Joint Public Review:

The flowering plant Capsella bursa-pastoris is an allotetraploid formed from the genomes of Capsella orientalis and Capsella grandiflora. An outstanding question in the evolution of allotetraploids is the relative contribution of immediate consequences of allopolyploidization vs. long-term evolution after the event. The authors address this question by re-synthesizing the allotetraploid in the lab using the two progenitor species, and comparing its phenotypic and gene expression variation to naturally occurring C. bursa-pastoris. They compared five categories of plant: the two progenitors of the allopolyploid, hybrids resynthesized from the progenitors with a whole-genome duplication either before or after the hybridization event, and the naturally occurring allopolyploid. Two lines of evidence were used: phenotypic data from the plants grown in a common environment, and RNAseq data from a subset of the plants.

The phenotypic data indicate that the selfing syndrome of C. bursa-pastoris likely evolved after the initial allopolyploidization event, and that pollen and seed viability recovered following the allopolyploidization event. They find evidence primarily for long-term phenotypic evolution towards a selfing syndrome in C. bursa-pastoris, and a combination of short and long-term changes to gene expression.

The manuscript is thorough and provides lots of new insights into the mechanisms driving evolution in allopolyploids. The work provides an interesting and valuable contribution to the field's understanding of how expression evolves in interaction with hybridization and polyploidy. Particularly in combination with the team's previous study on these lines, this experimental design is effective for separating the contributions of hybridization, WGD, and evolution over time.

>>The results are compelling but would benefit from small clarifications to the methods and statistics to account for possible positional effects in the growth chamber. Using a linear mixed model rather than a simple ANOVA would solve this problem.

>>The RNAseq data are used to explore overall expression patterns (using multi-dimensional scaling), patterns of differential expression (additive, dominant, or transgressive), and homeolog expression bias, and to determine the relative contributions of the original allopolyploidization event and subsequent evolution. Statistical cutoffs were used to categorize gene expression patterns, but the description and categorization of these patterns appears to have been largely qualitative and might be strengthened by including more statistical detail in questions like whether homeologous expression bias did indeed show more variation in resynthesized and evolved allopolyploids.

>>The study includes evidence that homeolog expression bias (overrepresentation of an allele from one species) results in part from homeologous synapsis (uneven inheritance of chromosome segments). These deviations from patterns consistent with 2:2 inheritance of genomic regions are highly variable between individuals in resynthesized allopolyploids but appeared to be mostly consistent within (but not between) populations in natural C. bursa-pastoris. This is intriguing evidence that segregation can be an important source of variation in allopolyploids. However, it was limited by the difficulty of inferring homeologous recombination breakpoints with RNAseq data because of the scale of recombination in wild populations (rather than resynthesized allopolyploids). In the future identifying such breakpoints will be an interesting direction for this and other allopolyploid systems.

>>The study could also valuably explore what kinds of genes experienced what forms of expression evolution. A brief description of GO terms frequently represented in genes which showed strong patterns of expression evolution might be suggestive of which selective pressures led to the changes in expression in the C. bursa-pastoris lineage, and to what extent they related to adaptation to polyploidization (e.g. cell-cycle regulators), compensating for the initial pollen and seed inviability or adapting to selfing (endosperm- or pollen-specific genes), or adaptation to abiotic conditions.

Reviewer #1 (Public Review):

The paper offers some potentially interesting insight into the allosteric communication pathways of the CTFR protein. A mutation to this protein can cause cystic fibrosis and both synthetic and endogenous ligands exert allosteric control of the function of this pivotal enzyme. The current study utilizes Gaussian Network Models (GNMs) of various substrate and mutational states of CFTR to quantify and characterize the role of individual residues in contributing to two main quantities that the authors deem important for allostery: transfer entropy (TE) and cross correlation. I found the TE of the Apo system and the corresponding statistical analysis particularly compelling. I found it difficult, however, to assess the limitations of the chosen model (GNM) and thus the degree of confidence I should have in the results. This mainly stems from a lack of a proposed mechanism by which allostery is achieved in the protein. Proposing a mechanism and presenting logical alternatives in the introduction would greatly benefit this manuscript. It would also allow the authors to place the allosteric mechanism of this protein in the broader context of protein allostery.

Reviewer #1 (Public Review):

Liu et al. investigated the brain functional lateralization in typically developing infants and infants with congenital sensorineural hearing loss (SNHL) to understand how early auditory deprivation disrupts the development of functional network organization using resting-state fNIRS imaging and the graph theory approach. They found that hemispheric asymmetry formed in early life and the initial lack of auditory exposure affected the typical development of functional network asymmetry in infants with hearing loss. Although the infants with hearing loss exhibited a balance between information segregation and integration within two hemispheres, consistent with the typically developing (TD) controls, the development of the leftward hemispheric asymmetry in network efficiency measures was disrupted. At the regional level, infants with hearing loss exhibited aberrant development of hemispheric network asymmetry especially in frontal regions.

Strengths:<br /> The strengths of this study include its focus on a relatively understudied area of research, namely the impact of hearing loss on brain network asymmetry in infants. The study used advanced neuroimaging techniques to examine the development of cerebral asymmetry in infants with hearing loss and compared their results to typically developing controls. The study's findings provide valuable insights into the importance of early auditory exposure for typical brain development. Overall, this study contributes to our understanding of the brain functional network changes underlying hearing loss and has important implications for early intervention and treatment strategies.

Weaknesses:<br /> Although this study does have strengths in principle, the weaknesses of this work are that the key claims cannot be fully supported due to inappropriate statistical analyses, the theoretical significance and the narrative logic are not well presented. In particular:

Theoretical significance: In the Introduction, the authors did not nicely explain why it is important to investigate how the brain functional network asymmetry develops in SNHL infants, and what new knowledge this analytical approach can tell the readers. It is insufficient to merely state that few studies have focused on this. The authors did not elaborate on the broader significance of studying the hemispherical asymmetry in SNHL infants.

Narrative logic: The organization of the Results part needs substantial improvement. The authors did not provide an overview of the analysis at the beginning of each results section, including the relationships between different measurements, the purpose of each analysis, the specific methods employed, and the meaning of the neural index used. It is therefore very difficult to understand why the authors conducted each analysis and how it contributes to the main narrative of the study. For instance, what is the relationship between the small-world properties within the hemisphere and the hemispherical asymmetry of network efficiency? What is the relationship between global/local efficiency and regional nodal efficiency? What global efficiency and local efficiency reflect? It is crucial to clarify and justify the analysis.

Problems on the statistics:<br /> 1) To support the major claim that the left hemisphere dominance of the functional network organization was significantly disrupted in SNHL infants, the authors should also report a statistically significant interaction (between leftward hemispheric asymmetry and type of infants), but instead they only reported that one effect (the leftward hemispheric asymmetry in the TD infants) was statistically significant, whereas the other effect (the leftward hemispheric asymmetry in the infants with SNHL) was not. And why not directly use asymmetry index and compare it between groups?<br /> 2) The necessary statistical values to support the conclusions are missing in several places. For example, Lines 111 - 113 and section 2.4.<br /> 3) The authors conducted multiple comparisons without correction in section 2.3 and section 2.5. It is likely that some of these comparisons would not survive the multiple comparison correction; therefore, the results need to be rephrased and the findings reinterpreted accordingly.<br /> 4) Inconsistent results exist. If "a significant group × age interaction effect on the mean AI of nodal efficiency was observed only in the frontal cortex, while other regions did not exhibit such an interaction" (Line 170-172), and the authors "investigated the group × age interaction effect on the mean nodal efficiencies of the frontal regions for each hemisphere" (Line 178-179), why "a significant interaction effect was observed in the frontal, temporal, parietal, and occipital regions of the left hemisphere" (Line 179-182)?

Reviewer #1 (Public Review):

Olszyński and colleagues present data showing variability from canonical "aversive calls", typically described as long 22 kHz calls rodents emit in aversive situations. Similarly long but higher-frequency (44 kHz) calls are presented as a distinct call type, including analyses both of their acoustic properties and animals' responses to hearing playback of these calls. While this work adds an intriguing and important reminder, namely that animal behavior is often more variable and complex than perhaps we would like it to be, there is some caution warranted in the interpretation of these data. The authors also do not provide adequate justification for the use of solely male rodents. With several reported sex differences in rat vocal behaviors this means caution should be exercised when generalizing from these findings.

Firstly, the authors argue that the shift to higher-frequency aversive calls is due to an increase in arousal (caused by the animals having received multiple aversive foot shocks towards the end of the protocols). However, it cannot be ruled out that this shift would be due to factors such as the passage of time and increase in fatigue of the animals as they make vocalizations (and other responses) for extended periods of time. In fact the gradual frequency increase reported for 22kHz calls and the drop in 44 kHz calls the next day in testing is in line with this.

Secondly, regarding the analysis where calls were sorted using DBSCAN based on peak frequency and duration, it is not surprising that the calls cluster based on frequency and duration, i.e. the features that are used to define the 44 kHz calls in the first place. Thus presenting this clustering as evidence of them being truly distinct call types comes across as a circular argument. The sparsity of calls in the 30-40 kHz range (shown in the individual animal panels in Figure 2C) could in theory be explained by some bioacoustics properties of rat vocal cords, without necessarily the calls below and above that range being ethologically distinct.

The behavioral response to call playback is intriguing, although again more in line with the hypothesis that these are not a distinct type of call but merely represent expected variation in vocalization parameters. Across the board animals respond rather similarly to hearing 22 kHz calls as they do to hearing 44 kHz calls, with occasional shifts of 44 kHz call responses to an intermediate between appetitive and aversive calls. This does raise interesting questions about how, ethologically, animals may interpret such variation and integrate this interpretation in their responses. However, the categorical approach employed here does not address these questions fully.

In sum, rather than describing the 44kHz long calls as a new call type, it may be more accurate to say that sometimes aversive calls can occur at frequencies above 22 kHz. Individual and situational variability in vocalization parameters seems to be expected, much more so than all members of a species strictly adhering to extremely non-variable behavioral outputs.

Reviewer #1 (Public Review):

The authors have studied the effect of temperature on the interspecific interaction strength of coastal marine fish communities, using eDNA samples. Their introduction describes the state of the art concerning the dynamics of interspecific interactions in ecological communities. This introduction is well written and highly information dense, summarizing all that the reader needs to know to further understand their study setup and execution.

The authors hypothesize that water temperature changes could have an effect on the interspecific interaction strength between marine fishes, and they studied this with a two year long, bi-weekly eDNA sampling campaign at 11 study sites in Japan with different temperature gradients. These 550 water samples were analysed for fish biodiversity through eDNA-metabarcoding using MiFish primers. By using the most abundant fish species as an internal spike in and quantifying the copy numbers from this species by qPCR, the authors were able estimate DNA copy numbers for the total dataset. From the 50 most frequently detected fish species in these samples they showed that temperature affected the interspecific interaction strength between some species. Their work provides a highly relevant approach to perform species-interaction strength analysis based on eDNA biodiversity assessments, and as such provides a research framework to study marine community dynamics by eDNA, which is highly relevant in the study of ecosystem dynamics. The models and analytical methods used are clearly described and made available, enabling application of these methods by anyone interested in applying it to their own site and species group of interest.

Strengths:

The authors have a study setup that is suitable to measure the effects of temperature of the eDNA diversity, and have taken a large number of samples and all appropriate controls to be able to accurately measure and describe these dynamics. The applied internal spike in to enable relative eDNA copy number quantification is convincing.

Weaknesses:

The authors were able to find a correlation between water temperature and interaction strengths observed. However, since water temperature is dependent on many environmental variables that are either directly or indirectly influencing ecosystem dynamics, it is hard to prove a direct correlation between the observed changes in community dynamics and the temperature alone

Reviewer #1 (Public Review):

This study by Cao et al. demonstrates role of Neutrophil in clearing apoptotic hepatocytes by directly burrowing into the apoptotic hepatocytes and ingesting the effete cells from inside without causing inflammation. The authors applied intravital microscopy, Immunostaining and electron microscopy to visualize perforocytosis of neutrophil in hepatocytes. They also found that neutrophil depletion impairs the clearance of apoptotic hepatocytes causing impaired liver function and generation of autoantibodies, implying a role of defective neutrophil- mediated clearance of apoptotic cells in Autoimmune Liver disease. The experiments were well designed and conducted, the results were reasonably interpreted, and the manuscript was clearly written with logical inputs.

Further studies to explore the signals/mechanisms that determine why neutrophil specifically target apoptotic hepatocytes in liver would be of great clinical significance.

Reviewer #1 (Public Review):

Summary:<br /> Codol et al. present a toolbox that allows simulating biomechanically realistic effectors and training Artificial Neural Networks (ANNs) to control them. The paper provides a detailed explanation of how the toolbox is structured and several examples that demonstrate its usefulness.

Main comments:<br /> 1. The paper is well written and easy to follow. The schematics help in understanding how the toolbox works and the examples provide an idea of the results that the user can obtain.

2. As I understand it, the main purpose of the paper should be to facilitate the usage of the toolbox. For this reason, I have missed a more explicit link to the actual code. As I see it, researchers will read this paper to figure out whether they can use MotorNet to simulate their experiments, and how they should proceed if they decide to use it. I'd say the paper provides an answer to the first question and assures that the toolbox is very easy to install and use. Maybe the authors could support this claim by adding "snippets" of code that show the key steps in building an actual example.

3. The results provided in Figures 1, 4, 5 and 6 are useful, because they provide examples of the type of things one can do with the toolbox. I have a few comments that might help improving them:<br /> a. The examples in Figures 1 and 5 seem a bit redundant (same effector, similar task). Maybe the authors could show an example with a different effector or task? (see point 4).<br /> b. I missed a discussion on the relevance of the results shown in Figure 4. The moment arms are barely mentioned outside section 2.3. Are these results new? How can they help with motor control research?<br /> c. The results in Figure 6 are important, since one key asset of ANNs is that they provide access to the activity of the whole population of units that produces a given behavior. For this reason, I think it would be interesting to show the actual "empirical observations" that the results shown in Fig. 6 are replicating, hence allowing a direct comparison between the results obtained for biological and simulated neurons.

4. All examples in the paper use the arm26 plant as effector. Although the authors say that "users can easily declare their own custom-made effector and task objects if desired by subclassing the base Plant and Task class, respectively", this does not sound straightforward. Table 1 does not really clarify how to do it. Maybe an example that shows the actual code (see point 2) that creates a new plant (e.g. the 3-joint arm in Figure 7) would be useful.

5. One potential limitation of the toolbox is that it is based on Tensorflow, when the field of Computational Neuroscience seems to be, or at least that's my impression, transitioning to pyTorch. How easy would it be to translate MotorNet to pyTorch? Maybe the authors could comment on this in the discussion.

6. Supervised learning (SL) is widely used in Systems Neuroscience, especially because it is faster than reinforcement learning (RL). Thus providing the possibility of training the ANNs with SL is an important asset of the toolbox. However, SL is not always ideal, especially when the optimal strategy is not known or when there are different alternative strategies and we want to know which is the one preferred by the subject. For instance, would it be possible to implement a setup in which the ANN has to choose between 2 different paths to reach a target? (e.g. Kaufman et al. 2015 eLife). In such a scenario, RL seems to be a more natural option Would it be easy to extend MotorNet so it allows training with RL? Maybe the authors could comment on this in the discussion.

Impact:<br /> MotorNet aims at simplifying the process of simulating complex experimental setups to rapidly test hypotheses about how the brain produces a specific movement. By providing an end-to-end pipeline to train ANNs on the simulated setup, it can greatly help guide experimenters to decide where to focus their experimental efforts.

Additional context:<br /> Being the main result a toolbox, the paper is complemented by a GitHub repository and a documentation webpage. Both the repository and the webpage are well organized and easy to navigate. The webpage walks the user through the installation of the toolbox and the building of the effectors and the ANNs.

Reviewer #1 (Public Review):

This study explored how expectations influence tactile perception. In summary, anticipating a tactile event enhances detection compared to when knowledge is lacking or ambiguous. However, prior information can also impair performance if the expected and actual stimuli are incongruent. The authors used fMRI and multivariate decoding analyses to investigate the underlying mechanisms of this behavioural phenomenon.

They stimulated two fingers (thumb and ring) of the left hand and analysed activity patterns in contralateral and ipsilateral somatosensory regions during and before stimulation. They were able to distinguish activity patterns for the two fingers during both stimulation and the pre-stimulation stage, specifically for the congruent condition. The authors suggest that congruent vibrotactile stimulation leads to higher multivariate information content and improved behavioural detection performance. They also found that the expectation of vibrotactile stimulation elicits somatotopic activity in contralateral S1, similar to the activity generated by actual stimulation.

