenly distributed throughout the anterior-posterior length of each OE and thereby mitigates the effects of section size and cell number variation among sections. In the case of UNO treated mice, the open and closed sides within the same section are paired, which further reduces the effects of section-to section variation. We have found that this approach yields reproducible quantities of newborn and total OSNs among biological replicate mice and enables accurate assessment of how quantities of OSNs of specific subtypes change as a result of altered olfactory experience, a key objective of this study.

(2) To assess whether the use of alternative approaches for normalizing newborn OSN quantities suggested by the reviewers would affect the present study’s findings, we compared three methods for normalizing the effects of exposure to male odors or muscone on quantities of newborn Olfr235 OSNs in the OEs of both UNO-treated and non-occluded mice: 1) OR+/EdU+ OSNs per half-section (used in this study), 2) OR+/EdU+ OSNs per total number of EdU+ cells (reviewer suggestion (i)), and 3) OR+/EdU+ OSNs per unit of DAPI+ area (an approximate measure of nuclei number; reviewer suggestion (ii)). The three normalization methods yielded statistically indistinguishable differences in assessing the effects of exposure of either UNO-treated or non-occluded mice to male odors (newly added Figure 2–figure supplement 2 and Figure 3–figure supplement 2), or of exposure of non-occluded mice to muscone (newly added Figure 4–figure supplement 3). Based on these findings, and the considerable time that would be required to renormalize all data in the manuscript, we have chosen to maintain the use of normalization per half-section.

Finally, the paper will benefit from improved data presentation and adequate statistical testing. Images in Figures 2 - 7, showing both EdU labeling of newborn neurons and OR-specific RNA-FISH, are hard to interpret. Moreover, t-tests should not be employed when data is not normally distributed (as is the case for most of their samples).

We have made extensive changes within the revised manuscript to increase the clarity and interpretability of the figures, including:

(1) Addition of a split-channel, high-magnification view of a representative image that shows the overlap of FISH and EdU signals (Figure 2D).

(2) Addition of experimental schematics and timelines corresponding to each set of experiments.

In the revised manuscript, several changes to the statistical tests have been made, as follows:

(1) To assess deviation from normality of the histological quantifications of newborn and total OSNs of specific subtypes in this study, all datasets were tested using the Shapiro-Wilk test for non-normality and the P values obtained are included in Supplementary file 1 (figure source data). Of the 274 datasets tested, 253 were found to have Shapiro-Wilk P values > 0.05, indicating that the vast majority (92%) do not show evidence of significant deviation from a normal distribution.

(2) A general lack of deviation of the datasets in this study from a normal distribution is further supported by quantile-quantile (QQ) plots, which compare actual data to a theoretically normal distribution (Appendix 4–figure 1). The datasets analyzed were separated into the following categories:

a. Quantities of newborn OSNs in UNO treated mice (Appendix 4-figure 1A)

b. Quantities of total OSNs in UNO treated mice (Appendix 4-figure 1B)

c. Quantities of newborn OSNs in non-occluded mice (Appendix 4-figure 1C)

d. UNO effect sizes for newborn or total OSNs (Appendix 4-figure 1D)

(3) Results of both parametric and non-parametric statistical tests of comparisons in this study have been included in Supplementary file 2 (statistical analyses). In general, the results from parametric and non-parametric tests are in good agreement.

(4) Statistical analyses of differences in OSN quantities in the OEs of non-occluded mice or UNO effect sizes in UNO-treated mice subjected more than two different experimental conditions have now been performed using one-way ANOVA tests, FDR-adjusted using the 2-stage linear step-up procedure of Benjamini, Krieger and Yekutieli.

Reviewer #2 (Recommendations for the Authors):

The manuscript by Hossain et al. would benefit from a thorough revision. Here, we outline several points that should be addressed:

Figure 3E - I & Figure 4E&F: Red lines that connect mean values are misleading.

Within the revised manuscript, the UNO effect size graphs have been modified for clarity, including removal of the lines between mean values except for those comparing changes over time post EdU injection (Figure 6 and Figure 6-figure supplement 1). For these latter graphs, we think that lines help to illustrate changes in effect sizes over time.

Figure 3E - I: UNO effect sizes (right) should be tested via ANOVA.

In the revised manuscript, statistical analyses of UNO effect sizes in UNO-treated mice subjected more than two different experimental conditions were done using one-way ANOVA tests, FDR-adjusted using the 2-stage linear step-up procedure of Benjamini, Krieger and Yekutieli (Figure 2-figure supplement 2; Figure 3; Figure 3-figure supplement 1; Figure 4; Figure 4-figure supplements 1, 2). The same tests were used for analysis of differences in OSN quantities in the OEs of non-occluded mice subjected more than two different experimental conditions (Figure 3; Figure 3-figure supplement 2; Figure 4; Figure 4-figure supplements 3, 4). For comparisons of differences in quantities of newborn OSNs of musk-responsive subtypes at 4 and 7 days post-EdU between non-occluded mice exposed and unexposed to muscone, a two sample ANOVA - fixed-test, using F distribution (right-tailed) was used (Figure 6; Figure 6-figure supplement 1).

Images in Figures 2 - 7, showing both EdU labeling of newborn neurons and OR-specific RNA-FISH: Colabeling is hard / often impossible to discern. Show zoom-ins and better explain the criteria for "colabeling" in the methods.

In the revised manuscript an enlarged and split-channel view of an image showing multiple newborn Olfr235 OSNs (OR+/EdU+) has been added (Figure 2D). A detailed description of the criteria for OR+/EdU+ OSNs is provided in Methods under the section “Histological quantification of newborn and total OSNs of specific subtypes.”

Figure 1C: add Olfr912.

As a control group for iOSN quantities of musk-responsive subtypes in Figure 1, we selected random subtypes that are expressed in the same zones: 2 and 3. Olfr912 OSNs were not included because this subtype was not randomly chosen, nor is it expressed the same zones (Olfr912 is expressed in zone 4). We also note that the scRNA-seq analysis was done to allow an initial exploration of the hypothesis that some OSN subtypes with that are more highly represented in mice exposed to male odors show stimulation-dependent neurogenesis. Considering that the scRNA-seq datasets contain only small numbers of iOSNs of specific subtypes, we think they are more useful for analyzing changes in birthrates within groups of subtypes (e.g., musk responsive, random) rather than individual subtypes.

The time of OE dissection is different for data shown in Figure 1 (P28) as compared to other figures (P35). Please comment/discuss.

Within the Results section of the revised manuscript, we have now clarified that the PD 28 timepoint chosen for EdU birthdating in the histological quantification of newborn OSNs of specific subtypes is analogous to the PD 28 timepoint chosen for identification of immature (Gap43-expressing) OSNs in the scRNA-seq samples. In the case of EdU birthdating, it is necessary to provide a chase period of sufficient length to enable robust and stable expression of an OR, which defines the subtype. A chase period of 7 days was chosen based on a previous study (C. J. van der Linden et al., 2020). Hence, a dissection date of PD 35 was chosen.

Figure 3F&G: please discuss the female à female effects

In the Results and Discussion sections of the revised manuscript, we discuss our observation that the Olfr1440 and Olfr1431 subtypes show significantly higher quantities of newborn OSNs on the open side compared to closed sides in UNO-treated females. We speculate that these subtypes may receive some odor stimulation in juvenile females, perhaps via musk or related odors emitted by females themselves or from elsewhere within the environment.

Figure 4E (and other examples): male à male displays two populations (no effect versus effect); please explain/speculate.

For some UNO effect sizes, there appears to be high degree of variation among mice, and, in some cases, this diversity appears to cause the data to separate into groups. We assessed whether this diversity might reflect mice that came from different litters, but this is not the case. Rather, we speculate that the observed diversity most likely reflects low representations of newborn OSNs of some subtypes and/or under specific conditions. The data referred to by the reviewer (now Figure 3–figure supplement 3D), for example, shows UNO effect sizes for quantities of newborn Olfr1431 OSNs, which has the lowest representation among the musk-responsive subtypes analyzed in this study.

Figure 5C-E: It is unclear why strong muscone concentrations (10%) have no effect, whereas no muscone sometimes (D&E) has an effect.

As discussed in response to comments from Reviewer #1, we speculate that fluctuations in UNO effect sizes in muscone-exposed mice, particularly at high muscone concentrations, may be due, at least in part, to transnasal and/or retronasal air flow [reviewed in (Coppola, 2012)], which would be expected to result in exposure of the closed side of the OE to muscone concentrations that increase with increasing environmental concentrations. In support of this, quantities of newborn Olfr235 (Figure 4C-middle) and Olfr1440 (Figure 4–figure supplement 1A-middle) OSNs increase on both the open and closed sides with increasing muscone concentration (except at the highest concentration, 10%, in the case of Olfr1440). We speculate that reductions in newborn Olfr1440 OSN quantities observed in the presence of 10% muscone may reflect overstimulation-dependent reductions in survival.

As emphasized above, our study also includes experiments on non-occluded animals (Figures 3, 4, 5). Findings from these experiments provide additional evidence that exposure to multiple musk odorants (muscone, ambretone) causes selective increases in the birthrates of multiple musk-responsive OSN subtypes (Olfr235, Olfr1440, Olfr1431).

We have included an extensive interpretation of UNO-based experiments, including their limitations, within the Results section of the revised manuscript.

Figure S1: please explain the large error bars regarding "Transcript level".

We have clarified that the error bars in this figure, which is now Appendix 1–figure 1, correspond to 95% confidence intervals.

The figure captions could be improved for ease of reading.

Figure captions have been revised for increased clarity.

Figure 4: Include Olfr235 data for consistency.

All OSN subtypes analyzed for the effects of exposure to adult mice on UNO-induced open-side biases in quantities of newborn OSNs have been included in a single figure, which is now Figure 3–figure supplement 3.

Figure S6F&G: Do not run statistics on n = 2 (G) or 3 (F) samples.

We have removed statistical test results from comparisons involving fewer than 4 observations.

Reviewer #3 (Public Review):

Summary:

Neurogenesis in the mammalian olfactory epithelium persists throughout the life of the animal. The process replaces damaged or dying olfactory sensory neurons. It has been tacitly that replacement of the OR subtypes is stochastic, although anecdotal evidence has suggested that this may not be the case. In this study, Santoro and colleagues systematically test this hypothesis by answering three questions: is there enrichment of specific OR subtypes associated with neurogenesis? Is the enrichment dependent on sensory stimulus? Is the enrichment the result of differential generation of the OR type or from differential cell death regulated by neural activity? The authors provide some solid evidence indicating that musk odor stimulus selectively promotes the OR types expressing the musk receptors. The evidence argues against a random selection of ORs in the regenerating neurons.

Strengths:

The strength of the study is a thorough and systematic investigation of the expression of multiple musk receptors with unilateral naris occlusion or under different stimulus conditions. The controls are properly performed. This study is the first to formulate the selective promotion hypothesis and the first systematic investigation to test it. The bulk of the study uses in situ hybridization and immunofluorescent staining to estimate the number of OR types. These results convincingly demonstrate the increased expression of musk receptors in response to male odor or muscone stimulation.

Weaknesses:

A major weakness of the current study is the single-cell RNASeq result. The authors use this piece of data as a broad survey of receptor expression in response to unilateral nasal occlusion. However, several issues with this data raise serious concerns about the quality of the experiment and the conclusions. First, the proportion of OSNs, including both the immature and mature types, constitutes only a small fraction of the total cells. In previous studies of the OSNs using the scRNASeq approach, OSNs constitute the largest cell population. It is curious why this is the case. Second, the authors did not annotate the cell types, making it difficult to assess the potential cause of this discrepancy. Third, given the small number of OSNs, it is surprising to have multiple musk receptors detected in the open side of the olfactory epithelium whereas almost none in the closed side. Since each OR type only constitutes ~0.1% of OSNs on average, the number of detected musk receptors is too high to be consistent with our current understanding and the rest of the data in the manuscript. Finally, unlike the other experiments, the authors did not describe any method details, nor was there any description of quality controls associated with the experiment. The concerns over the scRNASeq data do not diminish the value of the data presented in the bulk of the study but could be used for further analysis.

We are grateful to the reviewer for raising these important questions.

In the revised manuscript, we have clarified that the scRNA-seq dataset presented in the original version of the manuscript (now called dataset OE 1) was published and described in detail in a previous study (C. J. van der Linden et al., 2020). The reviewer is correct that the proportion of OSNs within that dataset was lower in that dataset than in other datasets that have been published more recently (using updated methods). We think this is likely because of the way that the cells were processed (e.g., from cryopreserved single cells followed by live/dead selection). However, because the open and closed sides were processed identically, we do not expect the ratios of OSNs of specific subtypes to be greatly affected. Hence, the differences observed for specific OSN subtypes on the open versus closed sides are expected to be valid.

As the reviewer notes, there is a surprisingly large difference between the number of OSNs of musk-responsive subtypes on the open and closed sides within the OE 1 dataset. This difference is a key piece of information that led us to formulate the hypothesis in the study: that musk responsive subtypes are born at a higher rate in the presence of male/musk odor stimulation. And while it is true that, on average, each subtype represents ~0.1% of the population, it is known that there is wide variance in representations among different subtypes [e.g., (Ibarra-Soria et al., 2017)]. The frequencies of the musk responsive subtypes among all OSNs on the open side of OE 1 (0.3% for Olfr235, 0.4% for olfr1440, 0.06% for Olfr1434, 0% for olfr1431, and 1% for Olfr1437) are in line with previous findings.

To confirm that the scRNA-seq findings from dataset OE 1 are not an artifact of the cell preparation methods used, we generated a second scRNA-seq dataset, OE 2, which has been added to the revised manuscript (Figure 1). The OE 2 dataset was prepared according to the same experimental timeline as OE 1, but the cells were captured immediately after dissociation and live/dead sorting via FACS. As expected, most cells within OE 2 dataset are OSNs (77% on the open side, 66% on the closed). Importantly, like the OE 1 dataset, the OE 2 dataset shows higher quantities of iOSNs of musk responsive subtypes on the open side of the OE compared to the closed (normalized for either total cells or total OSNs) (Figure 1–figure supplement 1D, E).

A weakness of the experiment assessing musk receptor expression is that the authors do not distinguish immature from mature OSNs. Immature OSNs express multiple receptor types before they commit to the expression of a single type. The experiments do not reveal whether mature OSNs maintain an elevated expression level of musk receptors.

While it is established that multiple ORs are coexpressed at a low level during OSN differentiation (Bashkirova et al., 2023; Fletcher et al., 2017; Hanchate et al., 2015; Pourmorady et al., 2024; Saraiva et al., 2015; Scholz et al., 2016; Tan et al., 2015), this has been found to occur primarily at the immediate neuronal precursor 3 (INP3) stage (Bashkirova et al., 2023; Fletcher et al., 2017), which is characterized by expression of Tex15 (Fletcher et al., 2017; Pourmorady et al., 2024) and precedes the immature OSN (iOSN) stage, which is characterized by expression of Gap43 (Fletcher et al., 2017; McIntyre et al., 2010; Verhaagen et al., 1989). Within the scRNA-seq datasets in the present study, iOSNs of specific subtypes are identified based on robust expression of Gap43 (Log² UMI > 1) and a specific OR gene (Log² UMI > 2), as described in the figures and methods. Thus, the cells defined as iOSNs are expected to express a single OR gene and this expression should be maintained as iOSNs transition to mOSNs. To confirm these predictions, we carried out a detailed analysis of OR expression at three different stages of OSN differentiation: INP3, iOSN, and mOSN (Figure 1–figure supplement 2). The cells chosen for analysis express the musk-responsive ORs Olfr235 or Olfr1440 or a randomly chosen OR Olfr701, in addition to markers that define INP3, iOSN, or mOSN cells. As expected, individual iOSNs and mOSNs of musk-responsive subtypes were found to exhibit robust and singular OR expression on the open and closed sides of OEs from UNO-treated mice. Moreover, and as observed previously, INP3 cells coexpress multiple OR transcripts at low levels. A detailed description of how the analysis was performed is included in the Methods section under Quantification and statistical analysis.

Within the histology-based quantifications, newborn OSNs are identified based on their robust RNA-FISH signals corresponding to a specific OR transcript and an EdU label. Considering the EdU chase time of 7 days, most EdU-positive cells are expected to have passed the INP3 stage and be iOSNs or mOSNs. Moreover, considering the low level of OR expression within INP3 cells, it is unlikely OR transcripts are expressed at a high enough level to be detectable and/or counted at this stage and thereby affect newborn OSN quantifications.

There are also two conceptual issues that are of concern. The first is the concept of selective neurogenesis. The data show an increased expression of musk receptors in response to male odor stimulation. The authors argue that this indicates selective neurogenesis of the musk receptor types. However, it is not clear what the distinction is between elevated receptor expression and a commitment to a specific fate at an early stage of development. As immature OSNs express multiple receptors, a likely scenario is that some newly differentiated immature OSNs have elevated expression of not only the musk receptors but also other receptors. The current experiments do not distinguish the two alternatives. Moreover, as pointed out above, it is not clear whether mature OSNs maintain the increased expression. Although a scRNASeq experiment can clarify it, the authors, unfortunately, did not perform an in-depth analysis to determine at which point of neurogenesis the cells commit to a specific musk receptor type. The quality of the scRNASeq data unfortunately also does not lend confidence for this type of analysis.

The addition of a second scRNA-seq dataset within the revised manuscript (Figure 1), combined with the new scRNA-seq-based analyses of OR expression in INP3, iOSN, and mOSN cells (Figure 1-figure supplement 2), provide strong evidence that iOSNs and mOSNs robustly express a single OR gene and that cellular expression is stable from the iOSN to the mOSN stage. These analyses do not support a scenario in which odor stimulation causes upregulated expression of multiple ORs and thereby causes apparent increases in quantities of newly generated OSNs that express musk-responsive ORs. Rather, the data firmly support a mechanism in which odor stimulation increases quantities of newly generated OSNs that have stably committed to the robust expression of a single musk-responsive OR.

A second conceptual issue, the idea of homeostasis in regeneration, which the authors presented in the Introduction, needs clarification. In its current form, it is confusing. It could mean that a maintenance of the distribution of receptor types, or it could mean the proper replacement of a specific OR type upon the loss of this type. The authors seem to refer to the latter and should define it properly.

We have revised the Introduction section to clarify our use of the term homeostatic in one instance (paragraph 4) and replace it with more specific language in a second instance (paragraph 5).

Reviewer #3 (Recommendations For The Authors):

Concerns over scRNASeq data. It appears that the samples may have included non-OE tissues, which reduced the representation of the OSNs. This experiment may need to be repeated to increase the number of OSNs.

As outlined in the response to the public comments, we think that the low proportion of OSNs in the OE 1 data set reflects how the cells were prepared and processed. We have now included a second scRNA-seq dataset to address this concern.

Cell types should be identified in the scRNASeq analysis, and the number of cells documented for each cell type, at least for the OSNs. The data should be made available for general access.

We have now clarified that the OE 1 dataset was published as part of a previous study (C. J. van der Linden et al., 2020) and was made publicly available as part of that study (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE157119). All cell types in the newly generated OE 2 dataset have been annotated (Figure 1) and this dataset has also been made publicly available (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE278693). The numbers and percentages of OSNs within OE 1 and OE 2 datasets have been added to the legend of Figure 1-figure supplement 1.

The specific OR types should be segregated for mature and immature OSNs. The percentage of a specific OR type should be normalized to the total number of OSNs, rather than the total cells. The current quantification is misleading because it gives the false sense that the muscone receptors represent ~0.1% of cells when the proportion is much higher if only OSNs are considered.

In the revised manuscript, quantities of iOSNs (Gap43+ cells) of specific subtypes within the OE 1 and OE 2 scRNA-seq datasets are graphed as percentages of both all OSNs (Figure 1E, Figure 1–figure supplement 1D) and all cells (Figure 1–figure supplement 1E). As a percentage of all OSNs, average quantities of iOSNs of musk responsive subtypes on the open side of the OE range from 0.005% (for Olfr1431) to 0.14% (for Olfr1440) (Figure 1E).

Within the feature plots for the two datasets, the differentiation stages of indicated OSNs have been clearly defined within the figures and figure legends. For the OE 1 dataset, iOSNs are differentiated from mOSNs by arrows (Figure 1–figure supplement 1C). For the OE 2 dataset (Figure 1D), only immature OSNs are shown for simplicity.

Technical details of the scRNASeq should be documented. In the feature plot of musk-response receptors (Figure. 1D), it is better to use the actual quantity of expression rather than binarized representation (with or without an OR). If one needs to use on/off to determine the number of cells for a given OR type, then the criteria of selection should be given.

Technical details of generation of the scRNA-seq datasets have been documented in the “Method details” section (for the OE 2 dataset) and in the method section of our previous publication of the OE 1 dataset (C. J. van der Linden et al., 2020). Details of the scRNA-seq analyses, including the criteria used to define immature OSNs of specific subtypes, are documented within the “Quantification and statistical analysis” section.

Within the feature plots, we have decided to show OSNs of a given subtype in a binary fashion using specific colors for the sake of simplicity (Figure 1D, Figure 1-figure supplement 1C). To address the reviewer’s cooncern, we have added a new figure that provides detailed information about OR transcript expression (levels and genes) within iOSNs and mOSNs of two different musk responsive subtypes and a randomly chosen subtype (Figure 1-figure supplement 2).

An in-depth analysis of the onset of OR expression in the GBC, INP, immature, and mature OSNs should be performed. It is also important to determine how many other receptors are detected in the cells that express the musk receptors. The current scRNASeq data may not be of sufficiently high quality and the experiment needs to be repeated. It is also important for the authors to take measures to eliminate ambient RNA contamination.

The revised manuscript includes a second scRNA-seq dataset (OE 2; Figure 1). Details of how both the original (OE 1) and new datasets were generated have been documented within the Methods sections of the corresponding publications [(C. J. van der Linden et al., 2020); present study]. For both datasets, live/dead selection of cells was performed, which was expected to reduce ambient RNA.

The revised manuscript also includes a new figure that provides detailed information about OR transcript expression within INP3, iOSN and mOSN cells that express one of two different musk responsive ORs or a randomly chosen OR (Figure 1-figure supplement 2). These data reveal, as reported previously (Bashkirova et al., 2023; Fletcher et al., 2017; Pourmorady et al., 2024), that low levels of multiple OR transcripts are detected in INP3 (Tex15+) cells. By contrast, iOSN (Gap43+) and mOSN (Omp+) cells robustly express a single OR, with little or no expression of other ORs.

Quantification of cells for Figure 2-7 should be changed. Instead of using cell number per 1/2 section, the data should be calculated using density (using the area of the epithelium or normalized to the total number of cells (based on DAPI staining). This is because multiple sections are taken from the same mouse along the A-P axis. These sections have different sizes and numbers of cells.

As noted in response to a similar concern of Reviewer #2, this has been addressed in two ways within the revised manuscript:

(1) We have noted within the Methods section that the approach of using half-sections for normalization has been used in multiple previous studies for quantifying newborn (OR+/EdU+) and total (OR+) OSN abundances (Hossain et al., 2023; Ibarra-Soria et al., 2017; C. van der Linden et al., 2018; C. J. van der Linden et al., 2020). Additionally, within the figure legends and Methods, we have more thoroughly described the approach used, including that it relies on averaging the quantifications from at least 5 high-quality coronal OE tissue sections that are evenly distributed throughout the anterior-posterior length of each OE and thereby mitigates the effects of section size and cell number variation among sections. In the case of UNO treated mice, the open and closed sides within the same section are paired, which further reduces the effects of section-to section variation. We have found that this approach yields reproducible quantities of newborn and total OSNs among biological replicate mice and enables accurate assessment of how quantities of OSNs of specific subtypes change as a result of altered olfactory experience, a key objective of this study.

(2) To assess whether the use of alternative approaches for normalizing newborn OSN quantities suggested by the reviewers would affect the present study’s findings, we compared three methods for normalizing the effects of exposure to male odors or muscone on quantities of newborn Olfr235 OSNs in the OEs of both UNO-treated and non-occluded mice: 1) OR+/EdU+ OSNs per half-section (used in this study), 2) OR+/EdU+ OSNs per total number of EdU+ cells (reviewer suggestion (i)), and 3) OR+/EdU+ OSNs per unit of DAPI+ area (an approximate measure of nuclei number; reviewer suggestion (ii)). The three normalization methods yielded statistically indistinguishable differences in assessing the effects of exposure of either UNO-treated or non-occluded mice to male odors (newly added Figure 2–figure supplement 2 and Figure 3–figure supplement 2), or of exposure of non-occluded mice to muscone (newly added Figure 4–figure supplement 3). Based on these findings, and the considerable time that would be required to renormalize all data in the manuscript, we have chosen to maintain the use of normalization per half-section.

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Author response:

The following is the authors’ response to the original reviews

Public Reviews:

Reviewer #1 (Public review):

Summary:

In this study, Le et al.. aimed to explore whether AAV-mediated overexpression of Oct4 could induce neurogenic competence in adult murine Müller glia, a cell type that, unlike its counterparts in cold-blooded vertebrates, lacks regenerative potential in mammals. The primary goal was to determine whether Oct4 alone, or in combination with Notch signaling inhibition, could drive Müller glia to transdifferentiate into bipolar neurons, offering a potential strategy for retinal regeneration.

The authors demonstrated that Oct4 overexpression alone resulted in the conversion of 5.1% of Müller glia into Otx2+ bipolar-like neurons by five weeks post-injury, compared to 1.1% at two weeks. To further enhance the efficiency of this conversion, they investigated the synergistic effect of Notch signaling inhibition by genetically disrupting Rbpj, a key Notch effector. Under these conditions, the percentage of Müller gliaderived bipolar cells increased significantly to 24.3%, compared to 4.5% in Rbpjdeficient controls without Oct4 overexpression. Similarly, in Notch1/2 double-knockout Müller glia, Oct4 overexpression increased the proportion of GFP+ bipolar cells from 6.6% to 15.8%.

To elucidate the molecular mechanisms driving this reprogramming, the authors performed single-cell RNA sequencing (scRNA-seq) and ATAC-seq, revealing that Oct4 overexpression significantly altered gene regulatory networks. They identified Rfx4, Sox2, and Klf4 as potential mediators of Oct4-induced neurogenic competence, suggesting that Oct4 cooperates with endogenously expressed neurogenic factors to reshape Müller glia identity.

Overall, this study aimed to establish Oct4 overexpression as a novel and efficient strategy to reprogram mammalian Müller glia into retinal neurons, demonstrating both its independent and synergistic effects with Notch pathway inhibition. The findings have important implications for regenerative therapies as they suggest that manipulating pluripotency factors in vivo could unlock the neurogenic potential of Müller glia for treating retinal degenerative diseases.

Strengths:

(1) Novelty: The study provides compelling evidence that Oct4 overexpression alone can induce Müller glia-to-bipolar neuron conversion, challenging the conventional view that mammalian Müller glia lacks neurogenic potential.

(2) Technological Advances: The combination of Muller glia-specific labeling and modifying mouse line, AAV-GFAP promoter-mediated gene expression, single-cell RNA-seq, and ATAC-seq provides a comprehensive mechanistic dissection of glial reprogramming.

(3) Synergistic Effects: The finding that Oct4 overexpression enhances neurogenesis in the absence of Notch signaling introduces a new avenue for retinal repair strategies.

Weaknesses:

(1) In this study, the authors did not perform a comprehensive functional assessment of the bipolar cells derived from Müller glia to confirm their neuronal identity and functionality.

(2) Demonstrating visual recovery in a bipolar cell-deficiency disease model would significantly enhance the translational impact of this work and further validate its therapeutic potential.

Response: We thank the Reviewer for their evaluation. We agree that functional analysis of Müller glia-derived bipolar cells is indeed important, but is beyond the current scope of the manuscript.

Reviewer #2 (Public review):

Summary:

The authors harness single-cell RNAseq data from zebrafish and mice to identify Oct4 as a candidate driver of neurogenesis. They then use adeno-associated virus vectors to show that while Oct4 overexpression alone converts rare adult Müller glia (MG) to bipolar cells, it synergizes with Notch pathway inhibition to cause this neurogenesis (achieved by Cre-mediated knockout of Rbpj floxed allele). Importantly, they genetically lineage-mark adult MG using a GLAST-CreER transgene and a Sun-GFP reporter, so that any non-MG cells that convert can be identified unambiguously. This is crucial because several high-profile papers made erroneous claims using short promoters in the viral delivery vector itself to mark MG, but those promoters are leaky and mark other non-MG cell types, making it impossible to definitively state whether manipulations studied were actually causing neurogenesis, or were merely the result of expression in pre-existing neurons. Once the authors establish Oct4 + RbpjKO synergy they use snRNAseq/ATACseq to identify known and novel transcription factors that could play a role in driving neurogenesis.

Strengths:

The system to mark MG is stringent, so the authors are studying transdifferentiation, not artifactual effects due to leaky viral promoters. The synergy between Oct4 and Notch pathway blockade is notable. The single-cell results add the potential involvement of new players such as Rfx4 in adult-MG-neurogenesis.

Weaknesses:

The existing version is difficult to read due to an unusually high number of text errors (e.g. references to the wrong figure panels etc.). A fuller explanation for the fraction of non-MG cells seen in control scRNAseq assays is required, particularly because the neurogenic trajectory which is enhanced in the Oct4/Rbpj-KO context is also evident in the control retina. Claims regarding the involvement of transcription factors in adult neurogenesis (such as Rfx4) need to be toned down unless they are backed up with functional data. It is possible that such factors are important, but equally, they may have no role or a redundant role, and without functional tests, it's impossible to say one way or the other.

Overall, the authors achieved what they set out to do, and have made new insights into how neurogenesis can be stimulated in MG. Ultimately, a major long-term goal in the field is to replace lost photoreceptors as this is most relevant to many human visual disorders, and while this paper (like all others before it) does not generate rods or cones, it opens new strategies to coax MG to form a related neuronal cell type. Their approach underscores the benefits of using a gold-standard approach for lineage tracing.

We thank the Reviewer for their evaluation. We have made extensive changes to the manuscript to correct errors and modify discussion as recommended. These are detailed below in our point-by-point responses to specific recommendations to the authors.

Recommendations for the authors:

Reviewer #1 (Recommendations for the authors):

Minor corrections:

(1) In Figure 1C top GFAP-mCherry panel, two dim GFP + cells have colocalized with Otx2, is it caused by optic imaging thickness or some muller glia cells having the Otx2 expression?

This indeed reflects the effects of optic imaging thickness. Colocalization of Sun1-GFP and Otx2 is not observed when Z-stack images are examined in GlastCreER;Sun1-GFP retinas. This can also be appreciated by the fact that, in cases of apparent overlap of nuclear envelope-targeted Sun1 and Otx2, the sizes of the labeled areas differ. In cases of true expression overlap, such as is seen following Oct4 overexpression, the labeled areas are the same size, or very nearly so.

Whether the Glast-CreERT2 x Rosa26-LSL-Sun1-GFP mouse line has cross-labeling with the Otx2+ bipolar cells, the author should image the mCherry ctrl sample with a thin optical imaging layer with a small pinhole for Z-stack to verify the co-labeling the GFP and Otx2 in mCherry ctrl sample.

Please see above. Since we first described this line (de Melo, et al. 2012), we have examined thousands of sections of GlastCreER;Sun1-GFP retinas, and have yet to see a single GFP-positive neuron. To avoid confusion, however, we have replaced these images with an additional image from a control mCherry-infected GlastCreER;Sun1-GFP retina processed for the same study.

In the middle upper panel, Oct4-mCherry group, the white arrows indicate the GFP colocalized with Otx2 signal, but seems not mCherry positive, by contrast, the neighbor cells have significant mCherry expression but no colocalization with Otx2. The GFAP promoter-Oct4-mCherry may have stopped expression after the Müller Glia cells were converted into Otx2+ bipolar cells, but is there any middle stage in which the Oct4mCherry and Otx2 co-expression? And after Müller glia to Bipolar conversion, why have Glast-CreERT2 driven GFP expressions not suppressed as GFAP promoter driven Oct4-mCherry? Could the author discuss this point?

We observed a significant number of Muller glia-derived cells expressing both Otx2 and weak mCherry signal. GFP expression is driven by the ubiquitous CAG promoter following Cre-dependent excision of a transcriptional stop cassette. We have modified the text to make this point explicit.

(2) In Figure S2b, the mouse is labeled with wild type; I assume it should be the same mouse line as Fig.1. Otherwise, the author should describe the source of the GFP signal.

“Wildtype” in this case refers to GlastCreER;Sun1-GFP controls, which as the Reviewer correctly points out, are not truly wildtype. The genotype of these animals is specified in all figure legends, and is now referred to as “control” rather than “wildtype” in the figures and main text throughout.

In Figure S2k and l, mCherry ctrl panel, the GFP+ cells looked co-labeling with Otx2, so again, is it the thicker optical imaging layer that caused overlapping vertically or the low specific of Müller Glia of the mouse line? Please describe the stars' meaning in Figure S2i,j in the figure legend. There are 2 figures labeled "n" of the quantification data.

This is, again, an example of the thicker optical imaging layer causing apparent overlap. We have previously demonstrated that the Sun1-GFP+ cells do not co-label with Otx2 in GFAP-mCherry AAV-injected control retinas (Le et al., 2022; Fig. 2C). The asterisks (*) indicate mouse-on-mouse vascular staining, which is now clarified in the figure legend. The 2 figures labeled ‘n’ have been relabeled as ‘m’ and ‘n’.

(3) In Figure 2c in the top panel, the Otx2 image was wrong; please replace it with the correct one.

We thank the Reviewer for spotting this error. This is an inadvertent duplication of the single-channel Otx2 staining for mCherry control sample. We have replaced this with the correct image.

(4) In Figure 3a, the Rbpj-cKO mouse line was used, but where was the GFP signal from? Please verify the mouse line you used in your work. The same question is also asked in Figure S3, S4b.

GlastCreER;Rpbj^lox/lox;Sun1-GFP were used in Figure 3a. As now specified in the Methods and all figure legends, all mice used in this study carry both the GlastCreER and Sun1-GFP transgenes.

(5) In Figure S4c,d, and 5 wks time point, if the authors quantify the GFP+/Sox2- cells changing, it will be more helpful to understand the percentage of the Müller glia cells conversion to Bipolar cells compared to the Figure 2D, and can be as a supplement to the conclusion Müller to Bipolar conversion rather the Müller proliferation.

Sox2-/GFP+ cells are a measure of Müller glia to bipolar cell conversion that complements that of GFP+/Otx2+ cells. This is now clarified in the text. We also include quantification of Sox2-/GFP+ neurons at 5 weeks post-injury in Fig. S5b.

(6) In Figure S1b,c, there is a large portion of cells that are activated Müller glia after NMDA injury. Did the activated Müller glial cells lose their Müller glial identity? Between the loss of Müller glial identity and neuronal reprogramming, are there any markers that can be used to assess whether Müller glial cells are truly transdifferentiating into neurons rather than remaining in a reactive glial state or an intermediate phase?

Wildtype Müller glia progressively revert to resting state, and by 72 hours post-injury have already lost expression of Klf4 and Myc (Hoang, et al. 2020), a point which is now specifically mentioned in the text. In GlastCreER;Sun1-GFP;Nfia/b/x^lox/lox;Rbpj^lox/lox Müller glia, reactive MG appear to largely convert to bipolar and amacrine-like cells, and it remains unclear if they eventually revert to a resting state (Le, et al. 2024).

Reviewer #2 (Recommendations for the authors):

This work demonstrates that Oct4 (Pou5f3) can induce neurogenesis in murine Müller glia (MG). Le et al start by showing that murine and zebrafish MG lack expression of Oct4 (Pou5f3) and its target Nanog. To assess the effect of Oct4 they first label adult MG with Sun1-GFP using tamoxifen-treated GlastCreER;Sun1-GFP mice, then later transduce in vivo with AAV vectors expressing mCherry alone or Oct4 + mCherry. Subsequently, they damage the retina with NMDA and assess the effects several weeks later. In Oct4+ cells at 2 weeks there is rare induction of the neural determinant Ascl1, down-regulation of the MG marker Sox2, induction of bipolar markers (Otx2, Scgn,Cabp5) but not amacrine (HuC/D) or rod (Nrl) markers. Combining Oct4 with

Notch inhibition (deleting floxed Rbpj) synergistically increases bipolar cell induction, with Otx2 staining rising to >20% of GFP-marked cells, and cells losing MG identify (loss of Sox2/9). EdU labeling was negligible suggesting direct trans-differentiation. Similar synergy was seen upon combining Oct4 expression with Notch1/2 double gene knockout. Attempts to combine Oct4 with Nfia, Nfib, and Nfix loss were unsuccessful as the GFAP promoter driving Oct4 in MG seems to require these three related transcription factors. scRNAseq confirmed the Oct4-overexpression/Rbpj-KO-driven increase in bipolar cells and decrease in MG cells and revealed that these manipulations may enhance bipolar cell genesis by repressing genes that define quiescent MG and enhancing expression of genes that define reactive MG and neurogenic cells. Finally, multiomic snRNA/scATAC-seq data was performed to assess the effect of Oct2 in wt or Rbpj null MG. This approach revealed that, as anticipated, more genes were up and down-regulated in the context of both manipulations vs Oct4 OE alone. Moreover, Oct4 and Rbpj KO reduced chromatin accessibility at target motifs for transcription factors involved in MG identify/quiescence, while MGPCs showed elevated accessibility for neurogenic factors. The combination of Oct4 OE and Rbpj KO induces accessibility at various interesting TF sites that may contribute to the synergistic neurogenesis, including Rfx4, Klf4, Insm1, and others.

This is an interesting paper that adds to the growing literature on how neurogenesis can be induced in mammalian MG. The focus on Oct4 is interesting and the synergistic effects are striking and analyzed in some detail with scRNAseq and multiomic snRNA/scATACseq. The latter results provide useful new insight into transcriptional programs that may be critical in driving neurogenesis. Functional insight into these new candidates is not explored in this manuscript, but that's beyond the scope of the current work and forms the basis for new studies. There are some overreaching statements in the Discussion that need to be toned down, but apart from that and a long list of textual errors that need to be fixed, this paper is a valuable contribution to the field.

Major comments

There are numerous textual errors (some, but not all, examples are detailed in minor comments). It was difficult to follow this paper given the unusually high number of textual errors and the abbreviated legends. Greater attention should be paid to harmonizing the text with the figures and ensuring that the legends are correct and complete.

The manuscript has been proofread carefully and errors corrected.

The opening section of the scRNAseq data should outline briefly why sorting for GFP labeled cells purifies a significant fraction of non-MG cell types, despite the earlier claim, (which agrees with other publications), that GLAST-CreER transgene expression is highly specific to MG. Presumably, it mainly/totally reflects the co-purification of cells, cell fragments, and/or cell-free mRNA from other lineages. Is it also possible that a fraction (however small) of these cells reflect low-level spurious/temporary activation of GLAST-CreER expression in non-MG? The "contamination" is present despite the addition of the GFP sequence to the reference genome (as explained in Methods). They mention: "a clear differentiation trajectory connecting Muller glia, neurogenic Muller gliaderived progenitor cells (MGPCs), and differentiating amacrine and bipolar cells (Fig. 3b)". However, the same trajectory is evident in control mCherry samples, so one could argue that this trajectory is active in normal retina at some low rate, but that would/should equate to rare sun-GFP+ non-MG in controls. Are there any such cells, even extremely rarely, or is it truly 0%? At any rate, the authors need to raise these concerns and offer some explanation(s) at the start of their scRNAseq Results section. If there are really no such sun-GFP+ cells, the authors should comment on the presence of the apparent inactive trajectory in the Discussion.

Since we first described this line (de Melo, et al. 2012), we have examined thousands of sections of GlastCreER;Sun1-GFP retinas, and have yet to see a single GFP-positive neuron. We have also previously shown (Hoang, et al. 2020) that FACSbased isolation of GFP-positive cells from GlastCreER;Sun1-GFP yields a roughly thirty-fold enrichment of Muller glia, implying the presence of small numbers of contaminating neurons. We thereby conclude that the presence of small numbers of neurons (rods, cones, bipolar, and amacrine cells) in the control GlastCreER;Sun1-GFP represents contamination rather than low levels of glia-to-neuron conversion, particularly since we are unable to detect the expression of genes such as neurogenic bHLH factors or immature photoreceptor precursor-specific factors such as Prdm1 that indicate the presence of intermediate cell states. This is now addressed in the Results section related to both Figures 3 and 4.

Discussion:

In reference to other strategies to induce neurogenesis the authors make the claim that Oct4 is fundamentally different: "In these cases, Müller glia broadly upregulate proneural genes and/or downregulate Notch signaling. Oct4 instead induces expression of the neurogenic transcription factor Rfx4, which is not expressed in developing retina. It is likely that activation of this parallel pathway to neurogenic competence in part accounts for synergistic induction of neurogenesis seen in Rbpj-deficient Müller glia". First, all these strategies, including Oct4, seem to activate bHLH factors, so they have that in common and the authors should note that overlap. More seriously, without functional tests (e.g. KO Rfx4) the authors need to dial back the over-reaching statement that Rfx4 is the fundamental mechanism driving the Oct4 effect. They can certainly suggest that this is one possibility, but equally, Rfx4 may have very little or no effect on neurogenesis, or it could act redundantly with some of the other factors the authors uncovered. It's impossible to know without functional data, so they either need to add the functional data, or hold back on the strong one-sided and overreaching claim.

Since both Rfx4 expression and motif accessibility are selectively observed following Oct4 overexpression, and Rfx4 also has known neurogenic activity, we stand by our conclusion that it is a particularly strong candidate for mediating the neurogenic effects of Oct4 overexpression. However, the Reviewer is correct that in the absence of functional data, speculation about its function should be qualified. We have done this in the revised manuscript.

Minor comments

This sentence in the Results is confusing: "While expression of neurogenic bHLH factors driven by the Gfap promoter was rapidly silenced in Muller glia and activated in amacrine and retinal ganglion cells, Gfap-Oct4-mCherry remained selectively expressed in Muller glia but did not induce detectable levels of Muller glia-derived neurogenesis in the uninjured retina (Le et al., 2022)". The cited reference is at the end so it sounds like the Oct4 assay was performed in Le et al 2022, and there is no reference to a Figure for the Oct4 data in the current paper.

As stated here, in Le, et al. 2022, we did not observe any conversion of Sun1-GFP-positive Muller glia to neurons in the absence of injury. In the current study, we instead test whether NMDA-induced excitotoxicity induced glia to neuron conversion in Muller glia overexpressing Oct4. This is now made clear in the revised text.

There are many errors and omissions regarding Figure S2:

Figure S2a, b legend, and panels do not match. 2a should be a schematic of the strategy to label MG with Sun1-GFP using GLAST-Cre and a floxed Sun1-GFP allele, but that's missing and instead, the current 2a is a schematic of AAV vectors. It seems that the current 2b legend may describe the combination of the current 2a and 2b panels.

This has been corrected.

Figure S2: Asterisks label certain stained elements in the Oct4 labeled panels, but there is no explanation in the legend. Are these meant to indicate non-specific staining? If so, what is the evidence that the signal is non-specific?

These asterisks represent non-specific mouse-on-mouse vascular staining observed with the mouse monoclonal anti-Oct4 used in this study. This is now indicated in the figure legend.

The text refers to Ascl1 staining in Figure S2e,f, but it's S2g,h.

This has been corrected.

Re this: "While Sun1-GFP-positive cells infected with Oct4-mCherry mostly express the Muller glial marker Sox2 (Fig. S2a,b), from 2 weeks post-injury onwards a subset of GFP positive cells did not show detectable Sox2 expression (Fig. S2b, yellow arrows)". Figure S2a, b are schematic diagrams, not immunofluorescence. They probably mean Figure S2c, d.

This has been corrected.

Fig S2m is mislabeled "n".

This has been corrected.

There are probably other errors with this figure, but I mostly gave up at this point. The authors should go through the paper to find and correct any additional mistakes/omissions in the text and legends.

The manuscript has been carefully proofread and errors corrected.

The figure panels are not always mentioned in the order that they appear. There are many examples.

Figure panels are now mentioned in the order that they appear.

Several schematics use "d-18-14" to indicate "day -18 to -14". The former is at first uninterpretable or at best unclear (could mean day -18 to day 14), perhaps d -18 to -14, or d -18:-14 would be clearer.

This has been corrected.

Re: "AAV-infected wildtype Muller glia could be readily identified by selective expression of Oct4 (Fig. 4e). Wildtype Oct4-expressing Muller glia give rise to both small numbers of neurogenic MGPCs (Fig. 4b),". Figure 4E is labeled Pou5f1, but it would be helpful to avoid confusion by also indicating on the figure that Pou5f1 = Oct4; and Fig 4b does not indicate neurogenic MGPCs (perhaps they mean 4c).

This has been corrected.

Some parts of the Results are written in the present tense and should be in the past tense (for guidance: https://www.nature.com/scitable/topicpage/effective-writing13815989/).

Past tense is now used throughout.

Pit1 (Pou1f1) is referred to as a "close variant" of Oct4/Pou4f5, but this is unclear (e.g. variant could mean a splice variant from the same locus) and the term "paralogue" should be used.

“Paralogue” is now used in this context.

Re: "Infection with Oct4-mCherry vector induced both Oct4 (Fig. S5e) and Ascl1 (Fig. S5d) expression in Notch1/2-deficient Müller glia." Supplementary image 5d is the one depicting Oct4 and 5e is the one showing Ascl1. However, the reference is reversed.

This has been corrected.

Author response:

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

We deeply appreciate the reviewer’s careful review and critiques. These are excellent critiques that we are working on and probably require a few more years of work. Published together, we believe these critiques add value to our manuscript.

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

Reviewer #2 (Public review):

Summary:

This manuscript by Yu and coworkers investigates the potential role of Secretory leukocyte protease inhibitor (SLPI) in Lyme arthritis. They show that, after needle inoculation of the Lyme disease (LD) agent, B. burgdorferi, compared to wild type mice, a SLPI-deficient mouse suffers elevated bacterial burden, joint swelling and inflammation, pro-inflammatory cytokines in the joint, and levels of serum neutrophil elastase (NE). They suggest that SLPI levels of Lyme disease patients are diminished relative to healthy controls. Finally, they find that SLPI may interact directly the B. burgdorferi.

Strengths:

Many of these observations are interesting and the use of SLPI-deficient mice is useful (and has not previously been done).

Weaknesses:

(a) The known role of SLPI in dampening inflammation and inflammatory damage by inhibition of NE makes the enhanced inflammation in the joint of B. burgdorferi-infected mice a predicted result; (b) The potential contribution of the greater bacterial burden to the enhanced inflammation is acknowledged but not experimentally addressed; (c) The relationship of SLPI binding by B. burgdorferi to the enhanced disease of SLPI-deficient mice is not addressed in this study, making the inclusion of this observation in this manuscript incomplete; and (d) assessment of SLPI levels in healthy controls vs. Lyme disease patients is inadequate.

We greatly appreciate the critiques, and we do agree. Even though the observation of NE level is predictable, we believe that it is important to actually demonstrate it in the context of murine Lyme arthritis. The function of SLPI goes beyond inhibiting NE level.  As an ongoing project in our lab, we believe that the current study serves as a good starting point to explore the pleiotropic effects SLPI in the pathogenesis of murine Lyme arthritis and in patients. And, the critiques here are of great value to our research.

Comments on revised version:

Several of the points were addressed in the revised manuscript, but the following issues remain:

Previous point that the relationship of SLPI binding to B. burgdorferi to the enhanced disease of SLPI-deficient mice is not investigated: The authors indicate that such investigations are ongoing. In the absence of any findings, I recommend that their interesting BASEHIT and subsequent studies be presented in a future study, which would have high impact.

We thank the reviewer for the critique. We do agree that this part of the story is not complete. However, we would like to keep the BASEHIT and binding data in the paper, as we believe that it is an important finding. We confirmed the binding using ELISA, flow cytometry, and immunofluorescent microscopy. We showed that the binding is specific to infectious strain of B. burgdorferi, thus likely to contribute to the pathogenesis of murine Lyme arthritis. Our data suggest that SLPI can directly interact with a B. burgdorferi protein. We are exploring the biological significance of the binding. And this finding can be further explored by other labs too.

Previous recommendation 1: (The authors added lines 267-68, not 287-68). This ambiguity is acknowledged but remains. In addition, in the revised manuscript, the authors state "However, these data also emphasize the importance of SLPI in controlling the development of inflammation in periarticular tissues of B. burgdorferi-infected mice." Given acknowledged limitations of interpretation, "suggest" would be more appropriate than "emphasize".

We thank the reviewer for the careful reading, and we apologize for the mistake. The change has been made accordingly (line 268).

Previous recommendation 5: The lack of clinical samples can be a challenge. Nevertheless, 4 of the 7 samples from LD patients are from individuals suffering from EM rather than arthritis (i.e., the manifestation that is the topic of the study) and some who are sampled multiple times, make an objective statistical comparison difficult. I don't have a suggestion as to how to address the difference in number of samples from a given subject. However, the authors could consider segregating EM vs. LA in their analysis (although it appears that limiting the comparison between HC and LA patients would not reveal a statistical difference).

We thank the reviewer for the critique. And we agree with the reviewer that the patient’s data presented are not ideal. We believe that at this point the combination of the samples is most logical, as the number of samples we have from patients with Lyme arthritis is fairly limited. We stated the limitation in the discussion. We do believe that the finding of the correlation is important. It suggests the potential function of SLPI in patients, beyond murine infection.

What’s more, various groups with large number of different samples can elucidate the relationship further.

Previous recommendation 6: Given that binding of SLPI to the bacterial surface is an essential aspect of the authors' model, and that the ELISA assay to indicate SLPI binding used cell lysates rather than intact bacteria, a control PI staining to validate the integrity of bacteria seems reasonable.

We appreciate the suggestion and has provided the propidium iodide staining in Supplemental Figure 5 (line 539-542, 568-569, 718-722).

Previous recommendation 8: The inclusion of a no serum control (that presumably shows 100% viability) would validate the authors' assertion that 20% serum has bactericidal activity.

We appreciate the suggestion. As stated in the manuscript (line 583-584), the percent viability was normalized to the control spirochetes culture without any treatment. Thus, the control spirochetes culture, without serum and SLPI treatment, showed 100% viability. We have revised Supplemental Figure 3 accordingly.

Author response:

The following is the authors’ response to the previous reviews

Public Reviews:

Reviewer #1 (Public review):

The paper proposes an interesting perspective on the spatio-temporal relationship between FC in fMRI and electrophysiology. The study found that while similar networks configurations are found in both modalities, there is a tendency for the networks to spatially converge more commonly at synchronous than asynchronous timepoints. However, my confidence in the findings and their interpretation is undermined by an incomplete justification for the expected outcomes for each of the proposed scenarios.

As detailed below, the reviewer’s comment motivated us to conduct simulations to establish the relationship between the scenarios that we seek to adjudicate and the empirical outcomes.

Main Concern

Fig 1 makes sense to me conceptually, including the schematics of the trajectories, i.e.:

- Scenario1. Temporally convergent, same trajectories through connectome state space

- Scenario2. Temporally divergent, different trajectories through connectome state space

However, based on my understanding (and apologies if I am mistaken), I am concerned that these scenarios do not necessarily translate into the schematic CRP plots shown in fig 2C, or the statements in the main text, i.e.:

- For scenario1, "epochs of cross-modal spatial similarity should occur more frequently at on-diagonal (synchronous) than off-diagonal (asynchronous) entries, resulting in an on-/off-diagonal ratio larger than unity"

- For scenario2, "epochs of spatial similarity could occur equally likely at on-diagonal and off-diagonal entries (ratio≈1)"

Where do the authors get these statements and the schematics in fig2C from? They do not seem to be fully justified via previous literature, theory, or simulations?

In particular, I am not convinced based on the evidence currently in the paper, that the ratio of off- to on-diagonal entries (and under what assumptions) is a definitive way to discriminate between scenarios 1 and 2.

For example, what about the case where the same network configuration reoccurs in both modalities at multiple time points. It seems to me that you would get a CRP with entries occurring equally on the on-diagonal as on the off-diagonal, regardless of whether the dynamics are matched between the two modalities or not (i.e. regardless of scenario 1 or 2 being true).

This thought experiment example might have a flaw in it, and the authors might ultimately be correct, but nonetheless a systematic justification needs to be provided for using the ratio of off- to on-diagonal entries to discriminate between scenario 1 and 2 (and under what assumptions it is valid).

Thank you for raising this important point. In response, we have now included simulation results to complement our earlier authors’ response, which provided literature references and a theoretical explanation of the on-/off-diagonal ratio metric.

In the absence of theory, the authors could use surrogate data for scenario 1 and 2. For example:

a. For scenario 1, run the CRP using a single modality. E.g. feed in the EEG into the analysis as both modality 1 AND modality 2. This should provide at least one example of CRP under scenario 1 (although it does not ensure that all CRPs under this scenario will look like this, it is at least a useful sanity check).

Note: This simulation was included in the previous round of author’s responses.

b. For scenario 2, run the CRP using a single modality plus a shuffled version. E.g. feed in the EEG into the analysis as both modality 1 AND a temporally shuffled version of the EEG as modality 2. The temporal shuffling of the EEG could be done by simple splitting the data into blocks of say ~10s and then shuffling them into a new order. This should provide a version of the CRP under scenario 2 (although it does not ensure that all CRPs under this scenario will look like this, it is at least a useful sanity check)

The authors have provided CRP plots for option a. It shows a CRP, as expected, consistent with scenario 1. This is a useful sanity check. However, as mentioned above, it does not ensure that all CRPs under this scenario will look like this.

However, the authors have not shown a CRP as per option b. As such, there is an incomplete justification for the expected outcomes of the scenarios.

Note that another option, which has not been carried out, is to use full simulations, with clearly specified assumptions, for scenario1 and 2. One way of doing this is to use a simplified (state-space) setup where you randomly simulate N spatially fixed networks that are independently switching on and off over time (i.e. "activation" is 0 or 1). Note that this would result in a N-dimensional connectome state space.

Using this, you can simulate and compute the CRPs for the two scenarios:

a. Scenario 1: where the simulated activation timecourses are set to be the same between both modalities

b. Scenario 2: where the simulated activation timecourses are simulated separately for each of the modalities

We followed the reviewer’s suggestion and have now included full simulations to address the concerns regarding the theory of the on-/off-diagonal ratio metric. As recommended, we defined a random quantized signal with N levels to represent the recurrent manifestation of N fixed connectome states. This setup was used to demonstrate the relationship between the two scenarios and the CRP observations used to adjudicate between the scenarios in our paper.

The CRP matrices in Fig. S10 provide an example illustration of this simulation. In the case where the two state timeseries are identical, there are more co-occurrences of the same state (white entries) on the diagonal than off the diagonal (left subplot). This is in line with Scenario 1, where both spatial and temporal convergence are present. Conversely, in Scenario 2, where state time courses are shuffled, co-occurrences of the same states are more dispersed, and the diagonal prominence vanishes (right subplot). This difference illustrates how the CRP reflects the presence or absence of temporal alignment, dissociating scenarios 1 and 2.

To quantitively validate this observation, we calculated the on-/off-diagonal ratio across simulations with varying N values. For Scenario 2 (shuffled version), the ratio consistently remained close to 1, indicating the absence of temporal synchronization. In contrast, Scenario 1 (non-shuffled version) produced significantly higher ratios, exceeding 1, confirming the metric's ability to capture meaningful synchrony. These results demonstrate that the simulations successfully replicate the expected relationship between the two scenarios and the CRPs, and validate the theoretical foundation of the ratio metric under the defined assumptions.

Minor Concern

Leakage correction. The paper states: "To mitigate this issue, we provide results from source-localized data both with and without leakage correction (supplementary and main text, respectively)." It is great that the authors provide both. However, given that FC in EEG is almost totally dominated by spatial leakage (see Hipp paper), the main results/figures for the scalp EEG should be done using spatial leakage corrected EEG data.

Thank you. We agree that source leakage is an important consideration, which is why the current work investigated the intracranial EEG-fMRI data as a primary approach and subsequently added the scalp EEG-fMRI approach. While source leakage correction is essential for addressing spurious connectivity, it can also risk removing genuine functional connectivity that includes zero-lag relationships. We are reassured by the observation that the scalp data both without and with leakage correction confirmed the findings of the intracranial data, i.e., the presence of spatial and a lack of temporal cross-modal convergence. As such we do not believe that source leakage had a considerable impact on the specific question at hand.

Reviewer #2 (Public review):

Summary:

The study investigates the brain's functional connectivity (FC) dynamics across different timescales using simultaneous recordings of intracranial EEG/source-localized EEG and fMRI. The primary research goal was to determine which of three convergence/divergence scenarios is the most likely to occur.

The results indicate that despite similar FC patterns found in different data modalities, the timepoints were not aligned, indicating spatial convergence but temporal divergence.

The researchers also found that FC patterns in different frequencies do not overlap significantly, emphasizing the multi-frequency nature of brain connectivity. Such asynchronous activity across frequency bands supports the idea of multiple connectivity states that operate independently and are organized into a multiplex system.

Strengths:

The data supporting the authors' claims are convincing and come from simultaneous recordings of fMRI and iEEG/EEG, which has been recently developed and adapted.

The analysis methods are solid and involved a novel approach to analyzing the co-occurrence of FC patterns across modalities (cross-modal recurrence plot, CRP) and robust statistics, including replication of the main results using multiple operationalizations of the functional connectome (e.g., amplitude, orthogonalized, and phase-based coupling).

In addition, the authors provided a detailed interpretation of the results, placing them in the context of recent advances and understanding of the relationships between functional connectivity and cognitive states.

The authors also did a control analysis and verified the effect of temporal window size or different functional connecvitity operationalizations. I also applaud their effort to make the analysis code open-sourced.

Recommendations for the authors:

Reviewer #2 (Recommendations for the authors):

The authors answer my concerns and they are resolved.

Author response:

The following is the authors’ response to the original reviews

Public Reviews:

Reviewer #1 (Public review):

This study investigates alterations in the autophagic-lysosomal pathway in the Q175 HD knock-in model crossed with the TRGL autophagy reporter mouse. The findings provide valuable insights into autophagy dynamics in HD and the potential therapeutic benefits of modulating this pathway. The study suggests that autophagy stimulation may offer therapeutic benefits in the early stages of HD progression, with mTOR inhibition showing promise in ameliorating lysosomal pathology and reducing mutant huntingtin accumulation.

However, the data raises concerns regarding the strength of the evidence. The observed changes in autophagic markers, such as autolysosome and lysosome numbers, are relatively modest, and the Western blot results do not fully match the quantitative results. These discrepancies highlight the need for further validation and more pronounced effects to strengthen the conclusions. While the study suggests the potential of autophagy regulation as a long-term therapeutic strategy, additional experiments and more reliable data are necessary to confirm the broader applicability of the TRGL/Q175 mouse model.

Furthermore, the 2004 publication by Ravikumar et al. demonstrated that inhibition of mTOR by rapamycin or the rapamycin ester CCI-779 induces autophagy and reduces the toxicity of polyglutamine expansions in fly and mouse models of Huntington's disease. mTOR is a key regulator of autophagy, and its inhibition has been explored as a therapeutic strategy for various neurodegenerative diseases, including HD. Studies suggest that inhibiting mTOR enhances autophagy, leading to the clearance of mHTT aggregates. Given that dysfunction of the autophagic-lysosomal pathway and lysosomal function in HD is already well-established, and that mTOR inhibition as a therapeutic approach for HD is also known, this study does not present entirely novel findings.

