Reviewer #1 (Public Review):

The proposed study provides an innovative framework for the identification of muscle synergies taking into account their task relevance. State-of-the-art techniques for extracting muscle interactions use unsupervised machine-learning algorithms applied to the envelopes of the electromyographic signals without taking into account the information related to the task being performed. In this work, the authors suggest including the task parameters in extracting muscle synergies using a network information framework previously proposed. This allows the identification of muscle interactions that are relevant, irrelevant, or redundant to the parameters of the task executed.

The proposed framework is a powerful tool to understand and identify muscle interactions for specific task parameters and it may be used to improve man-machine interfaces for the control of prostheses and robotic exoskeletons.

With respect to the network information framework recently published, this work added an important part to estimate the relevance of specific muscle interactions to the parameters of the task executed. However, the authors should better explain what is the added value of this contribution with respect to the previous one, also in terms of computational methods.

It is not clear how the well-known phenomenon of cross-talk during the recording of electromyographic muscle activity may affect the performance of the proposed technique and how it may bias the overall outcomes of the framework.

Reviewer #2 (Public Review):

This paper is an attempt to extend or augment muscle synergy and motor primitive ideas with task measures. The authors idea is to use information metrics (mutual information, co-information) in 'synergy' constraint creation that includes task information directly. By using task related information and muscle information sources and then sparsification, the methods construct task relevant network communities among muscles, together with task redundant communities, and task irrelevant communities. This process of creating network communities may then constrain and help to guide subsequent synergy identification using the authors published sNM3F algorithm to detect spatial and temporal synergies.

The revised paper is much clearer and examples are helpful in various ways. However, figure 2 as presented does not convincingly show why task muscle mutual information helps in separating synergies, though it is helpful in defining the various network communities used in the toy example.

The impact of the information theoretic constraints developed as network communities on subsequent synergy separation are posited to be benign and to improve over other methods (e.g., NNMF). However, not fully addressed are the possible impacts of the methods on compositionality links with physiological bases, and the possibility remains of the methods sometimes instead leading to modules that represent more descriptive ML frameworks that may not support physiological work easily. Accordingly, there is a caveat. This is recognized and acknowledged by the authors in their rebuttal of the prior review. It will remain for other work to explore this issue, likely through testing on detailed high degree of freedom artificial neuromechanical models and tasks. This possible issue with the strategy here likely needs to be fully acknowledged in the paper.

The approach of the methods seeks to identify task relevant coordinative couplings. This is a meta problem for more classical synergy analyses. Classical analyses seek compositional elements stable across tasks. These elements may then be explored in causal experiments and generative simulations of coupling and control strategies. However, task-based understanding of synergy roles and functional uses is significant and is clearly likely to be aided by methods in this study.

Information based separation has been used in muscle synergy analyses using infomax ICA, which is information based at core. Though linear mixing of sources is assumed in ICA, minimized mutual information among source (synergy) drives is the basis of the separation and detects low variance synergy contributions (e.g., see Yang, Logan, Giszter, 2019). In the work in this paper, instead, mutual information approaches are used to cluster muscles and task features into network communities preceding the SNM3F algorithm use for separation, rather than using minimized information in separation. This contrast of an accretive or agglomerative mutual information strategy here used to cluster into networks, versus a minimizing mutual information source separation used in infomax ICA epitomizes a key difference in approach here.

Physiological causal testing of synergy ideas is neglected in the literature reviews in the paper. Although these are only in animal work (Hart and Giszter, 2010; Takei and Seki, 2017), the clear connection of muscle synergy analysis choices to physiology is important, and eventually these issues need to be better managed and understood in relation to the new methods proposed here, even if not in this paper.

Analyses of synergies using the methods the paper has proposed will likely be very much dependent on the number and quality of task variables included and how these are managed, and the impacts of these on the ensuing sparsification and network communities used prior to SNM3F. The authors acknowledge this in their response. This caveat should likely be made very explicit in the paper.

It would be useful in the future to explore the approach described with a range of simulated data to better understand the caveats, and optimizations for best practices in this approach.

Reviewer #1 (Public Review):

Picard et al. report a novel neural signature of facial expressions of pain. In other words, they provide evidence that a specific set of brain activations, as measured by means of functional magnetic resonance imaging (fMRI), can tell us when someone is expressing pain via a concerted activation of distinctive facial muscles. They demonstrate that this signature provides a better characterization of this pain behaviour when compared with other signatures of pain reported by past research. The Facial Expression of Pain Signature (FEPS) thus enriches this collection and, if further validated, may allow scientists to identify the neural structures subserving important non-verbal pain behaviour. I have, however, some reservations about the strength of the evidence, relating to insufficient characterization of the underlying processes involved.

Strengths:<br /> The study relies on a robust machine-learning approach, able to capitalise on the multivariate nature of the fMRI data, an approach pioneered in the field of pain by one of the authors (Dr. Tor Wager). This paper extends Wager's and other colleagues' work attempting to identify specific combinations of brain structures subserving different aspects of the pain experience while examining the extent of similarity/dissimilarity with the other signatures. In doing so, the study provides further methodological insight into fine-grained network characterization that may inspire future work beyond this specific field.

Weaknesses:<br /> The main weakness concerns the lack of a targeted experimental design aimed to dissect the shared variance explained by activations both specific to facial expressions and to pain reports. In particular, I believe that two elements would have significantly increased the robustness of the findings:<br /> 1) Control conditions for both the facial expressions and the sensory input. An efficient signature should not be predictive of neutral and emotional facial expressions (e.g., disgust) other than pain expressions, as well as it should not be predictive of sensations originating from innocuous warm stimulation or other unpleasant but non-painful stimulation.<br /> 2) Graded intensity of the sensory stimulation: different intensities of the thermal stimulation would have caused a graded facial expression (from neutral to pain) and graded verbal reports (from no pain to strong pain), thus offering a sensitive characterisation of the signal associated with this condition (and the warm control condition).<br /> However, these conditions are missing from the current design, and therefore we cannot make a strong conclusion about the generalisability of the signature (regardless of whether it can predict better than other signatures - which may/may not suffer from similar or other methodological issues - another potential interesting scientific question!). The authors seem to work on the assumption that the trials where warm stimulation was delivered are of no use. I beg to disagree. As per my previous comment, warm trials (and associated neutral expressions) could be incorporated into the statistical model to increase the classification sensitivity and precision of the FEPS decoding.

Reviewer #2 (Public Review):

Summary:<br /> The objective of this study was to further our understanding of the brain mechanisms associated with facial expressions of pain. To achieve this, participants' facial expressions and brain activity were recorded while they received noxious heat stimulation. The authors then used a decoding approach to predict facial expressions from functional magnetic resonance imaging (fMRI) data. They found a distinctive brain signature for pain facial expressions. This signature had minimal overlap with brain signatures reflecting other components of pain phenomenology, such as signatures reflecting subjective pain intensity or negative effects.

Strength:<br /> The manuscript is clearly written. The authors used a rigorous approach involving multivariate brain decoding to predict the occurrence and intensity of pain facial expressions during noxious heat stimulation. The analyses seem solid and well-conducted. I think that this is an important study of fundamental and clinical relevance.

Weaknesses:<br /> Despite those major strengths, I felt that the authors did not suffciently explain their own interpretation of the significance of the findings. What does it mean, according to them, that the brain signature associated with facial expressions of pain shows a minimal overlap with other pain-related brain signatures?

A few questions also arose during my reading.

Question 1: Is the FEPS really specific to pain expressions? Is it possible that the signature includes a facial expression signal that would be shared with facial expressions of other emotions, especially since it involves socio-affective regulation processes? Perhaps this question should be discussed as a limit of the study?

Question 2: All AUs are combined together in a composite score for the regression. Given that the authors have other work showing that different AUs may be associated with different components of pain (affective vs. sensory), is it possible that combining all AUs together has decreased the correlation with other pain signatures? Or that the FEPS actually reflects multiple independent signatures?

Question 3: Is facial expressivity constant throughout the experiment? Is it possible that the expressivity changes between the beginning and the end of the experiment? For instance, if there is a habituation, or if the participant is less surprised by the pain, or in contrast if they get tired by the end of the experiment and do not inhibit their expression as much as they did at the beginning. If facial expressivity changes, this could perhaps affect the correlation with the pain ratings and/or with the brain signatures; perhaps time (trial number) could be added as one of the variables in the model to address this question.

Reviewer #3 (Public Review):

In this manuscript, Picard et al. propose a Facial Expression Pain Signature (FEPS) as a distinctive marker of pain processing in the brain. Specifically, they attempt to use functional magnetic resonance imaging (fMRI) data to predict facial expressions associated with painful heat stimulation.

The main strengths of the manuscript are that it is built on an extensive foundation of work from the research group, and that experience can be observed in the analysis of fMRI data and the development of the machine learning model. Additionally, it provides a comparative account of the similarities of the FEPS with other proposed pain signatures. The main weaknesses of the manuscript are the absence of a proper control condition to assess the specificity of the facial pain expressions, a few relevant omissions in the methodology regarding the original analysis of the data and its purpose, and a biased interpretation of the results.

I believe that the authors partially succeed in their aims, as described in the introduction, which are to assess the association between pain facial expression and existing pain-relevant brain signatures, and to develop a predictive brain activation model of the facial responses to painful thermal stimulation. However, I believe that there is a clear difference between those aims and the claim of the title, and that the interpretation of the results needs to be more rigorous.

Reviewer #1 (Public Review):

Activity has effects on the development of neural circuitry during almost any step of differentiation. In particular during specific time periods of circuit development, so-called critical periods (CP), altered neural activity can induce permanent changes in network excitability. In complex neural networks, it is often difficult to pinpoint the specific network components that are permanently altered by activity, and it often remains unclear how activity is integrated during the CP to set mature network excitability. This study combines electrophysiology with pharmacological and optogenetic manipulation in the Drosophila genetic model system to pinpoint the neural substrate that is influenced by altered activity during a critical period (CP) of larval locomotor circuit development. Moreover, it is then tested whether and how different manipulations of synaptic input are integrated during the CP to tune network excitability.

Strengths:<br /> Based on previous work, during the CP, network activity is increased by feeding the GABA-AR antagonist PTX. This results in permanent network activity changes, as highly convincingly assayed by a prolonged recovery period following induced seizure and by altered intersegmental locomotor network coordination. This is then used to provide two important findings: First, compelling electro- and optophysiological experiments track the site of network change down to the level of single neurons and pre- versus postsynaptic specializations. In short, increased activity during the CP increases both the magnitude of excitatory and inhibitory synaptic transmission to the aCC motoneuron, but excitation is affected more strongly. This results in altered excitation inhibition ratios. Fine electrophysiology shows that excitatory synapse strengthening occurs postsynaptically. High-quality anatomy shows that dendrite size and numbers of synaptic contacts remain unaltered. It is a major accomplishment to track the tuning of network excitability during the CP down to the physiology of specific synapses to identified neurons.

Second, additional experiments with single neuron resolution demonstrate that during the CP different forms of activity manipulation are integrated so that opposing manipulations can rescue altered setpoints. This provides novel insight into how developing neural network excitability is tuned, and it indicates that during the CP, training can rescue the effects of hyperactivity.

Weaknesses:<br /> There are no major weaknesses to the findings presented, but the molecular cause that underlies increased motoneuron postsynaptic responsiveness as well as the mechanism that integrates different forms of activity during the CP remain unknown. It is clear that addressing these experimentally is beyond the scope of this study, but some discussion about different candidates would be helpful.

Reviewer #2 (Public Review):

Summary:<br /> In this study, the authors use the tractable Drosophila embryonic/larval motor circuit to determine how manipulations of activity during a critical period (CP) modify the circuit in ways that persist into later developmental stages. Previously, this group demonstrated that manipulations to the aCC/MN-Ib neuron in embryonic stages enhance (or can rescue) susceptibility to seizures at later larval stages. Here, the authors demonstrate that following enhanced excitatory drive (by PTX feeding), the aCC neuron acquires increased sensitivity to cholinergic excitatory transmission, presumably due to increased postsynaptic receptor abundance and/or sensitivity, although this is not clarified. Although locomotion is not altered at later developmental larval stages, the authors suggest there is reduced "robustness" to induced seizures. The second part of the study then goes on to enhance inhibition during the CP in an attempt to counteract the enhanced excitation, and show that many aspects of the CP plasticity are rescued. The authors conclude that "average" E/I activity is integrated during the CP to determine the excitability of the mature locomotor network.

Overall, this study provides compelling mechanistic insight into how a final motor output neuron changes in response to enhanced excitatory drive during a CP to change the functionality of the circuit at later mature developmental stages. The first part of this study is strong, clearly showing the changes in the aCC neuron that result from enhanced excitatory input. This includes very nice electrophysiology and imaging data that assess synaptic function and structure onto aCC neurons from pre-motor inputs resulting from PTX exposure during development. However, the later experiments in Figures 6 and 7 designed to counteract the CP plasticity are somewhat difficult to interpret. In particular, the specificity of the manipulations of the ch neuron intended to counteract the CP plasticity is unclear, given the complexities of how these changes impact the excitability of all neurons during development. It is clear that CP plasticity is largely rescued in later stages, but it is hard to know if downstream or secondary adaptations may be masking the PTX-induced plasticity normally observed. Nonetheless, this study provides an important advance in our understanding of what parameters change during CPs to calibrate network dynamics at later developmental stages.

Reviewer #3 (Public Review):

Summary:<br /> In Hunter, Coulson et al, the authors seek to expand our understanding of how neural activity during developmental critical periods might control the function of the nervous system later in life. To achieve increased excitation, the authors build on their previous results and apply picrotoxin 17-19 hours after egg-laying, which is a critical period of nervous system development. This early enhancement of excitation leads to multiple effects in third-instar larvae, including prolonged recovery from electroshock, increased synchronization of motor neuron networks, and increased AP firing frequency. Using optogenetics and whole-cell patch clamp electrophysiology, the authors elegantly show that picrotoxin-induced over-excitation leads to increased strength of excitatory inputs and not loss of inhibitory inputs. To enhance inhibition, the authors chose an approach that involved the stimulation of mechanosensory neurons; this counteracts picrotoxin-induced signs of increased excitation. This approach to enhancing inhibition requires further control experiments and validation.

Strengths:<br /> • The authors confirm their previous results and show that 17-19 hours after egg laying is a critical period of nervous system development.<br /> • Using Ca2+/Sr2+ substitutions, the authors demonstrate that synaptic connections between A18a  aCC show increased mEPSP amplitudes. The authors show that this aCC input is what is driving enhanced excitation.<br /> • The authors demonstrate that the effects of over-excitation attributed to picrotoxin exposure are generalizable and also occur in bss mutant flies.

Weaknesses:<br /> • The authors build on their previous work and argue that the critical period (17-19h after egg-laying) is a uniquely sensitive period of development. Have the authors already demonstrated that exposure to picrotoxin at L1 or L2 (and even early L3 if experimentally possible) does not lead to changes in induced seizure at L3? This would further the authors' hypothesis of the uniqueness of the 17-19h AEL period. If this has already been established in prior publications, then this needs to be further explained. I do note in Gaicehllo and Baines (2015) that Fig 2E shows the identification of the 17-19h window.<br /> • Regarding experiments in Fig 2, authors only report changes in AP firing frequency. Can the authors also report other metrics of excitability, including measures of intrinsic excitability with and without picrotoxin exposure (including RMP, Rm)? Was a different amount of current injection needed to evoke stable 5-10 Hz firing with and without picrotoxin? In the representative figure (Fig. 2A), it appears that the baseline firing frequencies are different prior to optogenetic stimulation.<br /> • The ch-related experiments require further controls and explanation. Regarding experiments in Fig 6, what is the effect of ch neuron stimulation alone on time lag and AP frequency? Can the authors further clarify what is known about connections between aCC and ch neurons? It is difficult for this reviewer to conceptualize how enhancing ch-mediated inhibition would worsen seizures. While the cited study (Carreira-Rosario et al 2021) convincingly shows that inhibition of mechanosensory input leads to excessive spontaneous network activity, has it been shown that the converse - stimulation of ch neurons - indeed enhances network inhibition?<br /> • The interpretation of ch-related experiments is further complicated by the explanation in the Discussion that ch neuron stimulation depolarizes aCC neurons; this seems to undercut the authors' previous explanation that the increased E:I ratio is corrected by enhanced inhibition from ch neurons. The idea that ch neurons are placing neurons in a depolarized refractory state is not substantiated by data in the paper or citations.<br /> • In the Discussion, the authors suggest that enhanced proprioception leading to seizures is reminiscent of neurological conditions. This seems to be an oversimplification. Connecting abnormal proprioception to seizures is quite different from connecting abnormal proprioception to disorders of coordination. This should be revised.

Reviewer #1 (Public Review):

Summary:<br /> As adult-born granule neurons have been shown to play diverse roles, both positive and negative, to modulate hippocampal circuitry and function in epilepsy, understanding the mechanisms by which altered neurogenesis contributes to seizures is important for future therapeutic strategies. The work by Jain et al. demonstrates that increasing adult neurogenesis before status epilepticus (SE) leads to a suppression of chronic seizures in the pilocarpine model of temporal lobe epilepsy. This work is potentially interesting because previous studies showed suppressing neurogenesis led to reduced chronic seizures.

To increase neurogenesis, the authors conditionally delete the pro-apoptotic gene Bax using a tamoxifen-inducible Nestin-CreERT2 which has been previously published to increase proliferation and survival of adult-born neurons by Sahay et al. After 6 weeks of tamoxifen injection, the authors subjected male and female mice to pilocarpine-induced SE. In the first study, at 2 hours after pilocarpine, the authors examine latency to the first seizure, severity and total number of acute seizures, and power during SE. In the second study in a separate group of mice, at 3 weeks after pilocarpine, the authors examine chronic seizure number and frequency, seizure duration, postictal depression, and seizure distribution/cluster seizures. Overall, the study concludes that increasing adult neurogenesis in the normal adult brain can reduce epilepsy in females specifically. However, important BrdU birthdating experiments in both male and female mice need to be included to support the conclusions made by the authors. Furthermore, speculative mechanisms lacking direct evidence reduce enthusiasm for the findings.

Strengths:<br /> 1. The study is sex-matched and reveals differences in response to increasing adult neurogenesis in chronic seizures between males and females.

2. The EEG recording parameters are stringent, and the analysis of chronic seizures is comprehensive. In two separate experiments, the electrodes were implanted to record EEG from the cortex as well as the hippocampus. The recording was done for 10 hours post pilocarpine to analyze acute seizures, and for 3 weeks continuous video EEG recording was done to analyze chronic seizures.

Weaknesses:<br /> 1. Cells generated during acute seizures have different properties to cells generated in chronic seizures. In this study, the authors employ two bouts of neurogenesis stimuli (Bax deletion dependent and SE dependent), with two phases of epilepsy (acute and chronic). There are multiple confounding variables to effectively conclude that conditionally deleting Bax in Nestin-Cre+ cells leads to increased neurogenesis and hilar ectopic granule cells, thereby reducing chronic seizures.

2. Related to this is the degree of neurogenesis between Cre+ and Cre- mice and the nature of the sex differences. It is crucial to know the rate/fold change of increased neurogenesis before pilocarpine treatment and whether it is different between male and female mice.

3. The authors observe more hilar Prox1 cells in Cre+ mice compared to Cre- mice. The authors should confirm the source of the hilar Prox1+ cells.

4. The biggest weakness is the lack of mechanism. The authors postulate a hypothetical mechanism to reconcile how increasing and decreasing adult-born neurons in GCL and hilus and loss of hilar mossy and SOM cells would lead to opposite effects - more or fewer seizures. The authors suggest the reason could be due to rewiring or no rewiring of hilar ectopic GCs, respectively, but do not provide clear-cut evidence.

Reviewer #2 (Public Review):

Summary:<br /> In this manuscript, Jain et al explore whether increasing adult neurogenesis is protective against status epilepticus (SE) and the development of spontaneous recurrent seizures (chronic epilepsy) in a mouse pilocarpine model of TLE. The authors increase adult neurogenesis via conditional deletion of Bax, a pro-apoptotic gene, in Nestin-CreERT2Baxfl/fl mice. Cre- littermates are used as controls for comparisons. In addition to characterizing seizure phenotypes, the authors also compare the abundance of hilar ectopic granule cells, mossy cells, hilar SOM interneurons, and the degree of neuronal damage between mice with increased neurogenesis (Cre+) vs Cre- controls. The authors find less severe SE and a reduction in chronic seizures in female mice with pre-insult increased adult-born neurons. Immunolabeling experiments show these females also have preservation of hilar mossy cells and somatostatin interneurons, suggesting the pre-insult increase in adult neurogenesis is protective.

Strengths:<br /> 1. The finding that female mice with increased neurogenesis at the time of pilocarpine exposure have fewer seizures despite having increased hilar ectopic granule cells is very interesting.<br /> 2. The work builds nicely on the group's prior studies.<br /> 3. Apparent sex differences are a potentially important finding.<br /> 4, The immunohistochemistry data are compelling.<br /> 5. Good controls for EEG electrode implantation effects.<br /> 6. Nice analysis of most of the SE EEG data.

Weaknesses:<br /> 1. In addition to the Cre- littermate controls, a no Tamoxifen treatment group is necessary to control for both insertional effects and leaky expression of the Nestin-CreERT2 transgene.

2. The authors suggest sex differences; however, experimental procedures differed between male and female mice (as the authors note). Female mice received diazepam 40 minutes after the first pilocarpine-induced seizure onset, whereas male mice did not receive diazepam until 2 hours post-onset. The former would likely lessen the effects of SE on the female mice. Therefore, sex differences cannot be accurately assessed by comparing these two groups, and instead, should be compared between mice with matching diazepam time courses. Additionally, the authors state that female mice that received diazepam 2 hours post-onset had severe brain damage. This is concerning as it would suggest that SE is more severe in the female than in the male mice.

3. Some sample sizes are low, particularly when sex and genotypes are split (n=3-5), which could cause a type II statistical error.

4. Several figures show a datapoint in the sex and genotype-separated graphs that is missing from the corresponding male and female pooled graphs (Figs. 2C, 2D, 4B).

5. In Suppl Figs. 1B & 1C, subsections 1c and 2c, the EEG trace recording is described as the end of SE; however, SE appears to still be ongoing in these traces in the form of periodic discharges in the EEG.

6. In Results section II.D and associated Fig.3, what the authors refer to as "postictal EEG depression" is more appropriately termed "postictal EEG suppression". Also, postictal EEG suppression has established criteria to define it that should be used. The example traces in Fig. 3A and B should also be expanded to better show this potential phenomenon.

7. In Fig.5D, the area fraction of DCX in Cre+ female mice is comparable to that of Cre- and Cre+ male mice. Is it possible that there is a ceiling effect in DCX expression that may explain why male Cre+ mice do not have a significant increase compared to male Cre- mice?

8. In Suppl. Fig 6, the authors should include DCX immunolabeling quantification from conditional Cre+ male mice used in this study, rather than showing data from a previous publication.

9. In Fig 8, please also include Fluorojade-C staining and quantification for male mice.

10. Page 13: Please specify in the first paragraph of the discussion that findings were specific to female mice with pre-insult increases in adult-born neurogenesis.

Minor:<br /> 11. In Fig. 1 and suppl. figure 1, please clarify whether traces are from male or female mice.

12. Please be consistent with indicating whether immunolabeling images are from female or male mice.

a. Fig 5B images labeled as from "Cre- Females" and "Cre+ Females".

b. Suppl. Fig 8: Images labeled as "Cre- F" and "Cre+ F".

c. Fig 6: sex not specified.

d. Fig. 7: sex only specified in the figure legend.

e. Fig 8: only female mice were included in these experiments, but this is not clear from the figure title or legend.

13. Page 4: the last paragraph of the introduction belongs within the discussion section.

14. Page 6: The sentence "The data are consistent with prior studies..." is unnecessary.

15. Suppl. Fig 6A: Please include representative images of normal condition DCX immunolabeling.

16. In Suppl. Fig 7C, I believe the authors mean "no loss of hilar mossy and SOM cells" instead of "loss of hilar mossy and SOM cells".

Reviewer #1 (Public Review):

Summary:<br /> This study examined the role of statistical learning in pain perception, suggesting that individuals' expectations about a sequence of events influence their perception of pain intensity. They incorporated the components of volatility and stochasticity into their experimental design and asked participants (n = 27) to rate the pain intensity, their prediction, and their confidence level. They compared two different inference strategies - optimal Bayesian inference vs. heuristic-employing Kalman filters and model-free reinforcement learning and showed that the expectation-weighted Kalman filter best explained the temporal pattern of participants' ratings. These results provide evidence for a Bayesian inference perspective on pain, supported by a computational model that elucidates the underlying process.

Strengths:<br /> Their experimental design included a wide range of input intensities and the levels of volatility and stochasticity.

Weaknesses:<br /> - Selection of candidate computational models: While the paper juxtaposes the simple model-free RL model against a Kalman Filter model in the context of pain perception, the rationale behind this choice remains ambiguous. It prompts the question: could other RL-based models, such as model-based RL or hierarchical RL, offer additional insights? A more detailed explanation of their computational model selection would provide greater clarity and depth to the study.

- Effects of varying levels of volatility and stochasticity: The study commendably integrates varying levels of volatility and stochasticity into its experimental design. However, the depth of analysis concerning the effects of these variables on model fit appears shallow. A looming concern is whether the superior performance of the expectation-weighted Kalman Filter model might be a natural outcome of the experimental design. While the non-significant difference between eKF and eRL for the high stochasticity condition somewhat alleviates this concern, it raises another query: Would a more granular analysis of volatility and stochasticity effects reveal fine-grained model fit patterns?

- Rating instruction: According to Fig. 1A, participants were prompted to rate their responses to the question, "How much pain DID you just feel?" and to specify their confidence level regarding their pain. It is difficult for me to understand the meaning of confidence in this context, given that they were asked to report their *subjective* feelings. It might have been better to query participants about perceived stimulus intensity levels. This perspective is seemingly echoed in lines 100-101, "the primary aim of the experiment was to determine whether the expectations participants hold about the sequence inform their perceptual beliefs about the intensity of the stimuli."

- Relevance to clinical pain: While the authors underscore the relevance of their findings to chronic pain, they did not include data pertaining to clinical pain. Notably, their initial preprint seemed to encompass data from a clinical sample (https://www.medrxiv.org/content/10.1101/2023.03.23.23287656v1), which, for reasons unexplained, has been omitted in the current version. Clarification on this discrepancy would be instrumental in discerning the true relevance of the study's findings to clinical pain scenarios.

- Paper organization: The paper's organization appears a little bit weird, possibly due to the removal of significant content from their initial preprint. Sections 2.1-2.2 and 2.4 seem more suitable for the Methods section, while 2.3 and 2.4.1 are the only parts that present results. In addition, enhancing clarity through graphical diagrams, especially for the experimental design and computational models, would be quite beneficial. A reference point could be Fig. 1 and Fig. 5 from Jepma et al. (2018), which similarly explored RL and KF models.

Reviewer #2 (Public Review):

Summary:<br /> The present study aims to investigate whether learning about temporal regularities of painful events, i.e. statistical learning can influence pain perception. To this end, sequences of heat pain stimuli with fluctuating intensity are applied to 27 healthy human participants. The participants are asked to provide ratings of perceived as well as predicted pain intensity. Using an advanced modelling strategy, the results reveal that statistical expectations and confidence scale the judgment of pain in sequences of noxious stimuli as predicted by hierarchical Bayesian inference theory.

Strengths:<br /> This is a highly interesting and novel finding with potential implications for the understanding and treatment of chronic pain where pain regulation is deficient. The paradigm is clear, the analysis is state-of-the-art, the results are convincing, and the interpretation is adequate.

Reviewer #3 (Public Review):

Summary:<br /> I am pleased to have had the opportunity to review this manuscript, which investigated the role of statistical learning in the modulation of pain perception. In short, the study showed that statistical aspects of temperature sequences, with respect to specific manipulations of stochasticity (i.e., randomness of a sequence) and volatility (i.e., speed at which a sequence unfolded) influenced pain perception. Computational modelling of perceptual variables (i.e., multi-dimensional ratings of perceived or predicted stimuli) indicated that models of perception weighted by expectations were the best explanation for the data. My comments below are not intended to undermine or question the quality of this research. Rather, they are offered with the intention of enhancing what is already a significant contribution to the pain neuroscience field. Below, I highlight the strengths and weaknesses of the manuscript and offer suggestions for incorporating additional methodological details.

Strengths:<br /> - The manuscript is articulate, coherent, and skilfully written, making it accessible and engaging.

- The innovative stimulation paradigm enables the exploration of expectancy effects on perception without depending on external cues, lending a unique angle to the research.

- By including participants' ratings of both perceptual aspects and their confidence in what they perceived or predicted, the study provides an additional layer of information to the understanding of perceptual decision-making. This information was thoughtfully incorporated into the modelling, enabling the investigation of how confidence influences learning.

- The computational modelling techniques utilised here are methodologically robust. I commend the authors for their attention to model and parameter recovery, a facet often neglected in previous computational neuroscience studies.

- The well-chosen citations not only reflect a clear grasp of the current research landscape but also contribute thoughtfully to ongoing discussions within the field of pain neuroscience.

Weaknesses:<br /> - In Figure 1, panel C, the authors illustrate the stimulation intensity, perceived intensity, and prediction intensity on the same scale, facilitating a more direct comparison. It appears that the stimulation intensity has been mathematically transformed to fit a scale from 0 to 100, aligning it with the intensity ratings corresponding to either past or future stimuli. Given that the pain threshold is specifically marked at 50 on this scale, one could logically infer that all ratings falling below this value should be deemed non-painful. However, I find myself uncertain about this interpretation, especially in relation to the term "arbitrary units" used in the figure. I would greatly appreciate clarification on how to accurately interpret these units, as well as an explanation of the relationship between these values and the definition of pain threshold in this experiment.

- The method of generating fluctuations in stimulation temperatures, along with the handling of perceptual uncertainty in modelling, requires further elucidation. The current models appear to presume that participants perceive each stimulus accurately, introducing noise only at the response stage. This assumption may fail to capture the inherent uncertainty in the perception of each stimulus intensity, especially when differences in consecutive temperatures are as minimal as 1{degree sign}C.

- A key conclusion drawn is that eKF is a better model than eRL. However, a closer examination of the results reveals that the two models behave very similarly, and it is not clear that they can be readily distinguished based on model recovery and model comparison results.

Regarding model recovery, the distinction between the eKF and eRL models seems blurred. When the simulation is based on the eKF, there is no ability to distinguish whether either eKF or eRL is better. When the simulation is based on the eRL, the eRL appears to be the best model, but the difference with eKF is small. This raises a few more questions. What is the range of the parameters used for the simulations? Is it possible that either eRL or eKF are best when different parameters are simulated? Additionally, increasing the number of simulations to at least 100 could provide more convincing model recovery results.

Regarding model comparison, the authors reported that "the expectation-weighted KF model offered a better fit than the eRL, although in conditions of high stochasticity, this difference was short of significance against the eRL model." This interpretation is based on a significance test that hinges on the ratio between the ELPD and the surrounding standard error (SE). Unfortunately, there's no agreed-upon threshold of SEs that determines significance, but a general guideline is to consider "several SEs," with a higher number typically viewed as more robust. However, the text lacks clarity regarding the specific number of SEs applied in this test. At a cursory glance, it appears that the authors may have employed 2 SEs in their interpretation, while only depicting 1 SE in Figure 4.

- With respect to parameter recovery, a few additional details could be included for completeness. Specifically, while the range of the learning rate is understandably confined between 0 and 1, the range of other simulated parameters, particularly those without clear boundaries, remains ambiguous. Including scatter plots with the simulated parameters on the x-axis and the recovered parameters on the y-axis would effectively convey this missing information. Furthermore, it would be beneficial for the authors to clarify whether the same priors were used for both the modelling results presented in the main paper and the parameter recovery presented in the supplementary material.

- While the reliance on R-hat values for convergence in model fitting is standard, a more comprehensive assessment could include estimates of the effective sample size (bulk_ESS and/or tail_ESS) and the Estimated Bayesian Fraction of Missing Information (EBFMI), to show efficient sampling across the distribution. Consideration of divergences, if any, would further enhance the reliability of the results.

- The authors write: "Going beyond conditioning paradigms based in cuing of pain outcomes, our findings offer a more accurate description of endogenous pain regulation." Unfortunately, this statement isn't substantiated by the results. The authors did not engage in a direct comparison between conditioning and sequence-based paradigms. Moreover, even if such a comparison had been made, it remains unclear what would constitute the gold standard for quantifying "endogenous pain regulation."

Reviewer #1 (Public Review):

Summary:

In the current manuscript, the authors find distinct roles for the calcium sensors Syt7 and Doc2alpha in the regulation of asynchronous release and calcium-dependent synaptic vesicle docking in hippocampal neurons. The authors' data indicate that Doc2 functions in activating a component of asynchronous release beginning with the initial stimulus, while Syt7 does not appear to have a role at this early stage. A role for Syt7 in supporting both synchronous and asynchronous release appears during stimulation trains, where Syt7 is proposed to promote synaptic vesicle docking or capture during stimulation. Doc2 mutants show facilitation initially during a train and display higher levels of synchronous release initially, before reaching a similar plateau to controls later in the train. The authors contribute the increased synchronous release in Doc2 mutants to Syt1 having access to more SVs that can fuse synchronously. In contrast, Syt7 mutants show depression during a train, and continue to decline during stimulation. The authors contribute this to a role for Syt7 in promoting calcium-dependent SV docking and capture that feeds SVs to both synchronous and asynchronous fusion pathways. Importantly, phenotypes of a double Doc2/Syt7 mutant collapse onto the Doc2 phenotype, suggesting the two proteins are not additive in their role in supporting distinct aspects of SV release. Rapid freeze EM after stimulation provides support for a role for Syt7 in SV docking/capture at release sites, as they display less docked SVs after stimulation. In the case of Doc2, EM reveals fewer SVs fusion pits later during a stimulation, consistent with fewer asynchronous fusion events. The authors also provide modeling that supports aspects of their conclusions from the experimental data. I cannot evaluate the modeling data or the specific experimental subtleties of the GluSnFR quantification approach, as these are outside of my reviewer expertise.

Strengths:

The use of multiple approaches (optical imaging, physiology, rapid freeze EM, modeling, double mutant analysis) provides compelling support for the distinct roles of the two proteins in regulating SV release.

Weaknesses:

Some of the phenotypes for both Doc2 and Syt7 mutants have been reported in the authors' prior publications. It is not clear how well the GluSnFR approach is for accurately separating synchronous versus asynchronous release kinetics. The authors also tend to overstate the significance of the two proteins for asynchronous release in general, as a significant fraction of this release component is still intact in the double mutant, indicating these two proteins are only part of the asynchronous release mechanism.

Reviewer #2 (Public Review):

Summary:

The goal of this study is to provide a deeper understanding of the roles of syt7 and Doc2 in synaptic vesicle fusion. Depending on the system studied, and the nature of the preparation, it appears that syt7 functions as a sensor for asynchronous release, synaptic facilitation, both processes, or neither. The perspective offered by Chapman, Watanabe, and colleagues varies from those previously published and is therefore novel and interesting. However, the study is also burdened by some weaknesses which should be acknowledged and addressed.

Strengths:

The strengths of the study include the complementary imaging and electrophysiology approaches for assessing the function of syt7, and the use of appropriate knockout lines.

Weaknesses:

First, the manuscript strongly overstates the significance of the EM data which is interesting but not as definitive as the authors would suggest. As a consequence, the conclusion offered by the authors of syt7 "feeding" vesicles to Doc2 for asynchronous release is weakened. Second, it is not clear to this reviewer that the mathematical model is necessary or justified.

Reviewer #1 (Public Review):

The research addresses a key problem in life sciences: While there are millions of commercially available antibodies to human proteins, researchers often find that the reagents do not perform in the assays they are specified for. The consequence is wasted time and research funding, and the publication of misleading results.

Manufacturers' catalogues often contain images of western blots. Researchers are likely to select antibodies that stain a single band at the position expected from the mass of the intended target. However, the good results shown in catalogues are often not reproduced when researchers use the antibody in their own laboratories. A single band is also weak evidence since many proteins have similar mass and because assessment of mass by WB is at best approximate. In addition, results obtained by WB may not predict performance in applications where the antibody is used to recognize folded proteins. Examples include immunoprecipitation (IP) of native proteins in cell lysates and staining of viable or formalin-fixed/permeablized cells for flow cytometry or immunofluorescence microscopy (IF).

The authors of this manuscript are from the Canadian, public interest open-science company YCharos. The company webpage (ycharos.com) explains that they have partnered with many leading manufacturers of research antibodies and that their mission is to characterize commercially available antibody reagents for every human protein.

The authors have developed a standardized pipeline where antibodies are used in WB, IP of native proteins from cell lysates (WB readout) and IF (staining of cell lines that have been fixed with paraformaldehyde and permeabilized with Triton x100). A key component is the use of knockout cell lines as negative controls in WB and IF. Eight cell lines were selected as positive controls on the basis of mRNA expression data that are publicly available in the Expression 22Q1 database.

Reports for antibodies to each protein are made available online at https://ZENODO.org/communities/ycharos/ as images of western blots, and immunofluorescence staining. In addition, reports for each target are available at https://ycharos.com/data/ .

MANUSCRIPT:<br /> The manuscript describes validation criteria and results obtained with 614 commercially available antibodies to 65 proteins relevant for neuroscience A major achievement is the identification of successful renewable antibodies for 50/56 (77%) proteins in WB, 49/65 (75%) for IP and (54%) for IF. There can be little doubt that the approach represents a gold standard in antibody validation. The manuscript therefore represents a guide to a very valuable resource that should be of considerable interest to the scientific community.

While the results are convincing, they could be more accessible. In the current format, researchers have to download reports for each target and look through all images to identify the most useful antibodies from the images. The reports I reviewed did not draw conclusions on performance. A searchable database that returns validated antibodies for each application seems necessary.

It is worth noting that 95% of the tested antibodies were specified by the manufacturer for use in WB. This supports the view that manufacturers use WB as a first-pass test (Nat Methods. 2017 Feb 28;14(3):215) and that most commercial antibodies are developed to recognize epitopes that are exposed in unfolded proteins. Important exceptions are those used for ELISA or staining of viable cells for flow cytometry. 44% of antibodies specified for WB were classified as "successful" meaning a single band that was absent in the negative control (knockout/KO lysate). Another 35% detected the intended target but showed additional bands that were present also in the KO lysate. A key question is to what extent off-target binding was predictable from the WBs provided by the manufacturers. Thus, how often did the authors find multiple bands when the catalogue image showed a single band and vice versa?

The authors correctly point out that manufacturers rarely test their reagents in IP. Thus, there is little information about antibodies capable of binding folded proteins. It is encouraging that as many as 37% of those not specified for IP were able to enrich their targets from cell lysates. Yet it is important to explain that a test that involves readout by WB provides information about on-target binding only. Cross-reactive proteins will generally not be detected when blots are stained with an antibody reactive with a different epitope than the one used for IP. Possible solutions to overcome this limitation such as the use of mass spectrometry as readout should be discussed (Nature Methods volume 12, pages 725-731 (2015).

Performance in immunofluorescence microscopy was performed on cells that were fixed in 4% paraformaldehyde and then permeabilized with 0.1% Triton-X100. It seems reasonable to assume that this treatment mainly yields folded proteins wherein some epitopes are masked due to cross-linking. The expectation is therefore that results from IP are more predictive for on-target binding in IF than are WB results (Nature Methods volume 12, pages725-731 (2015). It is therefore surprising that IP and WB were found to have similar predictive value for performance in IF (supplemental Fig. 3). It would be useful to know if failure in IF was defined as lack of signal, lack of specificity (i.e. off-target binding) or both. Again, it is important to note the IP/western protocol used here does not test for specificity.

The authors report that recombinant antibodies perform better than standard monoclonals/mAbs or polyclonal antibodies. Again, a key question is to what extent this was predictable from the validation data provided by the manufacturers. It seems possible that the recombinant antibodies submitted by the manufacturers had undergone more extensive validation than standard mAbs and polyclonals.

Overall, the manuscript describes a landmark effort for systematic validation of research antibodies. The results are of great importance for the very large number of researchers who use antibodies in their research. The main limitations are the high cost and low throughput. While thorough testing of 614 antibodies is impressive and important, the feasibility of testing hundreds of thousands of antibodies on the market should be discussed in more detail.

Reviewer #2 (Public Review):

The paper nicely demonstrates the extent of the issue with the unreliability of commercial antibodies and describes a highly significant initiative for the robust validation of antibodies and recording this data so that others can benefit. It is a great idea to have all individual antibody characterisation reports available on Zenodo - these reports are comprehensive, clear and available to everyone.

A significant proportion of all life science research conclusions are based on data obtained through the use of antibodies. The quality and specificity of antibodies vary significantly. Until now there has been no uniform generally recognised approach to how to systematically assess and rate antibody specificity and quality. Furthermore, the applications that a particular antibody can be used in including western blot, immunofluorescence or immunoprecipitation are frequently not known. This paper provides important guidelines for how the quality of an antibody should be assessed and recorded and data made freely available via a Zenodo repository. This study will ensure that researchers only use well-validated antibodies for their work. A worrying aspect of this paper is that many poor-quality antibodies that failed validation are reportedly being widely used in the literature. More than 60% of all antibodies recommended for immunofluorescence failed QC. This study will have broad interest. I would recommend that all researchers select their antibodies using the database described in the paper and follow its recommendations for how antibodies should be thoroughly validated before being used in research. Hopefully, other researchers can contribute to this database in the future all widely used antibodies will eventually be well characterized. This should improve the quality and reproducibility of life science research.

Reviewer #1 (Public Review):

Summary:<br /> The findings in this manuscript are important in the gene editing in human-derived hematopoietic stem and progenitor cells. By optimizing the delivery tool, adding DNA-PK inhibitor and including spacer-breaking silent mutations, the editing efficiency is significantly increased, and the heterozygosity could be tuned. The editing is even across the hematopoietic hierarchy.

Strengths:<br /> The precise gene editing is important in gene therapy in vitro and in vivo. The manuscript provides solid evidence showing the efficacy and uniqueness of their gene editing approach.

Weaknesses:<br /> There are several extended and unique points shown in this paper but in a specific cell population.

Reviewer #2 (Public Review):

Summary:<br /> This work by Cloarec-Ung et al. sets out to uncover strategies that would allow for the efficient and precision editing of primitive human hematopoietic stem and progenitor cells (HSPCs). Such effective editing of HSPCs via homology directed repair has implications for the development of tractable gene therapy approaches for monogenic hematopoietic disorders as well as precise engineering of these cells for clinical regenerative and/or cell therapy strategies. In the setting of experimental hematology, precision introduction of disease relevant mutations would also open the door to more robust disease modeling approaches. It has been recognized that to encourage HDR, NHEJ as the dominant mode of repair in quiescent HSPCs must be inhibited. Testing editing of human cord blood HSPCs the authors first incorporate a prestimulation phase then identify optimal RNP amounts and donor types/amounts using standard editing culture conditions identifying optimal concentrations of AAV and short single-stranded oligonucleotide donors (ssODNs) that yield minimal impacts to cell viability while still enabling heightened integration efficiency. They then demonstrate the superiority of AZD7648, an inhibitor of NHEJ-promoting DNA-PK, in allowing for much increased HDR with toxicities imparted by this compound reduced substantially by siRNAs against p53 (mean targeting efficiencies at 57 and 80% for two different loci). Although AAV offered the highest HDR frequencies, differing from ssODN by a factor by ~2-fold, the authors show that spacer breaking sequence mutations introduced into the ssODN to better mimic the disruption of the spacer sequence provided by the synthetic intron in the AAV backbone yielded ssODN HDR frequencies equal to that attained by AAV. By examining editing efficiency across specific immunophenotypically identified subpopulations they further suggest that editing efficiency with their improved strategy is consistent across stem and early progenitors and use colony assays to quantify an approximate 4-fold drop in total colony numbers but no skewing in the potentiality of progenitors in the edited HSPC pool. Finally, the authors provide a strategy using mutation-introducing AAV mixed with different ratios of silent ssODN repair templates to enable tuning of zygosity in edited CD34+ cells.

Strengths:<br /> The methods are clearly described and the experiments for the most part also appropriately powered. In addition to using state of the art approaches the authors also provided useful insights into optimizing the practicalities of the experimental procedures that will aid bench scientists in effectively carrying out these editing approaches, for example avoiding longer handling times inherent when scaling up to editing over multiple conditions.

The sum of the adjustments to the editing procedure have yielded important advances towards minimizing editing toxicity while maximizing editing efficiency in HSPCs. In particular, the significant increase in HDR facilitated by the authors' described application of AZD7648 and the preservation of a pool of targeted progenitors is encouraging that functionally valuable cell types can be effectively edited.

The discovery of the effectiveness of spacer breaking changes in ssODNs allowing for substantially increased targeting efficiency is a promising advance towards democratizing these editing strategies given the ease of designing and synthesizing ssODNs relative to the production of viral donors.

The ability to zygosity tune was convincingly presented and provides a valuable strategy to modify this HDR procedure towards more accurate disease modelling.

Weaknesses:<br /> Despite providing convincing evidence that functional progenitors can be successfully edited by their procedure, as the authors acknowledge it remains to be verified to what degree the self-renewal capacity and in vivo regenerative potential of the more primitive fractions is maintained with their strategy.

Assessments of the potential for off-target effects via the authors' approach was somewhat cursory and would have benefitted from a more thorough evaluation.

Viability was assessed by live cell counting however given the short-term nature of the editing assay, more sensitive readouts of potentially compromised cell health could have provided a more stringent assessment of how the editing methodology impacted cell fitness.

Reviewer #1 (Public Review):

Summary: The manuscript by Heyndrickx et al describes protein crystal formation and function that bears similarity to Charcot-Leyden crystals made of galectin 10, found in humans under similar conditions. Therefore, the authors set out to investigate CLP crystal formation and their immunological effects in the lung. The authors reveal the crystal structure of both Ym1 and Ym2 and show that Ym1 crystals trigger innate immunity, activated dendritic cells in the lymph node, enhancing antigen uptake and migration to the lung, ultimately leading to induction of type 2 immunity.

Strengths:<br /> We know a lot about expression levels of CLPs in various settings in the mouse but still know very little about the functions of these proteins, especially in light of their ability to form crystal structures. As such data presented in this paper is a major advance to the field.

Resolving the crystal structure of Ym2 and the comparison between native and recombinant CLP crystals is a strength of this manuscript that will be a very powerful tool for further evaluation and understanding of receptor, binding partner studies including the ability to aid mutant protein generation.

The ability to recombinantly generate CLP crystals and study their function in vivo and ex vivo has provided a robust dataset whereby CLPs can activate innate immune responses, aid activation and trafficking of antigen presenting cells from the lymph node to the lung and further enhances type 2 immunity. By demonstrating these effects the authors directly address the aims for the study. A key point of this study is the generation of a model in which crystal formation/function an important feature of human eosinophilic diseases, can be studied utilising mouse models. Excitingly, using crystal structures combined with understanding the biochemistry of these proteins will provide a potential avenue whereby inhibitors could be used to dissolve or prevent crystal formation in vivo.

The data presented flows logically and formulates a well constructed overall picture of exactly what CLP crystals could be doing in an inflammatory setting in vivo. This leaves open a clear and exciting future avenue (currently beyond the scope of this work) for determining whether targeting crystal formation in vivo could limit pathology.

Weaknesses:<br /> Although resolving the crystal structure of Ym2 in particular is a strength of the authors work, the weaknesses are that further work or even discussion of Ym2 versus Ym1 has not been directly demonstrated. The authors suggest Ym2 crystals will likely function the same as Ym1, but there is insufficient discussion (or data) beyond sequence similarity as to why this is the case. If Ym1 and Ym2 crystals function the same way, from an evolutionary point, why do mice express two very similar proteins that are expressed under similar conditions that can both crystalise and as the authors suggest act in a similar way. Some discussion around these points would add further value.

Additionally, the crystal structure for Ym1 has been previously resolved (Tsai et al 2004, PMID 15522777) and it is unclear whether the data from the authors represents an advance in the 3D structure from what is previously known.

Whilst also generating a model to understand charcot-leyden crystals (CLCs), the authors fail to discuss whether crystal shape may be an important feature of crystal function. CLCs are typically needle like, and previous publications have shown using histology and TEM that Ym1 crystals are also needle like. However, the crystals presented in this paper show only formation of plate like structures. It is unclear whether these differences represent different methodologies (ie histology is 2D slides), or differences in CLP crystals that are intracellular versus extracellular. These findings highlight a key question over whether crystal shape could be important for function and has not been addressed by the authors.

Ym1/Ym2 crystals are often observed in conditions where strong eosinophilic inflammation is present. However, soluble Ym1 delivery in naïve mice shows crystal formation in the absence of a strong immune response. There is no clear discussion as to the conditions in which crystal formation occurs in vivo and how results presented in the paper in terms of priming or exacerbating an immune responses align with what is known about situations where Ym1 and Ym2 crystals have been observed.

Reviewer #2 (Public Review):

Summary: This interesting study addresses the ability of Ym1 protein crystals to promote pulmonary type 2 inflammation in vivo, in mice.

Strengths: The data are extremely high quality, clearly presented, significantly extending previous work from this group on the type 2 immunogenicity of protein crystals.

Weaknesses: There are no major weaknesses in this study. It would be interesting to see if Ym2 crystals behave similarly to Ym1 crystals in vivo. Some additional text in the Introduction and Discussion would enrich those sections.

Reviewer #1 (Public Review):

Summary:

Otarigho et al. presented a solid study revealing that in C. elegans, the neuropeptide Y receptor GPCR/NPR-15 mediates both molecular and behavioral immune responses to pathogen attack. Previously, three npr genes were found to be involved in worm defense. In this study, the authors screened mutants in the remaining npr genes against P. aeruginosa-mediated killing and found that npr-15 loss-of-function improved worm survival. npr-15 mutants also exhibited enhanced resistance to other pathogenic bacteria but displayed significantly reduced avoidance to S. aureus, independent of aerotaxis, pathogen intake and defecation. The enhanced resistance in npr-15 mutant worms was attributed to upregulation of immune and neuropeptide genes, many of which were controlled by the transcription factors ELT-2 and HLH-30. The authors found that NPR-15 regulates avoidance behavior via the TRPM gene, GON-2, which has a known role in modulating avoidance behavior through the intestine. The authors further showed that both NPR-15-dependent immune and behavioral responses to pathogen attack were mediated by the NPR-15-expressing neurons ASJ. Overall, the authors discovered that the NPR-15/ASJ neural circuit may regulate distinct defense mechanisms against pathogens under different circumstances. This study provides novel and useful information to researchers in the fields of neuroimmunology and C. elegans research.