I thoroughly enjoyed reading this well-written and clear work. The incorporation of multivariate decoding analysis alongside univariate analysis is a good choice for addressing the claimed questions. In the following sections, I will highlight the strengths and weaknesses of the study. While I generally agree with the authors' conclusions regarding the functional mechanisms underlying behavioural improvements, I believe there are limitations in the experimental design and chosen measures that constrain the interpretations drawn from the results. I hope that my comments can contribute to clarifying certain details and improving aspects of the study that may be considered weak. I believe this study holds significance for the field and provides a foundation for future investigations into the influence of top-down processing on tactile processing.

Strengths:<br /> 1) The research question is highly intriguing as it delves into the unexplored territory of top-down processes within the tactile domain that still needs to be well characterised.

2) The addition of multivariate decoding analysis alongside the univariate analysis was a good choice in my opinion, since activity level per se may not accurately reflect the underlying information content. Both high activity levels and absence of activity (as observed in this study) can still contain information. To be more specific, Figure 2C shows no significant activity in the congruent condition, but significant decoding for the two finger activity patterns is still possible in this condition (Figure 3A).

3) The utilization of a staircase before each functional run was also a good approach, although a potential limitation is noted (discussed below). Considering that prior knowledge can be particularly influential in the presence of weak or noisy stimuli, it is crucial to confirm that the stimulation was at threshold to maximize the likelihood of detecting differences in the pre-stimulus stage.

Weaknesses:<br /> 1) My main concern regarding this study lies in the choice of a detection paradigm, which may introduce response biases and affect the interpretation of results. If the threshold was set too low for some participants, it is possible that they reported feeling the touch more frequently on the cued finger, even when no actual sensation was present. Consequently, accuracy may be inflated for the congruent condition and reduced for the incongruent condition, making it difficult to attribute the observed improvements solely to enhanced detection. I think it would have been more appropriate to use a discriminatory task (e.g., discriminating pin patterns), as employed in Kok et al., 2012, where behavioural performance can be directly linked to decoding accuracy between related activity patterns. Additionally, incorporating trials with no stimulation (I am not sure whether this was the case in this study) and utilizing "None" responses to calculate accuracy could provide a more reliable measure of performance. Using dprime as a performance measure, which is bias-free, may be more appropriate. However, I remain concerned that participant responses are influenced more by the cue than the actual detection of stimuli.

2) While I appreciate the use of the staircase method, I was somewhat surprised by the relatively short length of each staircase (only 7 trials). I might not have extensive experience in this area, therefore this might still be ok for fingers, but I want to emphasize the importance of accurately determining the threshold for this study (as discussed in the previous point). However, I can see from Figure 1B that there seems to be consistency across runs (at least in the shown participant).

3) The absence of significant decoding in the incongruent condition (Figure 3A) raises some questions. It seems reasonable to expect that discrimination between the two finger activity patterns should still be possible in this condition, albeit with reduced accuracy as observed in Kok et al., 2012. Could this lack of significant decoding result from the detection task or possibly due to the smaller number of trials in the incongruent condition?

4) I am a bit confused about which specific region of interest (ROI) was used for both the univariate and decoding analysis during the stimulation stage, and the decoding analysis and RSA during the pre-stimulation phase. From my understanding, the entire S1 region (as defined using the SPM Anatomy toolbox) was included, encompassing not only the hand territory but the entire body. However, I may have misinterpreted the methodology. Given that an independent localizer was used to define ROIs for the univariate analysis during the pre-stimulation phase, it raises the question of why the same approach was not applied to the analyses during the stimulation phase and the multivariate analysis during the pre-stimulation phase.

5) By using a large ROI for analysis (as mentioned in point 4), the straightforward interpretation of BOLD level (i.e., no significant activity) in the congruent condition (Figure 2C) becomes less clear. It raises the question of whether there is truly no activity in the congruent condition or if the activity would be observed with a smaller region. This aligns with the findings of Kok et al., 2012, where they demonstrate activity in both expected and unexpected conditions, albeit reduced in the expected condition.

6) Point 5 raises another issue regarding the suggestion that significant decoding results imply higher multivariate information content in finger representations of congruent vibrotactile stimulations. Suppose a smaller ROI were used, revealing activity in the congruent condition and differential activity between the two finger conditions. In that case, the substantial difference in activation levels suggests that increased decoding accuracy may not necessarily require higher multivariate information content. It is conceivable that discrimination between the two conditions could be achieved with just two voxels-one in the thumb territory and one in the index territory.

Reviewer #1 (Public Review):

The modeling approaches are very sophisticated, and clearly demonstrate the selective nature of acute ketamine to reduce the impact of trial losses on subsequent performance, relative to neutral or gain outcomes. The authors then, not unreasonably, suggest that this effect is important in the context of the negative bias in interpreting events that is prominent in depression, in that if ketamine reduces the ability of negative outcomes to alter behavior, this may be a mechanism for its rapid acting antidepressant effects. However, there is a very strong assumption in this regard, as shown by the first sentence of the discussion which implies this is a systematic study of ketamine's acute antidepressant effects. In actuality, this is a study of the acute effects of ketamine on reinforcement learning (RL) modeled parameters. A primary concern here is that an effect presented as a "robust antidepressant-like behavioral effect" should be more enduring than just an alteration during the acute administration. As it is, the link to an "anti-depressant effect" is based solely on the selective effects on losses. This is not to say this is not an interesting observation, worthy of exploration. It is noted that a similar lack of enduring effects on outcome evaluation is observed in humans, as shown in supplemental fig. S4, but there is not accompanying citation for the human work. One question that comes to mind in terms of the selectivity observed is whether similar work has been done to examine the acute effects of any other drugs. If ketamine is unique in this regard, that would be quite interesting.

Reviewer #1 (Public Review):

In this article, Vardakalis et al. propose a novel model of hippocampal oscillations whereby an external input (emulating the medial septum) can drive theta rhythms. This model displays phase-amplitude coupling of gamma oscillations, as well as theta resetting, which are known features of physiological theta that have been missing in previous models. The end goal proposed by the authors is to have a framework to explore the mechanisms of neurostimulation, which have shown promising applications in pathological conditions, but for which the underlying dynamics remain largely unknown. To reach this objective, the authors implement an existing biophysical model of the hippocampus that is able to generate gamma oscillations, and receives inputs from a set of Kuramoto oscillators to emulate theta drive originating from the medial septum.

Overall, the hypotheses and results are clearly presented and supported by high quality figures. The study is presented in a didactic way, making it easy for a broad audience to understand the significance of the results. The study does present some weaknesses that could easily be addressed by the authors. First, there are some anatomical inaccuracies: line 129 and fig1C, the authors omit medial septum projections to area CA1 (in addition to the entorhinal cortex). Moreover, in addition to CA1, CA3 also provides monosynaptic feedback projections to the medial septum CA3. Finally, an indirect projection from CA1/3 excitatory neurons to the lateral septum, which in turn sends inhibitory projections to the medial septum could be included or mentioned by the authors. This could be of particular relevance to support claims related to effects of neurostimulations, whereby minutious implementation of anatomical data could be key. If not updating their model, the authors could add this point to their limitation section, where they already do a good job of mentioning some limitations of using the EC as a sole oscillatory input to CA1. The authors test conditions of low theta inputs, which they liken to pathological states (line 112). It is not clear what pathology the authors are referring to, especially since a large amount of 'oscillopathies' in the septohippocampal system are associated with decreased gamma/PAC, but not theta oscillations (e.g. Alzheimer's disease conditions). While relevant for the clinical field, there is overall a missed opportunity to explain many experimental accounts with this novel model. Although to this day, clinical use of DBS is mostly restricted to electrical (and thus cell-type agnostic) stimulation, recent studies focusing on mechanisms of neurostimulations have manipulated specific subtypes in the medial septum and observed effects on hippocampal oscillations (e.g. see Muller & Remy, 2017 for review). Focusing stimulations in CA1 is of course relevant for clinical studies but testing mechanistic hypotheses by focusing stimulation on specific cell types could be highly informative. For instance, could the author reproduce recent optogenetic studies (e.g. Bender et al. 2015 for stimulation of fornix fibers; Etter et al., 2019 & Zutshi et al. 2018 for stimulation of septal inhibitory neurons)? Cell specific manipulations should at least be discussed by the authors.

Beyond these weaknesses, this study has a strong utility for researchers wanting to explore hypotheses in the field of neurostimulations. In particular, I see value in such models for exploring more intricate, phase specific effects of continuous, as well as close loop stimulations which are on the rise in systems neuroscience.

Reviewer #1 (Public Review):

It is known that aberrant habit formation is a characteristic of obsessive-compulsive disorder (OCD). Habits can be defined according to the following features (Balleine and Dezfouli, 2019): rapid execution, invariant response topography and action 'chunking'. The extent to which OCD behavior is derived from enhanced habit formation relative to deficits in goal-directed behavior is a topic of debate in the current literature. This study examined habit-learning specifically (cf. deficits in goal-directed behavior) by regularly presenting, via smartphone, sequential learning tasks to patients with OCD and healthy controls. Participants engaged in the tasks every day over the course of a month. Automaticity, including the extent to which individual actions in the sequence become part of a unified 'chunk', was an important outcome variable. Following the 30 days of training, in-laboratory tasks were then administered to examine 1) if performing the learned sequences themselves had become rewarding 2) differences in goal-directed vs. habitual behavior.

Several hypotheses were tested, including:<br /> Patients would have impaired procedural learning vs. healthy volunteers (this was not supported, possibly because there were fewer demands on memory in the task used here)<br /> Once the task had been learned, patients would display automaticity faster (unexpectedly, patients were slower to display automaticity)<br /> Habits would form faster under a continuous (vs. variable) reinforcement schedule

Exploratory analyses were also conducted: an interesting finding was that OCD patients with higher self-reported symptoms voluntarily completed more sessions with the habit-training app and reported a reduction in symptoms.

Strengths

This paper is well situated theoretically within the habit learning/OCD literature.<br /> Daily training in a motor-learning task, delivered via smartphone, was innovative, ecologically valid and more likely to assay habitual behaviors specifically. Daily training is also more similar to studies with non-humans, making a better link with that literature. The use of a sequential-learning task (cf. tasks that require a single response) is also more ecologically valid.<br /> The in-laboratory tests (after the 1 month of training) allowed the researchers to test if the OCD group preferred familiar, but more difficult, sequences over newer, simpler sequences.

Weaknesses

The sample size was relatively small. Some potentially interesting individual differences within the OCD group could have been examined more thoroughly with a bigger sample (e.g., preference for familiar sequences). A larger sample may have allowed the statistical testing of any effects due to medication status.<br /> The authors were not able to test one criterion of habits, namely resistance to devaluation, due to the nature of the task

The authors achieved their aims in that two groups of participants (patients with OCD and controls) engaged with the task over the course of 30 days. The repeated nature of the task meant that 'overtraining' was almost certainly established, and automaticity was demonstrated. This allowed the authors to test their hypotheses about habit learning. The results are supportive of the author's conclusions.

This article is likely to be impactful -- the delivery of a task across 30 days to a patient group is innovative and represents a new approach for the study of habit learning that is superior to an in-laboratory approach.

An interesting aspect of this manuscript is that it prompts a comparison with previous studies of goal-directed/habitual responding in OCD that used devaluation protocols, and which may have had their effects due to deficits in goal-directed behavior and not enhanced habit learning per se.

Reviewer #1 (Public Review):

This manuscript describes a set of four passage-reading experiments which are paired with computational modeling to evaluate how task-optimization might modulate attention during reading. Broadly, participants show faster reading and modulated eye-movement patterns of short passages when given a preview of a question they will be asked. The attention weights of a Transformer-based neural network (BERT and variants) show a statistically reliable fit to these reading patterns above-and-beyond text- and semantic-similarity baseline metrics, as well as a recurrent-network-based baseline. Reading strategies are modulated when questions are not previewed, and when participants are L1 versus L2 readers, and these patterns are also statistically tracked by the same transformer-based network.

I should note that I served as a reviewer on an earlier version of this manuscript at a different venue. I had an overall positive view of the paper at that point, and the same opinion holds here as well.

Strengths:

- Task-optimization is a key notion in current models of reading and the current effort provides a computationally rigorous account of how such task effects might be modeled<br /> - Multiple experiments provide reasonable effort towards generalization across readers and different reading scenarios<br /> - Use of RNN-based baseline, text-based features, and semantic features provides a useful baseline for comparing Transformer-based models like BERT

Weaknesses:

- Generalization across neural network models seems, to me, somewhat limited: The transformer-based models differ from baseline models in numerous ways (model size, training data, scoring algorithm); it is thus not clear what properties of these models necessarily supports their fit to human reading patterns.<br /> - Inferential statistics are based on a series of linear regressions, but these differ markedly in model size (BERT models involve 144 attention-based regressor, while the RNN-based model uses just 1 attention-based regressor). How are improvements in model fit balanced against changes in model size? Also, it was not clear to me how participant-level variance was accounted for in the modeling effort (mixed-effects regression?) These questions may well be easily remedied by more complete reporting.<br /> - Experiment 1 was paired with a relatively comprehensive discussion of how attention weights mapped to reading times, but the same sort of analysis was not reported for Exps 2-4; this seems like a missed opportunity given the broader interest in testing how reading strategies might change across the different parameters of the four experiments.<br /> - Comparison of predictive power of BERT weights to human annotations of text relevance is limited: The annotation task asked participants to chose the 5 "most relevant" words for a given question; if >5 words carried utility in answering a question, this would not be captured by the annotation. It seems to me that the improvement of BERT over human annotations discussed around page 10-11 could well be due to this arbitrary limitation of the annotations.

Reviewer #1 (Public Review):

This manuscript describes extensive transcriptomic and epigenomic profiling for high-grade serous 'ovarian' cancer (HGSC) and its precancerous precursor the fallopian tube secretory epithelium cells (FTSEC). This study identifies MECOM, PAX8, SOX17 and WT1 as master transcription factors that regulate HGSC and FTSEC, as well as the transition from FTSEC to HGSC.

Overall, most the experiments described in the manuscript are well designed and executed. The data presented are of high quality, convincing, and in general support the conclusions made in the manuscript.

Given the complexity of the data and analysis, some clarification is needed to guide readers to better understand the results.

1) The definition of super enhancers should be clarified. In general, super enhancers are defined by large domains of enhancer clusters with high levels of H3K27ac, typically at least 10KB in size. The "super enhancers" presented in Figure 2 do not appear to be large clusters of enhancers.

2) Fig. 4D. Difficult to understand. Multiple bars seem to be represented by the same binding patterns by the four TFs. Need better description in both the text and figure legends.

3) "These data suggest that the antiproliferative effects of THZ1 and THZ531 in HGSC cells may be due to tumor-specific inhibition of MECOM, PAX8 and SOX17 expression by these drugs." Can the author expand the discussion on how CDK7/12 inhibitors could achieve tumor-specific inhibition of MECOM, PAX8 and SOX17?

Reviewer #1 (Public Review):

The authors prepared several Acinetobacter baumannii strains from which an essential protein of known or unknown function can be depleted. They chose to study one of the proteins (AdvA) in more detail. AdvA is a known essential cell division protein that accumulates at cell division sites together with other such proteins. No clear homologs are present in model bacteria such as E.coli, and the precise role(s) of AdvA is still unclear. The authors rename AdvA here as Aeg1. The authors searched for suppressors of lethality caused by AdvA-depletion and recovered an allele of ftsA (E202K) that is capable of doing so. Based on similar superfission alleles previously recovered in other division genes in E.coli, they test several mutant genes and find that certain alleles in ftsB, L and W can also suppress lethality of AdvA-minus cells.

In addition, the authors perform bacterial two-hybrid assays and protein sublocalization studies of AdvA and of other division proteins, but the results of these studies are either not new (confirming previous work) or not convincing.

Reviewer #1 (Public Review):

Bolumar et al. isolated and characterized EV subpopulations, apoptotic bodies (AB), Microvesicles (MV), and Exosomes (EXO), from endometrial fluid through the female menstrual cycle. By performing DNA sequencing, they found the MVs contain more specific DNA sequences than other EVs, and specifically, more mtDNA were encapsulated in MVs. They also found a reduction of mtDNA content in the human endometrium at the receptive and post-receptive period that is associated with an increase in mitophagy activity in the cells, and a higher mtDNA content in the secreted MVs was found at the same time. Last, they demonstrated that the endometrial Ishikawa cell-derived EVs could be taken by the mouse embryos and resulted in altered embryo metabolism.