Major Concerns:

(1) In Figure 3A1 and A2, delayed and/or deficient acidification of AL causes deficits in the reformation of LY to replenish the LY pool. However, in Figure S2D, there is no difference in AL formation or substrate degradation, as shown by the Western blotting results for CTSD and CTSB. How can these discrepancies be explained?

We appreciate the reviewer raising this point, and we agree with the concern. Please note that the material used for our immunoblotting was hemibrain homogenates, containing not only neurons but also glial cells, so the results for any protein, e.g., CTSD or CTSB in Fig. S2D, represented combined signals from neurons and glial cells. Our longstanding experience with western blot analysis of autophagy pathway markers is that signals from glial cells significantly interfere with/dilute the signals from neurons. By contrast, the immunofluorescence (IF) results in Fig. 3A, obtained with the assistance of tfLC3 probe and hue angle-based AV/LY subtype analysis, revealed the in situ conditions of the AL and LY within neurons selectively, which reflects the advantage of using the in vivo neuron-specific expression of the LC3 probe combined with IF with a LY marker in this study and our other related studies (Lee, Rao et al. 2019, Lee, Yang et al. 2022) as explained in the Introduction of this paper. Please also refer to a similar discussion regarding the WB-detected protein levels of p-ATG14 in L542-547. 

(2) The results demonstrate that in the brain sections of 17-month-old TRGL/Q175 mice, there was an increase in the number of acidic autolysosomes (AL), including poorly acidified autolysosomes (pa-AL), alongside a decrease in lysosome (LY) numbers. These AL/pa-AL changes were not significant in 2-month-old or 7-month-old TRGL/Q175 mice, where only a reduction in lysosome numbers was observed. This indicates that these changes, representing damage to the autophagy-lysosome pathway (ALP), manifest only at later stages of the disease. Considering that the ALP is affected predominantly in the advanced stages of the disease (e.g., at 17 months), why were 6-month-old TRGL/Q175 mice selected for oral mTORi INK treatment, and why was the treatment duration restricted to just 3 weeks?

We thank the reviewer for the comment. A key outcome measure in our evaluation of mTORi treatment was amelioration of mHTT pathology, i.e., mHTT aggregates/IBs. Before conducting the mTORi treatment experiments, we had learned from our assessments of age-associated progression of mHTT aggresomes/IBs in mice of different ages (e.g., 2-, 6-, 10- and 17-mo) that there were already severe mHTT accumulations in Q175 at 10-mo-old (e.g., Fig. 2A). This is consistent with a previous report (Carty, Berson et al. 2015) showing that striatal mHTT inclusions dynamically increase from 4 to 8 months. From a therapeutic point of view, more aggregates in the mouse brain would make it more difficult for the autophagy machinery to clear these aggregates. Thus, the high degree of aggregates in 10- or 17-mo may not be modifiable by the mTORi and/or prevent reliable/sensitive measurements on mTORi-induced phenotype changes. We then preferred to apply the treatment to younger (i.e., 6-mo-old) mice when the mHTT pathology was not so severe, with detectable, albeit mild, ALP abnormality.  Additionally, due to the 2-year funding limit for this project, there was insufficient time to generate a large set of old mice (e.g., ~18-mo) for another drug treatment experiment.  In future studies, it might be worthy to conduct the treatment “in the advanced stages of the disease (e.g., ~18-mo)” to further examine the modification potential of the mTORi on the ALP as well as the HTT aggregations. As for the treatment duration, we were interested in an acute treatment schedule given that, in our dosing tests, we observed rapid responses to the treatment (e.g., target engagement) in a few days even with one dose, and that the 14-15-day treatments produced consistent responses (e.g., Fig. S3A). Long-term treatment, however, would be worthy testing in the future although our current study informs a therapeutic approach that has been suggested by others involving intermittent/pulsatile administration of mTOR inhibitors to minimize side effects of chronic long-term administration.

(3) Is the extent of motor dysfunction in TRGL/Q175 mice comparable to that in Q175 mice? Does the administration of mTORi INK improve these symptoms?

Unfortunately, we were unable to investigate motor functions experimentally with specific assays such as open field or rotarod tests in this study (partially affected by the falling of the funded research period within the COVID-19 pandemic peak periods in 2020). Based on our experience in handling the mice, we did not notice any obvious differences between Q175 and TRGL/Q175, and any improvements after the acute mTORi INK treatment.  

(4) Why is eGFP expression not visible in Fig. 6A in TRGL-Veh mice? Additionally, why do normal (non-poly-Q) mice have fewer lysosomes (LY) than TRGL/Q175-INK mice? IHC results also show that CTSD levels are lower in TRGL mice compared to TRGL/Q175-INK mice. Does this suggest lysosome dysfunction in TRGL-Veh mice?

We appreciate the reviewer raising this point, which has been corrected (through slightly increasing the eGFP signal in the green channel and the merged channels equally for all genotypes), and the revised Fig. 6A is showing better eGFP signals. Regarding higher LY numbers/CTSD levels in TRGL/Q175-INK compared to the control TRGL-Veh mice, it does not necessarily imply LY dysfunction in TRGL mice, rather, it likely suggests mTORi treatment inducing LY biogenesis. Our original characterization of the TRGL mouse of varying ages, where low expression of the tgLC3 construct, produces only a very small increment of total LC3, resulting in no discernable functional changes in the autophagy pathway (Lee, Rao et al. 2019). The underlying mechanism, e.g., TFEB activation following mTOR inhibition, remains to be investigated in future studies. 

(5) In Figure 5A, the phosphorylation of ATG14 (S29) shows minimal differences in Western blotting, which appears inconsistent with the quantitative results. A similar issue is observed in the quantification of Endo-LC3.

We welcome the reviewer’s point, and therefore bands showing bigger differences of p-ATG14 (S29) have been used in the revised Fig. 5A, making the images and the quantitative results more consistent and representative. Similar changes have also been made to the Endo-LC3 data at the bottom of Fig. 5A.

(6) In Figure S2A and Figure S2B, 17-month-old TRGL/Q175 mice show a decrease in pp70S6K and the p-ULK1/ULK1 ratio, but no changes are observed in autophagy-related markers. Do these results indicate only a slight change in autophagy at this stage in TRGL/Q175 mice? Since the mTOR pathway regulates multiple cellular mechanisms, could mTOR also influence other processes? Is it possible that additional mechanisms are involved?

We completely agree with the reviewer. As mentioned in the text at multiple locations, LAP alterations in Q175 and TRGL/Q175 mice are mild even at a relatively old age (e.g., 17-mo), especially at the protein levels detected by immunoblotting. We agree that even if the mild alterations in the levels of pp70S6K (T389) and p-ULK1/ULK1 ratio may indicate “a slight change in autophagy”, it may also imply that other cell processes are involved given that mTOR signaling regulates multiple cellular functions. In particular, the p70S6K/p-p70S6K – a mTOR substrate used as a readout for mTOR activity in this study – is a key component of the protein synthesis pathway (Wang and Proud 2006, Magnuson, Ekim et al. 2012) , so its changes may serve as readouts for alterations in not only the autophagy pathway, but also the protein synthesis pathway. [A related discussion about mTOR/protein synthesis pathways, in response to a comment from Reviewer 2, has been incorporated into the text under Discussion, L633-640]

Reviewer #2 (Public review):

Summary:

In this manuscript, the authors have explored the beneficial effect of autophagy upregulation in the context of HD pathology in a disease stage-specific manner. The authors have observed functional autophagy lysosomal pathway (ALP) and its machineries at the early stage in the HD mouse model, whereas impairment of ALP has been documented at the later stages of the disease progression. Eventually, the authors took advantage of the operational ALP pathway at the early stage of HD pathology, in order to upregulate ALP and autophagy flux by inhibiting mTORC1 in vivo, which ultimately reverted back to multiple ALP-related abnormalities and phenotypes. Therefore, this manuscript is a promising effort to shed light on the therapeutic interventions with which HD pathology can be treated at the patient level in the future.

Strengths:

The study has shown the alteration of ALP in the HD mouse model in a very detailed manner. Such stage-dependent in vivo study will be informative and has not been done before. Also, this research provides possible therapeutic interventions for patients in the future.

Weaknesses:

Some constructive comments and suggestions in order to reflect the key aspects and concepts better in the manuscript :

(1) The authors have observed lysosome number alteration in a temporally regulated disease stage-specific manner. In this scenario investigation of regulation, localization, and level of TFEB, the transcription factor required for lysosome biogenesis, would be interesting and informative.

We thank the reviewer for this point and completely agree that exploring TFEBrelated aspects would be interesting which will be investigated in future studies. 

(2) For the general scientific community better clarification of the short forms will be useful. For example, in line 97, page 4, AP full form would be useful. Also 'metabolized via autophagy' can be replaced by 'degraded via autophagy'.

We appreciate the reviewer for raising this point. We introduced each abbreviation at the location where the full term first appears and, for the case of “AP”, it was introduced in (previous) Line 69 when “autophagosome” first appears. We agree with the reviewer about easy reading for the general scientific community and thus we have added an Abbreviation section after the Key Words section, listing abbreviations used in this manuscript.

Also, the word “metabolized” has been replaced with “degraded” as suggested. 

(3) The nuclear vs cytosolic localization of HTT aggregates shown in Figure 2, are very interesting. The increase in cytosolic HTT aggregate formation at 10 months compared to 6 months probably suggests spatio-temporal regulation of aggregate formation. The authors could comment in a more elaborate manner, on the reason and impact of this kind of regulation of aggregate formation in the context of HD pathology.

We value the reviewer’s important point. Previous studies have well documented that mHTT aggregates exist in both intranuclear and extranuclear locations in the brains of both human HD and mouse models (DiFiglia, Sapp et al. 1997, Li, Li et al. 1999, Carty, Berson et al. 2015, Peng, Wu et al. 2016, Berg, Veeranna et al. 2024). HTT can travel between the nucleus and cytoplasm and the default location for HTT is cytoplasmic, and thus the occurrence of nuclear mHTT aggregates is considered as a result of dysfunction in the nuclear exporting system for proteins (DiFiglia, Sapp et al. 1995, Gutekunst, Levey et al. 1995, Sharp, Loev et al. 1995, Cornett, Cao et al. 2005) while other factors such as phosphorylation of HTT may also affect nuclear targeting (DeGuire, Ruggeri et al. 2018). Extranuclear aggregates of mHTT usually appear later than nuclear aggregates and develop more aggressively in terms of numbers and pace after their appearance (Li, Li et al. 1999, Carty, Berson et al. 2015, Landles, Milton et al. 2020). The fact that there are neurons containing extranuclear aggregates without having nuclear aggregates within the same cells (Carty, Berson et al. 2015) does not support a nuclear-cytoplasmic sequence for aggregate formation, implying different mechanisms controlling the formation of these two types of aggregates. It was reported that there were no significant differences in toxicity associated with the presence of nuclear compared with extranuclear aggregates (Hackam, Singaraja et al. 1999), while other studies have proposed that nuclear aggregates correlate with transcriptional dysfunction while extranuclear aggregates may impair neuronal communication and can track disease progression (Li, Li et al. 1999, Benn, Landles et al. 2005, Landles, Milton et al. 2020). Thus, the observation of a higher level of extranuclear mHTT aggregates at 10-mo compared to 6-mo from the present study is consistent with previous findings mentioned above. In addition, our EM observations of homogenous granular/short fine fibril ultrastructure of both nuclear and extranuclear aggregates are consistent with findings from mouse model studies (Davies, Turmaine et al. 1997, Scherzinger, Lurz et al. 1997), which, interestingly, is different from in vitro studies where nuclear aggregates exhibited a core and shell structure but extranuclear aggregates did not possess the shell (Riguet, Mahul-Mellier et al. 2021), reflecting differences between in vivo and in vitro conditions. Taken together, even if efforts have been made in this and previous studies in trying to understand the differences between nuclear and extranuclear aggregates, the mechanisms regarding the spatial-temporal regulation of aggregate formation have so far not been fully revealed which will require additional investigations.

(4) In this manuscript, the authors have convincingly shown that mTOR inhibition is inducing autophagy in the HD mouse model in vivo. On the other hand, mTOR inhibition would also reduce overall cellular protein translation. This aspect of mTOR inhibition can also potentially contribute to the alleviation of disease phenotype and disease symptoms by reducing protein overload in HD pathology. The authors' comments regarding this aspect would be appreciated.

We recognize the value of the reviewer’s point which we completely agree with. Lowering mHTT via interfering protein translation (e.g., through RNAi, antisense oligonucleotides) has been an attractive strategy in HD therapeutic development (Kordasiewicz, Stanek et al. 2012, Tabrizi, Ghosh et al. 2019).  As mentioned above, mTOR regulates multiple cellular pathways including protein synthesis, and inhibition of mTOR as what was done in the present study is potentially affect protein synthesis as well. While our results of decreases in mHTT signals (Fig. 7) can be interpreted as a result of autophagymediated clearance of mHTT, certainly, a possibility cannot be excluded that mTOR inhibition may result in a reduction in HTT production which may also contribute to the observed results – future studies should determine how significant of such a contribution is. [The above description has been incorporated into the text under Discussion, L633-640] 

(5) The authors have shown nuclear inclusion formation and aggregation of mHTT and also commented on its potential removal with the UPS system (proteasomal degradation) in vivo. As there is also a reciprocal relationship present between autophagy and proteasomal machineries, upon upregulation of autophagy machinery by mTOR inhibition proteasomal activity may decrease. How nuclear proteasomal activity increases to tackle nuclear mHTT IBs, would be interesting to understand in the context of HD pathology. Comments from the authors in this aspect would clarify the role of multiple degradation pathways in handling mutant HTT protein in HD pathology.

We appreciate the reviewer raising this point. We agree that there are reciprocal relationships between autophagy and the UPS (Korolchuk, Menzies et al. 2010, Park and Cuervo 2013). In general, failure in one pathway would lead to compensatory upregulation of the other pathway, and vice versa (Lee, Park et al. 2019). So, as the reviewer pointed out, “upon upregulation of autophagy machinery by mTOR inhibition proteasomal activity may decrease”. However, we proposed in the Discussion that “It is possible that stimulation of autophagy is reducing the mHTT in the cytoplasm and thereby partially relieves the burden of the proteasome both in the cytoplasm and in the nucleus so that the nuclear proteasome operates more effectively”, which is inconsistent with the general expectation for a decreased UPS activity. However, please note that there are also instances where two pathways may act in the same direction, e.g., autophagy inhibition disturbs UPS degradative function (Korolchuk, Mansilla et al. 2009, Park and Cuervo 2013). Anyhow, our statement is just speculation, requiring verifications with additional experiments in the future. One of the observations reported here which may support the above speculation is the reductions of AV-non-associated form of mHTT/p62/Ub (Fig. 7B3), given that some of them might exist within the nucleus, whose reduced levels may reflect increased intranuclear UPS activity, besides the other possibility that they may travel from the nucleus to the cytosol for clearance as already discussed inside the text. [The last sentence has been incorporated into the text under Discussion, L628-632]

(6) For the treatment of neurodegenerative disorders taking the temporal regulation into consideration is extremely important, as that will determine the success rate of the treatments in patients. The authors in this manuscript have clearly discussed this scenario. However, for neurodegenerative disordered patients, in most cases, the symptom manifestation is a late onset scenario. In that case, it will be complicated to initiate an early treatment regime in HD patients. If the authors can comment on and discuss the practicality of the early treatment regime for therapeutic purposes that would be impactful.

We appreciate the reviewer raising this point and we agree with the main concern that “for neurodegenerative disordered patients, in most cases, the symptom manifestation is a late onset scenario.” This is really a common challenge in the therapeutic fields for neurodegeneration diseases. It should be first noted that the current study is an experimental therapeutical attempt in a mouse model which is consistent with previous reports (Ravikumar, Vacher et al. 2004) as a proof of concept for manipulating autophagy (i.e., via inhibiting mTOR in the current setting) as a potential therapeutic, whose clinical practicality requires further verifications. Moreover, in our opinion, early diagnosis (e.g., genetic testing in individuals with higher risk for HD) may be a key in overcoming the above challenges, i.e., if early diagnosis is enabled, it would become possible for earlier interventions. [The above description has been incorporated into the text under Discussion, L654-659] 

Recommendations for the authors: 

Reviewer #1 (Recommendations for the authors):

Minor concerns:

(1) Figures 1 and 2 should indicate the number of sections and mice/genotypes.

Thanks for the suggestion, and the info has been added in the figure legends. 

(2) Figure 3A2 should explain how AP, AL, pa-AL, and LY are quantified.

Thanks for raising this point. Please note that the quantitation of AP, AL, pa-AL and LY was performed by the hue angle-based analysis which was described under “Confocal image collection and hue angle-based quantitative analysis for AV/LY subtypes” within the Materials and Methods. A phrase “(see the Materials and Methods)” has been added after the existing description “Hue angle-based analysis was performed for AV/LY subtype determination using the methods described in Lee et al., 2019” in the figure legend.

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Author response:

The following is the authors’ response to the original reviews

Public Reviews:

Reviewer #1 (Public review):

This study offers a valuable investigation into the role of cholecystokinin (CCK) in thalamocortical plasticity during early development and adulthood, employing a range of experimental techniques. The authors demonstrate that tetanic stimulation of the auditory thalamus induces cortical long-term potentiation (LTP), which can be evoked through either electrical or optical stimulation of the thalamus or by noise bursts. They further show that thalamocortical LTP is abolished when thalamic CCK is knocked down or when cortical CCK receptors are blocked. Interestingly, in 18-month-old mice, thalamocortical LTP was largely absent but could be restored through the cortical application of CCK. The authors conclude that CCK contributes to thalamocortical plasticity and may enhance thalamocortical plasticity in aged subjects.

While the study presents compelling evidence, I would like to offer several suggestions for the authors' consideration:

(1) Thalamocortical LTP and NMDA-Dependence:

It is well established that thalamocortical LTP is NMDA receptor-dependent, and blocking cortical NMDA receptors can abolish LTP. This raises the question of why thalamocortical LTP is eliminated when thalamic CCK is knocked down or when cortical CCK receptors are blocked. If I correctly understand the authors' hypothesis - that CCK promotes LTP through CCKR-intracellular Ca2+-AMPAR. This pathway should not directly interfere with the NMDA-dependent mechanism. A clearer explanation of this interaction would be beneficial.

Thank you for your question regarding the role of CCK and NMDA receptors (NMDARs) in thalamocortical LTP. We propose that CCK receptor (CCKR) activation enhances intracellular calcium levels, which are crucial for thalamocortical LTP induction. Calcium influx through NMDARs is also essential to reach the threshold required for activating downstream signaling pathways that promote LTP (Heynen and Bear, 2001). Thus, CCKRs and NMDARs may function in a complementary manner to facilitate LTP, with both contributing to the elevation of intracellular calcium.

However, it is important to note that the postsynaptic mechanisms of thalamocortical LTP in the auditory cortex (ACx) differ from those in other sensory cortices. Studies have shown that thalamocortical LTP in the ACx appears to be less dependent on NMDARs (Chun et al., 2013), which is distinct from somatosensory or visual cortices. Our previous studies also found that while NMDAR antagonists can block HFS-induced LTP in the inner ACx, LTP can still be induced in the presence of CCK even after the NMDARs blockade (Chen et al. 2019). These findings suggest that CCK may act through an alternative mechanism involving CCKR-mediated calcium signaling and AMPAR modulation, which partially compensates for the loss of NMDAR signaling. This distinction may reflect functional differences between the ACx and other sensory cortices, as highlighted in previous studies (King and Nelken, 2009).

While our current study focuses on the role of CCKR-mediated plasticity in the auditory system, further investigations are needed to elucidate how CCKRs and NMDARs interact within the broader framework of thalamocortical neuroplasticity across different cortical regions. Understanding whether similar mechanisms operate in other sensory systems, such as the visual cortex, will be an important direction for future research.

Heynen, A.J., and Bear, M.F. (2001). Long-term potentiation of thalamocortical transmission in the adult visual cortex in vivo. J Neurosci 21, 9801-9813. 10.1523/jneurosci.21-24-09801.2001.

Chun, S., Bayazitov, I.T., Blundon, J.A., and Zakharenko, S.S. (2013). Thalamocortical Long-Term Potentiation Becomes Gated after the Early Critical Period in the Auditory Cortex. The Journal of Neuroscience 33, 7345-7357. 10.1523/jneurosci.4500-12.2013.

Chen, X., Li, X., Wong, Y.T., Zheng, X., Wang, H., Peng, Y., Feng, H., Feng, J., Baibado, J.T., Jesky, R., et al. (2019). Cholecystokinin release triggered by NMDA receptors produces LTP and sound-sound associative memory. Proc Natl Acad Sci U S A 116, 6397-6406. 10.1073/pnas.1816833116.

King, A. J., & Nelken, I. (2009). Unraveling the principles of auditory cortical processing: can we learn from the visual system? Nature neuroscience, 12(6), 698-701.

(2) Complexity of the Thalamocortical System:

The thalamocortical system is intricate, with different cortical and thalamic subdivisions serving distinct functions. In this study, it is not fully clear which subdivisions were targeted for stimulation and recording, which could significantly influence the interpretation of the findings. Clarifying this aspect would enhance the study's robustness.

Thank you for your valuable feedback. We would like to clarify that stimulation was conducted in the medial geniculate nucleus ventral (MGv), and recording was performed in layer IV of the ACx. Targeting the MGv allows us to investigate the influence of thalamic inputs on auditory cortical responses. Layer IV of the ACx is known to receive direct thalamic projections, making it an ideal site for assessing how thalamic activity influences cortical processing. We will incorporate this clarification into the revised manuscript to enhance the robustness of our study.

Results section:

“Stimulation electrodes were placed in the MGB (specifically in the medial geniculate nucleus ventral subdivision, MGv), and recording electrodes were inserted into layer IV of ACx”

“The recording electrodes were lowered into layer IV of ACx, while the stimulation electrodes were lowered into MGB (MGv subdivision). The final stimulating and recording positions were determined by maximizing the cortical fEPSP amplitude triggered by the ES in the MGB. The accuracy of electrode placement was verified through post-hoc histological examination and electrophysiological responses.”

(3) Statistical Variability:

Biological data, including field excitatory postsynaptic potentials (fEPSPs) and LTP, often exhibit significant variability between samples, sometimes resulting in a standard deviation that exceeds 50% of the mean value. The reported standard deviation of LTP in this study, however, appears unusually small, particularly given the relatively limited sample size. Further discussion of this observation might be warranted.

Thank you for your question. In our experiments, the sample size N represents the number of animals used, while n refers to the number of recordings, with each recording corresponding to a distinct stimulation and recording sites. To adhere to ethical guidelines and minimize animal usage, we often perform multiple recordings within a single animal, such as from different hemispheres of the brain. Although N may appear small, our statistical analyses are based on n, ensuring sufficient data points for reliable conclusions.

Furthermore, as our experiments are conducted in vivo, we observe lower variability in the increase of fEPSP slopes following LTP induction compared to brain slice preparations, where standard deviations exceeding 50% of the mean are common. This reduced variability likely reflects the robustness of the physiologically intact conditions in the in vivo setup.

(4) EYFP Expression and Virus Targeting:

The authors indicate that AAV9-EFIa-ChETA-EYFP was injected into the medial geniculate body (MGB) and subsequently expressed in both the MGB and cortex. If I understand correctly, the authors assume that cortical expression represents thalamocortical terminals rather than cortical neurons. However, co-expression of CCK receptors does not necessarily imply that the virus selectively infected thalamocortical terminals. The physiological data regarding cortical activation of thalamocortical terminals could be questioned if the cortical expression represents cortical neurons or both cortical neurons and thalamocortical terminals.

Thank you for your question. In Figure 2A, EYFP expression indicates thalamocortical projections, while the co-expression of EYFP with PSD95 confirms the identity of thalamocortical terminals. The CCK-B receptors (CCKBR) are located on postsynaptic cortical neurons. The observed co-labeling of thalamocortical terminals and postsynaptic CCKBR suggests that CCK-expressing neurons in the medial geniculate body (MGB) can release CCK, which subsequently acts on the postsynaptic CCKBR. This evidence supports our interpretation of the functional role of CCK modulating neural plasticity between thalamocortical inputs and cortical neurons. As shown in Figure 2A, we aim to demonstrate that the co-labeling of thalamocortical terminals with CCK receptors accounts for a substantial proportion of the thalamocortical terminals. We will ensure that this clarification is emphasized in the revised manuscript to address your concerns.

Results section:

“Cre-dependent AAV9-EFIa-DIO-ChETA-EYFP was injected into the MGB of CCK-Cre mice. EYFP labeling marked CCK-positive neurons in the MGB. The co-expression of EYFP thalamocortical projections with PSD95 confirms the identity of thalamocortical terminals (yellow), which primarily targeted layer IV of the ACx (Figure 2A, upper panel). Immunohistochemistry revealed that a substantial proportion (15 out of 19, Figure 2A lower right panel) of thalamocortical terminals (arrows) colocalize with CCK receptors (CCKBR) on postsynaptic cortical neurons in the ACx (Figure 2A lower panel), supporting the functional role of CCK in modulating thalamocortical plasticity.”

(5) Consideration of Previous Literature:

A number of studies have thoroughly characterized auditory thalamocortical LTP during early development and adulthood. It may be beneficial for the authors to integrate insights from this body of work, as reliance on data from the somatosensory thalamocortical system might not fully capture the nuances of the auditory pathway. A more comprehensive discussion of the relevant literature could enhance the study's context and impact.

Thank you for your valuable feedback. We will enhance our discussion on auditory thalamocortical LTP during early development and adulthood to provide a more comprehensive context for our study.

(6) Therapeutic Implications:

While the authors suggest potential therapeutic applications of their findings, it may be somewhat premature to draw such conclusions based on the current evidence. Although speculative discussion is not harmful, it may not significantly add to the study's conclusions at this stage.

Thank you for your thoughtful feedback. We agree that the therapeutic applications mentioned in our study are speculative at this stage and should be regarded as a forward-looking perspective rather than definitive conclusions. Our intention was to highlight the broader potential of our findings to inspire further research, rather than to propose immediate clinical applications.

In light of your feedback, we have adjusted the language in the manuscript to reflect a more cautious interpretation. Speculative discussions are now explicitly framed as hypotheses or possibilities for future exploration. We emphasize that our findings provide a foundation for further investigations into CCK-based plasticity and its implications.

We believe that appropriately framed forward-thinking discussions are valuable in guiding the direction of future research. We sincerely hope that our current and future work will contribute to a deeper understanding of thalamocortical plasticity and, over time, potentially lead to advancements in human health.

Reviewer #2 (Public review):

Summary:

This work used multiple approaches to show that CCK is critical for long-term potentiation (LTP) in the auditory thalamocortical pathway. They also showed that the CCK mediation of LTP is age-dependent and supports frequency discrimination. This work is important because it opens up a new avenue of investigation of the roles of neuropeptides in sensory plasticity.

Strengths:

The main strength is the multiple approaches used to comprehensively examine the role of CCK in auditory thalamocortical LTP. Thus, the authors do provide a compelling set of data that CCK mediates thalamocortical LTP in an age-dependent manner.

Weaknesses:

The behavioral assessment is relatively limited but may be fleshed out in future work.

Reviewer #3 (Public review):

Summary:

Cholecystokinin (CCK) is highly expressed in auditory thalamocortical (MGB) neurons and CCK has been found to shape cortical plasticity dynamics. In order to understand how CCK shapes synaptic plasticity in the auditory thalamocortical pathway, they assessed the role of CCK signaling across multiple mechanisms of LTP induction with the auditory thalamocortical (MGB - layer IV Auditory Cortex) circuit in mice. In these physiology experiments that leverage multiple mechanisms of LTP induction and a rigorous manipulation of CCK and CCK-dependent signaling, they establish an essential role of auditory thalamocortical LTP on the co-release of CCK from auditory thalamic neurons. By carefully assessing the development of this plasticity over time and CCK expression, they go on to identify a window of time that CCK is produced throughout early and middle adulthood in auditory thalamocortical neurons to establish a window for plasticity from 3 weeks to 1.5 years in mice, with limited LTP occurring outside of this window. The authors go on to show that CCK signaling and its effect on LTP in the auditory cortex is also capable of modifying frequency discrimination accuracy in an auditory PPI task. In evaluating the impact of CCK on modulating PPI task performance, it also seems that in mice <1.5 years old CCK-dependent effects on cortical plasticity are almost saturated. While exogenous CCK can modestly improve discrimination of only very similar tones, exogenous focal delivery of CCK in older mice can significantly improve learning in a PPI task to bring their discrimination ability in line with those from young adult mice.

Strengths:

(1) The clarity of the results along with the rigor multi-angled approach provide significant support for the claim that CCK is essential for auditory thalamocortical synaptic LTP. This approach uses a combination of electrical, acoustic, and optogenetic pathway stimulation alongside conditional expression approaches, germline knockout, viral RNA downregulation, and pharmacological blockade. Through the combination of these experimental configures the authors demonstrate that high-frequency stimulation-induced LTP is reliant on co-release of CCK from glutamatergic MGB terminals projecting to the auditory cortex.

(2) The careful analysis of the CCK, CCKB receptor, and LTP expression is also a strength that puts the finding into the context of mechanistic causes and potential therapies for age-dependent sensory/auditory processing changes. Similarly, not only do these data identify a fundamental biological mechanism, but they also provide support for the idea that exogenous asynchronous stimulation of the CCKBR is capable of restoring an age-dependent loss in plasticity.

(3) Although experiments to simultaneously relate LTP and behavioral change or identify a causal relationship between LTP and frequency discrimination are not made, there is still convincing evidence that CCK signaling in the auditory cortex (known to determine synaptic LTP) is important for auditory processing/frequency discrimination. These experiments are key for establishing the relevance of this mechanism.

Weaknesses:

(1) Given the magnitude of the evoked responses, one expects that pyramidal neurons in layer IV are primarily those that undergo CCK-dependent plasticity, but the degree to which PV-interneurons and pyramidal neurons participate in this process differently is unclear.

Thank you for this insightful comment. We agree that the differential roles of PV-interneurons and pyramidal neurons in CCK-dependent thalamocortical plasticity remain unclear and acknowledge this as an important limitation of our study. Our primary focus was on pyramidal neurons, as our in vivo electrophysiological recordings measured the fEPSP slope in layer IV of the auditory cortex, which primarily reflects excitatory synaptic activity. However, we recognize the critical role of the excitatory-inhibitory balance in cortical function and the potential contribution of PV-interneurons to this process. In future studies, we plan to utilize techniques such as optogenetics, two-photon calcium imaging and cell-type-specific recordings to investigate the distinct contributions of PV-interneurons and pyramidal neurons to CCK-dependent thalamocortical plasticity, thereby providing a more comprehensive understanding of how CCK modulates thalamocortical circuits.

(2) While these data support an important role for CCK in synaptic LTP in the auditory thalamocortical pathway, perhaps temporal processing of acoustic stimuli is as or more important than frequency discrimination. Given the enhanced responsivity of the system, it is unclear whether this mechanism would improve or reduce the fidelity of temporal processing in this circuit. Understanding this dynamic may also require consideration of cell type as raised in weakness #1.

Thank you for this thoughtful comment. We acknowledge that our study did not directly address the fidelity of temporal processing, which is indeed a critical aspect of auditory function. Our behavioral experiments primarily focused on linking frequency discrimination to the role of CCK in synaptic strengthening within the auditory thalamocortical pathway. However, we agree that enhanced responsivity of the system could also impact temporal processing dynamics, such as the precise timing of auditory responses. Whether this modulation improves or reduces the fidelity of temporal processing remains an open and important question.

As you noted, understanding these dynamics will require a deeper investigation into the interactions between different cell types, particularly the balance between excitatory and inhibitory neurons. Exploring how CCK modulation affects both the circuit and cellular levels in temporal processing is an important direction for future research, which we plan to pursue. Thank you again for raising this important point.

Disscusion section:

“While we focused on homosynaptic plasticity at thalamocortical synapses by recording only fEPSPs in layer IV of ACx, it is essential to further explore heterosynaptic effects of CCK released from thalamocortical synapses on intracortical circuits, particularly its role in modulating the excitatory-inhibitory balance. PV-interneurons, as key regulators of cortical inhibition, may contribute to the temporal fidelity of sensory processing, which is critical for auditory perception (Nocon et al., 2023; Cai et al., 2018). Additionally, CCK may facilitate cross-modal plasticity by modulating heterosynaptic plasticity in interconnected cortical areas. Future studies would provide valuable insights into the broader role of CCK in shaping sensory processing and cortical network dynamics.”

Nocon, J.C., Gritton, H.J., James, N.M., Mount, R.A., Qu, Z., Han, X., and Sen, K. (2023). Parvalbumin neurons enhance temporal coding and reduce cortical noise in complex auditory scenes. Communications Biology 6, 751. 10.1038/s42003-023-05126-0.

Cai, D., Han, R., Liu, M., Xie, F., You, L., Zheng, Y., Zhao, L., Yao, J., Wang, Y., Yue, Y., et al. (2018). A Critical Role of Inhibition in Temporal Processing Maturation in the Primary Auditory Cortex. Cereb Cortex 28, 1610-1624. 10.1093/cercor/bhx057.

(3) In Figure 1, an example of increased spontaneous and evoked firing activity of single neurons after HFS is provided. Yet it is surprising that the group data are analyzed only for the fEPSP. It seems that single-neuron data would also be useful at this point to provide insight into how CCK and HFS affect temporal processing and spontaneous activity/excitability, especially given the example in 1F.

Thank you for your insightful comment. In our in vivo electrophysiological experiments on LTP induction, we recorded neural activity for over 1.5 hours to assess changes in neuronal responses over time, both prior to and following the induction. While single neuron firing data can provide valuable insights, such measurements are inherently more variable due to factors like cortical state fluctuations and the condition of nearby neurons, which makes them less reliable for long-term analysis. For this reason, we focused on fEPSP, as it offers a more stable and robust readout of synaptic activity over extended periods.

We appreciate your suggestion and recognize the value of single-neuron data in understanding how CCK and HFS affect temporal processing and excitability. In future studies, we will consider to incorporate single-neuron analyses to complement our synaptic-level findings and provide a more comprehensive understanding of these mechanisms.

(4) The authors mention that CCK mRNA was absent in CCK-KO mice, but the data are not provided.

Thank you for your comment. Data from the CCK-KO mice are presented in Figure 3A (far right) and in the upper panel of Figure 3B (far right). In the lower panel of Figure 3B, data from the CCK-KO group are not shown because the normalized values for this group were essentially zero, as expected due to the absence of CCK mRNA.

(5) The circuitry that determines PPI requires multiple brain areas, including the auditory cortex. Given the complicated dynamics of this process, it may be helpful to consider what, if anything, is known specifically about how layer IV synaptic plasticity in the auditory cortex may shape this behavior.

Thank you for raising this important point. Pre-pulse inhibition (PPI) of the acoustic startle response indeed involves multiple brain regions, with the ascending auditory pathway playing a key role (Gómez-Nieto et al., 2020). Within the auditory cortex, layer IV neurons receive tonotopically organized inputs from the medial geniculate nucleus and are critical for integrating thalamic inputs and shaping auditory processing.

In our behavioral experiments, mice were required to discriminate pre-pulses of varying frequencies against a continuous background sound. Given the role of auditory cortical neurons in integrating thalamic inputs and shaping auditory processing, it is likely that synaptic plasticity in these neurons contributes to the enhanced discrimination of pre-pulses. Supporting this idea, our previous work demonstrated that local infusion of CCK, paired with weak acoustic stimuli, significantly increased auditory responses in the auditory cortex (Li et al., 2014). In the current study, we further showed that CCK release during high-frequency stimulation of the thalamocortical pathway induced LTP in layer IV of the auditory cortex. Together, these findings suggest that CCK-dependent synaptic plasticity in layer IV may amplify the cortical representation of weak auditory inputs, thereby improving pre-pulses detection and enhancing PPI performance.

It is also worth noting that aged mice with hearing loss typically exhibit PPI deficits due to impaired auditory processing (Ouagazzal et al., 2006 and Young et al., 2010). We propose that enhanced plasticity in the thalamocortical pathway, mediated by CCK, might partially compensate for these deficits by amplifying residual auditory signals in aged mice. However, the precise mechanisms by which layer IV synaptic plasticity modulates PPI behavior remain to be fully understood. Given the complex dynamics of sensory processing, future studies could explore how layer IV neurons interact with other cortical and subcortical circuits involved in PPI, as well as the specific contributions of excitatory and inhibitory cell types. These investigations will help provide a more comprehensive understanding of the role of CCK in modulating sensory gating and auditory processing.

Gómez-Nieto, R., Hormigo, S., & López, D. E. (2020). Prepulse inhibition of the auditory startle reflex assessment as a hallmark of brainstem sensorimotor gating mechanisms. Brain sciences, 10(9), 639.

Li, X., Yu, K., Zhang, Z., Sun, W., Yang, Z., Feng, J., Chen, X., Liu, C.-H., Wang, H., Guo, Y.P., and He, J. (2014). Cholecystokinin from the entorhinal cortex enables neural plasticity in the auditory cortex. Cell Research 24, 307-330. 10.1038/cr.2013.164.

Ouagazzal, A. M., Reiss, D., & Romand, R. (2006). Effects of age-related hearing loss on startle reflex and prepulse inhibition in mice on pure and mixed C57BL and 129 genetic background. Behavioural brain research, 172(2), 307-315.

Young, J. W., Wallace, C. K., Geyer, M. A., & Risbrough, V. B. (2010). Age-associated improvements in cross-modal prepulse inhibition in mice. Behavioral neuroscience, 124(1), 133.

Recommendations for the authors:

Reviewer #2 (Recommendations for the authors):

Major concerns:

(1) In Figure 1, the authors used different metrics for fEPSP strength. In Figure 1D, the authors used the slope, while they used the amplitude in Figure 1G. It is known that the two metrics are different from each other. While the slope is calculated from the linear regression between the voltage change per time of the rising phase of the fEPSP, the amplitude represents the voltage value of the fEPSP's peak. Please clarify here and in the method what metric you used, because the two terms are not interchangeable.

Thank you for pointing out this oversight in our manuscript. We confirm that we used the slope of the fEPSP as the metric for assessing synaptic strength throughout the study, including both Figure 1D and Figure 1G. We will make the necessary corrections to ensure clarity and consistency. Thank you for bringing this to our attention.

(2) It is not mentioned in the details of the methods about the CCK-KO mice. Please give such details. Although the authors used the CCK-KO mouse model as a control, I think that it is not a good choice to test the hypothesis mentioned in lines 165 and 166. The experiment was supposed to monitor the CCK-BR activity after HFS of the MGB and answer whether the CCK-BR will get activated by thalamic stimulation, but the CCK-KO mouse does not have CCK to be released after the optogenetic activation of the Chrimson probe. Therefore, it is expected to give nothing as if the experimenter runs an experiment without intervention. I think that the appropriate way to examine the hypothesis is to compare mice that were either injected with AAV9-Syn-FLEX-ChrimsonR-tdTomato or AAV9-Syn-FLEX-tdTomato. However, CCK-OK would be a perfect model to confirm that LTP can be only generated dependently on CCK, by simply running the HFS of the MGB that would be associated with the cortical recording of the fEPSP. This also will rule out the assumption that the authors mentioned in lines 191 and 192.

Thank you for your valuable feedback. The rationale behind our experimental design was to validate the newly developed CCK sensor and confirm its specificity. We aimed to verify CCK release post-HFS by comparing the responses of the CCK sensor in CCK-KO mice and CCK-Cre mice. This comparison allowed us to determine that the observed increase in fluorescence intensity post-HFS was specifically due to CCK release, rather than other neurotransmitters induced by HFS.

We appreciate your suggestion to compare mice injected with AAV9-Syn-FLEX-ChrimsonR-tdTomato and AAV9-Syn-FLEX-tdTomato, as it is indeed a valuable approach for directly testing the hypothesis regarding CCK-BR activation. However, we prioritized using the CCK-KO model to validate the CCK sensor's efficacy and specificity. The validation can be inferred by comparing the CCK sensor activity before and after HFS.

Regarding concerns mentioned in lines 191 and 192 about potential CCK release from other projections via indirect polysynaptic activation, CCK-KO mice were not suitable for this aspect due to their global knockout of CCK. To address this limitation, we utilized shRNA to specifically down-regulate Cck expression in MGB neurons. This approach focused on the necessity of CCK released from thalamocortical projections for the observed LTP and effectively ruled out the possibility of indirect polysynaptic activation.

We also acknowledge that the methods section lacked sufficient details about the CCK-KO mice, which may have caused confusion. In the revised methods section, we will add the following details:

(1) The genotype of the CCK-KO mice used in this study (CCK-ires-CreERT2, Jax#012710).

(2) A brief description of the CCK-KO validation, emphasizing the absence of CCK mRNA in these mice (as shown in Figure 3A and 3B).

(3) The experimental purpose of using CCK-KO mice to validate the specificity of the CCK sensor.

We believe these additions will clarify the rationale for using CCK-KO mice and their role in this study. Thank you again for highlighting these important points.

(3) Figure 3C: The authors should examine if there is a difference in the baseline of fEPSPs across different age groups as the dependence on the normalization in the analysis within each group would hide if there were any difference of the baseline slope of fEPSP between groups which could be related to any misleading difference after HFS. Also, I wonder about the absence of LTP in P20, which is a closer age to the critical period. Could the authors discuss that, please?

Thank you for your insightful feedback. To address your concern regarding baseline differences in fEPSP slopes across age groups, we conducted additional analysis. Baseline fEPSP across the three groups (P20, 8w, 18m), normalized to the 8w group, were 64.8± 13.1%, 100.0 ± 20.4%, and 58.8± 10.3%, respectively. While there was a trend suggesting smaller fEPSP slopes in the P20 and 18m groups compared to the young adult group, these differences were not statistically significant due to data variability (P20 vs. 8w, P = 0.319; 8w vs. 18m, P=0.147; P20 vs. 18m, P = 1.0, one-way ANOVA). These results suggest that baseline variability is unlikely to confound the observed differences in LTP after HFS. Furthermore, we ensured that normalization minimized any potential baseline effects.

Regarding the absence of LTP in P20, this likely reflects developmental regulation of CCKBR expression in the auditory cortex (ACx). The HFS-induced thalamocortical LTP observed in our study is CCK-dependent and mechanistically distinct from the NMDA-dependent thalamocortical LTP during the critical period. Specifically, correlated pre- and postsynaptic activity can induce NMDA-dependent thalamocortical LTP only during an early critical period corresponding to the first several postnatal days, after which this pairing becomes ineffective starting from the second postnatal week (Crair and Malenka, 1995; Isaac et al., 1997; Chun et al., 2013). In contrast, the CCK-dependent Thalamocortical LTP induced by HFS is robust in adult mice but appears absent in P20, likely due to the lack of postsynaptic CCKBR expression in the ACx at this developmental stage.

We will include these clarifications in the revised manuscript, particularly in the Discussion section, to provide a more comprehensive explanation of our findings. Thank you for your valuable comments and suggestions.

Crair, M.C., and Malenka, R.C. (1995). A critical period for long-term potentiation at thalamocortical synapses. Nature 375, 325-328. 10.1038/375325a0.

Isaac, J.T.R., Crair, M.C., Nicoll, R.A., and Malenka, R.C. (1997). Silent Synapses during Development of Thalamocortical Inputs. Neuron 18, 269-280. https://doi.org/10.1016/S0896-6273(00)80267-6.

Chun, S., Bayazitov, I.T., Blundon, J.A., and Zakharenko, S.S. (2013). Thalamocortical Long-Term Potentiation Becomes Gated after the Early Critical Period in the Auditory Cortex. The Journal of Neuroscience 33, 7345-7357. 10.1523/jneurosci.4500-12.2013.

(4) Figure 4F: It is noticed that the baseline fEPSP of the CCK group and ACSF groups were different, which raises a concern about the baseline differences between treatment groups.

Thank you for your valuable feedback and for pointing out this important detail. We apologize for any confusion caused by the presentation of the data. As noted in the figure legend, the scale bars for the fEPSPs were different between the left (0.1 mV) and right panels (20 µV). This difference in scale may have created the perception of baseline differences between the CCK and ACSF groups. To enhance clarity and avoid potential misunderstanding, we will unify the scale bar values in the revised figure. This adjustment will provide a clearer and more accurate comparison of fEPSPs between groups. Thank you again for bringing this issue to our attention.

(5) From Figure S2D, it seems that different animals were injected with the drug and ACSF. Therefore, how the authors validate the position of the recording electrode to the cortical area of certain CF and relative EF. Also, there is not enough information about the basis of the selection of the EF. Should it be lower than the CF with a certain value? Was the EF determined after the initial tuning curve in each case? To mitigate this difference, it would be appropriate if the authors examined the presence of a significant difference in the tuning width and CFs between animals exposed to ACSF and CCK-4. This will give some validation of a balanced experiment between ACSF and CCK-4. I wonder also why the authors used rats here not mice, as it will be easier to interpret the results came from the same species.

Thank you for your thoughtful comments. The effective frequency (EF) was determined after measuring the initial tuning curve for each case. The EF was selected to elicit a clear sound response while maintaining a sufficient distance from the characteristic frequency (CF) to allow measurable increases in response intensity. Specifically, EF was selected based on the starting point of the tuning peak, which corresponds to the onset of its fastest rising phase. From this point, EF was determined by moving 0.2 or 0.4 octaves toward the CF. While there were individual differences in EF selection among animals, the methodology for determining EF was standardized and applied consistently across both the ACSF and CCK-4 groups.

Regarding the use of rats in these experiments, these studies were conducted prior to our current work with mice. The findings in rat provide valuable insights that support our current results in mice. Since the rat data are supplementary to the primary findings, we included them as supplementary material to provide additional context and validation. Furthermore, in consideration of animal welfare, we chose not to replicate these experiments in mice, as the findings from rats were sufficient to support our conclusions.

Methods section:

“The tuning curve was determined by plotting the lowest intensity at which the neuron responded to different tones. The characteristic frequency (CF) is defined as the frequency corresponding to the lowest point on this curve. The effective frequency (EF) was determined to elicit a clear sound response while maintaining a sufficient distance from the CF to allow measurable increases in response intensity. Specifically, EF was selected based on the starting point of the tuning peak, which corresponds to the onset of its fastest rising phase. From this point, EF was determined by moving 0.2 or 0.4 octaves toward the CF.”

(6) Lines 384-386: There are no figures named 5H and I.

Thank you for pointing this out. The references to Figures 5H and 5I were incorrect and should have referred to Figures 5C and 5D. We sincerely apologize for this oversight and will correct these errors in the revised manuscript to ensure clarity and accuracy. Thank you again for bringing this to our attention.

(7) The authors should mention the sex of the animals used.

Thank you for your comment and for highlighting this important detail. The sex of the animals used in this study is specified in the Animals section of the Methods: "In the present study, male mice and rats were used to investigate thalamocortical LTP." We appreciate your careful attention to this point and will ensure that this detail remains clearly stated in the manuscript.

(8) Lines 534 and 648: These coordinates are difficult to understand. Since the experiment was done on both mice and rats, we need a clear description of the coordinates in both. Also, I think that you should mention the lateral distance from the sagittal suture as the ventral coordinates should be calculated from the surface of the skull above the AC and not from the sagittal suture.

Thank you for your valuable feedback and for pointing out this important issue. We apologize for any confusion caused by our description of the coordinates. The term “ventral” was deliberately used because the auditory cortex is located on the lateral side of the skull, which may have caused some misunderstanding.

To provide a clearer and more accurate descriptions of the coordinates, we will revise the text in the manuscript as follows: “A craniotomy was performed at the temporal bone (-2 to -4 mm posterior and -1.5 to -3 mm ventral to bregma for mice; -3.0 to -5.0 mm posterior and -2.5 to -6.5 mm ventral to bregma for rats) to access the auditory cortex.'

We appreciate your attention to these details and will ensure that the revised manuscript includes this clarification to improve accuracy and eliminate potential confusion. Thank you again for bringing this to our attention.

(9) Line 536: The author should specify that these coordinates are for the experiment done on mice.

Thank you for your valuable feedback. We will revise the manuscript to explicitly specify that these coordinates refer to the experiments conducted on mice. This clarification will help improve the clarity and precision of the manuscript. We greatly appreciate your attention to this point and your effort to enhance the quality of our work.

Methods section:

“and a hole was drilled in the skull according to the coordinates of the ventral division of the MGB (MGv, AP: -3.2 mm, ML: 2.1 mm, DV: 3.0 mm) for experiments conducted on mice.”

(10) Line 590: Please add the specifications of the stimulating electrode. Is it unipolar or bipolar? What is the cat.# provided by FHC?

Thank you for your valuable feedback. The electrodes used in the experiments are unipolar. We will include the catalog number provided by FHC in the revised manuscript for clarity. The revised text will be updated as follows:

“In HFS-induced thalamocortical LTP experiments, two customized microelectrode arrays with four tungsten unipolar electrodes each, impedance: 0.5-1.0 MΩ (recording: CAT.# UEWSFGSECNND, FHC, U.S.), and 200-500 kΩ (stimulating: CAT.# UEWSDGSEBNND, FHC, U.S.), were used for the auditory cortical neuronal activity recording and MGB ES, respectively.”

We appreciate your attention to this detail, and we will ensure that the revised manuscript reflects this clarification accurately.

(11) Lines 612-614: There are no details of how the optic fiber was inserted or post-examined. If there is a word limitation, the authors may reference another study showing these procedures.

Thank you for your insightful comment and for highlighting this important aspect of the methodology. To address this, we will reference the study by Sun et al. (2024) in the revised manuscript, which provides detailed procedures for optic fiber insertion and post-examination. We believe that this reference will help enhance the clarity and completeness of the methods section.

Sun, W., Wu, H., Peng, Y., Zheng, X., Li, J., Zeng, D., Tang, P., Zhao, M., Feng, H., Li, H., et al. (2024). Heterosynaptic plasticity of the visuo-auditory projection requires cholecystokinin released from entorhinal cortex afferents. eLife 13, e83356. 10.7554/eLife.83356.

We appreciate your valuable suggestion, which will contribute to improving the quality of the manuscript.

Minor concerns:

(1) The definition of HFS was repeated many times throughout the manuscript. Please mention the defined name for the first time in the manuscript only followed by its abbreviation (HFS).

Thank you for your suggestion and for pointing out this important detail. We will revise the manuscript to ensure that all abbreviations are defined only upon their first mention in the manuscript, with subsequent mentions using the abbreviations consistently. We appreciate your careful attention to detail and your effort to help improve the manuscript.

(2) Line 173: There is a difference between here and the methods section (620 nm here and 635 nm there) please correct which wavelength the authors used.

Thank you for your careful review and for bringing this discrepancy to our attention. We have corrected the inconsistency, and the wavelength has been unified throughout the manuscript to ensure accuracy and clarity. The revised text now reads as follows:

“The fluorescent signal was monitored for 25s before and 60s after the HFLS (5~10 mW, 620 nm) or HFS application.”

We appreciate your valuable feedback, which has helped us improve the precision and consistency of the manuscript.

(3) Line 185: I think the authors should refer to Figure 2G before mentioning the statistical results.

Thank you for your careful review and for pointing out this oversight. We have now added a reference to Figure 2G at the appropriate location to ensure clarity and logical flow in the manuscript, as recommended..

(4) Line 202: I think the authors should refer to Figure 2J before mentioning the statistical results.

Thank you again for your careful review and for highlighting this point. We have revised the manuscript to include a reference to Figure 2J before mentioning the statistical results.

We appreciate your valuable feedback, which has helped us improve the accuracy and presentation of the results.

(5) Line 260: Please add appropriate references at the end of the sentence to support the argument.

Thank you for your valuable suggestion. To address this, we have add appropriate references to support the statement regarding the multiple steps involved between mRNA expression and neuropeptide release. Additionally, we have revised the statement to adopt a more cautious interpretation. The revised text is as follows:

“It is widely recognized that mRNA levels do not always directly correlate with peptide levels due to multiple steps involved in peptide synthesis and processing, including translation, post-translational modifications, packaging, transportation, and proteolytic cleavage, all of which require various enzymes and regulatory mechanisms (38-41). A disruption at any stage in this process could lead to impaired CCK release, even when Cck mRNA is present.”

We have included the following references to support this statement:

38. Mierke, C.T. (2020). Translation and Post-translational Modifications in Protein Biosynthesis. In Cellular Mechanics and Biophysics: Structure and Function of Basic Cellular Components Regulating Cell Mechanics, C.T. Mierke, ed. (Springer International Publishing), pp. 595-665. 10.1007/978-3-030-58532-7_14.

39. Gualillo, O., Lago, F., Casanueva, F.F., and Dieguez, C. (2006). One ancestor, several peptides post-translational modifications of preproghrelin generate several peptides with antithetical effects. Mol Cell Endocrinol 256, 1-8. 10.1016/j.mce.2006.05.007.

40. Sossin, W.S., Fisher, J.M., and Scheller, R.H. (1989). Cellular and molecular biology of neuropeptide processing and packaging. Neuron 2, 1407-1417. https://doi.org/10.1016/0896-6273(89)90186-4.

41. Hook, V., Funkelstein, L., Lu, D., Bark, S., Wegrzyn, J., and Hwang, S.R. (2008). Proteases for processing proneuropeptides into peptide neurotransmitters and hormones. Annu Rev Pharmacol Toxicol 48, 393-423. 10.1146/annurev.pharmtox.48.113006.094812.

We greatly appreciate your helpful feedback, which has allowed us to improve both the accuracy and the depth of discussion in the manuscript.

(6) Line 278: The authors mentioned "due to the absence of CCK in aged animals", which was not an appropriate description. It should be a reduction of CCK gene expression or a possible deficient CCK release.

Thank you for your careful review and for pointing out the inaccuracy in our description. We agree with your suggestion and have revised the statement to more appropriately reflect the findings.

“Our findings revealed that thalamocortical LTP cannot be induced in aged mice, likely due to insufficient CCK release, despite intact CCKBR expression.”

This revision ensures a more accurate and precise description of the potential mechanisms underlying the observed phenomenon. We greatly appreciate your valuable feedback, which has helped us improve the clarity and accuracy of the manuscript.

(7) Line 291: The authors mentioned that "without MGB stimulation", which is confusing. The MGB was stimulated with a single electrical pulse to evoke cortical fEPSPs. Therefore it should be "without HFS of MGB".

Thank you for pointing this out and for highlighting the potential confusion caused by our original phrasing. Upon review, we recognize that our original phrasing "without MGB stimulation" may have been unclear and could have led to misinterpretation. To clarify, our intention was to describe the period during which CCK was present without any stimulation of the MGB.

It is important to note that, in the presence of CCK, LTP can be induced even with low-frequency stimulation, including in aged mice. This observation underscores the potent effect of CCK in facilitating thalamocortical LTP, regardless of the specific stimulation protocol used.

To address this issue, we have revised the sentence for improved clarity as follows::

" To investigate whether CCK alone is sufficient to induce thalamocortical LTP without activating thalamocortical projections, we infused CCK-4 into the ACx of young adult mice immediately after baseline fEPSPs recording. Stimulation was then paused for 15 min to allow for CCK degradation, after which recording resumed."

We believe this revision resolves the misunderstanding and provides a clearer and more accurate description of the experimental context. We greatly appreciate your insightful feedback, which has helped us refine the manuscript for clarity and precision.

Reviewer #3 (Recommendations for the authors):

Minor comments:

(1) Line 99, 134, possibly other locations: "site" to "sites".

Thank you for your careful review. We appreciate your attention to detail and have made the necessary corrections in the manuscript.

(2) Throughout the manuscript there are some minor issues with language choice and subtle phrasing errors and I suggest English language editing.

Thank you for your suggestion. In response, we have thoroughly reviewed the manuscript and addressed issues related to language choice and phrasing. The text has been carefully edited to ensure clarity, precision, and consistency. We believe these revisions have significantly enhanced the overall quality of the manuscript. We greatly appreciate your feedback, which has been invaluable in improving the presentation of our work.

(3) Based on the experimental configurations, I do not think it is a problematic caveat, but authors should be aware of the high likelihood of AAV9 jumping synapses relative to other AAV serotypes.

Thank you for bringing up the potential of AAV9 crossing synapses, a recognized characteristic of this serotype. We appreciate your observation regarding its relevance to our experimental design. In our study, we carefully considered the possibility of trans-synaptic transfer during both the experimental design and data interpretation phases. To minimize the likelihood of significant trans-synaptic spread, we implemented several measures, including controlling the injection volume, using a slow injection rate, and limiting the viral expression time. Post-hoc histological analyses confirmed that the expression of AAV9 was largely confined to the intended regions, with limited evidence of synaptic jumping under our experimental conditions.

While we acknowledge the inherent potential for AAV9 to cross synapses, we believe this effect does not substantially confound the interpretation of our findings in the current study. To address this concern, we have added a brief discussion on this point in the revised manuscript to enhance clarity. We greatly appreciate your insightful comment, which has helped us further refine our work.

Discussion section：

“ One potential limitation of our study is the trans-synaptic transfer property of AAV9. To mitigate this, we carefully controlled the injection volume, rate, and viral expression time, and conducted post-hoc histological analyses to minimize off-target effects, thereby reducing the likelihood of trans-synaptic transfer confounding the interpretation of our findings.”

(4) The trace identifiers (1-4) do not seem correctly placed/colored in Figure S1D. Please check others carefully.

Thank you for your careful review and for bringing this issue to our attention. We have corrected the trace identifiers in Figure S1D. Additionally, we have carefully reviewed all other figures to ensure their accuracy and consistency. We greatly appreciate your attention to detail, which has helped improve the overall quality of the manuscript.

(5) Please provide a value of the laser power range based on calibrated values.

Thank you for your suggestion. We have included the calibrated laser power range in the revised manuscript as follows:

“The laser stimulation was produced by a laser generator (5-20 mW(30), Wavelength: 473 nm, 620 nm; CNI laser, China) controlled by an RX6 system and delivered to the brain via an optic fiber (Thorlabs, U.S.) connected to the generator.”

We appreciate your feedback, which has helped improve the clarity and precision of our methodological description.

(6) It would be useful to annotate figures in a way that identifies in which transgenic mice experiments are being performed.

Thank you for your valuable suggestion. We will add annotations to the figures to explicitly identify the type of mice used in each experiment. We believe this enhancement will improve the clarity and accessibility of our results. We greatly appreciate your input in making our manuscript more informative.

(7) Please comment on the rigor you use to address the accuracy of viral injections. How often did they spread outside of the MGB/AC?

Thank you for raising this important question regarding the accuracy of viral injections and the potential spread outside the MGB or AC. Below, we provide details for each set of experiments:

shRNA Experiments:

For the shRNA experiments targeting the MGB, our primary goal was to achieve comprehensive coverage of the entire MGB. To this end, we used larger injection volumes and multiple injection sites, which inevitably resulted in some viral spread beyond the MGB. However, this approach was necessary to ensure robust knockdown effects that were representative of the entire MGB. While strict confinement to specific subregions could not be guaranteed, this strategy allowed us to prioritize the effectiveness of the knockdown within the target region.

Fiber photometry Experiments:

For the fiber photometry experiments targeting the auditory cortex (AC), we used larger injection volumes and multiple injection sites to cover its relatively large size. Although this approach might have resulted in some CCK-sensor virus spread outside the AC, the placement of the optic fiber was guided by the location of the auditory cortex. Consequently, any minor viral expression outside the AC would not affect the experimental results, as recordings were confined to the intended area through precise fiber placement.  

Optogenetic Experiments:

For the optogenetic experiments targeting the MGB, we specifically injected virus into the MGv subregion. To minimize viral spread, we employed several strategies, including the used fine injection needles, waiting for tissue stabilization (7 minutes post-needle insertion), delivering small volumes at a slow rate to prevent backflow, aspirating 5 nL of the solution post-injection, and raising the needle by 100 μm before waiting an additional 5 minutes prior to full retraction. These measures significantly reduced the risk of viral leakage to adjacent regions.