Strengths:

1. This study uncovered specific molecules and neuronal cells that regulate both molecular immune defense and behavior defense against pathogen attack and indicate that the same neural circuit may regulate distinct defense mechanisms under different circumstances. This discovery is significant because it not only reveals regulatory mechanisms of different defense strategies but also suggests how C. elegans utilize its limited neural resources to accomplish complex regulatory tasks.

2. Most conclusions in this study are supported by solid evidence, which are often derived from multiple approaches and/or experiments. Multiple pathogenic bacteria were tested to examine the effect of NPR-15 loss-of-function on immunity; the impacts of pharyngeal pumping and defecation on bacterial accumulation were ruled out when evaluating defense; RNA-seq and qPCR were used to measure gene expression; gene inactivation was done in multiple strains to assess gene function.

3. Gene differential expression, gene ontology and pathway analyses were performed to demonstrate that NPR-15 controls immunity through regulating immune pathways.

4. Elegant approaches were employed to examine avoidance behavior (partial lawn, full lawn, and lawn occupancy) and the involvement of neurons in regulating immunity and avoidance (the use of a diverse array of mutant strains).

5. Statistical analyses were appropriate and adequate.

Weaknesses:

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

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

3. The authors discovered that NPR-15 regulates avoidance behavior via the TRPM gene, GON-2. Only two factors (GON-2 and GTL-2) were examined in this study, and GON-2 happens to function through the intestine. It is possible that NPR-15 may broadly regulate multiple effectors in multiple tissues. Confining the regulation to the amphid sensory neuron-intestinal axis, as stated in the title and elsewhere in the manuscript, is not accurate.

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

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

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

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

Reviewer #2 (Public Review):

Summary:

The authors are studying the behavioral response to pathogen exposure. They and others have previously described the role that the G-protein coupled receptors in the nervous system plays in detecting pathogens, and initiating behavioral patterns (e.g. avoidance/learned avoidance) that minimize contact. The authors study this problem in C. elegans, which is amenable to genetic and cellular manipulations and allow the authors to define cellular and signaling mechanisms. This paper extends the original idea to now implicate signaling and transcriptional pathways within a particular neuron (ASJ) and the gut in mediating avoidance behaviour.

Strengths:

The work is rigorous and elegant and the data are convincing. The authors make superb use of mutant strains in C. elegans, as well tissue specific gene inactivation and expression and genetic methods of cell ablation. to demonstrate how a gene, NPR15 controls behavioral changes in pathogen infection. The results suggest that ASJ neurons and the gut mediate such effects. I expect the paper will constitute an important contribution to our understanding of how the nervous system coordinates immune and behavioral responses to infection.

Fig. 1/S1. Authors selected a mutant for further study, npr-15, which showed resistance to various pathogens, and less colonization. Data are convincing. Data also suggest that in response to S. aureus, where wt animals exhibit avoidance behavior measured as numbers of animals that move off a focal spot of bugs, the npr-15 mutants do not. The effect was abrogated when a full lawn was used, at least for S. aureus, where there was no place to run. The conclusion is that the NPR-15 mediates behavioral changes resulting in pathogen avoidance.

Comments: There is some variance in lawn occupancy of wt strains between the different trials in WT animals (e.g. in Fig. 1: 25 for wt vs 60% for npr mutant; S1c 5% for wt and 60% for npr mutant). Does this reflect rates of migration or re-occupancy in WT? Does pathogen avoidance persist and/or the rate of avoidance differ in npr mutant worms, and if animals were exposed then re-exposed, could the authors to determine whether a learned avoidance was similarly affected by this mutation by assessing rate changes?

Fig. 2/S2. NPR inhibits expression of immune and aversion pathway genes (ELT-2, HLH-30, PMK-1, and DAF-2/DAF-16). No concerns.

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

Fig. 3/S3. Let-2RNAi or hlh-30 RNAi abrogates immunity in both WT and npr mutants. Similar effects with mutants. pmk and daf-16 inactivation were without effect.

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

Fig. 4. Using neuronal and gut specific RNAi, the authors implicate the ASJ neurons in NPR-15 effects (ie in WT animals npr15 RNAi resulted in a pathogen resistance phenotype similar to that of the mutant animals. Specific expression of NPR-15 in the enhanced survival of the npr-15 mutants, an effect rescued by neuronal expression of NPR-15. Using strains lacking particular neurons, they found that strains lacking ASJ- strains phenocopies the npr mutant. Finally, sealing things nicely, they rescued NPR-15 in the mutant on an ASJ-specific Ptrx promoter.

Fig. 5. explores the dependence of pathogen avoidance on ASJ neurons and gut effects. Fig 5 shows that mutation of NPR in ASJ neuron alone phenocopies pathogen avoidance of the global npr mutant, indicating NPR expression in this and only this neurons is required. Fig. 5 also demonstrates that the loss of the ion channel GON-2 phenocopies the npr-15 mutant.

Comments: The authors suggest that the ASJ/NPR15 effect to limit avoidance acts via inhibition of GON-2 in the intestine. The observation that GON-2 inhibition effects on pathogen avoidance occur independently of neurons could suggest that it is a redundant way of accomplishing the same thing, which then makes one ask what the connection exists between the neuron and the gut. The effect of ASJ via NPR on pathogen avoidance is not neuropeptide dependent, which they show. So how does the neuronal-gut communication works. Specific Transmitters... perhaps. Since ASJ neurons control entry into dauer, perhaps isn't surprising that DAF-16 showed up as an NPR-15. induced factor (and dauer worms are resistant to a lot of stressors); that said dauer hormones might be involved as well. Is there any evidence that DAF-16 down-regulates GON-2 expression (see Murphy, Kenyon et al. 2005), and along these lines would GON-2 RNAi work in a DAF-16 mutant? I think addressing these issues are in my view the subject of future studies.

Weaknesses: The paper is solid and elegantly defines the genetic basis of behavioral avoidance via neurons and gut. The neuronal gut connection is shown, but how they are connected remains unsolved. I wouldn't suggest this is a weakness as much as an invitation for future work.

Reviewer #1 (Public Review):

Summary:<br /> The author uses CHAT GPT in the assessment linguistic characteristics of peer reviews published from August 2022 to February 2023 in Nature Communications in neuroscience field. The author analysed over 500 reviews, which greatly varied in terms of author characteristics, peer review length, subfield, number of reviews and writing style. Chat GPT analysed reviews and gave the scores regarding the language characteristics related to sentiment score and politeness.

Strengths:<br /> The innovative method is the biggest strength of this article. Moreover, the method can be implemented across fields and disciplines. I myself would like to see this method implemented in a grander scale. The author invested a lot of effort in data collection and I especially commend the that the chat GPT assessed the reviews twice, to ensure greater objectivity.

Weaknesses:<br /> The weaknesses listed in my Public Review of the previous version have been addressed in this revised version.

Reviewer #2 (Public Review):

Summary<br /> In this study, single author Jeroen Verharen investigates 500 publicly available peer review documents from 200 neuroscience papers. He uses ChatGPT to examine the sentiment and politeness of each review and performs a series of analyses including scores across reviewers, by field, institution ranking, and author gender. This is an impressive amount of analysis for a single author and uncovers an interesting pattern where female first authors receive consistently less polite reviews compared with male first authors. It is well known that women scientists face systematic discrimination across the field, and consistently in peer review. Using ChatGPT to examine these with a predefined scoring and metric system is novel and an accessible way for others in the future to evaluate these.<br /> Strengths include:<br /> 1) Given the variability in responses from ChatGPT, he pooled two scores for each review and demonstrated significant correlation between these two iterations. He confirmed also reasonable scoring by manipulating reviews. Finally, he compared a small subset (7 papers) to human scorers and again demonstrated correlation with sentiment and politeness.<br /> 2) The figures are consistently well presented and informative. Figure 2C nicely plots the scores with example reviews. The supplementary data are also thoughtful and include combination of first/last author genders. It is interesting that first author female last author male has the lowest score.<br /> 3) A series of detailed analysis including breaking down reviews by subfield (interesting to see the wide range of reviewer sentiment/politeness scores in Computational papers), institution, and author's name and inferred gender using Genderize. The author suggests that peer review to blind the reviewers to authors' gender may be helpful to mitigating the impoliteness seen.<br /> 4) The author has strengthened the analysis in this revision by comparing it to lexicon- and rule-based algorithms TextBlob and VADER.

Weaknesses:<br /> The weaknesses listed in my Public Review of the previous version have been adequately addressed in this revised version, and the article now acknowledges its limitations (ie, it is a pilot, proof-of-concept study, limited to articles about neuroscience). The author proposes further studies and it will be interesting to see the results of these.

Reviewer #2 (Public Review):

Summary:

The goal of the authors in this study is to develop a more reliable approach for quantifying codon usage such that it is more comparable across species. Specifically, the authors wish to estimate the degree of adaptive codon usage, which is potentially a general proxy for the strength of selection at the molecular level. To this end, the authors created the Codon Adaptation Index for Species (CAIS) that controls for differences in amino acid usage and GC% across species. Using their new metric, the authors find a previously unobserved negative correlation between the overall adaptiveness of codon usage and body size across 118 vertebrates. As body size is negatively correlated with effective population size and thus the general strength of natural selection, the negative correlation between CAIS and body size is expected. The authors argue this was previously unobserved due to failures of other popular metrics such as Codon Adaptation Index (CAI) and the Effective Number of Codons (ENC) to adequately control for differences in amino acid usage and GC content across species. Most surprisingly, the authors also find a positive relationship between CAIS and the overall "disorderedness" of a species protein domains. As some of these results are unexpected, which is acknowledged by the authors, I think it would be particularly beneficial to work with some simulated datasets. I think CAIS has the potential to be a valuable tool for those interested in comparing codon adaptation across species in certain situations. However, I have certain theoretical concerns about CAIS as a direct proxy for the efficiency of selection when the mutation bias changes across species.

Strengths:

(1) I appreciate that the authors recognize the potential issues of comparing CAI when amino acid usage varies and correct for this in CAIS. I think this is sometimes an under-appreciated point in the codon usage literature, as CAI is a relative measure of codon usage bias (i.e. only considers synonyms). However, the strength of natural selection on codon usage can potentially vary across amino acids, such that comparing mean CAI between protein regions with different amino acid biases may result in spurious signals of statistical significance (see Cope et al. Biochemica et Biophysica Acta - Biomembranes 2018 for a clear example of this).

(2) The authors present numerous analysis using both ENC and mean CAI as a comparison to CAIS, helping given a sense of how CAIS corrects for some of the issues with these other metrics. I also enjoyed that they examined the previously unobserved relationship between codon usage bias and body size, which has bugged me ever since I saw Kessler and Dean 2014. The result comparing protein disorder to CAIS was particularly interesting and unexpected.

(3) The CAIS metric presented here is generally applicable to any species that has an annotated genome with protein-coding sequences.

Weaknesses:

(1) The main weakness of this work is that it lacks simulated data to confirm that it works as expected. This would be particularly useful for assessing the relationship between CAIS and the overall effect of protein structure disorder, which the authors acknowledge is an unexpected result. I think simulations could also allow the authors to assess how their metric performs in situations where mutation bias and natural selection act in the same direction vs. opposite directions. Additionally, although I appreciate their comparisons to ENC and mean CAI, the lack of comparison to other popular codon metrics for calculating the overall adaptiveness of a genome (e.g. dos Reis et al.'s statistic, which is a function of tRNA Adaptation Index (tAI) and ENC) may be more appropriate. Even if results are similar to , CAIS has a noted advantage that it doesn't require identifying tRNA gene copy numbers or abundances, which I think are generally less readily available than genomic GC% and protein-coding sequences.

The authors mention the selection-mutation-drift equilibrium model, which underlies the basic ideas of this work (e.g. higher results in stronger selection on codon usage), but a more in-depth framing of CAIS in terms of this model is not given. I think this could be valuable, particularly in addressing the question "are we really estimating what we think we're estimating?"

Let's take a closer look at the formulation for RSCUS. From here on out, subscripts will only be used to denote the codon and it will be assumed that we are only considering the case of for some species

I think what the authors are attempting to do is "divide out" the effects of mutation bias (as given by , such that only the effects of natural selection remain, i.e. deviations from the expected frequency based on mutation bias alone represent adaptive codon usage. Consider Gilchrist et al. MBE 2015, which says that the expected frequency of codon at selection-mutation-drift equilibrium in gene for an amino acid with synonymous codons is

where is the mutation bias, is the strength of selection scaled by the strength of drift, and is the gene expression level of gene \(g\). In this case, \ and reflect the strength and direction of mutation bias and natural selection relative to a reference codon, for which . Assuming the selection-mutation-drift equilibrium model is generally adequate to model the true codon usage patterns in a genome (as I do and I think the authors do, too), the could be considered the expected observed frequency codon in gene .

Let's re-write the in the form of Gilchrist et al., such that it is a function of mutation bias . For simplicity, we will consider just the two-codon case and assume the amino acid sequence is fixed. Assuming GC% is at equilibrium, the term and can be written as

where is the mutation rate from nucleotides to. As described in Gilchrist et al. MBE 2015 and Shah and Gilchrist PNAS 2011, the mutation bias . This can be expressed in terms of the equilibrium GC content by recognizing that

As we are assuming the amino acid sequence is fixed, the probability of observing a synonymous codon at an amino acid becomes just a Bernoulli process.

If we do this, then

Recall that in the Gilchrist et al. framework, the reference codon has . Thus, we have recovered the Gilchrist et al. model from the formulation of under the assumption that natural selection has no impact on codon usage and codon NNG is the pre-defined reference codon. To see this, plug in 0 for in equation (1).

We can then calculate the expected RSCUS using equation (1) (using notation and equation (6) for the two codon case. For simplicity assume, we are only considering a gene of average expression (defined as . Assume in this case that NNG is the reference codon .

This shows that the expected value of RSCUS for a two-codon amino acid is expected to increase as the strength of selection increases, which is desired. Note that in Gilchrist et al. is formulated in terms of selection against a codon relative to the reference, such that a negative value represents that a codon is favored relative to the reference. If (i.e. selection does not favor either codon), then . Also note that the expected RSCUS does not remain independent of the mutation bias. This means that even if (i.e. the strength of natural selection) does not change between species, changes to the strength and direction of mutation bias across species could impact RSCUS. Assuming my math is right, I think one needs to be cautious when interpreting CAIS as representative of the differences in the efficiency of selection across species except under very particular circumstances. One such case could be when it is known that mutation bias varies little across the species of interest. Looking at the species used in this manuscript, most of them have a GC content ranging around 0.41, so I suspect their results are okay.

Although I have not done so, I am sure this could be extended to the 4 and 6 codon amino acids.

Another minor weakness of this work is that although the method is generally applicable to any species with an annotated genome and the code is publicly available, the code itself contains hard-coded values for GC% and amino acid frequencies across the 118 vertebrates. The lack of a more flexible tool may make it difficult for less computationally-experienced researchers to take advantage of this method.

Reviewer #1 (Public Review):

Summary:

This important manuscript investigates a subpopulation of glutamatergic neurons in the suprammamillary nucleus that projects to the pre-optic hypothalamus area (SuM-VGLUT2+::POA). First, they define the neural circuitry of these neurons, which make contact with many stress/threat-associated brain regions. Then they employ fibre photometry to measure the activity of these neurons during various threatening tasks and find the responses correlate well with threat stimuli. Finally, they stimulate these neurons and find multiple lines of evidence that mice find this aversive and will act to avoid receiving this stimulation. In sum, they provide compelling evidence that this neuronal population represents a new node in stress response circuitry that allows the animal to produce flexible behaviours in response to stress, which will be of interest to neuroscientists across several sub-fields.

Strengths:

Overall I found a lot to like about this manuscript and very little to dislike. It is very novel and interesting, and the evidence given to support the conclusions is compelling.

Specific strengths:

• The topic is highly novel.<br /> • The manuscript follows a logical structure and neatly moves through the central story. I found myself quite convinced of the evidence given for the conclusions that were made and many potential alternate interpretations are well-controlled for.<br /> • The manuscript employs an array of different tasks to provide converging evidence for their conclusions.<br /> • The authors provide excellent evidence of the specificity of the function of this neuronal population, both from anatomical studies and from behavioural studies (e.g. demonstrating that activity of gabaergic neurons in the same region does not correlate with behaviours in the same way).<br /> • The study is well-powered (sample sizes are good) and the effects are convincing.

Weaknesses:

• Despite the manuscript being generally well-written and easy to follow, there are several grammatical errors throughout that need to be addressed.<br /> • Only p values are given in the text to support statistical differences. This is not sufficient. F and/or t values should be given as well. Moreover, the fibre photometry data does not appear to have any statistical analyses reported - only confidence intervals represented in the figures without any mention of whether the null hypothesis that the elevations in activity observed are different from the baseline. This is particularly important where there is ambiguity, such as in Figure 3K, where the spontaneous activity of the animal appears to correlate with a spike in activity but the text mentions that there is no such difference. Without statistics, this is difficult to judge.<br /> • The use of photostimulation only is unfortunate, it would have been really nice to see some inactivation of these neurons as well. This is because of the well-documented issues with being able to determine whether photostimulation is occurring in a physiological manner, and therefore makes certain data difficult to interpret. For instance, with regards to the 'active coping' behaviours - is this really the correct characterisation of what's going on? I wonder if the mice simply had developed immobile responding as a coping strategy but when they experience stimulation of these neurons that they find aversive, immobility is not sufficient to deal with the summative effects of the aversion from the swimming task as well as from the neuronal activation? An inactivation study would be more convincing.<br /> • Nose poke is only nominally instrumental as it cannot be shown to have a unique relationship with the outcome that is independent of the stimuli-outcome relationships (in the same way that a lever press can, for example). Moreover, there is nothing here to show that the behaviours are goal-directed.

Reviewer #2 (Public Review):

The manuscript by Escobedo et al. is an interesting investigation addressing the involvement of a lesser-studied brain region/neuron population (SUM glutamate neurons that project to the POA and other places) in active coping and locomotor behavior. The authors present data that this small population of glutamate neurons is an important circuit hub recruited for active coping but not overall locomotion by employing several behavioral tests. The manuscript is straightforward and potentially interesting, but the strength of the evidence and the significance of the paper as a whole is limited due to some lack of rigor with regards to 1) validation and quantification of anatomical tracing data that serve as a basis for the behavioral testing, 2) the use of statistics, 3) sex as a biological variable, 4) genotype differences between experimental and control groups in behavioral tests, and other concerns laid out below.

1) These are very difficult, small brain regions to hit, and it is commendable to take on the circuit under investigation here. However, there is no evidence throughout the manuscript that the authors are reliably hitting the targets and the spread is comparable across experiments, groups, etc., decreasing the significance of the current findings. There are no hit/virus spread maps presented for any data, and the representative images are cropped to avoid showing the brain regions lateral and dorsal to the target regions. In images where you can see the adjacent regions, there appears expression of cell bodies (such as Supp 6B), suggesting a lack of SuM specificity to the injections.

2) In addition, the whole brain tracing is very valuable, but there is very little quantification of the tracing. As the tracing is the first several figures and supp figure and the basis for the interpretation of the behavior results, it is important to understand things including how robust the POA projection is compared to the collateral regions, etc. Just a rep image for each of the first two figures is insufficient, especially given the above issue raised. the combination of validation of the restricted expression of viruses, rep images, and quantified tracing would add rigor that made the behavioral effects have more significance.

For example, in Fig 2, how can one be sure that the nature of the difference between the nonspecific anterograde glutamate neuron tracing and the Sum-POA glutamate neuron tracing is real when there is no quantification or validation of the hits and expression, nor any quantification showing the effects replicate across mice? It could be due to many factors, such as the spread up the tract of the injection in the nonspecific experiment resulting in the labeling of additional regions, etc.

Relatedly, in Supp 4, why isn't C normalized to DAPI, which they show, or area? Similar for G -what is the mcherry coverage/expression, and why isn't Fos normalized to that?

3) The authors state that they use male and female mice, but they do not describe the n's for each experiment or address sex as a biological variable in the design here. As there are baseline sex differences in locomotion, stress responses, etc., these could easily factor into behavioral effects observed here.

4) In a similar vein as the above, the authors appear to use mice of different genotypes (however the exact genotypes and breeding strategy are not described) for their circuit manipulation studies without first validating that baseline behavioral expression, habituation, stress responses are not different. Therefore, it is unclear how to interpret the behavioral effects of circuit manipulation. For example in 7H, what would the VGLUT2-Cre mouse with control virus look like over time? Time is a confound for these behaviors, as mice often habituate to the task, and this varies from genotype to genotype. In Fig 8H, it looks like there may be some baseline differences between genotypes- what is normal food consumption like in these mice compared to each other? Do Cre+ mice just locomote and/or eat less? This issue exists across the figures and is related to issues of statistics, potential genotype differences, and other experimental design issues as described, as well as the question about the possibility of a general locomotor difference (vs only stress-induced). In addition, the authors use a control virus for the control groups in VGAT-Cre manipulation studies but do not explain the reasoning for the difference in approach.

5) The statistics used throughout are inappropriate. The authors use serial Mann-Whitney U tests without a description of data distributions within and across groups. Further, they do not use any overall F tests even though most of the data are presented with more than two bars on the same graph. Stats should be employed according to how the data are presented together on a graph. For example, stats for pre-stim, stim, and post-stim behavior X between Cre+ and Cre- groups should employ something like a two-way repeated measures ANOVA, with post-hoc comparisons following up on those effects and interactions. There are many instances in which one group changes over time or there could be overall main effects of genotype. Not only is serially using Mann-Whitney tests within the same panel misleading and statistically inaccurate, but it cherry-picks the comparisons to be made to avoid more complex results. It is difficult to comprehend the effects of the manipulations presented without more careful consideration of the appropriate options for statistical analysis.

Conceptual:<br /> 6) What does the signal look like at the terminals in the POA? Any suggestion from the data that the projection to the POA is important?

7) Is this distinguishing active coping behavior without a locomotor phenotype? For example, Fig. 5I and other figure panels show a distance effect of stimulation (but see issues raised about the genotype of comparison groups). In addition, locomotor behavior is not included for many behaviors, so it is hard to completely buy the interpretation presented.

8) What is the role of GABA neurons in the SuM and how does this relate to their function and interaction with glutamate neurons? In Supp 8, GABA neuron activation also modulates locomotion and in Fig 7 there is an effect on immobility, so this seems pretty important for the overall interpretation and should probably be mentioned in the abstract.

Questions about figure presentation:<br /> 9) In Fig 3, why are heat maps shown as a single animal for the first couple and a group average for the others? Why is the temporal resolution for J and K different even though the time scale shown is the same? What is the evidence that these signal changes are not due to movement per se?

10) In Fig 4, the authors carefully code various behaviors in mice. While they pick a few and show them as bars, they do not show the distribution of behaviors in Cre- vs Cre+ mice before manipulation (to show they have similar behaviors) or how these behaviors shift categories in each group with stimulation. Which behaviors in each group are shifting to others across the stim and post-stim periods compared to pre-stim?<br /> Of note, issues of statistics, genotype, and SABV are important here. For example, the hint that treading/digging may have a slightly different pre-stim basal expression, it seems important to first evaluate strain and sex differences before interpreting these data.

11) Why do the authors use 10 Hz stimulation primarily? is this a physiologically relevant stim frequency? They show that they get effects with 1 Hz, which can be quite different in terms of plasticity compared to 10 Hz.

12) In Fig 5A-F, it is unclear whether locomotion differences are playing a role. Entrances (which are low for both groups) are shown but distance traveled or velocity are not.

In B, there is no color in the lower left panel. where are these mice spending their time? How is the entirety of the upper left panel brighter than the lower left? If the heat map is based on time distribution during the session, there should be more color in between blue and red in the lower left when you start to lose the red hot spots in the upper left, for example. That is, the mice have to be somewhere in apparatus. If the heat map is based on distance, it would seem the Cre- mice move less during the stim.

13) By starting with 1 hz, are the experimenters inducing LTD in the circuit? what would happen if you stop stimming after the first epoch? Would the behavioral effect continue? What does the heat map for the 1 hz stim look like?

Relatedly, it is a lot of consistent stimulation over time and you likely would get glutamate depletion without a break in the stim for that long.

14) In Fig 6, the authors show that the Cre- mice just don't do the task, so it is unclear what the utility of the rest of the figure is (such as the PR part). Relatedly, the pause is dependent on the activation, so isn't C just the same as D? In G and H, why is a subset of Cre+ mice shown? Why not all mice, including Cre- mice?

15) In Fig 7, what does the GCaMP signal look like if aligned to the onset of immobility? It looks like since the hindpaw swimming is short and seems to precede immobility, and the increase in the signal is ramping up at the onset of hindpaw swimming, it may be that the calcium signal is aligned with the onset of immobility. What does it look like for swimming onset? In I, what is the temporal resolution for the decrease in immobility? Does it start prior to the termination of the stim, or does it require some elapsed time after the termination, etc?

Reviewer #3 (Public Review):

Summary:

Coping with stress by the animal in danger is essential for survival. The current study identified a novel population of neurons in the murine supramammillary nucleus (SuM) projecting to the POA as well as diverse brain regions relevant to the decision-making by combinatory labeling of the neurons with adeno-associated viruses (AAVs). Such a unique population of glutamatergic neurons was activated under a variety of acute stress, while the optogentic stimulation of them induced behaviors relevant to the active coping of the stress.

Strengths:

Discovery of the neural circuit converting the passive to the active stress coping strategy of the behavior in this study will provide deep insight into understanding how the animal survives with flexibility and must be informative for the neuroscience community.

Weaknesses:

Despite a large advance in understanding the role of this circuit in behavior in the study, I primarily have concerns about the interaction between SuM and other neural pathways.

Reviewer #1 (Public Review):

Sun and colleagues outline structural and mechanistic studies of the bacterial adhesin PrgB, an atypical microbial cell surface-anchored polypeptide that binds DNA. The manuscript includes a crystal structure of the Ig-like domains of PrgB, cryo-EM structures of the majority of the intact polypeptide in DNA-bound and free forms, and an assessment of the phenotypes of E. faecalis strains expressing various PrgB mutants. Generally, the study has been conducted with a good level of rigor, and there is consistency in the findings. Initial concerns about inferences initially made from low-resolution Cryo-EM structures have been addressed experimentally and the manuscript correspondingly updated.

Reviewer #2 (Public Review):

Having previously solved the X-ray crystallographic structure of the polymer adhesin domain (PAD) of PrgB from E. faecalis, the authors looked to build on that work by crystallizing a nearly full-length construct of PrgB. Though they were successful in their crystallization endeavors, the crystal contained only what was previously thought to be two domains with RGD motifs. The authors' high-resolution structure shows that in fact the C-terminal portion of PrgB is made up of four immunoglobulin-like domains. The authors then set out to collect single-particle cryoEM data in a bid to obtain a full-length structure of PrgB, both in the presence and absence of ssDNA. The authors were only able to obtain quite low-resolution data, which they fit their crystal structures into. The authors then used these structures to inform the design of novel deletion mutants and point mutations, as well as to rationalize years of phenotypic data from other published mutants.

The X-ray crystallographic structure is beautiful and in combination with their in vivo data allowed them to propose a model where PrgB positions cells at an appropriate distance for conjugation. The in vivo experiments appear to be done well and the authors' discovery that the Ser-Asn-Glu is not important for generalized aggregation but has an additional yet unknown role in conjugation and biofilm formation is exciting and well supported by their data.

[Editors' note: In response to reviews of a previous version of this manuscript, the authors have carried out additional experiments that have strengthened the already convincing aspects of the work. We commend the authors for responding to questions raised by the reviewers about the inference of interactions of in vivo importance inferred from low-resolution cryo-EM studies by carrying out and reporting on additional experiments that fail to confirm their initial speculative model. The current work is stronger and more convincing as a result.]

Reviewer #1 (Public Review):

Bacteria can adapt to extremely diverse environments via extensive gene reprogramming at transcriptional and post-transcriptional levels. Small RNAs are key regulators of gene expression that participate in this adaptive response in bacteria, and often act as post-transcriptional regulators via pairing to multiple mRNA-targets.

In this study, Melamed et al. identify four E. coli small RNAs whose expression is dependent on sigma 28 (FliA), involved in the regulation of flagellar gene expression. Even though they are all under the control of FliA, expression of these 4 sRNAs peaks under slightly different growth conditions and each has different effects on flagella synthesis/number and motility. Combining RILseq data, structural probing, northern-blots and reporter assays, the authors show that 3 of these sRNAs control fliC expression (negatively for FliX, positively for MotR and UhpU) and two of them regulate r-protein genes from the S10 operon (again positively for MotR, and negatively for FliX). UhpU also directly represses synthesis of the LrhA transcriptional regulator, that in turn regulates flhDC (at the top of flagella regulation cascade). Based on RILseq data, the fourth sRNA (FlgO) has very few targets and may act via a mechanism other than base-pairing.

As r-protein S10 is also implicated in anti-termination via the NusB-S10 complex, the authors further hypothesize that the up-regulation of S10 gene expression by MotR promotes expression of the long flagellar operons through anti-termination. Consistent with this possible connection between ribosome and flagella synthesis, they show that MotR overexpression leads to an increase in flagella number and in the mRNA levels of two long flagellar operons, and that both effects are dependent on the NusB protein. Lastly, they provide data supporting a more general activating and repressing role for MotR and FliX, respectively, in flagellar genes expression and motility, via a still unclear detailed mechanism.

This study brings a lot of new information on the regulation of flagellar genes, from the identification of novel sigma 28-dependent sRNAs to their effects on flagella production and motility. It represents a considerable amount of work; the experimental data are clear and solid and support the conclusions of the paper. Even though mechanistic details underlying the observed regulations by MotR or FliX sRNAs are lacking, the effect of these sRNAs on fliC, several rps/rpl genes, and flagellar genes and motility is convincing.<br /> The connection between r-protein genes regulation and flagellar operons is exciting, and so is the general effect of pMotR or pFliX on the expression of multiple middle and late flagellar genes.

Reviewer #2 (Public Review):

This manuscript discusses the posttranscriptional regulation of flagella synthesis in Escherichia coli. The bacterial flagellum is a complex structure that consists of three major domains, and its synthesis is an energy-intensive process that requires extensive use of ribosomes. The flagellar regulon encompasses more than 50 genes, and the genes are activated in a sequential manner to ensure that flagellar components are made in the order in which they are needed. Transcription of the genes is regulated by various factors in response to environmental signals. However, little is known about the posttranscriptional regulation of flagella synthesis. The manuscript describes four UTR-derived sRNAs (UhpU, MotR, FliX, and FlgO) that are controlled by the flagella sigma factor σ28 (fliA) in Escherichia coli. The sRNAs have varied effects on flagellin protein levels, flagella number, and cell motility, and they regulate different aspects of flagella synthesis.<br /> UhpU corresponds to the 3´ UTR of uhpT.

UhpU is transcribed from its own promoter inside the coding sequence of uhpT.

MotR originates from the 5´ UTR of motA. The promoter for motR is within the flhC CDS and is also the promoter of the downstream motAB-cheAW operon.

FliX originates from the 3´ UTR of fliC. Probably processed from parental mRNA.

FlgO originates from the 3´ UTR of flgL. Probably processed from parental mRNA.

This is a very interesting study that shows how sRNA-mediated regulation can create a complex network regulating flagella synthesis. The information is new and gives a fresh outlook at cellular mechanisms of flagellar synthesis.

Reviewer #3 (Public Review):

Flagella are crucial for bacterial motility and virulence of pathogens. They represent large molecular machines that require strict hierarchical expression control of their components. So far, mainly transcriptional control mechanisms have been described to control flagella biogenesis. While several sRNAs have been reported that are environmentally controlled and regulate motility mainly via control of flagella master regulators, less is known about sRNAs that are co-regulated with flagella genes and control later steps of flagella biogenesis.

In this carefully designed and well-written study, the authors explore the role of four E. coli σ28-dependent 3' or 5' UTR-derived sRNAs in regulating flagella biogenesis. UhpU and MotR sRNAs are generated from their own σ28(FliA)-dependent promoter, while FliX and FlgO sRNAs are processed from the 3'UTRs of flagella genes under control of FliA. The authors provide an impressive amount of data and different experiments, including phenotypic analyses, genomics approaches as well as in-vitro and in-vivo target identification and validation methods, to demonstrate varied effects of three of these sRNAs (UhpU, FliX and MotR) on flagella biogenesis and motility. For example, they show different and for some sRNAs opposing phenotypes upon overexpression: While UhpU sRNA slightly increases flagella number and motility, FliX has the opposite effect. MotR sRNA also increases the number of flagella, with minor effects on motility.

While the mechanisms and functions of the fourth sRNA, FlgO, remain elusive, the authors provide convincing experiments demonstrating that the three sRNAs directly act on different targets (identified through the analysis of previous RIL-seq datasets), with a variety of mechanisms. The authors demonstrate, UhpU sRNA, which derives from the 3´UTR of a metabolic gene, downregulates LrhA, a transcriptional repressor of the flhDC operon encoding the early genes that activate the flagellar cascade. According to their RIL-seq data analyses, UhpU has hundreds of additional potential targets, including multiple genes involved in carbon metabolism. Due to the focus on flagellar biogenesis, these are not further investigated in this study and the authors further characterize the two other flagella-associated sRNAs, FliX and MotR. Interestingly, they found that these sRNAs seem to target coding sequences rather than acting via canonical targeting of ribosome binding sites. The authors show FliX sRNA represses flagellin expression by interacting with the CDS of the fliC mRNA. Both FliX and MotR sRNA turn out to modulate the levels of ribosomal proteins of the S10 operon with opposite effects. MotR, which is expressed earlier, interacts with the leader and the CDS of rpsJ mRNA, leading to increased S10 protein levels and S10-NusB complex mediated anti-termination, promoting readthrough of long flagellar operons. FliX interacts with the CDSs of rplC, rpsQ, rpsS-rplV, repressing the production of the encoded ribosomal proteins. The authors also uncover MotR and FliX affect transcription selected representative flagellar genes, with an unknown mechanism.

Overall, this comprehensive study expands the repertoire of characterized UTR derived sRNAs and integrates new layers of post-transcriptional regulation into the highly complex flagellar regulatory cascade. Moreover, these new flagella regulators (MotR, FliX) act non-canonically, and impact protein expression of their target genes by base-pairing with the CDS of the transcripts. Their findings directly connect flagella biosynthesis and motility, highly energy consuming processes, to ribosome production (MotR and FliX) and possibly to carbon metabolism (UhpU). In their revised version, the authors have addressed many of the previously raised questions and comments. This made their manuscript easier to read and to follow.

Reviewer #1 (Public Review):

Lim W et al. investigated the mechanisms underlying doxorubicin resistance in triple negative breast cancer cells (TNBC). They use a new multifluidic cell culture chamber to grow MB-231 TNBC cells in the presence of doxorubicin and identify a cell population of large, resistant MB-231 cells they term L-DOXR cells. These cells maintain resistance when grown as a xenograft model, and patient tissues also display evidence for having cells with large nuclei and extra genomic content. RNA-seq analysis comparing L-DOXR cells to WT MB-231 cells revealed upregulation of NUPR1. Inhibition or knockdown of NUPR1 resulted in increased sensitivity to doxorubicin. NUPR1 expression was determined to be regulated via HDAC11 via promoter acetylation. The data presented could be used as a platform to understand resistance mechanisms to a variety of cancer therapeutics.

Reviewer #2 (Public Review):

Summary:<br /> In this paper, the authors induced large doxorubicin-resistant (L-DOXR) cells by generating DOX gradients using their Cancer Drug Resistance Accelerator (CDRA) chip. The L-DOXR cells showed enhanced proliferation rates, migration capacity, and carcinogenesis. Then the authors identified that the chemoresistance of L-DOXR cells is caused by failed epigenetic control of NUPR1/HDAC11 axis.

Strengths:

- Chemoresistant cancer cells were generated using a novel technique and their oncogenic properties were clearly demonstrated using both in vivo and in vitro analysis.<br /> - The mechanisms of chemoresistance of the L-DOXR cells could be elucidated using in vivo chemoresistant xenograft models, an unbiased genome-wide transcriptome analysis, and a patient data/tissue analysis.<br /> - This technique has great capability to be used for understanding the chemoresistant mechanisms of tumor cells.

Reviewer #3 (Public Review):

Summary:<br /> In this manuscript, Lim and colleagues use an innovative CDRA chip platform to derive and mechanistically elucidate the molecular wiring of doxorubicin-resistant (DOXR) MDA-MB-231 cells. Given their enlarged morphology and polyploidy, they termed these cells as Large-DOXR (L-DORX). Through comparative functional omics, they deduce the NUPR1/HDAC11 axis to be essential in imparting doxorubicin resistance and, consequently, genetic or pharmacologic inhibition of the NUPR1 to restore sensitivity to the drug.

Strengths:<br /> The study focuses on a major clinical problem of the eventual onset of resistance to chemotherapeutics in patients with triple-negative breast cancer (TNBC). They use an innovative chip-based platform to establish as well as molecularly characterize TNBC cells showing resistance to doxorubicin and uncover NUPR1 as a novel targetable driver of the resistant phenotype.

Weaknesses:<br /> Critical weaknesses are the use of a single cell line model (i.e., MDA-MB-231) for all the phenotypic and functional experiments and absolutely no mechanistic insights into how NUPR1 functionally imparts resistance to doxorubicin. It is imperative that the authors demonstrate the broader relevance of NUPR1 in driving dox resistance using independent disease models.

Reviewer #1 (Public Review):

Summary:

In this manuscript, the authors used machine learning algorithm to analyze published exosome datasets to find biomarkers to differentiate exosomes of different origin.

Strengths:

The performance of the algorithm are generally of good quality.

Weaknesses:

The source datasets are heterogeneous as described in Figure 1 and Figure 2, or Line 72-75; and therefore questionable.

Reviewer #2 (Public Review):

Summary:

This is a fine work on the development of computational approaches to detect cancer through exosomes. Exosomes are an emerging biomarker resource and have attracted considerable interests in the biomedical field. Kalluri and co-workers collected a large sample pool and used random forest to identify a group of protein markers that are universal to exosomes and to cancer exosomes. The results are very exciting and not only added new knowledge in cancer research but also a new and advanced method to detect cancer. Data was presented very nicely and the manuscript was well written.

Strengths:

Identified new biomarkers for cancer diagnosis via exosomes.<br /> Developed a new method to detect cancer non-invasively.<br /> Results were presented nicely and manuscript were well written.

Weaknesses:

N/A.

Reviewer #3 (Public Review):

In the current study, Li et al. address the difficulty in early non-invasive cancer diagnosis due to the limitations of current diagnostic methods in terms of sensitivity and specificity. The study brings attention to exosomes - membrane-bound nanovesicles secreted by cells, containing DNA, RNA, and proteins reflective of their originating cells. Given the prevalence of exosomes in various biological fluids, they offer potential as reliable biomarkers. Notably, the manuscript introduces a new computational approach, rooted in machine learning, to differentiate cancers by analyzing a set of proteins associated with exosomes. Utilizing exosome protein datasets from diverse sources, including cell lines, tissues, and various biological fluids, the study spotlights five proteins as predominant universal exosome biomarkers. Furthermore, it delineates three distinct panels of proteins that can discern cancer exosomes from non-cancerous ones and assist in cancer subtype classification using random forest models. Impressively, the models based on proteins from plasma, serum, or urine exosomes achieve AUROC scores above 0.91, outperforming other algorithms such as Support Vector Machine, K Nearest Neighbor Classifier, and Gaussian Naive Bayes. Overall, the study presents a promising protein biomarker signature tied to cancer exosomes and proposes a machine learning-driven diagnostic method that could potentially revolutionize non-invasive cancer diagnosis.

Reviewer #1 (Public Review):

The manuscript investigates how humans store temporal sequences of tones in working memory. The authors mainly focus on a theory named "Language of thought" (LoT). Here the structure of a stimulus sequence can be stored in a tree structure that integrates the dependencies of a stimulus stored in working memory. To investigate the LoT hypothesis, participants listened to multiple stimulus sequences that varied in complexity (e.g., alternating tones vs. nearly random sequence). Simultaneously, the authors collected fMRI or MEG data to investigate the neuronal correlates of LoT complexity in working memory. Critical analysis was based on a deviant tone that violated the stored sequence structure. Deviant detection behavior and a bracketing task allowed a behavioral analysis.

Results showed accurate bracketing and fast/correct responses when LoT complexity is low. fMRI data showed that LoT complexity correlated with the activation of 14 clusters. MEG data showed that LoT complexity correlated mainly with activation from 100-200 ms after stimulus onset. These and other analyses presented in the manuscript lead the authors to conclude that such tone sequences are represented in human memory using LoT in contrast to alternative representations that rely on distinct memory slot representations.

Strengths

The study provides a concise and easily accessible introduction. The task and stimuli are well described and allow a good understanding of what participants experience while their brain activation is recorded. Results are extensive as they include multiple behavioral investigations and brain activation data from two different measurement modalities. The presentation of the behavioral results is intuitive. The analysis provided a direct comparison of the LoT with an alternative model based on estimating a transition-probability measure of surprise.

For the fMRI data, the whole brain analysis was accompanied by detailed region of interest analyses, including time course analysis, for the activation clusters correlated with LoT complexity. In addition, the activation clusters have been set in relation (overlap and region of interest analyses) to a math and a language localizer. For the MEG data, the authors investigated the LoT complexity effect based on linear regression, including an analysis that also included transitional probabilities and multivariate decoding analysis. The discussion of the results focused on comparing the activation patterns of the task with the localizer tasks. Overall, the authors have provided considerable new data in multiple modalities on a well-designed experiment investigating how humans represent sequences in auditory working memory.

Weaknesses

The primary issue of the manuscript is the missing formal description of the LoT model and alternatives, inconsistencies in the model comparisons, and no clear argumentation that would allow the reader to understand the selection of the alternative model. Similar to a recent paper by similar authors (Planton et al., 2021 PLOS Computational Biology), an explicit model comparison analysis would allow a much stronger conclusion. Also, these analyses would provide a more extensive evidence base for the favored LoT model. Needed would be a clear argumentation for why the transitional probabilities were identified as the most optimal alternative model for a critical test. A clear description of the models (e.g., how many free parameters) and a description of the simulation procedure (e.g., are they trained, etc.) Here it would be strongly advised to provide the scripts that allow others to reproduce the simulations.

Furthermore, the manuscript needs a clear motivation for the type of sequences and some methodological decisions. Central here is the quadratic trend selectively used for the fMRI analysis but not for the other datasets. Also, the description of the linear mixed models is missing (e.g., the random effect structure, e.g., see Bates, D., Kliegl, R., Vasishth, S., & Baayen, H. (2015). Parsimonious mixed models. arXiv preprint arXiv:1506.04967.). Moreover, sample sizes have not been justified by a power analysis.

Reviewer #2 (Public Review):

Any stimulus that enters the human mind is in one way or another other compressed. A drawing with hundreds of lines might be turned into "picture of a seescape", a complex set of harmonically overlapping sine waves might be turned into "sad piano chord", and a weird set of utterances incomprehensible to most animals could be turned into "someone reading a review aloud" if prior experience permits. Understanding this process is essential to understanding the human mind. Understanding compression is even more critical to understanding working memory that - in its limited capacity - can most profit from compression, abstraction, or chunking.

Here, the authors provide some insight into how a sequence of binary pitch might be compressed during encoding into memory. They use a previously developed method to encapsulate sequences of 16 high and low pitches using a math-like description scheme (Planton et al., 2021). One can think of this scheme as a "language", "a categorization model", or "a process of segmenting patterns", but its central role in the experiment is to derive a 'rough' measure of complexity that is shown to covary with behavioral data, here and in prior work (Planton et al., 2021).

This language seems to be particularly useful in the context of this highly regularized task, where the set of possible sequences is limited to 20 (out of an overall number of 65.536 imaginable sequences). Instead of finding structures in random sequences, subjects can be expected to quickly learn that their task is to detect which particular structure (of a fairly limited class) is to be found in the given sequence. It is unclear whether such a language would also be useful for sequences of more natural stimuli that motivate the authors' research (e.g. syllables, tones, or shapes). What both more natural compression and the compression used in this task have in common is that long-term memory might play an instrumental role during the compression.

Thus, the authors provide clear evidence that these sequences are being compressed and some evidence that the compression used shares some features with the compression model employed, here. The neural data are consistent with this interpretation.

Regardless of our disagreement with the interpretation of the results the authors put forward, we find the research presented here elegantly designed, well grounded in a series of prior work, and inspiring. There is little known about the representation of sequences in memory and during perception and we believe that this work is a notable and helpful addition to our understanding of this question.

Reviewer #1 (Public Review):

The current manuscript by Liu et al entitled "Discovery and biological evaluation of a potent small molecule CRM1 inhibitor for its selective ablation of extranodal NK/T cell lymphoma" reports the identification of a novel CRM1 inhibitor and shows its efficiency against extranodal natural killer/T cell lymphoma cells (ENKTL).

This is a very timely and very original study with potential impact in a variety of pathologies not only in ENKTL. However, the main conclusions of the work are not supported by experimental evidence. The study claims that LFS-1107 reversibly inhibits the nuclear export receptor CRM1 but the authors only show that the compound binds to CRM1 and that the CRM1 substrate IκBα accumulates in the cell nucleus upon LFS-1107 treatment. The evidence is indirect and alternative scenarios are certainly possible. On the other hand, the manuscript is not always well-written and insufficiently referenced. The quality of the images is poor. The figure legends are incomplete. The nuclear translocation in figure 2G is not convincing. The western blot in figure 2G shows that LFS-1107 treatment induces IκBα expression, and both cytoplasmic and nuclear amounts increase in a dose-dependent manner. Together, these data do not support nuclear IκBα accumulation upon LFS-1107 treatment.

Reviewer #2 (Public Review):

Indeed, ENKTL is a rather deadly tumor with unmet medical needs. The work is novel in the sense that they designed and identified a very potent inhibitor homing at CRM1 via a deep-reinforcement learning model to suppress the overactivation of NF-κB signaling, an underlying mechanism of ENKTL pathogenesis. The authors demonstrated that LFS-1107 binds more strongly with CRM1 (approximately 40-fold) as compared to KPT-330, an existing CRM1 inhibitor. Another merit of the small-molecule inhibitor is that LFS-1107 can selectively eliminate ENKTL cells while sparing normal blood cells. Their animal results clearly demonstrated that the small-molecule inhibitor was able to extend mouse survival and eliminate tumor cells considerably. Overall, the manuscript may provide a possible therapeutic strategy to treat ENKTL with a good safety profile. The manuscript is also well-written. The weakness of the manuscript is that some details for the design and evaluation of the small-molecular inhibitor are missing.

Reviewer #1 (Public Review):

This paper describes the neural activity, measured by intrinsic optical imaging in reach-to-grasp, and reach-only conditions in relation to the Intra-cortical micro stimulation maps. The paper mostly describes a relatively unique and potentially useful data set. However, in the current version, no real hypotheses about the organization of M1 and PMd are tested convincingly. For example, the claim of "clustered neural activity" is not tested against any quantifiable alternative hypothesis of non-clustered activity, and support for this idea is therefore incomplete.

The combination of intrinsic optical imaging and intra-cortical micro-stimulation of the motor system of two macaque monkeys promised to be a unique and highly interesting dataset. The experiments are carefully conducted. In the analysis and interpretation of the results, however, the paper was disappointing to me. The two main weaknesses in my mind were:

a) The alternative hypotheses depicted in Figure 1B are not subjected to any quantifiable test. When is an activity considered to be clustered and when is it distributed? The fact that the observed actions only activate a small portion of the forelimb area (Figure 5G, H) is utterly unconvincing, as this analysis is highly threshold-dependent. Furthermore, it could be the case that the non-activated regions simply do not give a good intrinsic signal, as they are close to microvasculature (something that you actually seem to argue in Figure 6b). Until the authors can show that the other parts of the forelimb area are clearly activated for other forelimb actions (as you suggest on line 625), I believe the claim of cluster neural activity stands unsupported.

b) The most interesting part of the study (which cannot be easily replicated with human fMRI studies) is the correspondence between the evoked activity and intra-cortical stimulation maps. However, this is impeded by the subjective and low-dimensional description of the evoked movement during stimulation (mainly classifying the moving body part), and the relatively low-dimensional nature (4 conditions) of the evoked activity.

c) Many details about the statistical analysis remain unclear and seem not well motivated.

Reviewer #2 (Public Review):

Chehade and Gharbawie investigated motor and premotor cortex in macaque monkeys performing grasping and reaching tasks. They used intrinsic signal optical imaging (ISOI) covering an exceedingly large field-of-view extending from the IPS to the PS. They compared reaching and fine/power-grip grasping ISOI maps with "motor" maps which they obtained using extensive intracranial microstimulation. The grasping/reaching-induced activity activated relatively isolated portions of M1 and PMd, and did not cover the entire ICM-induced 'motor' maps of the upper limbs. The authors suggest that small subzones exist in M1 and PMd that are preferentially activated by different types of forelimb actions. In general, the authors address an important topic. The results are not only highly relevant for increasing our basic understanding of the functional architecture of the motor-premotor cortex and how it represents different types of forelimb actions, but also for the development of brain-machine interfaces. These are challenging experiments to perform and add to the existing yet complementary electrophysiology, fMRI, and optical imaging experiments that have been performed on this topic - due to the high sensitivity and large coverage of the particular IOSI methods employed by the authors. The manuscript is generally well written and the analyses seem overall adequate - but see below for some additional analyses that should be done. Although I'm generally enthusiastic about this manuscript, there are two major issues that should be clarified. These major questions relate mainly to potential thresholding issues and clustering issues.

Major:

1) The main claim of the authors is that specific forelimb actions activate only a small fraction of what they call the motor map (i.e., those parts of M1/PMd that evoke muscle contractions upon ICM). The action-related activity is measured by ISOI. When looking a the 'raw' reflectance maps, it is rather clear that relatively wide portions of the exposed cortex are activated by grasping/reaching, especially at later time points after the action. In fact, another reading of the results may be that there are two zones of 'deactivation' that split a large swath of motor-premotor cortex being activated by the grasping/reaching actions. (e.g. at 6 seconds after the cue in Fig 3A, 5A). At first sight, the 'deactivated' regions seem to be located in the cortex representing the trunk/shoulder/face - hence regions not necessarily activated (or only weakly) during the grasping/reaching actions. If true, this means that most of the relevant M1/PMd cortex IS activated during the latter actions - opposing the 'clustering' claims of the authors. This raises the question of whether the 'granularity' claimed by the authors is<br /> a. threshold dependent. In this context, the authors should provide an analysis whereby 'granularity' is shown independent of statistical thresholds of the ISOI maps.<br /> b. dependent on the time-point one assesses the maps. Given the sluggish hemodynamic responses, it is unclear which part of the ISOI maps conveys the most information relative to the cue and arm/hand movements. I suspect that timepoints > 6 s will reveal even larger 'homogeneous' activations compared to the maps < 6s.<br /> In fact, Fig 5F (which is highly thresholded) shows a surprisingly good match between the different forelimb actions, which argues against the existence of small subzones that are preferentially activated by different types of forelimb actions -the main claim of the authors.