This is a very interesting study and is the first one demonstrating the direct transmission of maternal mtDNA to embryos through EVs.

Reviewer #1 (Public Review):

Huan Wang et al. analyzed more than 10 million sequences and find that T12I, T102I and A104V were the top 3 frequently occurring mutations. They verified whether these mutations affect the stability and binding ability of NSP10, and whether there are structural changes. They find that three mutations destabilize the NSP10 by NMA prediction and determine their prediction by TSA. In addition, the Kd values shows that variants have similar binding ability or slightly improved affinity to NSP14 and NSP16 than native NSP10. Even though crystallization of the two variants is missing, the comparison of the crystallization of the T102I crystalline protein with the native shows that there is no structural change. Simultaneously, the dihedral angles in the variants do not explore any additional minima than that observed in wild-type NSP10, and there is no major conformational change.

Reviewer #1 (Public Review):

The manuscript by Goetz et al. takes a new perspective on sensory information processing in cells. In contrast to previous studies, which have used population data to build a response distribution and which estimate sensory information at about 1 bit, this work defines sensory information at the single cell level. To do so, the authors take two approaches. First, they estimate single cells' response distributions to various input levels from time-series data directly. Second, they infer these single-cell response distributions from the population data by assuming a biochemical model and extracting the cells' parameters with a maximum-entropy approach. In either case, they find, for two experimental examples, that single-cell sensory information is much higher than 1 bit, and that the reduction to 1 bit at the population level is due to the fact that cells' response functions are so different from each other. Finally, the authors identify examples of measurable cell properties that do or do not correlate with single-cell sensory information.

The work brings an important and distinct new insight to a research direction that generated strong interest about a decade ago: measuring sensory information in cells and understanding why it is so low. The manuscript is clear, the results are compelling, and the conclusions are well supported by the findings. Several contributions should be of interest to the quantitative biology community (e.g., the demonstration that single cells' sensory information is considerably larger than previously implied, and the approach of inferring single-cell data from population data with the help of a model and a maximum-entropy assumption).

Reviewer #1 (Public Review):

This manuscript sets out to implement a multi-stage fluorescence imaging essay to test two working models in understanding the folding states of RNA-binding proteins (RBPs) in stress-induced nuclear bodies. In conjunction with live-cell fluorescence lifetime imaging, the authors revealed and conformed a previously unclear phenomenon that the RBPs investigated in this work initially enter the nuclear bodies in native state in transient stress and then begin to misfold after prolonged stress. Comparing to conventional methods, the imaging strategy reported in this work is unique, comprehensive, and effective in surveying all three-stages (native, soluble oligomer, aggregates) of folding states for RBPs in one shot. Using this strategy, the authors then found that the heat shock protein 70 may protects RBPs from being degraded under stress. The manuscript is very well-written.

Reviewer #1 (Public Review):

In the manuscript titled "GABAergic synaptic scaling is triggered by changes in spiking activity rather than transmitter receptor activation," the authors present an investigation of the role of GABAergic synaptic scaling in the maintenance of spike rates in networks of cultured neurons. Their main findings suggest that GABAergic scaling exhibits features consistent with a key homeostatic mechanism that contributes to the stability of neuronal firing rates. Their data demonstrate that GABAergic scaling is multiplicative and emerges when postsynaptic spike rates are altered. Finally, their data suggest that, in contrast to their prior data on glutamatergic scaling, GABAergic scaling is driven by spike rates. The authors set the paper up as an argument that GABAergic scaling, rather than glutamatergic scaling, serves as the critical homeostatic mechanism for spike rate regulation.

While the paper is ambitious in its rhetorical scope and certainly presents intriguing findings, there are several serious concerns that need to be addressed to substantiate the interpretations of the data. For example, the CTZ data do not support the interpretations and conclusions drawn by the authors. Summarily, the authors argue that GABAergic scaling is measuring spiking (at the time scale of the homeostatic response, which they suggest is a key feature of a homeostat) yet their data in figure 5B show more convincingly that CTZ does not influence spiking levels - only one out of four time points is marginally significant (also, I suspect that the bootstrapping method mentioned in line 454-459 was conducted as a pairwise comparison of distributions. There is no mention of multiple comparisons corrections, and I have to assume that the significance at 3h would disappear with correction). Then, the fact that TTX applied on top of CTZ drives a increase in mIPSC amplitude is interpreted as a conclusive demonstration that GABAergic scaling is sensing spiking. It is inevitable, however, that TTX will also severely reduce AMAP-R activation - a very plausible alternative explanation is that the augmentation of AMPAR activation caused by CTZ is not sufficient to overcome the dramatic impact of TTX. All together, these data do not provide substantial evidence for the conclusion drawn by the authors.

Specific points:

- The logic of the basis for the argument is somewhat flawed: A homeostat does not require a multiplicative mechanism, nor does it even need to be synaptic. Membrane excitability is a locus of homeostatic regulation of firing, for example. In addition, synapse-specific modulation can also be homeostatic. The only requirement of the homeostat is that its deployment subserves the stabilization of a biological parameter (e.g., firing rate).<br /> - Line 63 parenthetically references an important, but contradictory study as a brief "however". Given the tone of the writing, it would be more balanced to give this study at least a full sentence of exposition.<br /> - The authors state (line 11) that expression of a hyperpolarizing conductance did not trigger scaling. More recent work ('Homeostatic synaptic scaling establishes the specificity of an associative memory') does this via expression of DREADDs and finds robust scaling.<br /> - Supplemental figure 1 looks largely linear to me? Out of curiosity, wouldn't you expect the left end to be aberrant because scaling up should theoretically increase the strength of some synapses that would have been previously below threshold for detection? Given that figure 2B also shows warping at the tail ends of similar distributions, how is this to be interpreted?<br /> - The readability of the figures is poor. Some of them have inconsistent boundary boxes, bizarre axes, text that appears skewed as if the figures were quickly thrown together and stretched to fit.<br /> - I'm concerned about the optogenetic restoration of activity experiment. Cortical pyramidal neuron mean firing rates are log normally distributed and span multiple orders of magnitude. The stimulation experiments can only address the total firing at a network-level - given than a network level "mean" is meaningless in a lognormal distribution, how are we to think about the effect of this manipulation when it comes to individual neurons homeostatically stabilizing their own activities? In essence, the argument is made at the single-neuron level, but the experiment is conducted with a network-level resolution.<br /> - Line 198-99: multiplicativity is not a requirement of a homeostatic mechanism.<br /> - Line 264-265 - again, neither multiplicativity and synaptic mechanisms are fundamentally any more necessary for a homeostatic locus than anything else that can modulate firing rate in via negative feedback.<br /> - 277: do you mean AMPAR?<br /> - Example: Figure 1A is frustratingly unreadable. The axes on the raster insets are microscopic, the arrows are strangely large, and it seems unnecessary to fill so much realestate with 4 rasters. Only one is necessary to show the concept of a network burst. The effect of time+CNQX on the frequency of burst is shown in B and C.<br /> - Example: Figure 2 appears warped and hastily assembled. Statistical indications are shown within and outside of bounding boxes. Axes are not aligned. Labels are not aligned. Font sizes are not equal on equivalent axes.<br /> - The discussion should include mention of the limitations and/or constraints of drawing general conclusions from cell culture.<br /> - The discussion should include mention of the role of developmental age in the expression of specific mechanisms. It is highly likely that what is studied at ~P14 is specific to early postnatal development.

It is essential to ensure that the data presented in the paper adequately supports the conclusions drawn. A more cautious approach in interpreting the results may lead to a stronger argument and a more robust understanding of the underlying mechanisms at play.

Public, mais sur une page protégée par WordPress.

Reviewer #1 (Public Review):

The authors sought to address the longstanding question of which cell types are infected during congenital or perinatal rubella virus infection. They used brain slice and organoid-microglia experimental models to demonstrate that the main cell types targeted by rubella virus are microglia. The authors further show that infection results in augmented interferon responses in neighboring neuronal cells but not in the microglia themselves. The data convincingly support the conclusions, with major strengths being the sophisticated primary cell models and single-cell RNA-Seq used to pinpoint microglia as the main cellular targets of rubella virus, and neurons as the bystander targets of immune signaling. This study reveals a new cellular target that will have important implications for basic studies on rubella virus-host interactions and for the potential development of therapies or improved vaccines targeting this virus. As rubella virus is a pathogen of high concern during human pregnancy, this study is also relevant in the field of neonatal infectious diseases.

Joint Public Review:

As nucleoporins can function at intact nuclear pore complexes (NPCs) or outside of NPCs as individual proteins or subcomplexes, it remains challenging to molecularly define the pool of molecules that exert a specific function. To address this challenge, here the authors develop a new method for specifically mapping NPC-associated loci by DamID with a recombinant fusion protein of the Dam methylase and the nuclear transport receptor, importin b (Dam-Impb), in permeabilized cells. The authors demonstrate that Dam-Impb is active, accumulates at the NPC and, using super-resolution microscopy, methylates NPC-adjacent regions; other observations further support the assertion that the approach is specific for NPC-associate chromatin regions. Furthermore, NPC-DamID does not require genetic manipulation and they show that it can be applied to both diverse cell lines as well as tissues. The authors confirm the association of nucleoporins with super-enhancers (SEs) in line with their prior work, now confirmed to occur at NPCs based on this study. Among SEs categorized as hierarchical enhancers (Nat Commun 9, 943 (2018)), hub enhancers are over-represented for methylation by Dam-Impb. The association of such enhancers with cohesin and CTCF suggests these regions could have a critical role in chromatin folding; enhancer-associated factors and marks such as H3K27 acetylation, RNA polymerase II, P300, CTCF and BRD4 also enrich at Dam-Impb methylation peaks. Using proximity ligation, the authors provide further evidence that Tpr, which interacts with the NPC basket, colocalizes with CTCF, BRD4 and P300. Based on these observations, the authors hypothesized that nucleoporin phase separation at SEs might potentiate phase separation of other factors at these elements. Consistent with previous work, over-expression of the intrinsically disordered region (IDR) of Nup153, a component of the NPC basket, forms nuclear droplets that are largely dispersed by 1,6-hexanediol. In this same condition, colocalization RNAPII and both Tpr and BRD4 is reduced, although some interactions between IDRs were not sensitive to this treatment. Last, using a lac operator array as a tethering site, the authors show that tethered Nup153 IDR recruits the carboxy terminal domain of RNAPII and Med1. However, whether the biology of how nucleoporins at NPCs influence SEs depends on biomolecular condensation will require future study.

Overall, the reviewers agree that this is an excellent manuscript that will impact our understanding of nuclear pore complex-genome interactions and how nucleoporins impact super enhancer function. The data are generally of high quality and are reasonably interpreted. There are, however, several important controls or analyses that would strengthen the conclusions of the paper, as outlined below.

1. NPC vs nucleoplasmic interactions: One of the main claims of the paper is that it provides a way to study specifically the NPC-associated loci and contrast them to the nucleoplasmic Nup-associated loci. Unfortunately, the authors do not devote much space to this comparison and many of the manipulations involve proteins that are in both locations (see below). This seems like an important, missed opportunity. The choices of Tpr or Nup153 should be more clearly justified. The Dpn8 staining appeared in regions outside of the nuclear envelope, which is inconsistent with the text. This should be addressed.

2. The PLA experiments: Although Tpr exists both at the NPC and in the nucleoplasm, the authors interpret these experiments as if they are exclusively reporting on proximity of enhancer proteins to the NPC. The images (e.g. Figure 5a, supplementary Figure 5) make it clear that the foci are throughout the nucleus. Where are the antigens recognized by the antibodies to Tpr and what this may mean for the findings? Further, PLA experiments are prone to artefacts and, while the authors have included a knockdown of Tpr as a negative control, additional controls would strengthen their conclusions. For example, what is the result when Tpr colocalization with NPC-specific proteins is assessed? How is that affected by hexanediol? A better PLA experiment might be to assess colocalization of Dam-ImpB or Dpn8 (bound to Dam-ImpB methylated sites) with super enhancer proteins such as Med1, CTCF, Brd4, etc. With regards to the PLA with

3. Analysis of genomic data: The normalization of the DNA sequencing tracks is not sufficiently explained. Moreover, some of the correlations using meta-site plots are not convincing. For example, the peaks of Nup153 or Nup98 methylation over Imp-B peaks are apparently weak. Although the authors report local maxima, these may not be strong associations. This raises the possibility that the stronger Nup153 or Nup98 peaks are not ImpB peaks. A better way to test for this would be to correlate the ImpB peak intensity to the Nup153 or Nup98 peak intensity globally. The expectation is that there will be both correlated peaks that show strong methylation by Nup153/Nup98 and ImpB, as well as peaks that do not (i.e. those in the nucleoplasm). Along these lines, the Dam alone control can be used for comparison. Peaks identified by Dam alone should not be correlated with ImpB, Nup153, Nup98, CTCF, RNAPII, Cohesin, H3K27Ac, Brd4, Mediator, super enhancers, hubs, etc. Also, what is the source of the Nup93 CUT&RUN data? It was unclear if it was from this study or a prior publication.

4. FISH experiments: these should be in the main figures of the paper and better described. How many loci were assessed in each category? Are the differences between the three classes significant? Also, the order of the legend is the opposite of the order of the bar segments, which is confusing to the reader. Related to Figure 2j: What are the FISH probes used here? How many cells were quantified?

5. The focus on IDRs as the primary functional mechanism for the NPC-SE connection was felt to be the least well-justified of the authors' conclusions. In particular, the quantitative effects in Fig. 6 are over-stated while caveats including possible over-expression artifacts and changes in the nuclear concentration of the IDRs due to efflux out of the nucleus in response to 1,6 hexanediol treatment as a consequence of the effect on the barrier of NPCs are not addressed. Additional experimental follow-up - for example does critical depletion of Nup153 (now possible with auxin degrons) disrupt the NPC-DamID profile? - would strengthen the support for the model.

6. Recent evidence points to the fact that 1,6-HD treatment probes the presence of hydrophobic interactions, rather than distinguishing between LLPS and interactions with spatially clustered binding sites (ICBS). These possibilities should be taken into account when interpreting the data, and should be discussed more thoroughly.

Joint Public Review:

This study is concerned with the general question as to how pools of synaptic vesicles are organized in presynaptic terminals to support different types of transmitter release, such as fast synchronous and asynchronous release. To address this issue, the authors employed the classical method of loading synaptic vesicle membranes with FM-styryl dyes and assessing dye destaining during repetitive synapse stimulation by live imaging as a readout of the mobilization of vesicles for fusion. Among other findings, the authors provide evidence indicating that there are multiple reserve vesicle pools, that quickly and slowly mobilized reserves do not mix, and that vesicle fusion does not follow a mono-exponential time course, leading to the notion that two separate reserve pools of vesicles - slowly vs. rapidly mobilizing - feed two distinct releasable pools - reluctantly vs. rapidly releasing. These findings are valuable to the field of synapse biology, where the organization of synaptic vesicle pools that support synaptic transmission in different temporal and stimulation regimes has been a focus of intense experimentation and discussion for more than two decades.

On the other hand, the present study has limitations, so that the authors' key conclusions remain incompletely supported by the data, and alternative interpretations of the data remain possible. The approach of using bulk FM-styryl dye destaining as a readout of precise vesicle arrangements and pools in a population of functionally very diverse synapses bears problems. In essence, the approach is 'blind' to many additional processes and confounding factors that operate in the background, from other forms of release to inter-synaptic vesicle exchange. Further, averaging signals over many - functionally very diverse - synapses makes it difficult to distinguish the dynamics of separate vesicle pools within single synapses from a scenario where different kinetics of release originate from different types of synapses with different release probabilities.

Reviewer #1 (Public Review):

This well written and designed study by Broca-Brisson et al describes the generation of a new in vitro model for creatine transporter deficiency (CTD), making use of human brain organoid cultures derived from CTD patients. This new model will certainly prove itself very useful to better understand this genetic disease essentially affecting CNS. As CTD has no satisfactory treatment so far (despite more than 20 years of research), this new model will also be very useful to design and develop new treatments.

In particular, through the use of immunohistochemistry, real time PCR, and proteomics combined with integrative bioinformatic and statistical analysis, authors provide very interesting new informations on the brain pathways affected in CTD (e.g. neurogenesis with down-regulation of SOX2 and PAX6 but up-regulation of GSK3b; and proteins involved in autistic spectrum, epilepsies or intellectual disabilities).