Histological Validation:

After the electrophysiological experiments, we systematically verified the accuracy of viral expression by examining histological sections to ensure that the expression was primarily localized within the intended regions.

Terminology in the Manuscript:

In the manuscript, we deliberately used the term "MGB" in the manuscript rather than specifically "MGv" to transparently acknowledge the potential for viral spread in some experiments.

We hope this explanation clarifies the strategies we employed to address the accuracy of viral injections, as well as how we managed potential viral spread. We have also added a brief information in the revised manuscript to reflect these points and acknowledge the inherent variability in viral delivery.

Author response:

The following is the authors’ response to the original reviews

We thank the reviewers for their constructive and helpful comments, which led us to make major changes in the model and manuscript, including adding the results of new experiments and analyses. We believe that the revised manuscript is much better than the previous version and that it addresses all issued raised by the reviewers. 

Summary of changes made in the revised manuscript:

(1) We increased the training set size from 39 video clips to 97 video clips and the testing set size from 25 video clips to 60 video clips. The increase in training set size improved the overall accuracy from a mean F1 score of 0.81 in the previous version to a mean F1 score of 0.891 (see Figure 2 and Figure 3) in the current version. Specifically, the F1 score for urine detection was improved from 0.79 to 0.88.

(2) We further evaluated the accuracy of the DeePosit algorithm in comparison to a second human annotator and found that the algorithm accuracy is comparable to human-level accuracy.

(3) The additional test videos allowed us to test the consistency of the algorithm performance across gender, space, time, and experiment type (SP, SxP, and ESPs). We found consistent levels of performance across all categories (see Figure 3), suggesting that errors made by the algorithm are uniform across conditions, hence should not create any bias of the results.

(4) In addition, we tested the algorithm performance on a second strain of mice (male C57BL/6) in a different environmental condition (white arena instead of a black one) and found that the algorithm achieves comparable accuracy, even though C57BL/6 mice and white arena were not included in the training set. Thus, the algorithm seems to be robust and efficient across various experimental conditions.

(5) Analyzing urination and defecation dynamics in an additional strain of mice revealed interesting strain-specific features, as discussed in the revised manuscript.

(6) Overall, we found DeePosit accuracy to be stable with no significant bias across stages of the experiment, types of the experiment, gender of the mice, strain of mice, and across experimental conditions.

(7) We also compared the performance of DeePosit to a classic object detection algorithm: YOLOv8. We trained YOLOv8 both on a single image input (YOLOv8 Gray) and on 3 image inputs representing a sequence of three time points around the ground truth event (t): t+0, t+10, and t+30 seconds (YOLOv8 RGB). DeePosit achieved significantly better accuracy over both YOLOv8 alternatives. YOLOv8 RGB achieved better accuracy than YOLOv8 Gray, suggesting that temporal information is important for this task. It's worth mentioning that while YOLOv8 requires the annotator to draw rectangles surrounding each urine spot or feces as part of the training set, our algorithm training set used just a single click inside each spot, allowing faster generation of training sets. 

(8) As for the algorithm parameters, we tested the effect of the main parameter of the preliminary detection (the temperature threshold for the detection of a new blob) and found that a threshold of 1.6°C gave the best accuracy and used this parameter for all of the experiments instead of 1.1°C which was used in the original manuscript. It's worth mentioning that the performance is quite stable (mean F1 score of 0.88-0.89) for the thresholds between 1.1°C and 3°C (Figure 3—Figure Supplement 2).

(9) We also checked if changing the input length of the video clip that is fed to the classifier affects the accuracy by training the classifier with -11..30 seconds video clips (41 seconds in total) instead of -11..60 seconds (71 seconds in total) and found no difference in accuracy. 

(10) In the revised paper, we report recall, precision, and F1 scores in the caption of the relevant figures and also supply Excel files with the full statistics for each of the figures.

Public Reviews:

Reviewer #1 (Public Review):

Summary:

The manuscript provides a novel method for the automated detection of scent marks from urine and feces in rodents. Given the importance of scent communication in these animals and their role as model organisms, this is a welcome tool.

We thank the reviewer for the positive assessment of our tool

Strengths:

The method uses a single video stream (thermal video) to allow for the distinction between urine and feces. It is automated.

Weaknesses:

The accuracy level shown is lower than may be practically useful for many studies. The accuracy of urine is 80%. 

We have trained the model better, using a larger number of video clips. The increase in training set size improved the overall accuracy from a mean F1 score of 0.81 in the previous version to a mean F1 score of 0.891 (see Figure 2 and Figure 3) in the current version. Specifically, the F1 score for urine detection was improved from 0.79 to 0.88. 

This is understandable given the variability of urine in its deposition, but makes it challenging to know if the data is accurate. If the same kinds of mistakes are maintained across many conditions it may be reasonable to use the software (i.e., if everyone is under/over counted to the same extent). Differences in deposition on the scale of 20% would be challenging to be confident in with the current method, though differences of the magnitude may be of biological interest. Understanding how well the data maintain the same relative ranking of individuals across various timing and spatial deposition metrics may help provide further evidence for the utility of the method.

The additional test videos allowed us to test the consistency of the algorithm performance across gender, space, time and experiment type (SP, SxP, and ESP). We found consistent levels of performance across all categories (see Figure 3), suggesting that errors made by the algorithm are uniform across conditions, hence should not create any bias of the results.

Reviewer #2 (Public Review):

Summary:

The authors built a tool to extract the timing and location of mouse urine and fecal deposits in their laboratory set up. They indicate that they are happy with the results they achieved in this effort.

Yes, we are.

The authors note urine is thought to be an important piece of an animal's behavioral repertoire and communication toolkit so methods that make studying these dynamics easier would be impactful.

We thank the reviewer for the positive assessment of our work.

Strengths:

With the proposed method, the authors are able to detect 79% of the urine that is present and 84% of the feces that is present in a mostly automated way.

Weaknesses:

The method proposed has a large number of design choices across two detection steps that aren't investigated. I.e. do other design choices make the performance better, worse, or the same? 

We chose to use a heuristic preliminary detection algorithm for the detection of warm blobs, since warm blobs can be robustly detected with heuristic algorithms without the need for a training set. This design selection might allow easier adaptation of our algorithm for different types of arenas. Another advantage of using a heuristic preliminary detection is the easy control of the preliminary detection parameters such as the minimum temperature difference for detecting a blob, size limits of the detected blob, cooldown rate and so on that may help in adopting it to new conditions. As for the classifier, we chose to feed it with a relatively small window surrounding each preliminary detection, and hence it is not affected by the arena’s appearance outside of its region of interest. This should allow lower sensitivity to the arena’s appearance.  

As for the algorithm parameters, we tested the effect of the main parameter of the preliminary detection (the temperature threshold for the detection of a new blob) and found that a threshold of 1.6°C gave the best accuracy and used this parameter for all of the experiments instead of 1.1°C which was used in the original manuscript. It's worth mentioning that the performance is quite stable (mean F1 score of 0.88-0.89) for the thresholds between 1.1°C and 3°.

We also checked if changing the input length of the video clip fed to the classifier affects the accuracy by training the classifier with -11..30 seconds video clips (41 seconds in total) instead of -11..60 seconds (71 seconds in total) and found no difference in accuracy. 

Overall, the algorithm's accuracy seems to be rather stable across various choices of parameters.

Are these choices robust across a range of laboratory environments?

We tested the algorithm performance on a second strain of mice (male C57BL/6) in a different environmental condition (white arena instead of a black one) and found that the algorithm achieves comparable accuracy, even though C57BL/6 mice and white arena were not included in the training set. Thus, the algorithm seems to be robust and efficient across various experimental conditions.

How much better are the demonstrated results compared to a simple object detection pipeline (i.e. FasterRCNN or YOLO on the raw heat images)?

We compared the performance of DeePosit to a classic object detection algorithm: YOLOv8. We trained YOLOv8 both on a single image input (YOLOv8 Gray) and on 3 image inputs representing a sequence of three time points around the ground truth event (t): t+0, t+10, and t+30 seconds (YOLOv8 RGB). DeePosit achieved significantly better accuracy over both YOLOv8 alternatives. YOLOv8 RGB achieved better accuracy than YOLOv8 Gray, suggesting that temporal information is important for this task. It's worth mentioning that while YOLOv8 requires annotator to draw rectangles surrounding each urine spot or feces as part of the training set, our algorithm training set used just a single click inside each spot, allowing faster generation of a training sets. 

The method is implemented with a mix of MATLAB and Python.

That is right.

One proposed reason why this method is better than a human annotator is that it "is not biased." While they may mean it isn't influenced by what the researcher wants to see, the model they present is still statistically biased since each object class has a different recall score. This wasn't investigated. In general, there was little discussion of the quality of the model. 

We tested the consistency of the algorithm performance across gender, space, time and experiment type (SP, SxP, and ESP). We found consistent levels of performance across all categories (see Figure 3), suggesting that errors made by the algorithm are uniform across conditions, hence should ne create any bias of the results. Specifically, the detection accuracy is similar between urine and feces, hence should not impose a bias between the various object classes.

Precision scores were not reported.

In the revised paper we report recall, precision, and F1 scores in the caption of the relevant figures and also supply Excel files with the full statistics for each of the figures.

Is a recall value of 78.6% good for the types of studies they and others want to carry out? What are the implications of using the resulting data in a study?

We have trained the model better, using a larger number of video clips. The increase in training set size improved the overall accuracy from a mean F1 score of 0.81 in the previous version to a mean F1 score of 0.891 (see Figure 2 and Figure 3) in the current version. Specifically, the F1 score for urine detection was improved from 0.79 to 0.88. 

How do these results compare to the data that would be generated by a "biased human?"

We further evaluated the accuracy of the DeePosit algorithm in comparison to a second human annotator and found that the algorithm accuracy is comparable to human-level accuracy (Figure 3).

5 out of the 6 figures in the paper relate not to the method but to results from a study whose data was generated from the method. This makes a paper, which, based on the title, is about the method, much longer and more complicated than if it focused on the method.

We appreciate the reviewer's comment, but the analysis of this new dataset by DeePosit demonstrates how the algorithm may be used to reveal novel and distinguishable dynamics of urination and defecation activities during social interactions, which were not yet reported. 

Also, even in the context of the experiments, there is no discussion of the implications of analyzing data that was generated from a method with precision and recall values of only 7080%. Surely this noise has an effect on how to correctly calculate p-values etc. Instead, the authors seem to proceed like the generated data is simply correct.

As mentioned above, the increase in training set size improved the overall accuracy from a mean F1 score of 0.81 in the previous version to a mean F1 score of 0.891 (see Figure 2 and Figure 3) in the current version. Specifically, the F1 score for urine detection was improved from 0.79 to 0.88.  

Reviewer #3 (Public Review):

Summary:

The authors introduce a tool that employs thermal cameras to automatically detect urine and feces deposits in rodents. The detection process involves a heuristic to identify potential thermal regions of interest, followed by a transformer network-based classifier to differentiate between urine, feces, and background noise. The tool's effectiveness is demonstrated through experiments analyzing social preference, stress response, and temporal dynamics of deposits, revealing differences between male and female mice.

Strengths:

The method effectively automates the identification of deposits

The application of the tool in various behavioral tests demonstrates its robustness and versatility.

The results highlight notable differences in behavior between male and female mice

We thank the reviewer for the positive assessment of our work.

Weaknesses:

The definition of 'start' and 'end' periods for statistical analysis is arbitrary. A robustness check with varying time windows would strengthen the conclusions.

In all the statistical tests conducted in the revised manuscript, we have used a time period of 4 minutes for the analysis. We did not used the last minute of each stage for the analysis since the input of DeePosit requires 1 minute of video after the event. Nevertheless, we also conducted the same tests using a 5-minute period and found similar results (Figure 5—Figure Supplement 1).

The paper could better address the generalizability of the tool to different experimental setups, environments, and potentially other species.

As mentioned above, we tested the algorithm performance on a second strain of mice (male C57BL/6) in a different environmental condition (white arena instead of a black one) and found that the algorithm achieves comparable accuracy, even though C57BL/6 mice and white arena were not included in the training set. Thus, the algorithm seems to be robust and efficient across various experimental conditions.

The results are based on tests of individual animals, and there is no discussion of how this method could be generalized to experiments tracking multiple animals simultaneously in the same arena (e.g., pair or collective behavior tests, where multiple animals may deposit urine or feces).

At the moment, the algorithm cannot be applied for multiple animals freely moving in the same arena. However, in the revised manuscript we explicitly discussed what is needed for adapting the algorithm to perform such analyses.

Recommendations for the authors: 

-  Add a note and/or perform additional calculations to show that the results do not depend on the specific definitions of 'start' and 'end' periods. For instance, vary the time window thresholds and recalculate the statistics using different windows (e.g., 1-5 minutes instead of 1-4 minutes).

In all the statistical tests conducted in the revised manuscript, we have used a time period of 4 minutes for the analysis. We did not use the last minute of each stage for the analysis since the input of DeePosit requires 1 minute of video after the event. Nevertheless, we also conducted the same tests using a 5-minute period and found similar results (Figure 5—Figure Supplement 1).

- Condense Figures 4, 5, and 6 to simplify the presentation. Focus on demonstrating the effectiveness of the tool rather than detailed experimental outcomes, as the primary contribution of this paper is methodological.

We have added to the revised manuscript one technical figure (Figure 3) comparing the accuracy of the algorithm performance across gender, space, time, and experiment type (SP, SxP, and ESP) as well as comparing its performance to a second human annotator and to YOLOv8. One more partially technical figure (Figure 5) compares the results of the algorithm between white ICR mice in the black arena and black C57BL/6 mice in the white arena. Thus, only Figures 4 and 6 show detailed experimental outcomes.

- Provide more detail on how the preliminary detection procedure and parameters might need adjustment for different experimental setups or conditions. Discuss potential adaptations for field settings or more complex environments.

As for the algorithm parameters, we tested the effect of the main parameter of the preliminary detection (the temperature threshold for the detection of a new blob) and found that a threshold of 1.6°C gave the best accuracy and used this parameter for all of the experiments instead of 1.1°C which was used in the original manuscript. It's worth mentioning that the performance is quite stable (mean F1 score of 0.88-0.89) for the thresholds between 1.1°C and 3°.

We also checked if changing the input length of the video clip that is fed to the classifier affects the accuracy by training the classifier with -11..30 seconds video clips (41 seconds in total) instead of -11..60 seconds (71 seconds in total) and found no difference in accuracy. 

Overall, the algorithm's accuracy seems to be rather stable across various choices of parameters.

Editor's note:

Should you choose to revise your manuscript, please ensure your manuscript includes full statistical reporting including exact p-values wherever possible alongside the summary statistics (test statistic and df) and 95% confidence intervals. These should be reported for all key questions and not only when the p-value is less than 0.05 in the main manuscript.

We have deposited the detailed statistics of each figure in https://github.com/davidpl2/DeePosit/tree/main/FigStat/PostRevision

Author response:

The following is the authors’ response to the original reviews

eLife Assessment

This valuable study investigates how hearing impairment affects neural encoding of speech, in particular the encoding of hierarchical linguistic information. The current analysis provides incomplete evidence that hearing impairment affects speech processing at multiple levels, since the novel analysis based on HM-LSTM needs further justification. The advantage of this method should also be further explained. The study can also benefit from building a stronger link between neural and behavioral data.

We sincerely thank the editors and reviewers for their detailed and constructive feedback.

We have revised the manuscript to address all of the reviewers’ comments and suggestions. The primary strength of our methods lies in the use of the HM-LSTM model, which simultaneously captures linguistic information at multiple levels, ranging from phonemes to sentences. As such, this model can be applied to other questions regarding hierarchical linguistic processing. We acknowledge that our current behavioral results from the intelligibility test may not fully differentiate between the perception of lower-level acoustic/phonetic information and higher-level meaning comprehension. However, it remains unclear what type of behavioral test would effectively address this distinction. We aim to xplore this connection further in future studies.

Public Reviews:

Reviewer #1 (Public Review):

The authors are attempting to use the internal workings of a language hierarchy model, comprising phonemes, syllables, words, phrases, and sentences, as regressors to predict EEG recorded during listening to speech. They also use standard acoustic features as regressors, such as the overall envelope and the envelopes in log-spaced frequency bands. This is valuable and timely research, including the attempt to show differences between normal-hearing and hearing-impaired people in these regards. I will start with a couple of broader questions/points, and then focus my comments on three aspects of this study: The HM-LSTM language model and its usage, the time windows of relevant EEG analysis, and the usage of ridge regression.

Firstly, as far as I can tell, the OSF repository of code, data, and stimuli is not accessible without requesting access. This needs to be changed so that reviewers and anybody who wants or needs to can access these materials. 

It is my understanding that keeping the repository private during the review process and making them public after acceptance is standard practice. As far as I understand, although the OSF repository was private, anyone with the link should be able to access it. I have now made the repository public.

What is the quantification of model fit? Does it mean that you generate predicted EEG time series from deconvolved TRFs, and then give the R2 coefficient of determination between the actual EEG and predicted EEG constructed from the convolution of TRFs and regressors? Whether or not this is exactly right, it should be made more explicit.

Model fit was measured by spatiotemporal cluster permutation tests (Maris & Oostenveld, 2007) on the contrasts of the timecourses of the z-transformed coefficient of determination (R²). For instance, to assess whether words from the attended stimuli better predict EEG signals during the mixed speech compared to words from the unattended stimuli, we used the 150dimensional vectors corresponding to the word layer from our LSTM model for the attended and unattended stimuli as regressors. We then fit these regressors to the EEG signals at 9 time points (spanning -100 ms to 300 ms around the sentence offsets, with 50 ms intervals). We then conducted one-tailed two-sample t-tests to determine whether the differences in the contrasts of the R² timecourses were statistically significant. Note that we did not perform TRF analyses. We have clarified this description in the “Spatiotemporal clustering analysis” section of the “Methods and Materials” on p.10 of the manuscript.

About the HM-LSTM:

• In the Methods paragraph about the HM-LSTM, a lot more detail is necessary to understand how you are using this model. Firstly, what do you mean that you "extended" it, and what was that procedure? 

The original HM-LSTM model developed by Chung et al. (2017) consists of only two levels: the word level and the phrase level (Figure 1b from their paper). By “extending” the model, we mean that we expanded its architecture to include five levels: phoneme, syllable, word, phrase, and sentence. Since our input consists of phoneme embeddings, we cannot directly apply their model, so we trained our model on the WenetSpeech corpus (Zhang et al., 2021), which provides phoneme-level transcripts. We have added this clarification on p.4 of the manuscript.

• And generally, this is the model that produces most of the "features", or regressors, whichever word we like, for the TRF deconvolution and EEG prediction, correct? 

Yes, we extracted the 2048-dimensional hidden layer activity from the model to represent features for each sentence in our speech stimuli at the phoneme, syllable, word, phrase and sentence levels. But we did not perform any TRF deconvolution, we fit these features (downsampled to 150-dimension using PCA) to the EEG signals at 9 timepoints around the offset of each sentence using ridge regression. We have now added a multivariate TRF (mTRF) analysis following Reviewer 3’s suggestions, and the results showed similar patterns to the current results (see Figure S2). We have added the clarification in the “Ridge regression at different time latencies” section of the “Methods and Materials” on p.10 of the manuscript.

Resutls from the mTRF analyses were added on p.7 of the manuscript.

• A lot more detail is necessary then, about what form these regressors take, and some example plots of the regressors alongside the sentences.

The linguistic regressors are just 5 150-dimensional vectors, each corresponding to one linguistic level, as shown in Figure 1B.

• Generally, it is necessary to know what these regressors look like compared to other similar language-related TRF and EEG/MEG prediction studies. Usually, in the case of e.g. Lalor lab papers or Simon lab papers, these regressors take the form of single-sample event markers, surrounded by zeros elsewhere. For example, a phoneme regressor might have a sample up at the onset of each phoneme, and a word onset regressor might have a sample up at the onset of each word, with zeros elsewhere in the regressor. A phoneme surprisal regressor might have a sample up at each phoneme onset, with the value of that sample corresponding to the rarity of that phoneme in common speech. Etc. Are these regressors like that? Or do they code for these 5 linguistic levels in some other way? Either way, much more description and plotting is necessary in order to compare the results here to others in the literature.

No, these regressors were not like that. They were 150-dimensional vectors (after PCA dimension reduction) extracted from the hidden layers of the HM-LSTM model. After training the model on the WenetSpeech corpus, we ran it on our speech stimuli and extracted representations from the five hidden layers to correspond to the five linguistic levels. As mentioned earlier, we did not perform TRF analyses; instead, we used ridge regression to predict EEG signals around the offset of each sentence, a method commonly employed in the literature (e.g., Caucheteux & King, 2022; Goldstein et al., 2022; Schmitt et al., 2021; Schrimpf et al., 2021). For instance, Goldstein et al. (2022) used word embeddings from GPT-2 to predict ECoG activity surrounding the onset of each word during naturalistic listening. We have included these literatures on p.3 in the manuscript, and the method is illustrated in Figure 1B.

• You say that the 5 regressors that are taken from the trained model's hidden layers do not have much correlation with each other. However, the highest correlations are between syllable and sentence (0.22), and syllable and word (0.17). It is necessary to give some reason and interpretation of these numbers. One would think the highest correlation might be between syllable and phoneme, but this one is almost zero. Why would the syllable and sentence regressors have such a relatively high correlation with each other, and what form do those regressors take such that this is the case?

All the regressors are represented as 2048-dimensional vectors derived from the hidden layers of the trained HM-LSTM model. We applied the trained model to all 284 sentences in our stimulus text, generating a set of 284 × 2048-dimensional vectors. Next, we performed Principal Component Analysis (PCA) on the 2048 dimensions and extracted the first 100 principal components (PCs), resulting in 284 × 100-dimensional vectors for each regressor. These 284 × 100 matrices were then flattened into 28,400-dimensional vectors. Subsequently, we computed the correlation matrix for the z-transformed 28,400-dimensional vectors of our five linguistic regressors. The code for this analysis, lstm_corr.py, can be found in our OSF repository. We have added a section “Correlation among linguistic features” in “Materials and Methods” on p.10 of the manuscript.

We consider the observed coefficients of 0.17 and 0.22 to be relatively low compared to prior model-brain alignment studies which report correlation coefficients above 0.5 for linguistic regressors (e.g., Gao et al., 2024; Sugimoto et al., 2024). In Chinese, a single syllable can also function as a word, potentially leading to higher correlations between regressors for syllables and words. However, we refrained from overinterpreting the results to suggest a higher correlation between syllable and sentence compared to syllable and word. A paired ttest of the syllable-word coefficients versus syllable-sentence coefficients across the 284 sentences revealed no significant difference (t(28399)=-3.96, p=1). We have incorporated this information into p.5 of the manuscript.

• If these regressors are something like the time series of zeros along with single sample event markers as described above, with the event marker samples indicating the onset of the relevant thing, then one would think e.g. the syllable regressor would be a subset of the phoneme regressor because the onset of every syllable is a phoneme. And the onset of every word is a syllable, etc.

All the regressors are aligned to 9 time points surrounding sentence offsets (-100 ms to 300 ms with a 50 ms interval). This is because all our regressors are taken from the HM-LSTM model, where the input is the phoneme representation of a sentence (e.g., “zh ə_4 y ie_3 j iəu_4 x iaŋ_4 sh uei_3 y ii_2 y aŋ_4”). For each unit in the sentence, the model generates five 2048dimensional vectors, each corresponding to the five linguistic levels of the entire sentence. We have added the clarification on p.11 of the manuscript.

For the time windows of analysis:

• I am very confused, because sometimes the times are relative to "sentence onset", which would mean the beginning of sentences, and sometimes they are relative to "sentence offset", which would mean the end of sentences. It seems to vary which is mentioned. Did you use sentence onsets, offsets, or both, and what is the motivation?

• If you used onsets, then the results at negative times would not seem to mean anything, because that would be during silence unless the stimulus sentences were all back to back with no gaps, which would also make that difficult to interpret.

• If you used offsets, then the results at positive times would not seem to mean anything, because that would be during silence after the sentence is done. Unless you want to interpret those as important brain activity after the stimuli are done, in which case a detailed discussion of this is warranted.

Thank you very much for pointing this out. All instances of “sentence onset” were typos and should be corrected to “sentence offset.” We chose offset because the regressors are derived from the hidden layer activity of our HM-LSTM model, which processes the entire sentence before generating outputs. We have now corrected all the typos. In continuous speech, there is no distinct silence period following sentence offsets. Additionally, lexical or phrasal processing typically occurs 200 ms after stimulus offsets (Bemis & Pylkkanen, 2011; Goldstein et al., 2022; Li et al., 2024; Li & Pylkkänen, 2021). Therefore, we included a 300 ms interval after sentence offsets in our analysis, as our regressors encompass linguistic levels up to the sentence level. We have added this motivation on p.11 of the manuscript.

• For the plots in the figures where the time windows and their regression outcomes are shown, it needs to be explicitly stated every time whether those time windows are relative to sentence onset, offset, or something else.

Completely agree and thank you very much for the suggestion. We have now added this information on Figure 4-6.

• Whether the running correlations are relative to sentence onset or offset, the fact that you can have numbers outside of the time of the sentence (negative times for onset, or positive times for offset) is highly confusing. Why would the regressors have values outside of the sentence, meaning before or after the sentence/utterance? In order to get the running correlations, you presumably had the regressor convolved with the TRF/impulse response to get the predicted EEG first. In order to get running correlation values outside the sentence to correlate with the EEG, you would have to have regressor values at those time points, correct? How does this work?

As mentioned earlier, we did not perform TRF analyses or convolve the regressors. Instead, we conducted regression analyses at each of the 9 time points surrounding the sentence offsets, following standard methods commonly used in model-brain alignment studies (e.g., Gao et al., 2024; Goldstein et al., 2022). The time window of -100 to 300 ms was selected based on prior findings that lexical and phrasal processing typically occurs 200–300 ms after word offsets (Bemis & Pylkkanen, 2011; Goldstein et al., 2022; Li et al., 2024; Li & Pylkkänen, 2021). Additionally, we included the -100 to 200 ms time period in our analysis to examine phoneme and syllable level processing (cf. Gwilliams et al., 2022). We have added the clarification on p. of the manuscript.

• In general, it seems arbitrary to choose sentence onset or offset, especially if the comparison is the correlation between predicted and actual EEG over the course of a sentence, with each regressor. What is going on with these correlations during the middle of the sentences, for example? In ridge regression TRF techniques for EEG/MEG, the relevant measure is often the overall correlation between the predicted and actual, calculated over a longer period of time, maybe the entire experiment. Here, you have calculated a running comparison between predicted and actual, and thus the time windows you choose to actually analyze can seem highly cherry-picked, because this means that most of the data is not actually analyzed.

The rationale for choosing sentence offsets instead of onsets is that we are aligning the HM-LSTM model’s activity with EEG responses, and the input to the model consists of phoneme representations of the entire sentence at one time. In other words, the model needs to process the whole sentence before generating representations at each linguistic level. Therefore, the corresponding EEG responses should also align with the sentence offsets, occurring after participants have seen the complete sentence. The ridge regression followed the common practice in model-brain alignment studies (e.g., Gao et al., 2024; Goldstein et al., 2022; Huth et al., 2016; Schmitt et al., 2021; Schrimpf et al., 2021), and the time window is not cherrypicked but based on prior literature reporting lexical and sublexical processing at these time period (e.g., Bemis & Pylkkanen, 2011; Goldstein et al., 2022; Gwilliams et al., 2022; Li et al., 2024; Li & Pylkkänen, 2021).

• In figures 5 and 6, some of the time window portions that are highlighted as significant between the two lines have the lines intersecting. This looks like, even though you have found that the two lines are significantly different during that period of time, the difference between those lines is not of a constant sign, even during that short period. For instance, in figure 5, for the syllable feature, the period of 0 - 200 ms is significantly different between the two populations, correct? But between 0 and 50, normal-hearing are higher, between 50 and 150, hearing-impaired are higher, and between 150 and 200, normal-hearing are higher again, correct? But somehow they still end up significantly different overall between 0 and 200 ms. More explanation of occurrences like these is needed.

The intersecting lines in Figures 5 and represent the significant time windows for withingroup comparisons (i.e., significant model fit compared to 0). They do not depict betweengroup comparisons, as no significant contrasts were found between the groups. For example, in Figure 1, the significant time windows for the acoustic models are shown separately for the hearing-impaired and normal-hearing groups. No significant differences were observed, as indicated by the sensor topography. We have now clarified this point in the captions for Figures 5 and 6.

Using ridge regression:

• What software package(s) and procedure(s) were specifically done to accomplish this? If this is ridge regression and not just ordinary least squares, then there was at least one non-zero regularization parameter in the process. What was it, how did it figure in the modeling and analysis, etc.?

The ridge regression was performed using customary python codes, making heavy use of the sklearn (v1.12.0) package. We used ridge regression instead of ordinary least squares regression because all our linguistic regressors are 150-dimensional dense vectors, and our acoustic regressors are 130-dimension vectors (see “Acoustic features of the speech stimuli” in “Materials and Methods”). We kept the default regularization parameter (i.e., 1). This ridge regression methods is commonly used in model-brain alignment studies, where the regressors are high-dimensional vectors taken from language models (e.g., Gao et al., 2024; Goldstein et al., 2022; Huth et al., 2016; Schmitt et al., 2021; Schrimpf et al., 2021). The code ridge_lstm.py can be found in our OSF repository, and we have added the more detailed description on p.11 of the manuscript.

• It sounds like the regressors are the hidden layer activations, which you reduced from 2,048 to 150 non-acoustic, or linguistic, regressors, per linguistic level, correct? So you have 150 regressors, for each of 5 linguistic levels. These regressors collectively contribute to the deconvolution and EEG prediction from the resulting TRFs, correct? This sounds like a lot of overfitting. How much correlation is there from one of these 150 regressors to the next? Elsewhere, it sounds like you end up with only one regressor for each of the 5 linguistic levels. So these aspects need to be clarified.

• For these regressors, you are comparing the "regression outcomes" for different conditions; "regression outcomes" are the R2 between predicted and actual EEG, which is the coefficient of determination, correct? If this is R2, how is it that you have some negative numbers in some of the plots? R2 should be only positive, between 0 and 1.

Yes we reduced 2048-dimensional vectors for each of the 5 linguistic levels to 150 using PCA, mainly for saving computational resources. We used ridge regression, following the standard practice in the field (e.g., Gao et al., 2024; Goldstein et al., 2022; Huth et al., 2016; Schmitt et al., 2021; Schrimpf et al., 2021). 

Yes, the regression outcomes are the R² values representing the fit between the predicted and actual EEG data. However, we reported normalized R² values which are ztransformed in the plots. All our spatiotemporal cluster permutation analyses were conducted using the z-transformed R² values. We have added this clarification both in the figure captions and on p.11 of the manuscript. As a side note, R² values can be negative because they are not the square of a correlation coefficient. Rather, R² compares the fit of the chosen model to that of a horizontal straight line (the null hypothesis). If the chosen model fits the data worse than the horizontal line, then R² value becomes negative: https://www.graphpad.com/support/faq/how-can-rsup2sup-be-negative 

Reviewer #2 (Public Review):

This study compares neural responses to speech in normal-hearing and hearing-impaired listeners, investigating how different levels of the linguistic hierarchy are impacted across the two cohorts, both in a single-talker and multi-talker listening scenario. It finds that, while normal-hearing listeners have a comparable cortical encoding of speech-in-quiet and attended speech from a multi-talker mixture, participants with hearing impairment instead show a reduced cortical encoding of speech when it is presented in a competing listening scenario. When looking across the different levels of the speech processing hierarchy in the multi-talker condition, normal-hearing participants show a greater cortical encoding of the attended compared to the unattended stream in all speech processing layers - from acoustics to sentencelevel information. Hearing-impaired listeners, on the other hand, only have increased cortical responses to the attended stream for the word and phrase levels, while all other levels do not differ between attended and unattended streams.

The methods for modelling the hierarchy of speech features (HM-LSTM) and the relationship between brain responses and specific speech features (ridge-regression) are appropriate for the research question, with some caveats on the experimental procedure. This work offers an interesting insight into the neural encoding of multi-talker speech in listeners with hearing impairment, and it represents a useful contribution towards understanding speech perception in cocktail-party scenarios across different hearing abilities. While the conclusions are overall supported by the data, there are limitations and certain aspects that require further clarification.

(1) In the multi-talker section of the experiment, participants were instructed to selectively attend to the male or the female talker, and to rate the intelligibility, but they did not have to perform any behavioural task (e.g., comprehension questions, word detection or repetition), which could have demonstrated at least an attempt to comply with the task instructions. As such, it is difficult to determine whether the lack of increased cortical encoding of Attended vs. Unattended speech across many speech features in hearing-impaired listeners is due to a different attentional strategy, which might be more oriented at "getting the gist" of the story (as the increased tracking of only word and phrase levels might suggest), or instead it is due to hearing-impaired listeners completely disengaging from the task and tuning back in for selected key-words or word combinations. Especially the lack of Attended vs. Unattended cortical benefit at the level of acoustics is puzzling and might indicate difficulties in performing the task. I think this caveat is important and should be highlighted in the Discussion section. RE: Thank you very much for the suggestion. We admit that the hearing-impaired listeners might adopt different attentional strategies or potentially disengage from the task due to comprehension difficulties. However, we would like to emphasize that our hearing-impaired participants have extended high-frequency (EHF) hearing loss, with impairment only at frequencies above 8 kHz. Their condition is likely not severe enough to cause them to adopt a markedly different attentional strategy for this task. Moreover, it is possible that our normalhearing listeners may also adopt varying attentional strategies, yet the comparison still revealed notable differences.We have added the caveat in the Discussion section on p.8 of the manuscript.

(2) In the EEG recording and preprocessing section, you state that the EEG was filtered between 0.1Hz and 45Hz. Why did you choose this very broadband frequency range? In the literature, speech responses are robustly identified between 0.5Hz/1Hz and 8Hz. Would these results emerge using a narrower and lower frequency band? Considering the goal of your study, it might also be interesting to run your analysis pipeline on conventional frequency bands, such as Delta and Theta, since you are looking into the processing of information at different temporal scales.

Indeed, we have decomposed the epoched EEG time series for each section into six classic frequency bands components (delta 1–3 Hz, theta 4–7 Hz, alpha 8–12 Hz, beta 12–20 Hz, gamma 30–45 Hz) by convolving the data with complex Morlet wavelets as implemented in MNE-Python (version 0.24.0). The number of cycles in the Morlet wavelets was set to frequency/4 for each frequency bin. The power values for each time point and frequency bin were obtained by taking the square root of the resulting time-frequency coefficients. These power values were normalized to reflect relative changes (expressed in dB) with respect to the 500 ms pre-stimulus baseline. This yielded a power value for each time point and frequency bin for each section. We specifically examined the delta and theta bands, and computed the correlation between the regression outcome (R² in the shape of number of subject * sensor * time were flattened for computing correlation) for the five linguistic predictors from these bands and those obtained using data from all frequency bands. The results showed high correlation coefficients (see the correlation matrix in Supplementary Figures S2 for the attended and unattended speech). Therefore, we opted to use the epoched EEG data from all frequency bands for our analyses. We have added this clarification in the Results section on p.5 and the “EEG recording and preprocessing” section in “Materials and Methods” on p.11 of the manuscript.

(3) A paragraph with more information on the HM-LSTM would be useful to understand the model used without relying on the Chung et al. (2017) paper. In particular, I think the updating mechanism of the model should be clarified. It would also be interesting to modify the updating factor of the model, along the lines of Schmitt et al. (2021), to assess whether a HM-LSTM with faster or slower updates can better describe the neural activity of hearing-impaired listeners. That is, perhaps the difference between hearing-impaired and normal-hearing participants lies in the temporal dynamics, and not necessarily in a completely different attentional strategy (or disengagement from the stimuli, as I mentioned above).

Thank you for the suggestion. We have added more details on our HM-LSTM model on p.10 “Hierarchical multiscale LSTM model” in “Materials and Methods”: Our HM-LSTM model consists of 4 layers, at each layer, the model implements a COPY or UPDATE operation at each time step t. The COPY operation maintains the current cell state of without any changes until it receives a summarized input from the lower layer. The UPDATE operation occurs when a linguistic boundary is detected in the layer below, but no boundary was detected at the previous time step t-1. In this case, the cell updates its summary representation, similar to standard RNNs. We agree that exploring modifications to the model’s updating factor would be an interesting direction. However, since we have already observed contrasts between normal-hearing and hearing-impaired listeners using the current model’s update parameters, we believe discussing additional hypotheses would overextend the scope of this paper.

(4) When explaining how you extracted phoneme information, you mention that "the inputs to the model were the vector representations of the phonemes". It is not clear to me whether you extracted specific phonetic features (e.g., "p" sound vs. "b" sound), or simply the phoneme onsets. Could you clarify this point in the text, please?

The model inputs were individual phonemes from two sentences, each transformed into a 1024-dimensional vector using a simple lookup table. This lookup table stores embeddings for a fixed dictionary of all unique phonemes in Chinese. This approach is a foundational technique in many advanced NLP models, enabling the representation of discrete input symbols in a continuous vector space. We have added this clarification on p.10 of the manuscript.

Reviewer #3 (Public Review):

Summary:

The authors aimed to investigate how the brain processes different linguistic units (from phonemes to sentences) in challenging listening conditions, such as multi-talker environments, and how this processing differs between individuals with normal hearing and those with hearing impairments. Using a hierarchical language model and EEG data, they sought to understand the neural underpinnings of speech comprehension at various temporal scales and identify specific challenges that hearing-impaired listeners face in noisy settings.

Strengths:

Overall, the combination of computational modeling, detailed EEG analysis, and comprehensive experimental design thoroughly investigates the neural mechanisms underlying speech comprehension in complex auditory environments.

The use of a hierarchical language model (HM-LSTM) offers a data-driven approach to dissect and analyze linguistic information at multiple temporal scales (phoneme, syllable, word, phrase, and sentence). This model allows for a comprehensive neural encoding examination of how different levels of linguistic processing are represented in the brain.

The study includes both single-talker and multi-talker conditions, as well as participants with normal hearing and those with hearing impairments. This design provides a robust framework for comparing neural processing across different listening scenarios and groups.

Weaknesses:

The analyses heavily rely on one specific computational model, which limits the robustness of the findings. The use of a single DNN-based hierarchical model to represent linguistic information, while innovative, may not capture the full range of neural coding present in different populations. A low-accuracy regression model-fit does not necessarily indicate the absence of neural coding for a specific type of information. The DNN model represents information in a manner constrained by its architecture and training objectives, which might fit one population better than another without proving the non-existence of such information in the other group. To address this limitation, the authors should consider evaluating alternative models and methods. For example, directly using spectrograms, discrete phoneme/syllable/word coding as features, and performing feature-based temporal response function (TRF) analysis could serve as valuable baseline models. This approach would provide a more comprehensive evaluation of the neural encoding of linguistic information.

Our acoustic features are indeed direct the broadband envelopes and the log-mel spectrograms of the speech streams. The amplitude envelope of the speech signal was extracted using the Hilbert transform. The 129-dimension spectrogram and 1-dimension envelope were concatenated to form a 130-dimension acoustic feature at every 10 ms of the speech stimuli. Given the duration of our EEG recordings, which span over 10 minutes, conducting multivariate TRF (mTRF) analysis with such high-dimensional predictors was not feasible. Instead, we used ridge regression to predict EEG responses across 9 temporal latencies, ranging from -100 ms to +300 ms, with additional 50 ms latencies surrounding sentence offsets. To evaluate the model's performance, we extracted the R² values at each latency, providing a temporal profile of regression performance over the analyzed time period. This approach is conceptually similar to TRF analysis.

We agree that including baseline models for the linguistic features is important, and we have now added results from mTRF analysis using phoneme, syllable, word, phrase, and sentence rates as discrete predictors (i.e., marking a value of 1 at each unit boundary offset). Our EEG data spans the entire 10-minute duration for each condition, sampled at 10-ms intervals. The TRF results for our main comparison—attended versus unattended conditions— showed similar patterns to those observed using features from our HM-LSTM model. At the phoneme and syllable levels, normal-hearing listeners showed marginally significantly higher TRF weights for attended speech compared to unattended speech at approximately -80 to 150 ms after phoneme offsets (t=2.75, Cohen’s d=0.87, p=0.057), and 120 to 210 ms after syllable offsets (t=3.96, Cohen’s d=0.73d = 0.73, p=0.083). At the word and phrase levels, normalhearing listeners exhibited significantly higher TRF weights for attended speech compared to unattended speech at 190 to 290 ms after word offsets (t=4, Cohen’s d=1.13, p=0.049), and around 120 to 290 ms after phrase offsets (t=5.27, Cohen’s d=1.09, p=0.045). For hearing-impaired listeners, marginally significant effects were observed at 190 to 290 ms after word offsets (t=1.54, Cohen’s d=0.6, p=0.059), and 180 to 290 ms after phrase offsets (t=3.63, Cohen’s d=0.89, p=0.09). These results have been added on p.7 of the manuscript, and the corresponding figure is included as Supplementary F2.

It is not entirely clear if the DNN model used in this study effectively serves the authors' goal of capturing different linguistic information at various layers. Specifically, the results presented in Figure 3C are somewhat confusing. While the phonemes are labeled, the syllables, words, phrases, and sentences are not, making it difficult to interpret how the model distinguishes between these levels of linguistic information. The claim that "Hidden-layer activity for samevowel sentences exhibited much more similar distributions at the phoneme and syllable levels compared to those at the word, phrase and sentence levels" is not convincingly supported by the provided visualizations. To strengthen their argument, the authors should use more quantified metrics to demonstrate that the model indeed captures phrase, word, syllable, and phoneme information at different layers. This is a crucial prerequisite for the subsequent analyses and claims about the hierarchical processing of linguistic information in the brain.

Quantitative measures such as mutual information, clustering metrics, or decoding accuracy for each linguistic level could provide clearer evidence of the model's effectiveness in this regard.

In Figure 3C, we used color-coding to represent the activity of five hidden layers after dimensionality reduction. Each dot on the plot corresponds to one test sentence. Only phonemes are labeled because each syllable in our test sentences contains the same vowels (see Table S1). The results demonstrate that the phoneme layer effectively distinguishes different phonemes, while the higher linguistic layers do not. We believe these findings provide evidence that different layers capture distinct linguistic information. Additionally, we computed the correlation coefficients between each pair of linguistic predictors, as shown in Figure 3B. We think this analysis serves a similar purpose to computing the mutual information between pairs of hidden-layer activities for our constructed sentences. Furthermore, the mTRF results based on rate models of the linguistic features we presented earlier align closely with the regression results using the hidden-layer activity from our HM-LSTM model. This further supports the conclusion that our model successfully captures relevant information across these linguistic levels. We have added the clarification on p.5 of the manuscript.

The formulation of the regression analysis is somewhat unclear. The choice of sentence offsets as the anchor point for the temporal analysis, and the focus on the [-100ms, +300ms] interval, needs further justification. Since EEG measures underlying neural activity in near real-time, it is expected that lower-level acoustic information, which is relatively transient, such as phonemes and syllables, would be distributed throughout the time course of the entire sentence. It is not evident if this limited time window effectively captures the neural responses to the entire sentence, especially for lower-level linguistic features. A more comprehensive analysis covering the entire time course of the sentence, or at least a longer temporal window, would provide a clearer understanding of how different linguistic units are processed over time. Additionally, explaining the rationale behind choosing this specific time window and how it aligns with the temporal dynamics of speech processing would enhance the clarity and validity of the regression analysis.

Thank you for pointing this out. We chose this time window as lexical or phrasal processing typically occurs 200 ms after stimulus offsets (Bemis & Pylkkanen, 2011; Goldstein et al., 2022; Li et al., 2024; Li & Pylkkänen, 2021). Additionally, we included the -100 to 200 ms time period in our analysis to examine phoneme and syllable level processing (e.g., Gwilliams et al., 2022). Using the entire sentence duration was not feasible, as the sentences in the stimuli vary in length, making statistical analysis challenging. Additionally, since the stimuli consist of continuous speech, extending the time window would risk including linguistic units from subsequent sentences. This would introduce ambiguity as to whether the EEG responses correspond to the current or the following sentence. We have added this clarification on p.12 of the manuscript.

Recommendations for the authors:

Reviewer #1 (Recommendations For The Authors):

As I mentioned, I think the OSF repo needs to be changed to give anyone access. I would recommend pursuing the lines of thought I mentioned in the public review to make this study complete and to allow it to fit into the already existing literature to facilitate comparisons.

Yes the OSF folder is now public. We have made revisions following all reviewers’ suggestions.

There are some typos in figure labels, e.g. 2B.

Thank you for pointing it out! We have now revised the typo in Figure 2B.

Reviewer #2 (Recommendations For The Authors):

(1) I was able to access all of the audio files and code for the study, but no EEG data was shared in the OSF repository. Unless there is some ethical and/or legal constraint, my understanding of eLife's policy is that the neural data should be made publicly available as well.

The preprocessed EEG data in .npy format in the OSF repository. 

(2) The line-plots in Figures 4B,5B, and 6B have very similar colours. They would be easier to interpret if you changed the line appearance as well as the colours. E.g., dotted line for hearingimpaired listeners, thick line for normal-hearing.

Thank you for the suggestion! We have now used thicker lines for normal-impaired listeners in all our line plots.

Reviewer #3 (Recommendations For The Authors):

(1) The authors may consider presenting raw event-related potentials (ERPs) or spatiotemporal response profiles before delving into the more complex regression encoding analysis. This would provide a clearer foundational understanding of the neural activity patterns. For example, it is not clear if the main claims, such as the neural activity in the normal-hearing group encoding phonetic information in attended speech better than in unattended speech, are directly observable. Showing ERP differences or spatiotemporal response pattern differences could support these claims more straightforwardly. Additionally, training pattern classifiers to test if different levels of information can be decoded from EEG activity in specific groups could provide further validation of the findings.

We have now included results from more traditional mTRF analyses using phoneme, syllable, word, phrase, and sentence rates as baseline models (see p.7 of the manuscript and Figure S3). The results show similar patterns to those observed in our current analyses. While we agree that classification analyses would be very interesting, our regression analyses have already demonstrated distinct EEG patterns for each linguistic level. Consequently, classification analyses would likely yield similar results unless a different method for representing linguistic information at these levels is employed. To the best of our knowledge, no other computational model currently exists that can simultaneously represent these linguistic levels.

(2) Is there any behavioral metric suggesting that these hearing-impaired participants do have deficits in comprehending long sentences? The self-rated intelligibility is useful, but cannot fully distinguish between perceiving lower-level phonetic information vs longer sentence comprehension.

In the current study, we included only self-rated intelligibility tests. We acknowledge that this approach might not fully distinguish between the perception of lower-level phonetic information and higher-level sentence comprehension. However, it remains unclear what type of behavioral test would effectively address this distinction. Furthermore, our primary aim was to use the behavioral results to demonstrate that our hearing-impaired listeners experienced speech comprehension difficulties in multi-talker environments, while relying on the EEG data to investigate comprehension challenges at various linguistic levels.

Minor:

(1) Page 2, second line in Introduction, "Phonemes occur over ..." should be lowercase.

According to APA format, the first word after the colon is capitalized if it begins a complete sentence (https://blog.apastyle.org/apastyle/2011/06/capitalization-after-colons.html). Here

the sentence is a complete sentence so we used uppercase for “phonemes”.

(2) Page 8, second paragraph "...-100ms to 100ms relative to sentence onsets", should it be onsets or offsets?

This is typo and it should be offsets. We have now revised it.

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Author response:

The following is the authors’ response to the original reviews

eLife Assessment

The study presents some useful findings on Mendelian randomization-phenome-wide association, with BMI associated with health outcomes, and there is a focus on sex differences. Although there are some solid phenotype and genotype data, some of the data are incomplete and could be better presented, perhaps benefiting from more rigorous approaches. Confirmation and further assessment of the observed sex differences will add further value.

Thank you for your positive comments. We have revised the analysis based on your feedback and that from the two reviewers. Specifically, we implemented a stricter multiple testing correction approach, improved the figures, included additional figures in the Supplementary Materials, considered the sex differences more rigorously and reported them in more detail. A comprehensive description of the revisions is provided below.

Public Reviews:

Reviewer #1 (Public review):

Summary:

This study uses information from the UK Biobank and aims to investigate the role of BMI on various health outcomes, with a focus on differences by sex. They confirm the relevance of many of the well-known associations between BMI and health outcomes for males and females and suggest that associations for some endpoints may differ by sex. Overall their conclusions appear supported by the data. The significance of the observed sex variations will require confirmation and further assessment.

Strengths:

This is one of the first systematic evaluations of sex differences between BMI and health outcomes. The hypothesis that BMI may be associated with health differentially based on sex is relevant and even expected. As muscle is heavier than adipose tissue, and as men typically have more muscle than women, as a body composition measure BMI is sometimes prone to classifying even normal weight/muscular men as obese, while this measure is more lenient when used in women. Confirmation of the many well-known associations is as expected and attests to the validity of their approach. Demonstration of the possible sex differences is interesting, with this work raising the need for further study.

Thank you for your valuable comments. We are grateful for the time and effort you have devoted to reviewing our manuscript. We have strengthened our paper by adding your insightful comment about the rationale for sex-specific analysis to the introduction:

Weaknesses:

(1) Many of the statistical decisions appeared to target power at the expense of quality/accuracy. For example, they chose to use self-reported information rather than doctor diagnoses for disease outcomes for which both types of data were available.

Thank you for your valuable comments. We apologize for the lack of clarity in our original description of the phenotypes. Information about health in the UK Biobank was obtained at baseline from tests, measurements and self reports. Subsequently comprehensive data linkage to hospital admissions, death registries and cancer registries was implemented. However, data linkage to primary care data, such as doctor diagnoses, has not been comprehensively implemented for the UK Biobank, possibly for logistic reasons. Doctor diagnoses are only available for about half the cohort, (https://www.ukbiobank.ac.uk/enable-your-research/about-our-data/health-related-outcomes-data). So, we used self-reported diagnoses because they are substantially more comprehensive than the doctor diagnoses. We have explained this point by making the following change to the Methods:

“Where attributes were available from both self-report and doctor diagnosis, we used self-reports. This is because comprehensive record linkage to doctor diagnoses has not yet been fully implemented for the UK Biobank, so information from doctor diagnoses may not fully represent the broader UK Biobank cohort.”

(2) Despite known problems and bias arising from the use of one sample approach, they chose to use instruments from the UK Biobank instead of those available from the independent GIANT GWAS, despite the difference in sample size being only marginally greater for UKB for the context. With the way the data is presented, it is difficult to assess the extent to which results are compatible across approaches.

Thank you for your comments. We agree completely about the issues with a one sample approach, please accept our apologies for not explaining our rationale. The sex-specific GIANT GWAS study is similar in size to the UK Biobank GWAS. However, the sex-specific GIANT GWAS is much less densely genotyped (~2,5 million variants) than the sex-specific UK Biobank GWAS (~10 million variants), so has less power, hence our use of the UK Biobank. To make this clear, we have added the number of variants in each study to the method section. Nevertheless, we also repeated analysis using sex-specific GIANT, as now given in the methods by making the following change

We amended the description in the first paragraph of the results section:

“Initial analysis using sex-specific BMI from GIANT yielded similar estimates as when using sex-specific BMI from the UK Biobank but had fewer SNPs resulting in wider confidence intervals (S Table 1) and fewer significant associations (S Table 1). Analysis using sex-combined GIANT yielded more significant associations but lacks granularity, so we presented the results obtained using sex-specific BMI from the UK Biobank.”

In the discussion we also made the following changes:

“Tenth, although this study primarily utilized sex-specific BMI, we also conducted analyses using overall BMI from GIANT including the UK Biobank, which gave a generally similar interpretation (S Table 1). Using sex-specific BMI from the UK Biobank and GIANT may lead to lower statistical power than using overall population BMI but allows for the detection of traits that are affected differently by BMI by sex. Including findings from the overall population BMI from sex-combined GIANT (S Table 1) makes the results more comparable to previous similar studies.”

(3) The approach to multiple testing correction appears very lenient, although the lack of accuracy in the reporting makes it difficult to know what was done exactly. The way it reads, FDR correction was done separately for men, and then for women (assuming that the duplication in tests following stratification does not affect the number of tests). In the second stage, they compared differences by sex using Z-test, apparently without accounting for multiple testing.

Thank you, we have accounted for multiple comparisons when considering differences by sex and have made corresponding changes. Specifically, in the methods, we changed:

“We obtained differences by sex using a z-test (Paternoster et al., 1998), which as recommended was on a linear scale for dichotomous outcomes (Knol et al., 2007; Rothman, 2008), then we identified which ones remained after allowing for false discovery”

We have made the following changes to the results section:

“We found significant differences by sex in the associations of BMI with 105 health-related attributes (p-value<0.05); 46 phenotypes remained after allowing for false discovery (Table 1). Of these 46 differences most (35) were in magnitude but not direction, such as for SHBG, ischemic heart disease, heart attack, and facial aging, while 11 were directionally different.

Notably, BMI was more strongly positively associated with myocardial infarction, major coronary heart disease events, ischemic heart disease, heart attack, and facial aging in men than in women. BMI was more strongly positively associated with diastolic blood pressure, and hypothyroidism/myxoedema in women than men. BMI was more strongly inversely associated with LDL-c, hay fever and allergic rhinitis in men than women. BMI was more strongly inversely associated with SHBG in women than men.

BMI was inversely associated with ApoB, iron deficiency anemia, hernia, and total testosterone in men, while positively associated with these traits in women (Table 1). BMI was inversely associated with sensitivity/hurt feelings, and ever seeking medical advice for nerves, anxiety, tension, or depression in men. However, BMI was positively associated with sensitivity/hurt feelings and ever seeking medical advice for these same issues in women. BMI was positively associated with muscle or soft tissue injuries and haemorrhage from respiratory passages in men, whilst inversely associated with these traits in women.”

We have correspondingly amended the discussion to reflect these changes by adding:

“Whether the difference in ischemic heart disease rates between men and women that emerged in the US and the UK the late 19th century (Nikiforov & Mamaev, 1998) is explained by rising BMI remains to be determined.”

(4) Presentation lacks accuracy in a few places, hence assessment of the accuracy of the statements made by the authors is difficult.

Thank you, we have revised the whole manuscript in order to improve clarity.

(5) Conclusion (Abstract) "These findings highlight the importance of retaining a healthy BMI" is rather uninformative, especially as they claim that for some attributes the effects of BMI may be opposite depending on sex/gender.

Thank you for your comments. We have changed the conclusion of the abstract, as given below:

“Our study revealed that BMI might affect a wide range of health-related attributes and also highlights notable sex differences in its impact, including opposite associations for certain attributes, such as ApoB; and stronger effects in men, such as for cardiovascular diseases. Our findings underscore the need for nuanced, sex-specific policy related to BMI to address inequities in health.”.

We have changed the Impact statement, as given below:

“BMI may affect a wide range of health-related attributes and there are notable sex differences in its impact, including opposite associations for certain attributes, such as ApoB; and stronger effects in men, such as for cardiovascular diseases. Our findings underscore the need for nuanced, sex-specific policy related to BMI.”

We have changed the conclusion of the paper, as given below:

“Our contemporary systematic examination found BMI associated with a broad range of health-related attributes. We also found significant sex differences in many traits, such as for cardiovascular diseases, underscoring the importance of addressing higher BMI in both men and women possibly as means of redressing differences in life expectancy. Ultimately, our study emphasizes the harmful effects of obesity and the importance of nuanced, sex-specific policy related to BMI to address inequities.in health.”

Reviewer #2 (Public review):

Summary:

In this present Mendelian randomization-phenome-wide association study, the authors found BMI to be positively associated with many health-related conditions, such as heart disease, heart failure, and hypertensive heart disease. They also found sex differences in some traits such as cancer, psychological disorders, and ApoB.

Strengths:

The use of the UK-biobank study with detailed phenotype and genotype information.

Thank you for your valuable comments. We are grateful for the time and effort you have devoted to reviewing our manuscript.

Weaknesses:

(1) Previous studies have performed this analysis using the same cohort, with in-depth analysis. See this paper: Searching for the causal effects of body mass index in over 300,000 participants in UK Biobank, using Mendelian randomization. https://journals.plos.org/plosgenetics/article?id=10.1371/journal.pgen.10079i51

Thank you for your valuable comments. We checked the paper carefully. It gives sex-specific estimates when the outcome was assessed in different ways in men and women, for example the question about number of children was asked in terms of live births in women and number of children fathered in men. In addition, for some significant findings, the authors investigated differences by sex. However, the paper did not use sex-specific BMI or sex-specific outcomes systematically. We have added this paper to our introduction and amended the text to explain the novelty of our study compared to previous studies.

“Previous phenome-wide association studies using MR (MR-PheWASs) have identified impacts of sex-combined BMI on endocrine disorders, circulatory diseases, inflammatory and dermatological conditions, some biomarkers and feelings of nervousness (Hyppönen et al., 2019; Millard et al., 2015; Millard et al. 2019), but did not systematically use sex-specific BMI for the exposure or sex-specific outcomes.”

(2) I believe that the authors' claim, "To our knowledge, no sex-specific PheWAS has investigated the effects of BMI on health outcomes," is not well supported. They have not cited a relevant paper that conducted both overall and sex-stratified PheWAS using UK Biobank data with a detailed analysis. Given the prior study linked above, I am uncertain about the additional contributions of the present research.

Thank you for your valuable comments, please accept our apologies for this oversight. As explained above, we have checked very carefully. There are three previous PheWAS for BMI, Hyppönen et al., 2019, Millard et al., 2015 and Millard et al. 2019. Hyppönen et al., 2019 and Millard et al., 2015 are not sex-specific. Millard et al. 2019 used sex-combined instruments, but some sex-specific outcomes, when the questions were asked sex-specifically, such as age at puberty asked as “age when periods started (menarche)” in women and “relative age of first facial hair” and “relative age voice broke” in men. When they found a factor significantly associated with BMI, they sometimes analyze it further including sex-specific analysis, but they did not do the analysis systematically for men and women with sex-specific BMI and sex-specific outcomes. We have amended the introduction to clarify this point.

“To our knowledge, no sex-specific PheWAS has investigated the effects of BMI on health outcomes (Hyppönen et al., 2019; Millard et al., 2015; Millard et al. 2009). To address this gap, we conducted a sex-specific PheWAS, using the largest available sex-specific GWAS of BMI, to explore the impact of sex-specific BMI on sex-specific health-related attributes”

Recommendations for the authors:

Reviewer #1 (Recommendations for the authors):

Presentation, accuracy, and referencing:

(1) The quality of the English language needs to be checked, including that all sentences carry all components required (including verbs).

We thank the reviewer for this suggestion. The manuscript has undergone language editing by a native English-speaker, with particular attention to grammatical completeness (including verb consistency and sentence structure). We have also clarified ambiguities and inconsistencies in terms pointed out by the native English speakers. All revisions have been implemented in the updated manuscript.

(2) The accuracy of statements needs to be checked. For example, in lines 82-83 it is not true that 2015/2019 was 'before the advent of large-scale GWAs studies". In the context of the above in lines 83-85, how can reference be made to a study published in 2020 calling that 'previous' MR studies and how a trial published in 2016 is 'recent'? Please revise, and please also check the manuscript for any other issues with accuracy of this kind.

We thank the reviewer for this suggestion. We have checked the manuscript and revised these sentences to be clearer, by making the following change.