2) Related to the previous point, the ROI selections/definitions for the time course analyses seem highly arbitrary. As indicated in the introduction, the clustering hypothesis dictates that "an arm function would be concentrated in subzones of the motor arm zones. Neural activity in adjacent subzones would be tuned for other arm functions." To test this hypothesis directly in a straightforward manner, the authors could use the results from the ICM experiment to construct independent ROIs and to evaluate the ISOI responses for the different actions. In that case, the authors could do a straightforward ANOVA (if the data permits parametric analyses) with ROI, action, and time point (and possibly subject) as factors.

Reviewer #1 (Public Review):

This paper evaluates the effect of knocking out CST7(Cystatin 5) on the APPNL-G-F Alzheimer's disease mouse model. They found sexually dimorphic outcomes, with differential transcriptional responses, increased phagocytosis (but interestingly a higher plaque burden) in females and suppressed inflammatory microglial activation in males (but interestingly no change in plaque burden). This study offers new insight into the functional role of CST7 that is upregulated in a subset of disease- associated microglia in AD models and human brain. Despite the discovery of disease-associated microglia several years ago, there has been little effort in understanding the function of the different genes that make up this profile, making this paper especially timely. Overall, the experiments are well-controlled and the data support the main conclusions and the manuscript could be strengthened by addressing the below comments and clarifying questions that could impact the interpretation of their data/ findings.

1. In the first section discussing CST7 expression levels in AD models, it would be good to involve a discussion of levels of CST7 change in human AD samples. There are sufficient available datasets to look at this, and it would help us understand how comparable the animal models are to human patients. For example, while in mice CST7 is highly enriched in microglia/macrophages, in human datasets it seems like it is not quite so specific to microglia - it is equally expressed in endothelial cells. This might have a significant impact on the interpretation of the data, and it would be good to introduce and assess the findings in mice through the human subjects lens. There is a discussion of the human data in the discussion section, but it would be more appropriately assessed in the same way as the mouse data and comparatively presented in the results section. The authors could also include the data from Gerrits et al. 2021 in their first figure.<br /> 2. The differential RNAseq data is perhaps one of the most striking results of this paper; however it is difficult to see exactly how similar the male v female APPNL-G-F profiles are, in addition to the genes shared or not between the KO condition. Venn diagrams, in addition to statistical tests, would enhance this part of the paper and add more clarity.<br /> 3. A major argument in the paper is a continuation of Sala-Frigiero 2019 which says that the female phenotype is an acceleration of the male phenotype. Does this mean that if males were assessed at later timepoints, they would be more similar to the females? Or are there intrinsic differences that never resolve? It would be helpful to see a later timepoint for males to get at the difference between these two options<br /> 4. If the central argument is that CST7 in females decreases phagocytosis and in males increases microglia activation, are there changes in amyloid plaque burden or structure in the APPNL-G-F /CST 7 KO mice compared to APPNL-G-F/CST7 WT that reflect these changes? Please address. If not, how does this affect the functional interpretation of differential expression observed in phagocytic/reactive microglia genes? Pieces of this are discussed but it could be clearer<br /> 5. It is confusing that increased phagocytosis in the APPNL-G-F/CST7 KO females leads to greater plaque burden, considering proteolysis is not affected. What might explain this observation? Additionally, it is interesting that suppression of microglial activation doesn't lead to an increase in plaques in the male APPNL-G-F/CST7 KO mice. How does the profile of phagocytic microglia in the male APPNL-G-F/CST7 KO mice differ from the APPNL-G-F males?<br /> 6. Seems that the authors have potentially discovered an unusual mechanism for how CST7 could regulate cell autonomous function without impacting its canonical protease target. The authors deal with this extensively in the discussion but an ELISA or ICC to localize CST7 to microglia in vitro or in vitro would help address this point.<br /> 7. The authors focus on plaques in their final figure, however dysregulated microglial phagocytosis could impact many other aspects of brain health. Simple immunohistochemistry for synapses and myelin/oligodendrocytes (especially given the results of the in vitro phagocytosis assay) could provide more insight here.

Reviewer #2 (Public Review):

In this article, Daniels et al evaluate the function of Cst7, a gene previously shown to be strongly expressed when microglia respond to Alzheimer's-like pathology. The reported findings include evidence for a sexually dimorphic role of Cst7 in microglia, including differences in lysosomal activity and ability to phagocytose. Some questions remain as to how many of these effects are 1) disease-independent, 2) age-dependent, and 3) ultimately affecting cognition

Strengths:<br /> -The approach taken here is sound, knocking out Cst7 in an animal model of Alzheimer's-like pathology, and analysing a range of variables associated with the pathology.<br /> -The authors have made good use of existing datasets, evidencing the advantages of data sharing and open data mining.<br /> -Data reporting is also excellent, as we can see the individual data points, and also observe how optimal group numbers were used. This adds solidity to the study.<br /> -The results are very well connected, with experiments focusing on the in vivo and in vitro lysosomal/phagocytic function<br /> -Exploring the effect of sex, as an independent variable, is a refreshing approach and clearly an important one by looking at the findings reported here.

Weaknesses:<br /> -The basis for the hypothesis of Cst7 displaying sexual dymorphism is not as strong as indicated by the text. Data presented in Figure 1 supports 1/2 models have statistically significant differences in expression of Cst7 between males and females.<br /> -As presented, it is hard to disentangle the differential impact of sex, in isolation, compared to the accelerated pathology/ageing observed in females. In other words, Cst7 could be playing a differential role in females not because that particular gene has sexually dimorphic roles, but because female microglia are generally more advanced in their phenotype and prone to Cst7-dependent effects that their younger counterparts (or male microglia) would not suffer. We also lack context when it comes to baseline effects of Cst7-/- compared to disease-related effects, since a crucial control (non-AD Cst7-/-) is missing from analyses, key in Figure 2 for example.<br /> -It is unclear how the knockout of Cst7 would selectively affect microglia. The expression of Cst7 is definitely very high in microglia in AD, but it's less clear whether other cells express this gene as well. If so, the effects of Cst7-/- could be microglia-independent in part.<br /> -Considering the large number of mice used in these studies, and the effort that very likely went into these, it is disappointing that we do not have any measure of cognition or any other behavioural task associated with the molecular data. Ultimately, changes in amyloid, for example, could or could not correlate with real pathology in APP models.

Reviewer #3 (Public Review):

In this manuscript, Daniels et al explored the role of Cystatin F in an A-driven mouse model of Alzheimer's disease. By crossing a constitutive knockout mouse lacking the gene that encodes Cystatin F, Cst7, to the AppNL-G-F mouse line, the authors describe impairments in microglial gene expression and phagocytic function that emerge more prominently in females versus males lacking Cst7. A strength of the study is its focus: given mounting evidence that microglia are a hub of neurological dysfunction with particular potential to trigger or exacerbate neurodegenerative disorders, it is essential to determine the changes in microglia that occur pathologically to promote disease progression. Similarly, the wide-spread identification of the gene in question, Cst7, as upregulated in AD models makes this gene a good target for mechanistic studies.

The paper in its current form also has several weaknesses which limit the insights derived, weaknesses that are largely related to the experimental tools and approaches chosen by the authors to test their hypotheses. For example, the paper begins with a figure replotting data from previous studies showing that Cst7 is upregulated in mouse models of Alzheimer's disease. Though relevant to the current study, there are no new insights provided here. Next, the authors perform bulk RNA-sequencing on microglia isolated from male and female mice in the Cst7-/-; AppNL-G-F mouse line. In the methods, it is unclear whether the authors took precautions to preserve the endogenous transcriptional state of these cells given evidence that microglia can acquire a DAM-like signature simply due to the process of dissociation (Marsh et al, Nature Neuroscience, 2022). If the authors did not control for this, their results may not support the conclusions they draw from the data. Relatedly, it appears the authors pooled all microglia together here, instead of just isolating DAMs specifically or analyzing microglia at single-cell resolution, which could reveal the heterogeneous nature of the role of Cst7 in microglia. In addition to losing information about heterogeneity, another concern is that they could be diluting out the major effects of the model on microglial function by including all microglia. Overall, the biggest issue I have with the RNA-sequencing data is the lack of validation of the gene expression changes identified using a different method that does not require dissociation, like immunohistochemistry or fluorescence in situ hybridization. Especially given the limited number of genes they found to be mis-regulated (see Fig. 2 E and G), I worry that these changes might simply be noise, especially since the authors provide no further evidence of their mis-regulation. Without further validation, the data presented are not sufficient to support the authors' claims.

In assessing the changes in microglial function and A pathology that occur in males and females of the Cst7-/-; AppNL-G-F line, the authors identify some differences between how females and males are affected by the loss of Cst7. While the statistical analyses the authors perform as given in the figure legends appear to be correct, the plots do not show significant changes between males and females for a given parameter. Take for example Figure 3H. Loss of Cst7 decreases IBA+Lamp+ microglia in males but increases this parameter in females. However, it does not appear that there is a significant difference in IBA+Lamp+ microglia in male versus female mice lacking Cst7. If there is no absolute difference between males and females, can the differential effects of Cst7 knockout on the sexes really be so relevant to the sexual dimorphism observed in the disease? I question this connection, but perhaps a greater discussion of what the result might mean by the authors would be helpful for placing this into context.

Finally, the use of in vitro assays of microglial function can be helpful as secondary analyses when coupled with in vivo or ex vivo approaches, but are not on their own sufficient to support the authors' conclusions. Quantitative engulfment assays (see Schafer et al, Neuron, 2012) on brain tissue showing that male and female microglia lacking Cst7 engulf different amounts of material (e.g. plaques, synapses, myelin) in the intact brain would be more convincing.

In general, a major limitation to the insights that can be derived in the study is the decision of the authors to perform all experiments at a single late-stage time point of 12 months of age. As this is quite far into disease progression for many AD models, phenotypic changes identified by the authors could arise due to the downstream effects of plaque deposition and therefore may not implicate Cst7 as a mechanism driving neurodegeneration rather than one of many inflammatory changes that accompany AD mouse models nearing the one-year time point. A related problem is that the study uses a constitutive KO mouse that has lacked Cst7 expression throughout life, not just during disease processes that increase with aging. In summary, the topic of the article is important and timely, but the connection between the data and the authors' conclusions is not as strong as it could be.

Reviewer #1 (Public Review):

The CFTR ion channel belongs to the family of ABC transporters, alternating between inward-facing (IF) and outward-facing (OF) conformations driven by binding and hydrolysis of ATP. ABC transporters are involved in a wide variety of physiologically essential transport processes. In contrast to all other ABC transporters, the OF conformation of CFTR includes an anion-conducting transmembrane pore, which has enabled investigators to use single-channel patch clamp electrophysiology to characterize the energetics of most of the relevant transitions that can be observed during a gating cycle. A transition that had remained elusive to quantify is the non-hydrolytic closing rate in which the channel switches back to an IF conformation even though both nucleotide-binding domain sites are occupied by non-hydrolyzed ATP molecules. The reason for this is that the rate of closure due to ATP hydrolysis occurs much faster, such that the non-hydrolytic closing rate cannot be quantified in WT channel recordings. Further, channels with mutations that are expected to disrupt ATP hydrolysis exhibit high variability between mutants in the non-hydrolytic closing rates, precluding an extrapolation for this rate onto the WT channel. It is presently unclear whether the large spread in the rates is caused by distinct degrees of remnant hydrolytic activity in each of the mutant channels. Regardless of this uncertainty, several of these mutations have been successfully employed in structural studies to stabilize the channel in an OF conformation with two ATP molecules bound. In the present manuscript, Márton A. Simon and collaborators use patch-clamp electrophysiology to measure the rates of non-hydrolytic channel closure in human CFTR channels containing single and double mutations expected to disrupt ATP hydrolysis. First, they find that the E1371Q but not the E1371S mutation significantly stabilizes the OF state and slows channel closure in the human but not the zebrafish channel. Looking at the structures of both human and zebrafish E1371Q mutant channels, a non-native hydrogen bond is identified between the side-chain of E1371Q and the main chain at position G576 that is observed only in the human structure and would be expected to stabilize the OF state. Double mutant cycle analysis is then utilized to compare the effect of removal of either of the hydrogen-bonding partners in the human CFTR, and found to be consistent with a large energy of interaction between the two sites that is interpreted to occur selectively in the OF state. Notably, none of these perturbations altered the intra-burst activity of the CFTR, indicating that those closures do not involve major changes in the NBD. The rates of closure for other mutants are then investigated, and it is found that combining two hydrolysis-disrupting mutations that retain relatively fast closure rates does not slow closure any further, suggesting that their fast closure rates are not due to residual ATP hydrolytic activity. Further, this observation also suggests that these mutations do not affect the intrinsic non-hydrolytic closing rate, allowing their rates to be used as a measure of what occurs in WT channels. The experiments presented are high-quality, the conclusions are well supported by the data and the findings clarify a series of questions that had remained in the field, in addition to finding and precisely quantitating the role of a hydrogen bond in stabilizing a conformation state of this channel. The results could have implications for other members of the ABC family that are harder to study because they do not produce ionic currents. Some methodological details could be explained better, such as the voltage at which each of the recordings was performed, and how data was normalized, which is presently unclear. Additional testing of the hypothesis could have been carried out through double mutant cycle analysis with E1371Q + G576Δ in the zebrafish receptor or the other non-hydrolytic mutants.

Reviewer #2 (Public Review):

Gating of the CFTR chloride channel is controlled by its nucleotide binding domains (NBDs) where ATP binding-induced dimerization leads to channel opening and ATP hydrolysis in the catalytic ATP binding site terminates CFTR's opening burst. Mutations that diminish ATP hydrolysis, including Walker A mutation K1250A, Walker B mutation D1370N, and catalytic glutamate mutations E1371Q and E1371S, have been used extensively to trap the channel in the open state by researchers studying CFTR function. The E1371Q human CFTR (hCFTR) has an extremely longer burst duration than all the other hydrolysis-deficient mutants, including E1371S hCFTR. An unexpected finding that the E-to-Q and E-to-S mutants of zebrafish CFTR (zCFTR) have similar non-hydrolytic closing rates inspired Simon et al to investigate the underlying mechanism for this discrepancy between the human and zebrafish CFTR orthologs, and examine how hydrolysis deficient mutations have differential effects on the CFTR's burst duration. Their data support the idea that all the above mutations completely abolish ATP hydrolysis. The closing rate of K1250A and E1371S CFTR represents the true non-hydrolytic closing rate of wildtype CFTR, while the closing rate of D1370N is accelerated presumably due to the lack of interaction between the negatively charged aspartate and magnesium ion in the ATP binding site. On the other hand, an artificial H-bond between the G576-Q1371 of hCFTR, which is absent in zCFTR, stabilizes the NBD dimer and slowers non-hydrolytic closure.

The conclusions of this paper are mostly well supported by the data, but some additional experiments will strengthen the claim on the role of the artificial inter-NBD hydrogen bond (point 1 below). Some aspects of data interpretation need to be further clarified (point 2-5 below).

1) The author hypothesized that in hCFTR an artificial H-bond between the side-chain of glutamine at position 1371 (i.e., in E1371Q mutant) and the backbone carbonyl at G576 of the D-loop stabilizes the NBD dimer. Such H-bond is absent in E1372Q zCFTR. The authors employed mutant cycle analysis on the G576Δ-E1371S mutation pair to demonstrate an energetic coupling between the hG576 and hE1371Q. However, how the deletion of G576 might alter the local structure is unpredictable. The result does not directly address the discrepancy between zCFTR and hCFTR, either. The D-loop is highly conserved across species with a consensus sequence PFGYLD (residue 574-579 in hCFTR), but in zCFTR the analogous sequence is PFTHLD. The backbone carbonyl oxygen could therefore be harder to access in zCFTR. A simple yet critical experiment would have strengthened the authors' claim that the interaction between Q1371 and G576 stabilizes the dimer: introducing mutation in the D-loop of zCFTR to match the sequence of hCFTR (and vice versa). The authors' hypothesis would predict that zCFTR with hCFTR's D-loop sequence should recapitulate hCFTR's phenotype: the E-to-Q mutation on the catalytic glutamate would further lengthen the burst duration compared to the E-to-S mutation.

2) The authors speculated that the reason for D1370N's relatively fast closing rate compared to other non-hydrolytic mutants is the loss of interaction between Mg2+ and the negatively charged aspartate. However, this reasoning fails to explain why non-hydrolytic closure of wildtype CFTR in the absence of Mg2+ (e.g., Levring et al. 2023 Extended Data Fig. 7g) is even slower than the non-hydrolytic closure of D1370N CFTR opened by MgATP, where at least the Mg2+ is present. The authors should caution the readers that so far no definitive experimental evidence can explain the destabilizing effect of D1370N.

3) Based on the results that the double mutant E1371S/K1250A hCFTR has similar burst duration as single mutant E1371S and K1250A, the authors made a strong claim that both mutations completely abolish ATP hydrolysis. Similar reasoning was applied to D1370N. The limitations in such interpretations should be discussed. The authors made the assumption that the termination of a burst is solely controlled by site 2 (Figure 1C). However, when hydrolysis is significantly diminished, binding of ATP in site 2 is very stable, and thus dissociation of ATP from site 2 versus site 1 becomes hard to distinguish. Whether all hydrolysis-deficient mutants share the same open-to-close transition by releasing ATP from site 2 but retaining ATP in site 1 is still a question. As the authors have elaborated in the text, it is known that mutations in the degenerate site 1 can affect non-hydrolytic closing. When mutations are introduced to site 2, they might as well result in allosteric effects on the stability of ATP binding in site 1, which could subsequently alter the channel's closing rate. The authors might want to make the readers aware of the complicated relationship between channel closure and CFTR's two ATP binding sites, and that the estimation of the "true non-hydrolytic closing rate" is based on an oversimplified gating scheme shown in Figure 1C.

4) It is known that non-hydrolytic closing rate of CFTR is phosphorylation dependent, which the authors briefly mentioned in the Discussion. Vergani et al. (2003) documented that τburst of K1250A and D1370N in PKA is ~80 s and ~4 s respectively, but both are reduced by roughly twofold when PKA was removed. In this study the burst durations of K1250A (~30 s, Figure 4C) and D1370N (~2 s, Figure 4E) indicate that these channels are not strongly phosphorylated. Similarly, the τburst of E1371S in PKA is over 100 s (Bompadre et al. 2005), significantly longer than that in the current study. Although it is unclear how a different degree of R domain phosphorylation affects non-hydrolytic closing, the fact that it does again suggests that the simplified scheme used as the base for data interpretation may have its limitation. The Discussion would benefit from a more cautionary note on the oversimplification of the IB1↔B1 transition, and clarify that channels are not strongly phosphorylated in the current experimental condition.

5) The τburst of E1371Q CFTR is over 400 second while the τburst of K1250A-E1371Q double mutant is shortened to ~200 second (Figure 3B, black vs Figure 4C, black). The K1250A-E1371S CFTR also seems to have a shorter τburst than E1371S CFTR (Figure 4C, blue vs Figure 3B, blue). Although the effect of the K1250A mutation on shortening τburst of E1371Q and E1371S CFTR is not as dramatic as the D1370N mutation, the authors might want to clearly state if there is indeed a significant difference and address how K1250A mutation has such destabilizing effect.

Reference:<br /> Bompadre, S. G., Cho, J. H., Wang, X., Zou, X., Sohma, Y., Li, M., and Hwang, T. C. (2005) CFTRgating II: Effects of nucleotide binding on the stability of open states. J Gen Physiol 125, 377-394

Levring,J., Terry,D.S., Kilic,Z., Fitzgerald,G., Blanchard,S.C., and Chen,J. (2023). CFTR function,<br /> pathology and pharmacology at single-molecule resolution. Nature 616, 606-614.

Vergani,P., Nairn,A.C., and Gadsby,D.C. (2003). On the mechanism of MgATP-dependent gating of CFTR Cl- channels. J. Gen. Physiol 121, 17-36.

Reviewer #3 (Public Review):

CFTR is an anion-selective channel that plays important roles in epithelial physiology. In this paper, Simon and colleagues focus on the step of the CFTR gating cycle that opens the pore. But the authors are particularly interested in the reversal of this opening step. Wild-type (WT) CFTR channels do not usually close by reversal of the opening step, as closure via this "non-hydrolytic" pathway is slow. Instead, hydrolysis of the ATP molecule bound at site 2 destabilizes the open (or bursting) channel and triggers rapid "hydrolytic" channel closure - before the open channel has time to overcome the energetic barrier on the non-hydrolytic pathway. While it is generally (but not universally) accepted that such a non-equilibrium kinetic scheme underlies CFTR gating, how tightly gating and ATPase cycles are coupled is still quite controversial.

Here, combining simple electrophysiology measurements on mutant channels with solid arguments, the authors provide an improved estimate for the backward rate on the opening transition (rate k-1) in WT-CFTR channels. It turns out that this rate is indeed slow, compared to the rate of the hydrolytic step (k1) allowing authors to conclude that WT CFTR channels close via reversal of the opening step only less than once every 100 gating cycles. In addition, results of thermodynamic mutant cycles and careful analysis of cryo-EM structures are used to support plausible molecular mechanisms that explain why different mutations in CFTR's catalytic site slow down, speed up or barely affect non-hydrolytic closure.

The strength of this study is twofold. First, the methods are sound, and the effects seen are clear-cut. Records are competently acquired, with a high number of repeats, are well analysed and very clearly presented. Second, the authors interpret their results with interdisciplinary competence, drawing on structural knowledge of ABC transporter catalytic mechanism, as well as on an in-depth understanding of studies investigating kinetics and thermodynamics of CFTR gating. This study, bringing together conclusions obtained in many previous studies, is a useful step forward towards a comprehensive description of the energetic landscape CFTR channel proteins wander through when gating. The Csanády lab has greatly contributed to developing this over the past years, and this paper reads as a "capstone".

However the reliance on previous conclusions is, in some ways, also a weakness. Many of the inferences made in interpreting the data depend on assumptions being met. There is evidence supporting the validity of these, but more clarity in stating implicit assumptions, and why the authors believe them to be valid, could improve the manuscript. The results fit well within the conceptual framework of CFTR's non-equilibrium gating. But some scientists, still sceptical of its basic premises, will not be convinced by these new results.

Within this context, the authors achieve their aim of estimating the microscopic rate constant for non-hydrolytic closure. The study will be of interest not only to scientists studying CFTR gating, but also to those wishing to understand how small-molecule drugs affect such gating. The mechanism of action of ivacaftor (currently taken by thousands of people for treatment of cystic fibrosis) is still not completely clear, and some evidence suggests that it stabilizes the pre-hydrolytic bursting state investigated here. Aspects of CFTR's conformational dynamics will probably also be true for some of its phylogenetic relatives. Thus, those studying other ABC transporters, many of which have medical relevance, will find it interesting to learn how CFTR couples its gating and hydrolytic cycles. This is especially true now, when cryogenic electron microscopy and other methods allow detailed structural comparisons between related ABC transporters, which can be correlated with differences in their function. Now more than ever CFTR could be a "model ABC protein".

Reviewer #1 (Public Review):

This paper presents an interesting data set from historic Western Eurasia and North Africa. Overall, I commend the authors for presenting a comprehensive paper that focuses the data analysis of a large project on the major points, and that is easy to follow and well-written. Thus, I have no major comments on how the data was generated, or is presented. Paradoxically, historical periods are undersampled for ancient DNA, and so I think this data will be useful. The presentation is clever in that it focuses on a few interesting cases that highlight the breadth of the data.

The analysis is likewise innovative, with a focus on detecting "outliers" that are atypical for the genetic context where they were found. This is mainly achieved by using PCA and qpAdm, established tools, in a novel way. Here I do have some concerns about technical aspects, where I think some additional work could greatly strengthen the major claims made, and lay out if and how the analysis framework presented here could be applied in other work.

## clustering analysis<br /> I have trouble following what exactly is going on here (particularly since the cited Fernandes et al. paper is also very ambiguous about what exactly is done, and doesn't provide a validation of this method). My understanding is the following: the goal is to test whether a pair of individuals (lets call them I1 and I2) are indistinguishable from each other, when we compare them to a set of reference populations. Formally, this is done by testing whether all statistics of the form F4(Ref_i, Ref_j; I1, I2) = 0, i.e. the difference between I1 and I2 is orthogonal to the space of reference populations, or that you test whether I1 and I2 project to the same point in the space of reference populations (which should be a subset of the PCA-space). Is this true? If so, I think it could be very helpful if you added a technical description of what precisely is done, and some validation on how well this framework works.

An independent concern is the transformation from p-values to distances. I am in particular worried about i) biases due to potentially different numbers of SNPs in different samples and ii) whether the resulting matrix is actually a sensible distance matrix (e.g. additive and satisfies the triangle inequality). To me, a summary that doesn't depend on data quality, like the F2-distance in the reference space (i.e. the sum of all F4-statistics, or an orthogonalized version thereof) would be easier to interpret. At the very least, it would be nice to show some intermediate results of this clustering step on at least a subset of the data, so that the reader can verify that the qpWave-statistics and their resulting p-values make sense.

The methodological concerns lead me to some questions about the data analysis. For example, in Fig2, Supp 2, very commonly outliers lie right on top of a projected cluster. To my understanding, apart from using a different reference set, the approach using qpWave is equivalent to using a PCA-based clustering and so I would expect very high concordance between the approaches. One possibility could be that the differences are only visible on higher PCs, but since that data is not displayed, the reader is left wondering. I think it would be very helpful to present a more detailed analysis for some of these "surprising" clustering where the PCA disagrees with the clustering so that suspicions that e.g. low-coverage samples might be separated out more often could be laid to rest.

One way the presentation could be improved would be to be more consistent in what a suitable reference data set is. The PCAs (Fig2, S1 and S2, and Fig6) argue that it makes most sense to present ancient data relative to present-day genetic variation, but the qpWave and qpAdm analysis compare the historic data to that of older populations. Granted, this is a common issue with ancient DNA papers, but the advantage of using a consistent reference data set is that the analyses become directly comparable, and the reader wouldn't have to wonder whether any discrepancies in the two ways of presenting the data are just due to the reference set.

## PCA over time<br /> It is a very interesting observation that the Fst-vs distance curve does not appear to change after the bronze age. However, I wonder if the comparison of the PCA to the projection could be solidified. In particular, it is not obvious to me how to compare Fig 6 B and C, since the data in C is projected onto that in Fig B, and so we are viewing the historic samples in the context of the present-day ones. Thus, to me, this suggests that ancient samples are most closely related to the folks that contribute to present-day people that roughly live in the same geographic location, at least for the middle east, north Africa and the Baltics, the three regions where the projections are well resolved.<br /> Ideally, it would be nice to have independent PCAs (something F-stats based, or using probabilistic PCA or some other framework that allows for missingness). Alternatively, it could be helpful to quantify the similarity and projection error.

Reviewer #2 (Public Review):

Antonio, Weiss, Gao, Sawyer, et al. provide new ancient DNA (aDNA) data for 200 individuals from Europe and the Mediterranean from the historical period, including Iron Age, Late Antiquity, Middle Ages, and early modernity. These data are used to characterize population structure in Europe across time and identify first-generation immigrants (roughly speaking, those who present genetic ancestry that is significantly different from others in the same archaeological site). Authors provide an estimate of an average across regions of >8% of individuals being first-generation immigrants. This observation, coupled with the observed genetic heterogeneity across regions, suggests high mobility of individuals during the historical period in Europe. In spite of that, Principal Component Analysis (PCA) indicates that the overall population structure in Europe has been rather stable in the last 3,000 years, i.e., the levels of genetic differentiation across space have been relatively stable. To understand whether population structure stability is compatible with a large number (>8%) of long-distance immigrants, authors use spatially-explicit Wright-Fisher simulations. They conclude these phenomena are incompatible and provide a thoughtful and convincing explanation for that.

Overall I think this manuscript is very well written and provides an exciting take-home message. The dataset with 200+ novel ancient human genomes will be a great resource for population genetics and paleogenomic studies. Methods are robust and well-detailed. Although the methods used are well-known and standard in the field of paleogenomics, the way the authors use these methods is very creative, insightful, and refreshing. Results provide a comprehensive and novel assessment of historical population genetic structure in Europe, including characterizing genetic heterogeneity within populations and interactions/migration across regions. Conclusions are fully supported by the data.

A few of the strengths of this manuscript are its dataset containing a large number of ancient human genomes, the novel insights about human migration provided by the results, the creative approach to characterize migration and population structure across time using aDNA, and the excellent figures describing research results. I see no major issues with this paper.

Reviewer #3 (Public Review):

This paper examines the existence of a fear memory engram in acetylcholine neurons of the basal forebrain and seeks to link this to the modulation of the amygdala for fear expression. Using genetically encoded ACh sensors, they show that ACh is released in the basolateral amygdala (BLA) in response to cues that had been paired with aversive shock (CS+) and by shock itself. They then use a cfos activity capture specifically of ACh neurons approach to show that an overlapping population of basal forebrain ACh neurons are activated during learning and recall, that chemogenetically silencing them reduced aversive memory recall, and that these cells have enhanced excitability. Moving on to examining the role of basal forebrain ACh neurons in regulating BLA, the authors show that chemogenetically inhibiting BLA projecting ACh neurons reduces memory recall-induced Fos activity in BLA neurons. Finally, they demonstrate the importance of these cells in producing freezing responses to both learned and innate aversive stimuli, though from different ACh populations.

The identification of specific activity-defined acetylcholine neurons for aversive memory expression as well as the role of basal forebrain ACh neurons in regulating BLA to produce expression of defensive behaviors is important and interesting. However, the paper is missing important control groups and experiments that are necessary to adequately support the authors' claims.

Reviewer #1 (Public Review):

The authors used MD simulations to investigate the role of N-terminal myristoylation and the presence of two SH domains on the allosteric regulation of c-Abl kinase. Standard established MD simulation methods and analyses were applied, including the force distribution analysis (FDA) method developed by Grater et al. some time ago.

The system is large and the conformational changes are complicated. In light of this, and aggravated by the fact that direct comparison with - and critical testing against - experimental data is not possible in the present case, I consider the overall simulation times to be rather short (several repeats, but only 500 ns). So there might be statistical convergence issues. Especially also because at least some of the starting structures were generated from available experimental structures after some modifications/modelling, and they might thus be out of equilibrium and need some time to fully relax during the MD simulations.

Unfortunately, I cannot find any convergence tests concerning the length of the simulations, which are usually considered to be standard analyses (Appendix Fig. 5 shows the effect of different thermostats and capping of the peptide chain, but no tests concerning simulation time). This could be critical in the present case, where the authors acknowledge themselves (e.g., on p. 4) that there are only subtle differences between the different simulation systems and the variations within a given system are larger than the relevant (putative) differences between systems (Fig. 1 C, D, E).

Issues with statistical convergence are expected not only for the standard MD simulations but also for the umbrella sampling simulations, as 50 ns sampling per window is nowadays not considered state of the art and is likely insufficient for quantitative binding free energy calculation, especially for membranes (see, e.g., DOI 10.1021/ct200316w). However, worrying about this latter aspect might neither be useful nor needed, because in our view the statement that myristoyl groups can bind to the membrane and that they can compete with binding in the hydrophobic protein pocket can hardly be considered a surprise and would not have required any simulation at all in my view because the experimental K_D values are available (Table 1). The very unfavourable K_d values for unbinding of Myr from both the hydrophobic protein pocket as well as from the membrane in fact show that this is not how it is expected to work in reality. The fully solvated state will be avoided due to its high free energy. Instead, isn't the myristoyl expected to directly transition from the pocket into the membrane, after membrane binding of the kinase in a proper orientation?

Concerning the metadynamics simulations, these are usually done to obtain a free energy landscape. Why was this not attempted here? In the present case, the authors seemed to have used metadynamics only for generating starting structures, with different degrees of helicity of the alpha_I part, for subsequent standard MD simulations. Not surprisingly, nothing much happened during the latter, and conformers with kinked/partially unfolded alpha_I as well as conformers with straight alpha_I were both found to be "stable", at least on the short simulation time scale. It could also not be expected that the SH domain would spontaneously detach in response to helix straightening - again, this would require much longer simulation times than 500 ns. Nevertheless, alpha_I straightening might very well reduce the binding affinity towards SH - this can only be explicitly studied with free energy simulations, however.

Reviewer #2 (Public Review):

The manuscript aims at understanding how the fatty acid ligand MYR inhibits the activity of Abl kinase. Despite a wealth of structural and biochemical data, a key mechanistic understanding of how MYR binding could inactive Abl was missing.

The authors used equilibrium and enhanced molecular dynamics (MD) simulations to masterfully answer open questions left by extensive experimental data in the mechanistic understanding of this system. The authors took advantage of several state-of-the-art simulation techniques and carefully planned simulations to extract a coherent understanding from a wealth of experimental facts.

The manuscript convincingly identifies an allosteric regulation by MYR. Allostery is often a source of confusion and sometimes is used as a magic catch-it-all explanation for poorly understood phenomena. Here, the authors show very compelling evidence of the existence of an allosteric mechanism. Also, they identify the physical origin of the allosteric pathway, providing a clear mechanistic understanding at the residue-level resolution. This is an impressive achievement.

By leaving a pocket in the protein, MYR enables the protein's activation. But MYR is a highly hydrophobic molecule surrounded by water. Where could it go rather than quickly binding back to the protein pocket? By asking this reasonable question, the authors propose an exciting mechanistic hypothesis. The physical proximity of Abl kinase to a cellular membrane could lead to a competition between the protein and the membrane for MYR, leading to a novel layer of regulation for this kinase. Free energy calculations performed by the authors show that this hypothesis is reasonable from the thermodynamic point of view.

From a broader perspective, this manuscript is an important contribution to the discussion of four outstanding topics. 1) myristoylation is an example of lipidation, a post-translational modification where an acyl chain is covalently linked to a protein. The role of post-translational modifications has been greatly underappreciated and investigated in the MD community. However, as all the work on Sars-Cov2 and this contribution show, post-translational modifications can be crucial to understanding function. Ignoring them could lead to severely biased results. 2) the debate on the nature of allostery is still on the rage. Some authors claim that looking for a residue-level mechanistic chain of events that explains the allosteric action does not make sense and that the only way of thinking about allostery is as a sudden global change of the conformational landscape. Here, the authors show that instead, it is possible and leads to an essential understanding. 3) The authors hypothesize a novel crosstalk between the Abl and cellular membranes mediated by MYR. This exciting and far-reaching hypothesis opens the door to new complex layers of regulation. I suspect that these crosstalks between cytosolic proteins, or the soluble domain of membrane-tethered proteins and membranes, are much more ubiquitous than what has been appreciated so far. 4) From a methodological point of view, this manuscript represents a masterful use of simulations to put existing experimental data in a coherent picture. It is an example of the use of MD simulations at its best, where the simulations make sense of experiments, integrate existing data into a unified picture, and lead to new hypotheses that can be tested in future experiments.

It would be superb if the authors could propose precise predictions that could inspire future experiments. Now that they present a residue-resolution allosteric pathway, can they suggest point mutations that would interrupt it?

Reviewer #1 (Public Review):

In this manuscript, Bilgic et al aim to identify the progenitor types (and their specific progeny) that underlie the expanded nature of gyrencephalic brains. To do this, they take a comparative scRNAseq (single cell transcriptomics) approach between neurodevelopment of the gyrencephalic ferret, and previously published primary human brain and organoid data.

They first improve gene annotations of the ferret genome and then collect a time series of scRNAseq data of 6 stages of the developing ferret brain spanning both embryonic and post-natal development. Among the various cell types they identify are a small proportion of truncated radial glial cells (tRGs), a population known to be enriched in humans and macaques that emerges late in neurogenesis as the RGC scaffold splits into an oRGC that contact the pial surface and a tRG that contacts the ventricular surface. They find that the tRGs consist of three distinct subpopulations two of which are committed to ependymal and astroglial fates.

By integrating these data with publicly available data of developing human brains and human brain organoids they make some important observations. Human and ferret tRGs have very similar transcriptional states, suggesting that the human tRGs too give rise to ependymal and astroglial fates. They also find that the current culture conditions of human brain organoids seem to lack tRGs, something that will need to be addressed if they are to be used to study tRGs. While the primary human data set did contain tRGs, the stage or the region sampled were likely not appropriate, and therefore, the number of cells they could retrieve was low.

The authors have spent considerable efforts in improving gene modeling of the ferret genome, which will be important for the field. They've generated valuable time series data for the developing ferret brain, and have proposed the lineal progeny for the tRGs in the human brain. Whether tRGs actually do give rise to the ependymal and astrogial fates needs to be validated in future studies.

Reviewer #2 (Public Review):

Bilgic et al first explored cellular diversity in the developing cerebral cortex of ferret, honing in on progenitor cell diversity by employing FACS sorting of HES5-positive cells. They have generated a novel single cell transcriptomic dataset capturing the diversity of cells in the developing ferret cerebral cortex, including diverse radial glial and excitatory neuron populations. Unexpectedly, this analysis revealed the presence of CRYAB-positive truncated radial glia previously described only in humans. Using bioinformatic analyses, the investigators proposed that truncated radial glia produce ependymal cells, astrocytes, and to a lesser degree, neurons. Of particular interest to the field, they identify enriched expression of FOXJ1 in late truncated radial glia strongly indicating that towards the end of neurogenesis, these cells likely give rise to ependymal cells. This study represents a major advancement in the field of cortical development and a valuable dataset for future studies of ferret cortical development.

Reviewer #1 (Public Review):

Summary:<br /> The current study examines the necessity of estrogen receptor alpha (ESR1) in GABA neurons located in the anteroventral and preoptic periventricular nuclei and the medial preoptic nucleus of the hypothalamus. This brain area is implicated in regulating the pre-ovulatory LH surge in females, but the identity of the estrogen-sensitive neurons that are required remains unknown. The data indicate that approximately 70% knockdown of ESR1 in GABA neurons resulted in variable reproductive phenotypes. However, when the ESR1 knockdown also results in a decrease in kisspeptin expression by these cells, the females had disrupted LH surges, but no alterations in pulsatile LH release. These data support the hypothesis that kisspeptin cells in this region are critical for the pre-ovulatory LH surge in females.

Strengths:<br /> The current study examined the efficacy of two guide RNAs to knockdown ESR1 in GABA neurons, resulting in an approximate 70% reduction in ESR1 in GABA neurons. The efficacy of this knockdown was confirmed in the brain via immunohistochemistry and the reproductive outcomes were analyzed several ways to account for differences in guide RNAs or the precise brain region with the ESR1 knockdown. The analysis was taken one step further by grouping mice based on kisspeptin expression following ESR1 knockdown and examining the reproductive phenotypes. Overall, the aims of the study were achieved, the methods were appropriate, and the data were analyzed extensively. This data supports the hypothesis that kisspeptin neurons in the anterior hypothalamus are critical for the preovulatory LH surge.

Weaknesses: One minor weakness in this study is the conclusion that the guide RNAs didn't seem to have unique effects on GnRH cFos expression or the reproductive phenotypes. Though the data indicate a 60-70% knockdown for both gRNA2 and gRNA3, 3 of the 4 gRNA2 mice had no cFos expression in GnRH neurons during the time of the LH surge, whereas all mice receiving gRNA3 had at least some cFos/GnRH co-expression. In addition, when mice were re-categorized based on reduction (>75%) in kisspeptin expression, most of the mice in the unilateral or bilateral groups received gRNA2, whereas many of the mice that received gRNA3 were in the "normal" group with no disruption in kisspeptin expression. Thus, additional experiments with increased sample sizes are needed, even if the efficacy of the ESR1 knockdown was comparable before concluding these 2 gRNAs don't result in unique reproductive effects.

Reviewer #2 (Public Review):

Clarkson et al investigated the impact of in vivo ESR1 gene disruption selectively in preoptic area GABA neurons on the estrogen regulation of LH secretion. The hypothalamic pathways by which estradiol controls the secretion of gonadotrophins are incompletely understood and relevant to a better understanding of the mechanisms driving fertility and reproduction. Using CRISPR-Cas9 methodology, the authors were able to effectively reduce the expression of estrogen receptor (ER)-alpha in GABA neurons located in the preoptic area of adult female mice. The results obtained were rather variable except in the animals with concomitant suppression of kisspeptin in the rostral periventricular region of the third ventricle (RP3V), which displayed interruption of ovarian cyclicity and an altered estradiol-induced LH surge. The experimental approach used allowed for a cell-selective, temporally-controlled suppression of ER-alpha expression, providing further evidence of the critical role of RP3V kisspeptin neurons in the estrogen positive-feedback effect. Nevertheless, the assessment of the estradiol-induced LH surge was limited to only one terminal blood collection. The preovulatory LH surge is a variable phenomenon and would require serial blood sampling for a conclusive evaluation of the surge occurrence or alteration, such as in shape, amplitude, or timing. The animals were not assessed for ovulation either, which might be a functional readout for the effectiveness of the LH surge. Thus, the actual effect on the preovulatory LH surge was not fully characterized. Finally, the study leaves unanswered the role of GABA itself. As there was no evident phenotype for the ESR1 knockdown in GABA neurons that do not coexpress kisspeptin, this suggests that GABA neurotransmission in the preoptic area is not involved in the estrogen regulation of LH secretion.

Reviewer #3 (Public Review):

Summary:<br /> Although the key role of estrogen receptor alpha (ERα ; encoded by ESR1 gene) in the control of reproduction has been known for more than two decades, the identity of the neuronal population(s) underlying the control of the negative and positive feedbacks exerted by estrogens on the hypothalamic pituitary ovary axis has been more complicated to pinpoint. Among the factors contributing to the difficulty in this endeavor are the cellular heterogeneity of the preoptic area and hypothalamus and the pulsatile activity of the axis. Several neuronal populations have been identified that control the activity of GnRH neurons, the hypothalamic headmasters of the axis. Among them, the kisspeptin neurons of the rostral periventricular aspect of the third ventricle (RP3V) have been considered the major candidates to convey preovulatory estrogen signals to GnRH neurons, which do not express ERα. Yet, the existence of other populations of kisspeptin neurons (notably in the arcuate nucleus) has made it difficult to selectively ablate ESR1 in one population. A first study (Wang et al., 2019) reported that knocking down ESR1 specifically in RP3V kisspeptin neurons led to decreased excitability of Kp neurons, blunted spontaneous as well estrogen-induced preovulatory LH surge, and reduced or absent corpora lutea indicative of impaired ovulation, but cyclicity was left unaltered.

As GABAergic afferences to GnRH neurons are also implicated in mediating the effects of estrogens on the HPG axis, the present study sought to investigate their role in the positive feedback using genetically driven Crispr-Cas9 mediated knockdown of ERα in VGAT expressing neurons in a specific subregion of the preoptic area. To this end, they stereotaxically delivered viral vectors expressing validated guide RNA into the preoptic area and evaluated their impact on estrus cyclicity and the ability of mice to mount an LH surge induced by estrogens and associated activation of GnRH neurons assessed by the co-expression of Fos. The results demonstrate that knocking down ESR1 in preoptic gabaergic neurons leads to an absence of LH surge and acyclicity when associated with severely reduced kisspeptin expression suggesting that a subpopulation of neurons co-expressing Kp and VGAT neurons are key for LH surge since total absence of Kp is associated with an absence of GnRH neuron activation and reduced LH surge (although this was not confirmed by the post-hoc). Although the implication of kisspeptin neurons was highly suspected already, the novelty of these results lies in the fact that estrogen signaling is necessary for only a selected fraction of them to maintain both regular cycles and LH surge capacity.

Strengths:<br /> Remarkable aspects of this study are, its dataset which allowed them to segregate animals based on distinct neuronal phenotypes matching specific physiological outcomes, the transparency in reporting the results (e.g. all statistical values being reported, all grouping variables being clearly defined, clarity about animals that were excluded and why) and the clarity of the writing. This allows the reader to understand clearly what has been done and how the analyses have been carried out. The same applies to the discussion which describes clearly possible interpretations as well as the limitations of this study based on a single in vivo experiment.

Another remarkable feature of this work lies in the analysis of the dataset. As opposed to the cre-lox approach which theoretically allows for the complete ablation of specific neuronal populations, but may lack specificity regarding timing of action and location, genetically driven in vivo Crispr-Cas9 editing offers both temporal and neuroanatomic selectivity but cannot achieve a complete knockdown. This approach based on stereotaxic delivery of the AAV-encoded guide RNAs comes with inevitable variability in the location where gene knockdown is achieved. By adjusting their original grouping of the animals based on the evaluation of the extent of kisspeptin expression in the target region, the authors obtained a much clearer and interpretable picture. Although only a few animals (n=4) displayed absent kisspeptin expression, the convergence of observations suggesting a central impairment of the reproductive axis is convincing.

In particular, the lack of activation of GnRH neurons in these mice despite a non-significant effect on the reduced LH levels in the post-hoc following a significant ANOVA (which is likely due to the limited number of concerned animals [n=4]), is convincing. Did the authors test whether LH concentrations correlated with the percentage of GnRH+ESR1 positive neurons? This could reinforce the conclusion.

Moreover, the apparent complete absence of kisspeptin expression in these 4 animals is compelling as it provides indirect confirmation of the key role of Kisspeptin neurons in this phenomenon using a different LH surge induction paradigm than Wang et al., 2019. Yet, the quality of the kisspeptin immunostaining in control animals does seem suboptimal and casts doubts about this conclusion (see the section on weaknesses for more details).

It is also interesting that a few animals with unilateral reduction in Kp expression also showed deficits in GnRH neuron activation suggesting that the impact of Kp may not be limited to the side of the brain where it is produced or the existence of a dose effect of Kp activation of GnRH neurons.

Finally, the observation that the pulsatile secretion of LH is maintained in the absence of Kp expression in the RP3V lends support to the notion that LH surge and pulsatility are regulated independently by distinct neuronal populations, a model put forward by the corresponding author a few years ago.

Weaknesses:<br /> One aspect for which I have ambiguous feelings is the minimal level of detail regarding the HPG axis and its regulation by estrogens. This limited amount of detail allows for an easy read with the well-articulated introduction quickly presenting the framework of the study. Although not presenting the axis itself nor mentioning the position of GnRH neurons in this axis or its lack of ERα expression is not detrimental to the understanding of the study, presenting at least the position of GnRH neurons in the axis and their critical role for fertility would likely broaden the impact of this work beyond a rather specialist audience.

The expression of kisspeptin constitutes a key element for the analysis and conclusion of the present work. However, the quality of the kisspeptin immunostaining seems suboptimal based on the representative images. The staining primarily consists of light punctuated structures and it is very difficult to delineate cytoplasmic immunoreactive material defining the shape of neurons in LacZ animals. For some of the cells marked by an arrow, it is also sometimes difficult to determine whether the staining for ESR1 and Kp are in the same focal plane and thus belong to the same neurons. Although this co-expression is not critical for the conclusions of the study, this begs the question of whether Kp expression was determined directly at the microscope (where the focal plan can be adjusted) or on the picture (without possible focal adjustment). Moreover, in the representative image of Kp loss, several nuclei stained for fos (black) show superimposed brown staining looking like a dense nucleus (but smaller than an actual nucleus). This suggests some sort of condensed accumulation of Kp immunoproduct in the nucleus which is not commented. Given the critical importance of this reported change in Kp expression for the interpretation of the present results, it is important to provide strong evidence of the quality/nature of this staining and its analysis which may help interpret the observed functional phenotype.

As acknowledged in the introduction, this study is not the first to use in vivo Crisp-Cas editing to demonstrate the role of kisspeptin neurons in the control of positive feedback. Although the present work achieved this indirectly by targeting VGAT neurons, I was surprised that the paper did not include more comparison of their results with those of Wang et al., 2019. In particular, why was the present approach more successful in achieving both lack of surge and complete acyclicity? Moreover, why is it that targeting ESR1 in a selected fraction of GABAergic neurons can lead to a near-complete absence of Kp expression in this region? This is briefly discussed in the penultimate paragraph but mostly focuses on the non-kisspeptinergic GABAneurons rather than those co-expressing the two markers.

Reviewer #1 (Public Review):

Summary:<br /> This work examined transcription factor Meis2 in the development of mouse and chick DRG neurons, using a combination of techniques, such as the generation of a new conditional mutant strain of Meis2, behavioral assays, in situ hybridization, transcriptomic study, immunohistochemistry, and electrophysiological (ex vivo skin-nerve preparation) recordings. The authors found that Meis2 was selectively expressed in A fiber LTMRs and that its disruption affects the A-LTMRs' end-organ innervation, transcriptome, electrophysiological properties, and light touch-sensation.

Strengths:<br /> 1) The authors utilized a well-designed mouse genetics strategy to generate a mouse model where the Meis2 is selectively ablated from pre- and post-mitotic mouse DRG neurons. They used a combination of readouts, such as in situ hybridization, immunhistochemistry, transcriptomic analysis, skin-nerve preparation, electrophysiological recordings, and behavioral assays to determine the role of Meis2 in mouse DRG afferents.

2) They observed a similar preferential expression of Meis2 in large-diameter DRG neurons during development in chicken, suggesting evolutionarily conserved functions of this transcription factor.

3) Conducted severe behavioral assays to probe the reduction of light-touch sensitivity in mouse glabrous and hairy skin. Their behavioral findings support the idea that the function of Meis2 is essential for the development and/or maturation of LTMRs.

4) RNAseq data provide potential molecular pathways through which Meis2 regulates embryonic target-field innervation.

5) Well-performed electrophysiological study using skin-nerve preparation and recordings from saphenous and tibial nerves to investigate physiological deficits of Meis2 mutant sensory afferents.

6) Nice whole-mount IHC of the hair skin, convincingly showing morphological deficits of Meis2 mutant SA- and RA- LTMRs.

Overall, this manuscript is well-written. The experimental design and data quality are good, and the conclusion from the experimental results is logical.

Weaknesses:<br /> 1) Although the authors justify this study for the involvement of Meis2 in Autism and Autism associated disorders, no experiments really investigated Autism-like specific behavior in the Meis2 ablated mice.

2) For mechanical force sensing-related behavioral assays, the authors performed VFH and dynamic cotton swabs for the glabrous skin, and sticky tape on the back (hairy skin) for the hairy skin. A few additional experiments involving glabrous skin plantar surfaces, such as stick tape or flow texture discrimination, would make the conclusion stronger.

3) The authors considered von Frey filaments (1 and 1.4 g) as noxious mechanical stimuli (Figure 1E and statement on lines 181-183), which is questionable. Alligator clips or pinpricks are more certain to activate mechanical nociceptors.