While the CTD human brain organoids show a decrease in Cr (in absence of Cr in the culture medium) as compared to control organoids (4 times less), they are not devoid of Cr. Do these organoids express the two enzymes allowing Cr synthesis (AGAT and GAMT), and in which brain cell types? If yes, how to explain the decrease in Cr in the CTD organoids?

The rescue experiment, re-establishing a functional Cr transporter (CRT or SLC6A8) in the CTD human brain organoids, is very interesting, as this may help the design and development of new treatments for CTD. However, authors claim that the functional CRT expressed in the rescued CTD organoids was expressed in each cell. This may be a difficulty in the development of new CTD treatments, as CRT should be expressed in neurons and oligodendrocytes, but not in astrocytes. Authors may want to comment on this point.

Reviewer #1 (Public Review):

Qin et al., demonstrate, convincingly, that plasticity of ocular dominance of binocular neurons in the visual thalamus persists in adulthood. The adult plasticity is similar to that described in critical period juveniles in that it is absent in transgenic mice with the deletion of the GABA a1 receptor in thalamus, which also blocks ocular dominance plasticity in primary visual cortex. However, the adult plasticity is not dependent on feedback from primary visual cortex, an important difference from juveniles. These findings are an important contribution of a growing body of work identifying plasticity in the adult visual system, and identifies the visual thalamus as a potential target for therapies to reverse adult amblyopia.

Reviewer #1 (Public Review):

This manuscript tackles an important question, namely how K+ affects substrate transport in the SLC6 family. K+ effects have previously been reported for DAT and SERT, but the prototypical SLC6-fold transporter LeuT was not known to be sensitive to the K+ concentration. In this manuscript, the authors demonstrate convincingly that K+ inhibits Na+ binding, and Na+-dependent amino acid binding at high concentrations, and that K+ inside of vesicles containing LeuT increases the transport rate. However, outside K+ apparently had very little effect. Uptake data are supplemented with binding data, using the scintillation proximity assay, and transition metal FRET, allowing the observation of the distribution of distinct conformational states of the transporter.<br /> Overall, the data are of high quality. I was initially concerned about the use of solutions of very high ionic strength (the Km for K+ is in the 200 mM range), however, the authors performed good controls with lower ionic strength solutions, suggesting that the K+ effect is specific and not caused by artifacts from the high salt concentrations.

The major issue I have with this manuscript is with the interpretation of the experimental data. Granted that the K+ effect seems to be complex. However, it seems counterintuitive that K+ competes with Na+ for the same binding site, while at the same time accelerating the transport rate. Even if K+ prevents rebinding of Na+ on the inside of vesicles, it would be expected that K+ then stabilizes this Na+-free conformation, resulting in a slowing of the transport rate. However, the opposite is found. I feel that it would be useful to perform some kinetic modeling of the transport cycle to identify a mechanism that would allow K+ to act as a competitive inhibitor of Na+ binding and rate-accelerator at the same time.

This ties into the second point: It is not mentioned in the manuscript what the configuration of the vesicles is after LeuT reconstitution. Are they right-side out? Is LeuT distributed evenly in inside-out and right-side out orientation? Is the distribution known? If yes, how does it affect the interpretation of the uptake data with and without K+ gradient?

Finally, mutations were only made to the Na1 cation binding site. These mutations have an effect mostly to be expected, if K+ would bind to this site. However, indirect effects of mutations can never be excluded, and the authors acknowledge this in the discussion section. It would be interesting to see the effect of K+ on a couple of mutants that are far away from Na+/substrate binding sites. This could be another piece of evidence to exclude indirect effects, if the K+ affinity is less affected.

Reviewer #1 (Public Review):

Astrocytes are known to express neuroligins 1-3. Within neurons, these cell adhesion molecules perform important roles in synapse formation and function. Within astrocytes, a significant role for neuroligin 2 in determining excitatory synapse formation and astrocyte morphology was shown in 2017. However, there has been no assessment of what happens to synapses or astrocyte morphology when all three major forms of neuroligins within astrocytes (isoforms 1-3) are deleted using a well characterized, astrocyte specific, and inducible cre line. By using such selective mouse genetic methods, the authors here show that astrocytic neuroligin 1-3 expression in astrocytes is not consequential for synapse function or for astrocyte morphology. They reach these conclusions with careful experiments employing quantitative western blot analyses, imaging and electrophysiology. They also characterize the specificity of the cre line they used. Overall, this is a very clear and strong paper that is supported by rigorous experiments. The discussion considers the findings carefully in relation to past work. This paper is of high importance, because it now raises the fundamental question of exactly what neuroligins 1-3 are actually doing in astrocytes. In addition, it enriches our understanding of the mechanisms by which astrocytes participate in synapse formation and function. The paper is very clear, well written and well illustrated with raw and average data.

Reviewer #1 (Public Review):

In this paper, Scholz and colleagues introduce a new paradigm aimed to bridge the gap between two domains that rely on hierarchical processing: language and memory. They find that, generally in line with their hypotheses, hierarchical processing is associated with activation in hippocampus (especially anterior), medial prefrontal cortex (mPFC), posterior superior temporal sulcus (pSTS), and inferior frontal gyrus (IFG). They also report that these effects in IFG are particularly strong late in the task, once participants have had a lot of experience and processing is presumably more automatic.

This work has many strengths. The goal to bridge these literatures by developing a new task is commendable. I appreciate also that the authors separately validated their new task behaviorally by comparing it to another accepted as tapping hierarchical processing. I also liked that the authors were transparent about their hypotheses, and certain analyses like the grid coding one that was planned but did not work out. I do however have a number of concerns about the interpretations of the findings, such as whether some patterns are ambiguous as to the true underlying effects. I also have a number of clarification questions. All concerns are described below.

1. Broadly, I would like to see the authors provide more information and logic on why hierarchical processing should be associated with a big reduction in univariate activation between P1 and P2-why would this signify item in contexts binding? How does this relate to existing work using other methods (e.g., like animal studies, which seem to make predictions more about representational structures)?

2. There are many differences between what kind of information participants are processing between Position 1 and Position 2 for the HIER but not ITER conditions, and these may not be related to the hierarchical structure specifically. Related to but I think distinct from some of the limitations mentioned in the Discussion is the fact that in the HIER condition, what is happening cognitively between Position 1 and Position 2 items is more distinct (attending to color for position 1, and shape for position 2), whereas the two positions are equivalent in the ITER condition. This is a bit different from the authors' intended manipulation of hierarchy, because it involves a specific dimension. A stronger design might have been to flip the dimensions with respect to position specifically, to make shape sometimes important for position 1, and color for position 2 (perhaps by counterbalancing across subjects, so half would see the current P1=color and P2=shape rules, and the other half P1=shape and P2=color rules). Another important difference between color and shape is that while color is a simple binary distinction that participants can make based on their preexisting knowledge of red versus green, and to which they can assign a verbal label; whereas, the shape distinction was something novel they acquired during the experiment, has no real-world validity or meaning, and would presumably rely more on visuospatial processing. The shape dimension was also much more variable, I believe. I should say that I do find comfort in a few things - (1) that behavior on this task is correlated with another one that also indexes hierarchy processing, and (2) that the results show regional specificity in a pattern at least not easily explained by this distinction. However, I do think future work will be needed to ask whether it is hierarchy processing per se or rather something to do with the particular cognitive states engaged during each phase in this particular task that is eliciting activation in this set of regions. It would strengthen the paper to discuss this issue directly so readers are alerted to the caveat.

3. I did not understand what data went into creating the schematic in Figure 2E. First, I think this depiction of a gradient might be easily misinterpreted because it seems to imply that the authors have a higher resolution analysis than they actually do. I believe the data were just analyzed in three subregions of hippocampus - head, body, and tail. Variability within each subregion (as seems to be implied by certain parts of a region being more grey and others more red/orange), is not something that could be assessed in this analysis. For example, why does the medial part of the head seem to be more "unspecific" whereas lateral regions look more HIER Pos1 specific? This type of depiction would only make sense in my mind if the authors had performed something like a voxelwise analysis to determine where specifically the interaction "peaks." I would recommend this visualization be cut or significantly changed to do away with the gradient.

4. I believe the authors have not reported enough information for us to know that hippocampus involvement indeed does not change with experience. It is interesting that hippocampus in the task x experience ROI analysis shows, if anything, bigger differentiation between the two tasks (numerically) for the late trials. This seems to go against the authors' hypothesis, and a lot of existing data, that hippocampus is preferentially involved in early (vs. late) learning. Given that the key signature in this region, though, is that it differentiates between position 1 and position 2 in HIER but not ITER, and doesn't show a big difference in magnitude across the two tasks, it makes me wonder whether the task x experience interaction collapsing across the two positions makes sense for this region. Did the authors consider a similar task x experience interaction within hippocampus, but additionally considering position? I think there are multiple ways to look at this question (e.g., either looking for a task x experience x position interaction, a task x experience within position 1, a task x position interaction separately in early vs. late portions of the task, or even a position x experience interaction only within the HIER task), and I'm sure the authors would be in a better place to decide on a specific path forward. The same logic might go for mPFC, which shows an interaction but no main effect of task. This relates to claims in the discussion as well, such as that "hippocampus was equally active in early and late trials," but given this analysis is collapsing across the dimension hippocampus (and mPFC) seem to be sensitive to (position), it seems like this could be masking an underlying effect in which hippocampus/mPFC might still be differentially involved early vs. late (i.e., they might show the task x position interaction preferentially during some task phases).

5. For the IFG regions, the task x experience interaction seems to be driven mainly by change (decrease in activation) for the ITER, rather than change in the HIER. The authors are at times careful to talk about this as "sustained" activity in IFG, which I appreciated, but other times talk about a "relative increase." I am not sure how I feel about that. I see the compelling evidence that there are task differences by experience, and that there is reduction for ITER that is interestingly not present for HIER, but I think I am still feeling uncomfortable with the term "increase" or even "relative increase" for HIER. For example, couldn't it simply be that the ITER task is requiring less processing with experience, whereas the HIER does not (perhaps because it requires more processing to begin with)? i.e., we do not know whether the reduction for ITER is simply a neural signal thing (i.e., activations diminish over time/experience) or a cognitive thing, specific to the ITER task. I think the authors are wanting to interpret the reductions as the former, but perhaps it would be more powerful to demonstrate if there was a baseline task that also showed reductions but for which not much would be expected in the way of cognitive change. Can the authors provide more justification for their choice of terminology (through either more logic or analyses), or if not, simply talk about it as sustained activity for HIER-which is especially interesting in the face of reductions for the ITER task?

6. Please define what is meant by the term "automaticity" in the introduction. A clearer definition of the concept would make the paper generally easier to follow, and it would also help foreshadow the hypotheses about mPFC activity in the introduction. To this end, it could be useful to elaborate on how learning takes place in this task, how it could foster increasing automaticity, and how automaticity maps onto behaviour (e.g., is it RT decrease alone, which happens for both conditions in this task?) the brain regions discussed.

7. There was no association between brain and behavior, which the authors interpret as a positive (as therefore task difficulty differences could not explain the effects). However in light of these null findings, it is on the flip side hard to know whether this neural engagement carries any behavioral significance. It seems to me as though the authors' framework makes predictions about brain-behavior correlations that were not tested in the manuscript. For example, I believe the authors asked whether behavior overall was correlated with activation. However, wouldn't the automaticity in IFG explanation for example predict that more engagement or an increase in engagement from early to late should be associated with e.g., faster RTs-not necessarily a relationship overall?

8. On p. 8, it is stated that "In the hippocampus, this effect is driven by higher betas for the presentation of the first object (H1 > I1) and lower betas for the second object (H2 < I2) when comparing across tasks." Can the authors confirm whether the pairwise comparisons following up on the interaction here are significant, or rather if they are referring to a numerical difference in the betas? It looked like the same (numerically) would be true for mPFC; is there a reason why the same information is not included for the mPFC ROI? Also, might the authors provide more speculation as to why one might see both enhanced and reduced activation for P1 and P2, respectively?

9. I was expecting some discussion of how hippocampus does not seem to show preferential involvement early, given that its potential role being restricted to early in learning (i.e., during acquisition only) was one of the primary motivators for using this task. As noted in my above comment (#4), I am not quite sure that I think there is evidence that the hippocampal role remains constant over this task, given the analyses provided (i.e., that they did not look at the position effect for early vs. late). However upon further analysis if it does seem to be more stable, and/or if it even increases over experience, the authors might want to talk about that in the Discussion.

10. The fact that the hierarchies in this paradigm unfolded over time makes them distinct on some level from the hierarchies present in the VRT task that was used to validate the HIER task's hierarchical processing demands. For example, there might be additional computations required to processes these temporally ordered structures, support online maintenance, and so on. It may be worth considering this aspect of the task, and whether/to what extent the results could be related to it, in the paper.

11. I also have many methodological and analytic clarification questions, which I detail in the recommendations for authors.

Reviewer #1 (Public Review):

The manuscript by Aguirre et al. describes an elegant approach for developing selective inhibitors of inositol hexakisphosphate kinases (IP6Ks). There are 3 IP6K isozymes (IP6K1-3) in humans, which catalyze the synthesis of inositol pyrophosphates. The lack of isozyme-selective inhibitors has hampered efforts to understand their individual physiological roles. While several inhibitors of IP6Ks have been described, their either lack isozyme selectivity or inhibit other kinases. To address this gap, Aguirre et al. used an analog-sensitive approach, which involves the identification of a mutant that, in an ideal world, doesn't impact the activity of the enzyme but renders it sensitive to an inhibitor that is absolutely selective for the engineered (analog-sensitive) enzyme. Initially, they generated the canonical gatekeeper (Leu210 in IP6K1) mutations (glycine and alanine); unfortunately, these mutations had a deleterious effect on the enzymatic activity of IP6K1. Interestingly, mutation of Leu210 to a valine, a subtly smaller amino acid, didn't affect enzymatic activity. The authors then designed a clever high-throughput assay to identify compounds that show selectivity for L210V IP6K1 versus WT IP6K1. The assay monitors the reverse reaction catalyzed by IP6Ks, monitoring the formation of ATP using a luminescence-based readout. After validating the screen, the authors screened 54,912 compounds. After culling the list of compounds using several criteria, the authors focused on one particular compound, referred to as FMP-201300. FMP-201300 was ~10-fold more potent against L210V IP6K1 compared to WT IP6K1. This selectivity was maintained for IP6K2. Mechanistic studies showed that FMP-201300 is an allosteric inhibitor of IP6K1. The authors also did a small SAR campaign to identify key functional groups required for inhibition.

Overall, this manuscript describes a unique and useful strategy for developing isozyme-selective inhibitors of IP6Ks. The serendipitous finding that subtle changes to the gatekeeper position can sensitize the IP6K1 mutant to allosteric inhibitors will undoubtedly inspire other analog-sensitive inhibitor studies. The manuscript is well-written and the experiments are generally well-controlled.

Reviewer #1 (Public Review):

In this study, the authors demonstrated a new model that B cell contraction after antigen encountering was dependent on N-WASP-branched actin polymerization. This statement is achieved by a systemic comparison of genetic modified mice vs wild type mice or inhibitor treated cells vs control cells. By imaging how B cells interact with antigen-coated planar lipid bilayer, the authors further suggested that the contraction event may provide B cells a channel to dismiss downstream kinase for a purpose to attenuate B cell activation signaling. Even though this manuscript is well written and packaged, however there are a few points that should be carefully addressed and revised.

The first major issue is related to the imaging and tracking experiment to examine the formation and migration of F-actin foci as illustrated in figure 3. The formation and centripetally migration of F-actin foci is a significant finding of this MS for the promotion of B cells to switch from spreading to contraction response. Thus, I may suggest to recommend the authors to conduct one more rigorous fluorescent molecular tracking experiment to confirm this phenomenon. Molecular tracking usually requires low labeling density, and the lifeact-GFP labeling here do not meet this requirement which may cause misidentification of the moving molecules. Permeable dye-based fluorescent speckle microscopy is recommended here to track the actin foci if applicable (P. Risteski, Nat. Rev. Mol. Cell Biol., 2023, DOI: 10.1038/s41580-023-00588-w & K. Hu, et al, Science, 2007, 315, 111-115). Additionally, kymograph is used for foci tracking in figure3 and figure4. Kymograph is indeed a powerful tool for tracking cell protrusion and retraction but is fairly suitable here, since a F-actin focus is a concentrated point which may not move strictly along the selected eight lines generating kymograph. Other imaging processing method should be used to track the foci, for example, time series max projection is recommended if applicable.