“Previous phenome-wide association studies using MR (MR-PheWASs) have identified impacts of sex-combined BMI on endocrine disorders, circulatory diseases, inflammatory and dermatological conditions, some biomarkers and feelings of nervousness (Hyppönen et al., 2019; Millard et al., 2015; Millard et al. 2019), but did not systematically use sex-specific BMI for the exposure or sex-specific outcomes. Previous MR studies and trials of incretins have expanded our knowledge about a broad range of effects of BMI (Larsson et al., 2020; Marso et al., 2016).”

(3) The adequacy of referencing will need to be checked, e.g. line 136 "as recommended by UK biobank" is vague and needs to be referenced.

We thank the reviewer for this suggestion. We have added citations.

“We categorized attributes as age at recruitment, physical measures, lifestyle and environmental, medical conditions, operations, physiological factors, cognitive function, health and medical history, sex-specific factors, blood assays and urine assays, based on the UK Biobank categories (https://biobank.ndph.ox.ac.uk/ukb/cats.cgi).”

(4) The accurate use of terminology needs to be checked. For example, BMI is a measure of adiposity, while high BMI (typically >30) is used to index obesity.

We thank you for your comments. We have changed the descriptions into “overweight/obesity” throughout.

(5) Figure 1, Please check that complete information is given for 'selection criteria' and that the rationale for all information included is clear. For example, it is currently unclear what is the distinction between the bottom two sections which both present a number of features included in the analyses? Also, the Box detailing exclusion of 3585 variables does not give the criteria for these exclusions. Please add.

Thank you for your comments. We have represented and revised Figure 1. Specifically, we have revised the bottom two sections to give each reason for exclusion and the number excluded for that reason. The updated “Excluded: 3,572 phenotypes, for the reason listed below:” box now contains bullet-points giving each reason for exclusion in the box (e.g. age of certain diseases/disorders onset: 26, alcohol: 56).

(6) Figure 4, does not look to be of typical publication quality.

We thank you for your comments. We have used different colors to make it smaller and more readable. Please see Table 1.

Analyses:

(1) As it stands, it is very difficult for a reader to confirm the conclusion that similar findings are obtained both when using instruments from the UKB and GIANT based on data presented (Stable 1 and 2). I suggested two things.

a) Organise stable 1 and 2 by significance and category, with separation by highlighting for those which are significant under correction. I would consider merging these two tables, such that it would be easy for the reader to make the comparisons side by side. Consider presenting separate tables for the analyses for women and men.

We thank you for your comments. We have followed your helpful advice and merged S Table 1 and S Table 2 into S Table 1. Furthermore, we have also merged S Table 5 to S Table 1.

b) In Stable 3, please add information from related comparisons using the GIANT instruments. To support the authors' claim that associations are similar, but only the precision of estimation differed, you could consider adding information for numbers of associations for those that are directionally consistent and which have an association at least under nominal significance'. For associations where this does not hold, I would refrain from making a claim that the results are not affected by the choice of instrument (or biases relating to the analysis conducted).

We thank you for your comments. Among 42 significant sex-specific associations identified in both the UK Biobank and the sex-specific GIANT consortium for men, all showed consistent directions of effect. Similarly, for women, all of the 45 significant associations exhibited consistent directions for UK Biobank compared with GIANT instruments.

In the sex-specific UK Biobank, there are 203 significant associations in men, and 232 significant associations in women. We have added: in the sex-specific GIANT, there are 46 significant associations in men, and 84 significant associations in women. In the sex-combined GIANT, there are 246 significant associations in men, and 276 significant associations in women. We have provided all this information in S Table 2.

We added the following descriptions at the end of the results section:

“Of the 42 significant sex-specific associations identified in both the UK Biobank and the sex-specific GIANT consortium for men, all were directionally consistent. Similarly, for women, all 45 such significant associations were directionally consistent.

We amended the following descriptions in the first paragraph of the results section:

“Initial analysis using sex-specific BMI from the GIANT yielded similar estimates as when using sex-specific BMI from the UK Biobank but had fewer SNPs resulting in wider confidence intervals (S Table 1) and fewer significant associations (S Table 2). Analysis using sex-combined GIANT yielded more significant associations but lacks granularity, so we presented the results obtained using sex-specific BMI from the UK Biobank.”

In the methods, we changed:

“We obtained differences by sex using a z-test (Paternoster et al., 1998), which as recommended was on a linear scale for dichotomous outcomes (Knol et al., 2007; Rothman, 2008), then we identified which ones remained after allowing for false discovery.”

We have made the following changes to the results section:

“We found significant differences by sex in the associations of BMI with 105 health-related attributes (p-value<0.05); 46 phenotypes remained after allowing for false discovery (Table 1). Of these 46 differences most (35) were in magnitude but not direction, such as for SHBG, ischemic heart disease, heart attack, and facial aging, while 11 were directionally different.

Notably, BMI was more strongly positively associated with myocardial infarction, major coronary heart disease events, ischemic heart disease, heart attack, and facial aging in men than in women. BMI was more strongly positively associated with diastolic blood pressure, and hypothyroidism/myxoedema in women than men. BMI was more strongly inversely associated with LDL-c, hay fever and allergic rhinitis in men than women. BMI was more strongly inversely associated with SHBG in women than men.

BMI was inversely associated with ApoB, iron deficiency anemia, hernia, and total testosterone in men, while positively associated with these traits in women (Table 1). BMI was inversely associated with sensitivity/hurt feelings, and ever seeking medical advice for nerves, anxiety, tension, or depression in men. However, BMI was positively associated with sensitivity/hurt feelings and ever seeking medical advice for these same issues in women. BMI was positively associated with muscle or soft tissue injuries and haemorrhage from respiratory passages in men, whilst inversely associated with these traits in women.”

(2) It is not clear what statistical criteria were used to determine sex differences, and the strategy/presentation should be clarified. In lines 229-231, it is implied that the 'significance' in one gender, but not in the other is used to indicate a difference. However, 'comparison of p-values' is not a valid statistical approach, and a more formal test (accounting for multiple testing would be warranted). It may be that a systematic approach has been implemented, but please check that it is adequately and accurately described to the reader.

Please accept our apologies for being unclear. Multiple comparisons are for independent phenotypes however, here, some phenotypes cannot be independent, therefore, using multiple comparisons in men and women separately is quite strict. We added multiple comparisons for the assessment of sex-differences, which is now given in Table 1. Initially, there were 105 significant associations (p value for sex-difference<0.05) (Table 1), and 46 associations remained after FDR correction (Table 1).  

Furthermore, we have made additional minor changes to clarify the wording.

Knol, M. J., van der Tweel, I., Grobbee, D. E., Numans, M. F., & Geerlings, M. I. (2007). Estimating interaction on an additive scale between continuous determinants in a logistic regression model. Int J Epidemiol, 36(5), 1111-1118.

Nikiforov, S. V., & Mamaev, V. B. (1998). The development of sex differences in cardiovascular disease mortality: a historical perspective. Am J Public Health, 88(9), 1348-1353. https://doi.org/10.2105/ajph.88.9.1348

Paternoster, R., Brame, R., Mazerolle, P., & Piquero, A. (1998). Using the correct statistical test for the equality of regression coefficients. Criminology, 36(4), 859-866.

Rothman, K. (2008). Greenland S, Lash TL (ed.). Modern Epidemiology. In: Philadelphia: Lippincott Wolliams & Wilkins.

Author response:

The following is the authors’ response to the previous reviews

Public Reviews: 

Reviewer #1 (Public review): 

Summary:

The study identifies two types of activation: one that is cue-triggered and nonspecific to motion directions, and another that is specific to the exposed motion directions but occurs in a reversed manner. The finding that activity in the medial temporal lobe (MTL) preceded that in the visual cortex suggests that the visual cortex may serve as a platform for the manifestation of replay events, which potentially enhance visual sequence learning.

Evaluations:

Identifying the two types of activation after exposure to a sequence of motion directions is very interesting. The experimental design, procedures and analyses are solid. The findings are interesting and novel.

In the original submission, it was not immediately clear to me why the second type of activation was suggested to occur spontaneously. The procedural differences in the analyses that distinguished between the two types of activation need to be a little better clarified. However, this concern has been satisfactorily addressed in the revision.

We thank the reviewer for his/her positive evaluation and thoughtful comments. 

Reviewer #2 (Public review):

This paper shows and analyzes an interesting phenomenon. It shows that when people are exposed to sequences of moving dots (That is moving dots in one direction, followed by another direction etc.), that showing either the starting movement direction, or ending movement direction causes a coarsegrained brain response that is similar to that elicited by the complete sequence of 4 directions. However, they show by decoding the sensor responses that this brain activity actually does not carry information about the actual sequence and the motion directions, at least not on the time scale of the initial sequence. They also show a reverse reply on a highly-compressed time scale, which is elicited during the period of elevated activity, and activated by the first and last elements of the sequence, but not others. Additionally, these replays seem to occur during periods of cortical ripples, similar to what is found in animal studies.

These results are intriguing. They are based on MEG recordings in humans, and finding such replays in humans is novel. Also, this is based on what seems to be sophisticated statistical analysis. The statistical methodology seems valid, but due to its complexity it is not easy to understand. The methods especially those described in figures 3 and 4 should be explained better.  

We thank the reviewer’s detailed evaluation. As suggested, we have further revised the Methods and Results sections, particularly the descriptions related to Figures 3 and 4, to enhance clarity. Please see the revisions highlighted in red in the revised manuscript.

Recommendations for the authors:

Reviewer #2 (Recommendations for the authors):

The most important results here are in Figure 4, and they rely on methods explained in Figure 3. Figure 4 and the results in the figure are confusing.

What is the red bar in 4B,E. What are the units of the Y axis in figure 4B,E?

Does sequenceness have units? How do we interpret these magnitudes apart from the line of statistical significance? Shouldn't there be two lines, one for forward replay and the other for backward replay rather than a single line with positive and negative values? The term sequnceness is defined in figure 3, and is key. The replayed sequence in figure 4A,D seems to last about 120 ms.

What is the meaning of having significance only within a window of 28-36 ms?

We thank the reviewer’s careful reading and insightful comments. We apologize for the lack of clarity regarding these details in the previous version. As mentioned above, we have revised the Methods and Results sections to enhance clarity throughout the manuscript. For convenience, we provide detailed explanations addressing the specific points raised by the reviewer below.

First, the red bars in Figures 4B and 4E indicate the lags when the evidence of sequenceness surpassed the statistical significance threshold, as determined by permutation testing. We have now explicitly clarified this in the revised figure captions.

Second, sequenceness doesn’t have units. It corresponds to the regression coefficient (β) obtained from the second-level GLM in the TDLM framework. Specifically, in the first step of TDLM, we constructed an empirical transition matrix that quantifies the evidence for all possible transitions (e.g., 0° → 90°) at each time lag (Δt). In the second step, we evaluated the extent to which each model transition matrix (e.g., forward or backward transitions) predicts the empirical transition matrix at each Δt, yielding second-level β values. Sequenceness is defined as the difference between the β values for the forward and backward transition models, reflecting the relative strength and directionality of sequential replay. As it is derived from regression coefficients, sequenceness is inherently a unitless measure.

Regarding the interpretation of sequenceness magnitudes beyond statistical significance, the β values reflect the extent to which the model transition matrix explains variance in the empirical transition matrix. While larger β values suggest stronger sequenceness, absolute magnitudes are influenced by various factors, such as between-participant noise. Therefore, the key criterion for interpreting these values is whether they surpass permutationbased significance thresholds, which indicate that the observed sequenceness is unlikely to have occurred by chance.

Third, as the reviewer correctly pointed out, we initially computed two separate regression lines, one for forward replay and the other for backward replay. We then defined sequenceness as the contrast between the forward and backward replay (forward minus backward). This contrast approach is commonly used in previous studies to remove between-participant variance in the sequential replay per se, which may arise due to variability in task engagement or measurement sensitivity (Liu et al., 2021; Nour et al., 2021).

Finally, regarding the duration of replay events, the example sequences shown in Figures 4A and 4D indeed span about 120 ms in total. However, the time lag (Δt) between successive reactivation peaks within these sequences is about 30 ms. This is in line with the findings shown in Figures 4B and 4E, where statistical significance is observed at a time lag window of 28 – 36 ms on the x-axis. It is important to note that the x-axis in these plots represents the time lag (Δt) between sequential reactivations, rather than absolute time.

We hope these clarifications address the reviewer’s concerns, and we have revised the manuscript accordingly to make these points clearer to readers.

The methods here are not simple and not simple to explain. The new version is easier to understand. From the new version it seems that the methodology is sound. It should be still clarified and better explained.

We have carefully revised the manuscript to better explain the methodology. We appreciate the reviewer’s feedback, which is valuable in improving the clarity of our work.

Now that I understand what they mean by decoding probability, I think that this term is confusing or even misleading. The decoding accuracy is the probability that the direction of motion classification was correct. It seems the so-called decoding probability is value of the logistic regression after normalizing the sum to 1. If this is a standard term it can probably be kept, if not another term would be better.

Thank you for the reviewer’s comment. We agree that the term decoding probability may initially seem confusing. However, decoding probability is a commonly used term in the neural decoding literature, particularly in human studies (e.g., Liu et al., 2019; Nour et al., 2021; Turner et al., 2023). To maintain consistency with previous work, we have kept this term in the manuscript. We appreciate the opportunity to clarify this point.

References

Liu, Y., Dolan, R. J., Higgins, C., Penagos, H., Woolrich, M. W., Ólafsdóttir, H. F., Barry, C., Kurth-Nelson, Z., & Behrens, T. E. (2021). Temporally delayed linear modelling (TDLM) measures replay in both animals and humans. eLife, 10, e66917. https://doi.org/10.7554/eLife.66917

Liu, Y., Dolan, R. J., Kurth-Nelson, Z., & Behrens, T. E. J. (2019). Human Replay Spontaneously Reorganizes Experience. Cell, 178(3), 640-652.e14. https://doi.org/10.1016/j.cell.2019.06.012

Nour, M. M., Liu, Y., Arumuham, A., Kurth-Nelson, Z., & Dolan, R. J. (2021). Impaired neural replay of inferred relationships in schizophrenia. Cell, 184(16), 4315-4328.e17. https://doi.org/10.1016/j.cell.2021.06.012

Turner, W., Blom, T., & Hogendoorn, H. (2023). Visual Information Is Predictively Encoded in Occipital Alpha/Low-Beta Oscillations. Journal of Neuroscience, 43(30), 5537–5545. https://doi.org/10.1523/JNEUROSCI.0135-23.2023

Author response:

We thank the editors and the reviewers for their valuable comments and for taking the time to evaluate our manuscript.

Answers to Reviewer 1:

(1) The core contribution of our method is that it learns meaningful spatiotemporal embeddings directly from image data without requiring pose estimation or eigenworm-based features as input. The learned embedding space can serve as a foundation for downstream tasks such as behavioral classification, clustering, or anomaly detection, further supporting its utility beyond visualization through eigenworm-derived features. Here we use the Tierpsy-derived features for latent space interpretation and for validation that our approach does indeed encode meaningful postural information. Additionally, without any Tierpsy-calculated features users can still color embeddings by known metadata like mutation or age and compare different strains to each other. 

(2) The numbers shown in Fig. 2.3 are illustrative placeholders intended to conceptually represent a vector of behavioral features. They do not correspond to any specific measurements or carry intrinsic meaning. We agree that this may lead to confusion, and we will clarify this in the revised manuscript.

(3) The visualizations in Figs. 4 (b) and (c) show the embeddings of sequences of behavior, rather than individual poses. Therefore, motion-related features such as speed are related to temporal patterns in those sequences rather than static postures. The color overlays reflect average motion characteristics (e.g., speed) of short behavior clips projected into the embedding space, rather than being directly linked to any single frame or pose.

Answers to Reviewer 2:

(1) In the abstract, our use of the term "unbiased" refers specifically to the avoidance of human-generated bias through feature engineering—i.e., the model does not rely on handcrafted features or predefined pose representations – the representations are based on data only. However, we agree that the model is still subject to dataset biases and will rectify this in the revised manuscript.

(2) The worm images are rotated to a common vertical orientation to remove orientation as a source of variability in the input. This ensures that the model focuses on learning pose and behavioral dynamics rather than arbitrary head-tail or angular positioning. While data augmentation could in theory account for this variability, we found in our preliminary experiments that applying this preprocessing step led to more stable and interpretable embeddings.

(3) We agree that simplifying the technical explanations would enhance the manuscript’s accessibility. In the revised version, we will briefly introduce contrastive learning in a less technical language.

(4) The gray points in Fig. 3a represent frames that Tierpsy could not resolve, primarily due to coiled, self-intersecting, or overlapping worm postures as Tierpsy uses skeletonization to estimate the centerline. This approach can fail if kind of challenging elements are part of the image.

(5) We appreciate this suggestion and consider it for a revised version of the manuscript.

(6) Although it may seem intuitive for highly bent (red) poses to lie near coiled (gray) ones in the embedding space, the clustering pattern observed reflects how the network organizes pose information. The red/orange cluster consists of distinguishable bent poses that are visually distinct and consistently separable from other postures. In contrast, the greenish and blueish poses are less strongly bent and may share more visual overlap with the unresolved (gray) images.

(7) The overlap occurs because some highly bent or coiled worms can still be (partially) resolved by Tierpsy, depending on specific pose conditions (e.g., head and tail not touching, not self-overlapping). However, Tierpsy fails to consistently resolve such frames. We will describe these cases in more detail in the revised manuscript.

(8) Thank you, we agree this claim needs to be better supported and will develop it in the revision.

(9) To support this statement we mainly visualized the respective sequences embedded in this area of the embedding space and found that it mostly consists of common behaviors such as forward locomotion. 

(10) We agree that interpretability is important and plan to include additional figures quantifications of the embedding space using more basic Tierpsy features.

(11) Fig. 5a is indeed based solely on N2 animals. In the revised manuscript we will include quantitative measures of behavioral variability and its change with age.

(12) We appreciate this suggestion and consider it for a revised version

(13) We agree this would be a valuable analysis. However, our current dataset primarily includes aging data for N2 animals. We acknowledge this limitation and consider adding more strains for future work.

(14) We will include links to our source code in the revised manuscript

Answers to Reviewer 3:

(1-2) Our current method is agnostic to head-tail orientation, which indeed restricts the ability to distinguish behaviors that rely on directional cues. We made this design choice as we believe that correctly identifying head/tail orientation can be a challenging task that may introduce additional biases or fail in difficult imaging conditions. However, we fully agree that integrating directional information would improve behavioral resolution, and this is a natural extension of our current framework. In future work, we aim to incorporate head-tail disambiguation.

(3) We explicitly designed our preprocessing and training pipeline to encourage size invariance, for example by resizing individuals to a consistent scale, as the focus of our work is to encode posture and movement only. However, we acknowledge that absolute size information is lost in this process, which can be informative for distinguishing genotypes or age-related changes.

(4) We agree that a direct quantitative comparison between our embedding-based representations and skeleton-based feature sets would strengthen the paper. Our current focus was to assess whether meaningful behavioral features could be learned from a skeleton-free representation.

Author response:

Reviewer 1:

(1) In general, the representation of target and distractor processing is a bit of a reach. Target processing is represented by SSVEP amplitude, which is most likely going to be related to the contrast of the dots, as opposed to representing coherent motion energy, which is the actual target. These may well be linked (e.g., greater attention to the coherent motion task might increase SSVEP amplitude), but I would call it a limitation of the interpretation. Decoding accuracy of emotional content makes sense as a measure of distractor processing, and the supplementary analysis comparing target SSVEP amplitude to distractor decoding accuracy is duly noted.

We agree with the reviewer. This is certainly a limitation and will be acknowledged as such in the revised manuscript.

(2) Comparing SSVEP amplitude to emotional category decoding accuracy feels a bit like comparing apples with oranges. They have different units and scales and probably reflect different neural processes. Is the result the authors find not a little surprising in this context? This relationship does predict performance and is thus intriguing, but I think this methodological aspect needs to be discussed further. For example, is the phase relationship with behaviour a result of a complex interaction between different levels of processing (fundamental contrast vs higher order emotional processing)?

Traditionally, the SSVEP amplitude at the distractor frequency is used to quantify distractor processing. Given that the target SSVEP amplitude is stronger than that for the distractor, it is possible that the distractor SSVEP amplitude is contaminated by the target SSVEP amplitude due to spectral power leakage; see Figure S4 for a demonstration of this. Because of this issue we therefore introduce the use of decoding accuracy as an index of distractor processing. This has not been done in the SSVEP literature. The lack of correlation between the distractor SSVEP amplitude and the distractor decoding accuracy, although it is kind of like comparing apples with oranges as pointed out by the reviewer, serves the purpose of showing that these two measures are not co-varying, and the use of decoding accuracy is free from the influence of the distractor SSVEP amplitude and thereby free from the influence by the target SSVEP amplitude. This is an important point. We will provide a more thorough discussion of this point in the revised manuscript. 

Reviewer 2:

(1) Incomplete Evidence for Rhythmicity at 1 Hz: The central claim of 1 Hz rhythmic sampling is insufficiently validated. The windowing procedure (0.5s windows with 0.25s step) inherently restricts frequency resolution, potentially biasing toward low-frequency components like 1 Hz. Testing different window durations or providing controls would significantly strengthen this claim.

This is an important point. We plan to follow the reviewer’s suggestion and repeat our analysis using different window sizes to test the robustness of the observed 1Hz rhythmicity. In addition, we plan to also apply the Hilbert transform to extract time-point-by-time-point amplitude envelopes, which will provide a window-free estimation of the distractor strength and further validate the presence of the low-frequency 1Hz dynamics.

(2) No-Distractor Control Condition: The study lacks a baseline or control condition without distractors. This makes it difficult to determine whether the distractor-related decoding signals or the 1 Hz effect reflect genuine distractor processing or more general task dynamics.

We agree with the reviewer. This is certainly a limitation and will be acknowledged as such in the revised manuscript.

(3) Decoding Near Chance Levels: The pairwise decoding accuracies for distractor categories hover close to chance (~55%), raising concerns about robustness. While statistically above chance, the small effect sizes need careful interpretation, particularly when linked to behavior.

This is a good point. In addition to acknowledging this in the revised manuscript, we will carry out two additional analyses to test this issue further. First, we will implement a random permutation procedure, in which the trial labels are randomly shuffled and the null-hypothesis distribution for decoding accuracy is built, and compare the decoding accuracy from the actual data to this distribution. Second, we will perform a temporal generalization analysis to examine whether the neural representations of the distractor drift over the course of an entire trial, which is 11 seconds long. Recent studies suggest that even when the stimulus stays the same, their neural representations may drift over time.

(4) No Clear Correlation Between SSVEP and Behavior: Neither target nor distractor signal strength (SSVEP amplitude) correlates with behavioral accuracy. The study instead relies heavily on relative phase, which - while interesting - may benefit from additional converging evidence.

We felt that what the reviewer pointed out is actually the main point of our study, namely, it is not the overall target or distractor strength that matters for behavior, it is their temporal relationship that matters for behavior. This reveals a novel neuroscience principle that has not been reported in the past. We will stress this point further in the revised manuscript.

(5) Phase-analysis: phase analysis is performed between different types of signals hindering their interpretability (time-resolved SSVEP amplitude and time-resolved decoding accuracy).

The time-resolved SSVEP amplitude is used to index the temporal dynamics of target processing whereas the time-resolved decoding accuracy is used to index the temporal dynamics of distractor processing. As such, they can be compared, using relative phase for example, to examine how temporal relations between the two types of processes impact behavior. This said, we do recognize the reviewer’s concern that these two processes are indexed by two different types of signals. We plan to normalize each time course, make them dimensionless, and then compute the temporal relations between them.   

Appraisal of Aims and Conclusions:

The authors largely achieved their stated goal of assessing rhythmic sampling of distractors. However, the conclusions drawn - particularly regarding the presence of 1 Hz rhythmicity - rest on analytical choices that should be scrutinized further. While the observed phase-performance relationship is interesting and potentially impactful, the lack of stronger and convergent evidence on the frequency component itself reduces confidence in the broader conclusions.

Impact and Utility to the Field:

If validated, the findings will advance our understanding of attentional dynamics and competition in complex visual environments. Demonstrating that ignored distractors can be rhythmically sampled at similar frequencies to targets has implications for models of attention and cognitive control. However, the methodological limitations currently constrain the paper's impact.

Thanks for these comments and positive assessment of our work’s potential implications and impact. We will try our best in the revision process to address the concerns.

Additional Context and Considerations:

(1) The use of EEG-fMRI is mentioned but not leveraged. If BOLD data were collected, even exploratory fMRI analyses (e.g., distractor modulation in visual cortex) could provide valuable converging evidence.

Indeed, leveraging fMRI data in EEG studies would be very beneficial, as having been demonstrated in our previous work. However, given that this study concerns the temporal relationship between target and distractor processing, it is felt that fMRI, given its well-known limitation in temporal resolution, has limited potential to contribute. We will be exploring this rich dataset in other ways where the two modalities are integrated to gain more insights not possible with either modality used alone.

(2) In turn, removal of fMRI artifacts might introduce biases or alter the data. For instance, the authors might consider investigating potential fMRI artifact harmonics around 1 Hz to address concerns regarding induced spectral components.

We have done extensive work in the area of simultaneous EEG-fMRI and have not encountered artifacts with a 1Hz rhythmicity. Also, the fact that the temporal relations between target processing and distractor processing at 1Hz predict behavior is another indication that the 1Hz rhythmicity is a neuroscientific effect not an artifact. However, we will be looking into this carefully and address this in the revision process.

Author response:

The following is the authors’ response to the original reviews

Public Reviews:

Reviewer #1 (Public Review):

Summary:

This computational modeling study builds on multiple previous lines of experimental and theoretical research to investigate how a single neuron can solve a nonlinear pattern classification task. The authors construct a detailed biophysical and morphological model of a single striatal medium spiny neuron, and endow excitatory and inhibitory synapses with dynamic synaptic plasticity mechanisms that are sensitive to (1) the presence or absence of a dopamine reward signal, and (2) spatiotemporal coincidence of synaptic activity in single dendritic branches. The latter coincidence is detected by voltage-dependent NMDA-type glutamate receptors, which can generate a type of dendritic spike referred to as a "plateau potential." The proposed mechanisms result in moderate performance on a nonlinear classification task when specific input features are segregated and clustered onto individual branches, but reduced performance when input features are randomly distributed across branches. Given the high level of complexity of all components of the model, it is not clear which features of which components are most important for its performance. There is also room for improvement in the narrative structure of the manuscript and the organization of concepts and data.

Strengths:

The integrative aspect of this study is its major strength. It is challenging to relate low-level details such as electrical spine compartmentalization, extrasynaptic neurotransmitter concentrations, dendritic nonlinearities, spatial clustering of correlated inputs, and plasticity of excitatory and inhibitory synapses to high-level computations such as nonlinear feature classification. Due to high simulation costs, it is rare to see highly biophysical and morphological models used for learning studies that require repeated stimulus presentations over the course of a training procedure. The study aspires to prove the principle that experimentally-supported biological mechanisms can explain complex learning.

Weaknesses:

The high level of complexity of each component of the model makes it difficult to gain an intuition for which aspects of the model are essential for its performance, or responsible for its poor performance under certain conditions. Stripping down some of the biophysical detail and comparing it to a simpler model may help better understand each component in isolation. That said, the fundamental concepts behind nonlinear feature binding in neurons with compartmentalized dendrites have been explored in previous work, so it is not clear how this study represents a significant conceptual advance. Finally, the presentation of the model, the motivation and justification of each design choice, and the interpretation of each result could be restructured for clarity to be better received by a wider audience.

Thank you for the feedback! We agree that the complexity of our model can make it challenging to intuitively understand the underlying mechanisms. To address this, we have revised the manuscript to include additional simulations and clearer explanations of the mechanisms at play.

In the revised introduction, we now explicitly state our primary aim: to assess to what extent a biophysically detailed neuron model can support the theory proposed by Tran-Van-Minh et al. and explore whether such computations can be learned by a single neuron, specifically a projection neuron in the striatum. To achieve this, we focus on several key mechanisms:

(1) A local learning rule: We develop a learning rule driven by local calcium dynamics in the synapse and by reward signals from the neuromodulator dopamine. This plasticity rule is based on the known synaptic machinery for triggering LTP or LTD in the corticostriatal synapse onto dSPNs (Shen et al., 2008). Importantly, the rule does not rely on supervised learning paradigms and neither is a separate training and testing phase needed.

(2) Robust dendritic nonlinearities: According to Tran-Van-Minh et al., (2015) sufficient supralinear integration is needed to ensure that e.g. two inputs (i.e. one feature combination in the NFBP, Figure 1A) on the same dendrite generate greater somatic depolarization than if those inputs were distributed across different dendrites. To accomplish this we generate sufficiently robust dendritic plateau potentials using the approach in Trpevski et al., (2023). 

(3) Metaplasticity: Although not discussed much in more theoretical work, our study demonstrates the necessity of metaplasticity for achieving stable and physiologically realistic synaptic weights. This mechanism ensures that synaptic strengths remain within biologically plausible ranges during training, regardless of initial synaptic weights.

We have also clarified our design choices and the rationale behind them, as well as restructured the interpretation of our results for greater accessibility. We hope these revisions make our approach and findings more transparent and easier to engage with for a broader audience.

Recommendations for the authors:

Reviewer #1 (Recommendations For The Authors):

This study extends three previous lines of work:  

(1) Prior computational/phenomenological work has shown that the presence of dendritic nonlinearities can enable single neurons to perform linearly non-separable tasks like XOR and feature binding (e.g. Tran-Van-Minh et al., Front. Cell. Neurosci., 2015).

Prior computational and phenomenological work, such as Tran-Van-Minh et al. (Front. Cell. Neurosci., 2015), directly inspired our study, as we now explicitly state in the introduction (page 4, lines 19-22). While Tran-Van-Minh theoretically demonstrated that these principles could solve the NFBP, it remains untested to what extent this can be achieved quantitatively in biophysically detailed neuron models using biologically plausible learning rules - which is what we test here.

(2) This study and a previous biophysical modeling study (Trpevski et al., Front. Cell. Neurosci., 2023) rely heavily on the finding from Chalifoux & Carter, J. Neurosci., 2011 that blocking glutamate transporters with TBOA increases dendritic calcium signals. The proposed model thus depends on a specific biophysical mechanism for dendritic plateau potential generation, where spatiotemporally clustered inputs must be co-activated on a single branch, and the voltage compartmentalization of the branch and the voltage-dependence of NMDARs is not enough, but additionally glutamate spillover from neighboring synapses must activate extrasynaptic NMDARs. If this specific biophysical implementation of dendritic plateau potentials is essential to the findings in this study, the authors have not made that connection clear. If it is a simple threshold nonlinearity in dendrites that is important for the model, and not the specific underlying biophysical mechanisms, then the study does not appear to provide a conceptual advance over previous studies demonstrating nonlinear feature binding with simpler implementations of dendritic nonlinearities.

We appreciate the feedback on the hypothesized role of glutamate spillover in our model. While the current manuscript and Trpevski et al. (2023) emphasize glutamate spillover as a plausible biophysical mechanism to provide sufficiently robust and supralinear plateau potentials, we acknowledge, however, that the mechanisms of supralinearity of dendritic integration, might not depend solely on this specific mechanism in other types of neurons. In Trpevski et al (2023) we, however, realized that if we allow too ‘graded’ dendritic plateaus, using the quite shallow Mg-block reported in experiments, it was difficult to solve the NFBP. The conceptual advance of our study lies in demonstrating that sufficiently nonlinear dendritic integration is needed and that this can be accounted for by assuming spillover in SPNs—but regardless of its biophysical source (e.g. NMDA spillover, steeper NMDA Mg block activation curves or other voltage dependent conductances that cause supralinear dendritic integration)—it enables biophysically detailed neurons to solve the nonlinear feature binding problem. To address this point and clarify the generality of our conclusions, we have revised the relevant sections in the manuscript to state this explicitly.

(3) Prior work has utilized "sliding-threshold," BCM-like plasticity rules to achieve neuronal selectivity and stability in synaptic weights. Other work has shown coordinated excitatory and inhibitory plasticity. The current manuscript combines "metaplasticity" at excitatory synapses with suppression of inhibitory strength onto strongly activated branches. This resembles the lateral inhibition scheme proposed by Olshausen (Christopher J. Rozell, Don H. Johnson, Richard G. Baraniuk, Bruno A. Olshausen; Sparse Coding via Thresholding and Local Competition in Neural Circuits. Neural Comput 2008; 20 (10): 2526-2563. doi: https://doi.org/10.1162/neco.2008.03-07-486). However, the complexity of the biophysical model makes it difficult to evaluate the relative importance of the additional complexity of the learning scheme.

We initially tried solving the NFBP with only excitatory plasticity, which worked reasonably well, especially if we assume a small population of neurons collaborates under physiological conditions. However, we observed that plateau potentials from distally located inputs were less effective, and we now explain this limitation in the revised manuscript (page 14, lines 23-37).

To address this, we added inhibitory plasticity inspired by mechanisms discussed in Castillo et al. (2011) , Ravasenga et al., and Chapman et al. (2022) , as now explicitly stated in the text (page 32, lines 23-26). While our GABA plasticity rule is speculative, it demonstrates that distal GABAergic plasticity can enhance nonlinear computations. These results are particularly encouraging, as it shows that implementing these mechanisms at the single-neuron level produces behavior consistent with network-level models like BCM-like plasticity rules and those proposed by Rozell et al. We hope this will inspire further experimental work on inhibitory plasticity mechanisms.

P2, paragraph 2: Grammar: "multiple dendritic regions, preferentially responsive to different input values or features, are known to form with close dendritic proximity." The meaning is not clear. "Dendritic regions" do not "form with close dendritic proximity."

Rewritten (current page 2, line 35)

P5, paragraph 3: Grammar: I think you mean "strengthened synapses" not "synapses strengthened".

Rewritten (current page 14, line 36)

P8, paragraph 1: Grammar: "equally often" not "equally much".

Updated (current page 10, line 2)

P8, paragraph 2: "This is because of the learning rule that successively slides the LTP NMDA Ca-dependent plasticity kernel over training." It is not clear what is meant by "sliding," either here or in the Methods. Please clarify.

We have updated the text and removed the word “sliding” throughout the manuscript to clarify that the calcium dependence of the kernels are in fact updated

P10, Figure 3C (left): After reading the accompanying text on P8, para 2, I am left not understanding what makes the difference between the two groups of synapses that both encode "yellow," on the same dendritic branch (d1) (so both see the same plateau potentials and dopamine) but one potentiates and one depresses. Please clarify.

Some "yellow" and "banana" synapses are initialized with weak conductances, limiting their ability to learn due to the relatively slow dynamics of the LTP kernel. These weak synapses fail to reach the calcium thresholds necessary for potentiation during a dopamine peak, yet they remain susceptible to depression under LTD conditions. Initially, the dynamics of the LTP kernel does not allow significant potentiation, even in the presence of appropriate signals such as plateau potentials and dopamine (page 10, lines 22–26). We have added a more detailed explanation of how the learning rule operates in the section “Characterization of the Synaptic Plasticity Rule” on page 9 and have clarified the specific reason why the weaker yellow synapses undergo LTD (page 11, lines 1–7).

As shown in Supplementary Figure 6, during subthreshold learning, the initial conductance is also low, which similarly hinders the synapses' ability to potentiate. However, with sufficient dopamine, the LTP kernel adapts by shifting closer to the observed calcium levels, allowing these synapses to eventually strengthen. This dynamic highlights how the model enables initially weak synapses to "catch up" under consistent activation and favorable dopaminergic conditions.

P9, paragraph 1: The phrase "the metaplasticity kernel" is introduced here without prior explanation or motivation for including this level of complexity in the model. Please set it up before you use it.

A sentence introducing metaplasticity has been added to the introduction (page 3, lines 36-42) as well as on page 9, where the kernel is introduced (page 9, lines 26-35)

P10, Figure 3D: "kernel midline" is not explained.

We have replotted fig 3 to make it easier to understand what is shown. Also, an explanation of the Kernel midpoint is added to the legend (current page 12, line 19)

P11, paragraph 1; P13, Fig. 4C: My interpretation of these data is that clustered connectivity with specific branches is essential for the performance of the model. Randomly distributing input features onto branches (allowing all 4 features to innervate single branches) results in poor performance. This is bad, right? The model can't learn unless a specific pre-wiring is assumed. There is not much interpretation provided at this stage of the manuscript, just a flat description of the result. Tell the reader what you think the implications of this are here.

Thanks for the suggestion - we have updated this section of the manuscript, adding an interpretation of the results that the model often fails to learn both relevant stimuli if all four features are clustered onto the same dendrite (page 13, lines 31-42). 

In summary, when multiple feature combinations are encoded in the same dendrite with similar conductances, the ability to determine which combination to store depends on the dynamics of the other dendrite. Small variations in conductance, training order, or other stochastic factors can influence the outcome. This challenge, known as the symmetry-breaking problem, has been previously acknowledged in abstract neuron models (Legenstein and Maass, 2011). To address this, additional mechanisms such as branch plasticity—amplifying or attenuating the plateau potential as it propagates from the dendrite to the soma—can be employed (Legenstein and Maass, 2011). 

P12, paragraph 2; P13, Figure 4E: This result seems suboptimal, that only synapses at a very specific distance from the soma can be used to effectively learn to solve a NFBP. It is not clear to what extent details of the biophysical and morphological model are contributing to this narrow distance-dependence, or whether it matches physiological data.

We have added Figure 5—figure supplement 1A to clarify why distal synapses may not optimally contribute to learning. This figure illustrates how inhibitory plasticity improves performance by reducing excessive LTD at distal dendrites, thereby enhancing stimulus discrimination. Relevant explanations have been integrated into Page 18, Lines 25-39 in the revised manuscript.

P14, paragraph 2: Now the authors are assuming that inhibitory synapses are highly tuned to stimulus features. The tuning of inhibitory cells in the hippocampus and cortex is controversial but seems generally weaker than excitatory cells, commensurate with their reduced number relative to excitatory cells. The model has accumulated a lot of assumptions at this point, many without strong experimental support, which again might make more sense when proposing a new theory, but this stitching together of complex mechanisms does not provide a strong intuition for whether the scheme is either biologically plausible or performant for a general class of problem.

We acknowledge that it is not currently known whether inhibitory synapses in the striatum are tuned to stimulus features. However, given that the striatum is a purely inhibitory structure, it is plausible that lateral inhibition from other projection neurons could be tuned to features, even if feedforward inhibition from interneurons is not. Therefore, we believe this assumption is reasonable in the context of our model. As noted earlier, the GABA plasticity rule in our study is speculative. However, we hope that our work will encourage further experimental investigations, as we demonstrate that if GABAergic inputs are sufficiently specific, they can significantly enhance computations (This is discussed on page 17, lines 8-15.).

P16, Figure 5E legend: The explanation of the meaning of T_max and T_min in the legend and text needs clarification.

The abbreviations  T_min and  T_max have been updated to CTL and CTH to better reflect their role in calcium threshold tracking. The Figure 5E legend and relevant text have been revised for clarity. Additionally, the Methods section has been reorganized for better readability.

P16, Figure 5B, C: When the reader reaches this paper, the conundrums presented in Figure 4 are resolved. The "winner-takes-all" inhibitory plasticity both increases the performance when all features are presented to a single branch and increases the range of somatodendritic distances where synapses can effectively be used for stimulus discrimination. The problem, then, is in the narrative. A lot more setup needs to be provided for the question related to whether or not dendritic nonlinearity and synaptic inhibition can be used to perform the NFBP. The authors may consider consolidating the results of Fig. 4 and 5 so that the comparison is made directly, rather than presenting them serially without much foreshadowing.

In order to facilitate readability, we have updated the following sections of the manuscript to clarify how inhibitory plasticity resolves challenges from Figure 4:

Figure 5B and Figure 5–figure supplement 1B: Two new panels illustrate the role of inhibitory plasticity in addressing symmetry problems.

Figure 5–figure supplement 1A: Shows how inhibitory plasticity extends the effective range of somatodendritic distances.

P18, Figure 6: This should be the most important figure, finally tying in all the previous complexity to show that NFBP can be partially solved with E and I plasticity even when features are distributed randomly across branches without clustering. However, now bringing in the comparison across spillover models is distracting and not necessary. Just show us the same plateau generation model used throughout the paper, with and without inhibition.

Figure updated. Accumulative spillover and no-spillover conditions have been removed.

P18, paragraph 2: "In Fig. 6C, we report that a subset of neurons (5 out of 31) successfully solved the NFBP." This study could be significantly strengthened if this phenomenon could (perhaps in parallel) be shown to occur in a simpler model with a simpler plateau generation mechanism. Furthermore, it could be significantly strengthened if the authors could show that, even if features are randomly distributed at initialization, a pruning mechanism could gradually transition the neuron into the state where fewer features are present on each branch, and the performance could approach the results presented in Figure 5 through dynamic connectivity.

To model structural plasticity is a good suggestion that should be investigated in later work, however, we feel that it goes beyond what we can do in the current manuscript.  We now acknowledge that structural plasticity might play a role. For example we show that if we can assume ‘branch-specific’ spillover, that leads to sufficiently development of local dendritic non-linearities, also one can learn with distributed inputs. In reality, structural plasticity is likely important here, as we now state (current page 22, line 35-42). 

P17, paragraph 2: "As shown in Fig. 6B, adding the hypothetical nonlinearities to the model increases the performance towards solving part of the NFBP, i.e. learning to respond to one relevant feature combination only. The performance increases with the amount of nonlinearity." This is not shown in Figure 6B.

Sentence removed. We have added a Figure 6 - figure supplement 1 to better explain the limitations.

P22, paragraph 1: The "w" parameter here is used to determine whether spatially localized synapses are co-active enough to generate a plateau potential. However, this is the same w learned through synaptic plasticity. Typically LTP and LTD are thought of as changing the number of postsynaptic AMPARs. Does this "w" also change the AMPAR weight in the model? Do the authors envision this as a presynaptic release probability quantity? If so, please state that and provide experimental justification. If not, please justify modifying the activation of postsynaptic NMDARs through plasticity.

This is an important remark. Our plasticity model differs from classical LTP models as it depends on the link between LTP and increased spillover as described by Henneberger et al., (2020).

We have updated the method section (page 27, lines 6-11), and we acknowledge, however, that in a real cell, learning might first strengthen the AMPA component, but after learning the ratio of NMDA/AMPA is unchanged ( Watt et al., 2004). This re-balancing between NMDA and AMPA might perhaps be a slower process.

Reviewer #2 (Public Review):

Summary:

The study explores how single striatal projection neurons (SPNs) utilize dendritic nonlinearities to solve complex integration tasks. It introduces a calcium-based synaptic learning rule that incorporates local calcium dynamics and dopaminergic signals, along with metaplasticity to ensure stability for synaptic weights. Results show SPNs can solve the nonlinear feature binding problem and enhance computational efficiency through inhibitory plasticity in dendrites, emphasizing the significant computational potential of individual neurons. In summary, the study provides a more biologically plausible solution to single-neuron learning and gives further mechanical insights into complex computations at the single-neuron level.

Strengths:

The paper introduces a novel learning rule for training a single multicompartmental neuron model to perform nonlinear feature binding tasks (NFBP), highlighting two main strengths: the learning rule is local, calcium-based, and requires only sparse reward signals, making it highly biologically plausible, and it applies to detailed neuron models that effectively preserve dendritic nonlinearities, contrasting with many previous studies that use simplified models.

Weaknesses:

I am concerned that the manuscript was submitted too hastily, as evidenced by the quality and logic of the writing and the presentation of the figures. These issues may compromise the integrity of the work. I would recommend a substantial revision of the manuscript to improve the clarity of the writing, incorporate more experiments, and better define the goals of the study.

Thanks for the valuable feedback. We have now gone through the whole manuscript updating the text, and also improved figures and added some supplementary figures to better explain model mechanisms. In particular, we state more clearly our goal already in the introduction.

Major Points:

(1) Quality of Scientific Writing: The current draft does not meet the expected standards. Key issues include:

i. Mathematical and Implementation Details: The manuscript lacks comprehensive mathematical descriptions and implementation details for the plasticity models (LTP/LTD/Meta) and the SPN model. Given the complexity of the biophysically detailed multicompartment model and the associated learning rules, the inclusion of only nine abstract equations (Eq. 1-9) in the Methods section is insufficient. I was surprised to find no supplementary material providing these crucial details. What parameters were used for the SPN model? What are the mathematical specifics for the extra-synaptic NMDA receptors utilized in this study? For instance, Eq. 3 references [Ca2+]-does this refer to calcium ions influenced by extra-synaptic NMDARs, or does it apply to other standard NMDARs? I also suggest the authors provide pseudocodes for the entire learning process to further clarify the learning rules.

The model is quite detailed but builds on previous work. For this reason, for model components used in earlier published work (and where models are already available via model repositories, such as ModelDB), we refer the reader to these resources in order to improve readability and to highlight what is novel in this paper - the learning rules itself. The learning rule is now explained in detail. For modelers that want to run the model, we have also provided a GitHub link to the simulation code. We hope this is a reasonable compromise to all readers, i.e, those that only want to understand what is new here (learning rule) and those that also want to test the model code. We explain this to the readers at the beginning of the Methods section.

ii. Figure quality. The authors seem not to carefully typeset the images, resulting in overcrowding and varying font sizes in the figures. Some of the fonts are too small and hard to read. The text in many of the diagrams is confusing. For example, in Panel A of Figure 3, two flattened images are combined, leading to small, distorted font sizes. In Panels C and D of Figure 7, the inconsistent use of terminology such as "kernels" further complicates the clarity of the presentation. I recommend that the authors thoroughly review all figures and accompanying text to ensure they meet the expected standards of clarity and quality.

Thanks for directing our attention to these oversights. We have gone through the entire manuscript, updating the figures where needed, and we are making sure that the text and the figure descriptions are clear and adequate and use consistent terminology for all quantities.

iii. Writing clarity. The manuscript often includes excessive and irrelevant details, particularly in the mathematical discussions. On page 24, within the "Metaplasticity" section, the authors introduce the biological background to support the proposed metaplasticity equation (Eq. 5). However, much of this biological detail is hypothesized rather than experimentally verified. For instance, the claim that "a pause in dopamine triggers a shift towards higher calcium concentrations while a peak in dopamine pushes the LTP kernel in the opposite direction" lacks cited experimental evidence. If evidence exists, it should be clearly referenced; otherwise, these assertions should be presented as theoretical hypotheses. Generally, Eq. 5 and related discussions should be described more concisely, with only a loose connection to dopamine effects until more experimental findings are available.

The “Metaplasticity” section (pages 30-32) has been updated to be more concise, and the abundant references to dopamine have been removed.

(2) Goals of the Study: The authors need to clearly define the primary objective of their research. Is it to showcase the computational advantages of the local learning rule, or to elucidate biological functions?

We have explicitly stated our goal in the introduction (page 4, lines 19-22). Please also see the response to reviewer 1.

i. Computational Advantage: If the intent is to demonstrate computational advantages, the current experimental results appear inadequate. The learning rule introduced in this work can only solve for four features, whereas previous research (e.g., Bicknell and Hausser, 2021) has shown capability with over 100 features. It is crucial for the authors to extend their demonstrations to prove that their learning rule can handle more than just three features. Furthermore, the requirement to fine-tune the midpoint of the synapse function indicates that the rule modifies the "activation function" of the synapses, as opposed to merely adjusting synaptic weights. In machine learning, modifying weights directly is typically more efficient than altering activation functions during learning tasks. This might account for why the current learning rule is restricted to a limited number of tasks. The authors should critically evaluate whether the proposed local learning rule, including meta-plasticity, actually offers any computational advantage. This evaluation is essential to understand the practical implications and effectiveness of the proposed learning rule.

Thank you for your feedback. To address the concern regarding feature complexity, we extended our simulations to include learning with 9 and 25 features, achieving accuracies of 80% and 75%, respectively (Figure 6—figure supplement 1A). While our results demonstrate effective performance, the absence of external stabilizers—such as error-modulated functions used in prior studies like Bicknell and Hausser (2021)—means that the model's performance can be more sensitive to occasional incorrect outcomes. For instance, while accuracy might reach 90%, a few errors can significantly affect overall performance due to the lack of mechanisms to stabilize learning.

In order to clarify the setup of the rule, we have added pseudocode in the revised manuscript (Pages 31-32) detailing how the learning rule and metaplasticity update synaptic weights based on calcium and dopamine signals. Additionally, we have included pseudocode for the inhibitory learning rule on Pages 34-35. In future work, we also aim to incorporate biologically plausible mechanisms, such as dopamine desensitization, to enhance stability.

ii. Biological Significance: If the goal is to interpret biological functions, the authors should dig deeper into the model behaviors to uncover their biological significance. This exploration should aim to link the observed computational features of the model more directly with biological mechanisms and outcomes.

As now clearly stated in the introduction, the goal of the study is to see whether and to what quantitative extent the theoretical solution of the NFBP proposed in Tran-Van-Minh et al. (2015) can be achieved with biophysically detailed neuron models and with a biologically inspired learning rule. The problem has so far been solved with abstract and phenomenological neuron models (Schiess et al., 2014; Legenstein and Maass, 2011) and also with a detailed neuron model but with a precalculated voltage-dependent learning rule (Bicknell and Häusser, 2021).

We have also tried to better explain the model mechanisms by adding supplementary figures.

Reviewer #2 (Recommendations For The Authors):

Minor:

(1) The [Ca]NMDA in Figure 2A and 2C can have large values even when very few synapses are activated. Why is that? Is this setting biologically realistic?

The elevated [Ca²⁺]NMDA with minimal synaptic activation arises from high spine input resistance, small spine volume, and NMDA receptor conductance, which scales calcium influx with synaptic strength. Physiological studies report spine calcium transients typically up to ~1 μM (Franks and Sejnowski 2002, DOI: 10.1002/bies.10193), while our model shows ~7 μM for 0.625 nS and around ~3 μM for 0.5 nS, exceeding this range. The calcium levels of the model might therefore be somewhat high compared to biologically measured levels - however, this does not impact the learning rule, as the functional dynamics of the rule remain robust across calcium variations.

(2) In the distributed synapses session, the study introduces two new mechanisms "Threshold spillover" and "Accumulative spillover". Both mechanisms are not basic concepts but quantitative descriptions of them are missing.

Thank you for your feedback. Based on the recommendations from Reviewer 1, we have simplified the paper by removing the "Accumulative spillover" and focusing solely on the "Thresholded spillover" mechanism. In the updated version of the paper, we refer to it only as glutamate spillover. However, we acknowledge (page 22, lines 40-42) that to create sufficient non-linearities, other mechanisms, like structural plasticity, might also be involved (although testing this in the model will have to be postponed to future work).

(3) The learning rule achieves moderate performance when feature-relevant synapses are organized in pre-designed clusters, but for more general distributed synaptic inputs, the model fails to faithfully solve the simple task (with its performance of ~ 75%). Performance results indicate the learning rule proposed, despite its delicate design, is still inefficient when the spatial distribution of synapses grows complex, which is often the case on biological neurons. Moreover, this inefficiency is not carefully analyzed in this paper (e.g. why the performance drops significantly and the possible computation mechanism underlying it).

The drop in performance when using distributed inputs (to a mean performance of 80%) is similar to the mean performance in the same situation in Bicknell and Hausser (2021), see their Fig. 3C. The drop in performance is due to that: i) the relevant feature combinations are not often colocalized on the same dendrite so that they can be strengthened together, and ii) even if they are, there may not be enough synapses to trigger the supralinear response from the branch spillover mechanism, i.e. the inputs are not summated in a supralinear way (Fig. 6B, most input configurations only reach 75%).

Because of this, at most one relevant feature combination can be learned. In the several cases when the random distribution of synapses is favorable for both relevant feature combinations to be learned, the NFBP is solved (Figs. 6B, some performance lines reach 100 % and 6C, example of such a case). We have extended the relevant sections of the paper trying to highlight the above mentioned mechanisms.

Further, the theoretical results in Tran-Van-Minh et al. 2015 already show that to solve the NFBP with supralinear dendrites requires features to be pre-clustered in order to evoke the supralinear dendritic response, which would activate the soma. The same number of synapses distributed across the dendrites i) would not excite the soma as strongly, and ii) would summate in the soma as in a point neuron, i.e. no supralinear events can be activated, which are necessary to solve the NFBP. Hence, one doesn’t expect distributed synaptic inputs to solve the NFBP with any kind of learning rule. 

(4) Figure 5B demonstrates that on average adding inhibitory synapses can enhance the learning capabilities to solve the NFBP for different pattern configurations (2, 3, or 4 features), but since the performance for excitatory-only setup varies greatly between different configurations (Figure 4B, using 2 or 3 features can solve while 4 cannot), can the results be more precise about whether adding inhibitory synapses can help improve the learning with 4 features?

In response to the question, we added a panel to Figure 5B showing that without inhibitory synapses, 5 out of 13 configurations with four features successfully learn, while with inhibitory synapses, this improves to 7 out of 13. Figure 5—figure supplement 1B provides an explanation for this improvement: page 18 line 10-24

(5) Also, in terms of the possible role of inhibitory plasticity in learning, as only on-site inhibition is studied here, can other types of inhibition be considered, like on-path or off-path? Do they have similar or different effects?

This is an interesting suggestion for future work. We observed relevant dynamics in Figure 6A, where inhibitory synapses increased their weights on-site when randomly distributed. Previous work by Gidon and Segev (2012) examined the effects of different inhibitory types on NMDA clusters, highlighting the role of on-site and off-path inhibition in shunting. In our context, on-site inhibition in the same branch, appears more relevant for maintaining compartmentalized dendritic processing.

(6) Figure 6A is mentioned in the context of excitatory-only setup, but it depicts the setup when both excitatory and inhibitory synapses are included, which is discussed later in the paper. A correction should be made to ensure consistency.

We have updated the figure and the text in order to make it more clear that simulations are run both with and without inhibition in this context (page 21 line 4-13)

(7) In the "Ca and kernel dynamics" plots (Fig 3,5), some of the kernel midlines (solid line) are overlapped by dots, e.g. the yellow line in Fig 3D, and some kernel midlines look like dots, which leads to confusion. Suggest to separate plots of Ca and kernel dynamics for clarity. 

The design of the figures has been updated to improve the visibility of the calcium and kernel dynamics during training.

(8) The formulations of the learning rule are not well-organized, and the naming of parameters is kind of confusing, e.g. T_min, T_max, which by default represent time, means "Ca concentration threshold" here.

The abbreviations of the thresholds  ( T_min,  T_max in the initial version) have been updated to CTL and CTH, respectively, to better reflect their role in tracking calcium levels. The mathematical formulations have further been reorganized for better readability. The revised Methods section now follows a more structured flow, first explaining the learning mechanisms, followed by the equations and their dependencies.

Author response:

The following is the authors’ response to the previous reviews

Public Reviews:

Reviewer #1 (Public review):

In this manuscript, the authors use a large dataset of neuroscience publications to elucidate the nature of self-citation within the neuroscience literature. The authors initially present descriptive measures of self-citation across time and author characteristics; they then produce an inclusive model to tease apart the potential role of various article and author features in shaping self-citation behavior. This is a valuable area of study, and the authors approach it with a rich dataset and solid methodology.

The revisions made by the authors in this version have greatly improved the validity and clarity of the statistical techniques, and as a result the paper's findings are more convincing.

This paper's primary strengths are: 1) its comprehensive dataset that allows for a snapshot of the dynamics of several related fields; 2) its thorough exploration of how self-citation behavior relates to characteristics of research and researchers.

Thank you for your positive view of our paper and for your previous comments.

Its primary weakness is that the study stops short of digging into potential mechanisms in areas where it is potentially feasible to do so - for example, studying international dynamics by identifying and studying researchers who move between countries, or quantifying more or less 'appropriate' self-citations via measures of abstract text similarity.

We agree that these are limitations of the existing study. We updated the limitations section as follows (page 15, line 539):

“Similarly, this study falls short in several potential mechanistic insights, such as by investigating citation appropriateness via text similarity or international dynamics in authors who move between countries.”

Yet while these types of questions were not determined to be in scope for this paper, the study is quite effective at laying the important groundwork for further study of mechanisms and motivations, and will be a highly valuable resource for both scientists within the field and those studying it.

Reviewer #2 (Public review):

The study presents valuable findings on self-citation rates in the field of Neuroscience, shedding light on potential strategic manipulation of citation metrics by first authors, regional variations in citation practices across continents, gender differences in early-career self-citation rates, and the influence of research specialization on self-citation rates in different subfields of Neuroscience. While some of the evidence supporting the claims of the authors is solid, some of the analysis seems incomplete and would benefit from more rigorous approaches.

Thank you for your comments. We have addressed your suggestions presented in the “Recommendations for the authors” section by performing your recommended sensitivity analysis that specifically identifies authors who could be considered neurologists, neuroscientists, and psychiatrists (as opposed to just papers that are published in these fields). Please see the “Recommendations for the authors” section for more details.

Reviewer #3 (Public review):

This paper analyses self-citation rates in the field of Neuroscience, comprising in this case, Neurology, Neuroscience and Psychiatry. Based on data from Scopus, the authors identify self-citations, that is, whether references from a paper by some authors cite work that is written by one of the same authors. They separately analyse this in terms of first-author self-citations and last-author self-citations. The analysis is well-executed and the analysis and results are written down clearly. The interpretation of some of the results might prove more challenging. That is, it is not always clear what is being estimated.

This issue of interpretability was already raised in my review of the previous revision, where I argued that the authors should take a more explicit causal framework. The authors have now revised some of the language in this revision, in order to downplay causal language. Although this is perfectly fine, this misses the broader point, namely that it is not clear what is being estimated. Perhaps it is best to refer to Lundberg et al. (2021) and ask the authors to clarify "What is your Estimand?" In my view, the theoretical estimands the authors are interested in are causal in nature. Perhaps the authors would argue that their estimands are descriptive. In either case, it would be good if the authors could clarify that theoretical estimand.

Thank you for your comment and for highlighting this insightful paper. After reading this paper, we believe that our theoretical estimand is descriptive in nature. For example, in the abstract of our paper, we state: “This work characterizes self-citation rates in basic, translational, and clinical Neuroscience literature by collating 100,347 articles from 63 journals between the years 2000-2020.” This goal seems consistent with the idea of a descriptive estimand, as we are not interested in any particular intervention or counterfactual at this stage. Instead, we seek to provide a broad characterization of subgroup differences in self-citations such that future work can ask more focused questions with causal estimands.

Our analysis included subgroup means and generalized additive models, both of which were described as empirical estimands for a theoretical descriptive estimand in Lundberg et al. We added the following text to the paper (page 3, line 112):

“Throughout this work, we characterized self-citation rates with descriptive, not causal, analyses. Our analyses included several theoretical estimands that are descriptive 17, such as the mean self-citation rates among published articles as a function of field, year, seniority, country, and gender. We adopted two forms of empirical estimands. First, we showed subgroup means in self-citation rates. We then developed smooth curves with generalized additive models (GAMs) to describe trends in self-citation rates across several variables.”

In addition, we added to the limitations section as follows (page 15, line 539):

“Yet, this study may lay the groundwork for future works to explore causal estimands.”

Finally, in my previous review, I raised the issue of when self-citations become "problematic". The authors have addressed this issue satisfactorily, I believe, and now formulate their conclusions more carefully.

Thank you for your previous comments. We agree that they improved the paper.