4) There are disconnections and inconsistencies among findings from morphological characterization, physiological recordings, and behavior assays. For example, Meis2 mutant SA-LTMRs show a deficiency in Merkel cell innervation in the glabrous skin but not in hairy skin. With no clear justification, the authors pooled recordings of SA-LTMRs from both glabrous and hairy skin and found a significant increase in mean vibration threshold. Will the results be significantly different if the data are analyzed separately? In addition, whole-mount IHC of Meissner's corpuscles showed morphological changes, but electrophysiological recordings didn't find significant alternation of RAI LTMRs. What does the morphological change mean then? Since the authors found that Meis2 mice are less sensitive to a dynamic cotton swab, which is usually considered as an RA-LTMR mediated behavior, is the SAI-LTMR deficit here responsible for this behavior? Connections among results from different methods are not clear, and the inconsistency should be discussed.

Reviewer #2 (Public Review):

Summary:<br /> Desiderio and colleagues investigated the role of the TALE (three amino acid loop extension) homeodomain transcription factor Meis2 during maturation and target innervation of mechanoreceptors and their sensation to touch. They start with a series of careful in situ hybridizations to examine Meis2 transcript expression in mouse and chick DRGs of different embryonic stages. By this approach, they identify Meis2+ neurons as slowly- and rapidly adapting A-beta LTMRs, respectively. Retrograde tracing experiments in newborn mice confirmed that Meis2-expressing sensory neurons project to the skin, while unilateral limb bud ablations in chick embryos in Ovo showed that these neurons require target-derived signals for survival. The authors further generated a conditional knock-out (cKO) mouse model in which Meis2 is selectively lost in Islet1-expressing, postmitotic neurons in the DRG (IsletCre/+::Meis2flox/flox, abbreviated below as cKO). WT and Islet1Cre/+ littermates served as controls. cKO mice did not exhibit any obvious alteration in volume or cellular composition of the DRGs but showed significantly reduced sensitivity to touch stimuli and various innervation defects to different end-organ targets. RNA-sequencing experiments of E18.5 DRGs taken from WT, Islet1Cre/+, and cKO mice reveal extensive gene expression differences between cKO cells and the two controls, including synaptic proteins and components of the GABAergic signaling system. Gene expression also differed considerably between WT and heterozygous Islet1Cre/+ mice while several of the other parameters tested did not. These findings suggest that Islet1 heterozygosity affects gene expression in sensory neurons but not sensory neuron functionality. However, only some of the parameters tested were assessed for all three genotypes. Histological analysis and electrophysiological recordings shed light on the physiological defects resulting from the loss of Meis2. By immunohistochemical approaches, the authors describe distinct innervation defects in glabrous and hairy skin (reduced innervation of Merkel cells by SA1-LTMRs in glabrous but not hairy skin, reduced complexity of A-beta RA1-LTMs innervating Meissner's corpuscles in glabrous skin, reduced branching and innervation of A-betA RA1-LTMRs in hairy skin). Electrophysiological recordings from ex vivo skin nerve preparations found that several, but not all of these histological defects are matched by altered responses to external stimuli, indicating that compensation may play a considerable role in this system.

Strengths:<br /> This is a well-conducted study that combines different experimental approaches to convincingly show that the transcription factor Meis2 plays an important role in the perception of light touch. The authors describe a new mouse model for compromised touch sensation and identify a number of genes whose expression depends on Meis2 in mouse DRGs. Given that dysbalanced MEIS2 expression in humans has been linked to autism and that autism seems to involve an inappropriate response to light touch, the present study makes a novel and important link between this gene and ASD.

Weaknesses:<br /> The authors make use of different experimental approaches to investigate the role of Meis2 in touch sensation, but the results obtained by these techniques could be connected better. For instance, the authors identify several genes involved in synapse formation, synaptic transmission, neuronal projections, or axon and dendrite maturation that are up- or downregulated upon targeted Meis2 deletion, but it is unresolved whether these chances can in any way explain the histological, electrophysiological, or behavioral deficits observed in cKO animals. The use of two different controls (WT and Islet1Cre/+) is unsatisfactory and it is not clear why some parameters were studied in all three genotypes (WT, Islet1Cre/+ and cKO) and others only in WT and cKO. In addition, Meis2 mutant mice apparently are less responsive to touch, whereas in humans, mutation or genomic deletion involving the MEIS2 gene locus is associated with ASD, a condition that, if anything, is associated with an elevated sensitivity to touch. It would be interesting to know how the authors reconcile these two findings. A minor weakness, the first manuscript suffers from some ambiguities and errors, but these can be easily corrected.

Reviewer #1 (Public Review):

Summary: The authors have used transcranial magnetic stimulation (TMS) and motor evoked potentials (MEPs) to determine whether the peripheral auditory confound arising from TUS can drive motor inhibition on its own. They gathered data from three international centers in four experiments testing:<br /> In Experiment 1 (n = 11), two different TUS durations and intensities under sound masking or without.<br /> Experiment 2 (n = 27) replicates Exp 1 with different intensities and a fixed TUS duration of 500ms.<br /> Experiment 3 ( n = 16) studied the effect of various auditory stimuli testing different duration and pitches while applying TUS in an active site, on-target or no TUS.<br /> Experiment 4 (n = 12) used an inactive control site to reproduce the sound without effective neuromodulation, while manipulating the volume of the auditory confound at different TUS intensities with and without continuous sound masking.

Strengths: This study comes from three very strong groups in noninvasive brain stimulation with long experience in neuromodulation, multimodal and electrophysiological recordings. Although complex to understand due to slightly different methodologies across centers, this study provides quantitative evidence alerting on the potential auditory confound of online US. Their results are in line with reductions seen in motor-evoked responses during online 1kHz TUS, and remarkable efforts were made to isolate peripheral confounds from actual neuromodulation factors, highlighting the confounding effect of sound itself.

Weaknesses: However, there are some points that need attention. In my view, the most important are:<br /> 1. Despite the main conclusion of the authors stating that there is no dose-response effects of TUS on corticospinal inhibition, the point estimates for change in MEP and Ipssa indicate a more complex picture. The present data and analyses cannot rule out that there is a dose-response function which cannot be fully attributed to difference in sound (since the relationship in inversed, lower intracranial Isppa leads to higher MEP decrease). These results suggest that dose-response function needs to be further studied in future studies.<br /> 2. Other methods to test or mask the auditory confound are possible (e.g., smoothed ramped US wave) which could substantially solve part of the sound issue in future studies or experiments in deaf animals etc...

Reviewer #2 (Public Review):

Summary:<br /> This study aims to test auditory confounds during transcranial ultrasound stimulation (TUS) protocols that rely on audible frequencies. In several experiments, the authors show that a commonly observed suppression of motor-evoked potentials (MEP) during TUS can be explained by acoustic stimulation. For instance, not only target TUS, but also stimulation of a control site and acoustic stimulation led to suppressed MEP.

Strengths:<br /> A clear strength of the study is the multitude of control conditions (control sites, acoustic masking, acoustic stimulation etc) that makes results very convincing.<br /> Indeed, I do not have much to criticise. The paper follows a clear structure and is easy to follow, the research question is clearly relevant, and analyses are sound. Figures are of high quality.<br /> Although auditory confounds during TUS have been demonstrated before, the thorough design of the study will lead to a strong impact in the field.

Weaknesses:<br /> I cannot see major weaknesses. A few minor ones are that (1) the overview of previous related work, and how frequent audible TUS protocols are in the field, could be a bit clearer/more detailed; (2) the acoustic control stimulus can be described in more detail; and (3) the finding that remaining motor inhibition is observed during acoustically masked trials deserves further discussion.

Joint Public Review:

The manuscript presented by Pabba et al. studied chromatin dynamics throughout the cell cycle. The authors used fluorescence signals and patterns of GFP-PCNA (GFP tagged proliferating cell nuclear antigen) and CY3-dUTP (which labels newly synthesized DNA but not the DNA template) to determine cell cycle stages in asynchronized HeLa (Kyoto) cells and track movements of chromatin domains. PCNA binds to replication forks and form replication foci during the S phase. The major conclusions are: (1) Labeled chromatin domains were more mobile in G1/G2 relative to the S-phase. (2) Restricted chromatin motion occurred at sites in proximity to DNA replication sites. (3) Chromatin motion was restricted by the loading of replisomes, independent of DNA synthesis. This work is based on previous work published in 2015, entitled "4D Visualization of replication foci in mammalian cells corresponding to individual replicons," in which the labeling method was demonstrated to be sound.

Comments on latest version: The revised manuscript has included data from a diploid cell line IMR90 (fibroblasts isolated from normal lung tissue) to strengthen the conclusions. Overall, quality of the work is substantially improved.

Reviewer #1 (Public Review):

Segas et al. present a novel solution to an upper-limb control problem which is often neglected by academia. The problem the authors are trying to solve is how to control the multiple degrees of freedom of the lower arm to enable grasp in people with transhumeral limb loss. The proposed solution is a neural network based approach which uses information from the position of the arm along with contextual information which defines the position and orientation of the target in space. Experimental work is presented, based on virtual simulations and a telerobotic proof of concept.

The strength of this paper is that it proposes a method of control for people with transhumeral limb loss which does not rely upon additional surgical intervention to enable grasping objects in the local environment. A challenge the work faces is that it can be argued that a great many problems in upper limb prosthesis control can be solved given precise knowledge of the object to be grasped, its relative position in 3D space and its orientation. It is difficult to know how directly results obtained in a virtual environment will translate to real world impact. Some of the comparisons made in the paper are to physical systems which attempt to solve the same problem. It is important to note that real world prosthesis control introduces numerous challenges which do not exist in virtual spaces or in teleoperation robotics.

The authors claim that the movement times obtained using their virtual system, and a teleoperation proof of concept demonstration, are comparable to natural movement times. The speed of movements obtained and presented are easier to understand by viewing the supplementary materials prior to reading the paper. The position of the upper arm and a given target are used as input to a classifier, which determines the positions of the lower arm, wrist and the end effector. The state of the virtual shoulder in the pick and place task is quite dynamic and includes humeral rotations which would be challenging to engineer in a real physical prosthesis above the elbow. Another question related to the pick and place task used is whether or not there are cases where both the pick position and the place position can be reached via the same, or very similar, shoulder positions? i.e. with the shoulder flexion-extension and abduction-adduction remaining fixed, can the ANN use the remaining five joint angles to solve the movement problem with little to no participant input, simply based on the new target position? If this was the case, movements times in the virtual space would present a very different distribution to natural movements, while the mean values could be similar. The arguments made in the paper could be supported by including individual participant data showing distributions of movement times and the distances travelled by the end effector where real movements are compared to those made by an ANN.

In the proposed approach users control where the hand is in space via the shoulder. The position of the upper arm and a given target are used as input to a classifier, which determines the positions of the lower arm, wrist and the effector. The supplementary materials suggest the output of the classifier occurs instantaneously, in that from the start of the trial the user can explore the 3D space associated with the shoulder in order to reach the object. When the object is reached a visual indicator appears. In a virtual space this feedback will allow rapid exploration of different end effector positions which may contribute to the movement times presented. In a real world application, movement of a distal end-effector via the shoulder is not to be as graceful and a speed accuracy trade off would be necessary to ensure objects are grasped, rather than knocked or moved.

Another aspect of the movement times presented which is of note, although it is not necessarily incorrect, is that the virtual prosthesis performance is close too perfect. In that, at the start of each trial period, either pick or place, the ANN appears to have already selected the position of the five joints it controls, leaving the user to position the upper arm such that the end effector reaches the target. This type of classification is achievable given a single object type to grasp and a limited number of orientations, however scaling this approach to work robustly in a real world environment will necessitate solving a number of challenges in machine learning and in particular computer vision which are not trivial in nature. On this topic, it is also important to note that, while very elegant, the teleoperation proof of concept of movement based control does not seem to feature a similar range of object distance from the user as the virtual environment. This would have been interesting to see and I look forward to seeing further real world demonstrations in the authors future work.

Reviewer #2 (Public Review):

Segas et al motivate their work by indicating that none of the existing myoelectric solution for people with trans-humeral limb difference offer four active degrees of freedom, namely forearm flexion/extension, forearm supination/pronation, wrist flexion/extension, and wrist radial/ulnar deviation. These degrees of freedom are essential for positioning the prosthesis in the correct plan in the space before a grasp can be selected. They offer a controller based on the movement of the stump.

The proposed solution is elegant for what it is trying to achieve in a laboratory setting. Using a simple neural network to estimate the arm position is an interesting approach, despite the limitations/challenges that the approach suffers from, namely, the availability of prosthetic hardware that offers such functionality, information about the target and the noise in estimation if computer vision methods are used. Segas et al indicate these challenges in the manuscript, although they could also briefly discuss how they foresee the method could be expanded to enable a grasp command beyond the proximity between the end-point and the target. Indeed, it would be interesting to see how these methods can be generalise to more than one grasp.

One bit of the results that is missing in the paper is the results during the familiarisation block. If the methods in "intuitive" I would have thought no familiarisation would be needed. Do participants show any sign of motor adaptation during the familiarisation block?

In Supplementary Videos 3 and 4, how would the authors explain the jerky movement of the virtual arm while the stump is stationary? How would be possible to distinguish the relative importance of the target information versus body posture in the estimation of the arm position? This does not seem to be easy/clear to address beyond looking at the weights in the neural network.

I am intrigued by how the Generic ANN model has been trained, i.e. with the use of the forward kinematics to remap the measurement. I would have taught an easier approach would have been to create an Own model with the native arm of the person with the limb loss, as all your participants are unilateral (as per Table 1). Alternatively, one would have assumed that your common model from all participants would just need to be 'recalibrated' to a few examples of the data from people with limb difference, i.e. few shot calibration methods.

Reviewer #3 (Public Review):

This work provides a new approach to simultaneously control elbow and wrist degrees of freedom using movement based inputs, and demonstrate performance in a virtual reality environment. The work is also demonstrated using a proof-of-concept physical system. This control algorithm is in contrast to prior approaches which electrophysiological signals, such as EMG, which do have limitations as described by the authors. In this work, the movements of proximal joints (eg shoulder), which generally remain under voluntary control after limb amputation, are used as input to neural networks to predict limb orientation. The results are tested by several participants within a virtual environment, and preliminary demonstrated using a physical device, albeit without it being physically attached to the user.

Strengths:<br /> Overall, the work has several interesting aspects. Perhaps the most interesting aspect of the work is that the approach worked well without requiring user calibration, meaning that users could use pre-trained networks to complete the tasks as requested. This could provide important benefits, and if successfully incorporated into a physical prosthesis allow the user to focus on completing functional tasks immediately. The work was also tested with a reasonable number of subjects, including those with limb-loss. Even with the limitations (see below) the approach could be used to help complete meaningful functional activities of daily living that require semi-consistent movements, such as feeding and grooming.

Weaknesses:<br /> While interesting, the work does have several limitations. In this reviewer's opinion, main limitations are: the number of 'movements' or tasks that would be required to train a controller that generalized across more tasks and limb-postures. The authors did a nice job spanning the workspace, but the unconstrained nature of reaches could make restoring additional activities problematic. This remains to be tested.

The weight of a device attached to a user will impact the shoulder movements that can be reliably generated. Testing with a physical prosthesis will need to ensure that the full desired workspace can be obtained when the limb is attached, and if not, then a procedure to scale inputs will need to be refined.

The reliance on target position is a complicating factor in deploying this technology. It would be interesting to see what performance may be achieved by simply using the input target positions to the controller and exclude the joint angles from the tracking devices (eg train with the target positions as input to the network to predict the desired angles).

Treating the humeral rotation degree of freedom is tricky, but for some subjects, such as those with OI, this would not be as large of an issue. Otherwise, the device would be constructed that allowed this movement.

Overall, this is an interesting preliminary study with some interesting aspects. Care must be taken to systematically evaluate the method to ensure clinical impact.

Joint Public Review:

The authors present a carefully controlled set of experiments that demonstrate an additional complexity for GPCR signaling in that endosomal signaling may be different when beta-arrestin is or isn't associated with a G protein-bound V2 vasopressin receptor. It uses state of the art biosensor-based approaches and beta-arrestin KO lines to assess this. It adds to a growing body of evidence that G proteins and beta-arresting can associate with GPCR complexes simultaneously. They also demonstrate the possibility that Gq might also be activated by the V2 receptor. My sense is one thing they may need to be considered is the possibility of such "megacomplexes" might actually involve receptor dimers or oligomers. They have added significant amounts of new data to address concerns I had about the sole use of mini-genes to assess G protein coupling and broadened discussions about the possible mechanisms underlying their observations. I would still argue that receptor oligomers are a more obvious way for such megacomplexes to be organized around.

Reviewer #1 (Public Review):

Summary:<br /> This manuscript by Estevam et al. reports new insights into the regulation of the receptor tyrosine kinase MET gained from two deep mutational scanning (DMS) datasets. In this paper, the authors use a classic selection system for oncogenic kinase signaling, the murine Ba/F3 cell line, to assess the functional effects of thousands of mutations in the kinase domains of MET in two contexts: (1) fusion of the whole MET intracellular region to the dimerization domain TPR, and (2) the same fusion protein, but with exon 14, which encodes part of the juxtamembrane region of MET, skipped. Critically, exon 14 skipping yields a version of MET that is found in many cancers and has higher signaling activity than the canonical MET isoform. The authors extensively analyze their DMS data to very convincingly show that their selection assay reports on kinase activity, by illustrating that many functionally important structural components of the kinase domain are not tolerant of mutation. Then, they turn their attention to a helical region of the juxtamembrane region (αJM), immediately after exon 14, which is posited to play a regulatory role in MET. Their DMS data illustrate that the strength and mutational tolerance of interactions between αJM and the key αC helix in the kinase domain depends on the presence or absence of exon 14. They also identify residues in the N-lobe of the kinase, such as P1153, which are not conserved across tyrosine kinases but appear to be essential for MET and MET-like kinases. Finally, the authors analyze their DMS data in the context of clinically-observed mutations and drug-resistance mutations.

Overall, this manuscript is exciting because it provides new insights into MET regulation in general, as well as the role of exon 14. It also reveals ways in which the JM region of MET is different from that of many other receptor tyrosinekinases. The exon 14-skipped fusion protein DMS data is somewhat underexplored and could be discussed in greater detail, which would elevate excitement about the work. Furthermore, some of the cell biological validation experiments and the juxtaposition with clinical data are perhaps not assessed/interpreted as clearly they could be. Some constructive suggestions are given below to enhance the impact of the manuscript.

Strengths:<br /> The main strengths of this paper, also summarized above in the summary, are as follows:

1. The authors very convincingly show that Ba/F3 cells can be coupled with deep mutational scanning to examine MET mutational effects. This is most clearly shown by highlighting how all of the known kinase structure and regulatory elements are highly sensitive to mutations, in accordance with a few other DMS datasets on other kinases.

2. A highlight of this paper is the juxtaposition of two DMS datasets for two different isoforms of the MET receptor. Very few comparisons like this exist in the literature, and they show how small changes to the overall architecture of a protein can impact its regulation and mutational sensitivity.

3. Another exciting advance in this manuscript is the deep structural analysis of the MET juxtamembrane region with respect to that of other tyrosine kinases - guided by the striking effect of mutations in the juxtamembrane helical region. The authors illustrate how the JM region of MET differs from that of other tyrosine kinases.

4. Overall, this manuscript will provide a resource for interpreting clinically relevant MET mutations.

Weaknesses:<br /> 1. The manuscript is front-loaded with extensive analysis of the first DMS dataset, in which exon 14 is present, however, the discussion and analysis of the exon 14-skipped dataset is somewhat limited. In particular, a deeper discussion of the differences between the two datasets is warranted, to lay out the full landscape of mutations that have different functional consequences in the two isoforms. Rather, the authors only focus on differences in the JM region. What are the broader structural effects of exon 14 skipping across the whole kinase domain?

2. It is unclear if gain-of-function mutations can actually be detected robustly in this specific system. This isn't a problem at face value, as different selection assays have different dynamic ranges. However, the authors don't discuss the statistical significance and reproducibility of gain- vs loss-of-function mutations, and none of the gain-of-function mutations are experimentally validated (some appear to show loss-of-function in their cellular validation assay with full-length MET). The manuscript would benefit from deeper statistical analysis (and discussion in the text) of gain-of-function mutations, as well as further validation of a broad range of activity scores in a functional assay. For the latter point, one option would be to express individual clones from their library in Ba/F3 cells and blot for MET activation loop phosphorylation (which is probably a reasonable proxy for activity/activation).

3. In light of point 2, above, much of the discussion about clinically-relevant gain-of-function mutations feels a bit stretched - although this section is definitely very interesting in premise. A clearer delineation of gain-of-function, with further statistical support and ideally also some validation, would greatly strengthen the claims in this section.

Reviewer #2 (Public Review):

Summary:<br /> The authors describe a deep mutational scanning (DMS) study of the kinase domain of the c-MET receptor tyrosine kinase. The screen is conducted with a highly activated fusion oncoprotein - Tpr-MET - in which the MET kinase domain is fused to the Tpr dimerization element. The mutagenized region includes the entire kinase domain and an alpha-helix in the juxtamembrane region that is essentially part of the MET kinase domain. The DMS screen is carried out in two contexts, one containing the entire cytoplasmic region of MET, and the other with an "exon 14 deletion" which removes a large portion of the juxtamembrane region (but retains the aforementioned alpha-helix). The work provides a robust and essentially exhaustive catalog of the effect of mutations (within the kinase domain) on the ability of the Tpr-MET fusion oncoproteins to drive IL3-independent growth of Ba/F3 cells. Every residue in the kinase is mutated to every natural amino acid. Given the design of the screen, one would expect it to be a powerful tool for identifying mutations that impair catalytic activity and therefore impair IL3-independent proliferation, but not the right tool for identifying gain-of-function mutations that operate by shifting the kinase from an inactive to active state (because the Tpr-Met fusion construct is already very highly activated). This is borne out by the data, which reveal many many deleterious mutations and few "gain-of-function" mutations (which are of uncertain significance, as discussed below).

Strengths:<br /> The authors take a very scholarly and thorough approach to interpreting the effect of mutations in light of available information for the structure and regulation of MET and other kinases. They examine the effect of mutations in the so-called catalytic (C) and regulatory (R) spines, the interface between the JM alpha-helix and the C-helix, the glycine-rich loop, and other key elements of the kinase, providing a structural rationale for the deleterious effect of mutations. Comparison of the panoply of deleterious mutations in the TPR-met versus TPR- exon14del-MET DMS screens reveals an interesting difference - the exon14 deletion MET is much more tolerant of mutations in the JM alpha-helix/C-helix interface. The reason for this is unclear, however.

Weaknesses:<br /> Because the screens were conducted with highly active Tpr-MET fusions, they have limited power to reveal gain-of-function mutations. Indeed, to the extent that Tpr-MET is as active or even more active than ligand-activated WT MET, one could argue that it is "fully" activated and that any additional gain of fitness would be "super-physiologic". I would expect such mutations to be rare (assuming that they could be detected at all in the Ba/F3 proliferation assay). Consistent with this, the authors note that gain-of-function mutations are rare in their screen (as judged by being more fit than the average of synonymous mutations). In their discussion of cancer-associated mutations, they highlight several "strong GOF variants in the DMS". It is unclear what the authors mean by "strong GOF", indeed it is unclear to this reviewer whether the screen has revealed any true gain of function mutations at all. A few points in this regard:

1) more active than the average of synonymous mutations (nucleotide changes that have no effect on the sequence of the expressed protein) seems to be an awfully low bar for GOF - by that measure, several synonymous mutations would presumably be classified as GOF.

2) In the +IL3 heatmap in supplemental Figure 1A, there is as much or more "blue" indicating GOF as in the -IL3 heatmap, which could suggest that the observed level of gain in fitness is noise, not signal.

3) And finally, consistent with this interpretation, in Supplemental Figure 1C, comparing the synonymous and missense panels in the IL3 withdrawal condition suggests that the most active missense mutations (characterized here as strong GOF) are no more active than the most active synonymous mutations.

My other major concern with the work as presented is that the authors conflate "activity" and "activation" in discussing the effects of mutations. "Activation" implies a role in regulation - affecting a switch between inactive and active conformations or states - at least in this reviewer's mind. As discussed above, the screen per se does not probe activation, only activity. To the extent that the residues discussed are important for activation/regulation of the kinase, that information is coming from prior structural/functional studies of MET and other kinases, not from the DMS screen conducted here. Of course, it is appropriate and interesting for the authors to consider residues that are known to form important structural/regulatory elements, but they should be careful with the use of activity vs. activation and make it clear to the reader that the screen probes the former. One example - in the abstract, the authors rightly note that their approach has revealed a critical hydrophobic interaction between the JM segment and the C-helix, but then they go on to assert that this points to differences in the regulation of MET and other RTKs. There is no evidence that this is a regulatory interaction, as opposed to simply a structural element present in MET (and indeed the authors' examination of prior crystal structures shows that the interaction is present in both active and inactive states.

Reviewer #1 (Public Review):

Summary:<br /> The article by Siachisumo, Luzzi and Aldalaquan et al. describes studies of RBMX and its role in maintaining proper splicing of ultra-long exons. They combine CLIP, RNA-seq, and individual example validations with manipulation of RBMX and its family members RBMY and RBMXL2 to show that the RBMX family plays a key role in maintaining proper splicing of these exons.

Strengths:<br /> I think one of the main strengths of the manuscript is its ability to explore a unique but interesting question (splicing of ultra-long exons), and derive a relatively simple model from the resulting genomics data. The results shown are quite clean, suggesting that RBMX plays an important role in the proper regulation of these exons. The ability of family members to rescue this phenotype (as well as only particular domains) is also quite intriguing and suggests that the mechanisms for keeping these exons properly spliced may be a quite important and highly conserved mechanism.

Weaknesses:<br /> I think my main critique is that a lot of the conclusions in the text are written with very broad and general claims, but these are often based on either a small number of examples or a non-transcriptome-wide analysis that I think would be necessary for such broad conclusions. For example, pg 5 - "The above data indicated that RBMX has a major role in repressing cryptic splicing patterns in human somatic cells." is based on seeing ~120 exons with a 2:1 ratio of exclusion to inclusion (and doesn't include analysis confirming which of these are cryptic). Similarly, on page 15, line 31 "The above experiments showed that RBMXL2 is able to globally replace RBMX activity" and Fig 5 as well - I think the 'globally' term here is a bit too much with most of the analysis derived from n=3 events.

Another weakness I think is the lack of context in the paper, where the basic description of how many 'long' and 'ultra-long' exons there are and what percent of those are bound by and/or regulated by RBMX is missing. The text (including the title, which to me is written to imply a general role for ultra-long exons') seems to imply that RBMX maintains proper splicing of ultra-long exons globally (which to me implies a significant percent of them), but I don't think the manuscript succeeds in strongly proving this global role.

Along those same lines, I think the manuscript claims that the described principles are specific to the RBMX family, but I think the lack of negative examples weakens the strength of this claim. For example, for Figure 2 (D/E/F/G), what I think would make this more powerful is a negative example - if you take CLIP + knockdown/RNA-seq data for a different splicing regulator (RBFOX2, PTBP1, etc.), do you not see this size difference? I think the analysis described here is sufficient to show that there is an overlap for RBMX, but I think this would make it much stronger in confirming it's not just a 'longer genes get more CLIP tags' artifact.

Reviewer #2 (Public Review):

Summary:<br /> One of the greatest challenges for the spliceosome is to be able to repress the many cryptic splice sites that can occur in both the intronic and exotic sequences of genes. Although many studies have focused on cryptic signals in introns (because of their common involvement in disease) the question still remained open as to the factors that repress cryptic exons in exons. Because exons are normally much shorter than introns, in many cases the problem does not exist. However, in human genes, a significant proportion of exons can be considerably longer than the average 150 nt length and this raises the question of how cryptic splicing can be prevented in long exons. To address this question, the authors have focused on the possible role played by an ancient mammalian RBD protein called RBMX. Using a combination of high-throughput and classic splicing methodologies, they have shown that there is a class of RBMX-dependent ultra-long exons connected where the RBMX, RBMXL2, and RBMY paralogs have closely related functional activity in repressing cryptic splice site selection.

Strengths:<br /> In general, the present work sheds light on what has been a rather understudied process in splicing research. The use of iCLIP and RNA-seq data has not only allowed us to identify the long exons where cryptic splicing is prevented by the RBMX proteins but has also allowed us to identify a network of genes mostly involved in genome stability and transcriptional control where these proteins seem to play a prominent role. This can therefore also shed additional information on the way splicing has shaped evolutionary processes in the mammalian lineage and will therefore be of interest to many researchers in this field.

Weaknesses:<br /> There are no major weaknesses.

Reviewer #3 (Public Review):

The manuscript by Siachisumo et al builds upon a previous publication from the same group of collaborators that showed that depletion of mouse RBMXL2 leads to a block in spermatogenesis associated with mis-splicing, particularly of large exons in genes associated with genome stability (Ehrmann et al eLife 2019). RBMXL2 is an RNA-binding protein and an autosomal retrotransposed paralog of the X-chromosomally encoded RBMX. RBMXL2 is expressed during meiosis when RBMX and the more distantly related RBMY (on the Y chromosome) are silenced. It is therefore an appealing hypothesis that RBMXL2 might provide cover for RBMX function during meiosis. To address this hypothesis the authors analysed the transcriptomic consequences of RBMX depletion by RNA-Seq in human cells (MDA-MB-231 and existing RNA-Seq data from HEK293 cells), complemented by iCLIP to analyze the binding targets of FLAG-tagged RBMX in HEK293 cells. The findings convincingly demonstrate that - like RBMXL2 - RBMX mainly acts as a splicing repressor and that it particularly acts to protect the integrity of very long ("ultra-long") exons. Upon RBMX depletion, many of these exons are shortened due to the use of cryptic 5' and/or 3' splice sites. Moreover, affected genes are particularly enriched for functions associated with genome integrity - indeed "comet assays" show that RBMX depletion leads to DNA damage defects.

The manuscrupt therefore delivers a clear affirmative answer to the question of whether the two highly related proteins have similar molecular functions, particularly with respect to suppressing cryptic splicing that affects ultra-long exons. This conclusion is reinforced by the ability of induced expression of either RBMXL2 or RBMY to fully complement the effects of RBMX knockdown upon three target events in the ETAA1, REV3L, and ATRX genes.

The manuscript also includes some experiments that address more mechanistic questions, such as the potential for RBMX to block access of spliceosome components to splice site elements and structure-function analyses of RBMX. These areas have a distinctly "preliminary" feel to them. For example, for one target (ETAA1) it is shown that CLIP tags are close to mapped branchpoints. However, no attempt is made to integrate the RNA-Seq and iCLIP data-sets to look for more generalized relationships between binding and activity. Likewise, one experiment shows that the RRM domain of RBMXL2 is not necessary for activity. Given that the RRM domain represents only ~25% of the total RBMXL2 sequence, this is a somewhat preliminary, albeit interesting, observation. Another surprising omission was that there was no global comparison of the consequences of RBMX depletion and complementation by RBMXL2, despite the fact that the relevant RNA-Seq data-sets had been generated (Figure 4 supplement 1 shows RNA-Seq IGV tracks that confirm the effects on ETAA1, REV3L and ATRX shown by RT-PCR in Figure 4).

In summary, this manuscript provides clear evidence to support the role of RBMX as a repressor of cryptic splice sites in ultra-long exons, similar to RBMXL2.

Reviewer #1 (Public Review):

Summary:<br /> Here, Boor et al focus on the regulation of daf-7 transcription in the ASJ chemosensory neurons, which has previously been shown to be sensitive to a variety of external and internal signals. Interestingly, they find that soluble (but not volatile) signals released by food activate daf-7 expression in ASJ, but that this is counteracted by signals from the ASIC channels del-3 and del-7, previously shown to detect the ingestion of food in the pharynx. Importantly, the authors find that ASJ-derived daf-7 can promote exploration, suggesting a feedback loop that influences locomotor states to promote feeding behavior. They also implicate signals known to regulate exploratory behavior (the neuropeptide receptor PDFR-1 and the neuromodulator serotonin) in the regulation of daf-7 expression in ASJ. Additionally, they identify a novel role for a pathway previously implicated in C. elegans sensory behavior, HEN-1/SCD-2, in the regulation of daf-7 in ASJ, suggesting that the SCD-2 homolog ALK may have a conserved role in feeding and metabolism.

Strengths:<br /> The studies reported here, particularly the quantitation of gene expression and the careful behavioral analysis, are rigorously done and interpreted appropriately. The results suggest that, with respect to food, DAF-7 expression encodes a state of "unmet need" - the availability of nearby food to animals that are not currently eating. This is an interesting finding that reinforces and extends our understanding of the neurobiological significance of this important signaling pathway. The identification of a role for ASJ-derived daf-7 in motor behavior is a valuable advance, as is the finding that SCD-2 acts in the AIA interneurons to influence daf-7 expression in ASJ.

Weaknesses:<br /> A limitation of the work is that some mechanistic relationships between the identified signaling pathways are not carefully examined, but this provides interesting opportunities for future work. A minor weakness concerns the experiment in which daf-7 is conditionally deleted from ASJ. This is an ideal approach for probing the function of daf-7, but these experiments seem to be carried out in the well-fed, on-food condition in which control animals should express little or no daf-7 in ASJ. Thus, the experimental design does not allow an assessment of the role of daf-7 under conditions in which its expression is activated (e.g., in animals exposed to un-ingestible food). An additional minor issue concerns the interpretation of the scd-2 experiments. The authors' findings do support a role for scd-2 signaling in the activation of daf-7 expression by un-ingestible food, but the data also suggest that scd-2 signaling is not essential for this effect, as there is still an effect in scd-2 mutants (Figure 4B).

Reviewer #2 (Public Review):

Summary:<br /> In this work, Boor and colleagues explored the role of microbial food cues in the regulation of neuroendocrine-controlled foraging behavior. Consistent with previous reports, the authors find that C. elegans foraging behavior is regulated by the neuroendocrine TGFβ ligand encoded by daf-7. In addition to its known role in the neuroendocrine/sensory ASI neurons, Boot and colleagues show that daf-7 expression is dynamically regulated in the ASJ sensory neurons by microbial food cues - and that this regulation is important for exploration/exploitation balance during foraging. They identify at least two independent pathways by which microbial cues regulate daf-7 expression in ASJ: a likely gustatory pathway that promotes daf-7 expression and an opposing interoceptive pathway, also likely chemosensory in nature but which requires microbial ingestion to inhibit daf-7 expression. Two neuroendocrine pathways known to regulate foraging (serotonin and PDF-1) appear to act at least in part via daf-7 induction. They further identify a novel role for the C. elegans ALK orthologue encoded by scd-2, which acts in interneurons to regulate daf-7 expression and foraging behavior. These results together imply that distinct cues from microbial food are used to regulate the balance between exploration and exploitation via conserved signaling pathways.

Strengths:<br /> The findings that gustatory and interoceptive inputs into foraging behavior are separable and opposing are novel and interesting, which they have shown clearly in Figure 1. It is also clear from their results that removal of the interoceptive cue (via transfer to non-digestible food) results in rapid induction of daf-7::gfp in ASJ, and that ASJ plays an important role in the regulation of foraging behavior.

The role of the hen-1/scd-2 pathway in mediating the effects of ingested food is also compelling and well-interpreted. The use of precise gain-of-function alleles further supports their conclusions. This implies that important elements of this food-sensing pathway may be conserved in mammals.

Weaknesses:<br /> What is less clear to me from the work at this stage is how the gustatory input fits into this picture and to what extent can it be strongly concluded that the daf-7-regulating pathways that they have identified (del-3/7, 5-HT, PDFR-1, scd-2) act via the interoceptive pathway as opposed to the gustatory pathway. It follows from the work of the Flavell lab that del-3/7 likely acts via the interoceptive pathway in this context as well but this isn't shown directly - e.g. comparing the effects of aztreonam-treated bacteria and complete food removal to controls. The roles of 5-HT and PDFR-1 are even a bit less clear. Are the authors proposing that these are entirely parallel pathways? This could be explained in better detail.

It would also be helpful to elaborate more on why the identified transcriptional positive feedback loop is predicted to extend roaming state duration - as opposed to some other mechanism of increasing roaming such as increased probability of roaming state initiation. This doesn't seem self-evident to me. Related to this point is the somewhat confusing conclusion that the effects of tph-1 and pdfr-1 mutations on daf-7 expression are due to changes in ingestion during roaming/dwelling. From my understanding (e.g. Cermak et al., 2020), pharyngeal pumping rate does not reliably decrease during roaming - so is it clear that there are in fact lower rates of ingestion during roaming in their experiments? If so, why does increased roaming (via tph-1 mutation) result in further increases in daf-7 expression in animals fed aztreonam-treated food (Fig 3B)? Alternatively, there could be a direct signaling connection between the 5-HT/PDFR-1 pathways and daf-7 expression which could be acknowledged or explained.

Reviewer #3 (Public Review):

Summary:<br /> In this interesting study, the authors examine the function of a C. elegans neuroendocrine TGF-beta ligand DAF-7 in regulating foraging movement in response to signals of food and ingestion. Building on their previous findings that demonstrate the critical role of daf-7 in a sensory neuron ASJ in behavioral response to pathogenic P. aeruginosa PA14 bacteria and different foraging behavior between hermaphrodite and male worms, the authors show, here, that ingestion of E. coli OP50, a common food for the worms, suppresses ASJ expression of daf-7 and secreted water-soluble cues of OP50 increases it. They further showed that the level of daf-7 expression in ASJ is positively associated with a higher level of roaming/exploration movement. Furthermore, the authors identify that a C. elegans ortholog of Anaplastic Lymphoma Kinase, scd-2, functions in an interneuron AIA to regulate ASJ expression of daf-7 in response to food ingestion and related cues. These findings place the DAF-7 TGF-beta ligand in the intersection of environmental food conditions, food intake, and food-searching behavior to provide insights into how orchestrated neural functions and behaviors are generated under various internal and external conditions.

Strengths:<br /> The study addresses an important question that appeals to a wide readership. The findings are demonstrated by generally strong results from carefully designed experiments.

Weaknesses:<br /> However, a few questions remain to provide a complete picture of the regulatory pathways and some analyses need to be strengthened. Specifically,

1. The authors show that diffusible cues of bacteria OP50 increase daf-7 expression in ASJ which is suppressed by ingestible food. Their results on del-3 and del-7 suggest that NSM neuron suppresses daf-7 ASJ expression. What sensory neurons respond to bacterial diffusible cues to increase daf-7 expression of ASJ? Since ASJ is able to respond to some bacterial metabolites, does it directly regulate daf-7 expression in response to diffusible cues of OP50 or does it depend on neurotransmission for the regulation? Some level of exploration in this question would provide more insights into the regulatory network of daf-7.

2. The results including those in Figure 2 strongly support that daf-7 in ASJ is required for roaming. Meanwhile, authors also observe increased daf-7 expression in ASJ under several conditions, such as non-ingestible food. Does non-ingestible food induce more roaming? It would complete the regulatory loop by testing whether a higher (than wild type) level of daf-7 in ASJ could further increase roaming. The results in pdf-1 and scd-2 gain-of-function alleles support more ASJ leads to more roaming, but the effect of these gain-of-function alleles may not be ASJ-specific and it would be interesting to know whether ASJ-specific increase of daf-7 leads to a higher level of roaming. In my opinion, either outcome would be informative and strengthen our understanding of the critical function of daf-7 in ASJ demonstrated here.

3. The analyses in Figure 4 cannot fully support "We further observed that the magnitude of upregulation of daf-7 expression in the ASJ neurons when animals were moved from ingestible food to non-ingestible food was reduced in scd-2(syb2455) to levels only about one-fourth of those seen in wild-type animals (Figure 4D)...", because the authors tested and found the difference in daf-7 expression between ingestible and non-ingestible food conditions in both wild type and the mutant worms. The authors did not analyze whether the induction was different between wild type and mutant. Under the ingestible food condition, ASJ expression of daf-7 already looks different in scd-2(syb2455).

4. The authors used unpaired two-tailed t-tests for all the statistical analyses, including when there are multiple groups of data and more than one treatment. In their previous study Meisel et al 2014, the authors used one-way ANOVA, followed by Dunnett's or Tukey's multiple comparison test when they analyzed daf-7 expression or lawn leaving in different mutants or under different bacterial conditions. It is not clear why a two-tailed t-test was used in similar analyses in this study.

Joint Public Review:

This study describes a group of CRH-releasing neurons, located in the paraventricular nucleus of the hypothalamus, which, in mice, affects both the state of sevoflurane anesthesia and a grooming behavior observed after it. PVH-CRH neurons showed elevated calcium activity during the post-anesthesia period. Optogenetic activation of these PVH-CRH neurons during sevoflurane anesthesia shifts the EEG from burst-suppression to a seemingly activated state (an apparent arousal effect), although without a behavioral correlate. Chemogenetic activation of the PVH-CRH neurons delays sevoflurane-induced loss of righting reflex (another apparent arousal effect). On the other hand, chemogenetic inhibition of PVH-CRH neurons delays recovery of the righting reflex and decreases sevoflurane-induced stress (an apparent decrease in the arousal effect). The authors conclude that PVH-CRH neurons are a common substrate for sevoflurane-induced anesthesia and stress. The PVH-CRH neurons are related to behavioral stress responses, and the authors claim that these findings provide direct evidence for a relationship between sevoflurane anesthesia and sevoflurane-mediated stress that might exist even when there is no surgical trauma, such as an incision. In its current form, the article does not achieve its intended goal.

Strengths<br /> The manuscript uses targeted manipulation of the PVH-CRH neurons, and is technically sound. Also, the number of experiments is substantial.

Weaknesses<br /> The most significant weaknesses are a) the lack of consideration and measurement of GABAergic mechanisms of sevoflurane anesthesia, b) the failure to use another anesthetic as a control, c) a failure to document a compelling post-anesthesia stress response to sevoflurane in humans, d) limitations in the novelty of the findings. These weaknesses are related to the primary concerns described below:

Concerns about the primary conclusion, that PVH-CRH neurons mediate "the anesthetic effects and post-anesthesia stress response of sevoflurane GA".

1) Just because the activity of a given neural cell type or neural circuit alters an anesthetic's response, this does not mean that those neurons play a role in how the anesthetic creates its anesthetic state. For example, sevoflurane is commonly used in children. Its primary mechanism of action is through enhancement of GABA-mediated inhibition. Children with ADHD on Ritalin (a dopamine reuptake inhibitor) who take it on the day of surgery can often require increased doses of sevoflurane to achieve the appropriate anesthetic state. The mesocortical pathway through which Ritalin acts is not part of the mechanism of action of sevoflurane. Through this pathway, Ritalin is simply increasing cortical excitability making it more challenging for the inhibitory effects of sevoflurane at GABAergic synapses to be effective. Similarly, here, altering the activity of the PVHCRH neurons and seeing a change in anesthetic response to sevoflurane does not mean that these neurons play a role in the fundamental mechanism of this anesthetic's action. With the current data set, the primary conclusions should be tempered.

2) It is important to compare the effects of sevoflurane with at least one other inhaled ether anesthetic. Isoflurane, desflurane, and enflurane are ether anesthetics that are very similar to each other, as well as being similar to sevoflurane. It is important to distinguish whether the effects of sevoflurane pertain to other anesthetics, or, alternatively, relate to unique idiosyncratic properties of this gas that may not be a part of its anesthetic properties.

For example, one study cited by the authors (Marana et al.. 2013) concludes that there is weak evidence for differences in stress-related hormones between sevoflurane and desflurane, with lower levels of cortisol and ACTH observed during the desflurane intraoperative period. It is not clear that this difference in some stress-related hormones is modeled by post-sevoflurane excess grooming in the mice, but using desflurane as a control could help determine this.

Concerns about the clinical relevance of the experiments<br /> In anesthesiology practice, perioperative stress observed in patients is more commonly related to the trauma of the surgical intervention, with inadequate levels of antinociception or unconsciousness intraoperatively and/or poor post-operative pain control. The authors seem to be suggesting that the anesthetic itself is causing stress, but there is no evidence of this from human patients cited. We were not aware that this is a documented clinical phenomenon. It is important to know whether sevoflurane effectively produces behavioral stress in the recovery room in patients that could be related to the putative stress response (excess grooming) observed in mice. For example, in surgeries or procedures that required only a brief period of unconsciousness that could be achieved by administering sevoflurane alone (comparable to the 30 min administered to the mice), is there clinical evidence of post-operative stress?

Patients who receive sevoflurane as the primary anesthetic do not wake up more stressed than if they had had one of the other GABAergic anesthetics. If there were signs of stress upon emergence (increased heart rate, blood pressure, thrashing movements) from general anesthesia, the anesthesiologist would treat this right away. The most likely cause of post-operative stress behaviors in humans is probably inadequate anti-nociception during the procedure, which translates into inadequate post-op analgesia and likely delirium. It is the case that children receiving sevoflurane do have a higher likelihood of post-operative delirium. Perhaps the authors' studies address a mechanism for delirium associated with sevoflurane, but this is not considered. Delirium seems likely to be the closest clinical phenomenon to what was studied.

Concerns about the novelty of the findings<br /> CRH is associated with arousal in numerous studies. In fact, the authors' own work, published in eLife in 2021, showed that stimulating the hypothalamic CRH cells leads to arousal and their inhibition promotes hypersomnia. In both papers, the authors use fos expression in CRH cells during a specific event to implicate the cells, then manipulate them and measure EEG responses. In the previous work, the cells were active during wakefulness; here- they were active in the awake state that follows anesthesia (Figure 1). Thus, the findings in the current work are incremental.

The activation of CRH cells in PVN has already been shown to result in grooming by Jaideep Bains (cited as reference 58). Thus, the involvement of these cells in this behavior is expected. The authors perform elaborate manipulations of CRH cells and numerous analyses of grooming and related behaviors. For example, they compare grooming and paw licking after anesthesia with those after other stressors such as forced swim, spraying mice with water, physical attack, and restraint. However, the relevance of these behaviors to humans and generalization to other types of anesthetics is not clear.

Reviewer #1 (Public Review):

Like the "preceding" co-submitted paper, this is again a very strong and interesting paper in which the authors address a question that is raised by the finding in their co-submitted paper - how does one factor induce two different fates. The authors provide an extremely satisfying answer - only one subset of the cells neighbors a source of signaling cells that trigger that subset to adopt a specific fate. The signal here is Delta and the read-out is Notch, whose intracellular domain, in conjunction with, presumably, SuH cooperates with Bsh to distinguish L4 from L5 fate (L5 is not neighbored by signal-providing cells). Like the back-to-back paper, the data is rigorous, well-presented and presents important conclusions. There's a wealth of data on the different functions of Notch (with and without Bsh). All very satisfying.

I have again one suggestion that the authors may want to consider discussing. I'm wondering whether the open chromatin that the author convincingly measure is the CAUSE or the CONSEQUENCE of Bsh being able to activate L4 target genes. What I mean by this is that currently the authors seem to be focused on a somewhat sequential model where Notch signaling opens chromatin and this then enables Bsh to activate a specific set of target genes. But isn't it equally possible that the combined activity of Bsh/Notch(intra)/SuH opens chromatin? That's not a semantic/minor difference, it's a fundamentally different mechanism, I would think. This mechanism also solves the conundrum of specificity - how does Notch know which genes to "open" up? It would seem more intuitive to me to think that it's working together with Bsh to open up chromatin, with chromatin accessibility than being a "mere" secondary consequence. If I'm not overlooking something fundamental here, there is actually also a way to distinguish between these models - test chromatin accessibility in a Bsh mutant. If the author's model is true, chromatin accessibility should be unchanged.<br /> I again finish by commending the authors for this terrific piece of work.

Reviewer #2 (Public Review):

Summary:

In this work, the authors explore how Notch activity acts together with Bsh homeodomain transcription factors to establish L4 and L5 fates in the lamina of the visual system of Drosophila. They propose a model in which differential Notch activity generates different chromatin landscapes in presumptive L4 and L5, allowing the differential binding of the primary homeodomain TF Bsh (as described in the co-submitted paper), which in turn activates downstream genes specific to either neuronal type. The requirement of Notch for L4 vs. L5 fate is well supported, and complete transformation from one cell type into the other is observed when altering Notch activity. However, the role of Notch in creating differential chromatin landscapes is not directly demonstrated. It is only based on correlation, but it remains a plausible and intriguing hypothesis.

Strengths:

The authors are successful in characterizing the role of Notch to distinguish between L4 and L5 cell fates. They show that the Notch pathway is active in L4 but not in L5. They identify L1, the neuron adjacent to L4 as expressing the Delta ligand, therefore being the potential source for Notch activation in L4. Moreover, the manuscript shows molecular and morphological/connectivity transformations from one cell type into the other when Notch activity is manipulated.

Using DamID, the authors characterize the chromatin landscape of L4 and L5 neurons. They show that Bsh occupies distinct loci in each cell type. This supports their model that Bsh acts as a primary selector gene in L4/L5 that activates different target genes in L4 vs L5 based on the differential availability of open chromatin loci.

Overall, the manuscript presents an interesting example of how Notch activity cooperates with TF expression to generate diverging cell fates. Together with the accompanying paper, it helps thoroughly describe how lamina cell types L4 and L5 are specified and provides an interesting hypothesis for the role of Notch and Bsh in increasing neuronal diversity in the lamina during evolution.

Weaknesses:

Differential Notch activity in L4 and L5:<br /> ● The manuscript focuses its attention on describing Notch activity in L4 vs L5 neurons. However, from the data presented, it is very likely that the pool of progenitors (LPCs) is already subdivided into at least two types of progenitors that will rise to L4 and L5, respectively. Evidence to support this is the activity of E(spl)-mɣ-GFP and the Dl puncta observed in the LPC region. Discussion should naturally follow that Notch-induced differences in L4/L5 might preexist L1-expressed Dl that affect newborn L4/L5. Therefore, the differences between L4 and L5 fates might be established earlier than discussed in the paper. The authors should acknowledge this possibility and discuss it in their model.<br /> ● The authors claim that Notch activation is caused by L1-expressed Delta. However, they use an LPC driver to knock down Dl. Dl-KD should be performed exclusively in L1, and the fate of L4 should be assessed.<br /> ● To test whether L4 neurons are derived from NotchON LPCs, I suggest performing MARCM clones in early pupa with an E(spl)-mɣ-GFP reporter.<br /> ● The expression of different Notch targets in LPCs and L4 neurons may be further explored. I suggest using different Notch-activity reporters (i.e., E(spl)-GFP reporters) to further characterize these differences. What cause the switch in Notch target expression from LPCs to L4 neurons should be a topic of discussion.

Notch role in establishing L4 vs L5 fates:<br /> ● The authors describe that 27G05-Gal4 causes a partial Notch Gain of Function caused by its genomic location between Notch target genes. However, this is not further elaborated. The use of this driver is especially problematic when performing Notch KD, as many of the resulting neurons express Ap, and therefore have some features of L4 neurons. Therefore, Pdm3+/Ap+ cells should always be counted as intermediate L4/L5 fate (i.e., Fig3 E-J, Fig3-Sup2), irrespective of what the mechanistic explanation for Ap activation might be. It's not accurate to assume their L5 identity. In Fig4 intermediate-fate cells are correctly counted as such.<br /> ● Lines 170-173: The temporal requirement for Notch activity in L5-to-L4 transformation is not clearly delineated. In Fig4-figure supplement 1D-E, it is not stated if the shift to 29{degree sign}C is performed as in Fig4-figure supplement 1A-C.<br /> ● Additionally, using the same approach, it would be interesting to explore the window of competence for Notch-induced L5-to-L4 transformation: at which point in L5 maturation can fate no longer be changed by Notch GoF?