The second major issue is about the relationship between actin foci formation and NMII recruitment in figure 5. The author concludes that 'N-WASP and Arp2/3 mediated branched actin polymerization promotes the recruitment and the reorganization of NMII ring-like structures by generating inner F-actin foci in the contact zone'. However, there is a lack of strong evidence to directly show the mechanism by which myosin is recruited and the up and down stream relationship between actin foci migration and myosin recruitment. Since myosin-induced actin retrograde flow is a classical model in adherent cells, is it possible that, here also in activated B cells, the recruited myosin driven the formation and migration of actin foci? This reviewer may recommend the author to investigate whether Myosin blocking (e.g., using Y27632) can eliminate the F-actin foci formation and migration.

Reviewer #1 (Public Review):

Transcriptional readthrough, intron retention, and transposon expression have been previously shown to be elevated in mammalian aging and senescence by multiple studies. The current manuscript claims that the increased intron retention and readthrough could completely explain the findings of elevated transposon expression seen in these conditions. To that end, they analyze multiple RNA-seq expression datasets of human aging, human senescence, and mouse aging, and establish a series of correlations between the overall expression of these three entities in all datasets.

While the findings are useful, the strength of the evidence is incomplete, as the individual analyses unfortunately do not support the claims. Specifically, to establish this claim there is a burden of proof on the authors to analyze both intron-by-intron and gene-by-gene, using internal matched regions, and, in addition, thoroughly quantify the extent of transcription of completely intergenic transposons and show that they do not contribute to the increase in aging/senescence. Furthermore, the authors chose to analyze the datasets as unstranded, even though strand information is crucial to their claim, as both introns and readthrough are stranded, and if there is causality, than opposite strand transposons should show no preferential increase in aging/senescence. Finally, there are some unclear figures that do not seem to show what the authors claim. Overall, the study is not convincing.

Major concerns:

1. Why were all datasets treated as unstanded? Strand information seems critical, and should not be discarded. Specifically, stranded information is crucial to increase the confidence in the causality claimed by the authors, since readthrough and intron retention are both strand specific, and therefore should influence only the same strand transposons and not the opposite-strand ones.

2. "Altogether this data suggests that intron retention contributes to the age-related<br /> increase in the expression of transposons" - this analysis doesn't demonstrate the claim. In order to prove this they need to show that transposons that are independent of introns are either negligible, or non-changing with age.

3. Additionally, the correct control regions should be intronic regions other than the transposon, which overall contributed to the read counts of the intron.

4. Furthermore, analysis of read spanning intron and partly transposons should more directly show this contribution.

5. "This contrasts with the almost completely even distribution of randomly permuted transposons." How was random permutation of transposons performed? Why is this contract not trivial, and why is this a good control?

6. Fig 4: the choice to analyze only the 10kb-20kb region downstream to TSE for readthrough regions has probably reduced the number of regions substantially (there are only 200 left) and to what extent this faithfully represent the overall trend is unclear at this point.

7. Fig. 5B shows the opposite of the authors claims: in the control samples there are more transposon reads than in the KCl samples.

8. "induced readthrough led to preferential expression of gene proximal transposons (i.e. those within 25 kb of genes), when compared with senescence or aging". A convincing analysis would show if there is indeed preferential proximity of induced transposons to TSEs. Since readthrough transcription decays as a function of distance from TSEs, the expression of transposons should show the same trends if indeed simply caused by readthrough. Also, these should be compared to the extent of transposon expression (not induction) in intergenic regions without any readthrough, in these conditions.

Reviewer #1 (Public Review):

The manuscript entitled: "TCR-pMHC complex formation triggers CD3 dynamics" by Van Eerden et al. mainly uses coarse-grained molecular dynamics to probe the dynamic changes, in terms of CDε spatial arrangements around 226 TCRs, before and after the engagements of MCC/I-Ek. The broader distributions of CDε iso-occupancies after pMHC binding correlate with the decreases of TCR-CD3 contacts and extensions of TCR conformations. Given the observed release of motion restrictions upon antigen recognition, the authors proposed a "drawbridge" model to describe the initial triggering processes from pMHC association to TCR straightening, FG-loop getaway, and CD3 enhanced mobility. In addition, the authors briefly investigated the functional effects of the rigidified connecting peptide (CP) in T-cell activation using in silico and in vitro mutagenesis. The manuscript raises an important and exciting hypothesis about the allostery of TCR-CD3 during TCR triggering; however, due to current not-yet-convincing evidence, both computationally and experimentally, in supporting their conclusions.

1. As mentioned by the authors, the TCR triggering and T cell activation have been illustrated by a number of models, such as mechanosensing and kinetic proofreading, "in which TCRs discriminate agonistic from antagonistic pMHCs." However, the critical feature of antigen discrimination is lacking in the drawbridge model. So far, the CDε movements qualitatively distinguish on and off states. The simulation of the antagonist or weaker binder would strengthen the manuscript by demonstrating the relevance of CDε mobility in the triggering mechanism. 226 TCR associated with K99E/I-Ek has been resolved in Ref (DOI: 10.4049/jimmunol.1100197), which can potentially serve as the "intermediate" system to formulate the gradual increase of CDε dynamics.

2. The linkage between conserved motifs in CP and CDε mobility is less apparent to this reviewer. The notion of the rigidified hinge (PP) requires further clarification. Computationally, the details of fine-grained simulations are required to justify the origin of the apparent mobility increase in PP. The direct comparison between Fig. 2 and Fig. 7 can help assess the relevance of CP through the alignment by FG-loop at a fixed direction in polar coordinates. Experimentally, anti-CD3 positive experiments and, ideally, another antagonist on 3A9 TCRs can strengthen the current functional assay. The baseline level of TCR expression (after positive selection) and 0h activation (Fig. S8) is missing.

3. Regarding the section "The TCRβ FG loop acts as a gatekeeper," besides contact analysis, additional motion analysis, such as RMSF or PCA, can further establish the importance of FG loops.

4. The discussion on anti-CD3 antibody effects and their potential contribution to CD3 mobility is highly recommended.

Reviewer #1 (Public Review):

Full activation of T cells requires not only antigen recognition through the T cell receptor, but also engagement of co-stimulation by the T cell. There are multiple co-stimulatory receptors that can be engaged by the T cell; yet, the downstream effects of signaling through these different receptors on T cell gene programs and function and are not yet fully understood. These questions are clinically important because genomic variants associated with immune and inflammatory disease map onto these different co-stimulatory receptors and, potentially, their downstream gene programs.

Based on these observations, the authors hypothesize that different modes of co-stimulation engage different genes and pathways that may be differentially associated with risk for inflammatory disease. To ask this question, the authors performs a comparative analysis of different co-stimulatory receptors, both CD28 - the most widely used form of co-stimulation for in vitro assays - as well as alternative modes of co-stimulation involving ICOS, CD6, CD27. They analyzing their effects on their T cell activation in vitro for human naive and memory CD4 cells, on gene expression using RNA-seq (at 24 hrs), on chromatin accessibility using ATAC-seq, and on specific proteins identified from transcriptomic data using flow cytometry.

From these experimental analysis, the authors conclude the following (1) alternative co-stimulation (ICOS, CD6, Cd27) can induce a *qualitatively* different gene and cellular program compared to canonical co-stim (CD28), resulting not only in less proliferation and cytokine production, as expected, but also in higher lysosome production and different metabolic programming. They also found that risk variants for inflammatory bowel disease mapped onto genes that were both shared across different modes of co-stimulation, as well as onto targets of specific co-stimulation.

This study and the authors' experimental system is well-designed to precisely identify genomic effects of co-stimulation, employing sorted subsets of human CD4 cells, as well as a in vitro setting that can effectively eliminate many confounding variables associated more complex scenarios. The transcriptome/chromatin accessibility measurements were also robustly analyzed and offer some support for the author's conclusion. However, there were two main weaknesses that limit that, if overcome, would enhance the authors' argument:

(1) It is not clear whether the qualitatively different effects of alternate co-stimulation compared to canonical CD28 co-stimulation, e.g. increased OXPHOS or lysosomal abundance for CD6, or heightened expression of genes or represent truly unique effects, or whether they simply represent effects of having quantitatively weaker strengths of CD28 co-stimulation. This concern would be addressed by an experiment doing a dose response curve for CD28 co-stimulation while measuring these variables (Fig. 6) or, more systematically, while performing RNA-seq. Also, to strengthen this argument, the authors would benefit from further in-depth literature discussion/analysis of the signaling pathways downstream of co-stimulation, to discuss molecular bases for different signaling, if any.

(2) There is no functional evidence to link differential activation of risk variant-associated genes by alternate co-stimulation with inflammatory disease. To show this, the authors can examine the activation of these genes (e.g. Bach2, Il18R1, from Table 2) using their assay, either using T cells from humans containing disease-associated variants at these gene loci, or by using T cells with a genetic disruption of the associated loci.

While providing insights for the pathogenesis of IBD, this study's main impact would be in the enhancing our understanding of how different modes of co-stimulation differ to activate T cells and prompt broader consideration of use of different co-stimulatory ligands in these in vitro assays and evaluation of their function in vivo.

Reviewer #1 (Public Review):

This manuscript provides an important case study for in-depth research on the adaptability of vertebrates in deep-sea environments. Through analysis of the genomic data of the hadal snailfish, the authors found that this species may have entered and fully adapted to extreme environments only in the last few million years. Additionally, the study revealed the adaptive features of hadal snailfish in terms of perceptions, circadian rhythms and metabolisms, and the role of ferritin in high-hydrostatic pressure adaptation. Besides, the reads mapping method used to identify events such as gene loss and duplication avoids false positives caused by genome assembly and annotation. This ensures the reliability of the results presented in this manuscript. Overall, these findings provide important clues for a better understanding of deep-sea ecosystems and vertebrate evolution.

Reviewer #1 (Public Review):

In the manuscript "Long‐read single‐cell sequencing reveals expressions of hypermutation clusters of isoforms in human liver cancer cells", S. Liu et al present a protocol combining 10x Genomics single-cell assay with Element LoopSeq synthetic long-read sequencing to study single nucleotide variants (SNVs) and gene fusions in Hepatocellular carcinoma (HCC) at single‐cell level. The authors were the first to combine LoopSeq synthetic long‐read sequencing technology and 10x Genomics barcoding for single cell sequencing. For each cell and each somatic mutation, they obtain fractions of mutated transcripts per gene and per each transcript isoform. The manuscript states that these values (as well as gene fusion information) provide better features for tumor-normal classification than gene expression levels. The authors identified many SNVs in genes of the human major histocompatibility complex (HLA) with up to 25 SNVs in the same molecule of HLA‐DQB1 transcript. The analysis shows that most mutations occur in HLA genes and suggests evolution pathways that led to these hypermutation clusters. Yet, very little is said about novel isoforms and alternative splicing in HCC cells, differences in isoform ratio between cells carrying different mutations, or diversity of alternative isoforms across cells. While the manuscript by Liu et al. presents a promising combination of technologies, it lacks significant insights, a comprehensive introduction, and has significant problems with data description and presentation.

Major comments:

1. The introduction section is scarce. It lacks description of important previous works focused on clustered mutations in cancers (for example, PMID35140399), on deriving the process of cancer development through somatic evolution (PMID32025013, from single cell data PMID32807900). Moreover, some key concepts e.g. mutational gene expression and mutational isoform expression are not defined. The introduction and the abstract contain slang expressions e.g. "protein mutation', a combination of terms I teach my students not to use.

2. In the results section, to select the mutations of interest, the authors apply UMAP dimensionality reduction to the mutation isoforms expression and cluster samples in UMAP space, then select the mutations that are present only in one cluster, then apply UMAP to the selected mutations only and cluster the samples again. The motivation for such a procedure seems unclear, could it be replaced with a more straightforward feature selection?

3. As I understand, the first "mutated isoform"-based UMAP clustering was built from expression levels of 205 "mutational isoforms". What was the purpose and outcome of the second "mutated isoform"-based UMAP clustering (Figure 2E)? In the manuscript the authors just describe the clusters and do not draw any conclusions or use the results of the clustering anywhere further.

4. The authors just cluster the data three times based on expression levels of different sets of "mutational isoforms" and describe the clusters. What do we need to gather from these clustering attempts besides the set of 113 mutations used for further analysis? What was the point of the re-clusterings? Did the authors observe improvement of the classification at each step?

5. The alignment of short reads generated from hypermutated transcriptomes is non-trivial. The proposed approach could address the issue without need for whole genome sequencing and offer insights about the cancer development through somatic evolution. Why didn't the authors use modern phylogenetic approaches in the "Evolution of mutations in HLA molecules" section or at least utilize the already performed clustering to infer cell lineages?

6. I am not sure I understood the definition of "mutated gene expression levels" and "mutated isoform expression levels" in the "Mutational gene expression and fusion transcript enhanced transcriptome clustering of benign hepatocytes and HCC" section. The authors mention that gene lists included all the isoforms within the same range of standard deviation. If I understand it correctly, they are equal if there is only one expressed transcript isoform. In that case, this overlap is not surprising at all.

7. "To investigate the roles of gene expression alterations that were not accompanied with isoform expression changes, UMAP analyses were performed based on the non‐overlapped genes." Venn diagrams (Sup Figure 8) show that there are much less "non-overlapped genes" than "genes that showed both gene and isoform level changes" for each SD threshold (for example, for SD>=0.8 59 vs 275). Could that be the reason why clustering based on the former group is worse i.e the cancer and normal cells are separated less clearly?

Reviewer #1 (Public Review):

The authors sought to craft a method, applicable to biobank-scale data but without necessarily using genotyping or sequencing, to detect the presence of de novo mutations and rare variants that stand out from the polygenic background of a given trait. Their method depends essentially on sibling pairs where one sibling is in an extreme tail of the phenotypic distribution and whether the other sibling's regression to the mean shows a systematic deviation from what is expected under a simple polygenic architecture.

Their method is successful in that it builds on a compelling intuition, rests on a rigorous derivation, and seems to show reasonable statistical power in the UK Biobank. (More biobanks of this size will probably become available in the near future.) It is somewhat unsuccessful in that rejection of the null hypothesis does not necessarily point to the favored hypothesis of de novo or rare variants. The authors discuss the alternative possibility of rare environmental events of large effect. Maybe attention should be drawn to this in the abstract or the introduction of the paper. Nevertheless, since either of these possibilities is interesting, the method remains valuable.

Reviewer #1 (Public Review):

In their manuscript entitled: "Is tumor mutational burden predictive of response to immunotherapy?", Gurjao and colleagues discuss the use of tumor mutational burden (TMB) as a predictive biomarker for cancer patients to respond to immune checkpoint blockage (ICB). By analyzing a large cohort of 882 patient samples across different tumor types they find either little or no association of TMB to the response of ICB. In addition, they showed that finding the optimal cutoff for patient stratification lead to a severe multiple testing problem. By rigorously addressing this multiple testing problem only non-small cell lung cancer out of 10 cancer types showed a statistically significant association of TMB and response to ICB. Nevertheless, it is clearly shown that in any case the rate of misclassification is too high that TMB alone would qualify as a clinically suitable biomarker for ICB response. Finally, the authors demonstrate with a simple mathematical model that only a few strong immunogenic mutations would be sufficient for an ICB response, thereby showing that also patients with a low TMB score could benefit from immunotherapy. The manuscript is clearly written, the results are well presented and the applied methods are state-of-the-art.

Reviewer #1 (Public Review):

This work presents findings on the cellular and ultrastructural organization of the nervous system in the freshwater polyp Hydra. Although the work presents potentially important data, there are several points that need to be addressed:

1) The antibody has to be properly validated as a tool for detecting all neurons. As it stands, the antibody might not recognize a cadherin and it is not clear whether it is specific and labels all neurons.

2) The lack of communication between the two nerve nets is an interesting observation, but its implications are limited due to technical reasons. This should be investigated further.

3) The apparent lack of typical terminal synaptic contacts and the predominant presence of "en passant" contacts in the neurite bundles could be the central element of the paper but this would have to be supported by more thorough observations and experiments.

4) The authors should highlight the novelty of the findings as compared to previous work that had already addressed some of these points.