Lundberg, I., Johnson, R., & Stewart, B. M. (2021). What Is Your Estimand? Defining the Target Quantity Connects Statistical Evidence to Theory. American Sociological Review, 86(3), 532-565. https://doi.org/10.1177/00031224211004187

Recommendations for the authors:

Reviewer #1 (Recommendations for the authors):

Thank you for your thorough revisions and responses to the reviews

Reviewer #2 (Recommendations for the authors):

I appreciate the authors' responses and am satisfied with all their replies except for my second comment. I still find the message conveyed slightly misleading, as the results seem to be generalized to neurologists, neuroscientists, and psychiatrists. It is important to refine the analysis to focus specifically on neuroscientists, identified as first or last authors based on their publication history. This approach is common in the science of science literature and would provide a more accurate representation of the findings specific to neuroscientists, avoiding the conflation with other related fields. This refinement could serve as a robustness check in the supplementary. I think adding this sub-analysis is essential to the validity of the results claimed in this paper.

Thank you for your comment. We added a sensitivity analysis where fields are defined by an author’s publication history, not by the journal of each article.

In the main text, we added the following:

(Page 3, line 129) “When determining fields by each author’s publication history instead of the journal of each article, we observed similar rates of self-citation (Table S7). The 95% confidence intervals for each field definition overlapped in most cases, except for Last Author self-citation rates in Neuroscience (7.54% defined by journal vs. 8.32% defined by author) and Psychiatry (8.41% defined by journal vs. 7.92% defined by author).”

Further details are provided in the methods section (page 21, line 801):

“4.11 Journal-based vs. author-based field sensitivity analyses

We refined our field-based analysis to focus only on authors who could be considered neuroscientists, neurologists, and psychiatrists. For each author, we looked at the number of articles they had in each subfield, as defined by Scopus. We considered 12 subfields that fell within Neurology, Neuroscience, and Psychiatry. These subfields are presented in Table S12. For each First Author and Last Author, we excluded them if any of their three most frequently published subfields did not include one of the 12 subfields of interest. If an author’s top three subfields included multiple broader fields (e.g., both Neuroscience and Psychiatry), then that author was categorized according to the field in which they published the most articles. Among First Authors, there were 86,220 remaining papers, split between 33,054 (38.33%) in Neurology, 23,216 (26.93%) in Neuroscience, and 29,950 (34.73%) in Psychiatry. Among Last Authors, there were 85,954 remaining papers, split between 31,793 (36.98%) in Neurology, 25,438 (29.59%) in Neuroscience, and 28,723 (33.42%) in Psychiatry.”

Reviewer #3 (Recommendations for the authors):

I would like to thank the authors for their responses the points that I raised, I do not have any new comments or further responses.

Author response:

We appreciate that the reviewers recognize the conceptual novelty of our work and find our work interesting.

Reviewer #1:

We thank Reviewer #1 for making us aware that the image presentation of some of what we see as very clear phenotypes in our work might not have been optimal in the reviewed pdf file, presumably due to the relatively low resolution and lack of appropriately magnified images in the merged pdf file. This issue– if not caught and corrected now– might have caused future readers to similarly not appreciate these clear phenotypes. We will carefully revise the figures and ensure maintenance of appropriate pdf resolution in the merged file so that image presentation is optimal and our findings are appropriately represented.

We appreciate that Reviewer #1 carefully and critically assessed the growth cone transcriptomic data. We agree that future additional validation is warranted, and this will be clearly stated in our revised paper. Because we judge that these data – even in their current form – will be of potential interest to other investigators sooner rather than later, we respectfully offer and request that we should share them in this paper as our attempt so far to identify elements of the relevant growth cone biology, rather than waiting for years before completing additional validation.

Even upon repeated reflection, we judge and respectfully submit that our CRISPR in utero electroporation experiments are, indeed, conducted with appropriate controls. We thought through the potential controls deeply prior to completing these complex experiments. We will describe our reasoning in detail in our point-by-point response.

Reviewer #2:

We thank Reviewer #2 for encouraging us to elaborate on the direction and cross- repressive interplay between Bcl11a and Bcl11b, which we previously identified (Woodworth*, Greig* et al., Cell Rep, 2016). We omitted deep discussion because we had already published this result, cited that work, and did not want to seem overly self- referential, as well as for reasons of length. Though we know and have reported that Bcl11a and Bcl11b are cross-repressive in SCPN development, we currently do not know whether increased Bcl11a expression in Bcl11b-null SCPN contributes to reduced Cdh13 expression. Also, we do not know if there is a similar Bcl11a-Bcl11b cross repression in striatal medium spiny neurons. This will be clarified in our revised paper.

We agree fully with the reviewer that “the common practice of picking from a list of differentially expressed genes the most likely ones” has been useful for and has substantially contributed to the elucidation of molecular mechanisms in many systems, including in CNS development. Indeed, the current paper identifies Cdh13 as a newly recognized functional molecule in SCPN axon development by in part using this approach. Cdh13 belongs to a well-known gene family, and its expression by SCPN was already reported by us (Arlotta*, Molyneauz* et al., Neuron, 2005). Despite these two facts, we newly identify its function in SCPN development, which has never been investigated or reported. We appreciate the reviewer encouraging us to elaborate on this here.

Recent technical advancement allows functional screening of a larger list of genes in vivo (Jin et al., Science, 2020; Ramani et al., bioRxiv, 2024; Zheng et al., Cell, 2024). That said, it is still a challenge to specifically access SCPN in vivo and apply such a high-throughput screening assay for axon development. We agree and predict that future work of this type might likely lead to identification of other new and unknown molecular regulators. We respectfully submit that our work reported here will provide useful foundation for many such future studies.

Author response:

The following is the authors’ response to the original reviews

Public Reviews:

Reviewer #1 (Public Review):

Summary:

The manuscript reports that expression of the E. coli operon topAI/yjhQ/yjhP is controlled by the translation status of a small open reading frame, that authors have discovered and named toiL, located in the leader region of the operon. The authors propose the following model for topAI activation: Under normal conditions, toiL is translated but topAI is not expressed because of Rho-dependent transcription termination within the topAI ORF and because its ribosome binding site and start codon are trapped in an mRNA hairpin. Ribosome stalling at various codons of the toiL ORF, caused by the presence of some ribosome-targeting antibiotics, triggers an mRNA conformational switch which allows translation of topAI and, in addition, activation of the operon's transcription because the presence of translating ribosomes at the topAI ORF blocks Rho from terminating transcription. Even though the model is appealing and several of the experimental data support some aspects of it, several inconsistencies remain to be solved. In addition, even though TopAI was shown to be an inhibitor of topoisomerase I (Yamaguchi & Inouye, 2015, NAR 43:10387), the authors suggest, without offering any experimental support, that, because ribosome-targeting antibiotics act as inducers, expression of the topAI/yjhQ/yjhP operon may confer resistance to these drugs.

Strengths:

- There is good experimental support of the transcriptional repression/activation switch aspect of the model, derived from well-designed transcriptional reporters and ChIP-qPCR approaches.

- There is a clever use of the topAI-lacZ reporter to find the 23S rRNA mutants where expression topAI was upregulated. This eventually led the authors to identify that translation events occurring at toiL are important to regulate the topAI/yjhQ/yjhP operon. Is there any published evidence that ribosomes with the identified mutations translate slowly (decreased fidelity does not necessarily mean slow translation, does it?)?

G2253 is in helix 80 of the 23S rRNA, which has been proposed to be involved in correct positioning of the tRNA. Mutations in helix 80 have been reported to cause defects in peptidyl transferase center activity, which could reduce the rate of ribosome movement along the mRNA. If ribosomes are sufficiently slowed when translating toiL, this could induce expression of topAI. G1911 and Ψ1917 are in helix 69 of the 23S rRNA, which is involved in forming the inter-subunit bridge, as well as interactions with release factors. Mutations in helix 69 cause a decrease in the processivity of translation, suggesting that the mutations we identified may increase the occupancy of ribosomes within toiL, thereby inducing expression of topAI. We have added text to the Discussion section to include this speculation.

- Authors incorporate relevant links to the antibiotic-mediated expression regulation of bacterial resistance genes. Authors can also mention the tryptophan-mediated ribosome stalling at the tnaC leader ORF that activates the expression of tryptophan metabolism genes through blockage of Rho-mediated transcriptional attenuation.

We have added a citation to a recent structural study of ribosomes translating the tnaC uORF. Specifically, we speculate in the Discussion that toiL may have evolved to sense a ribosome-targeting antibiotic, or another ribosome-targeting small molecule such as an amino acid.

Weaknesses:

The main weaknesses of the work are related to several experimental results that are not consistent with the model, or related to a lack of data that needs to be included to support the model.

The following are a few examples:

- It is surprising that authors do not mention that several published Ribo-seq data from E. coli cells show active translation of toiL (for example Li et al., 2014, Cell 157: 624). Therefore, it is hard to reconcile with the model that starts codon/Shine-Dalgarno mutations in the toiL-lux reporter have no effect on luciferase expression (Figure 2C, bar graphs of the no antibiotic control samples).

These data are for a topAI-lux reporter construct rather than toiL-lux. In our model, ribosome stalling within toiL is required to induce expression of the downstream genes; preventing translation of toiL by mutating the start codon or Shine-Dalgarno sequence would not cause ribosome stalling, consistent with the lack of an effect on topAI expression.

- The SHAPE reactivity data shown in Figure 5A are not consistent with the toiL ORF being translated. In addition, it is difficult to visualize the effect of tetracycline on mRNA conformation with the representation used in Figure 5B. It would be better to show SHAPE reactivity without/with Tet (as shown in panel A of the figure).

We have modified this figure (now Figure 6) so that we no longer show the SHAPE-seq data +/- tetracycline overlayed on the predicted RNA structure, since at best, the predicted structure likely only represents uninduced state. We have included the predicted structure together with the SHAPE-seq data for untreated cells as a separate panel because it is part of the basis for our model. We have also added a supplementary figure showing a similar RNA structure prediction based on conservation of the topAI upstream region across species (Figure 6 – figure supplement 1), and we describe this in the text.

- The "increased coverage" of topAI/yjhP/yjhQ in the presence of tetracycline from the Ribo-seq data shown in Figure 6A can be due to activation of translation, transcription, or both. For readers to know which of these possibilities apply, authors need to provide RNA-seq data and show the profiles of the topAI/yjhQ/yjhP genes in control/Tet-treated cells.

A previous study (Li et al., 2014, PMID 24766808) compared RNA-seq and Ribo-seq data for E. coli to measure normalized ribosome occupancy for each gene. However, sequence coverage for topAI was too low to confidently quantify either the RNA-seq or the Ribo-seq data. Presumably RNA levels were low because of Rho termination. Hence, we were not confident that RNA-seq would provide information on the regulation of topAI-yjhQP. Other data in our study provide strong evidence that regulation is primarily at the level of translation. And the key conclusion from Figure 6 (now Figure 7) is that tetracycline stalls ribosomes on start codons.

- Similarly, to support the data of increased ribosomal footprints at the toiL start codon in the presence of Tet (Figure 6B), authors should show the profile of the toiL gene from control and Tet-treated cells.

Figure 6B shows data for both treated and untreated cells. The overall ribosome occupancy is much lower for untreated cells, making it difficult to draw strong conclusions about the relative distribution of ribosomes across toiL.

- Representation of the mRNA structures in the model shown in Figure 5, does not help with visualizing 1) how ribosomes translate toiL since the ORF is trapped in double-stranded mRNA, and 2) how ribosome stalling on toiL would lead to the release of the initiation region of topAI to achieve expression activation.

We now show the predicted structure with only SHAPE-seq data for untreated cells. The comparison of SHAPE-seq +/- tetracycline is shown without reference to the predicted structure.

- The authors speculate that, because ribosome-targeting antibiotics act as expression inducers [by the way, authors should mention and comment that, more than a decade ago, it had been reported that kanamycin (PMID: 12736533) and gentamycin (PMID: 19013277) are inducers of topAI and yjhQ], the genes of the topAI/yjhQ/yjhP operon may confer resistance to these antibiotics. Such a suggestion can be experimentally checked by simply testing whether strains lacking these genes have increased sensitivity to the antibiotic inducers.

We thank the reviewer for pointing out these references, which we now cite. The fact that another group found that gentamycin induces topAI expression – it is one of the most highly induced genes in that paper – strongly suggests that we missed the key inducing concentrations for one or more antibiotics, meaning that topAI is induced by even more ribosome-targeting antibiotics than we realized.

We did some preliminary experiments to look for effects of TopAI, YjhQ, and/or YjhP on antibiotic sensitivity, but generated only negative results. Since these experiments were preliminary and far from exhaustive, we have chosen not to include them in the manuscript. Other studies of genes regulated by ribosome stalling in a uORF have looked at genes whose functions in responding to translation stress were already known, so the environmental triggers were more obvious. With so many possible triggers for topAI-yjhQP, it will likely require considerable effort to find the relevant trigger(s). Hence, we consider this an important question, but beyond the scope of this manuscript.

Reviewer #2 (Public Review):

Summary:

In this important study, Baniulyte and Wade describe how the translation of an 8-codon uORF denoted toiL upstream of the topAI-yjhQP operon is responsive to different ribosome-targeting antibiotics, consequently controlling translation of the TopAI toxin as well as Rho-dependent termination with the gene.

Strengths:

I appreciate that the authors used multiple different approaches such as a genetic screen to identify factors such as 23S rRNA mutations that affect topA1 expression and ribosome profiling to examine the consequences of various antibiotics on toiL-mediated regulation. The results are convincing and clearly described.

Weaknesses:

I have relatively minor suggestions for improving the manuscript. These mainly relate to the figures.

Reviewer #3 (Public Review):

Summary:

The authors nicely show that the translation and ribosome stalling within the ToiL uORF upstream of the co-transcribed topAI-yjhQ toxin-antitoxin genes unmask the topAI translational initiation site, thereby allowing ribosome loading and preventing premature Rho-dependent transcription termination in the topAI region. Although similar translational/transcriptional attenuation has been reported in other systems, the base pairing between the leader sequence and the repressed region by the long RNA looping is somehow unique in toiL-topAI-yjhQP. The experiments are solidly executed, and the manuscript is clear in most parts with areas that could be improved or better explained. The real impact of such a study is not easy to appreciate due to a lack of investigation on the physiological consequences of topAI-yjhQP activation upon antibiotic exposure (see details below).

Strengths:

Conclusion/model is supported by the integrated approaches consisting of genetics, in vivo SHAPE-seq and Ribo-Seq.

Provide an elegant example of cis-acting regulatory peptides to a growing list of functional small proteins in bacterial proteomes.

Recommendations for the authors:

Reviewing Editor Comments:

(1) Examine the consequences of mutations impeding translation of the topAI/yjhQ/yjhP operon on cell growth in the presence and absence of antibiotics.

See response to Reviewer 1’s comment.

(2) Resolve discrepancies between the SHAPE data indicating constitutive sequestration of the toiL Shine Dalgarno sequence with antibiotic-regulated translation of the toiL ORF.

See response to Reviewer 1’s comment.

(3) Reconcile published Ribo-Seq data with the model that start codon/Shine-Dalgarno mutations in the toiL-lux reporter have no effect on luciferase expression in the absence of antibiotics.

See response to Reviewer 1’s comment.

(4) Clarify whether antibiotic MIC values were employed to select antibiotic concentrations for different experiments.

The antibiotic concentrations we used are in line with reported MICs for E. coli. We now list the reported ECOFFs/MICs and include relevant citations.

(5) Provide RNA-seq data to complement the Ribo-Seq data for the topAI/yjhQ/yjhP genes in control vs. Tet-treated cells.

See response to Reviewer 1’s comment.

(6) Revise the text to address as many of the reviewers' suggestions as reasonably possible.

Changes to the text have been made as indicated in the responses to the reviewers’ comments.

Reviewer #2 (Recommendations for the Authors):

(1) Page 6: I would have liked to have more information about the 39 suppressor mutations in rho. Do any of the cis-acting mutations give support for the model proposed in Figure 8?

We only know the specific mutation for some of the strains, and we now list those mutations in the Methods section. For other mutants, we mapped the mutation to either the rho gene or to Rho activity, but we did not sequence the rho gene. Most of the specific mutations we did identify fall within the primary RNA-binding site of Rho and hence should be considered partial-loss-of-function mutations (complete loss of function would be lethal).

We identified cis-acting mutations by re-transforming the lacZ reporter plasmid into a wild-type strain. We did not sequence any of these plasmids.

(2) Page 12-13, Section entitled "Mapping ribosome stalling sites induced by different antibiotics": This section should start with a better transition regarding the logic of why the experiments were carried out and should end with an interpretation of the results.

We have added a few sentences at the start of this section to explain the rationale. We have also added two sentences at the end of this section to summarize the interpretation of the data.

(3) Page 15: The authors should discuss under what conditions the expression of TopAI (and YjhQ/YjhP might be induced? Is expression also elevated upon amino acid starvation?

We have looked through public RNA-seq data but have not identified growth conditions other than antibiotic treatment that induce expression of topAI, yjhQ or yjhP.

(4) References: The authors should be consistent about capitalization, italics, and abbreviations in the references.

These formatting errors will be fixed in the proofing stage.

(5) All graph figures: There should be more uniformity in the sizes of individual data points (some are almost impossible to see) and error bars across the figures.

We have tried to make the data points and error bars more visible for figures where they were smaller.

(6) Figure 1B: I do not think the left arrow labeling is very intuitive and suggest renaming these constructs.

We have removed the arrows to improve clarity.

(7) Figure 2A: toiL should be introduced at the first mention of Figure 2A.

We have added a schematic of the topAI-yjhQ-yjhP region as Figure 1A, including the toiL ORF, which we briefly mention in the text. We have opted to split Figure 2C into two panels. In Figure 2C we now only show data for the wild-type construct. Data for the mutant constructs are now shown in a new figure (Figure 5), alongside data for the wild-type constructs. We have simplified Figure 2A, since the mutations are not relevant to this revised figure, and we now show the schematic with the mutations as Figure 5A.

(8) Figure 3C and 3D: I suggest giving these graphs headings (or changing the color of the bars in Figure 3D) to make it more obvious that different things are measured in the two panels.

We have added headers to panels B-D make it clear that which graphs show ChIP-qPCR data which graph shows qRT-PCR data.

(9) Figure 6: It might be nice to show the topAI-yjhPQ operon here.

We now show the operon in Figure 1A.

(10) Figure 8: This figure could be optimized by adding 5' and 3' end labels and having more similarity with the model in Figure 7.

The constructs shown in Figure 7 lack most of the topAI upstream region, so they aren’t readily comparable to the schematic in Figure 8. However, we have changed the color of the ribosome in Figure 7 to match that in Figure 8. We also indicate the 5’ end of the RNA in Figure 8.

Reviewer #3 (Recommendations for the Authors):

Areas to improve:

(1) While it's important to learn about ToiL-dependent regulation of the downstream topAI-yjhQ toxin-antitoxin genes, the physiological consequence of topAI-yjhQ activation seems to be lost in the manuscript. Everything was done with a reporter lacZ/lux. In the absence of toiL translation (i.e. SD mutant) and/or ribosome stalling, does premature transcription termination result in non-stochiometric synthesis of toxin vs. antitoxin, leading to growth arrest or other measurable phenotype? Knowing the impact of ToiL in the native topAI-yjhQ context will be valuable.

See response to Reviewer 1’s comment.

(2) It was indicated in Figure 4-figure supplement 1 that toiL homologs are found in many other proteobacteria, are the UR sequences in those species also form a similar inhibitory RNA loop?? The nt sequence identity of toiL is likely to be constrained by the base pairing of the topAI 5' region.

We have added a supplementary figure panel showing an RNA structure prediction for the topAI upstream region based on sequence alignment of homologous regions from other species (Figure 6 – figure supplement 1).

What is the frequency of the MLENVII hepta-peptide in the E. coli genome-wide. Is the sequence disfavored to avoid spurious multi-antibiotic sensing?

LENVII is not found in any annotated E. coli K-12 protein. However, this is a sufficiently long sequence that we would expect few to no instances in the E. coli proteome.

(3) Figure 1A, it would be helpful to indicate the location of the toiL (red arrow as in Figure 2A) relative to the putative rut site early in the beginning of the results. Does TSS mark the transcription start site? There is no annotation of TSS in the figure legend. Was TSS previously mapped experimentally? Please include relevant citations.

We now indicate the position of the TSS relative to the topAI start codon. Similarly, we indicate the position of the start of toiL relative to the topAI start codon in Figure 2A. We now explain “TSS” in the figure legend. There is a reference in the text for the TSS (Thomason et al., 2015).

(4) Please consider rearranging the results section, perhaps more helpful to introduce the toiL in Figure 1 or earlier. The current format requires readers to switch back-and-forth between Figure 4 and Figure 2.

We have added a schematic of the topAI upstream region as Figure 1A, and we have separated Figure 2C as described in a response to a comment from Reviewer 2.

(5) Figure 2A and Figure 2-Figure Suppl 1A, for clarity, please mark the rut site upstream of the red arrow.

Rather than mark the rut on Figure 2A, which would make for a busy schematic, readers can compare the positions of the rut to those of toiL, which we have now added to Figures 1B (formerly Figure 1A) and 2A.

(6) The following conclusion seems speculative: "...but does not trigger termination until RNAP ..., >180 nt further downstream…". Shouldn't the authors already know where the termination site is based on their previous Term-seq data (see Ref 1, Adams PP et al 2021)?

Sites of Rho-dependent transcription termination cannot be mapped precisely from Term-seq data because exoribonucleases rapidly process the unstructured RNA 3’ ends.

(7) Genetic screen: Please discuss why the 23S rRNA mutations that cause translational infidelity could promote topAI translation. Wouldn't the mutant ribosome be affected in translating toiL?

See response to Reviewer 1’s comment.

(8) Although antibiotic concentrations were provided in Figure 2 legend, please provide the MIC values of each antibiotic, e.g., in Table S2, for the tested E. coli strain, to inform readers how specific subinhibitory concentrations were chosen.

See response to Reviewing Editor.

(9) Please clarify the calculation of luciferase units in the y-axis of Figure 2A, why the scale is drastically higher than that of Figure 7C using the same antibiotics?

These reporter assays use different constructs. The reporter construct used for experiments in Figure 7 includes a portion of the ermCL gene and associated downstream sequence. We have enlarged Figure 7A to highlight the difference in reporter constructs.

(10) Table S4 needs a few more details. It is unclear how those numbers in columns G-H were generated. Do those numbers correspond to ribosome density per nt/ORF?

We have added footnotes to Table S4 to indicate that the numbers in columns G and H represent sequence read coverage normalized by region length and by the upper quartile of gene expression.

(11) Figure 5, if the SHAPE results were true, the Shine Dalgarno sequence of toiL is sequestered in the hairpin structure with and without tetracycline treatment. It is inconceivable that translational initiation will occur efficiently, please discuss.

Our representation of the SHAPE-seq data was confusing since we overlayed the SHAPE-seq changes on a predicted structure that likely corresponds to the uninduced state. We hope that the new version of Figure 5 is clearer.

We presume the reviewer is referring to the Shine-Dalgarno sequence of topAI rather than toiL, since the Shine-Dalgarno sequence of toiL is predicted to be unstructured even in the absence of tetracycline treatment. The ribosome-binding site of topAI is more accessible in cells treated with tetracycline, although the SHAPE-seq data suggest that this is a transient event. The binding of the initiating ribosome may also reduce reactivity in this region under inducing conditions. We now discuss this briefly in the text.

Author response:

The following is the authors’ response to the previous reviews

Public Reviews: 

Reviewer #1 (Public review): 

The manuscript consists of two separate but interlinked investigations: genomic epidemiology and virulence assessment of Salmonella Dublin. ST10 dominates the epidemiological landscape of S. Dublin, while ST74 was uncommonly isolated. Detailed genomic epidemiology of ST10 unfolded the evolutionary history of this common genotype, highlighting clonal expansions linked to each distinct geography. Notably, North American ST10 was associated with more antimicrobial resistance compared to others. The authors also performed long read sequencing on a subset of isolates (ST10 and ST74), and uncovered a novel recombinant virulence plasmid in ST10 (IncX1/IncFII/IncN). Separately, the authors performed cell invasion and cytotoxicity assays on the two S. Dublin genotypes, showing differential responses between the two STs. ST74 replicates better intracellularly in macrophage compared to ST10, but both STs induced comparable cytotoxicity levels. Comparative genomic analyses between the two genotypes showed certain genetic content unique to each genotype, but no further analyses were conducted to investigate which genetic factors likely associated with the observed differences. The study provides a comprehensive and novel understanding on the evolution and adaptation of two S. Dublin genotypes, which can inform public health measures. The methodology included in both approaches were sound and written in sufficient detail, and data analysis were performed with rigour. Source data were fully presented and accessible to readers. 

Comments on revised version: 

The authors have addressed all the points raised by the reviewer. The manuscript is now much enhanced in clarity and accuracy. The re-written Discussion is more relevant and brings in comparison with other invasive Salmonella serotypes. 

Comments: 

In light of the metadata supplied in this revision, for Australian isolates, all human cases of ST74 (n=7) were from faeces (assuming from gastroenteritis) while 18/40 of ST10 were from invasive specimen (blood and abscess). This may contradict with the manuscript's finding and discussion on different experiment phenotypes of the two STs, with ST74 showing more replication in macrophages and potentially more invasive. Thus, the reviewer suggests the authors to mention this disparity in the Discussion, and discuss possible reasons underlying this disparity. This can strengthen the author's rationale for further in vivo studies. 

We thank the reviewer for pointing out this important observation. We have amended the text in the Discussion to address the differences in source of human cases as suggested by the Reviewer (lines 392-430). We have also included text highlighting the important knowledge gaps in understanding the drivers for emerging iNTS with broad host ranges and identify future avenues of research that could be explored to better understand the observed differences in the host-pathogen interactions.  

Reviewer #2 (Public review): 

This is a comprehensive analysis of Salmonella Dublin genomes that offers insights into the global spread of this pathogen and region-specific traits that are important to understand its evolution. The phenotyping of isolates of ST10 and ST74 also offer insights into the variability that can be seen in S. Dublin, which is also seen in other Salmonella serovars, and reminds the field that it is important to look beyond lab-adapted strains to truly understand these pathogens. This is a valuable contribution to the field. The only limitation, which the authors also acknowledge, is the bias towards S. Dublin genomes from high income settings. However, there is no selection bias; this is simply a consequence of publicly available sequences. 

We thank the reviewer for their comments and acknowledge the limitations of this study.

Author response:

The following is the authors’ response to the original reviews

Public Reviews:

Reviewer #1 (Public review):

(1) The authors repeatedly assert that an individual's behavior in the foraging assay depends on its prior history (particularly cultivation conditions). While this seems like a reasonable expectation, it is not fully fleshed out. The work would benefit from studies in which animals are raised on more or less abundant food before the behavioral task.

Cultivation density: While we agree with the reviewer that testing the effects of varying bacterial density during animal development (cultivation) is an interesting experiment, it is not feasible at this time. We previously attempted this experiment but found it nontrivial to maintain stable bacterial density conditions over long timescales as this requires matching the rate of bacterial growth with the rate of bacterial consumption. Despite our best efforts, we have not been able to identify conditions that satisfy these requirements. Thus, we focused our revised manuscript to include only assertions about the effects of recent experiences and added this inquiry as a future direction (lines 618-624).

(2) The authors convincingly show that the probability of particular behavioral outcomes occurring upon patch encounter depends on time-associated parameters (time since last patch encounter, time since last patch exploitation). There are two concerns here. First, it is not clear how these values are initialized - i.e., what values are used for the first occurrence of each behavioral state? More importantly, the authors don't seem to consider the simplest time parameter, the time since the start of the assay (or time since worm transfer). Transferring animals to a new environment can be associated with significant mechanical stimulus, and it seems quite possible that transferring animals causes them to enter a state of arousal. This arousal, which certainly could alter sensory function or decision-making, would likely decay with time. It would be interesting to know how well the model performs using time since assay starts as the only time-dependent parameter.

Parameter Initialization: We thank the reviewer for pointing out an oversight in our methods section regarding the model parameter values used for the first encounter. We clarified the initialization of parameters in the manuscript (lines 1162-1179). In short, for the first patch encounter where k = 1:

● ρ_k is the relative density of the first patch.

● τ_s is the duration of time spent off food since the beginning of the recorded experiment. For the first patch, this is equivalent to the total time elapsed.

● ρ_h is the approximated relative density of the bacterial patch on the acclimation plates (see Assay preparation and recording in Methods). Acclimation plates contained one large 200 µL patch seeded with OD₆₀₀ = 1 and grown for a total of ~48 hours. As with all patches, the relative density was estimated from experiments using fluorescent bacteria OP50-GFP as described in Bacterial patch density estimation in Methods.

● ρ_e is equivalent to ρ_h.

Transfer Method: We thank the reviewer for their thoughtful comment on how the stress of transferring animals to a new plate may have resulted in an increased arousal state and thus a greater probability of rejecting patches. We anticipated this possibility and, in order to mitigate the stress of moving, we used an agar plug method where animals were transferred using the flat surface of small cylinders of agar. Importantly, the use of agar as a medium to transfer animals provides minimal disruption to their environment as all physical properties (e.g. temperature, humidity, surface tension) are maintained. Qualitatively, we observed no marked change in behavior from before to after transfer with the agar plug method, especially as compared to the often drastic changes observed when using a metal or eyelash pick. We added these additional methodological details to the methods (lines 791-796).

Time Parameter: However, the reviewer’s concern that the simplest time parameter (time since start of the assay) might better predict animal behavior is valid. We thank the reviewer for pointing out the need to specifically test whether the time-dependent change in explore-exploit decision-making corresponds better with satiety (time off patch) or arousal (time since transfer/start of assay) state. To test this hypothesis, we ran our model with varying combinations of the satiety term τ_s and a transfer term τ_t. We found that when both terms were included in the model, the coefficient of the transfer term was non-significant. This result suggests that the relevant time-dependent term is more likely related to satiety than transfer-induced stress (lines 343-358; Figure 4 - supplement 4D).

(3) Similarly, Figures 2L and M clearly show that the probability of a search event occurring upon a patch encounter decreases markedly with time. Because search events are interpreted as a failure to detect a patch, this implies that the detection of (dilute) patches becomes more efficient with time. It would be useful for the authors to consider this possibility as well as potential explanations, which might be related to the point above.

Time-dependent changes in sensing: We agree with the reviewer that we observe increased responsiveness to dilute patches with time. Although this is interesting, our primary focus was on what decision an animal made given that they clearly sensed the presence of the bacterial patch. Nonetheless, we added this observation to the discussion as an area of future work to investigate the sensory mechanisms behind this effect (lines 563-568).

(4) Based on their results with mec-4 and osm-6 mutants, the authors assert that chemosensation, rather than mechanosensation, likely accounts for animals' ability to measure patch density. This argument is not well-supported: mec-4 is required only for the function of the six non-ciliated light-touch neurons (AVM, PVM, ALML/R, PLML/R). In contrast, osm-6 is expected to disrupt the function of the ciliated dopaminergic mechanosensory neurons CEP, ADE, and PDE, which have previously been shown to detect the presence of bacteria (Sawin et al 2000). Thus, the paper's results are entirely consistent with an important role of mechanosensation in detecting bacterial abundance. Along these lines, it would be useful for the authors to speculate on why osm-6 mutants are more, rather than less, likely to "accept" when encountering a patch.

Sensory mutant behavior: We thank the reviewer for pointing out the error in our interpretation of the behavior of osm-6 and mec-4 animals. We further elaborated on our findings and edited the text to better reflect that osm-6 mutants lack both chemosensory and mechanosensory ciliated sensory neurons (lines 406-448; lines 567-577). Specifically, we provided some commentary on the finding that osm-6 mutants show an augmented ability to detect the presence of bacterial patches but a reduced ability to assess their bacterial density. While this finding seems contradictory, it suggests that in the absence of the ability to assess bacterial density, animals must prioritize exploiting food resources when available.

(5) While the evidence for the accept-reject framework is strong, it would be useful for the authors to provide a bit more discussion about the null hypothesis and associated expectations. In other words, what would worm behavior in this assay look like if animals were not able to make accept-reject decisions, relying only on exploit-explore decisions that depend on modulation of food-leaving probability?

Accept-reject vs. stay-switch: We thank the reviewer for alerting us to this gap in our discussion. We have revised the text to further extrapolate upon our point of view on this somewhat philosophical distinction and what it predicts about C. elegans behavior (lines 507-533).

Reviewer #3 (Public review):

(1) Sensing vs. non-sensing

The authors claim that when animals encounter dilute food patches, they do not sense them, as evidenced by the shallow deceleration that occurs when animals encounter these patches. This seems ethologically inaccurate. There is a critical difference between not sensing a stimulus, and not reacting to it. Animals sense numerous stimuli from their environment, but often only behaviorally respond to a fraction of them, depending on their attention and arousal state. With regard to C. elegans, it is well-established that their amphid chemosensory neurons are capable of detecting very dilute concentrations of odors. In addition, the authors provide evidence that osm-6 animals have altered exploit behaviors, further supporting the importance of amphid chemosensory neurons in this behavior.

Interpretation of “non-sensing” encounters: We thank the reviewer for their comment and agree that we do not know for certain whether the animals sensed these patches or were merely non-responsive to them. We are, however, confident that these encounters lack evidence of sensing. Specifically, we note that our analyses used to classify events as sensing or non-sensing examined whether an animal’s slow-down upon patch entry could be distinguished from either that of events where animals exploited or that of encounters with patches lacking bacteria. We found that  “non-sensing” encounters are indeed indistinguishable from encounters with bacteria-free patches where there are no bacteria to be sensed (see Figure 2 - Supplement 8A-C and Patch encounter classification as sensing or non-responding in Methods). Regardless, we agree with the reviewer that all that can be asserted about these events is that animals do not appear to respond to the bacterial patch in any way that we measured. Therefore, we have replaced the term “non-sensing” with “non-responding” to better indicate the ethological interpretation of these events and clarified the text to reflect this change (lines 193-200; lines 211-212).

(2) Search vs. sample & sensing vs. non-sensing

In Figures 2H and 2I, the authors claim that there are three behavioral states based on quantifying average velocity, encounter duration, and acceleration, but I only see three. Based on density distributions alone, there really only seem to be 2 distributions, not 3. The authors claim there are three, but to come to this conclusion, they used a QDA, which inherently is based on the authors training the model to detect three states based on prior annotations. Did the authors perform a model test, such as the Bayesian Information Criterion, to confirm whether 2 vs. 3 Gaussians is statistically significant? It seems like the authors are trying to impose two states on a phenomenon with a broad distribution. This seems very similar to the results observed for roaming vs. dwelling experiments, which again, are essentially two behavioral states.

Validation of sensing clusters: We are grateful to the reviewer for pointing out the difficulty in visualizing the clusters and the need for additional clarity in explaining the semi-supervised QDA approach. We added additional visualizations and methods to validate the clusters we have discovered. Specifically, we used Silverman’s test to show that the sensing vs. non-responding data were bi-modal (i.e. a two-cluster classification method fits best) and accompanied this statistical test with heat maps which better illustrate the clusters (lines 171-173; lines 190-191; lines 948-972; lines 1003-1005; Figure 2 - supplement 6A-C; Figure 2 - supplement 7C-F).

Further, it seems that there may be some confusion as to how we arrived at 3 encounter types (i.e. search, sample, exploit). It’s important to note that two methods were used on two different (albeit related) sets of parameters. We first used a two-cluster GMM to classify encounters as explore or exploit. We then used a two-cluster semi-supervised QDA to classify encounters as sensing or non-sensing (now changed to “non-responding”, see above response) using a different set of parameters. We thus separated the explore cluster into two (sensing and non-responding exploratory events) resulting in three total encounter types: exploit, sample (explore/sensing), and search (explore/non-sensing).

(4) History-dependence of the GLM

The logistic GLM seems like a logical way to model a binary choice, and I think the parameters you chose are certainly important. However, the framing of them seems odd to me. I do not doubt the animals are assessing the current state of the patch with an assessment of past experience; that makes perfect logical sense. However, it seems odd to reduce past experience to the categories of recently exploited patch, recently encountered patch, and time since last exploitation. This implies the animals have some way of discriminating these past patch experiences and committing them to memory. Also, it seems logical that the time on these patches, not just their density, should also matter, just as the time without food matters. Time is inherent to memory. This model also imposes a prior categorization in trying to distinguish between sensed vs. not-sensed patches, which I criticized earlier. Only "sensed" patches are used in the model, but it is questionable whether worms genuinely do not "sense" these patches.

Model design: We thank the reviewer for their thoughtful comments on the model. We completed a number of analyses involving model selection including model selection criteria (AIC, BIC) and optimization with regularization techniques (LASSO and elastic nets) and found that the problem of model selection was compounded by the enormous array of highly-correlated variables we had to choose from. Additionally, we found that both interaction terms and non-linear terms of our task variables could be predictive of accept-reject decisions but that the precise set of terms selected depended sensitively on which model selection technique was used and generally made rather small contributions to prediction. The diverse array of results and combinatorial number of predictors to possibly include failed to add anything of interpretable value. We therefore chose to take a different approach to this problem. Rather than trying to determine what the “best” model was we instead asked whether a minimal model could be used to answer a set of core questions. Indeed, our goal was not maximal predictive performance but rather to distinguish between the effects of different influences enough to determine if encounter history had a significant, independent effect on decision making. We thus chose to only include task variables that spanned the most basic components of behavioral mechanisms to ask very specific questions. For example, we selected a time variable that we thought best encapsulated satiety. While we could have included many additional terms, or made different choices about which terms to include, based on our analyses these choices would not have qualitatively changed our results. Further, we sought to validate the parameters we chose with additional studies (i.e. food-deprived and sensory mutant animals). We regard our study as an initial foray into demonstrating accept-reject decision-making in nematodes. The exact mechanisms and, consequently, the best model design are therefore beyond the scope of this study.

Lastly, in regards to the use of only sensed patches in the model; while we acknowledge that we are not certain as to whether the “non-responding” encounters are truly not sensed, we find qualitatively similar results when including all exploratory patches in our analyses. However, we take the position that sensation is necessary for decision-making and thus believe that while our model’s predictive performance may be better using all encounters, the interpretation of our findings is stronger when we only include sensing events. We have added additional commentary about our model to the discussion section (lines 667-695).

(5) osm-6

The osm-6 results are interesting. This seems to indicate that the worms are still sensing the food, but are unable to assess quality, therefore the default response is to exploit. How do you think the worms are sensing the food? Clearly, they sense it, but without the amphid sensory neurons, and not mechanosensation. Perhaps feeding is important? Could you speculate on this?

We thank the reviewer for their thoughtful remarks. We have added additional commentary about the result of our sensory mutant experiments as described above in response to Reviewer #1 under Sensory mutant behavior.

(7) Impact:

I think this work will have a solid impact on the field, as it provides tangible variables to test how animals assess their environment and decide to exploit resources. I think the strength of this research could be strengthened by a reassessment of their model that would both simplify it and provide testable timescales of satiety/starvation memory.

Recommendations for the authors:

Reviewer #2 (Recommendations for the authors):

The authors title the work as an "ethological study" and emphasize the theme of "foraging in naturalistic environments" in contrast to typical laboratory conditions. The only difference in this study relative to typical laboratory conditions is that the food bacteria is distributed in many small patches as compared to one large patch. First, it is not clear to the reviewer that the size of the food patches in these experiments is more relevant to C. elegans in its natural context than the standard sizes of food patches. Furthermore, all the other highly unnatural conditions typical of laboratory cultivation still apply: the use of a 2D agar substrate, a single food bacteria that is not a component of a naturalistic diet, and the use of a laboratory-adapted strain of C. elegans with behavior quite distinct from that of natural isolates. The reviewer is not suggesting that the authors need to make their experiments more naturalistic, only that the experiments as described here should not be described as naturalistic or ethological as there is no support for such claims.

Ethological interpretation: We thank the reviewer for their comments about the use of the term ethological to describe this study. We chose to develop a patchy bacterial assay to mimic the naturalistic “boom-or-bust” environment. While we agree with the reviewer that we do not know if the size and distribution of the food patches in these experiments is more relevant to C. elegans, we maintain that these experiments were ecologically-inspired and revealed behavior that is difficult to observe in environments with large, densely-seeded bacterial patches. We have updated our text to better reflect that this study was “ecologically-inspired” rather than truly “ethological” in nature (lines 94, 693).

The main finding of the paper is that worms explore and then exploit, i.e. they frequently reject several bacterial patches before accepting one. This result requires additional scrutiny to reject other possible interpretations. In particular, when worms are transferred to a new plate we would expect some period of increased arousal due to the stressful handling process. A high arousal state might cause rejection of food patches. Could the measured accept/reject decisions be influenced by this effect? One approach to addressing this concern would be to allow the animals to acclimate to the new plate on a bare region before encountering the new food patches.

We thank the reviewer for their comment on how the stress of transferring animals to a new plate may have resulted in an increased arousal state and thus a greater probability of rejecting patches. We addressed this above in response to Reviewer #1 under Transfer Method and Time Parameter. In brief, we used a worm picking method that mitigated stress and added additional analyses showing that a transfer-related term was less predictive than a satiety-related term.

Related to the above, in what circumstances exactly are the authors claiming that worms first explore and then exploit? After being briefly deprived of food? After being handled?

Explore-then-exploit: All animals were well-fed and handled gently as described above under Transfer Method (lines 787-795). Our results suggest that the appearance of an explore-then-exploit strategy is a byproduct of being transferred from an environment with high bacterial density to an environment with low bacterial density as described in the manuscript (lines 461-466).

The authors emphasize their analysis of the accept/reject decision as a critical innovation. However, the accept/reject decision does not strike me as substantially different from the previously described stay/switch decision. When a worm encounters a new patch of bacteria, accepting this bacteria is equivalent to staying on it and rejecting (leaving) it is equivalent to switching away from it. The authors should explain how these concepts are significantly distinct.

Accept-reject vs. stay-switch: We thank the reviewer for alerting us to this gap in our discussion. We have revised the text to further extrapolate upon our point of view on this somewhat philosophical distinction and what it predicts about C. elegans behavior (lines 507-533).

During patch encounter classification, the authors computed three of the animals' behavioral metrics (Line 801-804) and claimed that the combination of these three metrics reveals two non-Gaussian clusters representing encounters where animals sensed the patch or did not appear to sense the patch. The authors also refer to a video to demonstrate the two clusters by rotating the 3-dimension scatter plot. However, the supposed clusters, if any, are difficult to see in a 3D (Video 5) or in a 2D scatter plot (Figure 3I). The authors need to clearly demonstrate the distinct clustering as claimed in the paper as this feature is fundamental and necessary for the model implementation and interpretation of results.

We are grateful to the reviewer for pointing out the difficulty in visualizing the clusters. We added additional visualizations and methods to validate the clusters we have discovered as described in our above response to Reviewer #3 under Validation of sensing clusters.

When selecting parameters (covariates) for their model, it is critical to avoid overfitting. Therefore, the authors used AIC and BIC (Figure 4- supplement 1) to demonstrate that the full GLM model has a better model performance than the other models which contain only a subset of the full covariates (in a total of 5). However, the authors compare the full set with only 4 other models whereas the total number of models that need to be compared with is 2^5-2. The authors at least need to include the AIC and BIC scores of all possible models in order to draw the conclusion about the performance of the full model.

Model selection criterion: We thank the reviewer for pointing out this gap in our methodology. We have now run the model with all combinations of subsets of model parameters and have confirmed that the model with all 5 covariates outperforms all other models even when using BIC, the strictest criterion for overfitting (Figure 1 - supplement 1A). The only other model that performs well (though not as often as the 5-term model) is the 4-term model lacking ρ_h. This result is not surprising as ρ_h only changes substantially once in an animal’s encounter history for the single-density, multi-patch data that this model was fit to. For example, for an animal foraging on patches of density 10, on the first encounter ρ_h = ~200 (see Parameter initialization above), but on every subsequent encounter ρ_h = ~10. Resultantly, the effect of ρ_h on the probability of exploiting is somewhat binary on the single-density, multi-patch data set. Nevertheless, we see significantly improved prediction of behavior in the novel multi-density, multi-patch data (Figure 4F) as we observe an effect of the most recently encountered patch. Additionally, we observe a similar impact (i.e., significant coefficient of negative sign) of the ρ_h term when the model is fit to the multi-density, multi-patch data set (Figure 4 - supplement 4D).

In any bacterial patch, the edges have a higher density of bacteria than the patch center. Thus, it is possible that a worm scans the patch edge density, on the basis of which it decides to accept or reject the patch whose average density is smaller. This could potentially cause an underestimate of the bacteria density used in the model. Furthermore, the potential inhomogeneity of the patch may further complicate the worm's decision-making, and the discrepancy between the reality and the model assumption will reduce the validity of the model. The authors need to estimate the inhomogeneity of the bacterial patches used in their assays and discuss how the edge effects may affect their results and conclusions.

Bacterial patch inhomogeneity: We extensively tested the landscape of the bacterial patches by imaging fluorescently-labeled bacteria OP50-GFP (Bacterial Patch Density in Methods; Figure 2 - supplement 1-3). As the reviewer mentions, we observe significantly greater bacterial density at the patch edge. This within-patch spatial inhomogeneity results from areas of active proliferation of bacteria and likely complicates an animal’s ability to accurately assess the quantity of bacteria within a patch and, consequently, our ability to accurately compute a metric related to our assumptions of what the animal is sensing. In our study, we used the relative density of the patch edge where bacterial density is highest as a proxy for an animal’s assessment of bacterial patch density (Figure 2 – supplement 1). This decision was based on a previous finding that the time spent on the edge of a bacterial patch affected the dynamics of subsequent area-restricted search. While within-patch spatial inhomogeneity likely affects an animal’s ability to assess patch density, we do not believe that this qualitatively affects the results of our study. Both the patch densities tested (Figure 2 – supplement 3A) as well as our observations of time-dependent changes in exploitation (Figure 2E,N-O; Figure 3H-I) maintained a monotonic relationship. Therefore, alternative methods of patch density estimation should yield similar results. We have added additional discussion on this topic to our manuscript (lines 578-593).

The authors claim that their methods (GMM and semi-supervised QDA) are unbiased. This seems unlikely as the QDA involves supervision. The authors need to provide additional explanation on this point.

Semi-supervised QDA labelling: We have removed the term “unbiased” to avoid any misinterpretation of the methodology and clarified our method of labelling used for “supervising” QDA. Specifically, we made two simple assumptions: 1) animals must have sensed the patch if they exploited it and 2) animals must not have sensed the patch if there was no bacteria to sense. Thus, we labeled encounters as sensing if they were found to be exploitatory as we assume that sensation is prerequisite to exploitation; and we labeled encounters as non-sensing for events where animals encountered patches lacking bacteria (OD₆₀₀ = 0). All other points were non-labeled prior to learning the model. In this way, our labels were based on the experimental design and results of the GMM, an unsupervised method; rather than any expectations we had about what sensing should look like. The semi-supervised QDA method then used these initial labels to iteratively fit a paraboloid that best separated these clusters, by minimizing the posterior variance of classification (lines 1012-1021). See Figure 2 - supplement 8A-B for a visualization showing the labelled data.

Based on the authors' result, worms behaviorally exhibit their preferences toward food abundance (density), which results in a preference scale for a range of densities. Does this scale vary with the worms' initial cultivation states? The author partially verified that by observing starved worms. This hypothesis could be better tested if the authors could analyze the decision-making of the worms that were initially cultivated with different densities of bacterial food.

While we agree with the reviewer that testing the effects of varying bacterial density during animal development (cultivation) is a very interesting experiment, it is not feasible at this time. We focused our revised manuscript to include only assertions about the effects of recent experiences and added this inquiry as a future direction as described above in our response to Reviewer #1 under Cultivation density.

It would be helpful to elaborate more on how the framework developed in this paper can be applied more broadly to other behaviors and/or organisms and how it may influence our understanding of decision-making across species.

We thank the reviewer for alerting us to this gap in our discussion. We have added additional commentary about our model and its utility to the discussion section (lines 667-695).

Reviewer #3 (Recommendations for the authors):

Sensing vs. non-sensing

Perhaps a more ethologically accurate term to describe this behavior would be "ignoring" rather than "not sensing". If the authors feel strongly about using the term "not sensing", then they should provide experimental evidence supporting this claim. However, I think simply changing the terminology negates these experiments.

We thank the reviewer for their thoughtful comments. While we agree with the reviewer that the term “non-sensing” may not be ethologically accurate (see response to Public Review above under Interpretation of “non-sensing” encounters), we interpret the term “ignoring” to mean that the animal sensed the patches but decided not to react. We have chosen to replace the term “non-sensing” with “non-responding” to best indicate the ethological interpretation of our observation. Nonetheless, we believe that it remains possible that animals are truly not sensing the bacterial patches as our method of classification compared the behavior against encounters with patches lacking bacteria (as described above in response to Reviewer #2 under Semi-supervised QDA labelling).

History-dependence of the GLM

Perhaps a simpler approach would be to say the worm senses everything, and this accumulative memory affects the decision to exploit. For example, the animal essentially experiences two feeding states: feeding on patches, and starvation off of patches.

The level of satiety could be modeled linearly:

Satiety(t_enter:t_leave) = k_feed*patch_density*delta_t

Where k_feed is some model parameter for rate of satiety signal accumulation, t_enter is the time the animal entered the patch, t_leave is the time the animal left the patch, and delta_t is the difference between the two. Perhaps you could add a saturation limit to this, but given your data, I doubt that is the case.

Starvation could be modeled as simply a decay from the last satiety signal:

Starvation(t_leave:t_enter) = Satiety(t_leave)*exp(-k_starve*delta_t).

Where starvation is the rate constant for the decay of the satiety signal.

For the logistic model, the logistic parameter is simply the difference between the current patch density and the current satiety signal.

A nice thing about this approach is that it negates the need to categorize your patches. All patch encounters matter. Brief patch encounters (categorized as non-sensing and not used in the prior GLM) naturally produce a very small satiety signal and contribute very little to the exploit decision. Another nice thing about this approach is that it gives you memory timescales, that are testable. There is a rate of satiety accumulation and a rate of satiety loss. You should be able to predict behavior with lower patch density, assuming the rate constants hold. (I am not advocating you do more experiments here, just pointing out a nice feature of this approach).

You could possibly apply this to a GLM for velocity on a non-exploited patch as well, though I assume this would be a linear GLM, given the velocity distributions you provided.

We thank the reviewer for their time and thoughtfulness in thinking about our model. The reviewer’s proposed model seems entirely reasonable and could aid in elucidating the time component of how prior experience affects decision-making. However, we decided to keep our paper focused on using a minimal model to answer a set of core questions (e.g., Does encounter history or satiety influence decision-making?) (see above under Model design for a more detailed response). Future studies investigating the mechanisms of these foraging decisions should open the door for more mechanistically accurate models. We have expanded our discussion of the model to include this assertion (lines 667-695).

Author response:

The following is the authors’ response to the original reviews

Recommendations for the authors:

Reviewer #1 (Recommendations For The Authors):

(1) Sample size: If the sample size of the study is increased, more confidence and new insights can be inferred about myometrial enhancer-mediated gene regulation in term pregnancy. Such a small sample size (N = 3) limits the statistical power of the study. As mentioned in the manuscript they failed to identify chromatin loops in the second subject's biopsy is observed due to a limited sample.

We agree with the reviewer’s comment about the sample size. We sincerely hope the result of this study would increase the interest of stakeholders to fund future projects in a larger scale.

(2) Figure quality: There is a lack of good representations of the results (e.g., screenshots of tables as figure panels!) as well as missing interpretations that might add value to the manuscript.

Figure 1B and 2B have been converted to the pie chart format.

(3) Definition of super-enhancer: The definition of super-enhancer is not clear. Also, the computational merging of enhancers to define super-enhancers should be described better.

Added more details about tool and parameter setting in the Method section of “Identification of super enhancers”:

“Identification of super enhancers

H3K27ac-positive enhancers were defined as regions of H3K27ac ChIP-seq peaks in each sample. The enhancers within 12.5Kb were merged by using bedtools merge function with parameter “-d 12500”. The combined enhancer regions were called super enhancers if they were larger than 15Kb. The common super enhancers from multiple samples were used for downstream analysis.”

Reference:

Whyte WA, Orlando DA, Hnisz D, Abraham BJ, Lin CY, Kagey MH, Rahl PB, Lee TI, Young RA. Master transcription factors and mediator establish super-enhancers at key cell identity genes. Cell. 2013 Apr 11;153(2):307-19. doi: 10.1016/j.cell.2013.03.035. PMID: 23582322; PMCID: PMC3653129.

(4) Assay-Specific Limitations: Each assay employed in the study, such as ChIP-Seq and CRISPRa-based Perturb-Seq, has its limitations, including potential biases, sensitivity issues, and technical challenges, which could impact the accuracy and reliability of the results. These limitations should be addressed properly to avoid false-positive results and improve the interpretability of the results.

The major limitations of the CRISPRa-based Perturb-Seq protocol in this study are the use of the hTERT-HM cells and the two-vector system for transduction. While hTERT-HM cells are a much easier platform in terms of technical operation, primary human myometrial cells are generally considered retaining a molecular context that is closer to the in vivo tissues. Due to the limitation on the efficiency of having two vectors simultaneously present in the same cell, hTERT-HM cells are much more affordable and operationally feasible to conduct the experiment. Future advancements on the increase of viral vector payload capacity may overcome this challenge and open the venue to perform the assay on primary human myometrial cells.

(5) Sample collection and comparison: There is mention of matched gravid term and non-gravid samples whereas no description or use of control samples was found in the results. Also, the comparison of non-labor samples with labor samples would provide a better understanding of epigenomic and transcriptomic events of myometrium leading to laboring events.

The description has been updated:

“Collection of myometrial specimens

Permission to collect human tissue specimens was prospectively obtained from individuals undergoing hysterectomy or cesarean section for benign clinical indications (H-33461). Gravid myometrial tissue was obtained from the margin of the hysterotomy in women undergoing term cesarean sections (>38 weeks estimated gestational age) without evidence of labor. Non-gravid myometrial tissue was collected from pre-menopausal women undergoing hysterectomy for benign conditions. Specimens from gravid women receiving treatment for pre-eclampsia, eclampsia, pregnancy-related hypertension, or pre-term labor were excluded.”

(6) Lack of clarity:

(6a) It is written as 'Chromatin Conformation Capture (Hi-C)'. I think Hi-C is Histone Capture and 3C is Chromosome Conformation Capture! This needs clear writing.

As the reviewer suggested, to make it clear, we have changed the text “A high throughput chromatin conformation capture (Hi-C) assay” to “A High-throughput Chromosome Conformation Capture (Hi-C) assay”.

(6b) In multiple places, 'PLCL2' gene is written as 'PCLC2'.

Corrected as suggested.

(6c) What is the biological relevance of considering 'active' genes with FPKM {greater than or equal to} 1? This needs clarification.

In RNA-seq analysis, the gene expression levels are often quantified using FPKM (Fragments Per Kilobase of transcript per Million mapped reads). Setting a threshold of FPKM for defining "active" genes in RNA-seq analysis is biologically relevant, because it helps to distinguish between genuinely expressed genes and background noise. It helps researchers focus on genes, which are more likely to have a significant biological impact. A common threshold for defining "active" genes is FPKM ≥ 1. Genes with FPKM values below this threshold may be transcribed at very low levels or could be background noise.

(6d) The understanding of differentially methylated genes at promoters is underrated as per the authors. But, why leaving DNA methylation apart, they selected histone modification as the basis of epigenetic reprogramming in terms of myometrium is unclear.

DNA methylation indeed plays a crucial role in evaluating the impact of cis-acting elements on gene regulation. Large-scale studies, such as the comprehensive analysis of the myometrial methylome landscape in human biopsies (Paul et al., JCI Insight, 2022, PMID: 36066972), have provided valuable insights. When integrated with histone modification and chromatin looping data, contributed by our group and collaborators, future secondary analyses leveraging machine learning are poised to further elucidate the mechanisms underlying myometrial transcriptional regulation.

(6e) How does the identification of PGR as an upstream regulator of PLCL2 gene expression in human myometrial cells contribute to our understanding of progesterone signaling in myometrial function?

In a previous study, we demonstrated a positive correlation between PLCL2 and PGR expression in a mouse model and identified PLCL2's role in negatively modulating oxytocin-induced myometrial cell contraction (Peavy et al., PNAS, 2021, PMID: 33707208). The present study builds on this by providing evidence for a direct regulatory mechanism in which PGR influences PLCL2 transcription, likely through a cis-acting element located 35 kb upstream. These findings suggest that PLCL2 acts as a mediator of PGR-dependent myometrial quiescence prior to labor, rather than merely participating in a parallel pathway. Further in vivo studies are necessary to delineate the extent to which PLCL2 mediates PGR activity, particularly the contraction-dampening function of the PGR-B isoform.

(7) Grammatical error: The manuscript has numerous grammatical errors. Please correct them.

Corrections have been made as suggested.

(8) Use of single-cell data: Though from the Methods section, it can be understood that single-cell RNA-seq was done to identify CRISPRa gRNA expressing cells to characterize the effect of gene activation, some results from single-cell data e.g., cell clustering, cell types, gRNA expression across clusters could be added for better elucidation.

As reviewer suggested, we have prepared a file “PerturbSeq_summary.xlsx” (Dataset S9) to provide additional results of perturb-seq data analysis. It includes 2 spreadsheets, “Cell_per_gRNA” for clustering and “Protospacer_calls_per_cell” for gRNA expression across clusters.

Reviewer #2 (Recommendations For The Authors):

(1) The following are a number of grammatical issues in the abstract. I suggest having a careful read of the entire manuscript to identify additional grammatical issues as I may not be able to highlight all of these issues.

(1a) "The myometrium plays a critical component during pregnancy." change component to role.

(1b) "It is responsible for the uterus' structural integrity and force generation at term," à replace "," with "."

(1c) Also, I suggest rephrasing the first 2 sentences to: The myometrium plays a critical role during pregnancy as it is responsible for both the structural integrity of the uterus and force generation at term.

(1d) "Here we investigated the human term pregnant nonlabor myometrial biopsies for transcriptome, enhancer histone mark cistrome, and chromatin conformation pattern mapping." Remove "the", and modify to "Here we investigated human term pregnant".

(1e) Missing period and sentence fragment, "PGR overexpression facilitated PLCL2 gene expression in myometrial cells Using CRISPR activation the functionality of a PGR putative enhancer 35-kilobases upstream of the contractile-restrictive gene PLCL2.

Corrections have been made as suggested.

(2) Sentence fragment: Studies on the role of steroid hormone receptors in myometrial remodeling have provided evidence that the withdrawal of functional progesterone signaling at term is due to a stoichiometric increase of progesterone receptor (PGR) A to B isoform-related estrogen receptor (ESR) alpha expression activation at term. (Mesiano, Chan et al. 2002) (Merlino, Welsh et al. 2007) (Nadeem, Shynlova et al. 2016).

The statement has been updated:

“Studies on the role of steroid hormone receptors in myometrial remodeling suggest that the withdrawal of functional progesterone signaling at term results from a stoichiometric shift favoring the PGR-A isoform over PGR-B. This shift is associated with increased activation of estrogen receptor alpha (ESR1) expression at term (Mesiano, Chan et al. 2002) (Merlino, Welsh et al. 2007) (Nadeem, Shynlova et al. 2016).”

(3) FOS:JUN heterodimers are implicated to be critical for the initiation of labor through transcriptional regulation of gap junction proteins such as Cx43 (Nadeem, Farine et al. 2018) (Balducci, Risek et al. 1993).

Use Gja1 (Gap junction alpha 1) as the current correct gene, not Cx43.

Also, several references predate Nadeem, Farine et al. 2018 and are more appropriate to use as references for the role of Ap-1 proteins in regulating Gja1; PMID: 15618352 and PMID: 12064606 were the first to show this relationship in myometrial cells.