L4-to-L3 conversion in the absence of Bsh<br /> ● Although interesting, the L4-to-L3 conversion in the absence of Bsh is never shown to be dependent on Notch activity. Importantly, L3 NotchON status is assumed based on their position next to Dl-expressing L1, but it is not empirically tested. Perhaps screening Notch target reporter expression in the lamina, as suggested above, could inform this issue.<br /> ● Otherwise, the analysis of Bsh Loss of Function in L4 might be better suited to be included in the accompanying manuscript that specifically deals with the role of Bsh as a selector gene for L4 and L5.

Different chromatin landscape in L4 and L5 neurons<br /> ● A major concern is that, although L4 and L5 neurons are shown to present different chromatin landscapes (as expected for different neuronal types), it is not demonstrated that this is caused by Notch activity. The paper proves unambiguously that Notch activity, in concert with Bsh, causes the fate choice between L4 and L5. However, that this is caused by Notch creating a differential chromatin landscape is based only in correlation (NotchON cells having a different profile than NotchOFF). Although the authors are careful not to claim that differential chromatin opening is caused directly by Notch, this is heavily suggested throughout the text and must be toned down.<br /> e.g.: Line 294: "With Notch signaling, L4 neurons generate distinct open chromatin landscape" and Line 298: "Our findings propose a model that the unique combination of HDTF and open chromatin landscape (e.g. by Notch signaling)" . These claims are not supported well enough, and alternative hypotheses should be provided in the discussion. An alternative hypothesis could be that LPCs are already specified towards L4 and L5 fates. In this context, different early Bsh targets in each cell type could play a pioneer role generating a differential chromatin landscape.

● The correlation between open chromatin and Bsh loci with Differentially Expressed genes is much higher for L4 than L5. It is not clear why this is the case, and should be discussed further by the authors.

Reviewer #1 (Public Review):

In this very strong and interesting paper the authors present a convincing series of experiments that reveal molecular mechanism of neuronal cell type diversification in the nervous system of Drosophila. The authors show that a homeodomain transcription factor, Bsh, fulfills several critical functions - repressing an alternative fate and inducing downstream homeodomain transcription factors with whom Bsh may collaborate to induce L4 and L5 fates (the author's accompanying paper reveals how Bsh can induce two distinct fates). The authors make elegant use of powerful genetic tools and an arsenal of satisfying cell identity markers.

I believe that this is an important study because it provides some fundamental insights into the conservation of neuronal diversification programs. It is very satisfying to see that similar organizational principles apply in different organisms to generate cell type diversity. The authors should also be commended for contextualizing their work very well, giving a broad, scholarly background to the problem of neuronal cell type diversification.

My one suggestion for the authors is to perhaps address in the Discussion (or experimentally address if they wish) how they reconcile that Bsh is on the one hand: (a) continuously expressed in L4/L4, (b) binding directly to a cohort of terminal effectors that are also continuously expressed but then, on the other hand, is not required for their maintaining L4 fate? A few questions: Is Bsh only NOT required for maintaining Ap expression or is it also NOT required for maintaining other terminal markers of L4? The former could be easily explained - Bsh simply kicks of Ap, Ap then autoregulates, but Bsh and Ap then continuously activate terminal effector genes. The second scenario would require a little more complex mechanism: Bsh binding of targets (with Notch) may open chromatin, but then once that's done, Bsh is no longer needed and Ap alone can continue to express genes. I feel that the authors should be at least discussing this. The postmitotic Bsh removal experiment in which they only checked Ap and depression of other markers is a little unsatisfying without further discussion (or experiments, such as testing terminal L4 markers). I hasten to add that this comment does not take away from my overall appreciation for the depth and quality of the data and the importance of their conclusions.

Reviewer #2 (Public Review):

Summary:<br /> In this paper, the authors explore the role of the Homeodomain Transcription Factor Bsh in the specification of Lamina neuronal types in the optic lobe of Drosophila. Using the framework of terminal selector genes and compelling data, they investigate whether the same factor that establishes early cell identity is responsible for the acquisition of terminal features of the neuron (i.e., cell connectivity and synaptogenesis).

The authors convincingly describe the sequential expression and activity of Bsh, termed here as 'primary HDTF', and of Ap in L4 or Pdm3 in L5 as 'secondary HDTFs' during the specification of these two neurons. The study demonstrates the requirement of Bsh to activate either Ap and Pdm3, and therefore to generate the L4 and L5 fates. Moreover, the authors show that in the absence of Bsh, L4 and L5 fates are transformed into a L1 or L3-like fates.

Finally, the authors used DamID and Bsh:DamID to profile the open chromatin signature and the Bsh binding sites in L4 neurons at the synaptogenesis stage. This allows the identification of putative Bsh target genes in L4, many of which were also found to be upregulated in L4 in a previous single-cell transcriptomic analysis. Among these genes, the paper focuses on Dip-β, a known regulator of L4 connectivity. They demonstrate that both Bsh and Ap are required for Dip-β, forming a feed-forward loop. Indeed, the loss of Bsh causes abnormal L4 synaptogenesis and therefore defects in several visual behaviors.

The authors also propose the intriguing hypothesis that the expression of Bsh expanded the diversity of Lamina neurons from a 3 cell-type state to the current 5 cell-type state in the optic lobe.

Strengths:

Overall, this work presents a beautiful practical example of the framework of terminal selectors: Bsh acts hierarchically with Ap or Pdm3 to establish the L4 or L5 cell fates and, at least in L4, participates in the expression of terminal features of the neuron (i.e., synaptogenesis through Dip-β regulation).

The hierarchical interactions among Bsh and the activation of Ap and Pdm3 expression in L4 and L5, respectively, are well established experimentally. Using different genetic drivers, the authors show a window of competence during L4 neuron specification during which Bsh activates Ap expression. Later, as the neuron matures, Ap becomes independent of Bsh. This allows the authors to propose a coherent and well-supported model in which Bsh acts as a 'primary' selector that activates the expression of L4-specific (Ap) and L5-specific (Pdm3) 'secondary' selector genes, that together establish neuronal fate.

Importantly, the authors describe a striking cell fate change when Bsh is knocked down from L4/L5 progenitor cells. In such cases, L1 and L3 neurons are generated at the expense of L4 and L5. The paper demonstrates that Bsh in L4/L5 represses Zfh1, which in turn acts as the primary selector for L1/L3 fates. These results point to a model where the acquisition of Bsh during evolution might have provided the grounds for the generation of new cell types, L4 and L5, expanding lamina neuronal diversity for a more refined visual behaviors in flies. This is an intriguing and novel hypothesis that should be tested from an evo-devo standpoint, for instance by identifying a species when L4 and L5 do not exist and/or Bsh is not expressed in L neurons.

To gain insight into how Bsh regulates neuronal fate and terminal features, the authors have profiled the open chromatin landscape and Bsh binding sites in L4 neurons at mid-pupation using the DamID technique. The paper describes a number of genes that have Bsh binding peaks in their regulatory regions and that are differentially expressed in L4 neurons, based on available scRNAseq data. Although the manuscript does not explore this candidate list in depth, many of these genes belong to classes that might explain terminal features of L4 neurons, such as neurotransmitter identity, neuropeptides or cytoskeletal regulators. Interestingly, one of these upregulated genes with a Bsh peak is Dip-β, an immunoglobulin superfamily protein that has been described by previous work from the author's lab to be relevant to establish L4 proper connectivity. This work proves that Bsh and Ap work in a feed-forward loop to regulate Dip-β expression, and therefore to establish normal L4 synapses. Furthermore, Bsh loss of function in L4 causes impairs visual behaviors.

Weaknesses:

● The last paragraph of the introduction is written using rhetorical questions and does not read well. I suggest rewriting it in a more conventional direct style to improve readability.

● A significant concern is the way in which information is conveyed in the Figures. Throughout the paper, understanding of the experimental results is hindered by the lack of information in the Figure headers. Specifically, the genetic driver used for each panel should be adequately noted, together with the age of the brain and the experimental condition. For example, R27G05-Gal4 drives early expression in LPCs and L4/L5, while the 31C06-AD, 34G07-DBD Split-Gal4 combination drives expression in older L4 neurons, and the use of one or the other to drive Bsh-KD has dramatic differences in Ap expression. The indication of the driver used in each panel will facilitate the reader's grasp of the experimental results.

● Bsh role in L4/L5 cell fate:<br /> o It is not clear whether Tll+/Bsh+ LPCs are the precursors of L4/L5. Morphologically, these cells sit very close to L5, but are much more distant from L4.<br /> o Somatic CRISPR knockout of Bsh seems to have a weaker phenotype than the knockdown using RNAi. However, in several experiments down the line, the authors use CRISPR-KO rather than RNAi to knock down Bsh activity: it should be explained why the authors made this decision. Alternatively, a null mutant could be used to consolidate the loss of function phenotype, although this is not strictly necessary given that the RNAi is highly efficient and almost completely abolishes Bsh protein.<br /> o Line 102: Rephrase "R27G05-Gal4 is expressed in all LPCs and turned off in lamina neurons" to "is turned off as lamina neurons mature", as it is kept on for a significant amount of time after the neurons have already been specified.<br /> o Line 121: "(a) that all known lamina neuron markers become independent of Bsh regulation in neurons" is not an accurate statement, as the markers tested were not shown to be dependent on Bsh in the first place.<br /> o Lines 129-134: Make explicit that the LPC-Gal4 was used in this experiment. This is especially important here, as these results are opposite to the Bsh Loss of Function in L4 neurons described in the previous section. This will help clarify the window of competence in which Bsh establishes L4/L5 neuronal identities through ap/pdm3 expression.

● DamID and Bsh binding profile:<br /> ○ Figure 5 - figure supplement 1C-E: The genotype of the Control in (C) has to be described within the panel. As it is, it can be confused with a wild type brain, when it is in fact a Bsh-KO mutant.<br /> ○ It Is not clear how L4-specific Differentially Expressed Genes were found. Are these genes DEG between Lamina neurons types, or are they upregulated genes with respect to all neuronal clusters? If the latter is the case, it could explain the discrepancy between scRNAseq DEGs and Bsh peaks in L4 neurons.

● Dip-β regulation:<br /> ○ Line 234: It is not clear why CRISPR KO is used in this case, when Bsh-RNAi presents a stronger phenotype.<br /> ○ Figure 6N-R shows results using LPC-Gal4. It is not clear why this driver was used, as it makes a less accurate comparison with the other panels in the figure, which use L4-Split-Gal4. This discrepancy should be acknowledged and explained, or the experiment repeated with L4-Split-Gal4>Ap-RNAi.<br /> ○ Line 271: It is also possible that L4 activity is dispensable for motion detection and only L5 is required.

● Discussion: It is necessary to de-emphasize the relevance of HDTFs, or at least acknowledge that other, non-homeodomain TFs, can act as selector genes to determine neuronal identity. By restricting the discussion to HDTFs, it is not mentioned that other classes of TFs could follow the same Primary-Secondary selector activation logic.

Joint Public Review:

Anthoney et al. provide an honorable attempt at furthering our understanding of the different sleep stages that may exist in Drosophila. The establishment of definable sleep stages/state in the fly model should be seen as a central goal in the field and this study represents an important step toward that goal. In particular, managing to draw parallels between sleep stages in flies and humans would make it relevant to use the power of fly genetics to better understand the molecular and cellular basis of these sleep stages. The authors use behavioral, physiological and transcriptomics approaches to describe the differences that exist between sleep triggered by optogenetic stimulation of dFB neurons and sleep induced by consumption of the sleep-promoting drug Gaboxadol (THIP). While there are still concerns regarding the interpretation of the major results, the authors have, in general, adequately responded to the reviewers' concerns.

The strengths of this work are:<br /> 1- The article is easy to read, and the figures are mostly informative.<br /> 2- The authors employ state-of-the-art techniques to measure neuronal activity and locomotion in a single assay.<br /> 3- The analysis of transcriptomic data is appropriate.<br /> 4- The authors identify many new genes regulated in response to specific methods for sleep induction. These are all potentially interesting candidates for further studies investigating the molecular basis of sleep. It would be interesting to know which of these genes are already known to display circadian expression patterns.

Concerns:<br /> 1- The fact that flies with dFB activation seem to keep a basal level of locomotor activity whereas THIP-treated ones don't is quite striking. Is it possible that there is an error with Figure 4C-D? Based on this, it is hard to believe that dFB stimulation and THIP consumption have similar behavioral effects on sleep.<br /> 2- The authors seem satisfied with the 'good correspondence' between their RNA-seq and qPCR results, this is true for only ~9/19 genes in Fig 6G and 2/6 genes in Fig 7G. The variability between the three biological replicates is not represented in the figure.

Comments<br /> 1- The fact that THIP-induced sleep persists long after THIP removal (Fig 3D) is intriguing. This suggests that the drug might trigger a sleep-inducing pathway that, once activated, can continue on its own without the drug.<br /> 2- The claim that induction of the two forms (active/quiet) of sleep produce distinct transcriptomic and physiological effects while producing highly similar behavioral effects makes it difficult to understand the relationships between the former changes with sleep state. In their response the authors argue that sleep behavior, as currently measured using duration of inactivity, is not necessarily expected to allow for a differentiation between active and quiet sleep. This further argues for a need for better physiological/molecular correlates of sleep state in the fly (a laudable goal of this very study). However, until clear behavioral correlates can be strongly associated with physiological/molecular correlates, we will be limited to speculation about this important issue.<br /> 3- The authors suggest that the duration of the periods of inactivity may not be particularly useful for defining sleep states/stages in the fly. If this is the case, it is certainly an important issue as this measurement is the behavioral criteria for defining sleep in the field. In this regard, it raises the question of the relationship between the active and quiet sleep states examined in this study and the growing evidence for a deep sleep state (characterized by, among other things, a lowered metabolism).<br /> 4- Although the methodological concerns regarding the dose of Gaboxadol and the controls for the optogenetic/transcriptomic experiments remain, the authors have explained the rationale for the experimental design they used.<br /> 5- Overall, the authors have managed to clearly illustrate the differences between dFB stimulation and THIP consumption on behavior, neuronal activity, and gene expression. In this regard, they have achieved what they claim in the title of the article. Overall, the results support the conclusions, however the main point to consider is that the methods employed here are artificial, and there is no guarantee at this stage that 'spontaneous' sleep has the same effects on the transcriptome than what is presented here.<br /> 5- This article represents an interesting attempt at addressing very important questions, and some of the data presented here, especially the RNA-seq, can be very useful for others. However, because of the lack of definitive conclusion about whether the results presented are applicable to 'natural' sleep, the impact of this article may remain limited beyond a relatively small field.

Comments to authors

The authors have suitably addressed all comments from this section.

Reviewer #1 (Public Review):

The study describes a new computational method for unsupervised (i.e., non-artificial intelligence) segmentation of objects in grayscale images that contain substantial noise, to differentiate object, no object, and noise. Such a problem is essential in biology because they are commonly confronted in the analysis of microscope images of biological samples and recently have been resolved by artificial intelligence, especially by deep neural networks. However, training artificial intelligence for specific sample images is a difficult task and not every biological laboratory can handle it. Therefore, the proposed method is particularly appealing to laboratories with little computational background. The method was shown to achieve better performance than a threshold-based method for artificial and natural test images. To demonstrate the usability, the authors applied the method to high-power confocal images of the thalamus for the identification and quantification of immunostained potassium ion channel clusters formed in the proximity of large axons in the thalamic neuropil and verified the results in comparison to electron micrographs. 

Strengths: <br /> The authors claim that the proposed method has higher pixel-wise accuracy than the threshold-based method when applied to gray-scale images with substantial noises.

Since the method does not use artificial intelligence, training and testing are not necessary, which would be appealing to biologists who are not familiar with machine learning technology.

The method does not require extensive tuning of adjustable parameters (trying different values of "Moran's order") given that the size of the object in question can be estimated in advance.

Weaknesses:<br /> It is understood that the strength of the method is that it does not depend on artificial intelligence and therefore the authors wanted to compare the performance with another non-AI method (i.e. the threshold-based method; TBM). However, the TBM used in this work seems too naive to be fairly compared to the expensive computation of "Moran's I" used for the proposed method. To provide convincing evidence that the proposed method advances object segmentation technology and can be used practically in various fields, it should be compared to other advanced methods, including AI-based ones, as well.

This method was claimed to be better than the TBM when the noise level was high. Related to the above, TBMs can be used in association with various denoising methods as a preprocess. It is questionable whether the claim is still valid when compared to the methods with adequate complexity used together with denoising. Consider for example, Weigert et al. (2018) https://doi.org/10.1038/s41592-018-0216-7; or Lehtinen et al (2018) https://doi.org/10.48550/arXiv.1803.04189.

The computational complexity of the method, determined by the convolution matrix size (Moran's order), linearly increases as the object size increases (Fig. S2b). Given that the convolution must be run separately for each pixel, the computation seems quite demanding for scale-up, e.g. when the method is applied for 3D image volumes. It will be helpful if the requirement for computer resources and time is provided.

Reviewer #2 (Public Review):

Summary:<br /> The manuscript by David et al. describes a novel image segmentation method, implementing Local Moran's method, which determines whether the value of a datapoint or a pixel is randomly distributed among all values, in differentiating pixel clusters from the background noise. The study includes several proof-of-concept analyses to validate the power of the new approach, revealing that implementation of Local Moran's method in image segmentation is superior to threshold-based segmentation methods commonly used in analyzing confocal images in neuroanatomical studies.

Strengths:<br /> Several proof-of-concept experiments are performed to confirm the sensitivity and validity of the proposed method. Using composed images with varying levels of background noise and analyzing them in parallel with the Local Moran's or a Threshold-Based Method (TBM), the study is able to compare these approaches directly and reveal their relative power in isolating clustered pixels.

Similarly, dual immuno-electron microscopy was used to test the biological relevance of a colocalization that was revealed by Local Moran's segmentation approach on dual-fluorescent labeled tissue using immuno-markers of the axon terminal and a membrane-protein (Figure 5). The EM revealed that the two markers were present in terminals and their post-synaptic partners, respectively. This is a strong approach to verify the validity of the new approach for determining object-based colocalization in fluorescent microscopy.

The methods section is clear in explaining the rationale and the steps of the new method (however, see the weaknesses section). Figures are appropriate and effective in illustrating the methods and the results of the study. The writing is clear; the references are appropriate and useful.

Weaknesses:<br /> While the steps of the mathematical calculations to implement Local Moran's principles for analyzing high-resolution images are clearly written, the manuscript currently does not provide a computation tool that could facilitate easy implementation of the method by other researchers. Without a user-friendly tool, such as an ImageJ plugin or a code, the use of the method developed by David et al by other investigators may remain limited.

Reviewer #2 (Public Review):

This manuscript by Muñoz-Reyes et al. presents studies on the molecular mechanisms by which NCS-1 regulates Ric-8A and its interaction with Ga. They have investigated how calcium ions and phosphorylation of Ric-8A affect these interactions. They found that NCS-1 induces a conformational change in Ric-8A that prevents its phosphorylation and subsequent interaction with Ga, and this can be reversed by increasing calcium ion concentration. Using structural biology methods, they determined the interaction surfaces between NCS-1 and Ric-8A. These mechanistic analyses are needed in the field and beneficial to helping us understand specificity in the regulation of G protein signaling.

Overall, this manuscript presents an abundance of data that supports the authors' conclusions. The introduction is thorough and well-written. The structure figures are beautiful and clear - well done. Most of the biochemical and biophysical experiments are convincing. In some cases, further elaborations and explanations of data interpretation are needed. The crystallographic data is solid. However, I have major concerns with the cryo-EM data presented due to its low quality and the conclusions drawn from it.

Reviewer #3 (Public Review):

The current manuscript investigates the molecular basis of calcium-sensitive regulation of the guanine exchange factor Ric8A by the neuronal calcium sensor 1 (NCS-1). The authors provide insight into a number of aspects of this interaction, including high-resolution structures of the NCS1-Ric8A binding interface (using peptides based on Ric8A), low-resolution cryo-EM data that hints at a structural rearrangement, and a biochemical investigation of the influence of calcium binding, sodium binding, and phosphorylation on this interaction. Altogether, this manuscript provides a comprehensive set of experiments that provide insight into this important interaction. In particular, the identification of ions bound to NCS-1 using crystallography and binding assays is very nicely done and convincing. The cryo-EM data is at low resolution and provides only weak support for the proposed mechanism.

Reviewer #1 (Public Review):

Summary:<br /> In this well-designed study, the authors of the manuscript have analyzed the impact of individually silencing 90 lipid transfer proteins on the overall lipid composition of a specific cell type. They confirmed some of the evidence obtained by their own and other research groups in the past, and additionally, they identified an unreported role for ORP9-ORP11 in sphingomyelin production at the trans-Golgi. As they delved into the nature of this effect, the authors discovered that ORP9 and ORP11 form a dimer through a helical region positioned between their PH and ORD domains.

Strengths:<br /> This well-designed study presents compelling new evidence regarding the role of lipid transfer proteins in controlling lipid metabolism. The discovery of ORP9 and ORP11's involvement in sphingolipid metabolism invites further investigation into the impact of the membrane environment on sphingomyelin synthase activity.

Weaknesses:<br /> There are a couple of weaknesses evident in this manuscript. Firstly, there's a lack of mechanistic understanding regarding the regulatory role of ORP9-11 in sphingomyelin synthase activity. Secondly, the broader role of hetero-dimerization of LTPs at ER-Golgi membrane contact sites is not thoroughly addressed. The emerging theme of LTP dimerization through coiled domains has been reported for proteins such as CERT, OSBP, ORP9, and ORP10. However, the specific ways in which these LTPs hetero and/or homo-dimerize and how this impacts lipid fluxes at ER-Golgi membrane contact sites remain to be fully understood.

Regardless of the unresolved points mentioned above, this manuscript presents a valuable conceptual advancement in the study of the impact of lipid transfer on overall lipid metabolism. Moreover, it encourages further exploration of the interplay among LTP actions across various cellular organelles.

Reviewer #2 (Public Review):

Summary:<br /> The authors set out to determine which lipid transfer proteins impact the lipids of Golgi apparatus, and they identified a reasonable number of "hits" where the lack of one lipid transfer protein affected a particular Golgi lipid or class of lipids. They then carried out something close to a "proof of concept" for one lipid (sphingomyelin) and two closely related lipid transfer proteins (ORP9/ORP11). They looked into that example in great detail and found a previously unknown relationship between the level of phosphatidylserine in the Golgi (presumably trans-Golgi, trans-Golgi Network) and the function of the sphingomyelin synthase enzyme. This was all convincingly done - results support their conclusions - showing that the authors achieved their aims.

Impact:<br /> There are likely to be 2 types of impact:<br /> (I) cell biology: sphoingomyelin synthase, ORP9/11 will be studied in the future in more informed ways to understand (a) the role of different Golgi lipids - this work opens that out and produces more questions than answers (b) the role of different ORPs: what distinguishes ORP11 from its paralogy ORP10?<br /> (ii) molecular biochemistry: combining knockdown miniscreen with organelle lipidomics must be time-consuming, but here it is shown to be quite a powerful way to discover new aspects of lipid-based regulation of protein function. This will be useful to others as an example, and if this kind of workflow could be automated, then the possible power of the method could be widely applied.

Strengths:<br /> Nicely controlled data;<br /> Wide-ranging lipidomics dataset with repeats and SDs - all data easily viewed.<br /> Simple take-home message that PS traffic to the TGN by ORP9/11 is required for some aspect of SMS1 function.

Weaknesses:<br /> Model and Discussion:<br /> No idea about the aspect of SMS1 function that is being affected. Even if no further experiments were carried out, the authors could discuss possibilities. One might speculate what the PS is being used for. For example, is it a co-factor for integral membrane proteins, such as flippases? Is it a co-factor for peripheral membrane proteins, such as yet more LTPs? The model could include the work of Peretti et al (2008), which linked Nir2 activity exchanging PI:PA (Yadav et al, 2015) to the eventual function of CERT. Could the PS have a role in removing/reducing DAG produced by CERT?

Reviewer #3 (Public Review):

Summary:<br /> The authors developed a lipid transfer protein knock-out library to identify lipid transfer proteins with roles in lipid homeostasis/metabolism. They investigated one of their hits, the ORP9/ORP11 dimer, which they found affects sphingomyelin synthesis. Further. they found that ORP9/11 localizes to ER-Golgi contact sites via interactions between a known FFAT motif in ORP9, which can interact with the ER protein VAP, and the PH domains of ORP9 and ORP11 that target PI4P at the Golgi. They showed defects in Golgi PI4P and PS levels when ORP9 or 11 were dysfunctional, supporting but not demonstrating that ORP9/11 might exchange PI4P and PS at these contact sites. Their in vitro data indicates that both ORP9 and 11 can transfer PS. They do not assess whether either protein can transfer PI4P (although there is a very nice recent paper by He et al et You, PMID 36853333, showing that ORP9 can transfer PI4P in vitro), and they do not assess the consequences of heterodimerization on either PS or PI4P transfer. The mechanisms by which PI4P/PS level perturbations affect sphingomyelin synthesis remain unclear.

Strengths:<br /> The authors have developed an LTP knock-out library that might generate hypotheses regarding lipid metabolism, although defects are not unexpected and mechanisms will be difficult to work out--as, in fact, evidenced by this manuscript.

The OPR9/11 localization data and imaging studies are well done; this is the first more comprehensive characterization of the ORP9/11 heterodimer, which was discovered in 2010.

Weaknesses:<br /> A major flaw is that the authors claim to but do not, in fact, provide evidence of PS/PI4P counter exchange in vitro. That the presence of PI4P on the acceptor liposomes accelerates PS transfer in the in vitro assays is not proof that there is a counter exchange. In fact, since the rate-determining step in the transfer reactions is lipid transfer between membrane and transfer protein and this depends on the association of the transfer protein with donor and/or acceptor liposomes, a more likely explanation for the more efficient transfer in the presence of PI4P is that PI4P allows for longer association of lipid transfer protein with acceptor liposomes. To show the plausibility of the counter-exchange idea as applied to the ORP9/ORP11 heterodimer, the authors would need to show that it can transfer PI4P. (The work by He et al et You, 2023, mentioned above, is a very nice study that the authors might use as a model.)

Mechanistic insights from the study are limited. How does a PI4P/PS imbalance affect sphingomyelin synthesis?

The ORP9/11 heterodimer seems to behave very much like ORP9/ORP10 heterodimer, including in its localization and dimerization mode. Is ORP9/11 just another version of 9/10? I wonder whether discussions of whether they are redundant or what their different roles are might be in order. There is little mechanistic or conceptual novelty arising from this study.

A minor point, but the statement (p2., lines 19-20) that "vesicular trafficking contributes only to a small portion of lipid trafficking" is not correct and raises eyebrows. What is more correct is that protein-mediated lipid transfer ALSO plays an important role in lipid transfer. It might even be said that LTP-mediated lipid transfer is critical in fine-tuning membrane lipid composition, including of phosphoinositides.

Reviewer #1 (Public Review):

Summary:<br /> In this study, the authors present a new mouse model with a deletion of the Tmem263 gene, also known as C12orf23, which encodes the transmembrane protein 263.

Strengths:<br /> The study indicates that Tmem263 interacts with growth hormone, and possibly participates in controlling body growth.

Weaknesses (Major):<br /> The current study confirms previous findings using a mouse model of TMEM263 gene deletion. However, it remains descriptive and does not provide critical insights into the mechanism of action of TMEM263 protein. While the study demonstrates Tmem263-mediated reduction in GHR gene expression in the liver and subsequent growth retardation, it does not elucidate the pathway through which Tmem263 affects GHR expression.

Weaknesses (minor):<br /> 1. Since GH-resistant dwarfism is typically associated with increased body adiposity and hepatic lipid accumulation, considering the high expression levels of Tmem263 in adipose tissue (Figure 1C), it would be valuable to measure body adiposity and hepatic lipid content.

2. It would be helpful to specify the age at which the growth plate parameters were tested. Additionally, were there any differences observed between male and female mice (Figure 3 L-N)? Information on the local (growth plate) expression of IGF-1, IGF-1R, and GHR would also be beneficial.

3. Given the low levels of blood glucose and serum insulin, it would be relevant to know if the mice were challenged with ITT or GTT.

4. The liver transcriptomics data presented in the study is impressive. However, there seems to be a missed opportunity to delve into the data and identify potential factors involved in the regulation of GHR expression.

5. The effects of whole-body Tmem263 nullification on osteoblast differentiation and function, as well as on osteoclastogenesis, were not investigated in the study. Considering the potential impact on bone health, these aspects could be explored in future research.

Reviewer #2 (Public Review):

Summary: The study demonstrates that deletion of a small cytoplasmic membrane protein, Tmem263, caused severe impairment of longitudinal bone growth and that the impaired bone growth was caused by suppression of expression and/or protein levels of growth hormone receptors in the liver.

Strengths: The experimental design of the study is sound and the results are in general supportive of the conclusions.

Weaknesses: The study lacks mechanistic investigation into how the deletion of a gene corresponding to a small cytoplasmic membrane protein would lead to a substantial reduction in the gene expression of growth hormone receptor, which takes place in the nuclei. Accordingly, the manuscript is of a largely descriptive nature.

Reviewer #3 (Public Review):

Prior studies in humans and in chickens suggested that TMEM263 could play an important role in longitudinal bone growth, but a definitive assessment of the function and potential mechanism of action of this species-conserved plasma membrane protein has been lacking. Here, the authors create a TMEM263 null mouse model and convincingly show a dramatic cessation of post-natal growth, which becomes apparent by day PND21. They report proportional dwarfism, highly significant bone and related phenotypes, as well as notable alterations of hepatic GH signaling to IGF1. A large body of prior work has established an essential role for GH and its stimulation of IGF1 production in liver and other tissues in post-natal growth. On this basis, the authors conclude that the observed decrease in serum IGF1 seen in TMEM263-KO mice is causal for the growth phenotype, which seems likely. Moreover, they ascribe the low serum IGF1 to the observed decreases in hepatic GH receptor (GHR) expression and GHR/JAK2/STAT5 signaling to IGF1, which is plausible but not proven by the experiments presented.

The finding that TMEM263 is essential for normal hepatic GHR/IGF1 signaling is an important, and unexpected finding, one that is likely to stimulate further research into the underlying mechanisms of TMEM263 action, including the distinct possibility that these effects involve direct protein-protein interactions between GHR and TMEM263 on the plasma membrane of hepatocytes, and perhaps on other mouse cell types and tissues as well, where TMEM263 expression is up to 100-fold higher (Fig. 1C).

An intriguing finding of this study, which is under-emphasized and should be noted in the Abstract, is the apparent feminization of liver gene expression in male TMEM263-KO mice, where many male-biased genes are downregulated, and many female-biased genes are upregulated. Further investigation of these liver gene responses by comparison to public datasets could be very useful, as it could help determine: (1) whether the TMEM263 liver phenotype is similar to that of hypophysectomized male mouse liver, where GH and GHR/STAT5/IGF1 signaling are both totally ablated; or alternatively, (2) whether the phenotype is more similar to that of a male mouse given GH as a continuous infusion, which induces widespread feminization of gene expression in the liver, and is perhaps similar to the gene responses seen in the TMEM263-KO mice. Answering this question could provide critical insight into the mechanistic basis for the hepatic effects of TMEM263 gene KO.

One notable weakness of this study is the conclusion (in the Abstract, and elsewhere), that the low serum IGF-I "is due to a deficit in hepatic GH receptor (GHR) expression, and GH-induced JAK2/STAT5 signaling." This conclusion is speculative in the absence of any experimental assessment of the impact of TMEM 263-KO on GHR/IGF1 signaling in other tissues that contribute to systematic IGF1 production and which likely also impact bone growth. More direct evidence for the impact of hepatic IGF1 production per se in this mouse model could be obtained by liver-specific delivery into the TMEM263-KO mice of a constitutively active. STAT5 construct, which was recently reported to normalize hepatic and serum IGF1 levels in liver-specific GHR-KO mice (PMID: 35396838).

Another weakness is the experiment presented in Fig. 5E, which is presented as evidence for the proposed GH resistance of TMEM263-KO mice. This experiment has several design flaws: 1) It uses human GH, which unlike mouse GH, activates mouse prolactin receptor as well as GH receptor; 2) the dose of hGH used, 3µg GH/g BW, is 100 times higher than is required to activate liver STAT5; and 3) the experiment lacks a set of control livers, which are needed to establish the level of STAT5 tyrosine phosphorylation in the absence of exogenous GH treatment. Moreover, if the mice used in Fig. 5E are males (the sex was not specified), then high variability in the basal phospho-STAT5 levels of control livers is expected, in which case n=6 or more individual control male livers may be required.

Reviewer #1 (Public Review):

In this manuscript, Huang and colleagues explored the role of iron in bacterial therapy for cancer. Using proteomics, they revealed the upregulation of bacterial genes that uptake iron, and reasoned that such regulation is an adaptation to the iron-deficient tumor microenvironment. Logically, they engineered E. Coli strains with enhanced iron-uptake efficiency, and showed that these strains, together with iron scavengers, suppress tumor growth in a mouse model. Lastly, they reported the tumor suppression by IroA-E. Coli provides immunological memory via CD8+ T cells. In general, I find the findings in the manuscript novel and the evidence convincing.

1. Although the genetic and proteomic data are convincing, would it be possible to directly quantify the iron concentration in (1) E. Coli in different growth environments, and (2) tumor microenvironment? This will provide the functional consequences of upregulating genes that import iron into the bacteria.

2. Related to 1, the experiment to study the synergistic effect of CDG and VLX600 (lines 139-175) is very nice and promising, but one flaw here is a lack of the measurement of iron concentration. Therefore, a possible explanation could be that CDG acts in another manner, unrelated to iron uptake, that synergizes with VLX600's function to deplete iron from cancer cells. Here, a direct measurement of iron concentration will show the effect of CDG on iron uptake, thus complementing the missing link.

3. Lines 250-268: Although statistically significant, I would recommend the authors characterize the CD8+ T cells a little more, as the mechanism now seems quite elusive. What signals or memories do CD8+ T cells acquire after IroA-E. Coli treatment to confer their long-term immunogenicity?

4. Perhaps this goes beyond the scope of the current manuscript, but how broadly applicable is the observed iron-transport phenomenon in other tumor models? I would recommend the authors to either experimentally test it in another model or at least discuss this question.

Reviewer #2 (Public Review):

Summary:

The authors provide strong evidence that bacteria, such as E. coli, compete with tumor cells for iron resources and consequently reduce tumor growth. When sequestration between LCN2 and bacterobactin is blocked by upregulating CDG(DGC-E. coli) or salmochelin(IroA-E.coli), E. coli increase iron uptake from the tumor microenvironment (TME) and restrict iron availability for tumor cells. Long-term remission in IroA-E.coli treated mice is associated with enhanced CD8+ T cell activity. Additionally, systemic delivery of IroA-E.coli shows a synergistic effect with chemotherapy reagent oxaliplatin to reduce tumor growth.

Strengths:

It is important to identify the iron-related crosstalk between E. coli and TME. Blocking lcn2-bacterobactin sequestration by different strategies consistently reduces tumor growth.

Weaknesses:

As engineered E.coli upregulate their function to uptake iron, they may increase the likelihood of escaping from nutritional immunity (LCN2 becomes insensitive to sequester iron from the bacteria). Would this raise the chance of developing sepsis? Do authors think that it is safe to administrate these engineered bacteria in mice or humans?

Reviewer #3 (Public Review):

Summary:

Based on their observation that tumor has an iron-deficient microenvironment, and the assumption that nutritional immunity is important in bacteria-mediated tumor modulation, the authors postulate that manipulation of iron homeostasis can affect tumor growth. They show that iron chelation and engineered DGC-E. coli have synergistic effects on tumor growth suppression. Using engineered IroA-E. coli that presumably have more resistance to LCN2, they show improved tumor suppression and survival rate. They also conclude that the IroA-E. coli treated mice develop immunological memory, as they are resistant to repeat tumor injections, and these effects are mediated by CD8+ T cells. Finally, they show synergistic effects of IroA-E. coli and oxaliplatin in tumor suppression, which may have important clinical implications.

Strengths:

This paper uses straightforward in vitro and in vivo techniques to examine a specific and important question of nutritional immunity in bacteria-mediated tumor therapy. They are successful in showing that manipulation of iron regulation during nutritional immunity does affect the virulence of the bacteria, and in turn the tumor. These findings open future avenues of investigation, including the use of different bacteria, different delivery systems for therapeutics, and different tumor types.

Weaknesses:

-- There is no discussion of the cancer type and why this cancer type was chosen. Colon cancer is not one of the more prominently studied cancer types for LCN2 activity. While this is a proof-of-concept paper, there should be some recognition of the potential different effects on different tumor types. For example, this model is dependent on significant LCN production, and different tumors have variable levels of LCN expression. Would the response of the tumor depend on the role of iron in that cancer type? For example, breast cancer aggressiveness has been shown to be influenced by FPN levels and labile iron pools.<br /> -- Are the effects on tumor suppression assumed to be from E. coli virulence, i.e. Does the higher number of bacteria result in increased immune-mediated tumor suppression? Or are the effects partially from iron status in the tumor cells and the TME?<br /> -- If the effects are iron-related, could the authors provide some quantification of iron status in tumor cells and/or the TME? Could the proteomic data be queried for this data?

Reviewer #1 (Public Review):

This work reports the use of pulsed high-intensity (1-3 W/cm2) 1 MHz ultrasonic waves to stimulate the secretion of extracellular vesicles (EVs) from skeletal muscle cells (C2C12 myotubes) through the modulation of intracellular calcium, and that these, in turn, regulate the inflammatory response in macrophages.

The authors first checked that the ultrasonic irradiation did not have any adverse effect on the structure (protein content) and function (proliferation and metabolic activity) of the myotubes, and showed an up to twofold increase in EV secretion, which they attributed to an increase in Ca2+ uptake into the cell. Finally, the authors show that the myotubes exposed to the ultrasonic irradiation wherein the EV concentration was found to be elevated led to a significant decrease in expression of IL-1b and IL-6 pro-inflammatory cytokines, therefore leading the authors to assert the potential of the use of ultrasonic irradiation for promoting anti-inflammatory effects on macrophages.

While the manuscript was reasonably clearly written and the methodology and results sound, it is not clear what the real contribution of the work is. The authors' findings - that ultrasonic stimulation is capable of altering intracellular Ca2+ to effect an increase in EV secretion from cells as long as the irradiation does not affect cell viability-is well established (see, for example, Ambattu et al., Commun Biol 3, 553, 2020; Deng et al., Theranostics, 11, 9 2021; Li et al., Cell Mol Biol Lett 28, 9, 2023). Moreover, the authors' own work (Maeshige et al., Ultrasonics 110, 106243, 2021) using the exact same stimulation (including the same parameters, i.e., intensity and frequency) and cells (C2C12 skeletal myotubes) reported this. Similarly, the authors themselves reported that EV secretion from C2C12 myotubes has the ability to regulate macrophage inflammatory response (Yamaguchi et al., Front Immunol 14, 1099799, 2023). It would then stand to reason that a reasonable and logical deduction from both studies is that the ultrasonic stimulation would lead to the same attenuation of inflammatory response in macrophages through enhanced secretion of EVs from the myotubes.

The authors' claim that 'the role of Ca2+ in ultrasound-induced EV release and its intensity-dependency are still unclear', and that the aim of the present work is to clarify the mechanism, is somewhat overstated. That ultrasonic stimulation alters intracellular Ca2+ to lead to EV release, therefore establishing their interdependency and hence demonstrating the mechanism by which EV secretion is enhanced by the ultrasonic stimulation, was detailed in Ambattu et al., Commun Biol 3, 553, 2020. While this was carried out at a slightly higher frequency (10 MHz) and slightly different form of ultrasonic stimulation, the same authors have appeared to since establish that a universal mechanism of transduction across an entire range of frequencies and stimuli (Ambattu, Biophysics Rev 4, 021301, 2023).

Similarly, the anti-inflammatory effects of EVs on macrophages have also been extensively reported (Li et al., J Nanobiotechnol 20, 38, 2022; Lo Sicco et al., Stem Cells Transl Med 6, 3, 2017; Hu et al., Acta Pharma Sin B 11, 6, 2021), including that by the authors themselves in a recent study on the same C2C12 myotubes (Yamaguchi et al., Front Immunol 14, 1099799, 2023). Moreover, the authors' stated aim for the present work - clarifying the mechanism of the anti-inflammatory effects of ultrasound-induced skeletal muscle-derived EVs on macrophages - appears to be somewhat redundant given that they simply repeated the microRNA profiling study they carried out in Yamaguchi et al., Front Immunol 14, 1099799, 2023. The only difference was that a fraction of the EVs (from identical cells) that they tested were now a consequence of the ultrasound stimulation they imposed.

That the authors have found that their specific type of ultrasonic stimulation maintains this EV content (i.e., microRNA profile) is novel, although this, in itself, appears to be of little consequence to the overall objective of the work which was to show the suppression of macrophage pro-inflammatory response due to enhanced EV secretion under the ultrasonic irradiation since it was the anti-inflammatory effects were attributed to the increase in EV concentration and not their content.

Reviewer #2 (Public Review):

Summary:<br /> The authors embarked on a journey to understand the mechanisms and intensity-dependency of ultrasound (US)-induced extracellular vesicle (EV) release from myotubes and the potential anti-inflammatory effects of these EVs on macrophages. This study builds on their prior work from 2021 that initially reported US-induced EV secretion.

Strengths:<br /> 1. The finding that US-treated myotube EVs can suppress macrophage inflammatory responses is particularly intriguing, hinting at potential therapeutic avenues in inflammation modulation.

Weaknesses:<br /> 1. The exploration of output parameters for US induction appears limited, with only three different output powers (intensities) tested, thus narrowing the scope of their findings.<br /> 2. Their claim of elucidating mechanisms seems to be only partially met, with a predominant focus on the correlation between calcium responses and EV release.<br /> 3. While the intracellular calcium response is a dynamic activity, the method used to measure it could risk a loss of kinetic information.<br /> 4. The inclusion of miRNA sequencing is commendable; however, the interpretation of this data fails to draw clear conclusions, diminishing the impact of this segment.

While the authors have shown the anti-inflammatory effects of US-induced EVs on macrophages, there are gaps in the comprehensive understanding of the mechanisms underlying US-induced EV release. Certain aspects, like the calcium response and the utility of miRNA sequencing, were not fully explored to their potential. Therefore, while the study establishes some findings, it leaves other aspects only partially substantiated.

Reviewer #2 (Public Review):

The manuscript of Akter et al is an important study that investigates the role of astrocytic Gi signaling in the anterior cingulate cortex in the modulation of extracellular L-lactate level and consequently impairment in flavor-place associates (PA) learning. However, whereas some of the behavioral observations and signaling mechanism data are compelling, the conclusions about the effect on memory are inadequate as they rely on an experimental design that does not allow to differentiate acute or learning effect from the effect outlasting pharmacological treatments, i.e. effect on memory retention. With the addition of a few experiments, this paper would be of interest to the larger group of researchers interested in neuron-glia interactions during complex behavior.<br /> • Largely, I agree with the authors' conclusion that activating Gi signaling in astrocytes impairs PA learning, however, the effect on memory retrieval is not that obvious. All behavioral and molecular signaling effects described in this study are obtained with the continuous presence of CNO, therefore it is not possible to exclude the acute effect of Gi pathway activation in astrocytes. What will happen with memory on retrieval test when CNO is omitted selectively during early, middle, or late session blocks of PA learning?<br /> • I found it truly exciting that the administration of exogenous L-lactate is capable to rescue CNO-induced PA learning impairment, when co-applied. Would it be possible that this treatment has a sensitivity to a particular stage of learning (acquisition, consolidation, or memory retrieval) when L-lactate administration would be the most efficacious?<br /> • The hypothesis that observed learning impairments could be associated with diminished mitochondrial biogenesis caused by decreased l-lactate in the result of astrocytic Gi-DREADDS stimulation is very appealing, but a few key pieces of evidence are missing. So far, the hypothesis is supported by experiments demonstrating reduced expression of several components of mitochondrial membrane ATP synthase and a decrease in relative mtDNA copy numbers in ACC of rats injected with Gi-DREADDs. L-lactate injections into ACC restored and even further increased the expression of the above-mentioned markers. Co-administration of NMDAR antagonist D-APV or MCT-2 (mostly neuronal) blocker 4-CIN with L-lactate, prevented L-lactate-induced increase in relative mtDNA copy. I am wondering how the interference with mitochondrial biogenesis is affecting neuronal physiology and if it would result in impaired PA learning or schema memory.

Reviewer #3 (Public Review):

Summary: This manuscript aims to unravel the mechanisms behind Aquaporin-0 (AQP0) tetramer array formation within lens membranes. The authors utilized electron crystallography and molecular dynamics (MD) simulations to shed light on the role of cholesterol in shaping the structural organization of AQP0. The evidence suggests that cholesterol not only defines the positions and orientations of associated molecules but also plays a crucial role in stabilizing AQP0 tetramer arrays. This study provides valuable insights into the potential principles driving protein clustering within lipid rafts, advancing our understanding of membrane biology.

In this review, I will focus on the MD simulations part, since this is my area of expertise. The authors conducted an impressive set of MD simulations aiming at understanding the role of cholesterol in structural organization of AQP0 arrays. These simulations clearly demonstrate the well-defined localization of cholesterol molecules around a single AQP0 tetramer, aligning with previous computational studies and the crystallographic structures presented in this manuscript. Interestingly, authors identified an unusual position for one cholesterol molecule, located near the center of the lipid bilayer, which was stabilized by the adjacent AQP0 tetramers. The authors showed that these adjacent tetramers can withstand a larger lateral detachment force when deep cholesterol molecules are present at the interface compared to scenarios with sphingomyelin (SM) molecules at the interface between two AQP0 tetramers. Authors interpret that result as evidence that deep cholesterol molecules mechanically stabilize the interface of the AQP0 tetramers. This conclusion has minor weaknesses, and the rigor of the lateral detachment simulations could be increased by establishing a reference point for the detachment force needed to separate AQP0 tetramers in a scenario without lipids at the interface between tetramers, and by increasing the number of repeats for the non-equilibrium steered MD simulations. Thermodynamic integration might be a better approach to compute the stabilization energy in the presence of cholesterol compared to the SM case.

Reviewer #1 (Public Review):

Aquaporin-0 forms 2D crystals in the lens of the eye. This propensity to form 2D crystals was originally exploited to solve the structure of aquaporin-0 reconstituted in membranes. Existing structures do not explain why the proteins spontaneously form these arrays, however. In this work the authors investigate the hypothesis that the main lipids in the native membranes, sphingomyelin and cholesterol, contribute to lattice formation. By titrating the cholesterol: sphingomyelin ratio, the authors identify cholesterol binding sites of increasing stability. The authors identify a cholesterol that interacts with adjacent tetramers and is bound at an unusual membrane depth. Computational simulations suggest that this cholesterol is only stable in the context of adjacent tetramers (ie lattice formation) and that the presence of the cholesterol increases the stability of that interface. The exact mechanism is not clear, but the authors propose that the so-called "deep cholesterol" improves shape complementarity between adjacent tetramers and modulates the kinetics of protein-protein interactions. Finally, the authors provide a reasonable model for the role of cholesterol in

Strengths of this manuscript include the analysis of multiple structures determined with different lipid compositions and lipid:cholesterol ratios. For each these, multiple lipids can be modelled, giving a good sense of the lipid specificity at various favorable lipid binding positions. In addition, multiple hypotheses are tested in a very thorough computational analysis that provides the framework for interpreting the structural observations. The authors also provide a thorough scholarly discussion that connects their work with other studies of membrane protein-cholesterol interactions.

The model presented by the authors is consistent with the data described. Further testing of this model, for example by mutating the deep cholesterol binding site, would strengthen the model. However, such experiments might be challenging due to the relatively non-specific/hydrophobic nature of the deep cholesterol binding site.

Reviewer #2 (Public Review):

Summary:<br /> In the manuscript by Chiu et al., "Structure and dynamics of cholesterol-mediated aquaporin-0 arrays and implications for lipid rafts," the authors address the effect of cholesterol on array formation by AQP0. Using a combination of electron crystallography and molecular dynamics simulations, the authors show binding of a "deep" cholesterol molecule between AQP0 tetramers. Each AQP0 tetramers binds four deep cholesterols to form a crystallographic array of AQP0.

Strengths:<br /> The combined approaches of electron crystallography and MD simulations under different lipid conditions (different sphingomyelin and cholesterol concentrations) are a strength of the study. The authors provide a thorough and convincing assessment of cholesterol binding, protein-protein interactions, and array formation by AQP0. The MD simulations allow the authors to consider the propensity of cholesterol to occupy the observed binding sites in the absence of crystal contacts. The combined methods and the breadth of analyses set a high standard in the field of membrane protein structural biology.

The findings of the authors fit nicely into a growing body of literature on cholesterol binding sites that mediate membrane protein-protein interactions. Cholesterol interacts with a variety of membrane proteins via its smooth alpha face of rough beta face. AQP0 is somewhat unique in that it binds the rough face of cholesterol in a "deep" binding site that places cholesterol in the middle of the membrane bilayer. So-called "deep" cholesterol binding sites have been described for GPCRs and docking studies suggest they may exist on other ion channels and transporters. In the case of AQP0, the deep cholesterol acts as a glue that holds two tetramers together. Since each tetramer has four binding sites for deep cholesterol, the assembly and mechanical stability of an extended two-dimensional array of AQP0 tetramers is a natural consequence in lens membranes.

Weaknesses:<br /> The authors report that the findings generally apply to raft formation in membranes. However, this point is less clear as the lens membrane in which AQP0 resides is rather unique in lipid and protein content and density. Nonetheless, the authors achieve the overall goal of evaluating cholesterol binding to AQP0, and there are many valuable and informative figures in the main manuscript and supplement that provide convincing results and interpretations.

Reviewer #3 (Public Review):

Summary:<br /> This paper by Sabelo et al. describes a new pathway by which lack of IgM in the mouse lowers bronchial hyperresponsiveness (BHR) in response to metacholine in several mouse models of allergic airway inflammation in Balb/c mice and C57/Bl6 mice. Strikingly, loss of IgM does not lead to less eosinophilic airway inflammation, Th2 cytokine production or mucus metaplasia, but to a selective loss of BHR. This occurs irrespective of the dose of allergen used. This was important to address since several prior models of HDM allergy have shown that the contribution of B cells to airway inflammation and BHR is dose dependent.