Reviewer #1 (Public Review):

The contribution of Klughammer et al reports on the fabrication and functionalization of zero-mode waveguides of different diameters as a mimic system for nuclear pore complexes. Moreover, the researchers performed molecular transport measurements on these mimic systems (together with molecular dynamic simulations) to assess the contribution of pore diameter and Nsp functionalization on the translocation rates of BSA, the nuclear transport protein Kap95 and finally the impact of different Kap95 concentrations on BSA translocation and overall selectivity of the mimicked pores as a function of their diameter. In order to assess the effect of the Nsp1 on the coated pores to the translocation rates and molecular selectivity they also conducted separated experiments on bare nano-pores, i.e., without coating, and of different diameters. One of the most novel aspects of this contribution is the detection scheme used to assess the translocation rates & selectivity, i.e., the use of an optical scheme based on single molecule fluorescence detection as compared to previous works that have mostly relied on conductance measurements. The results are in general convincing, the experiments carefully performed and the procedures explained in detail. Some weaknesses are identified on the FDTD simulations and interpretation of the single molecule data since they might be affected by quenching of the dye in close proximity to the pore. These weaknesses should be clarified and discussed properly.

Importantly, this study provides new insights on the mechanisms of nuclear transport contributing to further our understanding on how real nuclear-pore complexes (i.e., in living cell) can regulate molecular transport. The recent findings that the nuclear pore complexes are sensitive to mechanical stimulation by modulating their effective diameters, adds an additional level of interest to the work reported here, since the authors thoroughly explored different nano-pore diameters and quantified their impact on translocation and selectivity. There are multiple avenues for future research based on the system developed here, including higher throughout detection, extending to truly multicolor schemes or expanding the range of FG-Nups, nuclear transport proteins or cargos that need to be efficiently l transported to the nucleus through the nuclear pore complexes. As a whole, this is an important contribution to the field.

Joint Public Review:

Chen and collaborators first analysed in sheep embryonic gene editing using CRISPR-Cas9 technology to invalidate the two alleles of Mstn and Fgf5 genes by using different ratios of Cas9 mRNA and sgRNA. They showed that a ratio of 1:10 had highest efficiency and they successfully generated two sheep with biallelic mutations of both genes. Materials and Methods on the generation of gened edited sheep is entirely missing. The data on these gene edited sheep have been already published twice by the authors in different contexts. Other groups reported on gene editing of Mstn or Fgf5 in sheep embryos and the resulting phenotypes.

Although the findings are interesting, they do not provide sufficiently new scientific information or advancements in producing genetically modified livestock with improved production characteristics. While the MSTNDel273 sheep exhibited an increased number of muscle fibers, the data provided did not demonstrate a significant improvement in meat productions, quality or quantity in the MSTNDel273 sheep vs WT.

The authors indicate that sgRNA design changes in addition to changing the molar ratio of Cas9MRNA:sgRNA improved the ability to generate biallelic homozygous mutant sheep; however, the data provided to not demonstrate any significant difference. Given the small number of sheep that were actually produced and evaluated,it is extremely difficult to demonstrate anything that was analyzed to be significantly (statistically) different between MSTNDel273 sheep and WT, yet the authors seem to ignore this in much of their discussion. There is no explanation as to why the authors started with sheep that were FGF5 knockouts. The reviewer assumes that this was simply a line of sheep available from previous studies and the goal was to produce sheep with both improved hair/wool characteristics in addition to improved muscle development. However, the use of FGF5 knockout sheep complicates the ability to accurately decipher the unique aspects associated with targeting only myostatin for knock-out. At minimum, this is a variable that has to be considered in the statistical analysis. No information is provided on the methods used to produce the MSTNDel273 sheep, which is fundamentally important. It is assumed they were produced by injecting one-cell zygotes then transferring these into surrogate females. The methods employed might have a profound effect on the outcome.

Authors genotyped one sheep with a biallelic three base pair deletion in Mstn exon 3 and a compound heterozygote mutation in Fgf5 with a 5 nucleotides deletion on one allele and 37 nucleotides deletion on the other allele, partially spanning over the same region. This sheep developed a double muscle phenotype, which was documented using photography and CT scan. The hair phenotype was not further addressed, but authors referred to a previous publication.

Authors performed morphometric studies on two distinct muscles, longissimus dorsi and gluteus medius, and found a profound fiber hypotrophy in the Mstn-/-;Fgf5-/- double mutants, with a shift from larger fiber diameter to smaller fiber sizes. Morphometric studies showed only a low percentage of fibers in wt and mutant sheep had fiber cross sectional areas larger than 800 µm2, whereas about 30% in wt and about 60% in the mutant had CSA of <400 µm2. The report of one case, without reproducing the phenotype in other sheep, is scientifically insufficient. The fiber sizes in wt sheep remains far below previously published reports in sheep (about 3-5 times smaller) and as compared to other species, which suggests a methodological error in morphometric methods.

The authors also investigated the influence of Fgf5 mutation on muscle development. They determined fiber cross sectional area in heterozygous Fgf5 mutant (number of investigated animals not given) and conclude that Mstn mutation but not Fgf5 mutation caused the double muscle phenotype. Results are insufficient to support this conclusion. Firstly, authors investigated heterozygous FGF5 sheep and not homozygous mutants. Secondly, FGF5 has previously been shown to stimulate expansion of connective tissue fibroblasts and to inhibit skeletal muscle development during limb embryonic development (Clase et al. 2000). Of note, Mstn is also expressed during embryonic development. A combined knockout could therefore entail synergistic effects and cause muscle hyperplasia that is not found in individual knockout, a hypothesis that was not addressed by the authors.

The authors generated and studied an F1 generation of mutant sheep with heterozyogous mutation in Mstn and Fgf5. In Mstn+/-;Fgf5+/-, gluteus medius muscle was found to be larger compared to wt sheep, whereas other muscles were smaller, and overall meat quantity did not change. Morphometric studies revealed a similar muscle fiber hypotrophy and muscle hyperplasia as in the Mstn-/-;Fgf5-/- gluteus muscle.

In the next part of results, authors investigated the presence of myostatin protein in homozygous Mstn muscle using immunohistochemistry and found no differences compared to wt, however, positive and negative controls are missing. The also determined Mstn transcription and protein quantity using WB in heterozygous Mstn muscle and found no difference. The authors did not provide data to explain of why the herein generated Mstn mutation causes muscle fiber hypotrophy, whereas most work on myostatin abrogation demonstrated fiber hypertrophy.

Authors then isolated myoblasts from hind limbs of 3-month-old sheep fetuses and cultured in presence of 20% fetal bovine serum before switching to differentiation medium containing 2% horse serum. The cultures showed increased proliferation of Mstn+/-;Fgf5+/- myoblasts as well as downregulation of genes associated with muscle differentiation as well as reduced fusion index. No experiments were performed to assure whether the myostatin and FGF5 pathways were inhibited. No control experiments using supplementation with recombinant proteins and using growth factor depleted culture supplements were performed. As FGF5 and myostatin are secreted factors, evidence is missing whether this led to conditioning of the culture medium. Of note, previous work in mice demonstrated that the double muscle phenotype developed independent of satellite cells activity (Amthor et al. 2009).

Authors then performed RNA seq from Mstn+/-;Fgf5+/- muscle and found a number of differentially expressed genes, but none has been previously reported being involved in the myostatin signaling pathway, so the authors chose to only focus on FOSL1 and associated genes. Authors then demonstrated that Pdpn and Ankrd2 were upregulated during myogenic differentiation, whereas FOPSL1 was downregulated. Moreover, Fosl1 transcription was upregulated in myoblasts and myotubes from Mstn+/-;Fgf5+/- muscle. Authors showed an interaction between Fosl1 and Myod1. Moreover, authors demonstrated that Polsl1 directly binds to the Myod1 promoter. Authors also found decreased p38 MARPK protein levels in proliferating myoblasts from Mstn+/-;Fgf5+/- muscle and increased p38 MARPK in differentiating myotubes.

Furthermore, gain-of-function by overexpressing FOSL1 promoted cell proliferation and inhibited differentiation, and tert-butylhydroquinone, an indirect activator of FOSL1 also inhibited myogenic differentiation. The findings do not support the idea that FOSL1 is not involved, but neither do they strongly support the involvement of FOSL1. The observations made by the authors could be co-incidental and not causative in nature.

The manuscript by Chen et al. demonstrated successful gene editing in sheep embryos to obtain biallelic mutation of Mstn and FGF5. The resulting double muscle phenotype resulted from fiber hypotrophy and hyperplasia, which contradicts findings in the literature. Chen et al. generated F1 heterozygous offsprings, in which Mstn transcription and translation did not change. Myoblasts from these animals showed increased proliferation and decreased differentiation, which authors interpreted as the underlying cellular mechanism of the double muscle phenotype. However, no work on muscle development in these animals is presented. Important in vitro control experiments are missing. Chen and collaborators found Fosl1 as a differentially expressed gene in Mstn+/-;Fgf5+/- muscle. Fosl1 drives myoblast proliferation and has direct regulatory effect on the Myod1 promoter. The cellular and molecular mechanism of Fosl1 during myogenesis is novel and solid evidence. However, data remain inadequate to conclude whether Fosl1 indeed acts downstream of myostatin.

As the significant findings are minimal, the amount of text provided, figures and tables are disproportionally excessive. A large number of different molecular techniques are employed to try and decipher the mechanism(s) that result in the observed phenotype = double muscling. The authors focus on the MEK-ERK-FOSL1 pathway an suggest this the key pathway/mechanism resulting in the phenotype observed in MSTNDel273sheep. However, they provide very little solid evidence to support this notion.

The manuscript is very long, complicated and difficult to read, given the minimum amount of significant information that is provided. Further, it misses information in material methods, on the generation of animals, on histological techniques and morphometric studies. There is no information provided on the sex of the animals produced and then analyzed. There are also a number of editorial mistakes e.g. the authors refer to tables S1-S4 in the materials and methods and results section, but and there is no table S1-S4 provided.

Reviewer #1 (Public Review):

It is well established that tuberculosis (TB), which is caused by Mycobacterium tuberculosis (Mtb), is a leading cause of mortality and morbidity worldwide. However, the only vaccine licensed against tuberculosis is Bacille Calmette Guerin (BCG), has been around for nearly a century, and has limited efficacy in adults. Herein, the authors sought to investigate the effectiveness of a nanoparticle-based formulation of a subunit vaccine composed of Mtb lipid and protein antigens. The authors found that they were able to load the lipid, mycolic acid, into their nanoparticles without disrupting the architecture, and that the loaded particles activated T cells both in vitro and in vivo. Moreover, when they vaccinated with particles loaded with both lipid and protein antigens, they found that the lipid antigen persisted, and mycolic acid-specific T cells were able to be activated 6 weeks post-vaccination, in contrast to peptide-specific T cells. The authors investigated further and found that persistence required the nanoparticle encapsulation, rather than free lipid, and that it was independent of route (intratracheal, intravenous, or subcutaneous) of administration. To address the mechanisms underlying antigen persistence, the authors loaded the nanoparticles with a dye and demonstrated that the nanoparticle encapsulated lipid antigen was primarily stored in lung alveolar macrophages and that CD1b+ dendritic cells presented the antigen to mycolic acid specific T cells. Finally, the authors conducted mixed bone marrow chimera studies to examine the phenotype of the mycolic acid specific T cells and found that the memory T cell population phenotypically resembled T follicular helper, regulatory T cells, and exhausted T cells. Interestingly, while a large percentage of these lipid antigen specific T cells in the lymph nodes, lung and spleen were CXCR5+PD1+, the cells were still proliferating (Ki67+). Overall, this is a comprehensive study that has the potential to significantly enhance the field.

Reviewer #1 (Public Review):

The authors investigate the roles of ACOT12/8 in the production of acetate by the liver. They observe that acetate concentration parallels ketone concentrations during fasting and T1DM. They show that acetate is produced from fatty acids in hepatocytes. They also provide data from human subjects who were classified as either "healthy" or "diabetic," but there is no other characterization or description of these people, making it difficult to ascertain the context by which they were studied. Nevertheless, these findings could be gleaned from the literature, and yet there remains surprising uncertainty regarding the mechanism of acetate production by the liver. The authors use ShACOT12/8 and liver-specific ACOT12/8 knockout mice to demonstrate that these acetyl-CoA hydrolases are largely necessary for acetate production. There is data on this role for ACOTs in the literature, but they have yet to be widely studied. Using a 3H-palmitate assay, the authors then find that loss of these ACOTs inhibit fatty acid oxidation and propose that the mechanism involves scavenging CoA, analogous to the canonical role of ketogenesis. The idea is plausible but only partially proven. A related finding is that loss of these ACOTs inhibit ketogenesis, which the authors attribute to the loss of function of HMGC2S, partially through acetylation. These mechanisms suffer some limitations based on the cytosolic and mitochondrial compartmentation of the two processes, but the observations appear sound. Finally, the authors try to demonstrate that hepatic ACOT-mediated acetate production is necessary for normal motor function. The tracer data used to support the importance of acetate metabolism do not include loss of function models and generally need to be reported more transparently. Conceptually, one may be skeptical of the rather dramatic loss of motor function in the context of a relatively minor circulating nutrient. This may be a significant finding but requires more supporting evidence. Overall, the authors convincingly show that ACOT12/8 are critical for hepatic acetate production in mice, which will be helpful for the field, but the ramifications will require further investigation.

Reviewer #1 (Public Review):

Hermanns et al., investigated the virus diversity and prevalence patterns in conjunction with mosquito community compositions in natural and disturbed ecosystems (5 habitats) within the Tai National Park in Cote d'Ivorie. The ultimate aim was to analyse the interplay between viral biodiversity and prevalence with mosquito host biodiversity and prevalence. Pools of morphologically identified mosquitoes from pristine forest habitats through to habitats of high human disturbance were analysed for the presence of viruses of 12 major mosquito-borne virus taxa. While 15 of the viruses detected have been published previously, 34 potentially new viruses were detected of which the full genome of 5 was completely elucidated for phylogenetic analysis and temperature-dependent replication of 4 was performed. Via comprehensive analyses of the biodiversity of the viruses detected in mosquitoes collected within each habitat, it was shown that i) the highest virus richness was observed in the intermediately disturbed habitats, ii) that the prevalence of viruses corresponded to the relative abundance of the main mosquito host species that carried them, but iii) when just the main host mosquito for each virus was analysed alone in each habitat, that there was no trend in increasing or decreasing virus prevalence.

The conclusions within the paper were generally well-justified, but a caveat of the study is that the (likely) mechanisms of transmission of the viruses identified in the paper were not discussed. Many of these viruses are most likely maintained in nature via vertical transmission and thus, this information needs to be taken into consideration. Due to the fact that it is likely that many of these insect-specific viruses evolve with their mosquito host, it was not surprising that if there was an increased abundance of a particular mosquito species, that there was also increased prevalence of the virus which it hosts. Of course, this may differ depending on the viral family, but requires comment in the context of what is known. Furthermore, there requires clarification as to why analysing insect-specific virus prevalence and diversity will serve as a model for the study of typical arboviruses due to the differences in their maintenance in nature.

For many of the putative new viruses, only small sequences of less than 1200 nt were analysed. Granted that the RdRp is the most conserved gene, how was the 5% demarcation for a new species determined when established criteria differ to this.

Reviewer #1 (Public Review):

In this study, the authors study the effect of dynactin disruption on kinetochore fiber (k-fiber) length in spindles of dividing cultured mammalian cells. Dynactin disruption is known to interfere with dynein function and hence spindle pole formation. The main findings are that poles are not required for correct average k-fiber length and that severed k-fibers can regrow to their correct length both in the presence and absence of poles by modulating their dynamic properties at both k-fiber ends. In the presence of poles, regrowth is faster and the variation between k-fiber lengths is smaller. This is a very interesting study with high-quality quantitative imaging data that provides important new insight into potential mechanisms of spindle scaling, extending in an original manner previous work on this topic in cultured cells and in Xenopus egg extract. The Discussion is interesting to read as several possible mechanisms for k-fiber length control are discussed. The technical quality of the study is very high, the experiments are very original, and most conclusions are well supported by the data. Especially, the experiments observing the regrowth of k-fibers after severing and the study of the dynamic properties of these k-fibers provide very novel insight. Addressing the following concerns could potentially improve the manuscript:

(1) The phenotype generated here by disrupting dynactin via overexpressing p50 appears to be different from that caused by knocking down NuMA or dynein - as previously reported by the Dumont lab (Hueschen et al., 2019). In this study here, unfocused spindles are observed whereas earlier turbulent spindles were observed. This raises the question of whether dynein activity that contributes to pole focusing is really completely inhibited here. These discrepancies in phenotypes seem to deserve an explanation. Is k-fiber length in cultured mammalian cells only maintained in the case of this specific type of inhibition?