The statement has been updated as suggested:

“FOS:JUN heterodimers are implicated to be critical for the initiation of labor through transcriptional regulation of gap junction proteins such as GJA1 (Nadeem, Farine et al. 2018) (Balducci, Risek et al. 1993)”

(4) Define PLCL2 on first use.

Updated as suggested.

(5) There are a number of issues with this section, "Matched sSpecimens of gravid myometrium were collected at the margin of hysterotomy from women undergoing clinically indicated cesarean section at term (>38 weeks estimated gestation age) without evidence of labor. Specimens of healthy, non-gravid myometrium were also pecimens were collected from uteri removed from pre-menopausal women undergoing hysterectomy for benign clinical indications."

The description has been updated:

“Collection of myometrial specimens

Permission to collect human tissue specimens was prospectively obtained from individuals undergoing hysterectomy or cesarean section for benign clinical indications (H-33461). Gravid myometrial tissue was obtained from the margin of the hysterotomy in women undergoing term cesarean sections (>38 weeks estimated gestational age) without evidence of labor. Non-gravid myometrial tissue was collected from pre-menopausal women undergoing hysterectomy for benign conditions. Specimens from gravid women receiving treatment for pre-eclampsia, eclampsia, pregnancy-related hypertension, or pre-term labor were excluded.”

(6) Enriched motifs were identified by HOMER (Hypergeometric Optimization of Motif EnRichment) v4.11 (Heinz, Benner et al. 2010).

Please clarify what background is used for motif enrichment.

We used the default background sequences generated by HOMER from a set of random genomic sequences matching the input sequences in terms of basic properties, such as GC content and length. We have added more details in the Method section:

“DNA-binding factor motif enrichment analysis

Enriched motifs were identified by HOMER (Hypergeometric Optimization of Motif EnRichment) v4.11 with default background sequences matching the input sequences (Heinz, Benner et al. 2010).”

(7) "Six of the seven regions are also co-localized with previously published genome occupancy of transcription regulators curated by the ReMap Atlas"

Please clarify if this Atlas includes myometrial tissues or not and clarify the cell types included in the atlas.

According to the UCSC Genome Browser and the reference by Hammal et al. (2022), the current ReMap database includes PGR ChIP-seq data from human myometrial biopsies, available under NCBI GEO accession number GSE137550, alongside data from various other cell and tissue types. ReMap provides valuable insights into potential functional cis-acting elements in the genome from a systems biology perspective. However, tissue specificity requires independent validation.

(8) "Notably, 76% of the putative super-enhancers are co-localized with known PGR-occupied regions in the human myometrial tissue (Figure S2). This is significantly higher than the 20% co-localization in the regular enhancer group (Figure S2)."

Because there is a huge difference in the size of the putative super enhancer regions and the isolated enhancers this comparison is not appropriate as conducted. The comparison needs to account for the difference in size of the regions. Please provide P values for significance statements.

We acknowledge the reviewer's concern that our initial statement was overstated and potentially misleading, given the substantial difference in size between putative super-enhancer regions and regular enhancers. Rather than emphasizing the enrichment, it would be more accurate to simply describe our observation that super-enhancers encompass more PGR-occupied regions.

Here is the updated version:

“Notably, 76% of the putative super-enhancers co-localize with known PGR-occupied regions in human myometrial tissue, compared to 20% co-localization observed in regular enhancers (Figure S2).”

Reviewer #3 (Recommendations For The Authors):

(1) Title is extremely misleading, as here we do not get a view of the epigenomic landscape, but rather sparce data related to H3K27ac and H3K4me (focusing on enhancers) and chromatin conformation associated with the PLCL2 transcription start site (TSS).

As suggested, the title is modified to “Assessment of the Histone Mark-based Epigenomic Landscape in Human Myometrium at Term Pregnancy”.

(2) Improve the first result paragraph by providing a clear rationale for the experiments and their objectives, as well as introducing the samples used. Rather than simply listing approaches and end results in Table 1, offer concise explanations for the experiments alongside the supporting data presented in detailed figures. Using appropriate figures/graphs to effectively contextualize these datasets would be greatly appreciated by readers and would add more value to this research. Currently, it is difficult for us to assess and appreciate the quality of the data.

The following statement is included in the beginning of the Result section:

"To better understand the regulatory network shaping the myometrial transcriptome before labor, we analyzed transcriptome and putative enhancers in individual human myometrial specimens. Using RNA-seq, we identified actively expressed RNAs, while ChIP-seq for H3K27ac and H3K4me1 was used to map putative enhancers. Active genes were associated with nearby putative enhancers based on their genomic proximity. Additionally, chromatin looping patterns were mapped using Hi-C to further link active genes and putative enhancers within the same chromatin loops."

(3) The statistics for every sequencing approach need to be provided for each sample (e.g., RNA-seq: number of total reads, number of mapped reads, % of mapped reads; ChIP-Seq: number of mapped reads, % of mapped reads, % of duplicates).

We have generated the summary table of each dataset included in this study (Dataset S7) [NGS-summary.xls].

(4) Figure S1: The rationale behind comparing the Dotts study and yours regarding H3K27ac-positive regions needs to be better defined. Why is this performed if the data will not be used afterwards? What are the conserved regions associated with vs the ones that are variable? Is this biologically relevant? Why not use only the regions conserved between the 6 samples, to have more robust conclusions?

The purpose of comparing our data with the Dotts dataset is to highlight the degree of variation across studies. In this study, we focused on addressing specific biological questions using our own dataset rather than developing methodologies for meta-analysis. Future advancements in meta-analysis techniques could leverage the combined power of multiple datasets to provide deeper insights.

(5) Perhaps due to a lack of details, I am unable to ascertain how the putative myometrial enhancers were defined. In Dataset S1, it is stated, "we define the regions that have overlapping H3K27ac and H3K4me1 marks as putative myometrial enhancers at the term pregnant nonlabor stage (Dataset S1)". Within Dataset S1, for subjects 1, 2, and 3, H3K27ac and H3K4me1 double-positive enhancers are shown in term pregnant, non-labor human myometrial specimens, with approximately 100 regions corresponding to 131 (sample 1), 127 (sample 2), and 140 (sample 3) common peaks. However, in Figure 1a, reference is made to the 13114 putative enhancers commonly present across the three specimens. Is Dataset S1 intended to represent only a small fraction of the 13114 putative enhancers? Detailed analyses need to be conducted and better showcased.

Dataset S1 has been updated to list all 13,114 putative enhancers.

(6) For the gene expression analyses of RNA-seq data, FPKM values were utilized. However, it is unclear why the gene expression count matrix was normalized based on the ratio of total mapped read pairs in each sample to 56.5 million for the term myometrial specimens. I would recommend exercising caution regarding the use of FPKM expression units, as samples are normalized only within themselves, lacking cross-sample normalization. Consequently, due to external factors unaccounted for by this normalization method, a value of 10 in one sample may not equate to 10 in another.

We value the reviewer’s input. This question will be addressed in future secondary data analyses with suitable methodologies, as it is beyond the scope of this study.

(7) In Figure 1b, the authors have categorized their 12157 active genes into 3 bins based on FPKM values: >5 FPKM >1, >15 FPKM >5, and >15 FPKM. However, in the text, they describe these as 'actively high-expressing genes (FPKM >= 15)'. I would advise caution regarding the interpretation of these values, as an FPKM of 15 is not typically associated with highly expressed genes. According to literature and resources such as the Expression Atlas, an FPKM of 15 is generally considered to represent a low to medium expression level.

We appreciate the reviewer’s feedback. This question will be revisited during secondary data analyses using appropriate methodologies, as it falls outside the scope of the present study.

To increase readability and clarity, we modified the sentence as following: More than 40% of the 540 putative super enhancers are located within a 100-kilobase distance to high-expressing genes (FPKM >= 15), while only 7.3% of putative myometrial super enhancers are found near low-expressing genes (5 > FPKM >=1) (Figure 2B).

(8) Out of the 12157 active genes, approximately two-thirds have an FPKM >15. Was this expected? How does this correspond to what is observed in the literature, particularly in other similar studies (https://pubmed.ncbi.nlm.nih.gov/30988671/ ; https://pubmed.ncbi.nlm.nih.gov/35260533/ ) .

This is indeed an intriguing question that merits further exploration in future secondary analyses.

(9) It is also surprising to see that for the motif enrichment analysis (Fig. 1C), the P-values are small. This is probably because the percentage of target sequences with the motif is very similar to the percentage of background sequences with the motif. For instance, for selected genes in Figure 1C: AP-1 (50.68% vs. 46.50%), STAT5 (28.08% vs. 25.04%), PGR (17.90% vs. 16.12%), etc. Can one really say that you have a biologically relevant enrichment for values that are so close between target sequences and background sequences?

Reviewer’s comment is noted. Biological relevance shall be experimentally examined though wet-lab assays in future studies.

(10) For Figure 2, again not convinced that FPKM >= 15 can be used to say: Compared with the regular putative enhancers, the putative myometrial super-enhancers are found more frequently near active genes that are expressed at relatively higher levels (Figure 1B and Figure 2B). A higher threshold should be used if they want to say this.

To compare the association of putative enhancers with active genes expressed at different levels, we categorized the active genes into three groups based on their FPKM (Fragments Per Kilobase of transcript per Million mapped reads) values. These groups are defined as follows: the top third active genes (FPKM ≥ 15), the middle third active genes (5 ≤ FPKM < 15), and the bottom third active genes (1 ≤ FPKM < 5). By "active genes expressed at relatively higher levels," we refer specifically to the top third active genes with FPKM values of 15 or higher, indicating their relatively higher expression levels compared to the other groups of active genes.

(11) More detailed explanations and methods are needed regarding how the data for Figure S2 was obtained.

The following details were added to the methods section:

“Colocalization of super enhancers and PGR genome occupancy was compared by calling peaks from previously published PGR ChIP-seq data (GSM4081683 and GSM4081684). The percentages of enhancers and super enhancers that manifest PGR occupancy were calculated by overlapping the genomic regions in each category with PGR occupancy regions.”

(12) In Figure 2C, there is no information provided on the genes used to obtain the results. It would be helpful to include examples of these genes, along with their expression values, for instance.

The expression levels of the 346 active genes that are associated with myometrial super enhancers are included in Dataset S4, along with results of the updated gene ontology enrichment analysis using the Database for Annotation, Visualization, and Integrated Discovery (DAVID) of Knowledgebase v2024q4. Selected pathways of interest are listed in updated Figure 2C.

(13) The linking of PLCL2-related data to the first part of the story is lacking, and the rationale behind it is missing. This entire section should be more detailed, and the data should be expanded to better reflect the context.

As suggested, we included the following statement at the beginning of the section “Cis-acting elements for the control of the contractile gene PLCL2”:

“We previously demonstrated the positive correlation of PLCL2 and PGR expression in a mouse model and PLCL2’s function on negatively modulating oxytocin-induced myometrial cell contraction (Peavy et al., 2021). However, the mechanism underlies the PGR regulation of PLCL2 remains unclear. Taking advantage of the mapped myometrial cis-acting elements, we aimed to identify the cis-acting elements that may contribute to the PLCL2 transcriptional regulation with a special interest on the PGR-related enhancers.”

The context is that our results provide additional evidence to support a direct regulation mechanism of PGR on the PLCL2 transcription, likely though the 35-kb upstream cis-acting element. This finding suggests that PLCL2 likely plays a mediator’s role of PGR dependent myometrial quiescence before laboring rather than a mere passenger on a parallel pathway. Further studies using in vivo models are needed to determine the extent of PLCL2 in mediating PGR, especially PGR-B isoform’s contraction-dampening function.

(14) The entire Hi-C data should be presented to allow for the assessment of its quality and further value.

The revised manuscript has included the Hi-C quality control summary in Dataset S8 [HiC-QC-Summary.xlsx].

(15) The authors state: "For the purpose of functional screening, we focus on H3K27ac signals instead of using H3K27ac/H3K4me1 double positive criterium to cast a wider net." However, it is unclear how many of the targeted regions contained H3K27ac/H3K4me1 peaks. Were enhancers or super-enhancers targeted, and if so, how did they compare to H3K27ac sites?

The numbers of H3K27ac/H3K4me1 double positive peaks are recorded in Figure 1A. Compared to the numbers of H3K27ac intervals (Table 1), the H3K27ac/H3K4me1 double positive peaks are 62.9%, 70.7%, and 61.2% of corresponding H3K27ac intervals in each individual specimen.

(16) For the first set of data (Table 1), the authors state, "Together, these results reveal an epigenomic landscape in the human term pregnant myometrial tissue before the onset of labor, which we use as a resource to investigate the molecular mechanisms that prepare the myometrium for subsequent parturition." While it is acknowledged that an epigenetic landscape exists in all tissues, there is a lack of clarity regarding this landscape in the current manuscript, as we are only presented with a table containing numbers.

This sentence has been revised to: “Together, these results delineate a map of H3K27ac and H3K4me1 positive signals in the human term pregnant myometrial tissue before the onset of labor, which we use as a resource to investigate the molecular mechanisms that prepare the myometrium for subsequent parturition.”

(17) For S1, the authors conclude: These data together highlight the degree of variation in mapping the epigenome among specimens and datasets. This conclusion seems somewhat perplexing, and I find myself in partial disagreement. Firstly, providing a clear rationale for this section would strengthen the conclusions. It's important to consider what factors may contribute to this variability. It could simply be attributed to differences in experimental settings, such as variations in samples, protocols used, antibodies, sequencing departments, or overall data quality. Deeper analyses of the data could have provided more information.

We agree with the reviewer that deeper analyses are needed in order to extract more information among studies. However, appropriate methods for meta-analyses should be carefully evaluated and employed for this purpose. We humbly believe that such a task should belong to future studies that may combine available datasets for secondary analyses, leveraging the collective contribution of the reproductive biology community.

(18) In the methods section, please include an explanation of how enhancers and super-enhancers were defined or add appropriate citations for reference.

Added more details about tool and parameter setting in the Method section of “Identification of super enhancers”.

“Identification of super enhancers

H3K27ac-positive enhancers were defined as regions of H3K27ac ChIP-seq peaks in each sample. The enhancers within 12.5Kb were merged by using bedtools merge function with parameter “-d 12500”. The combined enhancer regions were called super enhancers if they were larger than 15Kb. The common super enhancers from multiple samples were used for downstream analysis.”

Reference:

Whyte WA, Orlando DA, Hnisz D, Abraham BJ, Lin CY, Kagey MH, Rahl PB, Lee TI, Young RA. Master transcription factors and mediator establish super-enhancers at key cell identity genes. Cell. 2013 Apr 11;153(2):307-19. doi: 10.1016/j.cell.2013.03.035. PMID: 23582322; PMCID: PMC3653129.

(19) Additional description on the "Inferred myometrial PGR activities and the correlation analysis "method section should be included to enhance clarity and understanding.

The description has been updated:

“The inferred PGR activities were represented by the T-score, which was derived by inputting the mouse myometrial Pgr gene signature, based on the differentially expressed genes between control and myometrial Pgr knockout groups at mid-pregnancy (Wu, Wang et al., 2022), into the SEMIPs application (Li, Bushel et al., 2021). The T-scores were computed using this signature alongside the normalized gene expression counts (FPKM) from 43 human myometrial biopsy specimens.”

(20) How was the qPCR analysis performed? Was the ddCT method utilized, and was a reference gene used for control? Additional information would be beneficial.

Quantifying relative mRNA levels was performed via the standard curve method.

The following details were added: “Relative levels of genes of interest were normalized to the 18S rRNA.”

(21) Regarding the RNA-Seq analysis of Provera-treated human Myometrial Specimens, the continued use of FPKM is not ideal due to potential differences in RNA composition between libraries. Additionally, clarification is needed on why Cufflinks 2.0.2 was used, considering it is no longer supported.

FPKM (Fragments Per Kilobase of transcript per Million mapped reads) is used in RNA-Seq analysis, because it allows for the normalization of gene expression data, accounting for differences in gene length and sequencing depth, and facilitates comparability across different genes and libraries. This makes it one of the essential tools for accurately measuring and comparing gene expression levels in various biological and clinical research contexts.

CuffLinks was once a popular tool for analyzing RNA-seq data, transcriptome assembly, and DEG identification. Its usage has declined in recent years due to the emergence of newer and more advanced tools. The main reason is that it was used for RNA-seq analysis at early stage of this study a few years ago. For the purpose of comparison and consistency, we continued using this tool for later RNA-seq analysis. If we start a new project now, we will choose newer tools, such as HISAT2, Salmon, and DEseq2.

(22) Overall, sentence structure and typos need to be corrected across the text. Here are some examples:

Line 17: at term, emerging studies.

Line 20-22: Here we investigated the human term pregnant nonlabor myometrial biopsies for transcriptome, enhancer histone mark cistrome, and chromatin conformation pattern mapping.

Line 30-32: PGR overexpression facilitated PLCL2 gene expression in myometrial cells Using CRISPR activation the functionality of a PGR putative enhancer 35-kilobases upstream of the contractile-restrictive gene PLCL2.

Line 66-70: However, the role of differential myometrial DNA methylation at contractility-driving gene promoter CpG islands in preterm birth is not thought to be major (Mitsuya, Singh et al. 2014), but given that DNA methylation-mediated gene regulation often occurs outside of CpG islands (Irizarry, Ladd-Acosta et al. 2009), there is still work to be done at this interface.

Line 80-83: Putative enhancers upstream of the PLCL2, a gene encoding for the protein PLCL2 which has been implicated in the modulation of calcium signaling (Uji, Matsuda et al. 2002) and maintenance of myometrial quiescence (Peavey, Wu et al. 2021), transcriptional start site were subject to functional assessment using CRISPR activation based assays.

Line 290 : sSpecimens

We appreciate the reviewer’s kind efforts and have made changes accordingly.

Author response:

The following is the authors’ response to the original reviews

Reviewer #1 (Recommendations for the authors):

Major comments

(1) The section on page 20 describing the proteomic analysis of EVs is poorly written and confusing, with a lot of data in the supplement. It is not clear what the proteomics data actually means.

We appreciate your feedback on the clarity of the proteomic analysis section. We have rewritten the section on page 20 with more detained information to provide a clearer explanation of the proteomics data and its biological significance. Additionally, we have incorporated a comparative analysis of the EV and total cell lysate proteomes (Fig. 8E, Supplementary Fig. S7A, Supplementary Tables 3 and 4) for supplemental data interpretation.

(2) The order of the data could be improved.

We appreciate your feedback regarding the data organization. We have reorganized the order and position of some data in a more structured and coherent manner, as suggested by the reviewers.

- Reorganization of the qPCR data (previously Fig. 1C) as Fig. 3A

- Removal of the data on the growth analysis on raffinose media (previously Fig. 7H).

-Reorganization of the spotting data of the double mutant (previously Fig 3B) to Supplementary Fig. S3B

- Reorganization of the subcellular localization data (previously Fig 3E) to Supplementary Fig. S4A

(3) The discussion is repetitive with the introduction and merely summarizes the results and speculates on the mechanism of how the absence of UGGT, leading to ERQC defects, results in defective EV biogenesis/cargo loading in C. neoformans.

We removed several repetitive sentences in the discussion and provided additional information on proteome analysis.

Other questions and comments

(1) Instead of comprehensively analyzing EVs from the UGG1 mutant, a more informative approach to better understanding how defects in N-linked glycosylation impact secretion, would be to do a proteomic analysis on the total secretions (including beta glucanase-treated cells to release classically secreted proteins from the cell wall) and EVs.

We agree that a comprehensive proteomic analysis of total secretions and classically secreted proteins would provide deeper insights into how defects in N-glycosylation impact secretion in C. neoformans. To address this concern, we performed an additional set of proteomic analyses, the proteome profiles of total cell lysates and the secretome of C. neoformans cultivated in SD broth and presented the results as Supplementary Table S5 and Supplementary Fig. S7B. These additional analyses provide further insights into the impact of UGG1 deletion on both conventional and unconventional secretion pathways, supporting a more pronounced effect of the UGG1 defect on EV-mediated trafficking. The discussion has been updated accordingly (Page 22, lines 509-514).

(2) The melanization defect in Ugg1 mutant is not strong. Could the reduction be due to partially compromised Ugg1 mutant growth at 30{degree sign}C as indicated in the spot tests. Were photos of the spot dilution assays taken at 1 and 2 days to investigate slower growth? Or alternatively were growth curves taken in a liquid culture?

For accuracy of melanin synthesis defect, in addition to analysis on L-DOPA plates, we had assessed melanin production in liquid L-DOPA medium following a 3-day incubation, and the melanin production in liquid media was normalized by cell density (OD₆₀₀). The data on normalized melanin production is now included as Fig. 4B in the revised manuscript. The defective laccase activity in the _ugg1_Δ mutant (Fig. 7C) further corroborates our melanization assay results, which is additionally mentioned in the text (Page 18, lines 393-395).

(3) Is it accurate to say that some virulence factors (i.e. melanin, capsule and phosphatases) are predominantly trafficked through EV's in C. neoformans? Have studies been done to determine the proportion of virulence factors trafficked via EV's versus traditional secretion?

We thank you for the thoughtful comments. Some virulence factors, such as urease, melanin and capsule polysaccharides, lack a signal peptide required for targeting for the conventional ER/Golgi secretion pathway. It is generally assumed that the trafficking of these factors in C. neoformans is predominantly mediated by non-conventional secretion via EVs. Additionally, even some virulence factors with signal peptides, such as laccase and phosphatases, are also transported via EVs besides the conventional secretion. The quantitative analysis to compare the proportion of virulence factors secretion via EVs versus the conventional pathway has not been yet reported, despite that genetic evidence suggests that conventional secretion also plays a significant role in the export of capsule polysaccharides. Thus, we were also careful not to highlight EV as the main route of virulence factors in the manuscript.

(4) There is insufficient background in the introduction linking what is known about the ERQC process to secretion in general. The topic changes from the ERQC process to fungal virulence factor, with a primary focus on non-classical (EV-based) secretion. Classical secretion should also be discussed without assuming that non classical (EV) secretion is the major pathway contributing to fungal virulence.

We appreciate your insightful comments highlighting the need for more background on the ERQC process and its relationship with secretion. To address the reviewer’s concerns, we have added sentences to describe the key roles of ERQC in conventional protein secretion in the Introduction (Page 5, lines 102-106).

(5) Figure 1A. What does the blue filled circle with the red outline signify? Fig 1 A legend is not well explained. A summary using material provided in the intro/discussion should be included to briefly explain the process and the differences between fungal species. Please also be aware that the intro starts describing the human ERQC process and then switches to what happens in S. cerevisiae.

We have revised Figure 1A by removing the red circle and updated the figure legend in the revised manuscript to include more detailed information about the ERQC differences across higher eukaryotes and fungal species.

(6) Figure 2A. There are no units on the Y-axis. Presumably, the scale is the same for all 3 strains.

Thank you for your comments. The Y-axis is the same for all three strains and, as in Fig. 2C, and represents the relative fluorescence intensity obtained from the HPLC analysis. We added the units on the Y-axis in Fig. 2A.

(7) If Mnl1 and 2 have proposed roles in proteasomal degradation, wouldn't they be expected to have ER retention signals, like Ugg1?

We appreciate your valuable insights regarding the absence of ER retention signals in Mnl1 and Mnl2. Previous studies have shown that Saccharomyces cerevisiae Mnl1/Htm1 does not possess canonical KDEL/HDEL-like ER retention signals. Instead, its retention in the ER lumen is facilitated through its interaction with protein disulfide isomerase Pdi1, which contains an HDEL sequence (Gauss et al. 2011). Thus, it is expected that non-canonical retention mechanisms—such as interactions with other ER proteins—could contribute to the retention of Mnl1 and Mnl2 within the ER. We added this information to the revised manuscript (Page 8, lines 154-159).

(8) Figure 1 C qPCR showing change in mRNA in response to ER stress should not be grouped in this figure. It could be standalone or discussed when the spot dilution assays are performed. Anyway, spots tests are more convincing of a role in stress response than qPCR as the ugg1 mutant is sensitive to tunicamycin, DTT and cell wall stressing agents.

As suggested by the reviewer, we have reorganized the qPCR data as a part of Figure 3 (Figure 3A) in the revised manuscript.

(9) It is odd that mns1/101 mutants are not sensitive to ER and CW stress given their proposed differing location/function in the pathway (Figure 1) determined from the N-linked profiling. Any explanation? Could there be redundancy?

We appreciate the reviewer’s observation regarding the lack of ER and CW stress sensitivity in the mns1_Δ and _mns101_Δ mutants, despite their proposed roles in _N-glycan processing. We had previously reported that the C. neoformans alg3_Δ mutant, lacking a critical enzyme responsible for the synthesis of Dol-PP-Man₆GlcNAc₂ in the _N-glycosylation pathway, exhibited clearly impaired N-glycan elongation, but showed no detectable growth defects even under stress conditions in vitro. However, alg3_Δ is avirulent in _in vivo pathogenicity (Thak et al., 2020). Similarly, the mns1_Δ_101_Δ double mutant shows glycan-processing defects that do not compromise cellular fitness under stress conditions but result in attenuated virulence in animal models. These findings suggest that some glycosylation-related defects may impact more severely _in vivo pathogenicity rather than in vitro stress sensitivity.

(10) Although the Silver-stained gels of the ugg1 mutant are not particularly informative, why weren't they (and Con A blots) performed for the other mutants?

The overall decrease of hypermannosylated glycans observed in the ugg1_Δ mutant allowed us to detect clear alterations in protein glycosylation patterns in the lectin blot using _Galanthus nivalis agglutinin, which recognizes terminal α1,2-, α1,3-, and α1,6-linked mannose residues. In contrast, the limited changes of a few glycan species in other mutants, including mns1_Δ, _mns101_Δ, and _mns1_Δ_101_Δ, are relatively subtle to be detected in the lectin blot, due to only minor differences in the average lengths of their _N-glycans compared to the WT. Therefore, we presented the lectin blotting data only for the _ugg1_Δ mutant.

(11) If there is ER stress under normal conditions in the Ugg1 mutant then technically this mutant should be growing more slowly under normal conditions. This is difficult to predict in a spot dilution assay where growth is only visualized at day three when any growth defect may have been corrected. The slower growth rather than the reduced secretion of GXM specifically is therefore more likely to be responsible for the reduced virulence.

We appreciate the reviewer’s insightful comment regarding the interplay between ER stress, growth defects, and virulence attenuation in the ugg1_Δ mutant. While retarded growth in _C. neoformans is often associated with reduced virulence, there are a few exceptions. For instance, disruptions in cell cycle progression in C. neoformans have been reported to result in larger capsule sizes, which rather enhance in vivo virulence when analyzed in Galleria mellonella infection models (García-Rodas et al., 2014). This highlights that growth defect alone is not sufficient for virulence attenuation. In the case of the _ugg1_Δ mutant, we speculate that the almost complete loss of virulence is attributed not only to its growth retardation but also to its impaired secretion of key virulence factors, including the polysaccharide capsule.

(12) The rationale for using leucine analogue 5',5',5'-trifluoroleucine (TFL), in a growth assay (Fig. 3C) to determine whether the defective ugg1Δ phenotypes are induced by ER stress caused by misfolded protein accumulation is not explained.

The leucine analogue 5',5',5'-trifluoroleucine (TFL) can be incorporated into newly synthesized proteins, disrupting normal folding and thus leading to the generation of misfolded proteins (Trotter et al., 2002; Cowie et al., 1959). In the context of a defective ERQC pathway, these misfolded proteins cannot be adequately repaired, resulting in their accumulation and triggering ER stress. Excessive ER stress may ultimately inhibit cell growth in the presence of TFL. This explanation has been incorporated into the revised manuscript (Page 11, lines 236–241).

(13) I would argue that only the Ugg1 and double Mns mutant were defective in virulence. For the single mutants, it looks like no difference was found relative to WT. The longer median survival of these mutants (if significant) is most likely due to poor infection technique.

We agree with the reviewer’s opinion that the mns1_Δ and _mns101_Δ single mutants have no significant difference in _in vivo virulence compared to the WT strain, unlike the _mns1_Δ_101_Δ double mutant which showed significant attenuated virulence. We had previously addressed that in the manuscript (Page 13, lines 267-269).

(14) The authors conclude that the ugg1Δ strain specifically is impaired in extracellular secretion of capsular polysaccharides but is this via classical (SAV1) secretion or EVs?

In addition to EV-mediated transport, capsular polysaccharide secretion can occur via the Sav1 (Sec4p)-mediated classical secretion pathway. However, our proteome data of total cell lysates indicated that the protein levels of Sav1 were comparable between the WT and _ugg1_Δ strains, suggesting that Sav1p function itself might not be impaired. Given that the _ugg1_Δ mutant exhibits altered vesicular structures (Supplementary Fig. S6) and loss of microvesicles (Fig. 8A), we speculate that a defect might occur at a post-Sav1p step, such as vesicle fusion with the plasma membrane, likely contributing to the complete defect in secretion of capsular polysaccharides in the _ugg1_Δ strain, in which EV biogenesis and defective cargo loading are severely impaired, producing EVs that lack capsular polysaccharides (Figure 8F). However, further studies should be carried out to define the contribution of SAV1 to the secretion of capsular polysaccharides in in the _ugg1_Δ strain.

(15) The rationale for doing 7 H is very confusing.

The experiment assessing raffinose utilization as a carbon source was inspired by the previous work of Garcia-Rivera et al., reporting that the _cap59_Δ mutant is unable to utilize raffinose due to a defect in the secretion of raffinose-hydrolyzing enzymes. As another way to investigate potential defects in the conventional secretion pathway, we investigated the growth of the _ugg1_Δ mutant in the presence of raffinose. Due to our extensive data length, we have decided to remove this complementary data from the manuscript.

(16) It is speculated in the discussion that ER stress impacts lipid/sterol synthesis and that LDs (lipid droplets?) aid the UPR and ERAD in degrading misfolded proteins during ER stress in S. cerevisiae. The authors mention that they observed a drastic increase in LDs in the ugg1Δ mutant. Where is this data? Even with the data, this is all speculation. The authors also speculate that increased numbers of vacuoles in ugg1 (where is the data?) could be the cause of the altered vesicular structures observed in the mutants, which may indicate abnormal lipid homeostasis caused by the ERQC defects, which could, in turn, affect EV biogenesis. Again, this is speculative.

The data on lipid droplets (LDs) and vacuole staining are presented in Supplementary Figure S6, showing a drastic increase in LDs and an increased in vacuolar size in the _ugg1_Δ mutant compared to the wild-type strain, especially in capsule-inducing conditions. In addition to such changes in vesicular structures, our preliminary data on sphingolipids and sterol analysis in the surface lipid fraction of the _ugg1_Δ mutant led us to propose the hypothesis that ERQC defects may impact lipid metabolism, which in turn could influence EV biogenesis and membrane properties. It is expected that these findings would provide a strong foundation for future studies exploring the link between ERQC, lipid homeostasis, and EV biogenesis. We have revised our speculation on the association of abnormal lipid homeostasis, caused by ERQC, with EV biogenesis more appropriately by adding the information on our preliminary data of lipid profiles and mentioning that the _ugg1_Δ mutant lacks microvesicles, which are derived from the plasma membrane (Page 24, lines 554-559).

Reviewer #2 (Recommendations for the authors):

(1) My suggestions for the authors are the same as those presented in the public review: (1) reducing the text in certain sections of the paper to improve readability for the audience, and (2) reconsidering the figures to reduce the amount of information in each one, moving some of the content to the supplementary material.

We thank the reviewer for their constructive suggestions regarding the organization and readability of the manuscript. As suggested, we addressed your concerns as follows:

(1) Reducing the text in the Introduction, Results, and Discussion sections by removing repetitive statements and simplifying complex descriptions where possible.

(2) Changing the presentation of figures: we have also reorganized the presentation of some data by moving non-essential data to the supplementary material. The updated figures and supplementary materials have been clearly referenced in the text to guide readers.

(3) Reorganization of materials and methods: some parts of methods were moved to Supplementary Information

(4) Removal of Figure 7H and the sentences describing the result

More detailed explanations on the reduction and reorganization are also described in the response to the major comments (2) and (3) made by Reviewer #1.

(2) Figure 3, for example, shows no difference in fungal growth under different cultivation conditions. This information is valuable but could be mentioned in the text, with the image provided as supplementary material, focusing the figure only on images that show significant growth differences among the strains. I suggest a similar approach for other figures so that the authors can include only the most relevant results in the main body of the article and move some figures to the supplementary materials.

For Fig. 3, the spotting data of the double mutant (previously Fig. 3B) is now presented in the supplementary information (Supplementary Fig. S3B). Additionally, the subcellular localization data (previously Fig 3E) was also moved to the supplementary material (Supplementary Fig. S4A).

Reviewer #3 (Recommendations for the authors):

(1) Line 43 "EV-mediated transport of virulence bags" doesn't make sense. EVs have been described as "virulence bags" (and are in this work later in the introduction) but this should here be "transport of virulence factors" or "compounds associated with virulence" but only if you have confirmed that the "cargo" is consistent with this- which is not evident in the abstract.

Thank you for your insightful comment. We have revised this to "EV-mediated transport of virulence factors" in line with your suggestion.

(2) Line 49 "secretory pathway" - is there not more than one secretion pathway?

Thank you for pointing this out. The term "secretory pathway" has been updated to "secretory pathways" to acknowledge the presence of both conventional and unconventional secretion mechanisms.

(3) Line 53 "recognizes folding defects, repairs them, and ensures the translocation of irreparable misfolded proteins" should be "recognizes folding defects and repairs them or ensures the translocation of irreparable misfolded proteins.

Thank you for pointing this out. We have revised the sentence as you suggested.

(4) Lines 88-90 ALG needs to be written out the first time - Asn-linked glycans. Also, consider adding that ALG genes are present in most eukaryotes as it is unclear what you are comparing C. neoformans to.

Thank you for your helpful comment. We have revised the text to write out "ALG" as "Asn-linked glycosylation" and added the sentence “ALG genes are evolutionary conserved in most eukaryotes” in the revised manuscript (Page 4, line 84).

(5) Line 99 Cryptococcus has already been abbreviated to C. so don't write it out again.

We have corrected "Cryptococcus" to “C.” throughout the manuscript after its first mention.

(6) Line 152- tunicamycin and DTT are not described yet, which may make it challenging for some readers to understand what these drugs are doing/why they were used. What is on lines 156 and 157 for these drugs should go up with the first mention of these drugs.

Thank you for your helpful suggestion. We have revised the manuscript to include the descriptions and purpose of using tunicamycin (TM) and dithiothreitol (DTT) immediately following their first mention, as recommended (Page 10, lines 208-210).

(7) The text for Figure 1 C is inaccurate. High temperature also induced KAR2, as noted above, but inaccurately stated in line 160. There is no comment on the significant UGG1 increase with tunicamycin or that KAR2 was highest in this condition.

Thank you for your thoughtful comment. We have better clarified the significant increase of UGG1 expression following tunicamycin treatment and KAR2 induction upon heat stress in the revised manuscript (Page 10, lines 216-217). Please note that Fig. 1C was revised and is now referred to as Fig. 3A.

(8) Figure 2B is not well explored/explained. There appears to be more protein in the mutant, including of higher weight in the intracellular compartment. It is difficult to ascertain if there is more too in the secretion phase with this gel. The methods do not specifically describe the concentration of protein added - just volume. Is what we are seeing a loading issue vs real differences?

Thank you for your insightful comments regarding Figure 2B. We added information on amounts of protein (30 µg per lane) in the legend of Figure 2B.

The main purpose of Fig. 2B is to examine the altered glycosylation pattern of ERQC by detecting glycoproteins using the Galanthus nivalis agglutinin, which specifically bind terminal α1,2-, α1,3-, and α1,6-linked mannose residues. The result of lectin blotting indicated that glycoproteins are more abundantly detected in the secretion fraction compared to in the soluble intracellular fraction, consistent with the general notion that more than 50% of secretory proteins are glycoproteins. Also, the more abundant proteins with decreased molecular weight in the secretion fraction of ugg1_Δ mutant supported the _N-glycan profiles with decreased hypermannosylation in _ugg1_Δ mutant. We added the purpose and more detailed interpretation on Figure 2B in the revised manuscript (Page 9, lines 174-179).

(9) Line 242 "melanin pigment" is redundant as melanin is a pigment.

We thank the reviewer for pointing out the redundancy in the phrase. We revised the text to simply state "melanin".

(10) Line 250 drops "completely" especially as the mutant did colonize the lungs of mice.

To avoid any possible misleading, we removed the term "completely" in the revised manuscript.

(11) Line 275- need to reference 18B7 as it is first introduced here.

We added the reference on the antibody 18B7 in the revised manuscript.

(12) Line 308- there are specific techniques to measure GXM size that could validate or refute the statement on "incomplete" polysaccharides. For example, DOI:10.1128/EC.00268-09.

We appreciated the valuable suggestion on specific techniques to measure GXM size, which will be one of key experiments in our future study. In the revised manuscript we cited the suggested reference to indicate the need for validation of our statement (Page 14, lines 316-318).

(13) Line 496 "mammals" - why is this used when the study is on a fungus, not a mammal? The structure of the first 2 paragraphs can be clearer to focus more on fungal biology.

We have compared both mammals and fungi to emphasize that the ERQC system is conserved among eukaryotes but diverged with a few species-specific features. This comparison is relevant in the context of understanding the evolutionary unique features of ERQC pathways in C. neoformans. We modified the first 2 paragraphs to clarify the main issue of our present study (Page 21, lines 472-483).

(14) Line 525- the ugg mutant was not avirulent as CFU was present and histopathology in the supplementary figures shows the tissue with ugg1 deletion was not normal (although the images are not especially easy to review). Yes, the mutant did not kill under your test conditions, but it was not avirulent (incapable of causing disease). Significantly attenuated or other descriptors should be utilized. Line 548 is also thus incorrect "complete loss of virulence").

We appreciate the reviewer’s concern regarding the description of the _ugg1_Δ mutant as avirulent. We agree that the use of merely “avirulent" may not fully capture the observed phenotypes in the CFU and histopathological data, since we cannot exclude the possibility that the _ugg1_Δ mutant retains the ability to establish an infection. Thus, we have revised the text by describing the _ugg1_Δ mutant as "almost avirulent".

(15) Line 597- the study by Fukuoka used kidney cells. It is misleading to not clearly state that this finding of ER stress was NOT done in fungi as the way it is presented makes it read as if this work was performed in C. neoformans. This should be clarified. This should also be double-checked and clarified for other statements, such as the reference to Harada in line 606, as this study used melanoma cells. These cell types are very different from cryptococcus- though I absolutely concur that lessons can be learned from comparative assessments.

We thank the reviewer for pointing out the need to clarify the experimental context of the cited studies. We explicitly stated the host cell types used in the referenced studies by Fukuoka et al. and by Harada et al., respectively, in the revised manuscript (Page 25, lines 560 and 568).

Author response:

Joint Public Review:

Summary:

In this study, Daniel et al. used three cognitive tasks to investigate behavioral signatures of cerebellar degeneration. In the first two tasks, the authors found that if an equation was incorrect, reaction times slowed significantly more for cerebellar patients than for healthy controls. In comparison, the slowing in the reaction times when the task required more operations was comparable to normal controls. In the third task, the authors show increased errors in cerebellar patients when they had to judge whether a letter string corresponded to an artificial grammar.

Strengths:

Overall, the work is methodologically sound and the manuscript well written. The data do show some evidence for specific cognitive deficits in cerebellar degeneration patients.

Thank you for the thoughtful summary and constructive feedback. We are pleased that the methodological rigor and clarity of the manuscript were appreciated, and that the data were recognized as providing meaningful evidence regarding cognitive deficits in cerebellar degeneration.

Weaknesses:

The current version has some weaknesses in the visual presentation of results. Overall, the study lacks a more precise discussion on how the patterns of deficits relate to the hypothesized cerebellar function. The reviewers and the editor agreed that the data are interesting and point to a specific cognitive deficit in cerebellar patients. However, in the discussion, we were somewhat confused about the interpretation of the result: If the cerebellum (as proposed in the introduction) is involved in forming expectations in a cognitive task, should they not show problems both in the expected (1+3 =4) and unexpected (1+3=2) conditions? Without having formed the correct expectation, how can you correctly say "yes" in the expected condition? No increase in error rate is observed - just slowing in the unexpected condition. But this increase in error rate was not observed. If the patients make up for the lack of prediction by using some other strategy, why are they only slowing in the unexpected case? If the cerebellum is NOT involved in making the prediction, but only involved in detecting the mismatch between predicted and real outcome, why would the patients not show specifically more errors in the unexpected condition?

Thank you for asking these important questions and initiating an interesting discussion. While decision errors and processing efficiency are not fully orthogonal and are likely related, they are not necessarily the same internal construct. The data from Experiments 1 and 2 suggest impaired processing efficiency rather than increased decision error. Reaction time slowing without increased error rates suggests that the CA group can form expectations but respond more slowly, possibly due to reduced processing efficiency. Thus, this analysis of our data can indicate that the cerebellum is not essential for forming expectations, but it plays a critical role in processing their violations.

Relatedly, two important questions remain open in the literature concerning the cerebellum’s role in expectation-related processes. The first is whether the cerebellum contributes to the formation of expectations or the processing of their violations. In Experiments 1 and 2, the CA group did not show impairments in the complexity manipulation. As mentioned by the editors, solving these problems requires the formation of expectations during the reasoning process. Given the intact performance of the CA group, these results suggest that they are not impaired in forming expectations. However, in both Experiments 1 and 2, patients exhibited selective impairments in solving incorrect problems compared to correct problems. Since expectation formation is required in both conditions, but only incorrect problems involve a violation of expectation (VE), we hypothesize that the cerebellum is involved in VE processes. We suggest that the CA group can form expectations in familiar tasks, but are impaired in processing unexpected compared to expected outcomes. This supports the notion that the cerebellum contributes to VE, rather than to forming expectations.

Importantly, while previous experimental manipulations(1–6) have provided important insights, some may have confounded these two internal constructs due to task design limitations (e.g., lack of baseline conditions). Notably, some of these previous studies did not include control conditions (e.g., correct trials) where there was no VE. In addition, other studies did not include a control measure (e.g., complexity effect), which limits their ability to infer the specific cerebellar role in expectation manipulation.

In addition to the editors’ question, we would like to raise a second important question regarding cerebellar contributions to expectations-related processes. While our findings point to a both unique and consistent cerebellar role in VE processes in sequential tasks, we do not aim to generalize this role to all forms of expectations(2,7,8). Another interesting process is how expectations are formed. Expectations can be formed by different processes(2,7,8), and this should be taken into account when defining cerebellar function. For instance, previous experimental paradigms(1–6), aiming to assess VE, utilized tasks that manipulated rule-based errors or probability-based errors, but did not fully dissociate these constructs. In our Experiments 1 and 2, we specifically manipulated error signals derived from previous top-down effects. However, in Experiment 3, the participant’s VE was derived from within-task processes. In Experiment 3, expectations were formed either by statistical learning or by rule-based learning. During the test stage, when evaluating sensitivity to correct and incorrect problems, the CA group showed deficits only when expectations were formed based on rules. These findings suggest that cerebellar patients may retain a general ability to form expectations. However, their deficit appears to be specific to processing rule-based VE, but not statistically derived VE. This pattern of results aligns with the results of Experiments 1 and 2 where the rules are known and based on pre-task knowledge.

We suggest that these two key questions are relevant to both motor and non-motor domains and were not fully addressed even in the previous, well-studied motor domain. Thus, the current experimental design used in three different experiments provides a valuable novel experimental perspective, allowing us to distinguish between some, but not all, of the processes involved in the formation of expectations and their violations. For instance, to our knowledge, this is the first study to demonstrate a selective impairment in rule-based VE processing in cerebellar patients across both numerical reasoning and artificial grammar tasks.

If feasible, we propose that future studies should disentangle different forms of VE by operationalizing them in experimental tasks in an orthogonal manner. This will allow us, as a scientific community, to achieve a more detailed, well-defined cerebellar motor and non-motor mechanistic account.

References

(1) Butcher, P. A. et al. The cerebellum does more than sensory prediction error-based learning in sensorimotor adaptation tasks. J. Neurophysiol. 118, 1622–1636 (2017).

(2) Moberget, T., Gullesen, E. H., Andersson, S., Ivry, R. B. & Endestad, T. Generalized role for the cerebellum in encoding internal models: Evidence from semantic processing. J. Neurosci. 34, 2871–2878 (2014).

(3) Riva, D. The cerebellar contribution to language and sequential functions: evidence from a child with cerebellitis. Cortex. 34, 279–287 (1998).

(4) Sokolov, A. A., Miall, R. C. & Ivry, R. B. The Cerebellum: Adaptive Prediction for Movement and Cognition. Trends Cogn. Sci. 21, 313–332 (2017).

(5) Fiez, J. A., Petersen, S. E., Cheney, M. K. & Raichle, M. E. Impaired non-motor learning and error detection associated with cerebellar damage. A single case study. Brain 115 Pt 1, 155–178 (1992).

(6) Taylor, J. A., Krakauer, J. W. & Ivry, R. B. Explicit and Implicit Contributions to Learning in a Sensorimotor Adaptation Task. J. Neurosci. 34, 3023–3032 (2014).

(7) Sokolov, A. A., Miall, R. C. & Ivry, R. B. The Cerebellum: Adaptive Prediction for Movement and Cognition. Trends Cogn. Sci. 21, 313–332 (2017).

(8) Fiez, J. A., Petersen, S. E., Cheney, M. K. & Raichle, M. E. IMPAIRED NON-MOTOR LEARNING AND ERROR DETECTION ASSOCIATED WITH CEREBELLAR DAMAGEA SINGLE CASE STUDY. Brain 115, 155–178 (1992).

(9) Picciotto, Y. De, Algon, A. L., Amit, I., Vakil, E. & Saban, W. Large-scale evidence for the validity of remote MoCA administration among people with cerebellar ataxia administration among people with cerebellar ataxia. Clin. Neuropsychol. 0, 1–17 (2024).

(10) Binoy, S., Monstaser-Kouhsari, L., Ponger, P. & Saban, W. Remote Assessment of Cognition in Parkinsons Disease and Cerebellar Ataxia: The MoCA Test in English and Hebrew. Front. Hum. Neurosci. 17, (2023).

(11) Saban, W. & Ivry, R. B. Pont: A protocol for online neuropsychological testing. J. Cogn. Neurosci. 33, 2413–2425 (2021).

(12) Algon, A. L. et al. Scale for the assessment and rating of ataxia : a live e ‑ version. J. Neurol. (2025). doi:10.1007/s00415-025-13071-7

(13) McDougle, S. D. et al. Continuous manipulation of mental representations is compromised in cerebellar degeneration. Brain 145, 4246–4263 (2022).

Author response:

eLife Assessment

This important study uses an innovative task design combined with eye tracking and fMRI to distinguish brain regions that encode the value of individual items from those that accumulate those values for value-based choices. It shows that distinct brain regions carry signals for currently evaluated and previously accumulated evidence. The study provides solid evidence in support of most of its claims, albeit with current minor weaknesses concerning the evidence in favour of gaze-modulation of the fMRI signal. The work will be of interest to neuroscientists working on attention and decision-making.

We thank the Editor and Reviewers for their summary of the strengths of our study, and for their thoughtful review and feedback on our manuscript. We plan to undertake some additional analyses suggested by the Reviewers to bolster the evidence in favor of gaze-modulation of the fMRI signal.

Reviewer #1 (Public review):

Summary:

This study builds upon a major theoretical account of value-based choice, the 'attentional drift diffusion model' (aDDM), and examines whether and how this might be implemented in the human brain using functional magnetic resonance imaging (fMRI). The aDDM states that the process of internal evidence accumulation across time should be weighted by the decision maker's gaze, with more weight being assigned to the currently fixated item. The present study aims to test whether there are (a) regions of the brain where signals related to the currently presented value are affected by the participant's gaze; (b) regions of the brain where previously accumulated information is weighted by gaze.

To examine this, the authors developed a novel paradigm that allowed them to dissociate currently and previously presented evidence, at a timescale amenable to measuring neural responses with fMRI. They asked participants to choose between bundles or 'lotteries' of food times, which they revealed sequentially and slowly to the participant across time. This allowed modelling of the haemodynamic response to each new observation in the lottery, separately for previously accumulated and currently presented evidence.

Using this approach, they find that regions of the brain supporting valuation (vmPFC and ventral striatum) have responses reflecting gaze-weighted valuation of the currently presented item, whereas regions previously associated with evidence accumulation (preSMA and IPS) have responses reflecting gaze-weighted modulation of previously accumulated evidence.

Strengths:

A major strength of the current paper is the design of the task, nicely allowing the researchers to examine evidence accumulation across time despite using a technique with poor temporal resolution. The dissociation between currently presented and previously accumulated evidence in different brain regions in GLM1 (before gaze-weighting), as presented in Figure 5, is already compelling. The result that regions such as preSMA respond positively to |AV| (absolute difference in accumulated value) is particularly interesting, as it would seem that the 'decision conflict' account of this region's activity might predict the exact opposite result. Additionally, the behaviour has been well modelled at the end of the paper when examining temporal weighting functions across the multiple samples.

Thank you!

Weaknesses:

The results relating to gaze-weighting in the fMRI signal could do with some further explication to become more complete. A major concern with GLM2, which looks at the same effects as GLM1 but now with gaze-weighting, is that these gaze-weighted regressors may be (at least partially) correlated with their non-gaze-weighted counterparts (e.g., SVgaze will correlate with SV). But the non-gaze-weighted regressors have been excluded from this model. In other words, the authors are not testing for effects of gaze-weighting of value signals *over and above* the base effects of value in this model. In my mind, this means that the GLM2 results could simply be a replication of the findings from GLM1 at present. GLM3 is potentially a stronger test, as it includes the value signals and the interaction with gaze in the same model. But here, while the link to the currently attended item is quite clear (and a replication of Lim et al, 2011), the link to previously accumulated evidence is a bit contorted, depending upon the interpretation of a behavioural regression to interpret the fMRI evidence. The results from GLM3 are also, by the authors' own admission, marginal in places.

We thank the Reviewer for their thoughtful critique. We acknowledge that our formulation of GLM2 does not test for the effects of gaze-weighted value signals beyond the base effects of value, only in place of the base effects of value. In our revision, we plan to examine alternative ways of quantifying the relative importance of gaze in these results.  

Reviewer #2 (Public review):

Summary:

In this paper, the authors seek to disentangle brain areas that encode the subjective value of individual stimuli/items (input regions) from those that accumulate those values into decision variables (integrators) for value-based choice. The authors used a novel task in which stimulus presentation was slowed down to ensure that such a dissociation was possible using fMRI despite its relatively low temporal resolution. In addition, the authors leveraged the fact that gaze increases item value, providing a means of distinguishing brain regions that encode decision variables from those that encode other quantities such as conflict or time-on-task. The authors adopt a region-of-interest approach based on an extensive previous literature and found that the ventral striatum and vmPFC correlated with the item values and not their accumulation, whereas the pre-SMA, IPS, and dlPFC correlated more strongly with their accumulation. Further analysis revealed that the pre-SMA was the only one of the three integrator regions to also exhibit gaze modulation.

Strengths:

The study uses a highly innovative design and addresses an important and timely topic. The manuscript is well-written and engaging, while the data analysis appears highly rigorous.

Weaknesses:

With 23 subjects, the study has relatively low statistical power for fMRI.

We thank the Reviewer for their comments on the strengths of the manuscript, and for highlighting an important limitation. We agree that the number of participants in the study, after exclusions, was lower than your typical fMRI study. However, it is important to note that we do have a lot of data for each subject. Due to our relatively fast, event-related design, we have on average 65 trials per subject (SD = 18) and 5.95 samples per trial (SD \= 4.03), for an average of 387 observations per subject (SD = 18). Our model-based analysis looks for very specific neural time courses across these ~387 observations, giving us substantial power to detect our effects of interest. Still, we acknowledge that our small number of subjects does still limit our power and our ability to generalize to other subjects. We plan to add the following disclaimer to the Discussion section:

“Together with our limited sample size (n = 23), we may not have had adequate statistical power required to observe consistent effects. Additional research with larger sample sizes is needed to resolve this issue.”

Author response:

The following is the authors’ response to the original reviews

Public Reviews:

Reviewer #1 (Public Review):

Summary:

A cortico-centric view is dominant in the study of the neural mechanisms of consciousness. This investigation represents the growing interest in understanding how subcortical regions are involved in conscious perception. To achieve this, the authors engaged in an ambitious and rare procedure in humans of directly recording from neurons in the subthalamic nucleus and thalamus. While participants were in surgery for the placement of deep brain stimulation devices for the treatment of essential tremor and Parkinson's disease, they were awakened and completed a perceptual-threshold tactile detection task. The authors identified individual neurons and analyzed single-unit activity corresponding with the task phases and tactile detection/perception. Among the neurons that were perception-responsive, the authors report changes in firing rate beginning ~150 milliseconds from the onset of the tactile stimulation. Curiously, the majority of the perception-responsive neurons had a higher firing rate for missed/not perceived trials. In summary, this investigation is a valuable addition to the growing literature on the role of subcortical regions in conscious perception.

Strengths:

The authors achieved the challenging task of recording human single-unit activity while participants performed a tactile perception task. The methods and statistics are clearly explained and rigorous, particularly for managing false positives and non-normal distributions. The results offer new detail at the level of individual neurons in the emerging recognition of the role of subcortical regions in conscious perception.

We thank the reviewer for their positive comments.

Weaknesses:

"Nonetheless, it remains unknown how the firing rate of subcortical neurons changes when a stimulus is consciously perceived." (lines 76-77) The authors could be more specific about what exactly single-unit recordings offer for interrogating the role of subcortical regions in conscious perception that is unique from alternative neural activity recordings (e.g., local field potential) or recordings that are used as proxies of neural activity (e.g., fMRI).

We agree with the reviewer that the contribution of micro-electrode recordings was not sufficiently put forward in our manuscript. We added the following sentences to the discussion, when discussing the multiple types of neurons we found:

Single-unit recordings provide a much higher temporal resolution than functional imaging, which helps assess how the neural correlates of consciousness unfold over time. Contrary to local field potentials, single-unit recordings can expose the variety of functional roles of neurons within subcortical regions, thereby offering a potential for a better mechanistic understanding of perceptual consciousness.

Related comment for the following excerpts:

"After a random delay ranging from 0.5 to 1 s, a "respond" cue was played, prompting participants to verbally report whether they felt a vibration or not. Therefore, none of the reported analyses are confounded by motor responses." (lines 97-99).

"These results show that subthalamic and thalamic neurons are modulated by stimulus onset, irrespective of whether it was reported or not, even though no immediate motor response was required." (lines 188190).

"By imposing a delay between the end of the tactile stimulation window and the subjective report, we ensured that neuronal responses reflected stimulus detection and not mere motor responses." (lines 245247).

It is a valuable feature of the paradigm that the reporting period was initiated hundreds of milliseconds after the stimulus presentation so that the neural responses should not represent "mere motor responses". However, verbal report of having perceived or not perceived a stimulus is a motor response and because the participants anticipate having to make these reports before the onset of the response period, there may be motor preparatory activity from the time of the perceived stimulus that is absent for the not perceived stimulus. The authors show sensitivity to this issue by identifying task-selective neurons and their discussion of the results that refer to the confound of post-perceptual processing. Still, direct treatment of this possible confound would help the rigor of the interpretation of the results.

We agree with the reviewer that direct treatment would have provided the best control. One way to avoid motor preparation is to only provide the stimulus-effector mapping after the stimulus presentation (Bennur & Gold, 2011; Twomey et al., 2016; Fang et al., 2024). Other controls to avoid post-perceptual processing used in consciousness research consist of using no-report paradigms (Tsuchiya et al., 2015) as we did in previous studies (Pereira et al., 2021; Stockart et al., 2024). Unfortunately, neither of these procedures was feasible during the 10 minutes allotted for the research task in an intraoperative setting with auditory cues and vocal responses. We would like to highlight nonetheless that the effects we report are shortlived and incompatible with sustained motor preparation activity.

We added the following sentence to the discussion:

Future studies ruling out the presence of motor preparation triggered by perceived stimuli (Bennur & Gold, 2011; Fang et al., 2024; Twomey et al., 2016) and verifying that similar neuronal activity occurs in the absence of task-demands (no-reports; Tsuchiya et al., 2015) or attention (Wyart & Tallon-Baudry, 2008) will be useful to support that subcortical neurons contribute specifically to perceptual consciousness.

"When analyzing tactile perception, we ensured that our results were not contaminated with spurious behavior (e.g. fluctuation of attention and arousal due to the surgical procedure)." (lines 118-117).

Confidence in the results would be improved if the authors clarified exactly what behaviors were considered as contaminating the results (e.g., eye closure, saccades, and bodily movements) and how they were determined.

This sentence was indeed unclear. It introduced the trial selection procedure we used to compensate for drifts in the perceptual threshold, which can result from fluctuations in attention or arousal. We modified the sentence, which now reads:

When analyzing tactile perception, we ensured that our results were not contaminated by fluctuating attention and arousal due to the surgical procedure. Based on objective criteria, we excluded specific series of trials from analyses and focused on time windows for which hits and misses occurred in commensurate proportions (see methods).

During the recordings, the experimenter stood next to the patients and monitored their bodily movements, ensuring they did not close their eyes or produce any other bodily movements synchronous with stimulus presentation.

The authors' discussion of the thalamic neurons could be more precise. The authors show that only certain areas of the thalamus were recorded (in or near the ventral lateral nucleus, according to Figure S3C). The ventral lateral nucleus has a unique relationship to tactile and motor systems, so do the authors hypothesize these same perception-selective neurons would be active in the same way for visual, auditory, olfactory, and taste perception? Moreover, the authors minimally interpret the location of the task, sensory, and perception-responsive neurons. Figure S3 suggests these neurons are overlapping. Did the authors expect this overlap and what does it mean for the functional organization of the ventral lateral nucleus and subthalamic nucleus in conscious perception?

These are excellent questions, the answers to which we can only speculate. In rodents, the LT is known as a hub for multisensory processing, as over 90% of LT neurons respond to at least two sensory modalities (for a review, see Yang et al., 2024). Yet, no study has compared how LT neurons in rodents encode perceived and nonperceived stimuli across modalities. Evidence in humans is scarce, with only a few studies documenting supramodal neural correlates of consciousness at the cortical level with noninvsasive methods (Noel et al., 2018; Sanchez et al., 2020; Filimonov et al., 2022). We now refer to these studies in the revised discussion: Moreover, given the prominent role of the thalamus in multisensory processing, it will be interesting to assess if it is specifically involved in tactile consciousness or if it has a supramodal contribution, akin to what is found in the cortex (Noel et al., 2018; Sanchez et al., 2020; Filimonov et al., 2022).

Concerning the anatomical overlap of neurons, we could not reconstruct the exact locations of the DBS tracts for all participants. Because of the limited number of recorded neurons, we preferred to refrain from drawing strong conclusions about the functional organization of the ventral lateral nucleus.

"We note that, 6 out of 8 neurons had higher firing rates for missed trials than hit trials, although this proportion was not significant (binomial test: p = 0.145)." (lines 215-216).

It appears that in the three example neurons shown in Figure 4, 2 out of 3 (#001 and #068) show a change in firing rate predominantly for the missed stimulations. Meanwhile, #034 shows a clear hit response (although there is an early missed response - decreased firing rate - around 150 ms that is not statistically significant). This is a counterintuitive finding when compared to previous results from the thalamus (e.g., local field potentials and fMRI) that show the opposite response profile (i.e., missed/not perceived trials display no change or reduced response relative to hit/perceived trials). The discussion of the results should address this, including if these seemingly competing findings can be rectified.