After a description of the phenotype, the authors try to elucidate the mechanisms. There is no loss of B cells in these mice. However, there is a lack of class switching to IgE and IgG1, with a concomitant increase in IgD. Restoring immunoglobulins with transfer of naïve serum in IgM deficient mice leads to restoration of allergen-specific IgE and IgG1 responses, which is not really explained in the paper how this might work. There is also no restoration of IgM responses, and concomitantly, the phenotype of reduced BHR still holds when serum is given, leading authors to conclude that the mechanism is IgE and IgG1 independent. Wild type B cell transfer also does not restore IgM responses, due to lack of engraftment of the B cells. Next authors do whole lung RNA sequencing and pinpoint reduced BAIAP2L1 mRNA as the culprit of the phenotype of IgM-/- mice. However, this cannot be validated fully on protein levels and immunohistology since differences between WT and IgM KO are not statistically significant, and B cell and IgM restoration are impossible. The histology and flow cytometry seems to suggest that expression is mainly found in alpha smooth muscle positive cells, which could still be smooth muscle cells or myofibroblasts. Next therefore, the authors move to CRISPR knock down of BAIAP2L1 in a human smooth muscle cell line, and show that loss leads to less contraction of these cells in vitro in a microscopic FLECS assay, in which smooth muscle cells bind to elastomeric contractible surfaces.

Strengths:<br /> 1. There is a strong reduction in BHR in IgM-deficient mice, without alterations in B cell number, disconnected from effects on eosinophilia or Th2 cytokine production<br /> 2. BAIAP2L1 has never been linked to asthma in mice or humans

Weaknesses:

1. While the observations of reduced BHR in IgM deficient mice are strong, there is insufficient mechanistic underpinning on how loss of IgM could lead to reduced expression of BAIAP2L1. Since it is impossible to restore IgM levels by either serum or B cell transfer and since protein levels of BAIAP2L1 are not significantly reduced, there is a lack of a causal relationship that this is the explanation for the lack of BHR in IgM-deficient mice. The reader is unclear if there is a fundamental (maybe developmental) difference in non-hematopoietic cells in these IgM-deficient mice (which might have accumulated another genetic mutation over the years). In this regard, it would be important to know if littermates were newly generated, or historically bred along with the KO line.<br /> 2. There is no mention of the potential role of complement in activation of AHR, which might be altered in IgM-deficient mice<br /> 3. What is the contribution of elevated IgD in the phenotype of the IgM-deficient mice. It has been described by this group that IgD levels are clearly elevated<br /> 4. How can transfer of naïve serum in class switching deficient IgM KO mice lead to restoration of allergen specific IgE and IgG1?<br /> 5. Alpha smooth muscle antigen is also expressed by myofibroblasts. This is insufficiently worked out. The histology mentions "expression in cells in close contact with smooth muscle". This needs more detail since it is a very vague term. Is it in smooth muscle or in myofibroblasts.<br /> 6. Have polymorphisms in BAIAP2L1 ever been linked to human asthma?<br /> 7. IgM deficient patients are at increased risk for asthma. This paper suggests the opposite. So the translational potential is unclear.

Reviewer #1 (Public Review):

Summary: The authors of this study sought to define a role for IgM in responses to house dust mites in the lung.

Strengths:

Unexpected observation about IgM biology<br /> Combination of experiments to elucidate function

Weaknesses:

Would love more connection to human disease

Reviewer #2 (Public Review):

Summary:<br /> The manuscript by Hadebe and colleagues describes a striking reduction in airway hyperresponsiveness in Igm-deficient mice in response to HDM, OVA and papain across the B6 and BALB-c backgrounds. The authors suggest that the deficit is not due to improper type 2 immune responses, nor an aberrant B cell response, despite a lack of class switching in these mice. Through RNA-Seq approaches, the authors identify few differences between the lungs of WT and Igm-deficient mice, but see that two genes involved in actin regulation are greatly reduced in IgM-deficient mice. The authors target these genes by CRISPR-Cas9 in in vitro assays of smooth muscle cells to show that these may regulate cell contraction. While the study is conceptually interesting, there are a number of limitations, which stop us from drawing meaningful conclusions.

Strengths:<br /> Fig. 1. The authors clearly show that IgMKO mice have striking reduced AHR in the HDM model, despite the presence of a good cellular B cell response.

Weaknesses:<br /> Fig. 2.<br /> The authors characterize the cd4 t cell response to HDM in IGMKO mice.<br /> They have restimulated medLN cells with antiCD3 for 5 days to look for IL-4 and IL-13, and find no discernible difference between WT and KO mice. The absence of PBS-treated WT and KO mice in this analysis means it is unclear if HDM-challenged mice are showing IL-4 or IL-13 levels above that seen at baseline in this assay. The choice of 5 days is strange, given that the response the authors want to see is in already primed cells. A 1-2 day assay would have been better. It is concerning that the authors state that HDM restimulation did not induce cytokine production from medLN cells, since countless studies have shown that restimulation of medLN would induce IL-13, IL-5 and IL-10 production from medLN. This indicates that the sensitization and challenge model used by the authors is not working as it should. The IL-13 staining shown in panel c is also not definitive. One should be able to optimize their assays to achieve a better level of staining, to my mind.

In d-f, the authors perform a serum transfer, but they only do this once. The half life of IgM is quite short. The authors should perform multiple naïve serum transfers to see if this is enough to induce FULL AHR.

The presence of negative values of total IgE in panel F would indicate some errors in calculation of serum IgE concentrations.

Overall, it is hard to be convinced that IgM-deficiency does not lead to a reduction in Th2 inflammation, since the assays appear suboptimal.

Fig. 3. Gene expression differences between WT and KO mice in PBS and HDM challenged settings are shown. PCA analysis does not show clear differences between all four groups, but genes are certainly up and downregulated, in particular when comparing PBS to HDM challenged mice. In both PBS and HDM challenged settings, three genes stand out as being upregulated in WT v KO mice. these are Baiap2l1, erdr1 and Chil1.

Fig. 4. The authors attempt to quantify BAIAP2L1 in mouse lungs. It is difficult to know if the antibody used really detects the correct protein. A BAIAP2L1-KO is not used as a control for staining, and I am not sure if competitive assays for BAIAP2L1 can be set up. The flow data is not convincing. The immunohistochemistry shows BAIAP2L1 (in red) in many, many cells, essentially throughout the section. There is also no discernible difference between WT and KO mice, which one might have expected based on the RNA-Seq data. So, from my perspective, it is hard to say if/where this protein is located, and whether there truly exists a difference in expression between wt and ko mice.

Fig. 5 and 6. The authors use a single cell contractility assay to measure whether BAIAP2L1 and ERDR1 impact on bronchial smooth muscle cell contractility. I am not familiar with the assay, but it looks like an interesting way of analysing contractility at the single cell level.<br /> The authors state that targeting these two genes with Cas9gRNA reduces smooth muscle cell contractility, and the data presented for contractility supports this observation. However, the efficiency of Cas9-mediated deletion is very unclear. The authors present a PCR in supp fig 9c as evidence of gene deletion, but it is entirely unclear with what efficiency the gene has been deleted. One should use sequencing to confirm deletion. Moreover, if the antibody was truly working, one should be able to use the antibody used in Fig 4 to detect BAIAP2L1 levels in these cells. The authors do not appear to have tried this.

Other impressions:<br /> The paper is lacking a link between the deficiency of IgM and the effects on smooth muscle cell contraction.<br /> The levels of IL-13 and TNF in lavage of WT and IGMKO mice could be analysed.

Moreover, what is the impact of IgM itself on smooth muscle cells? In the Fig. 7 schematic, are the authors proposing a direct role for IgM on smooth muscle cells? Does IgM in cell culture media induce contraction of SMC? This could be tested and would be interesting, to my mind.

Reviewer #2 (Public Review):

Summary:

The manuscript by Wohlwend et al. investigates the implications of inhibiting ceramide synthase Cers1 on skeletal muscle function during aging. The authors propose a role for Cers1 in muscle myogenesis and aging sarcopenia. Both pharmacological and AAV-driven genetic inhibition of Cers1 in 18-month-old mice lead to reduced C18 ceramides in skeletal muscle, exacerbating age-dependent features such as muscle atrophy, fibrosis, and center-nucleated fibers. Similarly, inhibition of the Cers1 orthologue in C. elegans reduces motility and causes alterations in muscle morphology.

Strengths:

The study is well-designed, carefully executed, and provides highly informative and novel findings that are relevant to the field.

Weaknesses:

The following points should be addressed to support the conclusions of the manuscript.

1) It would be essential to investigate whether P053 treatment of young mice induces age-dependent features besides muscle loss, such as muscle fibrosis or regeneration. This would help determine whether the exacerbation of age-dependent features solely depends on Cers1 inhibition or is associated with other factors related to age-dependent decline in cell function. Additionally, considering the reported role of Cers1 in whole-body adiposity, it is necessary to present data on mice body weight and fat mass in P053-treated aged-mice.

2) As grip and exercise performance tests evaluate muscle function across several muscles, it is not evident how intramuscular AAV-mediated Cers1 inhibition solely in the gastrocnemius muscle can have a systemic effect or impact different muscles. This point requires clarification.

3) To further substantiate the role of Cers1 in myogenesis, it would be crucial to investigate the consequences of Cers1 inhibition under conditions of muscle damage, such as cardiotoxin treatment or eccentric exercise.

4) It would be informative to determine whether the muscle defects are primarily dependent on the reduction of C18-ceramides or the compensatory increase of C24-ceramides or C24-dihydroceramides.

5) Previous studies from the research group (PMID 37118545) have shown that inhibiting the de novo sphingolipid pathway by blocking SPLC1-3 with myriocin counteracts muscle loss and that C18-ceramides increase during aging. In light of the current findings, certain issues need clarification and discussion. For instance, how would myriocin treatment, which reduces Cers1 activity because of the upstream inhibition of the pathway, have a positive effect on muscle? Additionally, it is essential to explain the association between the reduction of Cers1 gene expression with aging (Fig. 1B) and the age-dependent increase in C18-ceramides (PMID 37118545).

Addressing these points will strengthen the manuscript's conclusions and provide a more comprehensive understanding of the role of Cers1 in skeletal muscle function during aging.

Reviewer #1 (Public Review):

Summary: The authors identified that genetically and pharmacological inhibition of CERS1, an enzyme implicated in ceramides biosynthesis worsen muscle fibrosis and inflammation during aging.

Strengths: the study points out an interesting issue on excluding CERS1 inhibition as a therapeutic strategy for sarcopenia. Overall, the article it's well written and clear.

Weaknesses: Many of the experiments confirmed previous published data, which also show a decline of CERS1 in ageing and the generation and characterization of a muscle specific knockout mouse line. The mechanistic insights of how the increased amount of long ceramides (cer c24) and the decreased of shorter ones (cer c18) might influence muscle mass, force production, fibrosis and inflammation in aged mice have not been addressed.

Reviewer #3 (Public Review):

The valuable work shows some unique characteristics of long-lived PCs in comparison with bulk PCs. In particular, the authors clearly indicated the dependency of CXCR4 in PC longevity and provided a deal of resource of PC transcriptomes. Though CD93 is known as a marker for long-lived PCs, the authors can provide more data related to CD93.

Summary: Long-lived PCs are maintained with low motility and in a CXCR4-dependent manner.

Strengths: The reporter mice for fate-mapping can clearly distinguish long-lived PCs from total PCs and greatly contribute to the identification of long-lived PCs.

Weaknesses: The authors are unable to find a unique marker for long-lived PCs.

Reviewer #1 (Public Review):

The mechanisms underlying the generation and maintenance of LLPCs have been one of the unresolved issues. Recently, four groups have independently generated new genetic tools that allow fate tracing of murine plasma cells and have addressed how LLPCs are generated or maintained in homeostatic conditions or upon antigen immunization or viral infection. Here, Jing et al. have established another, but essentially the same, PC time stamping system, and tried to address the issues above. The question is whether the findings reported here provide significant conceptual advances from what has already been published.

1) Some of the observations in this manuscript have already been made by other studies (Xu et al. 2020, Robinson et al. 2022, Liu et al. 2022, Koike et al. 2023, Robinson et al. 2023). In my opinion, however, genetic analysis of the role of CXCR4 on PC localization or survival in BM (Figure 5) was well performed and provided some new aspects which have not been addressed in previous reports. The motility of CXCR4 cKO plasma cells in BM is not shown, but it could further support the idea that reduced mobility or increased clustering is required for longevity.

2) The combination of the several surface markers shown in Figure 3&4 doesn't seem to be practically applicable to identify or gate on LLPCs, because differential expression of CD81, CXCR4, CD326, CD44, or CD48 on LLPCs vs bulk PCs was very modest. EpCAMhi/CXCR3-, Ly6Ahi/Tigit- (Liu et al. 2022), B220lo/MHC-IIlo (Koike et al. 2023), or SLAMF6lo/MHC-IIlo (Robinson et al. 2023) has been reported as markers for LLPC population. It is unclear that the combination of surface markers presented here is superior to published markers. In addition, it is unclear why the authors did not use their own gene expression data (Fig.6), instead of using public datasets, for picking up candidate markers.

Reviewer #2 (Public Review):

In this study by Jing, Fooksman, and colleagues, a Blimp1-CreERT2-based genetic tracing study is employed to label plasma cells. Over the course of several months post-tamoxifen treatment, the only remaining labeled cells are long-lived plasma cells. This system provides a way to sort live long-lived plasma cells and compare them to unlabeled plasma cells, which contain a range of short-to-long-lived cells. From this analysis, several observations are made: 1) the turnover rate of plasma cells is greater in the spleen than in the bone marrow; 2) the turnover rate is highest early in life; 3) subtle transcriptional and cell surface marker differences distinguish long- from shorter-lived plasma cells; 4) long-lived plasma cells in the bone marrow are sessile and localize in clusters with each other; 5) CXCR4 is required for plasma cell retention in these clusters and in the bone marrow; 6) Repertoire analysis hints that the selection of long-lived plasma cells is not random for any cell that lands in the bone marrow.

Strengths:

1) The genetic timestamping approach is a clever and functional way to separate plasma cells of differing longevities.

2) This approach led to the identification of several markers that could help prospective separation of long-lived plasma cells from others.

3) Functional labeling of long-lived plasma cells allowed for a higher resolution analysis of transcriptomes and motility than was previously possible.

4) The genetic system allowed for a revisitation of the importance of CXCR4 in plasma cell retention and survival.

Weaknesses:

1) Most of the labeling studies, likely for practical reasons, were done on polyclonal rather than antigen-specific plasma cells. The triggers of these responses could vary based on age at the time of exposure, anatomical sites, etc. How these differences might influence markers and transcriptomes, independently of longevity, is not completely known.

2) The fraction of long-lived plasma cells in the unlabeled fraction varies with age, potentially diluting differences between long- and short-lived plasma cells.

3) The authors suggest their data favors a model by which plasma cells compete for niche space. Yet there is no evidence presented here that these niches are limiting.

4) The functional importance of the observed transcriptome differences between long- and shorter-lived plasma cells is unknown. An assessment as to whether these differences are conserved in human long- and short-lived bone marrow plasma cells might provide circumstantial supporting evidence that these changes are important for longevity.

Reviewer #1 (Public Review):

I have a major conceptual problem with this manuscript: How can the full deletion of a gene (PARG) sensitize a cell to further inhibition by its chemical inhibitor (PARGi) since the target protein is fully absent?

The authors state in the discussion section: "The residual PARG dePARylation activity observed in PARG KO cells likely supports cell growth, which can be further inhibited by PARGi". What does this statement mean? Is the authors' conclusion that their PARG KOs are not true KOs but partial hypomorphic knockdowns? Were the authors working with KO clones or CRISPR deletion in populations of cells?

Are there splice variants of PARG that were not knocked down? Are there PARP paralogues that can complement the biochemical activity of PARG in the PARG KOs? The authors do not discuss these critical issues nor engage with this problem.

These issues have to be dealt with upfront in the manuscript for the reader to make sense of their work.

Reviewer #2 (Public Review):

Summary:<br /> In this manuscript, Nie et al investigate the effect of PARG KO and PARG inhibition (PARGi) on pADPR, DNA damage, cell viability, and synthetic lethal interactions in HEK293A and Hela cells. Surprisingly, the authors report that PARG KO cells are sensitive to PARGi and show higher pADPR levels than PARG KO cells, which are abrogated upon deletion or inhibition of PARP1/PARP2. The authors explain the sensitivity of PARG KO to PARGi through incomplete PARG depletion and demonstrate complete loss of PARG activity when incomplete PARG KO cells are transfected with additional gRNAs in the presence of PARPi. Furthermore, the authors show that the sensitivity of PARG KO cells to PARGi is not caused by NAD depletion but by S-phase accumulation of pADPR on chromatin coming from unligated Okazaki fragments, which are recognized and bound by PARP1. Consistently, PARG KO or PARG inhibition shows synthetic lethality with Pol beta, which is required for Okazaki fragment maturation. PARG expression levels in ovarian cancer cell lines correlate negatively with their sensitivity to PARGi.

Strengths:<br /> The authors show that PARG is essential for removing ADP-ribosylation in S-phase.

Weaknesses:<br /> 1) This begs the question as to the relevant substrates of PARG in S-phase, which could be addressed, for example, by analysing PARylated proteins associated with replication forks in PARG-depleted cells (EdU pulldown and Af1521 enrichment followed by mass spectrometry).<br /> 2) The results showing the generation of a full PARG KO should be moved to the beginning of the Results section, right after the first Results chapter (PARG depletion leads to drastic sensitivity to PARGi), otherwise, the reader is left to wonder how PARG KO cells can be sensitive to PARGi when there should be presumably no PARG present.<br /> 3) Please indicate in the first figure which isoforms were targeted with gRNAs, given that there are 5 PARG isoforms. You should also highlight that the PARG antibody only recognizes the largest isoform, which is clearly absent in your PARG KO, but other isoforms may still be produced, depending on where the cleavage sites were located.<br /> 4) FACS data need to be quantified. Scatter plots can be moved to Supplementary while quantification histograms with statistical analysis should be placed in the main figures.<br /> 5) All colony formation assays should be quantified and sensitivity plots should be shown next to example plates.<br /> 6) Please indicate how many times each experiment was performed independently and include statistical analysis.

Reviewer #3 (Public Review):

Here the authors carried out a CRISPR/sgRNA screen with a DDR gene-targeted mini-library in HEK293A cells looking for genes whose loss increased sensitivity to treatment with the PARG inhibitor, PDD00017273 (PARGi). Surprisingly they found that PARG itself, which encodes the cellular poly(ADP-ribose) glycohydrolase (dePARylation) enzyme, was a major hit. Targeted PARG KO in 293A and HeLa cells also caused high sensitivity to PARGi. When PARG KO cells were reconstituted with catalytically-dead PARG, MMS treatment caused an increase in PARylation, not observed when cells were reconstituted with WT PARG or when the PARG KO was combined with PARP1/2 DKO, suggesting that loss of PARG leads to a strong PARP1/2-dependent increase in protein PARylation. The decrease in intracellular NADH+, the substrate for PARP-driven PARylation, observed in PARG KO cells was reversed by treatment with NMN or NAM, and this treatment partially rescued the PARG KO cell lethality. However, since NAD+ depletion with the FK868 nicotinamide phosphoribosyltransferase (NAMPT) inhibitor did not induce a similar lethality the authors concluded that NAD+ depletion/reduction was only partially responsible for the PARGi toxicity. Interestingly, PARylation was also observed in untreated PARG KO cells, specifically in S phase, without a significant rise in γH2AX signals. Using cells synchronized at G1/S by double thymidine blockade and release, they showed that entry into S phase was necessary for PARGi to induce PARylation in PARG KO cells. They found an increased association of PARP1 with a chromatin fraction in PARG KO cells independent of PARGi treatment, and suggested that PARP1 trapping on chromatin might account in part for the increased PARGi sensitivity. They also showed that prolonged PARGi treatment of PARG KO cells caused S phase accumulation of pADPr eventually leading to DNA damage, as evidenced by increased anti-γH2AX antibody signals and alkaline comet assays. Based on the use of emetine, they deduced that this response could be caused by unligated Okazaki fragments. Next, they carried out FACS-based CRISPR screens to identify genes that might be involved in cell lethality in WT and PARG KO cells, finding that loss of base excision repair (BER) and DNA repair genes led to increased PARylation and PARGi sensitivity, whereas loss of PARP1 had the opposite effects. They also found that BER pathway disruption exhibited synthetic lethality with PARGi treatment in both PARG KO cells and WT cells, and that loss of genes involved in Okazaki fragment ligation induced S phase pADPr signaling. In a panel of human ovarian cancer cell lines, PARGi sensitivity was found to correlate with low levels of PARG mRNA, and they showed that the PARGi sensitivity of cells could be reduced by PARPi treatment. Finally, they addressed the conundrum of why PARG KO cells should be sensitive to a specific PARG inhibitor if there is no PARG to inhibit and found that the PARG KO cells had significant residual PARG activity when measured in a lysate activity assay, which could be inhibited by PARGi, although the inhabited PARG activity levels remained higher than those of PARG cKO cells (see below). This led them to generate new, more complete PARG KO cells they called complete/conditional KO (cKO), whose survival required the inclusion of the olaparib PARPi in the growth medium. These PARG cKO cells exhibited extremely low levels of PARG activity in vitro, consistent with a true PARG KO phenotype.

The finding that human ovarian cancer cells with low levels of PARG are more sensitive to inhibition with a small molecule PARG inhibitor, presumably due to the accumulation of high levels of protein PARylation (pADPr) that are toxic to cells is quite interesting, and this could be useful in the future as a diagnostic marker for preselection of ovarian cancer patients for treatment with a PARG inhibitor drug. The finding that loss of base excision repair (BER) and DNA repair genes led to increased PARylation and PARGi sensitivity is in keeping with the conclusion that PARG activity is essential for cell fitness, because it prevents excessive protein PARylation. The observation that increased PARylation can be detected in an unperturbed S phase in PARG KO cells is also of interest. However, the functional importance of protein PARylation at the replication fork in the normal cell cycle was not fully investigated, and none of the key PARylation targets for PARG required for S phase progression were identified. Overall, there are some interesting findings in the paper, but their impact is significantly lessened by the confusing way in which the paper has been organized and written, and this needs to be rectified.

Reviewer #1 (Public Review):

The authors consider data by the Heisenberg group on rheological properties of non-confluent tissue in zebrafish embryos. These data had shown a steep increase and subsequent saturation in viscosity with cell density. The authors introduce a physical agent-based model of such tissues that accounts for the dispersion in cell size and the softness of the cells. The model is inspired by previous models to study glassy dynamics and reveals essential physical features that can explain the observed behavior. It goes beyond previous studies that had analysed the observations in terms of a percolation problem. The numerics is thoroughly done and could have a deep impact on how we describe non-confluent tissues.

Reviewer #2 (Public Review):

This paper explores how minimal active matter simulations can model tissue rheology, with applications to the in vivo situation of zebrafish morphogenesis. The authors explore the idea of active noise, particle softness and size heterogeneity cooperating to give rise to surprising features of experimental tissue rheologies (in particular an increase and then a plateau in viscosity with fluid fraction). In general, the paper is interesting from a theoretical standpoint, by providing a bridge between concepts from jamming of particulate systems and experiments in developmental biology. The idea of exploring a free space picture in this context is also interesting. However, I'm still unsure right now though of how much it can be applied to the specific system that the authors refer to - which could be fixed either by doing theoretical checks or considering other experimental systems/models reported in the recent literature.

Reviewer #3 (Public Review):

The authors successfully explain the sharp rise and subsequent saturation of the viscosity in dependence of cell packing fraction in zebrafish blastoderm with the help of a 2D model of soft deformable, polydisperse and self-propelled (active) disks. The main experimental observations can be reproduced and the unusual dependence of the viscosity on packing fraction can be explained by the available free area and the emergent motility of small sized cells facilitating multi-cell rearrangement in a highly jammed environment.

The paper is very well written, the results (experimental as well as theoretical) are original and scientifically valid. This is an important contribution to understanding the rheological properties of non-confluent tissues linking equilibrium and transport properties.

Reviewer #1 (Public Review):

This paper provides new evidence on the relationship between genetic/chromosome divergence and capacity for asexual reproduction (via unreduced, clonal gametes) in hybrid males or females. Whereas previous studies have focussed just on the hybrid combinations that have yielded asexual lineages in nature, the authors take an experimental approach, analysing meiotic processes in F1 hybrids for combinations of species spanning different levels of divergence, whether or not they form asexual lineages in nature. As such, the findings here are a substantial advance towards understanding how new asexual lineages form.

The quality of the work is high, the analyses are sound, and the authors sensibly link their observations to the speciation continuum. I should also add that the cytogenetic work here is just beautiful!

A key finding is that the precondition for asexual reproduction - the formation of unreduced gametes - is not unusual among hybrid females, so that we have to consider other factors to explain the rarity of asexual species - a major unresolved issue in evolutionary biology. This work also highlights a previously overlooked effect of chromosome organisation on speciation.

Reviewer #2 (Public Review):

The authors investigate the origin of asexual reproduction through hybridization between species. In loaches, diploid, polyploid, and asexual forms have been described in natural populations. The authors experimentally cross multiple species of loaches and conduct an impressively detailed characterization of gametogenesis using molecular cytogenetics to show that although meiosis arrests early in male hybrids, a subset of cells in females undergo endoreplication before meiosis, producing diploid eggs. This only occurred in hybrids of parental species that were of intermediate divergence. This work supports an expanding view of speciation where asexuality could emerge during a narrow evolutionary window where genomic divergence between species is not too high to cause hybrid inviability, but high enough to disrupt normal meiotic processes.

I enjoyed reading this study and I was impressed by the rigorous experiments. The authors provide strong evidence that premeiotic genome endoreplication is the mechanism behind asexually-reproducing females. In addition, I found the evidence convincing that this phenomenon is a consequence of combining two divergent genomes in an F1 hybrid female. The authors did not observe a single incidence of genome duplication in any of the parental species among a large number of surveyed oocytes.

Reviewer #1 (Public Review):

The manuscript "Diffusive lensing as a mechanism of intracellular transport and compartmentalization", explores the implications of heterogeneous viscosity on the diffusive dynamics of particles. The authors analyze three different scenarios:

(i) diffusion under a gradient of viscosity,

(ii) clustering of interacting particles in a viscosity gradient, and

(iii) diffusive dynamics of non-interacting particles with circular patches of heterogeneous viscous medium.

The implications of a heterogeneous environment on phase separation and reaction kinetics in cells are under-explored. This makes the general theme of this manuscript very relevant and interesting. However, the analysis in the manuscript is not rigorous, and the claims in the abstract are not supported by the analysis in the main text.

Following are my main comments on the work presented in this manuscript:

(a) The central theme of this work is that spatially varying viscosity leads to position-dependent diffusion constant. This, for an overdamped Langevin dynamics with Gaussian white noise, leads to the well-known issue of the interpretation of the noise term. The authors use the Ito interpretation of the noise term because their system is non-equilibrium.

One of the main criticisms I have is on this central point. The issue of interpretation arises only when there are ill-posed stochastic dynamics that do not have the relevant timescales required to analyze the noise term properly. Hence, if the authors want to start with an ill-posed equation it should be mentioned at the start. At least the Langevin dynamics considered should be explicitly mentioned in the main text. Since this work claims to be relevant to biological systems, it is also of significance to highlight the motivation for using the ill-posed equation rather than a well-posed equation. The authors refer to the non-equilibrium nature of the dynamics but it is not mentioned what non-equilibrium dynamics to authors have in mind. To properly analyze an overdamped Langevin dynamics a clear source of integrated timescales must be provided. As an example, one can write the dynamics as<br /> Eq. (1) \dot x = f(x) + g(x) \eta , which is ill-defined if the noise \eta is delta correlated in time but well-defined when \eta is exponentially correlated in time. One can of course look at the limit in which the exponential correlation goes to a delta correlation which leads to Eq. (1) interpreted in Stratonovich convention. The choice to use the Ito convention for Eq. (1) in this case is not justified.

(b) Generally, the manuscript talks of viscosity gradient but the equations deal with diffusion which is a combination of viscosity, temperature, particle size, and particle-medium interaction. There is no clear motivation provided for focus on viscosity (cytoplasm as such is a complex fluid) instead of just saying position-dependent diffusion constant. Maybe authors should use viscosity only when talking of a context where the existence of a viscosity gradient is established either in a real experiment or in a thought experiment.

(c) The section "Viscophoresis drives particle accumulation" seems to not have new results. Fig. 1 verifies the numerical code used to obtain the results in the later sections. If that is the case maybe this section can be moved to supplementary or at least it should be clearly stated that this is to establish the correctness of the simulation method. It would also be nice to comment a bit more on the choice of simulation methods with changing hopping sizes instead of, for example, numerically solving stochastic ODE.

A minor comment, the statement "the physically appropriate convention to use depends upon microscopic parameters and timescale hierarchies not captured in a coarse-grained model of diffusion." is not true as is noted in the references that authors mention, a correct coarse-grained model provides a suitable convention (see also Phys. Rev. E, 70(3), 036120., Phys. Rev. E, 100(6), 062602.).

(d) The section "Interaction-mediated clustering is affected by viscophoresis" makes an interesting statement about the positioning of clusters by a viscous gradient. As a theoretical calculation, the interplay between position-dependent diffusivity and phase separation is indeed interesting, but the problem needs more analysis than that offered in this manuscript. Just a plot showing clustering with and without a gradient of diffusion does not give enough insight into the interplay between density-dependent diffusion and position-dependent diffusion. A phase plot that somehow shows the relative contribution of the two effects would have been nice. Also, it should be emphasized in the main text that the inter-particle interaction is through a density-dependent diffusion constant and not a conservative coupling by an interaction potential.

(e) The section "In silico microrheology shows that viscophoresis manifests as anomalous diffusion" the authors show that the MSD with and without spatial heterogeneity is different. This is not a surprise - as the underlying equations are different the MSD should be different. There are various analogies drawn in this section without any justification:<br /> (i) "the saturation MSD was higher than what was seen in the homogeneous diffusion scenario possibly due to particles robustly populating the bulk milieu followed by directed motion into the viscous zone (similar to that of a Brownian ratchet, (Peskin et al., 1993))."<br /> (ii) "Note that lensing may cause particle displacements to deviate from a Gaussian distribution, which could explain anomalous behaviors observed both in our simulations and in experiments in cells (Parry et al., 2014)."<br /> Since the full trajectory of the particles is available, it can be analyzed to check if this is indeed the case.

(f) The final section "In silico FRAP in a heterogeneously viscous environment ... " studies the MSD of the particles in a medium with heterogeneous viscous patches which I find the most novel section of the work. As with the section on inter-particle interaction, this needs further analysis.

To summarise, as this is a theory paper, just showing MSD or in silico FRAP data is not sufficient. Unlike experiments where one is trying to understand the systems, here one has full access to the dynamics either analytically or in simulation. So just stating that the MSD in heterogeneous and homogeneous environments are not the same is not sufficient. With further analysis, this work can be of theoretical interest. Finally, just as a matter of personal taste, I am not in favor of the analogy with optical lensing. I don't see the connection.

Reviewer #2 (Public Review):

Summary:<br /> The authors study through theory and simulations the diffusion of microscopic particles and aim to account for the effects of inhomogeneous viscosity and diffusion - in particular regarding the intracellular environment. They propose a mechanism, termed "Diffusive lensing", by which particles are attracted towards high-viscosity regions where they remain trapped. To obtain these results, the authors rely on agent-based simulations using custom rules performed with the Ito stochastic calculus convention, without spurious drift. They acknowledge the fact that this convention does not describe equilibrium systems, and that their results would not hold at equilibrium - and discard these facts by invoking the fact that cells are out-of-equilibrium. Finally, they show some applications of their findings, in particular enhanced clustering in the high-viscosity regions. The authors conclude that as inhomogeneous diffusion is ubiquitous in life, so must their mechanism be, and hence it must be important.

Strengths:<br /> The article is well-written, and clearly intelligible, its hypotheses are stated relatively clearly and the models and mathematical derivations are compatible with these hypotheses.

Weaknesses:<br /> The main problem of the paper is these hypotheses. Indeed, it all relies on the Ito interpretation of the stochastic integrals. Stochastic conventions are a notoriously tricky business, but they are both mathematically and physically well-understood and do not result in any "dilemma" [some citations in the article, such as (Lau and Lubensky) and (Volpe and Wehr), make an unambiguous resolution of these]. Conventions are not an intrinsic, fixed property of a system, but a choice of writing; however, whenever going from one to another, one must include a "spurious drift" that compensates for the effect of this change - a mathematical subtlety that is entirely omitted in the article: if the drift is zero in one convention, it will thus be non-zero in another in the presence of diffusive gradients. It is well established that for equilibrium systems obeying fluctuation-dissipation, the spurious drift vanishes in the anti-Ito stochastic convention (which is not "anticipatory", contrarily to claims in the article, are the "steps" are local and infinitesimal). This ensures that the diffusion gradients do not induce currents and probability gradients, and thus that the steady-state PDF is the Gibbs measure. This equilibrium case should be seen as the default: a thermal system NOT obeying this law should warrant a strong justification (for instance in the Volpe and Wehr review this can occur through memory effects in robotic dynamics, or through strong fluctuation-dissipation breakdown). In near-equilibrium thermal systems such as the intracellular medium (where, although out-of-equilibrium, temperature remains a relevant and mostly homogeneous quantity), deviations from this behavior must be physically justified and go to zero when going towards equilibrium.

Here, drifts are arbitrarily set to zero in the Ito convention (the exact opposite of the equilibrium anti-Ito), which is the equilibrium equivalent to adding a force (with drift $- grad D$) exactly compensating the spurious drift. If we were to interpret this as a breakdown of detailed balance with inhomogeneous temperature, the "hot" region would be effectively at 4x higher temperature than the cold region (i.e. 1200K) in Fig 1A.

It is the effects of this arbitrary force (exactly compensating the Ito spurious drift) that are studied in the article. The fact that it results in probability gradients is trivial once formulated this way (and in no way is this new - many of the references, for instance, Volpe and Wehr, mention this). Enhanced clustering is also a trivial effect of this probability gradient (the local concentration is increased by this force field, so phase separation can occur). As a side note the "neighbor sensing" scheme to describe interactions is very peculiar and not physically motivated - it violates stochastic thermodynamics laws too, as the detailed balance is apparently not respected. Finally, the "anomalous diffusion" discussion is at odds with what the literature on this subject considers anomalous (the exponent does not appear anomalous).

The authors make no further justification of their choice of convention than the fact that cells are out-of-equilibrium, leaving the feeling that this is a detail. They make mentions of systems (eg glycogen, prebiotic environment) for which (near-)equilibrium physics should mostly prevail, and of fluctuation-dissipation ("Diffusivity varies inversely with viscosity", in the introduction). Yet the "phenomenon" they discuss is entirely reliant on an undiscussed mechanism by which these assumptions would be completely violated (the citations they make for this - Gnesotto '18 and Phillips '12 - are simply discussions of the fact that cells are out-of-equilibrium, not on any consequences on the convention).

Finally, while inhomogeneous diffusion is ubiquitous, the strength of this effect in realistic conditions is not discussed (this would be a significant problem if the effect were real, which it isn't). Gravitational attraction is also an ubiquitous effect, but it is not important for intracellular compartmentalization.

To conclude, the "diffusive lensing" effect presented here is not a deep physical discovery, but a well-known effect of sticking to the wrong stochastic convention.

Reviewer #1 (Public Review):

Summary: Yin Luo and colleagues describe a new regulatory mode of TRAIL (Tumor necrosis factor (TNF)-related apoptosis-inducing ligand) -induced apoptosis of tumor cells. The work is timely because high expectations existed on the possibility to induce tumor-specific apoptosis by activation of TRAIL-receptors DR4 and DR5. So far, however, attempted TRAIL-based anti-tumor therapies failed, and several tumor types were found to resist TRAIL-induced apoptosis. The work is also important because it aims at a better mechanistic understanding of TRAIL-induced apoptosis and TRAIL resistance of some tumor types.

Strengths: The major novel finding of this study is that extracellular heparan sulfate (HS) acts as a positive regulator of TRAIL-induced tumor cell apoptosis, and that HS expression of different tumor cell lines correlates with their capacity to induce cell death. The authors first show by affinity chromatography and SPR that murine and human TRAIL bind strongly to heparin (heparin is a highly sulfated, and thus strongly negatively charged form of HS that is derived from connective tissue type mast cells), and identify three basic amino acids on the TRAIL N-terminus that are required for the interaction. Size exclusion chromatography (SEC) and multiangle light scattering (MALS) revealed that TRAIL exists as a trimer that requires a minimum heparin length of 8 sugar residues for binding, and small angle X-ray scattering (SAXS) showed that TRAIL interaction with longer oligosaccharides induced higher order multimerization of TRAIL. Consistent with these biochemical and biophysical analyses, HS on tumor cells contributes to TRAIL-binding to their cell surface and subsequent apoptosis. Minor novel findings include domain swapping observed by TRAIL trimer crystallization and different degrees of HS core protein and DR receptor expression in different tumor cell types. These findings are well supported and together with the advanced and established methodology used by the authors are the strengths of this paper. The paper will be of great interest to medical biologists studying TRAIL-resistance of tumors, to biologists interested in DR4 and DR5 receptor function and the effects of receptor internalization, and to glycobiologists aiming to understand the multiple important roles that HS plays in development and disease. The authors also raise the important point (and support it well) that routine heparin treatment of cancer patients potentially interferes with TRAIL-based therapies, providing one possible reason for their failure.

Weaknesses: Despite the importance and the clear strengths of the paper, some of its aspects could have been developed further to better support the authors claims and their hypothesis that HS downregulation may represent one strategy to explain tumor resistance to TRAIL-induced apoptosis.

1) The authors demonstrate that HS at the tumor surface promotes TRAIL binding, and that HS promotes TRAIL-induced breast cancer and myeloma cell apoptosis. These findings are based on pre-treatment with heparinase to remove cell-surface HS prior to TRAIL-treatment, or presence of soluble heparin to compete with cell-surface HS for TRAIL binding. A more direct way to establish such new HS function could have been genetic manipulation of cancer cells to overexpress HS (e.g. by Syndecan 1 core-protein transgene expression in resistant cell types, e.g. IM-9 cells) or to express less or undersulfated HS (by interfering with the biosynthetic pathway in apoptosis-prone cells, for example by targeted inactivation/RNAi of the HS co-polymerase or sulfotransferases in RPMI-8226 cells). Changed susceptibility of these cells to TRAIL-induced apoptosis would greatly support the authors claim and suggest one promising new strategy to decrease tumor resistance against TRAIL-induced apoptosis.

2) The mechanistics of TRAIL-induced, HS-modulated tumor cell apoptosis could be more clearly defined. For example, the authors demonstrate convincingly that cell surface HS is essential for TRAIL-induced apoptosis in MDA-MB-453 breast cancer cells, and later show that a tumor cell line (IM-9 cells) that expresses HS and the core protein to which HS is attached to only limited degrees is the most resistant to TRAIL-induced apoptosis. Yet, these findings suggest, but do not prove a dose-dependent role of HS in TRAIL-induced apoptosis. Indeed, the authors later report that cell surface HS promotes TRAIL-induced myeloma cell apoptosis regardless of the sensitivity levels, and that other factors - the degree of TRAIL multimerization or DR4/DR5 receptor internalization - are also important. A cartoon could be added to the final figure to sort through these possible mechanisms and their relative importance.

3) The authors show that RPMI-8226 cell-surface HS promotes DR5 internalization despite the absence of direct DR5/heparin interactions. This is important because, as the authors are certainly aware of, HS manipulation at the cell surface (for example by heparinase treatment) changes a plethora of signaling pathways that may also indirectly affect apoptosis. HS-dependent internalization of TRAIL-receptor complexes, however, provides an important direct link from HS expression to TRAIL-induced apoptosis. To further support this possible link, it would therefore be worthwhile to include the binding characteristics and HS-dependent internalization of DR4 into this study.

Reviewer #2 (Public Review):

Summary:<br /> In the manuscript by Luo et al, the authors investigated the nature and function of TRAIL-HS binding for the regulation of TRAIL-mediated apoptosis in cancer cells. The authors discovered that TRAIL binds to 12mer HS and identified the amino acid residues critical for the binding. The authors further nicely showed that 12mer HS binds to TRAIL homotrimer and larger HS can further promote the formation of larger TRAIL oligomers. Structural analyses were conducted to characterize the binding of TRAIL/HS complexes. At functional level, the authors demonstrated that HS promotes the cell surface binding of TRAIL to enhance TRAIL-mediated apoptosis in a variety of cancer cells. Moreover, the ability of TRAIL to induce apoptosis is correlated with cell surface HS level. Lastly, the authors showed that HS forms complex with TRAIL and its receptor DR5 and promotes DR5 internalization.

Strengths:<br /> Overall, this is a nicely executed study providing both mechanistic and functional insight for TRAIL-mediated apoptosis. It conducted detailed characterization on the direct binding between HS and TRAIL and provided solid evidence supporting the role of such interaction for the regulation of TRAIL-induced apoptosis. The experiments were well-designed with proper controls included. The data interpretation is accurate. The manuscript was clearly written and easy to follow by general readers.

Weaknesses:<br /> There is no major weakness identified from this study. However, the role of HS for the formation of TRAIL homotrimer needs to be further clarified. In addition, the current relationship between cell surface HS level and sensitivity to TRAIL-mediated apoptosis is still correlative, as the authors indicated. Additional evidence to support the regulatory function of HS would further strengthen the significance of the study.

Reviewer #1 (Public Review):

Summary:<br /> Pulfer et al., describe the development and testing of a transformer-based deep learning architecture called ADeS, which the authors use to identify apoptotic events in cultured cells and live animals. The classifier is trained on large datasets and provides robust classification accuracies in test sets that are comparable to and even outperform existing deep learning architectures for apoptosis detection. Following this validation, the authors also design use cases for their technique both in vitro and in vivo, demonstrating the value of ADeS to the apoptosis research space.

Strengths:<br /> ADeS is a powerful tool in the arsenal of cell biologists interested in the spatio-temporal co-ordinates of apoptotic events in vitro, since live cell imaging typically generates densely packed fields of view that are challenging to parse by manual inspection. The authors also integrate ADeS into the analysis of data generated using different types of fluorescent markers in a variety of cell types and imaging modalities, which increases its adaptability by a larger number of researchers. ADeS is an example of the successful deployment of activity recognition (AR) in the automated bioimage analysis space, highlighting the potential benefits of AR to quantifying other intra- and intercellular processes observable using live cell imaging.

Weaknesses:<br /> A major drawback was the lack of access to the ADeS platform for the reviewers; the authors state that the code is available in the code availability section, which is missing from the current version of the manuscript. This prevented an evaluation of the usability of ADeS as a resource for other researchers. The authors also emphasize the need for label-free apoptotic cell detection in both their abstract and their introduction but have not demonstrated the performance of ADeS in a true label-free environment where the cells do not express any fluorescent markers. While Pulfer et al., provide a wealth of information about the generation and validation of their DL classifier for in vitro movies, and the utility of ADeS is obvious in identifying apoptotic events among FOVs containing ~1700 cells, the evidence is not as strong for in vivo use cases. They mention the technical challenges involved in identifying apoptotic events in vivo, and use 3D rotation to generate a larger dataset from their original acquisitions. However, it is not clear how this strategy would provide a suitable training dataset for understanding the duration of apoptotic events in vivo since the temporal information remains the same. The authors also provide examples of in vivo acquisitions in their paper, where the cell density appears to be quite low, questioning the need for automated apoptotic detection in those situations. In the use cases for in vivo apoptotic detection using ADeS (Fig 8), it appears that the location of the apoptotic event itself was obvious and did not need ADeS, as in the case of laser ablation in the spleen and the sparse distribution of GFP labeled neutrophils in the lymph nodes. Finally, the authors also mention that video quality altered the sensitivity of ADeS in vivo (Fig 6L) but fail to provide an example of ADeS implementation on a video of poor quality, which would be useful for end users to assess whether to adopt ADeS for their own live cell movies.

Reviewer #2 (Public Review):

Summary:<br /> Pulfer A. et al. developed a deep learning-based apoptosis detection system named ADeS, which outperforms the currently available computational tools for in vitro automatic detection. Furthermore, ADeS can automatically identify apoptotic cells in vivo in intravital microscopy time-lapses, preventing manual labeling with potential biases. The authors trained and successfully evaluated ADeS in packed epithelial monolayers and T cells distributed in 3D collagen hydrogels. Moreover, in vivo, training and evaluation were performed on polymorphonucleated leukocytes in lymph nodes and spleen.

Strengths:<br /> Pulfer A. et colleagues convincingly presented their results, thoroughly evaluated ADeS for potential toxicity assay, and compared its performance with available state-of-the-art tools.

Weaknesses:<br /> The use of ADeS is still restricted to samples where cells are fluorescently labeled either in the cytoplasm or in the nucleus, which limits its use for in vitro toxicity assays that are performed on primary cells or organoids (e.g., iPSCs-derived systems) that are normally harder to transfect. In conclusion, ADeS will be a useful tool to improve output quality and accelerate the evaluation of assays in several research areas with basic and applied aims.

Reviewer #1 (Public Review):

The prevalence of primary angle closure glaucoma (PACG) is high in Asia compared to all over the world. This study focused on characterizing the metabolite profile associated with PACG, identifying potential blood diagnostic markers, assessing their specificity for PACG, and verifying their applicability to predict the progression of visual field loss. To this end, Li et al. implemented a 5-phases multicenter prospective study to identify novel candidate biomarkers of PACG. A total of 616 individuals were recruited, identifying 1464 distinct metabolites in the serum by metabolomics and chemiluminescence immunoassays. By applying different machine learning algorithms the metabolite androstenedione showed good discrimination between PACG and control subjects, in both the discovery and validation phases. This metabolite also showed alterations in the aqueous humor and higher levels of androstenedione seemed to be associated with faster loss of visual field. Overall, the authors claimed that serum androstenedione levels may provide a new biomarker for early detection and monitoring/predicting PACG severity/progression.

Strengths:<br /> • Omics research on glaucoma is constrained by inadequate sample sizes, a dearth of validation sets to corroborate findings, and an absence of specificity analyses. The 5-phases study was designed to try to overcome these limitations. The study design is very robust, with well well-described discovery set (1 and 2), validation phase (1 and 2), supplemental phase, and cohort phase. Large and well-characterized patients with adequate control subjects contributed to the robustness of the results.<br /> • Combining untargeted and targeted metabolomics using mass spectrometry instruments (high resolution and low resolution) with an additional chemiluminiscence immunoassay determining androstenedione levels<br /> • Androstenedione achieved better diagnostic accuracy across the discovery and validation sets, with AUC varying between 0.85 and 1.0. Interestingly, baseline androstenedione levels can predict glaucoma progression via visual field loss results.<br /> • A positive correlation was observed between levels of androstenedione in serum and aqueous humor of PACG patients.<br /> • A level higher of 1.66 ng/mL of the metabolite androstenedione seems to imply a high risk of visual field loss. Androstenedione may serve as a predictor of glaucomatous visual field progression.

Weakness:<br /> • A single biomarker seems very unlikely to be of much help in the detection of glaucoma due to the etiological heterogeneity of the disease, the existence of different subtypes, and the genetic variability among patients. Rather, a panel of biomarkers may provide more useful information for clinical prediction, including better sensitivity and specificity. The inclusion of additional metabolites already identifying in the study, in combination, may provide more reliable and correct assignment results.<br /> • The number of samples in the supplementary phase is low, larger sample sizes are mandatory to confirm the diagnostic accuracy.<br /> • Cohorts from different populations are needed to verify the applicability of this candidate biomarker.<br /> • Sex hormones seem to be associated also with other types of glaucoma, such as primary open-angle glaucoma (POAG), although the molecular mechanisms are unclear (see doi:10.1167/iovs.17-22708). The inclusion of patients diagnosed with other subtypes of glaucoma, like POAG, may contribute to determining the sensitivity and specificity of the proposed biomarker. Androstenedione levels should be determined in POAG, NTG, or PEXG patients.<br /> • In addition, the levels of androstenedione were found significantly altered during other diseases as described by the authors or by conditions like polycystic ovary syndrome, limiting the utility of the proposed biomarker.<br /> • Uncertainty of the androstenedione levels compromises its usefulness in clinical practice.

Reviewer #2 (Public Review):

Summary:<br /> The objective of authors using metabolomics analysis of primary angle closure glaucoma (PACG) is to demonstrate that serum androstenedione is a novel biomarker that can be used to diagnose PACG and predict visual field progression.

Strengths:<br /> Use of widely targeted and untargeted metabolite detection conditions. Use of liquid chromatography-tandem mass spectrometry and a chemiluminescence method for confirmation of androstenedione.

Weaknesses:<br /> The "predict" part is on much less solid ground. The visual field progression and association with serum androstenedione within the current experimental design eludes to a correlation. It truly cannot be stated as predictive. To predict one needs to put the substance when nothing is there and demonstrate that the desired endpoint is reached. Conversely, the substance (androstenedione) can be removed, and show that the condition regresses. None of these are possible without model system experiments, which have not been done. The authors could put some additional details in the methods, such as: 1) how much sample was collected, 2) whether equal serum volume for analysis had equal serum proteins (or cells). They have used a LC-MS/MS and a Chemiluminescence method, but another independent method such as GC-MS/MS or NMR to detect androstenedione for a subset of patients with different stages of visual field defect would be desirable.

Reviewer #1 (Public Review):

The authors found that nifuroxazide has the potential to augment the efficacy of radiotherapy in HCC by reducing PD-L1 expression. This effect may be attributed to increased degradation of PD-L1 through the ubiquitination-proteasome pathway. The paper provides new ideas and insights to improve treatment effectiveness, however, there are additional points that could be addressed.

-The paper highlights that the combination of nifuroxazide increases tumor cell apoptosis. A discussion regarding the potential crosstalk or regulatory mechanisms between apoptotic pathways and PD-L1 expression would be valuable.

-The benefits and advantages of nifuroxazide combination could be compared to the current clinical treatment options.