(2) p50 addition and also p150-cc1 addition was often used in Xenopus egg extract in order to inhibit dynein function. Considerably larger concentrations of p50 than p150-cc1 needed to be used. Can the authors estimate the level of overexpression of p50 in the cells they study? It seems that could be possible given that a mCherry fusion protein can be overexpressed. Was it necessary to select cells with a particular level of mCherry-p50 overexpression to observe the reported phenotypes?

(3) Some comparison to previous experiments using p50 and p150-cc1 addition to Xenopus egg extract spindles could put this study better into the context of the available literature. It seems from previous publications that the p50 addition produced short, unfocused, barrel-shaped spindles, indicating that spindle length is maintained without poles, whereas the p150-cc1 addition produced elongating spindles (e.g. Gaetz & Kapoor, 2004).

(4) In this context, it seems that some more explanation is required for the observations presented in Fig. 1D and 1E. It appears that spindle length and k-fiber length have been measured quite differently. Not much information is provided for how spindle length was defined and measured (please expand this part of the Methods). Could the two different methods of measurement be the reason for the mean k-fiber length remaining unaltered in dynactin-disrupted spindles, whereas the spindle length increases in these cells? If not, do non-k-fiber microtubules contribute to unfocused spindles being longer or are chromosomes not aligned in the metaphase plate causing the increase in spindle length by misalignment of k-fiber sister pairs?

(5) It seems that in the Discussion it is implied that k-fibers can respond to severing in both focused and unfocused spindles by modulating their dynamics at both ends of the k-fibers, but in the Results section the wording is more cautious because of the difference in 'flux' in severed and unsevered unfocused spindles is not significant (Fig. 4D, blue data). It appears indeed that there is also a difference in flux between severed and unsevered unfocused spindles, but the number of data points is too small. Depending on how difficult these experiments are, it could be worth increasing the size of the data set to come to a clear conclusion, given that the data shown in Figs. 3 and 4 are quite remarkable and form the core of the study.

(6) Can the authors exclude that the stopping of 'flux' at minus ends after severing is due to some sort of permanent damage induced by ablation? In other words, do severed spindles begin to flux again once they have regrown to their original length?

(7) To this reader, the conceptualization of distinguishing between 'global' and 'local' effects/behavior was a little confusing, both in the title and also later in the text. The concept of 'local' regulation of k-fiber length appears to contradict the observation that k-fiber length can be regained after severing by changes in the dynamics at both ends (so at two very different locations) which is a rather remarkable finding. Maybe distinguishing between 'individual' and 'collective' k-fiber behavior could be clearer.

(8) Can the authors exclude that some of the differences between unfocused and focused spindles could be due to altered dynein activity at kinetochores? Or due to the dynein-dependent accumulation of certain spindle proteins along microtubules towards the minus ends of k-fibers or other spindle microtubules, instead of being due to only the presence versus absence of poles? Could this be tested by ablating both poles? If this is too challenging, a discussion of these possibilities could be justified.

Reviewer #1 (Public Review):

One aim of this paper was to study historical migration from Botswana during the time of the development of the HIV epidemic. The second aim was to test whether the migration networks impacted the development of the epidemic. The first aim was achieved: this paper used historical census data in a clear way, to describe the qualities of characteristics of migration in the country at four points in time, from 1981 to 2011. Very detailed data are presented in clear ways, using network chord diagrams, sharing age- and sex-specific migration rates, and urban-rural classifications. However, data was not presented to achieve the second aim. The authors reviewed some important literature about migration and HIV. They suggested that the migration patterns, such as from specific mining towns and mostly between districts, could have been important in supporting the generalized spread of HIV. But without evidence linking HIV prevalence over time in the linked districts in Botswana, this aim was not supported.

One other limitation of the paper was that very little context, outside of migration rates, was provided. Is there any additional information about economic growth, or political event for example, that could clarify or add context to these migration flows? As it stands now, these analyses are quite basic and don't take into account underlying demographic, economic, or political trends.

The data presented in this paper has potential impact. As the paper stands now, it could be quite useful for future work when linked to additional data sources on HIV prevalence over time (or other questions that could have been influenced by migration patterns).

Reviewer #1 (Public Review):

Studies of the p38g/d MAP kinase signaling pathways using loss-of-function approaches are compromised the finding that the expression of the ERK family MAP3K Tpl2 is down-regulated. Dissection of the specific roles of p38g/d is therefore difficult. Here the authors report that compound mutant mice with a kinase-inactive p38y MAPK mutation and p38d deficiency show no defects in Tpl2 expression. The importance of this study is therefore that they describe a mouse model that can be used to examine p38g/d MAP kinase function. The data presented are solid and convincing. The authors show that p38g/d MAP kinase signaling contributes to macrophage responses to endotoxin. Moreover, the authors identify Ser44 as an inhibitory site of MEF2D phosphorylation by p38d.

Reviewer #1 (Public Review):

In this manuscript, the authors have demonstrated the direct effect of androgen receptor activation on B - cell frequencies. In the clinical part of the research, they have found increased frequencies of age-associated double-negative B memory cells and elevated levels of circulating immunoglobulin M (IgM) in women with hyperandrogenic phenotypes of PCOS. The major study strengths are driven by their experimental part. It was shown that the transfer of serum IgG from women with PCOS into wild-type female mice increases body weight, whereas RAG1 knock-out mice, which lack mature T- and B cells, do not demonstrate any signs of hyperandrogenism. Simultaneously, an androgen receptor antagonist prevents increased B cell numbers induced by androgens, whereas B cell-deficient mice are not protected from developing a PCOS-like phenotype when exposed to DHT. Generally, the author's conclusions are based on evidence, and this study opens up a new direction of research in this area.

Reviewer #1 (Public Review):

This paper provides valuable (and impressive) data on the geometry of cerebellar foliation among 56 species of mammals and gives novel insights into the evolution of cerebellar foliation and its relationship with the anatomy of the cerebrum. Thus far, the majority of the research on brain folding focuses on the cerebral cortex with little research on the cerebellum. The results from Heuer et al confirm that the evolution of the cerebellum and cerebrum follows a concerted fashion across mammals. Moreover, they suggest that both the cerebrum and cerebellum folding are explained by a similar mechanistic process.

1. Although I found the introduction well written, I think it lacks some information or needs to develop more on some ideas (e.g., differences between the cerebellum and cerebral cortex, and folding patterns of both structures). For example, after stating that "Many aspects of the organization of the cerebellum and cerebrum are, however, very different" (1st paragraph), I think the authors need to develop more on what these differences are. Perhaps just rearranging some of the text/paragraphs will help make it better for a broad audience (e.g., authors could move the next paragraph up, i.e., "While the cx is unique to mammals (...)").

2. Given that the authors compare the folding patterns between the cerebrum and cerebellum, another point that could be mentioned in the introduction is the fact that the cerebellum is convoluted in every mammalian species (and non-mammalian spp as well) while the cerebrum tends to be convoluted in species with larger brains. Why is that so? Do we know about it (check Van Essen et al., 2018)? I think this is an important point to raise in the introduction and to bring it back into the discussion with the results.

3. In the results, first paragraph, what do the authors mean by the volume of the medial cerebellum? This needs clarification.

4. In the results: When the authors mention 'frequency of cerebellar folding', do they mean the degree of folding in the cerebellum? At least in non-mammalian species, many studies have tried to compare the 'degree or frequency of folding' in the cerebellum by different proxies/measurements (see Iwaniuk et al., 2006; Yopak et al., 2007; Lisney et al., 2007; Yopak et al., 2016; Cunha et al., 2022). Perhaps change the phrase in the second paragraph of the result to: "There are no comparative analyses of the frequency of cerebellar folding in mammals, to our knowledge".

5. Sultan and Braitenberg (1993) measured cerebella that were sagittally sectioned (instead of coronal), right? Do you think this difference in the plane of the section could be one of the reasons explaining different results on folial width between studies? Why does the foliation index calculated by Sultan and Braitenberg (1993) not provide information about folding frequency?

6. Another point that needs to be clarified is the log transformation of the data. Did the authors use log-transformed data for all types of analyses done in the study? Write this information in the material and methods.

7. The discussion needs to be expanded. The focus of the paper is on the folding pattern of the cerebellum (among different mammalian species) and its relationship with the anatomy of the cerebrum. Therefore, the discussion on this topic needs to be better developed, in my opinion (especially given the interesting results of this paper). For example, with the findings of this study, what can we say about how the folding of the cerebellum is determined across mammals? The authors found that the folial width, folial perimeter, and thickness of the molecular layer increase at a relatively slow rate across the species studied. Does this mean that these parameters have little influence on the cerebellar folding pattern? What mostly defines the folding patterns of the cerebellum given the results? Is it the interaction between section length and area? Can the authors explain why size does not seem to be a "limiting factor" for the folding of the cerebellum (for example, even relatively small cerebella are folded)? Is that because the 'white matter' core of the cerebellum is relatively small (thus more stress on it)?

8. One caveat or point to be raised is the fact that the authors use the median of the variables measured for the whole cerebellum (e.g., median width and median perimeter across all folia). Although the cerebellum is highly uniform in its gross internal morphology and circuitry's organization across most vertebrates, there is evidence showing that the cerebellum may be organized in different functional modules. In that way, different regions or folia of the cerebellum would have different olivo-cortico-nuclear circuitries, forming, each one, a single cerebellar zone. Although it is not completely clear how these modules/zones are organized within the cerebellum, I think the authors could acknowledge this at the end of their discussion, and raise potential ideas for future studies (e.g., analyse folding of the cerebellum within the brain structure - vermis vs lateral cerebellum, for example). I think this would be a good way to emphasize the importance of the results of this study and what are the main questions remaining to be answered. For example, the expansion of the lateral cerebellum in mammals is suggested to be linked with the evolution of vocal learning in different clades (see Smaers et al., 2018). An interesting question would be to understand how foliation within the lateral cerebellum varies across mammalian clades and whether this has something to do with the cellular composition or any other aspect of the microanatomy as well as the evolution of different cognitive skills in mammals.

Reviewer #1 (Public Review):

This is a very elegant study of the dynamics of the longitudinal surface pH profile in growing Arabidopsis roots. The authors first present a new powerful method for the visualization of the surface pH profiles using the pH-sensitive fluorescent dye fluoresceine-5 (or 6)-sulfonic acid. This is an interesting new tool for studying surface pH in plants and perhaps other organisms. The main findings are that the presence of an alkaline band at the transition zone does not depend on AHA abundance (shown by immunolocalization) or activity shown by pharmacology (FC treatment) or by using plants expressing hyperactive, or PP2CD1- inhibited AHA2 or by using KO mutants aha2 or pp2c-d respectively. This band depends on auxin and AUX1-mediated auxin influx and rapid auxin response components AFB1 and CNGC14. The latter has a distribution along the root fitting the longitudinal surface pH zonation and are both required for it. Canonical auxin signaling (TIR) has more quantitative effects on the extent of the auxin-induced alkalinization. They also observe that the rapid auxin response module is constantly activated and inactivated as shown by the time-dependent variations in surface pH within the alkaline zone on both sides of the root and the rapid AUX1, AFB1, and CNGC14-dependent acidification of the upper surface and alkalinisation of the lower surface during gravitropic responses. Finally, they provide some evidence for the role of the rapid auxin responses in avoiding physical obstacles in the environment of the root.

The data look very sound. The originality of the approach used is the observation of dynamic responses at a second-to-minute time scale and to systematically correlate between the observed changes in the longitudinal surface pH profile with changes in growth rate. The manuscript is well-written with clear figures.

Reviewer #1 (Public Review):

This paper studies color vision in anemonefish. The central conclusion of the paper is that anemonefish use signals from their UV cones to discriminate colors that would not otherwise be distinguishable; this differs from other fish in which UV cones extend the range of wavelengths of sensitivity but do not add a dimension to color vision. The work fits into a rich history of studies investigating how color vision fits into an animal's ecological niche. My primary concerns regard the microspectrophotometry data from single cones and some aspects of the presentation of the behavioral data.

Microspectrophotometry<br /> The spectral properties of the cone types are a key issue for interpreting the results. These were measured using MSP, and fits are shown in Figure 2. The raw data shown in Fig. S1 appears more complicated than indicated in the main text. The templates miss the measurements across broad wavelength bands in each cone type. Particularly concerning is the high UV absorbance across cone types and the long-wavelength absorbance in the UV cone. It is not clear how this picture supports the relatively simple description of cone types and spectral sensitivities given in the main text and which forms the basis of the modeling.

Presentation<br /> The results are not presented in a straightforward way - at least for this reviewer. What is missing for me is a clear link between the psychometric curves in Figure 3A and the discrimination thresholds indicated in Figure 3B and Figure 4. Figure 3A is only discussed in the text on line 289 - after Figure 4 has been introduced and discussed. It would have been very helpful for me if the psychometric curves were first introduced and described, then the relation to Figure 3B was clearly indicated (perhaps with a single psychometric curve as an example). Similarly for Figure 4 the relationship between specific psychometric curves and the threshold plotted would be quite helpful. Currently it takes a careful reading to understand why being below the dashed line in Figure 4 is important.

RNL model<br /> The data is fit and interpreted in the context of the receptor noise limited model. The paragraph in the discussion about complementary color pairs suggests that this model is incorrect (text around line 332). Consideration of how the results depend on the RNL model is important, especially given the interpretation here.

Figure 3B<br /> This is the key figure in the paper. But several issues make seeing the data in this figure difficult. First, the important part of the figure is buried near the origin and hard to see. Can you show a surface that connects the thresholds in the different chromatic directions, or otherwise highlight the regions of discriminable and not discriminable colors?

Reviewer #1 (Public Review):

The technical approach is novel, exciting, and very carefully calibrated, and can certainly lead to many interesting downstream applications, e.g. enhanced throughput and consistency for screening purposes. Compared to traditional single-assay designs, this solution eliminates some sources of human error associated with manual dilution of reagents and reproducibility and facilitates the study of a wide spectrum of concentrations particularly at the low-concentration (below nanomolar), high-sensitivity range.

However, the study itself does not generate any fundamentally novel insights or new understanding of the biology or biophysics of the chemotactic response. It mainly reproduces previously measured trends in a more efficient and controlled manner. The novelty of the paper is purely in the technology, whereas the major weakness is that this new technology was not used to demonstrate or discover some new biological phenomenon.

Reviewer #1 (Public Review):

Croft, Pearce, and colleagues used a combination of spatial transcriptomics and analysis of publicly available scRNA-sequencing data of patients with PDAC to assess differences in the transcriptome of tumor proximal and distal stromal cells and associated these differences with survival data. Focusing on aSMA+ cancer-associated fibroblasts (CAFs), they find that tumor-proximal CAFs were defined by high expression of PDPN and Wnt ligands, and tumor-distal CAFs expressed inflammatory, complement, and Wnt inhibitor genes. While gene expression in relation to tumor distance did not per se correlate with clinical outcome, the authors find that individual genes within the tumor proximal and distal subsets were associated with increased or decreased survival. Based on this, the authors suggest a combination of targeting tumor-proximal CAFs defined by PDPN expression and inhibition of HIF-1a in the tumor distal stroma. Using an innovative approach to combine their spatial transcriptomics with single-cell transcriptomics data, the authors further identify an association between the expression of proximal CAF marker genes with elevated expression of complement and retinoic acid metabolism genes, and that complement genes were associated with increased survival.

While spatial differences in the abundance of different CAF subsets have been suggested before, the spatial transcriptomic data presented in this study provide a fresh look at CAF heterogeneity in PDAC and will be a useful resource for hypothesis generation and testing on the spatial regulation of CAF heterogeneity. However, there are major concerns associated with the interpretation of the data given the markers used to generate it, the association with single-cell data, and an overinterpretation of transcriptomic data, as detailed below.

1) The spatial transcriptomic data on fibroblasts relies on aSMA as a fibroblast marker. However, several studies in PDAC and other tumors have shown that there are subtypes of CAFs that do not (or only weakly) express aSMA, including inflammatory CAFs (iCAFs) and antigen-presenting CAFs (apCAFs) which therefore might have been missed by the authors. While there is no universal CAF marker, more recently the pancreatic cancer field has been using PDPN as a pan-CAF marker. aSMA is a classical myofibroblast marker across tissues and tumor types and the authors should state that they focused on myofibroblasts in their study.