We thank the reviewer for pointing out this limitation of the discussion. We avoided putting too much emphasis on these aspects due to the limited number of perception-selective neurons. Although subcortical connectivity models would predict that neurons in the thalamus should increase their firing rate for perceived stimuli, we were not surprised to see this heterogeneity as we had previously found neurons decreasing their firing rates for missed stimuli in the posterior parietal cortex (Pereira et al., 2021). We answer these points in response to the reviewer’s last comment below on the latencies of the effects.

The authors report 8 perception-responsive neurons, but there are only 5 recording sites highlighted (i.e., filled-in squares and circles) in Figures S3C and 4D. Was this an omission or were three neurons removed from the perception-responsive analysis?

Unfortunately, we could not obtain anatomical images for all participants. This information was present in the methods section, although not clearly enough:

For 34 / 50 neurons, preoperative MRI and postoperative CT scans (co-registered in patient native space using CranialSuite) were available to precisely reconstruct surgical trajectories and recording locations (for the remaining 16 neurons, localizations were based on neurosurgical planning and confirmed by electrophysiological recordings at various depths).

Therefore, we added the following sentence in Figures 2, 3, 4 and S3.

[...] for patients for which we could obtain anatomical images.

Could the authors speak to the timing of the responses reported in Figure 4? The statistically significant intervals suggested both early (~160-200ms) to late responses (~300ms). Some have hypothesized that subcortical regions are early - ahead of cortical activation that may be linked with conscious perception. Do these results say anything about this temporal model for when subcortical regions are active in conscious perception?

We agree that response timing could have been better described. We performed a new analysis of the latencies at which our main effects were observed. This analysis revealed the existence of the two clusters mentioned by the reviewer very clearly. We now include this analysis in a new Figure 5 in the revised manuscript.

We also performed a new analysis to support the existence of bimodal distributions and quantified the latencies. We added this text to the result section:

We note that the timings of sensory and perception effects in Figures 3 and 4 showed a bimodal distribution with an early cluster (149 ms for sensory neurons; 121 ms for perception neurons; c.f. methods) and a later cluster (330 ms for sensory neurons; 315 ms for perception neurons; Figure 5). and this section to the methods:

To measure bimodal timings of effect latencies, we fitted a two-component Gaussian mixture distribution to the data in Figure 5 by minimizing the mean square error with an interior-point method. We took the best of 20 runs with random initialization points and verified that the resulting mean square error was markedly (> 4 times) better than using a single component.

We updated the discussion, including the points made in the comment about higher activity for missed stimuli (above):

The early cluster’s average timing around 150 ms post-stimulus corresponds to the onset of a putative cortical correlate of tactile consciousness, the somatosensory awareness negativity (Dembski et al., 2021). Similar electroencephalographic markers are found in the visual and auditory modality. It is unclear, however, whether these markers are related to perceptual consciousness or selective attention (Dembski et al., 2021). The later cluster is centered around 300 ms and could correspond to a well known electroencephalographic marker, the P3b (Polich, 2007) whose association with perceptual consciousness has been questioned (Pitts et al., 2014; Dembski et al., 2021) although brain activity related to consciousness has been observed at similar timing even in the absence of report demands (Sergent et al., 2021; Stockart et al., 2024). It is also important to note that these clusters contain neurons with both increased and decreased firing rates following stimulus onset, similar to what was observed previously in the posterior parietal cortex (Pereira et al., 2021).

Reviewer #2 (Public Review):

The authors have studied subpopulations of individual neurons recorded in the thalamus and subthalamic nucleus (STN) of awake humans performing a simple cognitive task. They have carefully designed their task structure to eliminate motor components that could confound their analyses in these subcortical structures, given that the data was recorded in patients with Parkinson's Disease (PD) and diagnosed with an Essential Tremor (ET). The recorded data represents a promising addition to the field. The analyses that the authors have applied can serve as a strong starting point for exploring the kinds of complex signals that can emerge within a single neuron's activity. Pereira et. al conclude that their results from single neurons indicate that task-related activity occurs, purportedly separate from previously identified sensory signals. These conclusions are a promising and novel perspective for how the field thinks about the emergence of decisions and sensory perception across the entire brain as a unit.

We thank the reviewer for these positive comments.

Despite the strength of the data that was obtained and the relevant nature of the conclusions that were drawn, there are certain limitations that must be taken into consideration:

(1) The authors make several claims that their findings are direct representations of consciousnessidentifiable in subcortical structures. The current context for consciousness does not sufficiently define how the consciousness is related to the perceptual task.

This is indeed a complex issue in all studies concerned with perceptual consciousness and we were careful not to make such “direct” claims. Instead, we used the state-of-the-art tools available to study consciousness (see below) and only interpreted our findings with respect to consciousness in the discussion. For example, in the abstract, our claim is that “Our results provide direct neurophysiological evidence of the involvement of the subthalamic nucleus and the thalamus for the detection of vibrotactile stimuli, thereby calling for a less cortico-centric view of the neural correlates of consciousness.”

In brief, first, we used near-threshold stimuli which allowed us to contrast reported vs. unreported trials while keeping the physical properties of the stimulus comparable. Second, we used subjective reports without incentive for participants to be more conservative or liberal in their response (e.g. through reward). Third, we introduced a random delay before the responses to limit confounding effects due to the report. We also acknowledged that “... it will be important in future studies to examine if similar subcortical responses are obtained when stimuli are unattended (Wyart & Tallon-Baudry, 2008), task-irrelevant (Shafto & Pitts, 2015), or when participants passively experience stimuli without the instruction to report them (i.e., no-report paradigms) (Tsuchyia et al., 2015)”. This last sentence now reads (to address a point made by Reviewer 1 about motor preparation):

Future studies ruling out the presence of motor preparation triggered by perceived stimuli (Bennur & Gold, 2011; Fang et al., 2024; Twomey et al., 2016) and verifying that similar neuronal activity occurs in the absence of task-demands (no-reports; Tsuchiya et al., 2015) or attention (Wyart & Tallon-Baudry, 2008) will be useful to support that subcortical neurons contribute specifically to perceptual consciousness.

(2) The current work would benefit greatly from a description and clarification of what all the neurons thathave been recorded are doing. The authors' criteria for selecting subpopulations with task-relevant activity are appropriate, but understanding the heterogeneity in a population of single neurons is important for broader considerations that are being studied within the field.

We followed the reviewer’s suggestions and added new results regarding the latencies of the reported effects (new Figure 5). We also now show firing rates for hits, misses and overall sensory activity (hits and misses combined) for all perception-selective or sensory-selective (when behavior was good enough; Figure S5). Although a more detailed characterization of the heterogeneity of the neurons identified would have been relevant, it seems beyond the scope of the present study, especially given the relatively small number of neurons we identified, as well as the relative simplicity of the paradigm imposed by the clinical context in which we worked.

(3) The authors have omitted a proper set of controls for comparison against the active trials, forexample, where a response was not necessary. Please explain why this choice was made and what implications are necessary to consider.

We had mentioned this limitation in the discussion: Nevertheless, it will be important in future studies to examine if similar subcortical responses are obtained when stimuli are unattended (Wyart & TallonBaudry, 2008), task-irrelevant (Shafto & Pitts, 2015), or when participants passively experience stimuli without the instruction to report them (i.e., no-report paradigms) (Tsuchyia et al., 2015). We agree that such a control would have been relevant, but this was not feasible during the 10 minutes allotted for the research task in an intraoperative setting. These constraints are both clinical, to minimize discomfort for patients and practical, as is difficult to track neurons in an intraoperative setting for more than 10 minutes.

We added a sentence to this effect in the discussion.

Reviewer #3 (Public Review):

Summary:

This important study relies on a rare dataset: intracranial recordings within the thalamus and the subthalamic nucleus in awake humans, while they were performing a tactile detection task. This procedure allowed the authors to identify a small but significant proportion of individual neurons, in both structures, whose activity correlated with the task (e.g. their firing rate changed following the audio cue signalling the start of a trial) and/or with the stimulus presentation (change in firing rate around 200 ms following tactile stimulation) and/or with participant's reported subjective perception of the stimulus (difference between hits and misses around 200 ms following tactile stimulation). Whereas most studies interested in the neural underpinnings of conscious perception focus on cortical areas, these results suggest that subcortical structures might also play a role in conscious perception, notably tactile detection.

Strengths:

There are two strongly valuable aspects in this study that make the evidence convincing and even compelling. First, these types of data are exceptional, the authors could have access to subcortical recordings in awake and behaving humans during surgery. Additionally, the methods are solid. The behavioral study meets the best standards of the domain, with a careful calibration of the stimulation levels (staircase) to maintain them around the detection threshold, and an additional selection of time intervals where the behavior was stable. The authors also checked that stimulus intensity was the same on average for hits and misses within these selected periods, which warrants that the effects of detection that are observed here are not confounded by stimulus intensity. The neural data analysis is also very sound and well-conducted. The statistical approach complies with current best practices, although I found that, in some instances, it was not entirely clear which type of permutations had been performed, and I would advocate for more clarity in these instances. Globally the figures are nice, clear, and well presented. I appreciated the fact that the precise anatomical location of the neurons was directly shown in each figure.

We thank the reviewer for this positive evaluation.

Weaknesses:

Some clarification is needed for interpreting Figure 3, top rows: in my understanding the black curve is already the result of a subtraction between stimulus present trials and catch trials, to remove potential drifts; if so, it does not make sense to compare it with the firing rate recorded for catch trials.

The black curve represents the firing rate without any subtraction. We only subtracted the firing rates of catch trials in the statistical procedure, as the reviewer noted, to remove potential drift. We added (before baseline correction) to the legend of Figure 3.

I also think that the article could benefit from a more thorough presentation of the data and that this could help refine the interpretation which seems to be a bit incomplete in the current version. There are 8 stimulus-responsive neurons and 8 perception-selective neurons, with only one showing both effects, resulting in a total of 15 individual neurons being in either category or 13 neurons if we exclude those in which the behavior is not good enough for the hit versus miss analysis (Figure S4A). In my opinion, it should be feasible to show the data for all of them (either in a main figure, or at least in supplementary), but in the present version, we get to see the data for only 3 neurons for each analysis. This very small selection includes the only neuron that shows both effects (neuron #001; which is also cue selective), but this is not highlighted in the text. It would be interesting to see both the stimulus-response data and the hit versus miss data for all 13 neurons as it could help develop the interpretation of exactly how these neurons might be involved in stimulus processing and conscious perception. This should give rise to distinct interpretations for the three possible categories. Neurons that are stimulus-responsive but not perception-selective should show the same response for both hits and misses and hence carry out indifferently conscious and unconscious responses. The fact that some neurons show the opposite pattern is particularly intriguing and might give rise to a very specific interpretation: if the neuron really doesn't tend to respond to the stimulus when hits and misses are put together, it might be a neuron that does not directly respond to the stimulus, but whose spontaneous fluctuations across trials affect how the stimulus is perceived when they occur in a specific time window after the stimulus. Finally, neuron #001 responds with what looks like a real burst of evoked activity to stimulation and also shows a difference between hits and misses, but intriguingly, the response is strongest for misses. In the discussion, the interesting interpretation in terms of a specific gating of information by subcortical structures seems to apply well to this last example, but not necessarily to the other categories.

We now provide a supplementary Figure showing firing rates for hits, misses and the combination of both. The reviewer’s analysis about whether a perception-selective neuron also has to respond to the stimulus to be involved in gating is interesting. With more data, a finer characterization of these neurons would have been possible. In our study, it is possible that more neurons have similar characteristics as #001 (e.g. #032, #062, #068) but do not show a significant difference with respect to baseline when both hits and misses are considered. We now avoid interpreting null effects, especially considering the low number of trials with near-threshold detection behavior we could collect in 10 minutes. 

We also realized that we had not updated Figure S7 after the last revision in which we had corrected for possible drifts to obtain sensory-selective neurons. The corrected panel A is provided below.

Recommendations for the authors:

Reviewer #1 (Recommendations For The Authors):

It appears that the correct rejection was low for most participants. It would improve interpretation of the behavioral results if correct rejection was shown as a rate (i.e., # of correct rejection trials / total number of no stimulus/blank trials) rather than or in addition to reporting the number of correct rejection trials (Figure 1C).

We added the following figure to the supplementary information.

The axis tick marks in Figure 5A late versus early are incorrect (appears the axis was duplicated).

Thank you for spotting this, it has been corrected.

Reviewer #2 (Recommendations For The Authors):

We would like to congratulate the authors on this strongly supported contribution to the field. The manuscript is well-written, although a little bit too concise in sections. See the following comments for the methods that could benefit the present conclusions:

Thank you for these suggestions that we believe improved our interpretations.

Major Points

(1) The subpopulations of neurons that are considered are small, but it is not a confounding issue for the conclusions drawn. However, the behavior of the neurons that were excluded should be considered by calculating the percentage of neurons that are selective for the distinct parameters, as a function of time. This would greatly strengthen the understanding of what can be observed in the two subcortical structures.

We thank the reviewer for this suggestion. We performed a new analysis of the latencies at which our main effects were observed. This analysis revealed the existence of two clusters, as shown in the new Figure 5 copied below

We also performed a new analysis to support the existence of bimodal distributions and quantified the latencies. We added this text to the result section:

We note that the timings of sensory and perception effects in Figures 3 and 4 showed a bimodal distribution with an early cluster (149 ms for sensory neurons; 121 ms for perception neurons; c.f. methods) and a later cluster (330 ms for sensory neurons; 315 ms for perception neurons; Figure 5). and this section to the methods:

To measure bimodal timings of effect latencies, we fitted a two-component Gaussian mixture distribution to the data in Figure 5 by minimizing the mean square error with an interior-point method. We took the best of 20 runs with random initialization points and verified that the resulting mean square error was markedly (> 4 times) better than using a single component.

We also updated the discussion:

The early cluster’s average timing around 150 ms post-stimulus corresponds to the onset of a putative cortical correlate of tactile consciousness, the somatosensory awareness negativity (Dembski et al., 2021). Similar electroencephalographic markers are found in the visual and auditory modality. It is unclear, however, whether these markers are related to perceptual consciousness or selective attention (Dembski et al., 2021). The later cluster is centered around 300 ms and could correspond to a well known electroencephalographic marker, the P3b (Polich, 2007) whose association with perceptual consciousness has been questioned (Pitts et al., 2014; Dembski et al., 2021) although brain activity related to consciousness has been observed at similar timing even in the absence of report demands (Sergent et al., 2021; Stockart et al., 2024). It is also important to note that these clusters contain neurons with both increased and decreased firing rates following stimulus onset, similar to what was observed previously in the posterior parietal cortex (Pereira et al., 2021).

(2) We highly recommend that the authors consider employing some analysis that decodes therepresentations observable in the activity of individual neurons as a function of time (e.g. Shannon's Mutual Information). This would reinforce and emphasize the most relevant conclusions.

We thank the reviewers for this suggestion. Unfortunately, such methods would require many more trials than what we were able to collect in the 10-minute slots available in the operating room.

(3) Although there are small populations recorded in each of the two subcortical structures, they aresufficient to attempt a study using population dynamics (primarily, PCA can still work with smaller populations). Given the broad range of dynamics that are observed in a population of single units typically involved in decision-making, it would be interesting to consider whether heterogeneity is a hallmark of decision-making, and trying to summarize the variance in the activity of the entire population should provide a certain understanding of the cue-selective versus the perception-selective qualities, as an example.

We now present all 13 neurons that were sensory- or perception-selective for which we had good enough behavior to show hit vs. miss differences in Supplementary Figure S5. Although population-level analyses would be relevant, they are not compatible with the number of neurons we identified.

(4) A stronger presentation of what the expectations are for the results would also benefit theinterpretability of the manuscript when added to the introduction and discussion sections.

Due to the scarcity of single-neuron data related to perceptual consciousness, especially in the subcortical structures we explored, our prior expectations did not exceed finding perception-selective neurons. We would prefer to avoid refining these expectations post-hoc. 

Minor Comments

(1) Add the shared overlap between differently selective neurons explicitly in the manuscript.

We added this information at the end of the results section.

(2) Add a consideration in the methods of why the Wilcoxon test or permutation test was selected forseparate uses. How do the results compare?

Sorry for this misunderstanding. We clarified this in revised methods:

To deal with possibly non-parametric distributions, we used Wilcoxon rank sum test or sign test instead of t-tests to test differences between distributions. We used permutation tests instead of Binomial tests to test whether a reported number of neurons could have been obtained by chance.

Reviewer #3 (Recommendations For The Authors):

Suggestions for improved or additional experiments, data or analysis:

As suggested already in the public review, it might be worth showing all 13 neurons with either stimulusresponsive or perception-selective behaviour and, based on that, deepen the potential interpretation of the results for the different categories.

We agree that this information improves the understanding of the underlying data and this addition was also proposed by reviewer 2. We added it in a new supplementary Figure S5.

Recommendations for improving the writing and presentation

As mentioned in the public review, I think Figure 3 needs clarification. I found that, in some instances, it was not entirely clear which type of analyses or permutation tests had been performed, and I would advocate for more clarity in these instances. For example:

Page 6 line 146 "permuting trial labels 1000 times": do you mean randomly attributing a trial to aneuron? Or something else?

We agree that this was somewhat unclear. We modified the sentence to:

permuting the sign of the trial-wise differences

We now define a sign permutation test for paired tests and a trial permutation test for two-sample tests in the methods and specify which test was used in the maintext.

Page 7, neurons which have their firing rate modulated by the stimulus: I think you ought to be moreexplicit about the analysis so that we grasp it on the first read. To understand what is shown in Figure 3 I had to go back and forth between the main text and the method, and I am still not sure I completely understood. You compare the firing rate in sliding windows following stimulus onset with the mean firing rate during the 300ms baseline. Sliding windows are between 0 and 400 ms post-stim (according to methods ?) and a neuron is deemed responsive if you find at least one temporal cluster that shows a significant difference with baseline activity (using cluster permutation). Is that correct? Either way, I would recommend being a bit more precise about the analysis that was carried out in the main text, so that we only need to refer to methods when we need specialized information.

We agree that the methods section was unclear. We re-wrote the following two paragraphs:

To identify sensory-selective neurons, we assumed that subcortical signatures of stimulus detection ought to be found early following its onset and looked for differences in the firing rates during the first 400 ms post-stimulus onset compared to a 300 ms pre-stimulus baseline. To correct for possible drifts occurring during the trial, we subtracted the average cue-locked activity from catch trials to the cuelocked activity of each stimulus-present trials before realigning to stimulus onset. We defined a cluster as a set of adjacent time points for which the firing rates were significantly different between hits and misses, as assessed by a non-parametric sign rank test. A putative neuron was considered sensory-selective when the length of a cluster was above 80 ms, corresponding to twice the standard deviation of the smoothing kernel used to compute the firing rate. Whether for the shuffled data or the observed data, if more than one cluster was obtained, we discarded all but the longest cluster. This permutation test allowed us to control for multiple comparisons across time and participants.

For perception-selective neurons, we looked for differences in the firing rates between hit and miss trials during the first 400 ms post-stimulus onset. We defined a cluster as a set of adjacent time points for which the firing rates were significantly different between hits and misses as assessed by a nonparametric Wilcoxon rank sum test. As for sensory-selective neurons, a putative neuron was considered perception-selective when the length of a cluster was above 80 ms, corresponding to twice the standard deviation of the smoothing kernel used to compute the firing rate and we discarded all but the longest cluster.

Minor points:

Figure 3: inset showing action potentials, please also provide the time scale (in the legend for example), so that it's clear that it is not commensurate with the firing rate curve below, but rather corresponds to the dots of the raster plot.

We added the text ”[...], duration: 2.5 ms” in Figures 2, 3, and 4.

Line 210: I recommend: “we found 8 neurons [...] showing a significant difference *between hits and misses* after stimulus onset."

We made the change.

Top of page 9, the following sentence is misleading “This result suggests that neurons in these two subcortical structures have mostly different functional roles ; this could read as meaning that functional roles are different between the two structures. Probably what you mean is rather something along this line : “these two subcortical structures both contain neurons displaying several different functional roles”

Changed.

Line 329: remove double “when”

We made the change, thank you for spotting this.

Author response:

The following is the authors’ response to the previous reviews

We would like to thank you for your valuable comments and suggestions, which have greatly contributed to improving our manuscript.

We have carefully addressed all the reviewers' suggestions, and detailed responses for each Reviewer are provided at the end of this letter. In summary:

• The Introduction has been revised to provide a more focused discussion on results, toning down the speculative discussion on seasonal host shifts.

• The methodology section has been clarified, particularly the power analysis, which now includes a clearer explanation. The random effects in the models have been better described to ensure transparency.

• The Results section was reorganized to highlight the key findings more effectively.

• The Discussion has been restructured for clarity and conciseness, ensuring the interpretation of the results is clearer and better aligned with the study objectives.

• Minor edits throughout the manuscript were made to improve readability and accuracy.

We hope you find this revised version of the manuscript satisfactory.

Reviewer #1 (Public review):

Summary:

This study examines the role of host blood meal source, temperature, and photoperiod on the reproductive traits of Cx. quinquefasciatus, an important vector of numerous pathogens of medical importance. The host use pattern of Cx. quinquefasciatus is interesting in that it feeds on birds during spring and shifts to feeding on mammals towards fall. Various hypotheses have been proposed to explain the seasonal shift in host use in this species but have provided limited evidence. This study examines whether the shifting of host classes from birds to mammals towards autumn offers any reproductive advantages to Cx.

quinquefasciatus in terms of enhanced fecundity, fertility, and hatchability of the offspring. The authors found no evidence of this, suggesting that alternate mechanisms may drive the seasonal shift in host use in Cx. quinquefasciatus.

Strengths:

Host blood meal source, temperature, and photoperiod were all examined together.

Weaknesses:

The study was conducted in laboratory conditions with a local population of Cx. quinquefasciatus from Argentina. I'm not sure if there is any evidence for a seasonal shift in the host use pattern in Cx. quinquefasciatus populations from the southern latitudes.

Comments on the revision:

Overall, the manuscript is much improved. However, the introduction and parts of the discussion that talk about addressing the question of seasonal shift in host use pattern of Cx. quin are still way too strong and must be toned down. There is no strong evidence to show this host shift in Argentinian mosquito populations. Therefore, it is just misleading. I suggest removing all this and sticking to discussing only the effects of blood meal source and seasonality on the reproductive outcomes of Cx. quin.

Introduction and discussion have been modified, toned down and sticked to discuss the results as suggested.

Reviewer #1 (Recommendations for the authors):

Some more minor comments are mentioned below.

Line 51: Because 'of' this,

Changed as suggested.

Line 56: specialists 'or' generalists

Changed as suggested.

Line 56: primarily

Changed as suggested.

Line 98: Because 'of' this,

Changed as suggested.

Reviewer #2 (Public review):

Summary:

Conceptually, this study is interesting and is the first attempt to account for the potentially interactive effects of seasonality and blood source on mosquito fitness, which the authors frame as a possible explanation for previously observed hostswitching of Culex quinquefasciatus from birds to mammals in the fall. The authors hypothesize that if changes in fitness by blood source change between seasons, higher fitness on birds in the summer and on mammals in the autumn could drive observed host switching. To test this, the authors fed individuals from a colony of Cx. quinquefasciatus on chickens (bird model) and mice (mammal model) and subjected each of these two groups to two different environmental conditions reflecting the high and low temperatures and photoperiod experienced in summer and autumn in Córdoba, Argentina (aka seasonality). They measured fecundity, fertility, and hatchability over two gonotrophic cycles. The authors then used generalized linear mixed models to evaluate the impact of host species, seasonality, and gonotrophic cycle on fecundity, fertility, and hatchability. The authors were trying to test their hypothesis by determining whether there was an interactive effect of season and host species on mosquito fitness. This is an interesting hypothesis; if it had been supported, it would provide support for a new mechanism driving host switching. While the authors did report an interactive impact of seasonality and host species, the directionality of the effect was the opposite from that hypothesized. The authors have done a very good job of addressing many of the reviewer's concerns, especially by adding two additional replicates. Several minor concerns remain, especially regarding unclear statements in the discussion.

Strengths:

(1) Using a combination of laboratory feedings and incubators to simulate seasonal environmental conditions is a good, controlled way to assess the potentially interactive impact of host species and seasonality on the fitness of Culex quinquefasciatus in the lab.

(2) The driving hypothesis is an interesting and creative way to think about a potential driver of host switching observed in the field.

Weaknesses:

(1) The methods would be improved by some additional details. For example, clarifying the number of generations for which mosquitoes were maintained in colony (which was changed from 20 to several) and whether replicates were conducted at different time points.

Changed as suggested.

(2) The statistical analysis requires some additional explanation. For example, you suggest that the power analysis was conducted a priori, but this was not mentioned in your first two drafts, so I wonder if it was actually conducted after the first replicate. It would be helpful to include further detail, such as how the parameters were estimated. Also, it would be helpful to clarify why replicate was included as a random effect for fecundity and fertility but as a fixed effect for hatchability. This might explain why there were no significant differences for hatchability given that you were estimating for more parameters.

The power analysis was conducted a posteriori, as you correctly inferred. While I did not indicate that it was performed a priori, you are right in noting that this was not explicitly mentioned. As you suggested, the methodology for the power analysis has been revised to clarify any potential doubts.

Regarding the model for hatchability, a model without a random effect variable was used, as all attempts to fit models with random effects resulted in poor validation. These points have now been clarified and explained in the corresponding section.

(3) A number of statements in the discussion are not clear. For example, what do you mean by a mixed perspective in the first paragraph? Also, why is the expectation mentioned in the second paragraph different from the hypothesis you described in your introduction?

Changed as suggested.

(4) According to eLife policy, data must be made freely available (not just upon request).

Data and code will be publicly available. The corresponding section was modified.

Reviewer #2 (Recommendations for the authors):

Your manuscript is much improved by the inclusion of two additional replicates! The results are much more robust when we can see that the trends that you report are replicable across 3 iterations of the experiment. Congratulations on a greatly improved study and paper! I have several minor concerns and suggestions, listed below:

38-39: I think it is clearer to say "no statistically significant effect of season on hatchability of eggs" ... or specify if you are referring to blood or the interaction of blood and season. It isn't clear which treatment you are referring to here.

Changed as suggested.

54-57: This could be stated more succinctly. Instead of citing papers that deal with specific examples of patterns, I would suggest citing a review paper that defines these terms.

Changed as suggested.

83-84: What if another migratory bird is the preferred host in Argentina? I would state this more cautiously (e.g. "may not be applicable...").

Changed as suggested.

95-96: I don't understand what you mean by this. These hypotheses are specifically meant to understand mosquitoes that DO have a distinct seasonal phenology, so I'm not sure why this caveat is relevant. And naturally this hypothesis is host dependent, since it is based on specific host reproductive investments. I think that the strongest caveat to this hypothesis is simply that it hasn't been proven.

Changed as suggested.

97-115: This is a great paragraph! Very clear and compelling.

Thanks for your words!

118: Do you have an exact or estimated number of rafts collected?

Sorry, I have not the exact number of rafts, but it was at leas more than 20-30.

135: "over twenty" was changed to "several"; several would imply about 3 generations, so this is misleading. If the colony was actually maintained for over twenty generations, then you should keep that wording.

Changed as suggested.

163-164: Can you please clarify whether the replicates were conducted a separate time points?

Changed as suggested.

Note: the track changes did not capture all of the changes made; e.g. 163-164 should show as new text but does not.

You are absolutely right; when I uploaded the last version, I unfortunately deleted all tracked changes and cannot recover them. In this new version, I will ensure that all minimal changes are included as tracked changes.

186 - 189: the terms should be "fixed effect" and "random effect"

Changed as suggested.

191: Edit: linear

Changed as suggested.

194: why was replicate not included as a random effect here when it was above? Also, can you please clarify "interaction effects"? Which interactions did you include?

Changed as suggested. Explained above and in methodology. Hatchability models with random effect variable were poor fitted and validated. The interactions for hatchability were a four-way (season, blood source, cycle and replicate)

207-208: I'm not sure what you mean by "aimed to achieve"? Weren't you doing this after you conducted the experiments, so wouldn't this be determining the power of your model (post-hoc power analysis)? Also, I think you should provide the parameter estimates that were used (e.g. effect size - did you use the effect size you estimated across the 3 replicates?).

Changed as suggested.

214-215: this should be reworded to acknowledge that this is estimated for the given effect size; for example, something like "This sample size was sufficient to detect the observed effect with a statistical power of 0.8" or something along those lines (unless I am misunderstanding how you conducted this test).

Changed as suggested.

246. Abbreviate Culex

Changed as suggested.

253-255: This sentence isn't clear. What do you mean by mixed? Also, the season really seemed to mainly impact the fitness of mosquitoes fed on mouse blood and here the way it is phrased seems to indicate that season has an impact on the fitness of those fed with chicken blood.

Changed as suggested.

258-260: You stated your hypothesis as the relative fitness shifting between seasons, but this statement about the expectation is different from your hypothesis stated earlier. Please clarify.

You are right. Thank you for noting this. It was changed as suggested.  

263-266: I also don't understand this sentence; what does the first half of the sentence have to do with the second?

Changed as suggested.

269-270: This doesn't align with your observation exactly; you say first AND second are generally most productive, but you observed a drop in the second. Please clarify this.

Changed as suggested.

280: I suggest removing "as same as other studies"; your caveats are distinct because your experimental design was unique

Changed as suggested.

287: you shouldn't be looking for a "desired" effect; I suggest removing this word

Changed as suggested.

288: It wasn't really a priori though, since you conducted it after your first replicate (unless you didn't use the results from the first replicate you reported in the original drafts?)

It was a posteriori. Changed as suggested.

290: Why is 290 written here?

It was a mistype. Deleted as suggested.

291-298: The meaning of this section of your paragraph is not clear.

Improve as suggested.

304-313: This list of 3 explanations are directed at different underlying questions. Explanations 1 and 2 are alternative explanations for why host switching occurs if not due to differences in fitness. This isn't really an explanation of your results so much as alternative explanations for a previously reported phenomenon. And the third is an explanation for why you may not have observed the expected effect. I suggest restructuring this to include the fact that Argentinian quinqs may not host switch as part of your previous list of caveats. Then you can include your two alternative explanations for host switching as a possible future direction (although I would say that it is really just one explanation because "vector biology" is too broad of a statement to be testable). Also, you haven't discussed possible explanations for your actual result, which showed that mosquito fitness decreased when feeding on mouse blood in autumn conditions and in the second gonotrophic, while those that fed on chicken did not experience these changes. Why might that be?

The discussion was restructured to include all these suggested changes. Additionally, it was also discussed some possible explanations of our results.

315-317: This statement is vague without a direct explanation of how this will provide insight. I suggest removing or providing an explanation of how this provides insight to transmission and forecasting.

Changed as suggested.

319-320: According to eLife policy, all data should be publicly available. From guidelines: "Media Policy FAQs Data Availability Purpose and General Principles To maintain high standards of research reproducibility, and to promote the reuse of new findings, eLife requires all data associated with an article to be made freely and widely available. These must be in the most useful formats and according to the relevant reporting standards, unless there are compelling legal or ethical reasons to restrict access. The provision of data should comply with FAIR principles (Findable, Accessible, Interoperable, Reusable). Specifically, authors must make all original data used to support the claims of the paper, or that is required to reproduce them, available in the manuscript text, tables, figures or supplementary materials, or at a trusted digital repository (the latter is recommended). This must include all variables, treatment conditions, and observations described in the manuscript. The authors must also provide a full account of the materials and procedures used to collect, pre-process, clean, generate and analyze the data that would enable it to be independently reproduced by other researchers."

- so you need to make your data available online; I also understand the last sentence to indicate that code should be made available.  

Data and code will be publicly available.

Table 1: it is notable that in replicate 2, the autumn:mouse:gonotrophic cycle II fecundity and fertility are actually higher than in the summer, which is the opposite of reps 1 and 3 and the overall effect you reported from the model. This might be worth mentioning in the discussion.

Mentioned in the discussion as suggested.

Tables 1 and 2: shouldn't this just be 8 treatments? You included replicate as a random effect, so it isn't really a separate set of treatments.

This table reflects the output of the whole experiment, that is why it is present the 24 expetiments.

Figure 3: Can you please clarify if this is showing raw data?

Changed as suggested.

Note: grammatical copy editing would be beneficial throughout

Grammar was improved as suggested.

Author response:

The following is the authors’ response to the previous reviews

Public Reviews:

Reviewer #1 (Public review):

In this study, Tian et al. explore the role of ubiquitination of non-structural protein 16 (nsp16) in the SARS-CoV-2 life cycle. nsp16, in conjunction with nsp10, performs the final step of viral mRNA capping through its 2'-O-methylase activity. This modification allows the virus to evade host immune responses and protects its mRNA from degradation. The authors demonstrate that nsp16 undergoes ubiquitination and subsequent degradation by the host E3 ubiquitin ligases UBR5 and MARCHF7 via the ubiquitin-proteasome system (UPS). Specifically, UBR5 and MARCHF7 mediate nsp16 degradation through K48- and K27-linked ubiquitination, respectively. Notably, degradation of nsp16 by either UBR5 or MARCHF7 operates independently, with both mechanisms effectively inhibiting SARS-CoV-2 replication in vitro and in vivo. Furthermore, UBR5 and MARCHF7 exhibit broad-spectrum antiviral activity by targeting nsp16 variants from various SARS-CoV-2 strains. This research advances our understanding of how nsp16 ubiquitination impacts viral replication and highlights potential targets for developing broadly effective antiviral therapies.

Strengths:

The proposed study is of significant interest to the virology community because it aims to elucidate the biological role of ubiquitination in coronavirus proteins and its impact on the viral life cycle. Understanding these mechanisms will address broadly applicable questions about coronavirus biology and enhance our overall knowledge of ubiquitination's diverse functions in cell biology. Employing in vivo studies is a strength.

Weaknesses:

Minor comments:

Figure 5A- The authors should ensure that the figure is properly labeled to clearly distinguish between the IP (Immunoprecipitation) panel and the input panel.

Thank you for your suggestion. We have exchanged Figure 5 in this version.

Reviewer #3 (Public review):

Summary:

The manuscript "SARS-CoV-2 nsp16 is regulated by host E3 ubiquitin ligases, UBR5 and MARCHF7" is an interesting work by Tian et al. describing the degradation/ stability of NSP16 of SARS CoV2 via K48 and K27-linked Ubiquitination and proteasomal degradation. The authors have demonstrated that UBR5 and MARCHF7, an E3 ubiquitin ligase bring about the ubiquitination of NSP16. The concept, and experimental approach to prove the hypothesis looks ok. The in vivo data looks ok with the controls. Overall, the manuscript is good.

Strengths:

The study identified important E3 ligases (MARCHF7 and UBR5) that can ubiquitinate NSP16, an important viral factor.

Comments on revisions:

I had gone through the revised form of the manuscript thoroughly. The authors have addressed all of my concerns. To me, the experimental approach looks convincing that the host E3 ubiquitin ligases (UBR5 and MARCHF7) ubiquitinate NSP16 and mark it for proteasomal degradation via K48- and K27- linkage. The authors have represented the final figure (Fig.8) in a convincing manner, opening a new window to explore the mechanism of capping the vRNA bu NSP16.

Thank you for your recognition.

Author response:

The following is the authors’ response to the original reviews

Reviewer #1 (Public review):

Summary, and Strengths:

The authors and their team have investigated the role of Vimentin Cysteine 328 in epithelial-mesenchymal transition (EMT) and tumorigenesis. Vimentin is a type III intermediate filament, and cysteine 328 is a crucial site for interactions between vimentin and actin. These interactions can significantly influence cell movement, proliferation, and invasion. The team has specifically examined how Vimentin Cysteine 328 affects cancer cell proliferation, the acquisition of stemness markers, and the upregulation of the non-coding RNA XIST. Additionally, functional assays were conducted using both wild-type (WT) and Vimentin Cysteine 328 mutant cells to demonstrate their effects on invasion, EMT, and cancer progression. Overall, the data supports the essential role of Vimentin Cysteine 328 in regulating EMT, cancer stemness, and tumor progression. Overall, the data and its interpretation are on point and support the hypothesis. I believe the manuscript has great potential.

The authors are thankful to the reviewers for carefully reading the manuscript and evaluating the data to make positive comments and supporting our conclusions.

Weaknesses:

Minor issues are related to the visibility and data representation in Figures 2E and 3 A-F

We have revised the figures (Figure 2E and Figure 3A-F) to increase the data visibility.

Reviewer #2 (Public review):

The aim of the investigation was to find out more about the mechanism(s) by which the structural protein vimentin can facilitate the epithelial-mesenchymal transition in breast cancer cells.

The authors focussed on a key amino acid of vimentin, C238, its role in the interaction between vimentin and actin microfilaments, and the downstream molecular and cellular consequences. They model the binding between vimentin and actin in silico to demonstrate the potential involvement of C238, but the outcome is described vaguely.

We have expanded the discussion of these results in the manuscript to more explicitly describe the critical role of C238 in the vimentin-actin interaction. Specifically, we highlight that C238 lies within a region of the vimentin rod domain known to mediate key protein-protein interactions. Our modeling shows that the thiol group of C238 enables specific hydrogen bonding and potential disulfide-mediated interactions with actin, which are disrupted upon mutation to serine. These findings provide mechanistic insight into the functional importance of this residue.

The phenotype of a non-metastatic breast cancer cell line MCF7, which doesn't express vimentin, could be changed to a metastatic phenotype when mutant C238S vimentin, but not wild-type vimentin, was expressed in the cells. Expression of vimentin was confirmed at the level of mRNA, protein, and microscopically. Patterns of expression of vimentin and actin reflected the distinct morphology of the two cell lines. Phenotypic changes were assessed through assay of cell adhesion, proliferation, migration, and morphology and were consistent with greater metastatic potential in the C238S MCF7 cells. Changes in the transcriptome of MCF7 cells expressing wild-type and C238S vimentins were compared and expression of Xist long ncRNA was found to be the transcript most markedly increased in the metastatic cells expressing C238S vimentin. Moreover changes in expression of many other genes in the C238S cells are consistent with an epithelial mesenchymal transition. Tumourigenic potential of MCF7 cells carrying C238S but not wild-type, vimentin was confirmed by inoculation of cells into nude mice. This assay is a measure of the stem-cell quality of the cells and not a measure of metastasis. It does demonstrate phenotypic changes that could be linked to metastasis.

shRNA was used to down-regulate vimentin or Xist in the MCF7 C238S cells. The description of the data is limited in parts and data sets require careful scrutiny to understand the full picture. Down-regulation of vimentin reversed the morphological changes to some degree, but down-regulation of Xist didn't.

This is understandable given the fact that vimentin interacts with actin which is known to determine cell shape. XIST being a non-coding RNA will not have the same effect.

Conversely, down-regulation of XIST inhibited cell growth, a sign of reversing metastatic potential, but down-regulation of vimentin had no effect on growth.

XIST is known to get induced in a number of cancers (see Figure 3E) which is consistent with our observation that its downregulation will inhibit cell growth. However, downregulation of vimentin had no effect on growth which is consistent with our previously published observation that ectopic expression of wildtype vimentin in MCF-7 cells did not influence cell growth (Usman et al Cells 2022, 11(24), 4035; https://doi.org/10.3390/cells11244035).

Down-regulation of either did inhibit cell migration, another sign of metastatic reversal.

We have previously shown that ectopic expression of wildtype vimentin in MCF-7 stimulate cell migration due to downregulation of CDH5 (endothelial cadherins) (Usman et al Cells 2022, 11(24), 4035). Therefore, downregulation of vimentin is expected to inhibit cell migration which is what we observed in this study. Why downregulation of XIST inhibited cell migration is not clear. It is conceivable that XIST downregulation affects Lamin expression which may suppress intercellular interactions to increase cell migration. This hypothesis is supported by the fact that vimentin expression in MCF-7 affects Lamin expression (Usman et al Cells 2022, 11(24), 4035).

The interpretation of this type of experiment is handicapped when full reversal of expression is not achieved, as was the case in this study.

Full reversal of any biological effect is almost impossible to achieve which is because the shRNAs by nature are not 100% effective. This can however be tested using crispr Cas 9 gene editing to completely knockdown a protein (can’t be used for XIST as it is a non-coding RNA). In that case one has to assume that it will have no off-target effect.

Overall the study describes an intriguing model of metastasis that is worthy of further investigation, especially at the molecular level to unravel the connection between vimentin and metastasis. The identification of a potential role for Xist in metastasis, beyond its normal role in female cells to inactivate one of the X chromosomes, corroborates the work of others demonstrating increased levels in a variety of tumours in women and even in some tumours in men. It would be of great interest to see where in metastatic cells Xist is expressed and what it binds to.

The authors fully agree that it is an interesting model of metastasis/oncogenesis that requires further investigation.

Author response:

The following is the authors’ response to the original reviews

Public Reviews:

Reviewer #1 (Public review):

Summary:

Hua et al show how targeting amino acid metabolism can overcome Trastuzumab resistance in HER2+ breast cancer.

Strengths:

The authors used metabolomics, transcriptomics and epigenomics approaches in vitro and in preclinical models to demonstrate how trastuzumab-resistant cells utilize cysteine metabolism.

Thank you for your valuable comments. We would like to extend our appreciation for your efforts. Your constructive suggestion would help improve our research.

Weaknesses:

However, there are some key aspects that needs to be addressed.

Major:

(1) Patient Samples for Transcriptomic Analysis: It is unclear from the text whether tumor tissues or blood samples were used for the transcriptomic analysis. This distinction is crucial, as these two sample types would yield vastly different inferences. The authors should clarify the source of these samples.

Thank you for your valuable comments. In the transcriptomic analysis, we included the data of HER2 positive breast cancer patients who received trastuzumab in I-SPY2 trial (GSE181574). Tumor tissues were used in this dataset. We highlighted the usage of “pre-treatment breast cancer tumors” in Line 309 and included the overview of transcriptomic data analysis in I-SPY2 trial in Figure S1F.

(2) The study only tested one trastuzumab-resistant and one trastuzumab-sensitive cell line. It is unclear whether these findings are applicable to other HER2-positive tumor cell lines, such as HCC1954. The authors should validate their results in additional cell lines to strengthen their conclusions.

Thank you for your valuable comments. We agree with your opinion, and the exploration of multiple cell lines would make our research findings more comprehensive. This is a limitation of our study, and we would continue to improve our design and methods in future experiments.

(3) Relevance to Metastatic Disease: Trastuzumab resistance often arises in patients during disease recurrence, which is frequently associated with metastasis. However, the mouse experiments described in this paper were conducted only in the primary tumors. This article would have more impact if the authors could demonstrate that the combination of Erastin or cysteine starvation with trastuzumab can also improve outcomes in metastasis models.

Thank you for your valuable comments. We agree with your suggestions. The exploration of metastatic disease would make our research more meaningful and help better address clinical key issues. In our future studies, we will continue to investigate the association between the invasive and metastatic capabilities of trastuzumab resistant HER2 positive breast cancer and cysteine metabolism.

Minor:

(1) The figures lack information about the specific statistical tests used. Including this information is essential to show the robustness of the results.

Thank you for your valuable comments. We added statistical information in our figure legends, including Line 849-850, Line 865-867, Line 881-882, Line 898-900, Line 910-911 and Line 923-924.

(2) Figure 3K Interpretation: The significance asterisks in Figure 3K do not specify the comparison being made. Are they relative to the DMSO control? This should be clarified.

Thank you for your valuable comments. We have modified this figure to demonstrate it more clearly. In Figure 3K, the significance was determined by one-way ANOVA and the comparison presented was relative to the DMSO control. It was indicated that the combination of erastin or cysteine starvation and trastuzumab could increase lipid peroxidation, although trastuzumab monotherapy did not induce ferroptosis.

Additionally, the combination of erastin and trastuzumab could result in more lipid peroxidation than erastin alone. Similar results were also found in the combination of cysteine starvation and trastuzumab. These results showed that targeting cysteine metabolism plus trastuzumab could have synergic effects to induce ferroptosis in trastuzumab resistant HER2 positive breast cancer.

Reviewer #2 (Public review):

In this manuscript, Hua et al. proposed SLC7A11, a protein facilitating cellular cystine uptake, as a potential target for the treatment of trastuzumab-resistant HER2-positive breast cancer. If this claim holds true, the finding would be of significance and might be translated to clinical practice. Nevertheless, this reviewer finds that the conclusion was poorly supported by the data.

Notably, most of the data (Figures 2-6) were based on two cell lines - JIMT1 as a representative of trastuzumab-resistant cell line, and SKBR3 as a representative of trastuzumab sensitive cell line. As such, these findings could be cell-line specific while irrelevant to trastuzumab sensitivity at all. Furthermore, the authors claimed ferroptosis simply based on lipid peroxidation (Figure 3). Cell viability was not determined, and the rescuing effects of ferroptosis inhibitors were missing. The xenograft experiments were also suspicious (Figure 4). The description of how cysteine starvation was performed on xenograft tumors was lacking, and the compound (i.e., erastin) used by the authors is not suitable for in vivo experiments due to low solubility and low metabolic stability. Finally, it is confusing why the authors focused on epigenetic regulations (Figures 5 & 6), without measuring major transcription factors (e.g., NRF2, ATF4) which are known to regulate SLC7A11.

To sum up, this reviewer finds that the most valuable data in this manuscript is perhaps Figure 1, which provides unbiased information concerning the metabolic patterns in trastuzumab-sensitive and primary resistant HER2-positive breast cancer patients.

Thank you for your valuable comments. We agree with your suggestions. Your feedback would help enhance the quality of our research.

(1) Our research was mainly conducted in JIMT1 (trastuzumab resistant) and SKBR3 (trastuzumab sensitive), and this is a limitation of our study. The experimental validation using different cell lines will make our research findings more persuasive. In our future research, we will continuously optimize experimental design and methods to make our findings more comprehensive.

(2) The detection of ferroptosis in our research was mainly performed by evaluating the lipid peroxidation. Experiments measuring cell viability and rescuing effects would help provide more evidence.

We utilized CCK8 tests to compare cell viabilities of JIMT1 and SKBR3 in different erastin and RSL3 concentrations, as well as different exposure time of cysteine starvation. It was shown that JIMT1 was more sensitive to erastin and RSL3, but tolerant to cysteine starvation, which was consistent with the previous lipid peroxidation tests. This data was included in Figure S5C-E. We added the description in Line 375-379.

In addition, we also performed experiments to explore the rescuing effects of ferroptosis inhibitor Fer-1. It was indicated that Fer-1 could suppress the lipid peroxidation resulted from erastin, RSL3 and cysteine starvation in both JIMT1 and SKBR3. This provided more evidence that cysteine metabolism played a vital role in modulating HER2 positive breast cancer ferroptosis. This data was included in Figure S5G and S5H. We added the description to Line 387-391.

(3) In xenograft experiments, the cysteine starvation was performed by feeding cystine/cysteine-deficient diet (Xietong Bio). We added details of this diet on Line 236-237 in Methods.

We agree with your opinion on the role of erastin in experiments in vivo. We have tried to optimize drug dissolution and other conditions by referring to previous relevant literature. We would continue to improve our experimental design and methods.

(4) Epigenetic modifications have been recognized as crucial factors in drug resistance formation. An increasing number of studies have emphasized the importance of epigenetic changes in regulating the abnormal expression of oncogenes and tumor suppressor genes related to drug resistance. Currently, the role of epigenetic changes in the development of trastuzumab resistance in HER2 positive breast cancer is still in exploration. We tried to investigate the dysregulation of histone modifications and DNA methylation in trastuzumab resistant HER2 positive breast cancer. Our findings indicated that targeting H3K4me3 and DNA methylation could decrease SLC7A11 expression and induce ferroptosis. This would provide more evidence in exploring trastuzumab resistance mechanisms. We have provided a detailed discussion on Line 598-607.

We would like to extend our appreciation for your constructive suggestions and continue to improve our research in future experiments.

Recommendations for the authors:

Reviewer #2 (Recommendations for the authors):

(1) Line 334: it would be helpful to clarify that JIMT1 cells are trastuzumab-resistant while SKBR3 cells are trastuzumab sensitive, especially for those not familiar with breast cancer cell lines.

Thank you for your valuable recommendations. We added the description of trastuzumab sensitive SKBR3 and trastuzumab resistant JIMT1 on Line 334-335.

(2) Figure 3: the concentrations of erastin and RSL3 should be indicated.

Thank you for your valuable recommendations. In Figure 3, the concentration of erastin was 10μm and RSL3 was 1μm. We added these details in the figure legends on Line 872-873.

(3) Figure 3: lipid peroxidation does not necessarily mean ferroptosis. Cell viability data and rescuing effects of ferroptosis inhibitors should be shown.

Thank you for your valuable recommendations. As we mentioned above, we utilized CCK8 tests to compare cell viabilities of JIMT1 and SKBR3 in different erastin and RSL3 concentrations, as well as different exposure time of cysteine starvation. It was consistent with lipid peroxidation tests that JIMT1 was more sensitive to erastin and RSL3, but tolerant to cysteine starvation. This data was included in Figure S5C-E. We added the description in Line 375-379.

As described above, we also performed experiments to explore the rescuing effects of ferroptosis inhibitor Fer-1. It was indicated that Fer-1 could suppress the lipid peroxidation resulted from erastin, RSL3 and cysteine starvation in both JIMT1 and SKBR3. This provided more evidence that cysteine metabolism played a vital role in modulating HER2 positive breast cancer ferroptosis. This data was included in Figure S5G and S5H. We added the description to Line 387-391.

(4) Figure 3H: how cysteine starvation was performed should be clarified in the Methods section.

Thank you for your valuable recommendations. We performed cell culture with cysteine starvation by utilizing cystine/cysteine-deficient DMEM (BIOTREE) and 1% penicillin streptomycin at 37℃ with 5% CO2. We added details of this diet on Line 141-143 in Methods.

(5) Figure 4: the meaning of "H" should be clarified.

Thank you for your valuable recommendations. H was indicated as trastuzumab. We clarified the meaning of “H” in the figure legends on Line 898.

(6) Figure 4B & 4C: the data of "H" group and "Erastin" group are inconsistent.

Thank you for your valuable recommendations. In the vivo experiments, the tumor volume changes were analyzed using a paired approach, comparing the tumor size of each individual mouse before and after treatment. We noticed the confusion caused and added more details about our vivo experiments on Line 240 in Methods and Line 892-893 in figure legends.

(7) Figure 4: how cysteine starvation was performed should be clarified in the Methods section.

Thank you for your valuable recommendations. We performed cysteine starvation by utilizing cystine/cysteine-deficient diet (Xietong Bio). We added details of this diet on Line 236-237 in Methods.

We have also corrected some grammatical errors in the manuscript and We would like to extend our great appreciation to all editors and reviewers for their invaluable contributions.

Author response:

The following is the authors’ response to the original reviews

Summary of revisions:

Thanks to the careful review and comments from the reviewers, we restructured the introduction and the discussion to improve clarity and better contextualise findings. We notably discuss further the f_sphere decrease observations in the cerebellum and the Tau-specific findings (Tau being a possible marker for Purkinje cells development and Tau switching compartment in the thalamus). We added material in Supplementary Information to support these discussion points. We added a figure to show the metabolic profiles normalised by water or by macromolecules and a figure and table related to a rough approximation of f_sphere, leaning on existing literature. We report the DTI results for thoroughness.

Public Reviews:

Reviewer #1 (Public Review):

In this work, Ligneul and coauthors implemented diffusion-weighted MRS in young rats to follow longitudinally and in vivo the microstructural changes occurring during brain development. Diffusion-weighted MRS is here instrumental in assessing microstructure in a cell-specific manner, as opposed to the claimed gold-standard (manganese-enhanced MRI) that can only probe changes in brain volume. Differential microstructure and complexification of the cerebellum and the thalamus during rat brain development were observed noninvasively. In particular, lower metabolite ADC with increasing age were measured in both brain regions, reflecting increasing cellular restriction with brain maturation. Higher sphere (representing cell bodies) fraction for neuronal metabolites (total NAA, glutamate) and total creatine and taurine in the cerebellum compared to the thalamus were estimated, reflecting the unique structure of the cerebellar granular layer with a high density of cell bodies. Decreasing sphere fraction with age was observed in the cerebellum, reflecting the development of the dendritic tree of Purkinje cells and Bergmann glia. From morphometric analyses, the authors could probe non-monotonic branching evolution in the cerebellum, matching 3D representations of Purkinje cells expansion and complexification with age. Finally, the authors highlighted taurine as a potential new marker of cerebellar development.

From a technical standpoint, this work clearly demonstrates the potential of diffusion-weighted MRS at probing microstructure changes of the developing brain non-invasively, paving the way for its application in pathological cases. Ligneul and coauthors also show that diffusionweighted MRS acquisitions in neonates are feasible, despite the known technical challenges of such measurements, even in adult rats. They also provide all necessary resources to reproduce and build upon their work, which is highly valuable for the community.

From a biological standpoint, claims are well supported by the microstructure parameters derived from advanced biophysical modelling of the diffusion MRS data. The assumption of metabolite compartmentation, forming the basis of cell-specific microstructure interpretation of dMRS data, remains debated and should be considered with care (Rae, Neurochem Res, 2014, https://doi.org/10.1007/s11064-013-1199-5). External cross-validation of some of the authors' claims, in particular taurine in the thalamus switching from neurons to astrocytes during brain development, would be a highly valuable addition to this study.

R1.1: We understand the reviewer's concerns. Metabolic compartmentation is not a one-toone correspondence. Although we interpret the results in the light of metabolic compartmentation, our results are not driven by this assumption. We could not perform a direct cross-validation of the taurine switch in the thalamus, but we now clarify in the discussion why the dMRS results themselves indicate a switch, and we integrate our results better with existing literature on taurine. We now discuss this in more detail for the cerebellar results too.

Specific strengths:

(1) The interpretation of dMRS data in terms of cell-specific microstructure through advanced biophysical modelling (e.g. the sphere fraction, modelling the fraction of cell bodies versus neuronal or astrocytic processes) is a strong asset of the study, going beyond the more commonly used signal representation metrics such as the apparent diffusion coefficient, which lacks specificity to biological phenomena.

(2) The fairly good data quality despite the complexity of the experimental framework should be praised: diffusion-weighted MRS was acquired in two brain regions (although not in the same animals) and longitudinally, in neonates, including data at high b-values and multiple diffusion times, which altogether constitutes a large-scale dataset of high value for the diffusion-weighted MRS community.

(3) The authors have shared publicly data and codes used for processing and fitting, which will allow one to reproduce or extend the scope of this work to disease populations, and which goes in line with the current effort of the MR(S) community for data sharing.

Specific weaknesses:

(1) This work lacks an introduction and a discussion about diffusion MRI, which is already a validated technique to assess brain development non-invasively. Although water lacks cellspecificity compared to metabolites, several studies have reported a decrease in water ADC and increased fractional anisotropy with brain maturation, associated with the myelination process and decreased water content (overview in Hüppi, Chapt. 30 of "Diffusion MRI: Theory, Methods, and Applications", Oxford University Press, 2010). Interestingly, the same observations are found in this work (decreased ADC with age for most metabolites in both brain regions), which should have been commented on. Moreover, the authors could have reported water diffusion properties in addition to metabolites', as I believe the water signal, used for coil combination and/or Eddy currents corrections, is usually naturally acquired during diffusion-weighted MRS scans.

R1.2: Thank you for these helpful suggestions. We have now improved our introduction of the various modalities, and we contextualise the study in light of previous DTI findings in the as suggested by the reviewer. We agree with the reviewer that the comparison with previous human DTI is relevant, and we now mention it at the beginning of the discussion. However, the very different nature of the dMRS signal compared to dMRI (intracellular and absence of exchange for metabolites) prevents us from drawing any strong conclusions.

(2) It is unclear why the authors have normalized metabolite concentrations (measured from low b-values diffusion-weighted MRS spectra) to the macromolecule concentrations. First, it is not specified whether in vivo macromolecules were acquired at each age or just at one time point. Second, such ratios are not standard practice in the MRS community so this choice should have been explained. Third, the macromolecule content was reported to change with age (Tkac et al., Magn Reson Med, 2003), therefore a change in metabolite to macromolecule ratio with age cannot be interpreted unequivocally.

R1.3: We agree with the reviewer that this needed further explanations. We now clarify in the Results section “Metabolic profile changes with age” the reasoning behind choosing macromolecules for normalisation. We also added in the Supplementary Information the metabolite concentrations change with age when normalising by water, and a direct comparison with MM normalisation (Figure S2).

(3) Some discussion is missing about the choice of the analytical biophysical model (although a few are compared in Supplementary Materials), in particular: is a model of macroscopic anisotropy relevant in cerebellum, made of a large fraction of oriented white matter tracks, and does the model remain valid at different ages given white matter maturation and the ongoing myelination process?

R1.4: We agree with the reviewer that this is a valid concern. We actually acquired some standard DTI at the end of the acquisition sessions (where possible) having in mind the fibre dispersion estimation. However, data could not be acquired in all animals, and the data quality was poor (see Figure S8, the experimental conditions would have required further optimisation). We now add a couple of sentences at the beginning and in the end of discussion to address this limitation, and we include the DTI data in Supplementary Information.

Reviewer #2 (Public Review):

Summary:

The authors set out to non-invasively track neuronal development in rat neonates, which they achieved with notable success. However, the direct relationship between the results and broader conclusions regarding developmental biology and potential human implications is somewhat overstretched without further validation.

Strengths:

If adequately revised and validated, this work could have a significant impact on the field, providing a non-invasive tool for longitudinal studies of brain development and neurodevelopmental disorders in preclinical settings.

Weaknesses:

(1) Consistency and Logical Flow:

The manuscript suffers from a lack of strategic flow in some sections. Specifically, transitions between major findings and methodological discussions need refinement to ensure a logical progression of ideas. For example, the jump from the introduction of developmental trajectories and the technicalities of MRS (Magnetic Resonance Spectroscopy) processing on page 3 could benefit from a bridging paragraph that explicitly states the study's hypotheses based on existing literature gaps.

R2.1: Thank you for this general feedback (along with your point (3)) that helped us restructure the introduction and the discussion to improve the clarity and flow.

(2)  Scientific Rigour:

While the novel application of diffusion-weighted MRS is commendable, there's a notable gap in the rigorous validation of this approach against gold-standard histological or molecular techniques. Particularly, the assertions regarding the sphere fraction and morphological changes inferred from biophysical modelling mandates direct validation to solidify the claims made. A study comparing these in vivo findings with ex vivo confirmation in at least a subset of samples would significantly enhance the reliability of these conclusions.

R2.2: We agree with the reviewer that this would have been a great addition to the manuscript. Although we could not run new experiments to address these flaws, we now discuss the results more quantitatively, leaning on existing literature (addition of Figure S11 and Table S2). This helps us understand the results around Tau in both regions better, and illustrate the R_sphere trend.

(3) Clarity and Novelty:

- The manuscript often delves deeply into technical specifics at the expense of accessibility to readers not deeply familiar with MRS technology. The introduction and discussions would benefit from a clearer elucidation of why these specific metabolite markers were chosen and their known relevance to neuronal and glial cells, placing this in the context of what is novel compared to existing literature.

- The novelty aspect could be reinforced by a more structured discussion on how this method could change the current understanding or practices within neurodevelopmental research, compared to the current state of the art.

R2.3: See answer to (1). By restructuring the introduction and the discussion, we hope to have addressed this point. We now discuss how these findings compare to the state of the art (notably added comparison with dMRI research). Along with the next comment, we better discuss potential implications of these findings for neurodevelopmental research.