Reviewer #2 (Public Review):

Summary:<br /> Zhao et al. aimed to explore an important question - how to overcome the resistance of hepatocellular carcinoma cells to radiotherapy? Given that the immune-suppressive microenvironment is a major mechanism underlying resistance to radiotherapy, they reasoned that a drug that blocks the PD-1/PD-L1 pathway could improve the efficacy of radiation therapy and chose to investigate the effect of Nifuroxazide, an inhibitor of stat3 activation, on radiotherapy efficacy in treating hepatocellular carcinoma cells. From in vitro experiments, they find combination treatment (Nifuroxazide+ radiotherapy) increases apoptosis and reduces proliferation and migration, in comparison to radiotherapy alone. From in vivo experiments, they demonstrate that combined treatment reduces the size and weight of tumors in vivo and enhances mice survival. These data indicate a better efficacy of combination therapy compared to radiotherapy alone. Moreover, they also determined the effect of combination therapy on tumor microenvironment as well as peripheral immune response. They find that combination therapy increases infiltration of CD4+ and CD8+ cells as well as M1 macrophages in the tumor microenvironment. Interestingly, they find that the ratio of Treg cells in spleen is increased by radiotherapy but decreased by Nifuroxazide. Considering the immune-suppressive role of Treg cells, this finding is consistent with reduced tumor growth by combination therapy. However, it is unclear whether the combined therapy affects the ratio of Treg cells in the tumors or not. The most intriguing part of the study is the determination of the effect of Nifuroxazide on PD-L1 expression in the context of radiotherapy. Considering Nifuroxazide is a stat3 activation inhibitor and stat3 inhibition leads to reduced expression of PD-L1, one would expect Nifuroxazide decreases PD-L1 expression through stat3. However, they found that the effect of Nifuroxazide on PD-L1 is dependent on GSK3 mediated Proteasome pathways and independent of stat3, in the given experimental context. To determine the relevance to human hepatocellular carcinoma, they also measured the PD-L1 expression in human tumor tissues of HCC patients pre- and post-radiotherapy. The increased PD-L1 expression level in HCC after radiotherapy is impressive. However, it is unclear whether the patients being selected in the study had resistant disease to radiotherapy or not.

Overall, the data are convincing and supportive to the conclusions.

Strengths:<br /> 1) Novel finding: Identified novel mechanism underlying the effect of Nifuroxazide on PD-L1 expression in hepatocellular carcinoma cells in the context of radiotherapy.<br /> 2) Comprehensive experimental approaches: using different approaches to prove the same finding. For example, in Fig 4, both IHC and WB were used. In Fig 5, both IF and WB were used.<br /> 3) Human disease relevance: Compared observations in mice with human tumor samples.

Weaknesses:<br /> 1. It is hard to tell whether the observed phenotype and mechanism are generic or specific to the limited cell lines used in the study. The in vitro experiments were performed in one human cell line and the in vivo experiments were performed in one mouse cell line.<br /> 2. The study did not distinguish the effect of increased radiosensitivity by nifuroxazide from combined anti-tumor effects by two different treatments.

Reviewer #3 (Public Review):

Summary:<br /> In this study, the authors embarked on an exploration of how nifuroxazide could enhance the responsiveness to radiotherapy by employing both an in vitro cell culture system and an in vivo mouse tumor model.

Strengths:<br /> The researchers conducted an array of experiments aimed at revealing the function of nifuroxazide in aiding the radiotherapy-induced reduction of proliferation, migration, and invasion of HepG2 cells.

Weaknesses:<br /> The authors did not provide the molecular mechanism through which nifuroxazide collaborates with radiotherapy to effectively curtail the proliferation, migration, and invasion of HCC cells. Moreover, the evidence supporting the assertion that nifuroxazide contributes to the degradation of radiotherapy-induced upregulation of PD-L1 via the ubiquitin-proteasome pathway appears to be insufficient. Importantly, further validation of this discovery should involve the utilization of an additional syngeneic mouse HCC tumor model or an orthotopic HCC tumor model.

Reviewer #1 (Public Review):

Summary:<br /> The data clearly demonstrate that arpin is important for vessel barrier function, yet its genetic loss via a CRISPR strategy was not lethality, but led to viable animals in C57Blk strain at 12 weeks of age, albeit with leaky blood vessels. Pharmacological approaches were employed to demonstrate that loss of arpin led to ROCK1-dependent stress fiber formation that promoted increased permeability.

Strengths:<br /> The results clearly demonstrate that arpin is expressed in the endothelium of blood vessels and its deficiency leads to leaky blood vessels in in vivo and in vitro models.

Weaknesses:<br /> They conclude vessel leak was not related to enhanced Arp2/3 function through arpin deficiency, but no direct evidence of Arp2/3 activity is provided to support this conclusion. Instead, the authors concluded that ROCK1 activity was elevated in arpin knockdown cells and caused robust stress fiber formation. This idea could be strengthened by testing if ROCK1 inhibition by pharmacological block in arpin KO mice leads to less vascular leakage while pharmacological inhibition of Arp2/3 does not attenuate increased vessel permeability.

Reviewer #2 (Public Review):

Summary:<br /> The authors have taken their previous finding that arpin is important for epithelial junctions and extended this to endothelial cells. They find that the positive effects of arpin on endothelial junctions are not dependent on Arp2/3 activity but instead on suppression of actinomyosin contractility.

Strengths:<br /> The study uses standard approaches to test each of the components in the model. The quality of the experimental work is good and the amount of experimental evidence is sufficient to support this straightforward story.

Weaknesses:<br /> The major weakness is that the story is a simple extension of the previous work on arpin and junctions in epithelial cells. The additional information is that the effects are not via Arp2/3 directly, but instead through an increase in actinomyosin contractility. However, the connection between arpin and increased ROCK activity is not revealed.

Reviewer #1 (Public Review):

The authors investigate the alpha chain TCR landscape in conventional vs regulatory CD4 T cells. Overall I think it is a very well thought out and executed study with interesting conclusions. The authors have investigated CDR3 alpha repertoires coupled with a transgenic fixed CDR3beta in a mouse system.

Strengths:<br /> - One of a kind evidence and dataset.

- State-of-the-art analyses using tools that are well-accepted in the literature.

- Interesting conclusions on the breadth of immune response to challenges across different types of challenges (tumor, viral and parasitic).

Weaknesses:<br /> - Some conclusions regarding the eCD4->eTreg transition are not so strong using only the data.

- Some formatting issues.

Reviewer #2 (Public Review):

This study investigates T-cell repertoire responses in a mouse model with a transgenic beta chain, such that all T-cells in all mice share a fixed beta chain, and repertoire diversity is determined solely by alpha chain rearrangements. Each mouse is exposed to one of a few distinct immune challenges, sacrificed, and T-cells are sampled from multiple tissues. FACS is used to sort CD4 and Treg cell populations from each sample, and TCR repertoire sequencing from UMI-tagged cDNA is done.

Various analyses using repertoire diversity, overlap, and clustering are presented to support several principal findings: 1) TCR repertoires in this fixed beta system have highly distinct clonal compositions for each immune challenge and each cell type, 2) these are highly consistent across mice, so that mice with shared challenges have shared clones, and 3) induction of CD4-to-Treg cell type transitions is challenge-specific.

The beta chain used for this mouse model was previously isolated based on specificity for Ovalbumin. Because the beta chain is essential for determining TCR antigen specificity, and is highly diverse in wildtype mice, I found it surprising that these mice are reported to have robust and consistently focused clonal responses to very diverse immune challenges, for which a fixed OVA-specific beta chain is unlikely to be useful. The authors don't comment on this aspect of their findings, but I would think it is not expected *a priori* that this would work. If this does work as reported, it is a valuable model system: due to massively reduced diversity, the TCR repertoire response is much more stereotyped across individual samples, and it is much easier to detect challenge-specific TCRs via the statistics of convergent responses.

While the data and analyses present interesting signals, they are flawed in several ways that undermine the reported findings. I summarize below what I think are the most substantive data and analysis issues.

1. There may be systematic inconsistencies in repertoire sampling depth that are not described in the manuscript. Looking at the supplementary tables (and making some plots), I found that the control samples (mice with mock challenge) have consistently much shallower sampling-in terms of both read count and UMI count-compared with the other challenge samples. There is also a strong pattern of lower counts for Treg vs CD4 cell samples within each challenge.

2. FACS data are not reported. Although the graphical abstract shows a schematic FACS plot, there are no such plots in the manuscript. Related to the issue above, it would be important to know the FACS cell counts for each sample.

3. For diversity estimation, UMI-wise downsampling was performed to normalize samples to 1000 random UMIs, but this procedure is not validated (the optimal normalization would require downsampling cells). What is the influence of possible sampling depth discrepancies mentioned above on diversity estimation? All of the Treg control samples have fewer than 1000 total UMIs-doesn't that pose a problem for sampling 1000 random UMIs? Indeed, I simulated this procedure and found systematic effects on diversity estimates when taking samples of different numbers of cells (each with a simulated UMI count) from the same underlying repertoire, even after normalizing to 1000 random UMIs. I don't think UMI downsampling corrects for cell sampling depth differences in diversity estimation, so it's not clear that the trends in Fig 1A are not artifactual-they would seem to show higher diversity for control samples, but these are the very same samples with an apparent systematic sampling depth bias.

4. The Figures may be inconsistent with the data. I downloaded the Supplementary Table corresponding to Fig 1 and made my own version of panels A-C. This looked quite different from the diversity estimations depicted in the manuscript. The data does not match the scale or trends shown in the manuscript figure.

5. For the overlap analysis, a different kind of normalization was performed, but also not validated. Instead of sampling 1000 UMIs, the repertoires were reduced to their top 1000 most frequent clones. It is not made clear why a different normalization would be needed here. There are several samples (including all Treg control samples) with only a couple hundred clones. It's also likely that the noted systematic sampling depth differences may drive the separation seen in MDS1 between Treg and CD4 cell types. I also simulated this alternative downsampling procedure and found strong effects on MDS clustering due to sampling effects alone.

It is not made clear how the overlap scores were converted to distances for MDS. It's hard to interpret this without seeing the overlap matrix.

6. The cluster analysis is superficial, and appears to have been cherry-picked. The clusters reported in the main text have illegibly small logo plots, and no information about V/J gene enrichments. More importantly, as the caption states they were chosen from the columns of a large (and messier-looking) cluster matrix in the supplementary figure based on association with each specific challenge. There's no detail about how this association was calculated, or how it controlled for multiple tests. I don't think it is legitimate to simply display a set of clusters that visually correlate; in a sufficiently wide random matrix you will find columns that seem to correlate with any given pattern across rows.

7. The findings on differential plasticity and CD4 to Treg conversion are not supported. If CD4 cells are converting to Tregs, we expect more nucleotide-level overlap of clones. This intuition makes sense. But it seems that this section affirms the consequent: variation in nucleotide-level clone overlap is a readout of variation in CD4 to Treg conversion. It is claimed, based on elevated nucleotide-level overlap, that the LLC and PYMT challenges induce conversion more readily than the other challenges. It is not noted in the textual interpretations, but Fig 4 also shows that the control samples had a substantially elevated nucleotide-level overlap. There is no mention of a null hypothesis for what we'd expect if there was no induced conversion going on at all. This is a reduced-diversity mouse model, so convergent recombination is more likely than usual, and the challenges could be expected to differ in the parts of TCR sequence space they induce focus on. They use the top 100 clones for normalization in this case, but don't say why (this is the 3rd distinct normalization procedure).

Although interpretations of the reported findings are limited due to the issues above, this is an interesting model system in which to explore convergent responses. Follow-up experimental work could validate some of the reported signals, and the data set may also be useful for other specific questions.

Reviewer #3 (Public Review):

Nakonechnaya et al present a valuable and comprehensive exploration of CD4+ T cell response in mice across stimuli and tissues through the analysis of their TCR-alpha repertoires.

The authors compare repertoires by looking at the relative overlap of shared clonotypes and observe that they sometimes cluster by tissue and sometimes by stimulus. They also compare different CD4+ subsets (conventional and Tregs) and find distinct yet convergent responses with occasional plasticity across subsets for some stimuli.

The observed lack of a general behaviour highlights the need for careful comparison of immune repertoires across cell subsets and tissues in order to better understand their role in the adaptive immune response.

In conclusion, this is an important paper to the community as it suggests several future directions of exploration.

Unfortunately, the lack of code and data availability does not allow the reproducibility of the results.

Joint Public Review:

In this study, Cacho-Navas et al. describe the role of ICAM-1 expressed on the apical membrane of bile canaliculi and its function to control the bile canaliculi (BCs) homeostasis. This is a previously unrecognized function of this protein in hepatocytes. The same authors have previously shown that basolateral ICAM-1 plays a role in controlling lymphocyte adhesion to hepatocytes during inflammation and that this interaction is responsible for the loss of polarity of hepatocytes during disease states.

This new study shows that ICAM-1 is mainly localized in the apical domain of the BC and in association with EBP-50, communicates with the subapical acto-myosin ring to regulate the size and morphology of the BC. They used the well-known immortal cell line of liver cells (HepG2) in which they deleted ICAM-1 gene by CRISPR-Cas9 editing and hepatic organoids derived from WT and ICAM-1-KO mice. alternating KO as well as rescue experiments. They show that in the absence of apical ICAM-1, the BC become dilated.

The data sufficiently support the conclusions of the study.

Reviewer #1 (Public Review):

In this report, Hariharan and colleagues describe a protocol based on machine learning to differentiate wild type and DMD myofibers differentiated on a micropattern. They use images generated by immunofluorescence against the proteins of the DAPC complex, (which is disrupted in absence of dystrophin) to train a classifier to recognize healthy and diseased myofibers. They show that combining images generated with utrophin and alpha-sarcoglycan provide the most effective discrimination. They then validate the approach by applying their pipeline to wild type cells treated with DMD siRNA to mimic the DMD mutation or with antisense oligonucleotides to restore the DMD coding frame by exon skipping. They show that their strategy groups the siRNA treated myofibers with the DMD ones efficiently. Grouping of the oligonucleotide treated DMD myofibers with the healthy also works but is less efficient.

Overall the work is well done and I don't have any significant technical critique. I found this highly technical study interesting for a specialized audience.

Reviewer #2 (Public Review):

Summary:<br /> The authors have developed a Myoscreen platform, which is a scalable and physiologically relevant system for generating and characterizing patient-derived myotubes. The platform can be used to accurately predict the DMD disease phenotype in a disease-relevant cell type and has wide applications in the drug development process.

Strengths:<br /> The Myoscreen platform is scalable, meaning that it can be used to generate and characterize a large number of patient-derived myotubes. This is important for drug discovery, as it allows researchers to test a wider range of potential treatments. The Myoscreen platform also uses a physiologically relevant system for generating and characterizing myotubes. This means that the results obtained from the platform are more likely to be relevant to the human disease. This compared for example to using C2C12 myotubes. The Myoscreen platform has been shown to be effective in predicting the DMD disease phenotype. This means that it can be used to identify potential treatments that are likely to be effective in patients with DMD.

Weaknesses:<br /> The study has several limitations. The method and material section could be improved. The authors rely heavily on UMAP to identify differences between non-DMD and DMD donor myotubes. They do not validate their findings using pharmacological small drugs. Additionally, the biological replicates used are extremely low, which raises concerns about the reproducibility of the findings.

Reviewer #3 (Public Review):

Summary: Hariharan et al. establish an analysis pipeline using automated microscopy to detect features identified by morphological profiling from images of common dystrophin complex proteins present in differentiated diseased and unaffected human myoblasts. Ultimately, using a machine learning algorithm to generate high dimensional phenotypes, the authors can distinguish Duchenne patient myotubes from unaffected patient controls based on the morphological features of several Dystrophin complex proteins. Initially analyzed on their own or in pairs the authors identify an optimal combination of Utrophin and a-sarcoglycan and subsequently test their ability to distinguish perturbations of Dystrophin either by knock down (siRNA) in unaffected controls or following treatment with a splicing modifier, vivo-phosphorodiamidate morpholino oligonucleomer (vivo-PMO) to ameliorate the DMD phenotype. It is unclear whether this methodology will see widespread adoption due to the combination of unique methods (micro-patterned plates and machine learning based image analysis) combined with a lack of detail on the specific features responsible for supporting the high dimensional phenotypes generated using their machine learning algorithm.

Strengths: The overall concept of this paper is interesting in that subtle morphological phenotypes, not readily observable by the eye, exhibited by dystrophin complex associated proteins can distinguish DMD samples from unaffected controls. It is interesting In Fig. 3B to see Utrophin and a-Sarcoglycan distinguish DMD and non-DMD lines from each other. This finding is the core of the paper and yet little information on how or why this is detected by image analysis is presented. An argument could be made that Combinations 1-7 all "work" to a certain degree at segregating DMD from non-DMD lines. This finding is exciting and has broad applicability both within and beyond the muscle field.

Weaknesses: Significantly more detail on the 235 features that are identified would greatly benefit the paper. What are the most critical features that give rise to high F-Scores for Utrophin and a-Sarcoglycan? What do the image masks display for the top ~10 features (or 5)? In Fig. 3B what metric(s) is critical in this segregation? What is the effect on the dimensional display if PCA is conducted as opposed to a tSNE?

Biological replicates are lacking to draw conclusions upon. Non-DMD #4 is present in certain figures and absent from others. With 2 replicates (non-DMD) and 2 replicates (DMD) it is difficult to draw statistical conclusions on the data. Non-DMD #4 is identified as a poor line (37% Desmin compared to the other lines being >88%) in Table 1. If this line is a poor line please remove it from the data analysis.

It is not appropriate to calculate Euclidan distance based on tSNE plots. PCA, MDS or UMAP are the appropriate high dimensional visual representations that allow for Euclidian distance calculations. This brings into question the validity of Fig. 4D and Fig. 5D. The link below outlines the limitations of tSNE plots. https://distill.pub/2016/misread-tsne/

It is unclear why treatment (siRNA) results in a statistically significant F-score (>0.9) when comparing non-DMD samples treated with siRNA against Dystrophin with DMD samples. Given that the siRNA knock-down appears to be quite robust this was unexpected and brings into question whether Dystrophin protein is the primary driver for the high dimensional phenotypes observed.

Joint Public Review:

The study by Ding et al demonstrated that microbial metabolite I3A reduced western diet induced steatosis and inflammation in mice. They showed that I3A mediates its anti-inflammatory activities through AMP-activated protein kinase (AMPK)-dependent manner in macrophages. Translationally, they proposed that I3A could be a potential therapeutic molecule in preventing the progression of steatosis to NASH.

Major strengths<br /> • Authors clearly demonstrated that the Western Diet (WD)-induced steatosis and I3A treatment reduced steatosis and inflammation in pre-clinical models. Data clearly supports these statements.<br /> • I3A treatment rescued WD-altered bile acids as well as partially rescued the metabolome, proteome in the liver.<br /> • I3A treatment reduced the levels of enzymes in fatty acid transport, de novo lipogenesis and β-oxidation<br /> • I3A mediates its anti-inflammatory activities through AMP-activated protein kinase (AMPK)-dependent manner in macrophages.

Minor<br /> I agree with the authors that investigating known other AhR ligands in comparison may be beyond the scope of this study.

Reviewer #1 (Public Review):

Summary:

This study by Lee et al. is a direct follow-up on their previous study that described an evolutionary conservancy among placental mammals of two motifs (a transmembrane motif and a juxtamembrane palmitoylation site) in CD4, an antigen co-receptor, and showed their relevance for T-cell antigen signaling. In this study, they describe the contribution of these two motifs to the CD4-mediated antigen signaling in the absence of CD4-LCK binding. Their approach was the comparison of antigen-induced proximal TCR signaling and distal IL-2 production in 58-/- T-cell hybridoma expressing exogenous truncated version of CD4 (without the interaction with LCK), called T1 with T1 version with the mutations in either or both of the conserved motifs. They show that the T1 CD4 can support signaling to the extend similar to WT CD4, but the mutation of the conserved motifs substantially reduced the signaling. The authors conclude that the role of these motifs is independent of the LCK-binding.

Strengths:<br /> The authors convincingly show that T1 CD4, lacking the interaction with LCK supports the TCR signaling and also that the two studied motifs have a significant contribution to it.

Weaknesses:<br /> The study has several weaknesses.

1. The whole study is based on a single experimental system, genetically modified 58-/- hybridoma. It is unclear at this moment, how the molecular motifs studied here contribute to the signaling in a real T cell. The evolutionary conservancy suggests that these motifs are important for T cell biology. However, the LCK-binding motif is conserved as well (perhaps even more) and it plays a very minor role in their model. Without verifying their results in primary cells, the quantitative, but even qualitative, importance of these motifs for T-cell signaling and biology is unclear. Although the authors discuss this issue in the Discussion, it should be noted in all important parts of the manuscript, where conclusions are made (abstract, end of introduction, perhaps also in the title) that the results are coming from the hybridoma cells.

2. Many of the experiments lack the negative control. I believe that two types of negative controls should be included in all experiments. First, hybridoma cells without CD4 (or with CD4 mutant unable to bind MHCII). Second, no peptide control, i.e., activation of the hybridoma cells with the APC not loaded with the cognate peptide. These controls are required to distinguish the basal levels of phoshorylation and CD4-independent antigen-induced phosphorylation to quantify, what is the contribution of the particular motifs to the CD4-mediated support. Although these controls are included in some of the experiments, they are missing in other ones. The binding mutant appears in some FC results as a horizontal bar (without any error bar/variability), showing that CD4 does not give a huge advantage in these readouts. Why don't the authors show no peptide controls here as well? Why the primary FC data (histograms) are not shown? Why neither of these two controls is shown for the % of responders plots? Although the IL-2 production is a very robust and convincing readout, the phosphoflow is much less sensitive. It seems that the signaling is elevated only marginally. Without the mentioned controls and showing the raw data, the precise interpretation is not possible.

3. The processing of the data is not clear. Some of the figures seem to be overprocessed. For instance, I am not sure what "Normalized % responders of pCD3zeta" means (e.g., Fig. 1C and elsewhere)? Why do not the authors show the actual % of pCD3zeta+ cells including the gating strategy? Why do the authors subtract the two histograms in Fig. 2- Fig.S3? It is very unusual.

4. The manuscript lacks Materials and Methods. It only refers to the previous paper, which is very unusual. Although most of the methods are the same, they still should be mentioned here. Moreover, some of the mutants presented here were not generated in the previous study, as far as I understand. Perhaps the authors plan to include Materials and Methods during the revision...

5. Membrane rafts are a very controversial topic. I recommend the authors stick to the more consensual term "detergent resistant microdomains" in all cases/occurances.

6. Last, but not least, the mechanistic explanation (beyond the independence of LCK binding) of the role of these motifs is very unclear at the moment.

Reviewer #2 (Public Review):

Summary:<br /> The paper by Kuhn and colleagues follows upon a 2022 paper in which they identified residues in CD4 constrained by evolutionary purifying selection in placental mammals and then performed functional analyses of these conserved sequences. They showed that sequences distinct from the CXC "clamp" involved in recruitment of Lck have critical roles in TCR signaling, and these include a glycine-rich motif in the transmembrane (TM) domain and the cys-containing juxtamembrane (JM) motif that undergoes palmitoylation, both of which promote TCR signaling, and a cytoplasmic domain helical motif, also involved in Lck binding, that constrains signaling. Mutations in the transmembrane and juxtamembrane sequences led to reduced proximal signaling and IL-2 production in a hybridoma's response to antigen presentation, despite retention of abundant CD4 association with Lck in the detergent-soluble membrane fraction, presumably mislocalized outside of lipid rafts and distal to the TCR. A major conclusion of that study was that CD4 sequences required for Lck association, including the CXC "clasp" motif, are not as consequential for CD4 co-receptor function in TCR signaling as the conserved TM and JM motifs. However, the experiments did not determine whether the functions of the TM and JM motifs are dependent on the Lck-binding properties of CD4 - the mutations in those motifs could result in free Lck redistributing to associate with CD4 in signaling-incompetent membrane domains or could function independently of CD4-Lck association. The current study addresses this specific question.

Using the same model system as in the earlier paper (the entire methods section is a citation to the earlier paper), the authors show that truncation of the Lck-binding intracellular domain resulted in a moderate reduction in IL-2 response, as previously shown, but there was no apparent effect on proximal phosphorylation events (CD3z, Lck, ZAP70, PLCg1). They then evaluated a series of TM and JM motif mutations in the context of the truncated Lck-nonbinding molecule, and showed that these had substantially impaired co-receptor function in the IL-2 assay and reduced proximal signaling. The proximal signaling could be observed at high ligand density even with a MHC non-binding mutation in CD4, although there was still impaired IL-2 production. This result additionally illustrates that phosphorylation of the proximal signaling molecules is not sufficient to activate IL-2 expression in the context of antigen presentation.

Strengths:<br /> The strength of the paper is the further clear demonstration that the classical model of CD4 co-receptor function (MHCII-binding CD4 bringing Lck to the TCR complex, for phosphorylation of the CD3 chain ITAMs and of the ZAP70 kinase) is not sufficient to explain TCR activation. The data, combined with the earlier paper, further implicate the gly-rich TM sequence and the palmitoylation targets in the JM region as having critical roles in productive co-receptor-dependent TCR activation.

Weaknesses:<br /> The major weakness of the paper is the lack of mechanistic insight into how the TM and JM motifs function. The new results are largely incremental in light of the earlier paper from this group as well as other literature, cited by the authors, that implicates "free" Lck, not associated with co-receptors, as having the major role in TCR activation. It is clear that the two motifs are important for CD4 function at low pMHCII ligand density. The proposal that they modulate interactions of TCR complex with cholesterol or other membrane lipids is an interesting one, and it would be worth further exploring by employing approaches that alter membrane lipid composition. The JM sequence presumably dictates localization within the membrane, by way of palmitoylation, which may be critical to regulate avidity of the TCR:CD4 complex for pMHCII or TCR complex allosteric effects that influence the activation threshold. Experiments that explore the basis of the mutant phenotype could substantially enhance the impact of this study.

Reviewer #1 (Public Review):

Summary: Zai et al test if songbirds can recover the capacity to sing auditory targets without singing experience or sensory feedback. Past work showed that after the pitch of targeted song syllables is driven outside of birds' preferred target range with external reinforcement, birds revert to baseline (i.e. restore their song to their target). Here the authors tested the extent to which this restoration occurs in muted or deafened birds. If these birds can restore, this would suggest an internal model that allows for sensory-to-motor mapping. If they cannot, this would suggest that learning relies entirely on feedback-dependent mechanisms, e.g. reinforcement learning (RL). The authors find that deafened birds exhibit moderate but significant restoration, consistent with the existence of a previously under-appreciated internal model in songbirds.

Strengths:<br /> The experimental approach of studying vocal plasticity in deafened or muted birds is innovative, technically difficult, and perfectly suited for the question of feedback-independent learning. The finding in Figure 4 that deafened birds exhibit subtle but significant plasticity toward restoration of their pre-deafening target is surprising and important for the songbird and vocal learning fields, in general.

Weaknesses:<br /> The evidence and analyses related to the directed plasticity in deafened birds are confusing, and the magnitude of the plasticity is far less than the plasticity observed in control birds with intact feedback. The authors acknowledge this difference in a two-system model of vocal plasticity, but one wonders why the feedback-independent model, which could powerfully enhance learning speed, is weak in this songbird system.

There remains some confusion about the precise pitch-change methods used to study the deafened birds, including the possibility that a critical cohort of birds was not suitably balanced in a way where deafened birds were tested on their ability to implement both pitch increases and decreases toward target restoration.

Reviewer #2 (Public Review):

Summary: This paper investigates the role of motor practice and sensory feedback when a motor action returns to a learned or established baseline. Adult male zebra finches perform a stereotyped, learned vocalization (song). It is possible to shift the pitch of particular syllables away from the learned baseline pitch using contingent white noise reinforcement. When the reinforcement is stopped, birds will return to their baseline over time. During the return, they often sing hundreds of renditions of the song. However, whether motor action, sensory feedback, or both during singing is necessary to return to baseline is unknown.

Previous work has shown that there is covert learning of the pitch shift. If the output of a song plasticity pathway is blocked during learning, there is no change in pitch during the training. However, as soon as the pathway is unblocked, the pitch immediately shifts to the target location, implying that there is learning of the shift even without performance. Here, they ask whether the return to baseline from such a pitch shift also involves covert or overt learning processes. They perform a series of studies to address these questions, using muting and deafening of birds at different time points. learning.

Strengths: The overall premise is interesting and the use of muting and deafening to manipulate different aspects of motor practice vs. sensory feedback is a solid approach.

Weaknesses: One of the main conclusions, which stems primarily from birds deafened after being pitch-shifted using white noise (WNd) birds in comparison to birds deafened before being pitch-shifted with light as a reinforcer (LOd), is that recent auditory experience can drive motor plasticity even when an individual is deprived of such experience. While the lack of shift back to baseline pitch in the LOd birds is convincing, the main conclusion hinges on the responses of just a few WNd individuals who are closer to baseline in the early period. Moreover, only 2 WNd individuals reached baseline in the late period, though neither of these were individuals who were closer to baseline in the early phase. Most individuals remain or return toward the reinforced pitch. These data highlight that while it may be possible for previous auditory experience during reinforcement to drive motor plasticity, the effect is very limited. Importantly, it's not clear if there are other explanations for the changes in these birds, for example, whether there are differences in the number of renditions performed or changes to other aspects of syllable structure that could influence measurements of pitch.

While there are examples where the authors perform direct comparisons between particular manipulations and the controls, many of the statistical analyses test whether each group is above or below a threshold (e.g. baseline) separately and then make qualitative comparisons between those groups. Given the variation within the manipulated groups, it seems especially important to determine not just whether these are different from the threshold, but how they compare to the controls. In particular, a full model with time (early, late), treatment (deafened, muted, etc), and individual ID (random variable) would substantially strengthen the analysis.

The muted birds seem to take longer to return to baseline than controls even after they are unmuted. Presumably, there is some time required to recover from surgery, however, it's unclear whether muting has longer-term effects on syrinx function or the ability to pass air. In particular, it's possible that the birds still haven't recovered by 4 days after unmuting as a consequence of the muting and unmuting procedure or that the lack of recovery is indicative of an additional effect that muting has on pitch recovery. For example, the methods state that muted birds perform some quiet vocalizations. However, if birds also attempt to sing, but just do so silently, perhaps the aberrant somatosensory or other input from singing while muted has additional effects on the ability to regain pitch. It would also be useful to know if there is a relationship between how long they are muted and how quickly they return to baseline.

Reviewer #3 (Public Review):

Summary:<br /> Zai et al. test whether birds can modify their vocal behavior in a manner consistent with planning. They point out that while some animals are known to be capable of volitional control of vocalizations, it has been unclear if animals are capable of planning vocalizations -that is, modifying vocalizations towards a desired target without the need to learn this modification by practicing and comparing sensory feedback of practiced behavior to the behavioral target. They study zebra finches that have been trained to shift the pitch of song syllables away from their baseline values. It is known that once this training ends, zebra finches have a drive to modify pitch so that it is restored back to its baseline value. They take advantage of this drive to ask whether birds can implement this targeted pitch modification in a manner that looks like planning, by comparing the time course and magnitude of pitch modification in separate groups of birds who have undergone different manipulations of sensory and motor capabilities. A key finding is that birds who are deafened immediately before the onset of this pitch restoration paradigm, but after they have been shifted away from baseline, are able to shift pitch partially back towards their baseline target. In other words, this targeted pitch shift occurs even when birds don't have access to auditory feedback, which argues that this shift is not due to reinforcement-learning-guided practice, but is instead planned based on the difference between an internal representation of the target (baseline pitch) and current behavior (pitch the bird was singing immediately before deafening).

The authors present additional behavioral studies arguing that this pitch shift requires auditory experience of the song in its state after it has been shifted away from baseline (birds deafened early on, before the initial pitch shift away from baseline, do not exhibit any shift back towards baseline), and that a full shift back to baseline requires auditory feedback. The authors synthesize these results to argue that different mechanisms operate for small shifts (planning, does not need auditory feedback) and large shifts (reinforcement learning, requires auditory feedback).

The authors also make a distinction between two kinds of planning: covert-not requiring any motor practice and overt-requiring motor practice but without access to auditory experience from which target mismatch could be computed. They argue that birds plan overtly, based on these deafening experiments as well as an analogous experiment involving temporary muting, which suggests that indeed motor practice is required for pitch shifts.

Strengths:<br /> The primary finding (that partially restorative pitch shift occurs even after deafening) rests on strong behavioral evidence. It is less clear to what extent this shift requires practice, since their analysis of pitch after deafening takes the average over within the first two hours of singing. If this shift is already evident in the first few renditions then this would be evidence for covert planning. This analysis might not be feasible without a larger dataset. Similarly, the authors could test whether the first few renditions after recovery from muting already exhibit a shift back toward baseline.

This work will be a valuable addition to others studying birdsong learning and its neural mechanisms. It documents features of birdsong plasticity that are unexpected in standard models of birdsong learning based on reinforcement and are consistent with an additional, perhaps more cognitive, mechanism involving planning. As the authors point out, perhaps this framework offers a reinterpretation of the neural mechanisms underlying a prior finding of covert pitch learning in songbirds (Charlesworth et al., 2012).

A strength of this work is the variety and detail in its behavioral studies, combined with sensory and motor manipulations, which on their own form a rich set of observations that are useful behavioral constraints on future studies.

Weaknesses:<br /> The argument that pitch modification in deafened birds requires some experience hearing their song in its shifted state prior to deafening (Fig. 4) is solid but has an important caveat. Their argument rests on comparing two experimental conditions: one with and one without auditory experience of shifted pitch. However, these conditions also differ in the pitch training paradigm: the "with experience" condition was performed using white noise training, while the "without experience" condition used "lights off" training (Fig. 4A). It is possible that the differences in the ability for these two groups to restore pitch to baseline reflect the training paradigm, not whether subjects had auditory experience of the pitch shift. Ideally, a control study would use one of the training paradigms for both conditions, which would be "lights off" or electrical stimulation (McGregor et al. 2022), since WN training cannot be performed in deafened birds. This is difficult, in part because the authors previously showed that "lights off" training has different valences for deafened vs. hearing birds (Zai et al. 2020). Realistically, this would be a point to add to in discussion rather than a new experiment.

A minor caveat, perhaps worth noting in the discussion, is that this partial pitch shift after deafening could potentially be attributed to the birds "gaining access to some pitch information via somatosensory stretch and vibration receptors and/or air pressure sensing", as the authors acknowledge earlier in the paper. This does not strongly detract from their findings as it does not explain why they found a difference between the "mismatch experience" and "no mismatch experience groups" (Fig. 4).

More broadly, it is not clear to me what kind of planning these birds are doing, or even whether the "overt planning" here is consistent with "planning" as usually implied in the literature, which in many cases really means covert planning. The idea of using internal models to compute motor output indeed is planning, but why would this not occur immediately (or in a few renditions), instead of taking tens to hundreds of renditions? To resolve confusion, it would be useful to discuss and add references relating "overt" planning to the broader literature on planning, including in the introduction when the concept is introduced. Indeed, muddying the interpretation of this behavior as planning is that there are other explanations for the findings, such as use-dependent forgetting, which the authors acknowledge in the introduction, but don't clearly revisit as a possible explanation of their results. Perhaps this is because the authors equate use-dependent forgetting and overt planning, in which case this could be stated more clearly in the introduction or discussion.

Reviewer #3 (Public Review):

Summary:<br /> The neural retina is one of the most energetically active tissues in the body and research into retinal metabolism has a rich history. Prevailing dogma in the field is that the photoreceptors of the neural retina (rods and cones) are heavily reliant on glycolysis, and as oxygen tension at the level of photoreceptors is very low, these specialized sensory neurons carry out aerobic glycolysis, akin to the Warburg effect in cancer cells. It has been found that this unique metabolism changes in many retinal diseases, and targeting retinal metabolism may be a viable treatment strategy. The neural retina is composed of 11 different cell types, and many research groups over the past century have contributed to our current understanding of cell-specific metabolism of retinal cells. More recently, it has been shown in mouse models and co-culture of the mouse neural retina with human RPE cultures that photoreceptors are reliant on the underlying retinal pigment epithelium for supplying nutrients. Chen and colleagues add to this body of work by studying an ex vivo culture of the developing mouse retina that maintained contact with the retinal pigment epithelium. They exposed such ex vivo cultures to small molecule inhibitors of specific metabolic pathways, performing targeted metabolomics on the tissue and staining the tissue with key metabolic enzymes to lay the groundwork for what metabolic pathways may be active in particular cell types of the retina. The authors conclude that rod and cone photoreceptors are reliant on different metabolic pathways to maintain their cell viability - in particular, that rods rely on oxidative phosphorylation and cones rely on glycolysis. Further, their data support multiple mechanisms whereby glycolysis may occur simultaneously with anapleurosis to provide abundant energy to photoreceptors. The data from metabolomics revealed several novel findings in retinal metabolism, including the use of glutamine to fuel the mini-Krebs cycle, the utilization of the Cahill cycle in photoreceptors, and a taurine/hypotaurine shuttle between the underlying retinal pigment epithelium and photoreceptors to transfer reducing equivalents from the RPE to photoreceptors. In addition, this study provides robust quantitative metabolomics datasets that can be compared across experiments and groups. The use of this platform will allow for rapid testing of novel hypotheses regarding the metabolic ecosystem in the neural retina.

Strengths:<br /> The data on differences in the susceptibility of rods and cones to mitochondrial dysfunction versus glycolysis provides novel hypothesis-generating conjectures that can be tested in animal models. The multiple mechanisms that allow anapleurosis and glycolysis to run side-by-side add significant novelty to the field of retinal metabolism, setting the stage for further testing of these hypotheses as well.

Weaknesses:<br /> Almost all of the conclusions from the paper are preliminary, based on data showing enzymes necessary for a metabolic process are present and the metabolites for that process are also present. However, to truly prove whether these processes are happening, C13 labeling or knock-out or over-expression experiments are necessary. Further, while there is good data that RPE cultures in vitro strongly recapitulate RPE phenotypes in vivo, ex vivo neural retina cultures undergo rapid death. Thus, conclusions about metabolism from explants should either be well correlated with existing literature or lead to targeted in vivo studies. This paper currently lacks both.

Reviewer #1 (Public Review):

Summary: In the manuscript by Chen et al. entitled, "The retina uncouples glycolysis and oxidative phosphorylation via Cori-, Cahill-, and mini-Krebs-cycle", the authors look to provide insight on retinal metabolism and substrate utilization by using a murine explant model with various pharmacological treatments in conjunction with metabolomics. The authors conclude that photoreceptors, a specific cell within the explant, which also includes retinal pigment epithelium (RPE) and many other types of cells, are able to uncouple glycolytic and Krebs-cycle metabolism via three different pathways: 1) the mini-Krebs-cycle, fueled by glutamine and branched-chain amino acids; 2) the alanine-generating Cahill-cycle; and 3) the lactate-releasing Cori-cycle. While intriguing if determined to be true, these cell-specific conclusions are called into question due to the ex vivo experimental setup with the inclusion of RPE, the fact that the treatments were not cell-specific nor targeted at an enzyme specific to a certain cell within the retina, and no stable isotope tracing nor mitochondrial function assays were performed. Hence, without significant cell-specific methods and future experimentation, the primary claims are not supported.

Strengths: This study attempts to improve on the issues that have limited the results obtained from previous ex vivo retinal explant studies by culturing in the presence of the RPE, which is a major player in the outer retinal metabolic microenvironment. Additionally, the study utilizes multiple pharmacologic methods to define retinal metabolism and substrate utilization.

Weaknesses: A major weakness of this study is the lack of in vivo supporting data. Explant cultures remove the retina from its dual blood supply. Typically, retinal explant cultures are done without RPE. However, the authors included RPE in the majority of experimental conditions herein. However, it is unclear if the metabolomics samples included the RPE or not. The inclusion of the RPE, which is metabolically active and can be altered by the treatments investigated herein, further confounds the claims made regarding the neuroretina. Considering the pharmacologic treatments utilized with the explant cultures are not cell-specific and/or have significant off-target effects, it is difficult to ascertain that the metabolic changes are secondary to the effects on photoreceptors alone, which the authors claim. Additionally, the explants are taken at a very early age when photoreceptors are known to still be maturing. No mention or data is presented on how these metabolic changes are altered in retinal explants after photoreceptors have fully matured. Likewise, significant assumptions are made based on a single metabolomics experiment with no stable isotope tracing to support the pathways suggested. While the authors use immunofluorescence to support their claims at multiple points, demonstrating the presence of certain enzymes in the photoreceptors, many of these enzymes are present throughout the retina and likely the RPE. Finally, the claims presented here are in direction contradiction to recent in vivo studies that used cell-specific methods when examining retinal metabolism. No discussion of this difference in results is attempted.

Reviewer #2 (Public Review):

Summary: The authors aim to learn about retinal cell-specific metabolic pathways, which could substantially improve the way retinal diseases are understood and treated. They culture ex vivo mouse retinas for 6 days with 2 - 4 days of various drug treatments targeting different metabolic pathways or by removing the RPE/choroid tissue from the neural retina. They then look at photoreceptor survival, stain for various metabolic enzymes, and quantify a broad panel of metabolites. While this is an important question to address, the results are not sufficient to support the conclusions.

Strengths: The questions the authors are exploring at extremely valuable and I commend the authors and working to learn more about retina metabolism. The different sensitivity of the cones to various drugs is interesting and may suggest key differences between rods and cones. The authors also provide a thoughtful discussion of various metabolic pathways in the context of previous publications.

Weaknesses: As the authors point out, ex vivo culture models allow for control over multiple aspects of the environment (such as drug delivery) not available in vivo. Ex vivo cultures can provide good hints as to what pathways are available between interacting tissues. However, there are many limitations to ex vivo cultures, including shifting to a very artificial culture media condition that is extremely different than the native environment of the retina. It is well appreciated that cells have flexible metabolism and will adapt to the conditions provided. Therefore, observations of metabolic responses obtained under culture conditions need to be interpreted with caution, they indicate what the tissue is doing under those specific conditions (which include cells adapting and dying).

Chen et al use pharmacological interventions to the impact of various metabolic pathways on photoreceptor survival and "long term" metabolic changes. The dose and timing of these drug treatments are not examined though. It is also hard to know how these drugs penetrate the tissue and it needs to be validated that the intended targets are being accurately hit. These relatively long-term treatments should be causing numerous downstream changes to metabolism, cell function, and survival, which makes looking at a snapshot of metabolite levels hard to interpret. It would be more valuable to look at multiple time points after drug treatment, especially easy time points (closer to 1 hr). The authors use metabolite ratios to make conclusions about pathway activity. It would be more valuable to directly measure pathway activity by looking a metabolite production rates in the media and/or with metabolic tracers again in time scales closer to minutes and hours instead of days.

It is not clear from the text if the ex vivo samples with RPE/choroid intact are analyzed for metabolomics with the RPE/choroid still intact or if this is removed. If it is not removed, the comparison to the retina without RPE/choroid needs to be re-interpreted for the contribution of metabolites from the added tissue. The composition of the tissue is different and cannot be disentangled from the changes to the neural retina specifically.

While the data is interesting and may give insights into some rod and cone-specific metabolic susceptibility, more work is needed to validate these conclusions. Given the limitations of the model the authors have over-interpreted their findings and the conclusions are not supported by the results. They need to either dramatically limit the scope of their conclusions or validate these hypotheses with additional models and tools.

Reviewer #2 (Public Review):

Erk2 is an essential element of the MAP kinase signaling cascade and directly controls cell proliferation, migration, and survival. Therefore, it is one of the most important drug targets for cancer therapy. The catalytic subunit of Erk2 has a bilobal architecture, with the small lobe harboring the nucleotide-binding pocket and the large lobe harboring the substrate-binding cleft. Several studies by the Ahn group revealed that the catalytic domain hops between (at least) two conformational states: active (R) and inactive (L), which exchange in the millisecond time scale based on the chemical shift mapping. The R state is a signature of the double phosphorylated Erk2 (2P-Erk2), while the L state has been associated with the unphosphorylated kinase (0P-Erk2). Interestingly, the X-ray structures reveal only minimal differences between these two states, a feature that led to the conclusion that active and inactive states are structurally similar but dynamically very different. The Ahn group also found that ATP-competitive inhibitors can steer the populations of Erk2 either toward the R or the L state, depending on their chemical nature. The latter opens up the possibility of modulating the activity of this kinase by changing the chemistry of the ATP-competitive inhibitor. To prove this point, the authors present a set of nineteen compounds with diverse chemical substituents. From their combined NMR and HDX-Mass Spec analyses, fourteen inhibitors drive the kinase toward the R state, while four compounds keep the kinase hopping between the R and L states. Based on these data, the authors rationalize the effects of these inhibitors and the importance of the nature of the substituents on the central scaffold to steer the kinase activity. While all these inhibitors target the ATP binding pocket, they display diverse structural and dynamic effects on the kinase, selecting a specific structural state. Although the inhibited kinase is no longer able to phosphorylate substrates, it can initiate signaling events functioning as scaffolds for other proteins. Therefore, by changing the chemistry of the inhibitors it may be possible to affect the MAP cascade in a predictable manner. This concept, recently introduced as proof of principle, finds here its significance and practical implications. The design of the next-generation inhibitors must be taken into account for these design principles. The research is well executed, and the data support the author's conclusions.

Reviewer #3 (Public Review):

Summary:<br /> Anderson et al utilize an array of orthogonal techniques to highlight the importance of protein dynamics for the function and inhibition of the kinase ERK2. ERK2 is important for a large variety of biological functions.

Strengths:<br /> This is a thorough and detailed study that uses a variety of techniques to identify critical molecular/chemical parameters that drive ERK2 in specific states.

Weaknesses:<br /> No details rules were identified so that novel inhibitors could be designed. Nevertheless, the mode of action of these existing inhibitors is much better defined.

Reviewer #1 (Public Review):

Summary:<br /> The authors set out to determine how chemical variation on kinase inhibitors determines the selection of Erk2 conformations and how inhibitor binding affects ERk2 structure and dynamics.

Strengths:<br /> The study is beautifully presented both verbally and visually. The NMR experiments and the HDX experiments complement each other for the study of Erk2 solution dynamics. X-ray crystallography of Erk2 complexes with inhibitors shows small but distinct structural changes that support the proposed model for the impact of inhibitor binding.

Weaknesses:<br /> A discussion of compound residence time for the different compounds and kinase constructs and how it could affect the very slow HDX rates might be helpful. For example, could any of the observed effects in Figure 4 be due to slow compound dissociation rather than slowed down kinase dynamics? What would be the implications?

Reviewer #1 (Public Review):

This is an elegant didactic exposition showing how dendritic plateau potentials can enable neurons to perform reliable 'binary' computations in the face of realistic spike time jitter in cortical networks. The authors make many good arguments, and the general concept underlying the paper is sound. A strength is their systematic progression from biophiysical to simplified models of single neurons, and their parallel investigation of spiking and binary neural networks, with training happening in the binary neural network.

Reviewer #2 (Public Review):

Summary:

Artificial intelligence (AI) could be useful in some applications and could help humankind. Some forms of AI work on the platform of artificial neural networks (ANN). ANNs are inspired by real brains and real neurons. Therefore understanding the repertoire and logic of real neurons could potentially improve AANs. Cell bodies of real neurons, and axons of real neurons, fire nerve impulses (nerve impulses are very brief ~2 ms, and very tall ~100 mV). Dendrites, which comprise ~80% of the total neuronal membrane (80% of the total neuronal apparatus) typically generate smaller (~50 mV amplitude) but much longer (~100 ms duration) electrical transients, called glutamate-mediated dendritic plateau potentials. The authors have built artificial neurons capable of generating such dendritic plateau potentials, and through computer simulations the authors concluded that long-lasting dendritic signals (plateau potentials) reduce negative impact of temporal jitter occurring in real brain, or in AANs. The authors showed that in AANs equipped with neurons whose dendrites are capable of generating local dendritic plateau potentials, the sparse, yet reliable spiking computations may not require precisely synchronized inputs. That means, the real world can impose notable fluctuations in the network activity and yet neurons could still recognize and pair the related network events. In the AANs equipped with dendritic plateaus, the computations are very robust even when inputs are only partially synchronized. In summary, dendritic plateau potentials endow neurons with ability to hold information longer and connect two events which did not happen at the same moment of time. Dendritic plateaus circumvent the negative impact, which the short membrane time constants arduously inflict on the action potential generation (in both real neurons and model neurons). Interestingly, one of the indirect conclusions of the current study is that neurons equipped with dendritic plateau potentials may reduce the total number of cells (nodes, units) required to perform robust computations.

Strengths:<br /> The majority of published studies are descriptive in nature. Researchers report what they see or measure. A smaller number of studies embark on a more difficult task, which is to explain the logic and rationale of a particular natural design. The current study falls into that second category. The authors first recognize that conduction delays and noise make asynchrony unavoidable in communication between circuits in the real brain. This poses a fundamental problem for the integration of related inputs in real (noisy) world. Neurons with short membrane time constants can only integrate coincident inputs that arrive simultaneously within 2-3 ms of one another. Then the authors considered the role for dendritic plateau potentials. Glutamate-mediated depolarization events within individual dendritic branches, can remedy the situation by widening the integration time window of neurons. In summary, the authors recognized that one important feature of neurons, their dendrites, are built-in to solve the major problems of rapid signal processing: [1] temporal jitter, [2] variation, [3] stochasticity, and [4] reliability of computation. In one word, the dendritic plateau potentials have evolved in the central nervous systems to make rapid CNS computations robust.

Weaknesses:<br /> The authors made some unsupported statements, which should either be deleted, or thoroughly defended in the manuscript. But first of all, the authors failed to bring this study to the readers who are not experts in computational modeling or Artificial Neural Networks. Critical terms (syntax) and ideas have not been explained. For example: [1] binary feature space? [2] 13 dimensions binary vectors? [3] the binary network could still cope with the loss of information due to the binarization of the continuous coordinates? [4] accurate summation?

Reviewer #1 (Public Review):

Summary:<br /> In this manuscript, Abe and colleagues employ in vivo 2-photon calcium imaging of dopaminergic axons in the mPFC. The study reveals that these axons primarily respond to unconditioned aversive stimuli (US) and enhance their responses to initially-neutral stimuli after classical association learning. The manuscript is well-structured and presents results clearly. The utilization of a refined prism-based imaging technique, though not entirely novel, is well-implemented. The study's significance lies in its contribution to the existing literature by offering single-axon resolution functional insights, supplementing prior bulk measurements of calcium or dopamine release. Given the current focus on neuromodulator neuron heterogeneity, the work aligns well with current research trends and will greatly interest researchers in the field.

However, I would like to highlight that the authors could further enhance their manuscript by addressing study limitations more comprehensively and by providing essential details to ensure the reproducibility of their research. In light of this, I have a number of comments and suggestions that, if incorporated, would significantly contribute to the manuscript's value to the field.

Strengths:<br /> -Descriptive.<br /> -Utilization of a well-optimized prism-based imaging method.<br /> -Provides valuable single-axon resolution functional observations, filling a gap in existing literature.<br /> -Timely contribution to the study of neuromodulator neuron heterogeneity.

Weaknesses:<br /> 1. It's important to fully discuss the fact that the measurements were carried out only on superficial layers (30-100um), while major dopamine projections target deep layers of the mPFC as discussed in the cited literature (Vander Weele et al., 2018) and as illustrated in FigS1B,C. This limitation should be explicitly acknowledged and discussed in the manuscript, especially given the potential functional heterogeneity among dopamine neurons in different layers. This potential across-layer heterogeneity could also be the cause of discrepancy among past recording studies with different measurement modalities. Also, mentioning technical limitations would be informative. For example: how deep the authors can perform 2p-imaging through the prism? was the "30-100um" maximum depth the authors could get?

2. In the introduction, it seems that the authors intended to refer to Poulin et al. 2018 regarding molecular/anatomical heterogeneity of dopamine neurons, but they inadvertently cited Poulin et al. 2016 (a general review on scRNAseq). Additionally, the statement that "dopamine neurons that project to the PFC show unique genetic profiles (line 85)" requires clarification, as Poulin et al. 2018 did not specifically establish this point. Instead, they found at least the Vglut2/Cck+ population projects into mPFC, and they did not reject the possibility of other subclasses projecting to mPFC. Rather, they observed denser innervation with DAT-cre, suggesting that non-Vglut2/Cck populations would also project to mPFC. Discuss the potential molecular heterogeneity among mPFC dopamine axons in light of the sampling limitation mentioned earlier.

3. I find the data presented in Figure 2 to be odd. Firstly, the latency of shock responses in the representative axons (right panels of G, H) is consistently very long - nearly 500ms. It raises a query whether this is a biological phenomenon or if it stems from a potential technical artifact, possibly arising from an issue in synchronization between the 2-photon imaging and stimulus presentation. My reservations are compounded by the notable absence of comprehensive information concerning the synchronization of the experimental system in the method section. Secondly, there appear to be irregularities in Panel J. While the authors indicate that "Significant axons were classified as either reward-preferring (cyan) or aversive-preferring (magenta), based on whether the axons are above or below the unity line of the reward/aversive scatter plot (Line 566)," a cyan dot slightly but clearly deviates above the unity line (around coordinates (x, y) = (20, 21)). This needs clarification. Lastly, when categorizing axons for analysis of conditioning data in Fig3 (not Fig2), the authors stated "The color-coded classification (cyan/magenta) was based on k-means clustering, using the responses before classical conditioning (Figure 2J)". I do not understand why the authors used different classification methods for two almost identical datasets.

4. In connection with Point 3, conducting separate statistical analyses for aversive and rewarding stimuli would offer a fairer approach. This could potentially reveal a subset of axons that display responses to both aversive and appetitive stimuli, aligning more accurately with the true underlying dynamics. Moreover, the characterization of Figure 2J as a bimodal distribution while disregarding the presence of axons responsive to both aversive and appetitive cues seems somewhat arbitrary and circular logic. A more inclusive consideration of this dual-responsive population could contribute to a more comprehensive interpretation.

5. The contrast in initialization to novel cues between aversive and appetitive axons mirrors findings in other areas, such as the tail-of-striatum (TS) and ventral striatum (VS) projecting dopamine neurons (Menegas et al., 2017, not 2018). You might consider citing this very relevant study and discussing potential collateral projections between mPFC and TS or VS.

6. The use of correlation values (here >0.65) to group ROIs into axons is common but should be justified based on axon density in the FOV and imaging quality. It's important to present the distribution of correlation values and demonstrate the consistency of results with varying cut-off values. Also, provide insights into the reliability of aversive/appetitive classifications for individual ROIs with high correlations. Importantly, if you do the statistical testing and aversive/appetitive classifications for individual ROIs with above-threshold high correlation (to be grouped into the same axon), do they always fall into the same category? How many false positives/false negatives are observed?
<br /> "Our results remained similar for different correlation threshold values (Line 556)" (data not shown) is obsolete.

Reviewer #2 (Public Review):

Summary:<br /> This study aims to address existing differences in the literature regarding the extent of reward versus aversive dopamine signaling in the prefrontal cortex. To do so, the authors chose to present mice with both a reward and an aversive stimulus during different trials each day. The authors used high spatial resolution two-photon calcium imaging of individual dopaminergic axons in the medial PFC to characterize the response of these axons to determine the selectivity of responses in unique axons. They also paired the reward (water) and an aversive stimulus (tail shock) with auditory tones and recorded across 12 days of associative learning.

The authors find that some axons respond to both reward and aversive unconditioned stimuli, but overall, there is a strong preference to respond to aversive stimuli consistent with expectations from prior studies that used other recording methods. The authors find that both of their two auditory stimuli initially drive responses in axons, but that with training axons develop more selective responses for the shock associated tone indicating that associative learning led to changes in these axon's responses. Finally, the authors use anticipatory behaviors during the conditioned stimuli and facial expressions to determine stimulus discrimination and relate dopamine axons signals with this behavioral evidence of discrimination. This study takes advantage of cutting-edge imaging approaches to resolve the extent to which dopamine axons in PFC respond appetitive or aversive stimuli. They conclude that there is a strong bias to respond to the aversive tail shock in most axons and weaker more sparse representation of water reward.

Strengths:<br /> The strength of this study is the imaging approach that allows for investigation of the heterogeneity of response across individual dopamine axons, unlike other common approaches such as fiber photometry which provide a measure of the average population activity. The use of appetitive and aversive stimuli to probe responses across individual axons is another strength.

Weaknesses:<br /> A weakness of this study is the design of the associative conditioning paradigm. The use of only a single reward and single aversive stimulus makes it difficult to know whether these results are specific to the valence of the stimuli versus the specific identity of the stimuli. Further, the reward presentations are more numerous than the aversive trials making it unclear how much novelty and habituation account for results. Moreover, the training seems somewhat limited by the low number of trials and did not result in strong associative conditioning. The lack of omission responses reported may reflect weak associative conditioning. Finally, the study provides a small advance in our understanding of dopamine signaling in the PFC and lacks evidence for if and what might be the consequence of these axonal responses on PFC dopamine concentrations and PFC neuron activity.

Reviewer #3 (Public Review):

Summary:

The authors image dopamine axons in medial prefrontal cortex (mPFC) using microprism-mediated two-photon calcium imaging. They image these axons as mice learn that two auditory cues predict two distinct outcomes, tailshock or water delivery. They find that some axons show a preference for encoding of the shock and some show a preference for encoding of water. The authors report a greater number of dopamine axons in mPFC that respond to shock. Across time, the shock-preferring axons begin to respond preferentially to the cue predicting shock, while there is a less pronounced increase in the water-responsive axons that acquire a response to the water-predictive cue (these axons also increase non-significantly to the shock-predictive cue). These data lead the authors to argue that dopamine axons in mPFC preferentially encode aversive stimuli.

Strengths:

The experiments are beautifully executed and the authors have mastered an impressively complex technique. Specifically, they are able to image and track individual dopamine axons in mPFC across days of learning. This technique is used the way it should be: the authors isolate distinct dopamine axons in mPFC and characterize their encoding preferences and how this evolves across learning of cue-shock and cue-water contingencies. Thus, these experiments are revealing novel information about how aversive and rewarding stimuli is encoded at the level of individual axons, in a way that has not been done before. This is timely and important.

Weaknesses:

The overarching conclusion of the paper is that dopamine axons preferentially encode aversive stimuli. This is prevalent in the title, abstract, and throughout the manuscript. This is fundamentally confounded. As the authors point out themselves, the axonal response to stimuli is sensitive to outcome magnitude (Supp Fig 3). That is, if you increase the magnitude of water or shock that is delivered, you increase the change in fluorescence that is seen in the axons. Unsurprisingly, the change in fluorescence that is seen to shock is considerably higher than water reward. Further, when the mice are first given unexpected water delivery and have not yet experienced the aversive stimuli, over 40% of the axons respond [yet just a few lines below the authors write: "Previous studies have demonstrated that the overall dopamine release at the mPFC or the summed activity of mPFC dopamine axons exhibits a strong response to aversive stimuli (e.g., tail shock), but little to rewards", which seems inconsistent with their own data]. Given these aspects of the data, it could be the case that the dopamine axons in mPFC encodes different types of information and delegates preferential processing to the most salient outcome across time. The use of two similar sounding tones (9Khz and 12KHz) for the reward and aversive predicting cues are likely to enhance this as it requires a fine-grained distinction between the two cues in order to learn effectively.

There is considerable literature on mPFC function across species that would support such a view. Specifically, theories of mPFC function (in particular prelimbic cortex, which is where the axon images are mostly taken) generally center around resolution of conflict in what to respond, learn about, and attend to. That is, mPFC is important for devoting the most resources (learning, behavior) to the most relevant outcomes in the environment. This data then, provides a mechanism for this to occur in mPFC. That is, dopamine axons signal to the mPFC the most salient aspects of the environment, which should be preferentially learned about and responded towards. This is also consistent with the absence of a negative prediction error during omission: the dopamine axons show increases in responses during receipt of unexpected outcomes, but do not encode negative errors. This supports a role for this projection in helping to allocate resources to the most salient outcomes and their predictors, and not learning per se. Below are a just few references from the rich literature on mPFC function (some consider rodent mPFC analogous to DLPFC, some mPFC), which advocate for a role in this region in allocating attention and cognitive resources to most relevant stimuli, and do not indicate preferential processing of aversive stimuli.

References:<br /> 1. Miller, E. K., & Cohen, J. D. (2001). An integrative theory of prefrontal cortex function. Annual review of neuroscience, 24(1), 167-202.<br /> 2. Bissonette, G. B., Powell, E. M., & Roesch, M. R. (2013). Neural structures underlying set-shifting: roles of medial prefrontal cortex and anterior cingulate cortex. Behavioural brain research, 250, 91-101.<br /> 3. Desimone, R., & Duncan, J. (1995). Neural mechanisms of selective visual attention. Annual review of neuroscience, 18(1), 193-222.<br /> 4. Sharpe, M. J., Stalnaker, T., Schuck, N. W., Killcross, S., Schoenbaum, G., & Niv, Y. (2019). An integrated model of action selection: distinct modes of cortical control of striatal decision making. Annual review of psychology, 70, 53-76.<br /> 5. Ridderinkhof, K. R., Ullsperger, M., Crone, E. A., & Nieuwenhuis, S. (2004). The role of the medial frontal cortex in cognitive control. science, 306(5695), 443-447.<br /> 6. Nee, D. E., Kastner, S., & Brown, J. W. (2011). Functional heterogeneity of conflict, error, task-switching, and unexpectedness effects within medial prefrontal cortex. Neuroimage, 54(1), 528-540.<br /> 7. Isoda, M., & Hikosaka, O. (2007). Switching from automatic to controlled action by monkey medial frontal cortex. Nature neuroscience, 10(2), 240-248.

Joint Public Review:

Neuropeptide signaling is an important component of nervous systems, where neuropeptides typically act via G-protein coupled receptors (GPCRs) to regulate many physiological and behavioral processes. Neuropeptides and their cognate GPCRs have been extensively characterized in bilaterian animals, revealing that a core set of neuropeptide signaling systems originated in common ancestors of extant Bilateria. Neuropeptides have also been identified in cnidarians, which are a sister group to the Bilateria. However, the GPCRs that mediate the effects of neuropeptides in cnidarians have not been identified.

In this paper the authors perform a phylogenetic analysis of GPCRs in metazoans and report that the orthologs of bilaterian neuropeptide receptors are not found in cnidarians. This indicates that neuropeptide signaling systems have largely evolved independently in cnidarians and bilaterians. To accomplish this, they generated a library of putative and known neuropeptides computationally identified in the genome of the cnidarian sea anemone Nematostella vectensis. These peptides were systematically screened for their ability to activate any of the 161 putative Nematostella GPCRs.

This work identified 31 validated GPCRs. These, together with GPCRs that cluster with them, were then used to demonstrate the independent expansion of GPCRs in cnidarian and bilaterian lineages. The authors then mapped validated receptors and ligands to the Nematostella single cell data to provide an overview of the cell types expressing these signaling genes. In addition, the authors have begun to analyze neuropeptide signaling networks in N. vectensis by showing potential signaling connections between cell types expressing neuropeptides and cell types expressing cognate receptors.

This work is the most extensive pharmacological characterization of neuropeptide GPCRs in a cnidarian to date and thus represents an important accomplishment, and is one that will improve our understanding of how peptidergic signaling evolved in animals and its impact on evolution of nervous systems."

The reviewers did not detect any weaknesses in the work but ask that the authors comment on the following points:<br /> 1- It was not clear why the phylogenetic analysis included non-validated GPCRs that clustered with the validated peptidergic receptors. Would restricting the phylogenetic analyses only to confirmed peptidergic GPCRs alter the topology of the tree and subsequent conclusions of independent expansion?<br /> 2- Clearly, other neuropeptide signaling systems in cnidarians remain to be discovered but this paper represents a huge step forward.<br /> 3- There are limitations in what can be interpreted from single cell transcriptomic data but the data nevertheless provide the foundations for future studies involving i). detailed anatomical analysis of neuropeptide and neuropeptide receptor expression in N. vectensis using mRNA in situ hybridization and/or immunohistochemical methods and ii). functional analysis of the physiological/behavioral roles of neuropeptide signaling systems in N. vectensis

Reviewer #1 (Public Review):

Summary:

This research study utilizes a realistic motoneuron model to explore the potential to trace back the appropriate levels of excitation, inhibition, and neuromodulation in the firing patterns of motoneurons observed in in-vitro and in-vivo experiments in mammals. The research employs high-performance computing power to achieve its objectives. The work introduces a new framework that enhances understanding of the neural inputs to motoneuron pools, thereby opening up new avenues for hypothesis testing research.

Strengths:<br /> The significance of the study holds relevance for all neuroscientists. Motoneurons are a unique class of neurons with known distribution of outputs for a wide range of voluntary and involuntary motor commands, and their physiological function is precisely understood. More importantly, they can be recorded in-vivo using minimally invasive methods, and they are directly impacted by many neurodegenerative diseases at the spinal cord level.<br /> The computational framework developed in this research offers the potential to reverse engineer the synaptic input distribution when assessing motor unit activity in humans, which holds particular importance.<br /> Overall, the strength of the findings focuses on providing a novel framework for studying and understanding the inputs that govern motoneuron behavior, with broad applications in neuroscience and potential implications for understanding neurodegenerative diseases. It highlights the significance of the study for various research domains, making it valuable to the scientific community.

Weaknesses: The exact levels of inhibition, excitation, and neuromodulatory inputs to neural networks are unknown. Therefore the work is based on fine-tuned measures that are indirectly based on experimental results. However, obtaining such physiological information is challenging and currently impossible. From a computational perspective it is a challenge that in theory can be solved. Thus, although we have no ground-truth evidence, this framework can provide compelling evidence for all hypothesis testing research and potentially solve this physiological problem with the use of computers.

Reviewer #2 (Public Review):

The study presents an extensive computational approach to identify the motor neuron input from the characteristics of single motor neuron discharge patterns during a ramp up/down contraction. This reverse engineering approach is relevant due to limitations in our ability to estimate this input experimentally. Using well-established models of single motor neurons, a (very) large number of simulations were performed that allowed identification of this relation. In this way, the results enable researchers to measure motor neuron behavior and from those results determine the underlying neural input scheme. Overall, the results are very convincing and represent an important step forward in understanding the neural strategies for controlling movement.

Nevertheless, I would suggest that the authors consider the following recommendations to strengthen the message further. First, I believe that the relation between individual motor neuron behavioral characteristics (delta F, brace height etc.) and the motor neuron input properties can be illustrated more clearly. Although this is explained in the text, I believe that this is not optimally supported by figures. Figure 6 to some extent shows this, but figures 8 and 9 as well as Table 1 shows primarily the goodness of fit rather than the actual fit. Second, I would have expected the discussion to have addressed specifically the question of which of the two primary schemes (push-pull, balanced) is the most prevalent. This is the main research question of the study, but it is to some degree left unanswered. Now that the authors have identified the relation between the characteristics of motor neuron behaviors (which has been reported in many previous studies), why not exploit this finding by summarizing the results of previous studies (at least a few representative ones) and discuss the most likely underlying input scheme? Is there a consistent trend towards one of the schemes, or are both strategies commonly used?

In addition, it seems striking to me that highly non-linear excitation profiles are necessary to obtain a linear CST ramp in many model configurations. Although somewhat speculative, one may expect that an approximately linear relation is desired for robust and intuitive motor control. It seems to me that humans generally have a good ability to accurately grade the magnitude of the motor output, which implies that either a non-linear relation has been learnt (complex task), or that the central nervous system can generally rely on a somewhat linear relation between the neural drive to the muscle and the output (simpler task). Following this reasoning, it could be interesting to report also for which input scheme, the excitation profile is most linear. I understand that this is not the primary aim of the study, but it may be an interesting way to elaborate on the finding that in many cases non-linear excitation profiles were needed to produce the linear ramp.

Reviewer #1 (Public Review):

Summary:<br /> I read the paper by Parrotta et al with great interest. The authors are asking an interesting and important question regarding pain perception, which is derived from predictive processing accounts of brain function. They ask: If the brain indeed integrates information coming from within the body (interoceptive information) to comprise predictions about the expected incoming input and how to respond to it, could we provide false interoceptive information to modulate its predictions, and subsequently alter the perception of such input? To test this question, they use pain as the input and the sounds of heartbeats (falsified or accurate) as the interoceptive signal.

Strengths:<br /> I found the question well-established, interesting, and important, with important implications and contributions for several fields, including neuroscience of prediction-perception, pain research, placebo research, and health psychology. The paper is well-written, the methods are adequate, and the findings largely support the hypothesis of the authors. The authors carried out a control experiment to rule out an alternative explanation of their finding, which was important.

Weaknesses:<br /> I will list here one theoretical weakness or concern I had, and several methodological weaknesses.

The theoretical concern regards what I see as a misalignment between a hypothesis and a result, which could influence our understanding of the manipulation of heartbeats, and its meaning: The authors indicate from prior literature and find in their own findings, that when preparing for an aversive incoming stimulus, heartbeats *decrease*. However, in their findings, manipulating the heartbeats that participants hear to be slower than their own prior to receiving a painful stimulus had *no effect* on participants' actual heartbeats, nor on their pain perceptions. What authors did find is that when listening to heartbeats that are *increased* in frequency - that was when their own heartbeats decreased (meaning they expected an aversive stimulus) and their pain perceptions increased.

This is quite complex - but here is my concern: If the assumption is that the brain is collecting evidence from both outside and inside the body to prepare for an upcoming stimulus, and we know that *slowing down* of heartbeats predicts an aversive stimulus, why is it that participants responded in a change in pain perception and physiological response when listened to *increased heartbeats* and not decreased? My interpretation is that the manipulation did not fool the interoceptive signals that the brain collects, but rather the more conscious experience of participants, which may then have been translated to fear/preparation for the incoming stimulus. As the authors indicate in the discussion (lines 704-705), participants do not *know* that decreased heartbeats indicate upcoming aversive stimulus, and I would even argue the opposite - the common knowledge or intuitive response is to increase alertness when we hear increased heartbeats, like in horror films or similar scenarios. Therefore, the unfortunate conclusion is that what the authors assume is a manipulation of interoception - to me seems like a manipulation of participants' alertness or conscious experience of possible danger. I hope the (important) distinction between the two is clear enough because I find this issue of utmost importance for the point the paper is trying to make. If to summarize in one sentence - if it is decreased heartbeats that lead the brain to predict an approaching aversive input, and we assume the manipulation is altering the brain's interoceptive data collection, why isn't it responding to the decreased signal? --> My conclusion is, that this is not in fact a manipulation of interoception, unfortunately.

I will add that the control experiment - with an exteroceptive signal (knocking of wood) manipulated in a similar manner - could be seen as evidence of the fact that heartbeats are regarded as an interoceptive signal, and it is an important control experiment, however, to me it seems that what it is showing is the importance of human-relevant signals to pain prediction/perception, and not directly proves that it is considered interoceptive. For example, it could be experienced as a social cue of human anxiety/fear etc, and induce alertness.

Several additional, more methodological weaknesses include the very small number of trials per condition - the methods mention 18 test trials per participant for the 3 conditions, with varying pain intensities, which are later averaged (and whether this is appropriate is a different issue). This means 6 trials per condition, and only 2 trials per condition and pain intensity. I thought that this number could be increased, though it is not a huge concern of the paper. It is, however, needed to show some statistics about the distribution of responses, given the very small trial number (see recommendations for authors). The sample size is also rather small, on the verge of "just right" to meet the required sample size according to the authors' calculations. Finally, and just as important, the data exists to analyze participants' physiological responses (ECG) after receiving the painful stimulus - this could support the authors' claims about the change in both subjective and objective responses to pain. It could also strengthen the physiological evidence, which is rather weak in terms of its effect. Nevertheless, this is missing from the paper.

I have several additional recommendations regarding data analysis (using an ANOVA rather than multiple t-tests, using raw normalized data rather than change scores, questioning the averaging across 3 pain intensities) - which I will detail in the "recommendations for authors" section.

Conclusion:<br /> To conclude, the authors have shown in their findings that predictions about an upcoming aversive (pain) stimulus - and its subsequent subjective perception - can be altered not only by external expectations, or manipulating the pain cue, as was done in studies so far, but also by manipulating a cue that has fundamental importance to human physiological status, namely heartbeats. Whether this is a manipulation of actual interoception as sensed by the brain is - in my view - left to be proven.<br /> Still, the paper has important implications in several fields of science ranging from neuroscience prediction-perception research, to pain and placebo research, and may have implications for clinical disorders, as the authors propose. Furthermore, it may lead - either the authors or someone else - to further test this interesting question of manipulation of interoception in a different or more controlled manner.

I salute the authors for coming up with this interesting question and encourage them to continue and explore ways to study it and related follow-up questions.

Reviewer #2 (Public Review):

In this manuscript, Parrotta et al. tested whether it is possible to modulate pain perception and heart rate by providing false HR acoustic feedback before administering electrical cutaneous shocks. To this end, they performed two experiments. The first experiment tested whether false HR acoustic feedback alters pain perception and the cardiac anticipatory response. The second experiment tested whether the same perceptual and physiological changes are observed when participants are exposed to a non-interoceptive feedback. The main results of the first experiment showed a modulatory effect for faster HR acoustic feedback on pain intensity, unpleasantness, and cardiac anticipatory response compared to a control (acoustic feedback congruent to the participant's actual HR). However, the results of the second experiment also showed an increase in pain ratings for the faster non-interoceptive acoustic feedback compared to the control condition, with no differences in pain unpleasantness or cardiac response.

The main strengths of the manuscript are the clarity with which it was written, and its solid theoretical and conceptual framework. The researchers make an in-depth review of predictive processing models to account for the complex experience of pain, and how these models are updated by perceptual and active inference. They follow with an account of how pain expectations modulate physiological responses and draw attention to the fact that most previous studies focus on exteroceptive cues. At this point, they make the link between pain experience and heart rate changes, and introduce their own previous work showing that people may illusorily perceive a higher cardiac frequency when expecting painful stimulation, even though anticipating pain typically goes along with a decrease in HR. From here, they hypothesize that false HR acoustic feedback evokes more intense and unpleasant pain perception, although the actual HR actually decreases due to the orienting cardiac response. Furthermore, they also test the hypothesis that an exteroceptive cue will lead to no (or less) changes in those variables. The discussion of their results is also well-rooted in the existing bibliography, and for the most part, provides a credible account of the findings.

The main weaknesses of the manuscript lies in a few choices in methodology and data analysis that hinder the interpretation of the results and the conclusions as they stand. The first peculiar choice is the convoluted definition of the outcomes. Specifically, pain intensity and unpleasantness are first normalized and then transformed into variation rates (sic) or deltas, which makes the interpretation of the results unnecessarily complicated. This is also linked to the definitions of the smallest effect of interest (SESOI) in terms of these outcomes, which is crucial to determining the sample size and gauging the differences between conditions. However, the choice of SESOI is not properly justified, and strangely, it changes from the first experiment to the second.

Furthermore, the researchers propose the comparison of faster vs. slower delta HR acoustic feedback throughout the manuscript when the natural comparison is the incongruent vs. the congruent feedback. This could be influenced by the fact that the faster HR exteroceptive cue in experiment 2 also shows a significant modulatory effect on pain intensity compared to congruent HR feedback, which puts into question the hypothesized differences between interoceptive vs. exteroceptive cues. These results could also be influenced by the specific choice of exteroceptive cue: the researchers imply that the main driver of the effect is the nature of the cue (interoceptive vs. exteroceptive) and not its frequency. However, they attempt to generalize their findings using knocking wood sounds to all possible sounds, but it is possible that some features of these sounds (e.g., auditory roughness or loomingness) could be the drivers behind the observed effects. Finally, it is noteworthy that the researchers divided the study into two experiments when it would have been optimal to test all the conditions with the same subjects in a randomized order in a single cross-over experiment to reduce between-subject variability.

Taking this into consideration, I believe that the conclusions are only partially supported by the evidence. Despite of the outcome transformations, a clear effect of faster HR acoustic feedback can be observed in the first experiment, which is larger than the proposed exteroceptive counterpart. This work could be of broad interest to pain researchers, particularly those working on predictive coding of pain.

Reviewer #3 (Public Review):

Summary:

In their manuscript titled "Exposure to false cardiac feedback alters pain perception and anticipatory cardiac frequency", Parrotta and colleagues describe an experimental study on the interplay between false heart rate feedback and pain experience in healthy, adult humans. The experimental design is derived from Bayesian perspectives on interoceptive inference. In Experiment 1 (N=34), participants rated the intensity and unpleasantness of an electrical pulse presented to their middle fingers. Participants received auditory cardiac feedback prior to the electrical pulse. This feedback was congruent with the participant's heart rate or manipulated to have a higher or lower frequency than the participant's true heart rate (incongruent high/ low feedback). The authors find heightened ratings of pain intensity and unpleasantness as well as a decreased heart rate in participants who were exposed to the incongruent-high cardiac feedback. Experiment 2 (N=29) is equivalent to Experiment 1 with the exception that non-interoceptive auditory feedback was presented. Here, mean pain intensity and unpleasantness ratings were unaffected by feedback frequency.

Strengths:

The authors present interesting experimental data that was derived from modern theoretical accounts of interoceptive inference and pain processing.

1. The motivation for the study is well-explained and rooted within the current literature, whereas pain is the result of a multimodal, inferential process. The separation of nociceptive stimulation and pain experience is explained clearly and stringently throughout the text.

2. The idea of manipulating pain-related expectations via an internal, instead of an external cue, is very innovative.

3. An appropriate control experiment was implemented, where an external (non-physiological) auditory cue with parallel frequency to the cardiac cue was presented.

4. The chosen statistical methods are appropriate, albeit averaging may limit the opportunity for mechanistic insight, see weaknesses section.

5. The behavioral data, showing increased unpleasantness and intensity ratings after exposure to incongruent-high cardiac feedback, but not exteroceptive high-frequency auditory feedback, is backed up by ECG data. Here, the decrease in heart rate during the incongruent-high condition speaks towards a specific, expectation-induced physiological effect that can be seen as resulting from interoceptive inference.

Weaknesses:

Additional analyses and/ or more extensive discussion are needed to address these limitations:

1. I would like to know more about potential learning effects during the study. Is there a significant change in ∆ intensity and ∆ unpleasantness over time; e.g. in early trials compared to later trials? It would be helpful to exclude the alternative explanation that over time, participants learned to interpret the exteroceptive cue more in line with the cardiac cue, and the effect is driven by a lack of learning about the slightly less familiar cue (the exteroceptive cue) in early trials. In other words, the heartbeat-like auditory feedback might be "overlearned", compared to the less naturalistic tone, and more exposure to the less naturalistic cue might rule out any differences between them w.r.t. pain unpleasantness ratings.

2. The origin of the difference in Cohen's d (Exp. 1: .57, Exp. 2: .62) and subsequently sample size in the sensitivity analyses remains unclear, it would be helpful to clarify where these values are coming from (are they related to the effects reported in the results? If so, they should be marked as post-hoc analyses).

3. As an alternative explanation, it is conceivable that the cardiac cue may have just increased unspecific arousal or attention to a larger extent than the exteroceptive cue. It would be helpful to discuss the role of these rather unspecific mechanisms, and how it may have differed between experiments.

4. The hypothesis (increased pain intensity with incongruent-high cardiac feedback) should be motivated by some additional literature.

5. The discussion section does not address the study's limitations in a sufficient manner. For example, I would expect a more thorough discussion on the lack of correlation between participant ratings and self-reported bodily awareness and reactivity, as assessed with the BPQ.<br /> a. Some short, additional information on why the authors chose to focus on body awareness and supradiaphragmatic reactivity subscales would be helpful.

6. The analyses presented in this version of the manuscript allow only limited mechanistic conclusions - a computational model of participant's behavior would be a very strong addition to the paper. While this may be out of the scope of the article, it would be helpful for the reader to discuss the limitations of the presented analyses and outline avenues towards a more mechanistic understanding and analysis of the data. The computational model in [7] might contain some starting ideas.

Some additional topics were not considered in the first version of the manuscript:<br /> 1. The possible advantages of a computational model of task behavior should be discussed.<br /> 2. Across both experiments, there was a slightly larger number of female participants. Research suggests significant sex-related differences in pain processing [1,2]. It would be interesting to see what role this may have played in this data.<br /> 3. There are a few very relevant papers that come to mind which may be of interest. These sources might be particularly useful when discussing the roadmap towards a mechanistic understanding of the inferential processes underlying the task responses [3,4] and their clinical implications.<br /> 4. In this version of the paper, we only see plots that illustrate ∆ scores, averaged across pain intensities - to better understand participant responses and the relationship with stimulus intensity, it would be helpful to see a more descriptive plot of task behavior (e.g. stimulus intensity and raw pain ratings)

[1] Mogil, J. S. (2020). Qualitative sex differences in pain processing: emerging evidence of a biased literature. Nature Reviews Neuroscience, 21(7), 353-365. https://www.nature.com/articles/s41583-020-0310-6<br /> [2] Sorge, R. E., & Strath, L. J. (2018). Sex differences in pain responses. Current Opinion in Physiology, 6, 75-81. https://www.sciencedirect.com/science/article/abs/pii/S2468867318300786?via%3Dihub<br /> [3] Unal, O., Eren, O. C., Alkan, G., Petzschner, F. H., Yao, Y., & Stephan, K. E. (2021). Inference on homeostatic belief precision. Biological Psychology, 165, 108190.<br /> [4] Allen, M., Levy, A., Parr, T., & Friston, K. J. (2022). In the body's eye: the computational anatomy of interoceptive inference. PLoS Computational Biology, 18(9), e1010490.<br /> [5] Stephan, K. E., Manjaly, Z. M., Mathys, C. D., Weber, L. A., Paliwal, S., Gard, T., ... & Petzschner, F. H. (2016). Allostatic self-efficacy: A metacognitive theory of dyshomeostasis-induced fatigue and depression. Frontiers in human neuroscience, 10, 550.<br /> [6] Friston, K. J., Stephan, K. E., Montague, R., & Dolan, R. J. (2014). Computational psychiatry: the brain as a phantastic organ. The Lancet Psychiatry, 1(2), 148-158.<br /> [7] Eckert, A. L., Pabst, K., & Endres, D. M. (2022). A Bayesian model for chronic pain. Frontiers in Pain Research, 3, 966034.

Reviewer #1 (Public Review):

Summary:<br /> The question at hand is whether astrocytes contribute to the mechanism of long-term synaptic potentiation (LTP) at synaptic contacts between excitatory glutamatergic neurons and inhibitory neurons (E-I synapses). This is a legitimate query considering the immense body of work that has now established synaptic plasticity (LTP, LTD and spike-timing dependent plasticity) as an astrocyte-dependent process at excitatory synapses and, by contrast, the lack of knowledge on whether and how astrocytes control IN activity. Taking direct inspiration from that same body of work, authors recapitulate a number of experiments and approaches from prior seminal studies and provide evidence that E-I synapses in the stratum radiatum of the hippocampus display NMDAR-dependent plasticity, which can be suppressed by pharmacologically hindering astrocytes physiology, preventing astrocyte Ca2+ transients or blocking endocannabinoid CB1 receptors. Under any of these conditions, LTP can still be rescued by exogenously applying D-serine, a naturally occurring co-agonist of NMDARs primarily released by astrocytes. Coincidently, authors show that the conditions used to elicit LTP also cause a transient increase in NMDAR co-agonist site occupancy. Lastly, based on some evidence that gamma-CaMKII is predominantly expressed in INs rather than excitatory neurons, authors conducted AAV-mediated IN-specific gamma-CaMKII shRNA experiments and found that this is sufficient to suppress LTP at E-I synapses. They found that this approach also impairs contextual fear learning in behaving mice. Authors conclude that astrocytes gate LTP at E-I synapses via a mechanism wherein neuronal depolarization during LTP induction elicits endocannabinoid release which drives CB1-dependent astrocyte Ca2+ activity, causing the release of the NMDAR co-agonist D-serine (required for NMDAR activation).

Strengths:<br /> This is an important question and the experimental work seems to have been conducted at high standards. The electrophysiology traces are impeccable, the experiments are well powered, including the behavioral testing, and multiple controls and validations are provided throughout. The figures are clear and easy to understand. Overall, the conclusions from the study are consistent, or partially consistent, by the findings.

Main Weaknesses:<br /> 1- A major point of concern is the lack of proper acknowledgment of the seminal studies that were mimicked in this manuscript, notably Henneberger et al, Nature 2010, Adamsky et al, Cell 2018; and Robin et al., Neuron 2017. The entire study design is a replica of these landmark studies: it isn't built upon or inspired from them, it exactly repeats the experiments and methods performed in them, coming dangerously close to being simply a hidden attempt to plagiarize published work. The resemblance goes as far as using an identical figure display (see Fig4.D vs Fig 2D of Ref#4). The issue is that authors frame the problem, scientist logic, reasoning, technical tricks, approaches, and interpretations as their own whereas, in reality, they were taken verbatim out of previous work and applied to a (shockingly) similar problem. The probity of the present study is thus in question. Authors need to clearly acknowledge, in all relevant instances, that the work presented here recapitulates the approach, reasoning and methodology used in past seminal studies that tackled the mechanisms of astrocyte regulation of LTP.

2-Relatedly, in past work, field recordings were used to monitor LTP in hippocampal slices (refs 4, 26 and others). This method captures indiscriminately all excitatory synapses where glutamate is released to cause AMPAR-dependent (and NMDAR) transmembrane flux of cations in the postsynaptic element, including E-I synapses and not just E-E synapse like the authors claim. Therefore, a strong argument can be made that there is no actual ground to differentiate the present results from past ones.

3-There is a general lack of excitement about this study. One reason is that it replicates almost identically past work, as mentioned above. Another is that the scientific question and importance of the findings are not framed appropriately. The work is presented as an astrocyte-focused investigation, but it has very limited value to the astrocyte field. The findings are, in all accounts, identical to those unveiled by previous work especially because E-I synapses are, in fact, excitatory synapses. Where this study does bring value, however, is to the field of interneurons, but it would need to be reframed to shift the emphasis from astrocytes to E-I connections. Authors would need to elevate the text by framing their work around relevant considerations, such as IN diversity, mechanisms of LTP in IN subtypes, role of E-I connections in hippocampal circuit function, information processing, behavior, spatial learning, navigation, or grid cells activity etc...

4-A clear weakness of the study is that it fails to consider the molecular and functional diversity of interneurons in the stratum radiatum and provides no insights or considerations related to it. Authors provide no information on what type of IN were patched, or the location of their cell body in the s.r., effectively treating all patched IN as a homogeneous ensemble of cells - which they are not. Relatedly, the study is extremely evasive on the importance of the results in the context of inhibitory interneurons. This renders the significance of the insights highly uncertain and dampens both the impact of the study and the excitement it generates. Hippocampal interneurons are very diverse in molecular identity, sub-anatomical location, morphology, projections, connectivity and functional importance. Some experts go as far as recognizing 29 subtypes in the CA1, including 9 in the stratum radiatum alone (based on the location of their soma). However, this is neither addressed nor acknowledged by the authors, with the exception of a statement (line 659) where they claim to have "focused on a subpopulation of interneurons in the stratum radiatum" without providing any precision or evidence to corroborate this assertion. This diversity, alone, could explain why not all cells showed LTP, or why the mechanisms authors describe in the radiatum do not seem to be at play in the oriens. Hence, carefully considering the diversity of INs in the present work is necessary. It would refine and augment the conclusions of the paper. Instead of a sub-region specificity, the study might fuel the notion of an IN subtype specificity of LTP mechanisms, which is more useful to the field.

5-Authors take several shortcuts. Some of the conclusions are a leap from the experiments and are only acceptable due to the close analogy with very similar investigations conducted in the past that provided identical results. For instance, the present study provides no evidence of any sort that D-serine is involved - rather, it provides evidence that the pathway at hand contributes to increasing the occupancy of the co-agonist binding site of NMDARs. Considering the absence of work demonstrating that D-serine is the endogenous co-agonist of NMDARs at E-I synapses, most of the authors claims on D-serine are unfounded. This would necessitate using tools such as the canonical D-serine scavengers DAAS or DsDA, serine racemase KO mice etc. Similarly, authors provide no compelling evidence that endocannabinoid CB1 receptors involved in this pathway are located on astrocytes

6-An important caveat in this study is the protocol employed to induce LTP, which includes steps of sustained depolarization of the patched IN to -10mV. Neuronal depolarization is known to induce endocannabinoids production. In several instances, this was shown to 'activate' astrocytes and elicit the release of astrocyte-derived transmitters at nearby synapses. This implies that the endocannabinoid-dependent pathway described in the study is, most likely, artificially engaged by the protocol itself. Hence, the present work only provides evidence that an astrocyte-dependent, CB1-D-serine-pathway can be artificially called upon with this specific LTP protocol, but does not convincingly demonstrate that it is naturally occurring or necessary for plasticity at E-I synapses. Authors would need to thoroughly address this caveat by replicating some of their key findings (AM251, calcium-clamp, D-serine and CaMKII shRNA) using a protocol that does not entail the artificial depolarization of the patched interneuron.

7-Reading and understanding are hindered by a rather vast array of issues with the text itself. It needs thorough editing for typos, misnomers, meaning-altering errors in syntax, and a number of issues with English.

Reviewer #2 (Public Review):

Summary:<br /> This work explores the implication of astrocytes in the regulation of long-term potentiation of excitatory synapses onto inhibitory neurons in CA1 hippocampus. They found that astrocytes of a sub-region of CA1 regulate this plasticity through their activation of endocannabinoids that lead to the release of the NMDA receptor co-agonist, D-serine.

Strengths:<br /> The experiments are well considered and conceptualized, and use appropriate tools to explore the role of astrocytes in the tripartite synapse. The results highlight a novel role of astrocytes in an important aspect of the synaptic regulation of the hippocampal circuit. There are extensive levels of analysis for each experimental group of evidence.

Weaknesses:<br /> The authors underscore and used an oversimplified view of the heterogeneity of interneuron populations and their selective roles in the hippocampal network. Also, there is an uneven level of astrocyte-selective tools used in the different experiments which creates an uneven strength of arguments and conclusions regarding the role of glial cells. Finally, the wording used by the authors often lead to some confusion or sense of overinterpretation.

Reviewer #1 (Public Review):

This paper presents a set of experiments designed to test whether gravity in people's intuitive physics engine is implemented as a simple deterministic representation of gravity or as a Gaussian distribution. The work shows experimentally that the probabilistic representation of gravity does a better job at capturing both human judgments, including biases in stability inferences. The work further shows that Gaussian representations of gravity can evolve in a simple agent-environment reinforcement learning problem setup.

Strengths:<br /> The paper approaches the problem from three different angles in an impressive way. The first is through a direct comparison of human judgments against model predictions. The second is through an analysis of whether the model correctly predicts cognitive illusions. The third is through a computational exploration of how these representations emerge in a reinforcement-learning setup. The idea of approaching the same problem from multiple independent angles, and seeking confirming evidence is laudable.

Weaknesses:<br /> There are two differences between the "natural gravity" account and the "mental gravity" account. The first difference lies in the implementation of gravity. The second, however, is simply that the mental gravity model is integrating more uncertainty into the simulator. In my understanding, adding small amounts of noise to computational models will often increase their fit to human judgments (with softmaxing perhaps being the most common example of this). While counter-intuitive, this is because 'noiseless' models have perfect representations of the stimuli, which is an unrealistic assumption. In the case of intuitive physics, people might have noisy perceptual representations of exactly how flat the table is, the exact location of each block, what small disturbances might be happening in the environment, and so on. The absence of these sources of uncertainty in deterministic models can make them perform in a non-human-like manner.

While all the data presented in the paper is consistent with the possibility that people have a stochastic representation of gravity. It is possible that people have uncertainty over what unobservable forces a block tower might be under (e.g., wind, bumps to the table, etc). Therefore, even if you have a firm belief that gravity goes down, you may want to add noise in your simulations to account for the fact that, in the real world, gravity is almost never the only force acting on an object that has started to move. While the paper acknowledges that such an account would be mathematically equivalent, it does not acknowledge that this raises the question of whether people actually have stochastic representations of gravity.

This alternative account could be particularly important because I believe it might be a more accurate representation of what people believe. I may be wrong, but I believe that it is common to emphasize the probabilistic nature of the models and the importance of implementing forces as distributions (e.g., the concept of 'noisy newtons').

Reviewer #2 (Public Review):

Summary:<br /> Through a set of experiments and model simulations, the authors tested whether the commonly assumed world model of gravity was a faithful replica of the physical world. They found that participants did not model gravity as a single, fixed vector for gravity but instead as a distribution of possible vectors. Surprisingly, the width of this distribution was quite large (~20 degrees). While previous accounts had suggested that this uncertainty was due to perceptual noise or an inferred external perturbation, the authors suggest that this uncertainty simply arises from a noisy distribution of the representation of gravity's direction. A reinforcement learning model with an initial uniform distribution for gravity's direction ultimately converged to a precision in the same order as the human participants, which lends support to the authors' conclusion and suggests that this distribution is learned through experience. What's more, further simulations suggest that representing gravity with such a wide distribution may balance speed and accuracy, providing a potentially normative explanation for the world model with gravity as a distribution.

Strengths:<br /> The authors present surprising findings in a relatively straightforward way in a now classic experimental task. They provide a normative explanation based on a resource-rational framework for why people may have a stochastic world model instead of a deterministic world model.

Weaknesses:<br /> Support for gravity being represented as a Gaussian distribution (stochastic world model), as opposed to perceptual uncertainty or (inferred) external perturbations, is from an RL model simulation. It would be more convincing if the authors could experimentally demonstrate that potential external perturbations did not affect the distribution of gravity.

Reviewer #3 (Public Review):

Summary:<br /> Previous studies suggest that humans may infer objects' stability through a world model that performs mental simulations with a priori knowledge of gravity acting upon objects. In this study, the authors test two alternative hypotheses about the nature of this a priori knowledge. According to the Natural Gravity assumption, the direction of gravity encoded in this world model is straight downwards as in the physical world. According to the alternative Mental Gravity assumption, that gravity direction is encoded in a Gaussian distribution, with the vertical direction as the maximum likelihood. They present two experiments and computer simulations as evidence in support of the Mental Gravity assumption. Their conclusion is that when the brain is tasked to determine the stability of a given structure it runs a mental simulation, termed Mental Gravity Simulation, which averages the estimated temporal evolutions of that structure arising from different gravity directions sampled from a Gaussian distribution.

Weaknesses:<br /> In spite of the fact that the Mental Gravity Simulation (MGS) seems to predict the data of the two experiments, it is an untenable hypothesis. I give the main reason for this conclusion by illustrating a simple thought experiment. Suppose you ask subjects to determine whether a single block (like those used in the simulations) is about to fall. We can think of blocks of varying heights. No matter how tall a block is, if it is standing on a horizontal surface it will not fall until some external perturbation disturbs its equilibrium. I am confident that most human observers would predict this outcome as well. However, the MSG simulation would not produce this outcome. Instead, it would predict a non-zero probability of the block to tip over. A gravitational field that is not perpendicular to the base has the equivalent effect of a horizontal force applied on the block at the height corresponding to the vertical position of the center of gravity. Depending on the friction determined by the contact between the base of the block and the surface where it stands there is a critical height where any horizontal force being applied would cause the block to fall while pivoting about one of the edges at the base (the one opposite to where the force has been applied). This critical height depends on both the size of the base and the friction coefficient. For short objects this critical height is larger than the height of the object, so that object would not fall. But for taller blocks, this is not the case. Indeed, the taller the block the smaller the deviation from a vertical gravitational field is needed for a fall to be expected. The discrepancy between this prediction and the most likely outcome of the simple experiment I have just outlined makes the MSG model implausible. Note also that a gravitational field that is not perpendicular to the ground surface is equivalent to the force field experienced by the block while standing on an inclined plane. For small friction values, the block is expected to slide down the incline, therefore another prediction of this MSG model is that when we observe an object on a surface exerting negligible friction (think of a puck on ice) we should expect that object to spontaneously move. But of course, we don't, as we do not expect tall objects that are standing to suddenly fall if left unperturbed. In summary, a stochastic world model cannot explain these simple observations.

The question remains as to how we can interpret the empirical data from the two experiments and their agreement with the predictions of the stochastic world model if we assume that the brain has internalized a vertical gravitational field. First, we need to look more closely at the questions posed to the subjects in the two experiments. In the first experiment, subjects are asked about how "normal" a fall of a block construction looks. Subjects seem to accept 50% of the time a fall is normal when the gravitational field is about 20 deg away from the vertical direction. The authors conclude that according to the brain, such an unusual gravitational field is possible. However, there are alternative explanations for these findings that do not require a perceptual error in the estimation of the direction of gravity. There are several aspects of the scene that may be misjudged by the observer. First, the 3D interpretation of the scene and the 3D motion of the objects can be inaccurate. Indeed, the simulation of a normal fall uploaded by the authors seems to show objects falling in a much weaker gravitational field than the one on Earth since the blocks seem to fall in "slow motion". This is probably because the perceived height of the structure is much smaller than the simulated height. In general, there are even more severe biases affecting the perception of 3D structures that depend on many factors, for instance, the viewpoint. Second, the distribution of weight among the objects and the friction coefficients acting between the surfaces are also unknown parameters. In other words, there are several parameters that depend on the viewing conditions and material composition of the blocks that are unknown and need to be estimated. The authors assume that these parameters are derived accurately and only that assumption allows them to attribute the observed biases to an error in the estimate of the gravitational field. Of course, if the direction of gravity is the only parameter allowed to vary freely then it is no surprise that it explains the results. Instead, a simulation with a titled angle of gravity may give rise to a display that is interpreted as rendering a vertical gravitational field while other parameters are misperceived. Moreover, there is an additional factor that is intentionally dismissed by the authors that is a possible cause of the fall of a stack of cubes: an external force. Stacks that are initially standing should not fall all of a sudden unless some unwanted force is applied to the construction. For instance, a sudden gust of wind would create a force field on a stack that is equivalent to that produced by a tilted gravitational field. Such an explanation would easily apply to the findings of the second experiment. In that experiment subjects are explicitly asked if a stack of blocks looks "stable". This is an ambiguous question because the stability of a structure is always judged by imagining what would happen to the structure if an external perturbation is applied. The right question should be: "do you think this structure would fall if unperturbed". However, if stability is judged in the face of possible external perturbations then a tall structure would certainly be judged as less stable than a short structure occupying the same ground area. This is what the authors find. What they consider as a bias (tall structures are perceived as less stable than short structures) is instead a wrong interpretation of the mental process that determines stability. If subjects are asked the question "Is it going to fall?" then tall stacks of sound structure would be judged as stable as short stacks, just more precarious.

The RL model used as a proof of concept for how the brain may build a stochastic prior for the direction of gravity is based on very strong and unverified assumptions. The first assumption is that the brain already knows about the force of gravity, but it lacks knowledge of the direction of this force of gravity. The second assumption is that before learning the brain knows the effect of a gravitational field on a stack of blocks. How can the brain simulate the effect of a non-vertical gravitational field on a structure if it has never observed such an event? The third assumption is that from the visual input, the brain is able to figure out the exact 3D coordinates of the blocks. This has been proven to be untrue in a large number of studies. Given these assumptions and the fact that the only parameters the RL model modifies through learning specify the direction of gravity, I am not surprised that the model produces the desired results.

Finally, the argument that the MGS is more efficient than the NGS model is based on an incorrect analysis of the results of the simulation. It is true that 80% accuracy is reached faster by the MGS model than the 95% accuracy level is reached by the NGS model. But the question is: how fast does the NGS model reach 80% accuracy (before reaching the plateau)?

Reviewer #1 (Public Review):

Summary:<br /> The authors made significant updates to Hippacampome.org including 50 new cell types.

Strengths:<br /> The authors have been thorough in basing their views on peer-reviewed literature. They have made the data highly accessible and the user has the ability to control what is included.

Weaknesses:<br /> There are many inconsistencies in the literature regarding cell types and how these are incorporated into hippocampome.org is not clear.

Properties are often a result of modeling and not biological data, and caveats to this approach, and other assumptions are unclear.

Several interneuron subtypes in the dentate gyrus do not appear to be listed, such as neurogliaform cells.

The nomenclature HIPROM should be distinguished or made synonymous with HIPP. Same for MOCAP and MOPP/HICAP.

Dorsal ventral and sex differences are not mentioned.

Reviewer #2 (Public Review):

Summary and strengths:<br /> The authors have developed a helpful resource for the community regarding hippocampal cell types and their interactions from many perspectives. There have been many updates to hippocampome v1.0 to v1.12, that are nicely summarized and explained (e.g., Table 1). The content and impact are also presented (Fig. 4).

Weaknesses:<br /> My main comment is that it is not completely clear and/or it is a bit buried as to what makes this v2.0 (rather than v1.13). The title would seem to encompass it ('... enabling data-driven spiking neural network simulations...), but in the introduction, the authors seem to emphasize "50 newly identified neuron types...". Is it the case that launching network simulations (using CARLsim) was not possible up to v1.12? I don't think so? I think that this research advance is to announce and summarize the various updates and to demonstrate how network simulations can be easily done? If so, this should and could be made more clear so that the reader does not necessarily have to go through all the previous versions to understand what is 'special' or different about v2.0. This could perhaps be achieved by situating their tool and its goals relative to other efforts (e.g., blue brain project) that are mentioned in the Discussion?

Reviewer #3 (Public Review):

Summary:<br /> The authors aim to provide a multidisciplinary resource on the structural and physiological organization of the hippocampal system and make the available experimental data available for further theoretical work, providing tools to do so in a very flexible and user-friendly way. Since this is a new version of an already existing data-resource, the authors certainly reach their aim and fulfil expectations that the reader might have. The content of the database is as good as the original data, collected from the published knowledge-database, sometimes with the help of the original authors, and the overall quality depends further on how the data are curated by the team of authors and many others who helped them. That process is briefly described and more details are available in descriptions of previous versions and on the website. The data extraction, examples of how data can be used, and the part on attempts to model the hippocampus are exciting and open doors to new and exciting research opportunities.

Strengths:<br /> Excellent description with many outlined opportunities. Nicely illustrated and inviting to explore the online database.

Weaknesses:<br /> The figures are complex, containing a heavy information load with many abbreviations. You need some general knowledge of the system in order to grasp the enormous potential of what is provided.