(4) Completeness:

- The Discussion section requires expansion to offer a more comprehensive interpretation of how these findings impact the broader field of neurodevelopment and psychiatric disorders. Specifically, the implications for human studies or clinical translation are touched upon but not fully explored.

- Further, while supplementary material provides necessary detail on methodology, key findings from these analyses should be summarized and discussed in the main text to ensure the manuscript stands complete on its own.

R2.4: Thank you for these helpful suggestions. We now integrate the findings better into the existing literature. We notably discuss how the results might translate to humans.

(5) Grammar, Style, Orthography:

There are sporadic grammatical and typographical errors throughout the text which, while minor, detract from the overall readability. For example, inconsistencies in metabolite abbreviations (e.g., tCr vs Cr+PCr) should be standardized.

R2.5: Thank you for the careful review. This has been corrected.

(6) References and Additional Context:

The current reference list is extensive but lacks integration into the narrative. Direct comparisons with existing studies, especially those with conflicting or supportive findings, are scant. More dedicated effort to contextualize this work within the existing body of knowledge would be beneficial.

R2.6: Because the nature of this work is novel, it is difficult to find directly conflicting/similar works. However, we now integrate the findings into the broader literature.

Recommendations for the authors:

Reviewer #1 (Recommendations For The Authors):

Minor comments:

Thank you for the careful review, we have addressed most of the minor comments, except for the last one, which we discuss below.

- Some figures appear blurred in the printed PDF- Introduction: "constrained and hindered by cell membranes," - maybe use "restricted" instead of "constrained", like everywhere else in the text

- Introduction: "(typically ~8cm3 vs ~8mm3 in dMRI in humans)" - here I suggest to put the rat brain sizes instead to help the reader understand how small the voxel was at P5 in this study, thus explaining the challenges

- Fig 1 - numbers 1 and 2 on panel A,B should be clarified and they do not match 1 and 2 on panel C, which is confusing- Fig 2 - I am guessing the large dots are the mean and small are individual data points? Please clarify

- Please specify "Relative CRLB" rather than just "CRLB", in supp. mat as well

- Fig 3 - title of panel B, I would change "signal" into "concentration"

- Fig 3 - end of caption: "and levelled to get Signal(tCr,P30)/Signal(MM,P30)=8", I think "in the thalamus" is missing

- The results section "Biophysical modelling underlines different developmental trajectories of cell microstructure between the cerebellum and the thalamus" is sometimes unprecise, e.g.: "Cerebellum: The sphere fraction and the radius estimated from tNAA diffusion properties vary with age." but the tNAA sphere fraction seems to vary more with age in the thalamus according to table 1 "Cerebellum: fsphere decreases from 0.63 (P10) to 0.41 (P30), but R is stable" this is for tCr I presume

- Table 1 - "pvalues" please add "before multiple comparison correction"

- Figure 5 - Panel B, the L-segment subpanel is unclear -which metabolites is it referring to? Why does Tau have a * in panel A?

- Update Ref 37 to the journal version

- Methods: "A STELASER (Ligneul et al., MRM 2017) sequence", add numbered reference instead

- Please specify that the DIVE toolbox uses Gaussian phase distribution approximation, it is important for the dMRS reader given that your diffusion gradient length is long and cannot be neglected, and that the SGP approximation does not apply.

The Gaussian phase distribution approximation and the SGP approximation are two different concepts. The gradient duration ∂ (7 ms) is short compared to the gradient separation ∆ (100 ms), but it could still be considered too long for the SGP approximation to hold. However, the gradient duration is accounted for in DIVE in any case.

Author response:

The following is the authors’ response to the original reviews

Public reviews:

Reviewer #1 (Public review):

Summary:

In this manuscript, the authors Eapen et al. investigated the peptide inhibitors of Cdc20. They applied a rational design approach, substituting residues found in the D-box consensus sequences to better align the peptides with the Cdc20-degron interface. In the process, the authors designed and tested a series of more potent binders, including ones that contain unnatural amino acids, and verified binding modes by elucidating the Cdc-20-peptide structures. The authors further showed that these peptides can engage with Cdc20 in the cellular context, and can inhibit APC/CCdc20 ubiquitination activity. Finally, the authors demonstrated that these peptides could be used as portable degron motifs that drive the degradation of a fused fluorescent protein.

Strengths:

This manuscript is clear and straightforward to follow. The investigation of different peptide variations was comprehensive and well-executed. This work provided the groundwork for the development of peptide drug modalities to inhibit degradation or apply peptides as portable motifs to achieve targeted degradation. Both of which are impactful.

Weaknesses:

A few minor comments:

(1) In my opinion, more attention to the solubility issue needs to be discussed and/or tested. On page 10, what is the solubility of D2 before a modification was made? The authors mentioned that position 2 is likely solvent exposed, it is not immediately clear to me why the mutation made was from one hydrophobic residue to another. What was the level of improvement in solubility? Are there any affinity data associated with the peptide that differ with D2 only at position 2?

The reviewer is correct that we have not done any detailed solubility characterisation; we refer only to observations rather than quantitative analysis. We wrote that we reverted from Leu to Ala due to solubility - we have clarified this statement (page 11) to say that that we reverted to Ala, as it was the residue present in D1, for which we observed a measurable affinity by SPR and saw a concentration-dependent response in the thermal shift analysis. We do not have any peptides or affinity data that explore single-site mutations with the parental peptide of D2. D2 is included in the paper because of its link to the consensus D-box sequence and thus was the logical path to the investigations into positions 3 and 7 that come later in the manuscript.

(2) I'm not entirely convinced that the D19 density not observed in the crystal structure was due to crystal packing. This peptide is peculiar as it also did not induce any thermal stabilization of Cdc20 in the cellular thermal shift assay. Perhaps the binding of this peptide could be investigated in more detail (i.e., NMR?) Or at least more explanation could be provided.

This section has been clarified (page 16). The lack of observed density was likely due to the relatively low affinity of D19 and also to the lack of binding of the three C-terminal residues in the crystal, and consequently it has a further reduced affinity. The current wording in the manuscript puts greater emphasis on this second aspect being a D19-specific issue, even though it applies to all four soaked peptides. The extent of peptide-induced thermal stabilisations observed by TSA and CETSA is different, with the latter experiment consistently showing smaller shifts. This observation may be due to the more complex medium (cell lysate vs. purified protein) and/or different concentrations of the proteins in solution. In the CETSA, we over-expressed a HiBiT-tagged Cdc20, which is present in addition to any endogenously expressed Cdc20. Although we did not investigate it, the near identical D-box binding sites on Cdc20 and Cdh1 would suggest that there will be cross-specificity, which could further influence the CETSA experiments.

The section now reads:

“We therefore assume that this is the reason for the lack of observed density in this region of the peptides D20 and D21 (Fig. S3E and S3F, respectively). We believe that it causes a reduction in binding affinities of all peptides in crystallo, given the evidence from SPR highlighting a role of position 7 in the interaction (Table 1). Interestingly, the observed electron density of the peptide correlates with Cdc20 binding affinity: D21 and D20, having the highest affinities, display the clearest electron density allowing six amino acids to be modeled, whereas D7 shows relatively poor density permitting modelling of only four residues. For D19, the lack of density observed likely reflects its intrinsically weaker affinity compared to the other peptides, in addition to losing the interactions from position 7 due to crystal packing.”

Reviewer #2 (Public review):

Summary:

The authors took a well-characterised (partly by them), important E3 ligase, in the anaphase-promoting complex, and decided to design peptide inhibitors for it based on one of the known interacting motifs (called D-box) from its substrates. They incorporate unnatural amino acids to better occupy the interaction site, improve the binding affinity, and lay foundations for future therapeutics - maybe combining their findings with additional target sites.

Strengths:

The paper is mostly strengths - a logical progression of experiments, very well explained and carried out to a high standard. The authors use a carefully chosen variety of techniques (including X-ray crystallography, multiple binding analyses, and ubiquitination assays) to verify their findings - and they impressively achieve their goals by honing in on tight-binders.

Weaknesses:

Some things are not explained fully and it would be useful to have some clarification. Why did the authors decide to model their inhibitors on the D-box motif and not the other two SLiMs that they describe?

For completeness, in addition to the D-box we did originally construct peptides based on the ABBA and KEN-box motifs, but they did not show any shift in melting temperature of cdc20 in the thermal shift assay whereas the D-box peptides did; consequently, we focused our efforts on the D-box peptides. Moreover, there is much evidence from the literature that points to the unique importance of the D-box motif in mediating productive interactions of substrates with the APC/C (i.e. those leading to polyubiquitination & degradation). One of the clearest examples is a study by Mark Hall’s lab (described in Qin et al. 2016), which tested the degradation of 15 substrates of yeast APC/C in strains carrying alleles of Cdh1 in which the docking sites for D-box, KEN or ABBA were mutated. They observed that whereas degradation of all 15 substrates depended on D-box binding, only a subset required the KEN binding site on Cdh1 and only one required the ABBA binding site. A more recent study from David Morgan’s lab (Hartooni et al. 2022) looking at binding affinities of different degron peptides concluded that KEN motif has very low affinity for Cdc20 and is unlikely to mediate degradation of APC/C-Cdc20 substrates. Engagement of substrate with the D-box receptor is therefore the most critical event mediating APC/C activity and the interaction that needs to be blocked for most effective inhibition of substrate degradation.

We have added the following text to the Results section “Design of D-box peptides” (page 10):

“We focused on D-box peptides, as there is much evidence from the literature that points to the unique importance of the D-box motif in mediating productive interactions of substrates with the APC/C (i.e. those leading to polyubiquitination & degradation). One of the clearest examples is a study that tested the degradation of 15 substrates of yeast APC/C in strains carrying alleles of Cdh1 in which the docking sites for D-box, KEN or ABBA were mutated ((Qin et al. 2017)). They observed that, whereas degradation of all 15 substrates depended on D-box binding, only a subset required the KEN binding site on Cdh1 and only one required the ABBA binding site. A more recent study (Hartooni et al. 2022) of binding affinities of different degron peptides concluded that KEN motif has very low affinity for Cdc20 and is unlikely to mediate degradation of APC/C-Cdc20 substrates. Engagement of substrate with the D-box receptor is therefore the most critical event mediating APC/C activity and the interaction that needs to be blocked for most effective inhibition of substrate degradation.”

What exactly do they mean when they say their 'observation is consistent with the idea that high-affinity binding at degron binding sites on APC/C, such as in the case of the yeast 'pseudo-substrate' inhibitor Acm1, acts to impede polyubiquitination of the bound protein'? It's an interesting thing to think about, and probably the paper they cite explains it more but I would like to know without having to find that other paper.

Interesting results from a number of labs (Choi et al. 2008,  Enquist-Newman et al. 2008,  Burton et al. 2011, Qin et al. 2019) have shown that mutation of degron SLiMs in Acm1 that weaken interaction with the APC/C have the unexpected consequence of converting Acm1 from APC/C inhibitor to APC/C substrate. A necessary conclusion of these studies is that the outcome of degron binding (i.e. whether the binder functions as substrate or inhibitor) depends on factors other than D-box affinity and that D-box affinity can counteract them. One idea is that if a binder interacts too tightly, this removes some flexibility required for the polyubiquitination process. The most recent study on this question (Qin et al.2019) specifically pins the explanation for the inhibitory function of the high affinity D-box in Acm1 on its ‘D-box Extension’ (i.e. residues 8-12) preventing interaction with APC10.  In our current study, the binding affinity of peptides is measured against Cdc20. In cellular assays however, the D-box must also engage APC10 for degradation to occur. It may be that the peptide binding most strongly to the D-box pocket on Cdc20 is less able to bind to APC10 and therefore less effective in triggering APC10-dependent steps in the polyubiquitination pathway. The important Hartooni et al. paper from David Morgan’s lab confirms that even though the binding of D-box residues to APC10 is very weak on its own, it can contribute 100X increase in affinity of a peptide by adding cooperativity to the interaction of D-box with co-activator. Re Figure 6 and the fact that we did look at peptide binding in cells, these experiments were done in unsynchronised cells, so most Cdc20 would not be bound to APC/C.

We have modified the text (page 18) from:

“However, we found the opposite effect: D2 and D3 showed increased rates of mNeon degradation compared to D1 and D19 (Fig. 8C,D). This observation is consistent with the idea that high-affinity binding at degron binding sites on APC/C, such as in the case of the yeast ‘pseudo-substrate’ inhibitor Acm1, acts to impede polyubiquitination of the bound protein (Qin et al. 2019). Indeed, there is no evidence that Hsl1, which is the highest affinity natural D-box (D1) used in our study, is degraded any more rapidly than other substrates of APC/C in yeast mitosis. As shown in Qin et al., mutation of the high affinity D-box in Acm1 converts it from inhibitor to substrate (Qin et al. 2019). Overall, our results support the conclusions that all the D-box peptides engage productively with the APC/C and that the highest affinity interactors act as inhibitors rather than functional degrons of APC/C.”

to:

“However, we found the opposite effect: D2 and D3 showed increased rates of mNeon degradation compared to D1 and D19 (Fig. 8C,D). This observation is consistent with conclusions from other studies that affinity of degron binding does not necessarily correlate with efficiency of degradation.  Indeed, there is no evidence that Hsl1, which is the highest affinity natural D-box (D1) used in our study, is degraded any more rapidly than other substrates of APC/C in yeast mitosis. A number of studies of a yeast ‘pseudo-substrate’ inhibitor Acm1, have shown that mutation of the high affinity D-box in Acm1 converts it from inhibitor to substrate (Choi et al. 2008,  Enquist-Newman et al. 2008,  Burton et al. 2011) through a mechanism that governs recruitment of APC10 (Qin et al. 2019). Our study does not consider the contribution of APC10 to binding of our peptides to APC/C^Cdc20 complex, but since there is strong cooperativity provided by this additional interaction (Hartooni et al. 2022) we propose this as the critical factor in determining the ability of the different peptides to mediate degradation of associated mNeon.”

Reviewer #3 (Public review):

Summary:

Eapen and coworkers use a rational design approach to generate new peptide-inspired ligands at the D-box interface of cdc20. These new peptides serve as new starting points for blocking APC/C in the context of cancer, as well as manipulating APC/C for targeted protein degradation therapeutic approaches.

Strengths:

The characterization of new peptide-like ligands is generally solid and multifaceted, including binding assays, thermal stability enhancement in vitro and in cells, X-ray crystallography, and degradation assays.

Weaknesses:

One important finding of the study is that the strongest binders did not correlate with the fastest degradation in a cellular assay, but explanations for this behavior were not supported experimentally. Some minor issues regarding experimental replicates and details were also noted.

Interesting results from a number of labs (Choi et al. 2008,  Enquist-Newman et al. 2008,  Burton et al. 2011, Qin et al. 2019) have shown that mutation of degron SLiMs in Acm1 that weaken interaction with the APC/C have the unexpected consequence of converting Acm1 from APC/C inhibitor to APC/C substrate. A necessary conclusion of these studies is that the outcome of degron binding (i.e. whether the binder functions as substrate or inhibitor) depends on factors other than D-box affinity and that D-box affinity can counteract them. One idea is that if a binder interacts too tightly, this removes some flexibility required for the polyubiquitination process. The most recent study on this question (Qin et al.2019) specifically pins the explanation for the inhibitory function of the high affinity D-box in Acm1 on its ‘D-box Extension’ (i.e. residues 8-12) preventing interaction with APC10.  In our current study, the binding affinity of peptides is measured against Cdc20. In cellular assays however, the D-box must also engage APC10 for degradation to occur. It may be that the peptide binding most strongly to the D-box pocket on Cdc20 is less able to bind to APC10 and therefore less effective in triggering APC10-dependent steps in the polyubiquitination pathway. The important Hartooni et al. paper from David Morgan’s lab confirms that even though the binding of D-box residues to APC10 is very weak on its own, it can contribute 100X increase in affinity of a peptide by adding cooperativity to the interaction of D-box with co-activator. Re Figure 6 and the fact that we did look at peptide binding in cells, these experiments were done in unsynchronised cells, so most Cdc20 would not be bound to APC/C.

We have modified the text (page 18) from:

“However, we found the opposite effect: D2 and D3 showed increased rates of mNeon degradation compared to D1 and D19 (Fig. 8C,D). This observation is consistent with the idea that high-affinity binding at degron binding sites on APC/C, such as in the case of the yeast ‘pseudo-substrate’ inhibitor Acm1, acts to impede polyubiquitination of the bound protein (Qin et al. 2019). Indeed, there is no evidence that Hsl1, which is the highest affinity natural D-box (D1) used in our study, is degraded any more rapidly than other substrates of APC/C in yeast mitosis. As shown in Qin et al., mutation of the high affinity D-box in Acm1 converts it from inhibitor to substrate (Qin et al. 2019). Overall, our results support the conclusions that all the D-box peptides engage productively with the APC/C and that the highest affinity interactors act as inhibitors rather than functional degrons of APC/C.”

to:

“However, we found the opposite effect: D2 and D3 showed increased rates of mNeon degradation compared to D1 and D19 (Fig. 8C,D). This observation is consistent with conclusions from other studies that affinity of degron binding does not necessarily correlate with efficiency of degradation.  Indeed, there is no evidence that Hsl1, which is the highest affinity natural D-box (D1) used in our study, is degraded any more rapidly than other substrates of APC/C in yeast mitosis. A number of studies of a yeast ‘pseudo-substrate’ inhibitor Acm1, have shown that mutation of the high affinity D-box in Acm1 converts it from inhibitor to substrate (Choi et al. 2008,  Enquist-Newman et al. 2008,  Burton et al. 2011) through a mechanism that governs recruitment of APC10 (Qin et al. 2019). Our study does not consider the contribution of APC10 to binding of our peptides to APC/C^Cdc20 complex, but since there is strong cooperativity provided by this additional interaction (Hartooni et al. 2022) we propose this as the critical factor in determining the ability of the different peptides to mediate degradation of associated mNeon.”

Recommendations for the authors:

Reviewer #1 (Recommendations for the authors):

(1) On page 12 (towards the end), the author stated D10 contained an A3P mutation, they meant P3A right? 'To test this hypothesis, we proceeded to synthesise D10, a derivative of D4 containing an A3P single point mutation.'

We thank the reviewer for spotting this typo, which we have corrected.

(2) Have the authors considered other orthogonal approaches to cross-examine/validate binding affinities? That said, I do not think extra experiments are necessary.

We did not explore further orthogonal approaches due to the challenges of producing sufficient amounts of the Cdc20 protein. Due to the low affinities of many peptides for Cdc20, many techniques would have required more protein than we were able to produce. We believe that the qualitative TSA combined with the SPR is sufficient to convince the readers; indeed there is a correlation between SPR-determined binding affinities and the thermal shifts: For the natural amino acid-containing peptides (Table 1) D19 has the highest affinity and causes the largest thermal shift in the Cdc20 melting temperature, D10 has the lowest affinity and causes the smallest thermal shift, and D1, D3, D4, and D5 and all rank in the middle by both techniques. For those peptides containing unnatural amino acids (Table 2), again higher affinities are reflected in larger thermal shifts.

Reviewer #2 (Recommendations for the authors):

The data seem fine to me. I would appreciate a little more detail on the points mentioned in the public review. Also a thorough reread, maybe by a disinterested party as there are various typos that could be corrected - all in all an excellent clear paper that encompasses a lot of work.

A colleague has carefully checked the manuscript, and typos have been corrected.

Author response:

We wish to express our gratitude to the reviewers for their insightful and constructive comments on the initial version of our manuscript. We greatly value their observations and have every intention of addressing their remarks in a thorough and constructive manner. Based on the editors’ and reviewers’ feedback, we realize that it was not entirely clear that we intended this work primarily to be a resource and not yield strong insights into DNN-human alignment. Since our method also covers the broad range of natural objects - as used in the vast majority of studies on object processing - we also feel we did not sufficiently highlight the breadth of the tool. Based on the editors’ assessment, our explorations into the limits of the method - which we saw as a strength, not a weakness of our work - perhaps overshadowed the otherwise broad applicability somewhat. We hope to clarify this in the revised manuscript. Beyond these general points, we would like to address the following four points:

• Where feasible, we intend to undertake additional analyses and refine existing ones. For instance, we plan to provide noise ceilings for all datasets where such calculations are possible, and we plan to give careful consideration to implementing a permutation or label-shuffling test to explore some of the ideas shared by the reviewers.

• We plan to discuss more thoroughly several topics raised by the reviewers (e.g., how our approach might contend with different experimental situations such when using line drawings as stimuli).

• We aim to enhance the clarity of our manuscript throughout. This will include refining the wording of our abstract and offering a more detailed explanation of the methods employed in the fMRI analyses.

• We plan to elaborate further on our line of reasoning by addressing potential sources of misunderstanding—such as clarifying what we mean by a “lack of data” and providing greater detail regarding the nature of the 49-dimensional embedding.

Author response:

The evidence supporting this mechanism is incomplete, with additional work needed to clarify SHP-1's role, the contribution of Fc receptor crosslinking, and the biological relevance across normal and malignant B cells. 

We will address these points by:

- including SHP-1 inhibitors in the DuoHexaBody-CD37 cytotoxicity experiments to address the role of SHP-1

- investigating which Fc receptors are involved in the crosslinking using FcR blocking antibodies and/or use purified fixed effector cells that express different Fc receptors in the DuoHexaBody-CD37 cytotoxicity experiments 

- study the effect of DuoHexaBody-CD37 on normal B cells

As the findings are based primarily on in vitro models, further validation would be required to support broader translational conclusions.

We would like to refer to previous studies that showed potent cytotoxicity of DuoHexaBody-CD37 in vivo, including xenograft and PDX lymphoma models supporting broader translational conclusions:

Oostindie et al. Blood Cancer Journal (2020) 10:30 https://doi.org/10.1038/s41408-020-0292-7

Author response:

We thank the reviewers for their comments and for their constructive suggestions. We intend to submit a revised manuscript where we address the comments made in the Public Reviews as well as in the Recommendations for the Authors.

One of our most interesting findings, as noted by the reviewers, was the discovery of a small subpopulation of cells likely arrested in G2 that accounts for a disproportionate amount of radiation-induced gene expression. In addition, to the responses indicated below, we are planning to include additional “wet lab” experiments in the revised manuscript that address the properties of this seemingly important subpopulation of cells.

Reviewer 1:

Strengths:

(1) The authors have used robust methods for rearing Drosophila larvae, irradiating wing discs, and analyzing the data with Seurat v5 and HHI.

(2) These data will be informative for the field.

(3) Most of the data is well-presented.

(4) The literature is appropriately cited.

Thank you for these comments

Weaknesses:

(1) The data in Figure 1 are single-image representations. I assume that counting the number of nuclei that are positive for these markers is difficult, but it would be good to get a sense of how representative these images are and how many discs were analyzed for each condition in B-M.

(2) Some of the figures are unclear.

In the revised manuscript, we will provide a more detailed quantitative analysis. For each condition, we analyzed 4 - 9 discs.

We assume that the reviewer in referring to panels in Figure 1. We will review these images and if necessary, repeat the experiments or choose alternative images that appear clearer.

Reviewer 2:

Overall, the data presented in the manuscript are of high quality but are largely descriptive. This study is therefore perceived as a resource that can serve as an inspiration for the field to carry out follow-up experiments.

We intend to include more  “wet lab” experiments in our revised manuscript to address the identity and properties of the high-trbl cells that we have identified using the clustering approach based on cell-cycle gene expression.

Reviewer 3:

Strengths:

Overall, the manuscript makes a compelling case for heterogeneity in gene expression changes that occur in response to uniform induction of damage by X-rays in a single-layer epithelium. This is an important finding that would be of interest to researchers in the field of DNA damage responses, regeneration, and development.

Thank you.

Weaknesses:

This work would be more useful to the field if the authors could provide a more comprehensive discussion of both the impact and the limitations of their findings, as explained below.

Propidium iodide staining was used as a quality control step to exclude cells with a compromised cell membrane. But this would exclude dead/dying cells that result from irradiation. What fraction of the total do these cells represent? Based on the literature, including works cited by the authors, up to 85% of cells die at 4000R, but this likely happens over a longer period than 4 hours after irradiation. Even if only half of the 85% are PI-positive by 4 hr, this still removes about 40% of the cell population from analysis. The remaining cells that manage to stay alive (excluding PI) at 4 hours and included in the analysis may or may not be representative of the whole disc. More relevant time points that anticipate apoptosis at 4 hr may be 2 hr after irradiation, at which time pro-apoptotic gene expression peaks (Wichmann 2006). Can the authors rule out the possibility that there is heterogeneity in apoptosis gene expression, but cells with higher expression are dead by 4 hours, and what is left behind (and analyzed in this study) may be the ones with more uniform, lower expression? I am not asking the authors to redo the study with a shorter time point, but to incorporate the known schedule of events into their data interpretation.

We thank the reviewer for these important comments. The generation of single-cell RNAseq data from irradiated cells is tricky. Many cells have already died. Even those that do not incorporate propidium iodide are likely in early stages of apoptosis or are physiologically unhealthy and likely made it through our FACS filters. Indeed, in irradiated samples up to  57% of sequenced cells were not included in our analysis since their RNA content seemed to be of low quality. It is therefore likely that our data are biased towards cells that are less damaged. As advised by the reviewer, we will include a clearer discussion of these issues as well as the time course of events and how our analysis captures RNA levels only at a single time point.

If cluster 3 is G1/S, cluster 5 is late S/G2, and cluster 4 is G2/M, what are clusters 0, 1, and 2 that collectively account for more than half of the cells in the wing disc? Are the proportions of clusters 3, 4, and 5 in agreement with prior studies that used FACS to quantify wing disc cells according to cell cycle stage?

Clusters 0, 1, and 2 likely contain cells in other stages of the cell cycle, including early G1. Other studies indicate that more than 70% of cells are expected to have a 4C DNA content 4 h after irradiation at 4000 Rad. The high-trbl cluster only accounts for 18% of cells. Thus clusters 0, 1 and 2 could potentially contain other populations that also have a 4C DNA content. Importantly, similar proportions of cells in these clusters are also observed in unirradiated discs. We are mining the gene expression patterns in these clusters with the goal of estimating their location in the cell cycle and will include those data in the revised manuscript.

The EdU data in Figure 1 is very interesting, especially the persistence in the hinge. The authors speculate that this may be due to cells staying in S phase or performing a higher level of repair-related DNA synthesis. If so, wouldn't you expect 'High PCNA' cells to overlap with the hinge clusters in Figures 6G-G'? Again, no new experiments are needed. Just a more thorough discussion of the data.

We have found that the locations of elevated PCNA expression do not always correlate with the location of EdU incorporation either by examining scRNA-seq data or by using HCR to detect PCNA. PCNA expression is far more widespread. We intend to present additional data that address this point and also a more thorough discussion in the revised manuscript.

Trbl/G2/M cluster shows Ets21C induction, while the pattern of Ets21C induction as detected by HCR in Figures 5H-I appears in localized clusters. I thought G2/M cells are not spatially confined. Are Ets21C+ cells in Figure 5 in G2/M? Can the overlap be confirmed, for example, by co-staining for Trbl or a G2/M marker with Ets21C?

The data show that the high_-trbl_ cells are higher in Ets21C transcripts relative to other cell-cycle-based clusters after irradiation. This does not imply that high-trbl-cells in all regions of the disc upregulate Ets21C equally. Ets21C expression is likely heterogeneous in both ways – by location in the disc and by cell-cycle state. We will attempt to look for co-localization as suggested by the reviewer.

Induction of dysf in some but not all discs is interesting. What were the proportions? Any possibility of a sex-linked induction that can be addressed by separating male and female larvae?

We can separate the cells in our dataset into male and female cells by expression of lncRNA:roX1/2. When we do this, we see X-ray induced dysf expressed similarly in both male and female cells. We think that it is therefore unlikely that this difference in expression can be attributed to cell sex. We are investigating other possibilities such as the maturity of discs.

Author response:

The following is the authors’ response to the original reviews

Public Reviews:

Reviewer #1 (Public review):

Summary:

Epiney et al. use single-nuclei RNA sequencing (snRNA-seq) to characterize the lineage of Type-2 (T2) neuroblasts (NBs) in the adult Drosophila brain. To isolate cells born from T2 NBs, the authors used a genetic tool that specifically allows the permanent labeling of T2-derived cell types, which are then FAC-sorted for snRNA-seq. This effective labeling approach also allows them to compare the isolated T2 lineage cells with T1-derived cell types by a simple exclusion method. The authors begin by describing a transcriptomic atlas for all T1 and T2-derived neuronal and glia clusters, reporting that the T2-derived lineage comprises 161 neuronal clusters, in contrast to the T1 lineage which comprises 114 of them. The authors then use the expression of VAChT, VGlut, Gad1, Tbh, Ple, SerT, and Tdc2 to show that T2 neuroblasts generate all major neuron classes of fast-acting neurotransmitters. Strikingly, they show that a subset of glia and neuronal clusters have disproportionate enrichment in males or females, suggesting that T2 neuroblasts generate sex-biased cell types. The authors then proceed to characterize neuropeptide expression across T2-derived neuronal clusters and argue that the same neuropeptide can be expressed across different cell types, while similar cell types can express distinct neuropeptides. The functional implication of both observations, however, remains to be tested. Furthermore, the authors describe combinatorial transcription factor (TF) codes that are correlated with neuropeptide expression for T2-derived neurons along with an overall TF code for all T2-derived cell types, both of which will serve as an important starting point for future investigations. Finally, the authors map well-studied neuronal types of the central complex to the clusters of their T2-derived snRNA-seq dataset. They use known marker combinations, bulk RNA-seq data and highly specific split-GAL4 driver lines to annotate their T2-derived atlas, establishing a comprehensive transcriptomic atlas that would guide future studies in this field.

Thanks for the clear and accurate summary of our findings.

Strengths:

This study provides an in-depth transcriptomic characterization of neurons and glia derived from Type-2 neuroblast lineages. The results of this manuscript offer several future directions to investigate the mechanisms of diversifying neuronal identity. The datasets of T1-derived and T2-derived cells will pave the way for studies focused on the functional analysis of combinatorial TF codes specifying cell identity, sex-based differences in neurogenesis and gliogenesis, the relationship between neuropeptide (co)expression and cell identity, and the differential contributions of distinct progenitor populations to the same cell type.

Thank you for the positive comments.

Weaknesses:

The study presents several important observations based on the characterization of Type II neuroblast-derived lineages. However, a mechanistic insight is missing for most observations. The idea that there is a sex-specific bias to certain T2-derived neurons and glial clusters is quite interesting, however, the functional significance of this observation is not tested or discussed extensively. Finally, the authors do not show whether the combinatorial TF code is indeed necessary for neuropeptide expression or if this is just a correlation due to cell identity being defined by TFs. Functional knockdown of some candidate TFs for a subset of neuropeptide-expressing cells would have been helpful in this case.

We agree that we do not provide mechanistic or functional insights. Our goal was to produce hypothesis generating datasets for our lab and others to use to direct functional or mechanistic studies.

Reviewer #2 (Public review):

In this manuscript, Epiney et al., present a single-nucleus sequencing analysis of Drosophila adult central brain neurons and glia. By employing an ingenious permanent labeling technique, they trace the progeny of T2 neuroblasts, which play a key role in the formation of the central complex. This transcriptomic dataset is poised to become a valuable resource for future research on neurogenesis, neuron morphology, and behavior.

Thank you for the positive comments.

The authors further delve into this dataset with several analyses, including the characterization of neurotransmitter expression profiles in T2-derived neurons. While some of the bioinformatic analyses are preliminary, they would benefit from additional experimental validation in future studies.

Thank you for the positive comments. We too hope that future research will benefit from this dataset.

Reviewer #1 (Recommendations for the authors):

Major points

(1) In Figures 1E and 4A, the T1 and T2 glia subsets reveal sub-clusters for several cell types as seen by the distribution of points on the UMAP. This observation is never validated or discussed. Do these sub-clusters represent true differences in identities or are they artifacts of the single-nucleus preparation? For Figure 1E, it is not clear whether specific sub-clusters (see Ensheathing-4 vs Ensheathing-5 and Astrocyte-2 vs. Astrocyte-6) are differentially enriched between the T1 and T2 lineages. The existence of these sub-clusters must be discussed or dismissed.  

We agree that this needs to be addressed more clearly in the manuscript and have made text changes in the Results and Discussion sections to clarify. We note that a recent glial cell atlas (Lago-Baldaia et al., 2023: PMID: 37862379) of the developing fly VNC and optic lobes found sub-clusters that mapped to the same subtype annotations. Interestingly, Lago-Baldaia and colleagues found that the transcriptional diversity of glia cell types did not match the morphological diversity of glia validated in vivo. See text changes below.

Lines 131-133: “Similar to a previous glial cell atlas (Lago-Baldaia et al., 2023) we found some glial subtypes (astrocytes, ensheathing, and subperineurial) mapped to multiple clusters (Figure 1E, 1F).”

Lines 206-208: “In line with our T1+T2 atlas and previous glia cell atlas (Lago-Baldaia et al., 2023), some subtypes mapped to several subclusters including ensheathing, astrocytes, and chiasm (Figure 4A-B).”

Lines 397-401: “Similar to a recent glial cell atlas (Lago-Baldaia et al., 2023), we found glial subtypes like astrocytes, ensheathing, and subperineurial glia mapped to several sub-clusters (Figure 1E-F). It remains unclear if these sub-clusters with the same cell type annotation represent distinct glial identities or different transcriptional states within these populations.”

(2) The authors present evidence for sex-specific neuronal and glia subtypes and find differential expression of specific yolk proteins and long non-coding RNAs. However, whether any of these differences are driven by other canonical sex-specific genes such as Fruitless (Fru) or Double-sex (Dbx) has not been reported or discussed. The authors must re-analyze their data for these genes and claim whether they have any contribution to sex-specific sub-clusters.

Thank you for pointing this out. We have made text changes and clarifications to highlight the expression of other canonical sex-specific genes. Fru was enriched in male nuclei as expected. Interestingly, dbx was enriched in female nuclei. It remains to be determined if these genes are mechanisms that may be driving sex-specific changes.

Lines 224-226: “Additionally, female nuclei were enriched for dbx (Supp Table 8). Male glial nuclei expressed higher levels of genes including the male-specific genes lncRNA:rox1/2 and fru (Figure 5C; Supp Table 8) (Ryner et al., 1996; Amrein and Axel, 1997; Meller et al., 1997).”

Lines 237-239: “Male nuclei expressed higher levels of genes including the male-specific genes lncRNA:rox1/2 and fru (Figure 5G; Supp Table 9) (Ryner et al., 1996; Amrein and Axel, 1997; Meller et al., 1997).”

Lines 428-431:” We found the expected differential expression of yolk proteins (yp1, yp2, yp3) enriched in female nuclei and the long non-coding RNAs rox1/2 and fru enriched in male neuronal nuclei (Ryner et al., 1996; Amrein and Axel, 1997; Meller et al., 1997; Warren et al., 1979). Interestingly, we found dbx to be enriched in both glial and neuronal female nuclei.”

Lines 433-435: “It remains to be determined if these genes are driving these sex-specific differences in glia and neurons.”

(3) In Figure 6C, it is unclear whether the Ms-2A-LexA-expressing neurons of clusters 157 and 160 project to two different neuropils or share projects to both neuropils. However, it is not explicitly shown in the immunostaining data whether indeed there are two populations to begin with. The authors must check for cluster 157 and 160 specific markers (such as Dh44 and ple) and test whether they appear mutually exclusively in the Ms-2A-LexA-expressing neurons. The same reasoning would apply to the data shown in Figures 6D and 6E, where the authors must test whether the NPF and AstA expressing cells are indeed neurons from clusters 100 and 128, using orthogonal cluster markers to conclude that they are similar (or the same) neurons.

We changed the focus of the paragraph to confirm that these neurons indeed come from type II and that they target the central complex. Although due to the lack of reagents we cannot test the identity of each one of these neurons, we could make meaningful interpretations of the staining to validate our ideas about neuropeptidergic cells in the central complex. We made sure to mention the limitation of our experiment to avoid any wrong conclusions.

Minor points

(1) Line 115 - "cluster that represents optic lobe neurons". How was this cluster identified?

We reexamined the most significant genes enriched in this cluster 124, and found they are Rh2, ninaC, trpl, and phototransduction related genes (Supplemental table 1). We reassigned the identity of this cluster as ocelli, which also express photoreceptor genes but can’t be easily removed during dissection. We modified the text as follows:

"We used known markers (Croset et al., 2018; Davie et al., 2018; Supp Table 2) to identify distinct cell types in the central brain, including glia, mushroom body neurons, olfactory projection neurons, clock neurons, Poxn+ neurons, serotonergic neurons, dopaminergic neurons, octopaminergic neurons, corazonergic neurons, hemocytes, and ocelli (Figure 1B, Supp. Table 1)."

(2) As the separation in Figure 1B is not obvious, annotated cell type clusters must be re-colored instead of being labelled as the exact dots are indistinguishable. This would especially be helpful for OCTY, SER, OPN, and CLK clusters.

(3) Cluster labels in Figure 1C are barely visible and the font size must be increased for the reader. Recoloring the cluster identities and attaching a legend would again help in this case.

We recolored the atlas in Figure 1B, 1C and 1C’ and increased the font size in Figure 1C’.

(4) For Figure 4A, clusters should be labelled on the UMAP along with the legend as it is difficult for the reader to match identities using Seurat colors. The same is true for the UMAPs in Figure 5A.

Yes, we agree that labeling would improve readability and have done so for UMAPs in Figure 4A and 5A-A’’.

Reviewer #2 (Recommendations for the authors):

In this manuscript, Epiney et al., present a single-nucleus sequencing analysis of adult central brain neurons and glia Through the use of a ingenious permanent labeling technique, they are able to trace the progeny of T2 neuroblasts, which contribute significantly to the formation of the central complex. This transcriptomic dataset is the first of its kind and will likely serve as a valuable resource for future studies.

The authors further explore this dataset through several analyses, including the characterization of neurotransmitter expression profiles in T2-derived neurons. However, the approach used to identify the identity of each neuron cluster could be more clearly articulated, and some of the authors' conclusions are more generalized - either already well-established or lacking sufficient support.

Detailed comments:

Abstract - "Our data support the hypothesis that each transcriptional cluster represents one or a few closely related neuron subtypes. - Is this a novel finding? If so, it would be helpful if the authors could explain why this is the case more clearly.

Our results are not generally novel, and many single cell/single nuclei RNA-seq papers have been published (more citations added to Introduction). Our work is novel in that we analyze Type 1 and Type 2 neuroblasts in the central brain.

Line 53 - In the introduction the authors should also reference other single-cell studies done in the Drosophila brain.

Done.

Line 59 - There are some typos here. The authors could also mention type zero.

Both done.

Figure 1 and Sup Table 1 - Authors show in sup table 1 the top cell markers by cluster but there is no correspondence between cluster number and identity. The authors do not say which known markers were used to give the identity to each cluster.

We have added the cell identity in the Supplemental Table 1. For the unknown cells, we left the column blank. We have also added a Supplemental Table 2 to show the markers we used to give identity to the clusters.

Supplementary Tables - For each table, more detailed information should be provided regarding what is being compared and the methods used for these comparisons.

We have added the methods we used in Seurat to generate each individual table.

Line 138 - Differential gene expression analysis between T1 and T2 glial progeny did not show differences across any glial cell types (Supp Table 4). - Was this comparison done per cluster? Is differential gene expression of top markers, which are anyway the genes that define each glial cell type, enough for this type of analysis?

Yes, we performed the differential expression analysis using all genes (i.e., not just marker defining) at a cluster-by-cluster resolution with results in Supplemental Table 4. We have edited the text to make this clarification.

Lines 139-141: “Differential gene expression analysis for all genes between T1 and T2 glial progeny did not show differences across any glial cell types or clusters (Supp Table 4).”

Line 146 - We identified T1-derived neurons by excluding cells co-expressing T2-specific. Markers FLP+/GFP+/RFP+ plus repo+ glial clusters. - Bioinformatically, correct?

Yes. We clarified the sentence as follows:

"We identified T1-derived neurons by bioinformatically excluding cells co-expressing T2-specific markers FLP+/GFP+/RFP+ plus repo+ glial clusters."

Line 156 - We found that each cluster strongly expressed a unique combination of genes. - As they are grouped by seurat in different clusters, why is this surprising?

Line 175 - "top 10 significantly enriched genes gathered from each T2 neuron cluster" - can these lists be included?

Yes they are grouped by Seurat. We toned down the sentence and refer each combination of genes as cluster markers. We modified the sentences as follows:

Each unique combination of enriched genes could be referred to as cluster markers.

Line 211- How did the authors identify sex-biased clusters? How did the authors separate the samples/cells by sex? Was it done bioinformatically by the expression of certain genes? If so, which?

We collected male and female nuclei separately. We have added text in the methods section as follows:

"Equal amounts of male and female central brains (excluding optic lobes) were dissected at room temperature within 1 hour. The samples were flash-frozen in liquid nitrogen and stored separately at -80°.

In the first round, we pooled male and female brains together to select GFP+ nuclei and used particle-templated instant partitions to capture single nuclei to generate cDNA library (Fluent BioSciences, Waterton, MA). In the second and third rounds, RFP+ nuclei from male and female brains were collected separately. The split-pool method was then used to generate barcoded cDNA libraries from each individual nucleus."

Are there sex-specific differences in genes in glia other than genes that were previously known to be sex-specific?

We report the comprehensive list of sex-specific differences in gene expression for both glia and neurons in Supp tables 8 and 9.

Line 237 - When the authors mention "We conclude that male and female adult T2 neurons have sex-specific differences in gene expression within the same neuronal subtype" does this mean that these neurons are the same in male and in female brains, but they additionally specifically express sex-specific genes?

Yes, we report that male and females contain the same neurons defined by their transcriptional profile. It remains to be seen if this sex-specific differences changes how these same neuronal subtypes function between male and females. We have added additional text in the discussion to expand on this thought.

Lines 437-441: “It remains to be determined if these genes are driving sex-specific differences within glial and neuronal subtypes. These genes may reflect sex-specific differences in the adult central brain and may provide insight into how behavioral circuits are linked to sex-specific behaviors. Future work should aim to characterize and test these genes.”

Line 250 - The idea behind these sections "What is the relationship between neuropeptide expression and cluster identity?" "relation between cluster and morphology" lacks clarity. As clusters are defined based on principal component analysis, and the genes used to define a cluster are dependent on this method, there is no assumption that each cluster represents only one type of neuron or that it should include only neurons expressing the same neurotransmitter genes. Even if some clusters consist of a single neuron type, this should not be generalized to all clusters (and vice-versa).

Correct, we cannot determine from the transcriptome data whether distinct clusters will have different morphology. We have changed the focus of the question to address that we are confirming they come from type 2 and that they target the central complex while comparing to known cells that express the neuropeptide.

Line 265 - We first assayed the neuronal morphology of Ms+ neurons - why did the authors choose these neurons?

Resolved in main text: “we found that type II-derived Ms-2A-LexA-expressing neurons project to multiple layers of the dorsal fan-shaped body and the entire ellipsoid body, suggesting an unknown class of Ms+ neurons targeting to EB and/orFB".

Line 268 - "Currently we can't determine whether Ms+ neurons in clusters 157 and 160 project to different CX neuropils, or whether neurons from both clusters share projections into both neuropils. " - The purpose of this point is unclear.

Resolved in text: “we found that type II-derived Ms-2A-LexA-expressing neurons project to multiple layers of the dorsal fan-shaped body and the entire ellipsoid body, suggesting an unknown class of Ms+ neurons targeting to EB and/or FB”.

Line 279 - This analysis could be more explored.

Thank you for your feedback. As the comment was somewhat broad, we were unsure of the specific revisions needed and have therefore left the text unchanged.

Line 301 - The text regarding this section, and the description and details of respective figures should be proofread to ensure clarity.

Done.

Line 386 - Alternatively, co-expression may be due to background from RNAs released during dissociation. - RNA in soup could be bioinformatically analysed.

Correct. We opted to delete this sentence since our split-pool based method does not create background RNA expression. Additionally, the analysis is performed on scaled expression >2, and any background RNA is unlikely to yield such high expression.

Discussion - Some of the conclusions are a bit too general, suggesting that the results might be meaningful, but also acknowledging the possibility of artifacts. If the authors could refine this, it would strengthen the manuscript.

We are sorry but we are uncertain what you are asking; we don't know what you want us to refine. Our apologies for the misunderstanding.

Author response:

The following is the authors’ response to the original reviews

Reviewer #1 (Public Review):

This review evaluates the SCellBOW framework, which applies phenotype algebra to obtain vectors from cancer subclusters or user-defined subclusters.

Strengths:

SCellBOW employs an innovative application of NLP-inspired techniques to analyze scRNA-seq data, facilitating the identification and visualization of phenotypically divergent cell subpopulations. The framework demonstrates robustness in accurately representing various cell types across multiple datasets, highlighting its versatility and utility in different biological contexts. By simulating the impact of specific malignant subpopulations on disease prognosis, SCellBOW provides valuable insights into the relative risk and aggressiveness of cancer subpopulations, which is crucial for personalized therapeutic strategies. The identification of a previously unknown and aggressive AR−/NElow subpopulation in metastatic prostate cancer underscores the potential of SCellBOW in uncovering clinically significant findings.

Major concerns:

The reliance on bulk RNA-seq data as a reference raises concerns about potentially misleading results due to the presence of RNA expression from immune cells in the TME. It is unclear if SCellBOW adequately addresses this issue, which could affect the accuracy of the cancer subcluster vectors.

We appreciate the reviewer's concerns. To address the concern about potentially misleading results due to the TME when using bulk RNA-seq data as a reference:

a. We account for systematic biases between the single-cell and bulk transcriptomics readouts by creating pseudo-bulk profiles for single-cell clusters, enabling more accurate comparisons [Section Materials and methods, Data preparation for phenotype algebra].

b. We encode expressions into word vectors and co-embed them together. By doing this, we mitigate any possibility of systematic differences in the embedding. It is imperative that we subject both single-cell and bulk data through the same treatments because otherwise, it will be difficult to perform algebraic operations on them [Section Materials and methods, Generating vectors for phenotype algebra].

c. In our new analysis of the tumor microenvironment, we have shown that SCellBOW effectively differentiates between malignant and non-malignant cells, confirming that it is not biased by the immune cell composition in the bulk RNA-seq data [Section SCellBOW facilitates survival-risk attribution of tumor subpopulations, Fig. 5g-h].

The method of extracting vectors in phenotype algebra appears to be a straightforward subtraction operation. This simplicity might limit its efficiency in excluding associations with phenotypes from specific subpopulations, potentially leading to inaccurate interpretations of the data.

Thanks for this excellent query. Vector algebra operations are not done in the gene expression space (i.e., gene expression vectors associated with tumor samples), rather we process the single cell and bulk expression profiles through multiple steps (pseudo-bulk vector generation for single cell clusters, mapping gene expression values to word frequencies as better understood by the Doc2vec neural networks etc.) to ensure their embeddings are consistent and capture intricate phenotypic information. We have demonstrated this through rigorous validation of the clusters yielded on various types of healthy and diseased samples. Furthermore, we have demonstrated the consistency of the vector algebra operations on known cancer subtypes in breast cancer, glioblastoma, and prostate cancer. We have clarified this further in text. [Section Materials and methods, ‘Generating vectors for phenotype algebra’, ‘Survival risk attribution’].

The review would benefit from additional validation studies to assess the effectiveness of SCellBOW in distinguishing between cancerous and non-cancerous signals, particularly in heterogeneous tumor environments.

We thank the reviewer for advising this additional validation. While our study primarily focused on signals from malignant cells, we have now considered the impact of the tumor microenvironment. We observed that the predicted risk score increases when the immune component is subtracted from the tumor, suggesting that tumor aggressiveness increases in the absence of immune components. Importantly, the aggressiveness ranking of tumor subtypes (NE > ARAL > ARAH) remained consistent, confirming that SCellBOW effectively preserves subtype-specific risk stratification [Section SCellBOW facilitates survival-risk attribution of tumor subpopulations, Fig. 5g-h].

Further clarification on how SCellBOW handles mixed-cell populations within bulk RNA-seq data would strengthen the evaluation of its applicability and reliability in diverse research settings.

We really appreciate the reviewer’s observation. We clarify that rather than relying on absolute gene expression values, SCellBOW maps bulk RNA-seq data into an embedding space, where we extract the latent representation of the tumor. This process effectively masks the influence of mixed-cell populations, reducing biases introduced by immune or stromal components. Furthermore, phenotype algebra operates within this embedding space by comparing cosine similarities between latent representations of bulk and pseudo-bulk datasets, rather than using direct gene expression values. This allows SCellBOW to capture biologically meaningful relationships and infer tumor-specific signals effectively, even in the presence of heterogeneous cell populations. Our benchmarking across diverse cancer types confirms its effectiveness [Section Results, ‘SCellBOW enables pseudo-grading of metastatic prostate cancer tumor microenvironment’, ‘Unsupervised risk-stratification of metastatic prostate cancer clusters using SCellBOW’].

Reviewer #2 (Public Review):

The authors developed a novel tool, SCellBOW, to perform cell clustering and infer survival risks on individual cancer cell clusters from the single-cell RNA seq dataset. The key ideas/techniques used in the tool include transfer learning, bag of words (BOW), and phenotype algebra which is similar to word algebra from natural language processing (NLP). Comparisons with existing methods demonstrated that SCellBOW provides superior clustering results and exhibits robust performance across a wide range of datasets. Importantly, a distinguishing feature of SCellBOW compared to other tools is its ability to assign risk scores to specific cancer cell clusters. Using SCellBOW, the authors identified a new group of prostate cancer cells characterized by a highly aggressive and dedifferentiated phenotype.

Strengths:

The application of natural language processing (NLP) to single-cell RNA sequencing (scRNA-seq) datasets is both smart and insightful. Encoding gene expression levels as word frequencies is a creative way to apply text analysis techniques to biological data. When combined with transfer learning, this approach enhances our ability to describe the heterogeneity of different cells, offering a novel method for understanding the biological behavior of individual cells and surpassing the capabilities of existing cell clustering methods. Moreover, the ability of the package to predict risk, particularly within cancer datasets, significantly expands the potential applications.

Major concerns:

Given the promising nature of this tool, it would be beneficial for the authors to test the risk-stratification functionality on other types of tumors with high heterogeneity, such as liver and pancreatic cancers, which currently lack clinically relevant and well-recognized stratification methods. Additionally, it would be worthwhile to investigate how the tool could be applied to spatial transcriptomics by analyzing cell embeddings from different layers within these tissue

(1) We completely agree with the reviewer’s view. Our selection of glioblastoma and breast cancer for this study was primarily driven by the focus on extensively studied and well-defined cancer types. To demonstrate the effectiveness of our model, we tested it on advanced prostate cancer, which currently lacks clinically relevant and well-recognized stratification methods. This application to metastatic prostate cancer serves as a proof of concept, illustrating our model's potential to provide valuable insights into cancer types where established stratification approaches are limited or absent.

(2) Regarding the application of our tool to spatial transcriptomics, we have already analyzed data from Digital Spatial Profiling (DSP). The article is already quite complex and involved, and we are afraid the inclusion of spatial transcriptomics may amount to a significant extension of the method. To this end, although we will discuss the future possibilities, we will skip the method validity check on spatial transcriptomics data.

Reviewer #2 (Recommendations For The Authors):

(1) "SCellBOW adapts the popular document-embedding model Doc2vec for single-cell latent representation learning, which can be used for downstream analysis...": Using only simple gene frequency might overlook the dependent relationships between genes, potentially compromising the biological significance. This could be discussed further.

This is an excellent point raised by the reviewer. We acknowledge that using only simple gene frequency may overlook dependent relationships between genes, potentially compromising biological significance. To address this, we have now compared SCellBOW on the specific task of phenotype algebra and demonstrated its effectiveness in capturing meaningful biological relationships which is overlooked by simple gene frequency. We have now added the results of this comparison and showed that gene expression data alone couldn't cut it for accurate risk stratification [Section Overall discussion, Supplementary Note 7, Supplementary Fig. 8i-k].

(2) "While existing methods effectively reveal the subpopulations, they are insufficient in associating malignant risk with specific cellular subpopulations identified from scRNA-seq data....": Perhaps I missed it in the methods section, but how does SCellBOW compare to simply performing pseudobulk analysis on separate cell clusters, treating them as bulk RNA-seq, and then associating the signatures with disease prognosis?

This is an insightful point, and we appreciate the opportunity to clarify it.

(1) While pseudobulk analysis on separate cell clusters, followed by associating their signatures with disease prognosis, is a common approach, SCellBOW achieves this without requiring a priori knowledge of prognostic biomarkers to determine whether a subpopulation is aggressive.

(2) Moreover, pseudobulk analysis aggregates gene expression across cells, which can potentially mask intra-cluster heterogeneity, thereby obscuring important signatures associated with disease prognosis. In contrast, the latent representation in SCellBOW captures the semantic meaning of disease aggressiveness, allowing for a more nuanced and biologically meaningful risk assessment.

(3) "The proposed approach, SCellBOW, can effectively capture the heterogeneity and risk associated with each phenotype, enabling the identification and assessment of malignant cell subtypes in tumors directly from scRNA-seq gene expression profiles, thereby eliminating the need for marker genes...": Have the author compared the resulting group with well-known markers and do they overlap?

We appreciate this thoughtful question. While SCellBOW does not rely on predefined marker genes for clustering or risk stratification, we have systematically evaluated whether the resulting subpopulations align with well-known markers. To assess this, we compared SCellBOW-derived clusters with established marker-based annotations across multiple datasets. We observed a significant overlap between SCellBOW clusters and canonical marker-defined cell types in various cancers, including GBM, BRCA, and mCRPC.

(4) "We constructed three use cases leveraging publicly available scRNA-seq datasets...": The three training and testing datasets are all from healthy tissue. How about in tumor tissue? i.e., Could SCellBOW also identify better cell clusters in tumor datasets?

We appreciate the reviewer’s inquiry. For benchmarking and method validation, we primarily selected normal tissue datasets as they are heavily annotated and well-characterized. Our goal was to extensively evaluate SCellBOW across different clustering metrics, including ARI, NMI, and SI, which required datasets with reliable ground truth. Tumor datasets, in contrast, often lack confirmatory ground truth, making direct benchmarking more challenging. However, to assess SCellBOW’s applicability in tumor settings, we performed downstream analyses on tumor scRNA-seq datasets using phenotype algebra. Our results demonstrate that SCellBOW effectively identifies distinct cell clusters, including malignant and non-malignant populations, reinforcing its applicability in tumor settings [Section Results, ‘Unsupervised risk-stratification of metastatic prostate cancer clusters using SCellBOW’].

Minor issues:

(1) Labels of subplots within the manu/figure should be revised to ensure correct order (missing Figures 3a-d, 4b before 4a, etc).

We thank the reviewer for pointing this out. We have corrected the figure labels and ensured that all subplots follow the correct order, aligning with the manuscript.

(2) "reaffirmed the clinically known aggressiveness order, i.e., CLA >-MES >-PRO, where CLA succeeds the rest of the subtypes in aggressiveness48 (Figures 4c, d)...": "Fig. 4c, d" should be "Fig. 4e, f". Also please put Figure 4a before 4b. Overall the order of Figure 4 needs to be revised to match the order in the manu. Similar to Figure 6.

We have corrected the figure reference to Fig. 4e, f and revised the order of Figure 4 to maintain consistency with the manuscript.

(3) "Our results showed that SCellBOW learned latent representation of single-cells accurately captures the 'semantics' associated with cellular phenotypes and allows algebraic operations such as'+' and'-'." Figure 5f (SCellBOW performances on mCRPC) should also be cited here since Supplementary Figure 6 contains three datasets (GBM, BRCA, mCRPC) while in Figure 4 only GBM and BRCA were shown?

We thank the reviewer for this suggestion. We have now cited Figure 5f in this section to ensure that all datasets, including mCRPC, are appropriately referenced.

(4) Under the subheading "SCellBOW facilitates survival-risk attribution of tumor subpopulations", the lines start with "We refer to this as phenotype algebra. We utilized this ability to find an association between the embedding vectors, representing total tumor - a specific malignant cell cluster with tumor aggressiveness..." could be reduced a little bit especially the re-intro of phenotype algebra since the author has already discussed previously (under "overview of SCellBOW").

We appreciate the feedback and have condensed this section to avoid redundancy while maintaining clarity in connecting phenotype algebra to survival-risk attribution.

(5) "Most CD4+ T cells map to CL0 and CL9 (here, CL is used as an abbreviation for cluster) (Figure 3f)..." "(here, CL is used as an abbreviation for cluster)" this note could be moved forward to SF2 since CL is first introduced in SF2.

We thank the reviewer for the suggestion. We have moved the definition of CL (cluster) to Supplementary Figure 2 (SF2), where it is first introduced, for improved clarity.

Author response:

We sincerely thank the editor and both reviewers for their time and thoughtful feedback on our manuscript. We have addressed several of the concerns in the responses below and are currently working on additional analyses to further strengthen the study. These results will be incorporated into the final version of the research paper.

Reviewer #1 (Public review):

Summary:

The authors investigated the population structure of the invasive weed Lantana camara from 36 localities in India using 19,008 genome-wide SNPs obtained through ddRAD sequencing.

Strengths:<br /> The manuscript is well-written, the analyses are sound, and the figures are of great quality.

Weaknesses:

The narrative almost completely ignores the fact that this plant is popular in horticultural trade and the different color morphs that form genetic populations are most likely the result of artificial selection by humans for certain colors for trade, and not the result of natural selfing. Although it may be possible that the genetic clustering of color morphs is maintained in the wild through selfing, there is no evidence in this study to support that. The high levels of homozygosity are more likely explained as a result of artificial selection in horticulture and relatively recent introductions in India. Therefore, the claim of the title that "the population structure.. is shaped by its mating system" is in part moot, because any population structure is in large part shaped by the mating system of the organism, but further misleading because it is much more likely artificial selection that caused the patterns observed.

The reviewer raises the possibility that the observed genetic patterns may have originated through the selection of different varieties by the horticultural industry. While it is plausible that artificial selection can lead to the formation of distinct morphs, the presence of a strong structure between them in the wild populations cannot be explained just based on selection. In the wild, different flower colour variants frequently occur in close physical proximity and should, in principle, allow for cross-fertilization. Over time, this gene flow would be expected to erode any genetic structure shaped solely by past selection. However, our results show no evidence of such a breakdown in structure. Despite co-occurring in immediate proximity, the flower colour variants maintain distinct genetic identities. This suggests the presence of a barrier to gene flow, likely maintained by the species' mating system. Moreover, the presence of many of these flower colour morphs in the native range—as documented through observations on platforms like iNaturalist—suggests that these variants may have a natural origin rather than being solely products of horticultural selection.

While it is plausible that horticultural breeding involved efforts to generate new varieties through crossing—resulting in the emergence of some of the observed morphs—even if this were the case, the dynamics of a self-fertilizing species would still lead to rapid genetic structuring. Following hybridization, just a few generations of selfing are sufficient to produce inbred lines, which can then maintain distinct genetic identities. As discussed in our manuscript, such inbred lines could be associated with specific flower colour morphs and persist through predominant self-fertilization. This mechanism provides a compelling explanation for the strong genetic structure observed among co-occurring flower colour variants in the wild.

While a recent bottleneck may have increased inbreeding, the strong and consistent genetic structuring we observe within populations is more indicative of predominant self-fertilization. To further validate this, we conducted a bagging experiment on Lantana camara inflorescences to exclude insect-mediated cross-pollination. The results showed no significant difference in seed set between bagged and open-pollinated flowers, supporting the conclusion that L. camara is primarily self-fertilizing in India.