2) While the association of the spatial data and single-cell data is innovative, it is flawed by the use of a single marker gene (DCN) to define the fibroblast population and the small number of cells (229) within this population. The authors need to corroborate their findings using a combination of fibroblast marker genes as well as other studies comprising a larger number of cells of fibroblast origin.

3) The authors find HIF1a expression to be associated with poor survival and enriched within the tumor-distal stroma. They interpret this as a reflection of the hypoxic environment of PDAC. However, the authors only ever look at HIF1a mRNA, but hypoxic regulation of HIF-1a occurs post-transcriptionally. Transcriptional regulation of HIF1a has been reported through pro-inflammatory cytokines and NFkB signaling. Therefore, the authors should either stain for HIF1a to confirm enrichment on the protein level or adjust their discussion to reflect the important biology behind HIF1a regulation.

4) The authors often misinterpret the correlations and associations they observe as causation and explanation - they should either adjust their language or perform experiments to show causation.

Reviewer #1 (Public Review):

In this manuscript, the authors present a computational framework based on Bayesian observer models that parameterises several different sources of noise and bias in perceptual decision-making. The authors show that these sources of suboptimality cannot be dissociated in many typical decision-making tasks. They present an analysis of two previously published sets of experimental data, where the experimental design should allow them to dissociate suboptimalities parameterised in the model. They fit various versions of the model including different suboptimalities and in general show that including the suboptimalities improves the fit, depending on whether the data is aggregated across participants or not.

The major strengths of the methods and results include 1. The clear theoretical delineation of different forms of suboptimalities may help to guide research understanding them on the behavioural and neural levels. 2. The attention to scientific rigor in model fitting, including the use of power analysis and corrections for the number of model parameters. 3. Clear figures that are helpful in understanding the model.

The major weaknesses of the methods and results include 1. The lack of model/parameter recovery analysis shows the extent to which the model can separate sources of suboptimality against some ground truth. 2. The lack of generalisability, where the model parameters can only be dissociated using specific experimental manipulations, and a large number of trials. 3. It is unclear to what extent the assumptions of the model (and its parameterisation) limit the realisability of the proposed computational framework.

The authors achieve their aim of outlining a computational framework that accounts for various sources of suboptimalities and shows some evidence that this model may be useful for making inferences about these suboptimalities given careful experimental manipulation.

The work adds to the movement toward delineating the specific sources of suboptimalities as opposed to capturing 'noise' and 'bias' as overarching variables, and the model may prove useful for other researchers. However, given the model requires special experimental tasks to dissociate the parameters, it is unclear how this model improves upon the traditional approach of designing experiments to dissociate sources of suboptimalities directly.

Reviewer #1 (Public Review):

Precise regulation of gamete fusion ensures that offspring will have the same ploidy as the parents. However, breaking this regulation can be useful for plant breeding. Haploid induction followed by chemical-induced genome doubling can be used to fix desirable genotypes, while triparental hybrids where two sperm cells with two different genotypes fertilize an egg cell can be advantageous for bypassing hybridization barriers to create interspecies hybrids with increased fitness. This manuscript follows up on a previous study from the same research group that used a clever high throughput polyspermy detection assay (HIPOD) to show that wild-type Arabidopsis naturally forms triparental hybrids at very low frequencies (less than 0.05% of progeny) and that these triparental hybrids can bypass dosage barriers in the endosperm (Nakel, et al., 2017). Mao and co-authors hypothesized that mutants that conferred polytubey, the attraction of multiple pollen tubes by mutant female gametophytes, would also increase the rate of triparental hybrids. They used a double mutant in the endopeptidase genes ECS1 and ECS2 which had previously been reported to induce supernumerary pollen tube attraction to test this hypothesis with their two-component HIPOD system in which one pollen donor constitutively expresses the mGAL4-VP16 transcription factor while the second pollen donor carries an herbicide resistance gene regulated by the GAL4-responsive UAS promoter. Triparental hybrids are detected as herbicide-resistant progeny from wild-type Arabidopsis flowers that have been pollinated by the two paternal genotypes. The authors convincingly show that the ecs1 ecs2-1 double mutant more than doubled the frequency of triparental, triploid hybrids in HIPOD crosses. They next tested the hypothesis that this increase in triparental hybrids was due to a gametophytic effect by using an ecs1-/- ecs2-1/ECS2 maternal parent in the HIPOD assay and testing whether the ecs2-1 mutant allele was preferentially inherited in triparental hybrids. The mutant allele was inherited at a much higher rate than expected, confirming their hypothesis.

The triparental hybrid results with the ecs1 ecs2 mutant were not that surprising since the presence of extra sperm cells gives more opportunities for triparental hybrids to form, especially if gamete fusion is misregulated. However, an unexpected result came when the authors used aniline blue staining to analyze the ecs1 ecs2 polytubey phenotype. They confirmed that the double mutant had increased levels of polytubey compared to wild-type ovules, but they also noticed that 13% of seeds were not developing normally. This phenotype was confirmed with a second ecs2 allele and was complemented with both ECS1 and ECS2 transgenes under their native promoters. Microscopic analysis revealed normal gametophyte morphology before fertilization, but 8% of pollinated ovules failed to develop an embryo and 7% failed to develop endosperm, suggesting single fertilization events. In a logical set of experiments, they followed up on this result by crossing ecs1 ecs2 with pollen carrying a fluorescent reporter that would be expressed in developing embryos and endosperm. In this experiment, they were again surprised. Some of the wild-type-looking seeds lacked a paternal contribution (i.e. no fluorescent signal from the paternal reporter construct) in the embryo. This prompted them to look more closely at the progeny, upon which they detected small plants that were haploid. They confirmed the haploid nature by chromosome spreads. Finally, they used interaccession crosses between ecs1 ecs2 (Col-0) and Landsberg to verify that haploid progeny only carried maternal alleles of markers on all five chromosomes, indicating that the ecs1 ecs2 genotype can induce maternal haploids.

This interesting study highlights the importance of following up on unexpected results. The conclusions are well-supported by the data and quite exciting. Paternal haploid inducers have been discovered in several species, but this is one of only two examples of maternal haploid induction. While the percentage of maternal haploids is very low, this phenomenon could be useful for plant breeding.

Weaknesses<br /> The data in the manuscript is intriguing, but the question of how the same mutant combination promotes the formation of both triploid and haploid progeny remains unanswered and is not thoroughly discussed, nor is any model suggested for how the ECS1/2 peptidases could play a role in regulating gamete fusion and/or repressing parthenogenesis. A second unanswered question is whether the maternal haploids are a result of failed plasmogamy or karyogamy between the egg and sperm leading to parthenogenesis or a result of paternal genome elimination after plasmogamy. In figure 3B, the authors attempted to test whether plasmogamy occurs between the male and female gametes in ecs1 ecs2 ovules by crosses with pollen that expresses a mitochondrial marker under control of the pRPS5a promoter which is active in sperm cells as well as embryos and endosperm of fertilized ovules. This experiment allowed them to detect sperm cells that had not fused with the egg and central cell at 2 days after pollination. They also counted the percentage of seeds that expressed the mitochondrial marker in both embryo and endosperm at 2 DAP and found that ecs1 ecs2 mutants had a 20% reduction of visible mitochondria in embryo sacs compared to wildtype. They conclude that the result indicates a potential plasmogamy defect. However, the dependability of this marker is questionable since only ~55% of wild-type seeds had detectable signal in the embryo and endosperm. The authors imply that this experiment could be used to test plasmogamy, but it is not clear how any conclusions related to the abnormal seed phenotype could be drawn from examining the rate of signal in both the embryo and endosperm. Since the mitochondrial marker was not expressed from a sperm-specific promoter, the fluorescent signal at 2DAP is likely due to new gene expression from pRPS5a in the fertilized embryo and endosperm, not an indication of the presence of sperm-derived mitochondria. Perhaps an earlier timepoint could be used as well as a sperm-specific promoter instead of pRPS5a to answer the question of whether plasmogamy is happening in the ecs1 ecs2 ovules.

Reviewer #1 (Public Reviews):

The article describes the development of a highly concentrated antibody formulation for MS-Hu6, a first-in-class FSH-blocking humanized antibody proposed for clinical use in osteoporosis, obesity, and Alzheimer's disease. The authors utilized various techniques, including protein thermal shift, size exclusion chromatography, and dynamic light scattering, to examine the stability and physiochemical properties of the formulated MS-Hu6 at concentrations ranging from 1 to 100 mg/mL. They found that the thermal, monomeric, and colloidal stability of the formulated MS-Hu6 was maintained at a concentration of 100 mg/mL, and the addition of L-methionine and disodium EDTA improved its long-term colloidal and thermal stability. The authors further confirmed the thermal stability of the formulation through Nano differential scanning calorimetry (DSC). They demonstrated that MS-Hu6's structural integrity was maintained through Circular Dichroism (CD) and Fourier Transform Infrared (FTIR) spectroscopy. The formulated MS-Hu6 displayed excellent thermal and colloidal stability even after three rapid freeze-thaw cycles and storage for more than 90 days at 4{degree sign}C and 25{degree sign}C. The authors concluded that they had developed a stable, manufacturable, and transportable MS-Hu6 formulation at an ultra-high concentration, meeting acceptable industry standards. The study's findings may serve as a resource for developing biologics formulation in academic medical centers.

Reviewer #1 (Public Review):

The authors solved cryoEM structural maps for the pLGIC homolog Alpo4 from an extreme thermophile worm in apo and CHAPS-bound conditions. The data appear to be of good quality and in addition to a first structural model of Alpo4 also provide several interesting observations. Notably, the detergent CHAPS was observed to occupy the orthosteric binding pocket where it induces a quaternary twist of extracellular relative to transmembrane domains opposite to that observed for activation in canonical pLGICs. Given recent advances in lipid modulation of pLGICs, this structural model of how a detergent can bind to the orthosteric site will be of interest. Additionally, a ring of 16' methionines forms a potential alternate pore gate to the 9' leucine ring in canonical pLGICs. Unfortunately, testing these hypotheses such as the alternate pore gate remains difficult due to the fact that the activating ligand for Alpo4 remains unknown. This also makes it hard to relate the observed changes upon CHAPS binding to the Alpo4 function. Nonetheless, the structures will aid in hypotheses for functional mechanisms of Alpo4 and pLGICs.

Reviewer #1 (Public Review):

This manuscript examines how subjects change their decision strategy in a visual motion change detection task between blocks of trials where they sought to detect stronger versus weaker signals. The authors hypothesized that decision bounds would be reduced for the weaker signal condition. While behavioral changes were reasonably consistent with this hypothesis, it was challenged by EEG measures that have been previously found to relate to decision variables. In particular, a Beta-band EEG measure suggested decision bounds being reduced for the stronger signal condition, in distinct contrast to the initial hypothesis. Based on this, the authors developed an alternative behavioral model that could explain behavioral adjustments while having decision bounds that were constrained by the putative signature of the decision variable derived from the EEG Beta amplitude. This alternative model has two central features: 1) Sensory evidence is referenced to an adjustable criterion before accumulation such that evidence below the criterion provides negative input to the decision variable. 2) There is a lower reflecting bound on the decision variable such that it cannot go to negative values.

This experiment makes a strong case for the benefit of reconciling behavioral modeling with ideas in the literature about neural signatures of the corresponding decision processes. In this work, the standard behavioral modeling and the EEG-derived putative signatures of the decision process did not initially agree. This is an important finding. The authors also go further. Something must give. Either the standard modeling approach for this type of task provides an incorrect account or the putative mapping of EEG Beta amplitude to the decision process is incorrect. The authors argue for the former. They build from the starting point that the neural signatures are correct and develop alternative behavioral models that they argue to be consistent with these while also explaining behavior. One of these models (described above) fits the data as well as the standard model. I think this approach and the resulting model are interesting, but I have concerns.

1) I think the authors should give greater consideration to the possibility that the relationship between the decision process and EEG-derived neural signatures - despite having a basis in previous results - is either incorrect in some ways, not the full story, or might not fully apply to this paradigm. The authors include important analyses that already suggest that point to some degree, such as the analysis of the raw Beta amplitude in Figure 4. I give the authors credit for including that analysis and the critical discussion they have surrounding it. I suggest that the authors should include further discussion on this general point. What is the main evidence for the relationship of Beta amplitude to decision bound? Are there any differences from the previous studies that might break the relationship in this paradigm? With the approach taken here so heavily dependent on that relationship, discussing how it might not be correct seems of utmost importance.

2) My biggest concern revolves around where the Beta amplitude measurement fits into the model. Figure 3 (neurally informed modeling) shows it informing an evidence-independent component that adds to the decision variable, and it is described as such in the methods. However, Beta amplitude is clearly affected by the evidence (Figure 2A). And in the results, it is described as best corresponding to a decision variable, which would include the influence of the sensory evidence. If Beta amplitude depends on evidence, then an adjustment of the evidence criterion would influence Beta amplitude, even during the ITI. So, I don't see how it can be properly used as the constraining factor for the evidence-independent urgency in neurally-informed modeling.

To illustrate my concern, consider the evidence criterion adjustment model. In this model, the average DV value during the ITI will be closer to the decision bound in the weaker signal condition compared to the stronger signal condition - that is how the model fits the false alarm rate differences. This should be reflected in Beta amplitude if the latter reflects the DV. However, the Beta-derived urgency is highest for the condition with the lowest false alarm rate and lowest for the condition with the highest false alarm rate. It seems there is an inescapable conclusion that Beta amplitude does not fully reflect the main behavioral-driving features of the DV in this paradigm, even in the criterion-adjustment model. That said, I do think the development and consideration of the criterion-adjustment model is an important contribution, even with this shortcoming.

Additional comments:

1) The criterion-adjustment model appears equivalent to one with a constant negative drift added to the decision variable in addition to the contribution of evidence (and a lower reflecting bound). If true, it seems that there could be an alternative equivalent account that does not involve regulation of the transfer of incoming evidence.

2) I understand why one of the model parameters (e.g., noise or bound) must be fixed, but I don't understand why the authors didn't keep it the same parameter across all models. Couldn't they have fixed the noise parameters for all the models? Having it different makes it difficult to compare parameter values across the models, especially because the fitted noise term in the neurally-informed models appears dramatically reduced compared to the fixed value of noise used for the bound-adjustment model. On the topic of parameters, can the leak parameter be reported in more intuitive units? It seems to be parameterized as a fraction leak per time step, and I couldn't easily find the time step used. Reporting the leak as a time constant would be immediately understandable.

Reviewer #1 (Public Review):

This paper provides compelling and clear analyses that show that the coding level (sparsity) of the granule-cell layer of a cerebellar-like network does not only change the dimensionality of the representation, but also the inductive bias of the network: Higher sparsity biases the network to learning more high-frequency representations. Depending on the dominant frequencies of the target function, different coding levels are therefore optimal. The results are important/fundamental to theories of cerebellar learning and speak to a relevant ongoing debate in cerebellar learning, but will be of interest to readers outside of cerebellar neurophysiology.

Reviewer #1 (Public Review):

This study combines in vitro somatic and dendritic recordings and computational modeling to study how cholinergic agonists modulate the response of CA1 pyramidal neurons to triangular current injections. The authors have previously used a similar approach (Upchurch, 2022, JNeuroscience) to show that CA1 neurons exhibit asymmetric AP firing (more firing on the upward ramp) in response to such current injections and that this effect is due to Na channel inactivation. The present work builds on these results by showing that cholinergic modulation changes this response, i.e., there is more firing on the downward part of the ramp. This change appears to require an intracellular Ca2+ concentration increase (mediated via IP3 and voltage-gated Ca2+ channels), which activates TRPM4 channels. In this scheme, cholinergic activity increases IP3, and the depolarizing current injection opens voltage-gated Ca2+ channels. This study will be of some interest to cellular neurophysiology experts working on the hippocampus.

1) This study claims that the triangular current injections recapitulate hippocampal place cell activity. However, it has been shown recently that the asymmetric firing of CA1 place cells is due to synaptic weight changes resulting from synaptic plasticity (e.g., Bittner et al., 2017). This suggests that the asymmetric firing of place cells is primarily the result of asymmetric synaptic input. Therefore, the authors should test whether carbachol similarly affects a synaptically driven membrane potential ramp. If this is not the case, the strong claim that this work has implications for place cell firing is not justified, in my opinion.

2) Along the same lines, it has been shown before that the precision of spike timing depends on the stimulation pattern in vitro (Mainen and Sejnowski, 1995). Constant stimuli led to imprecise AP firing trains, whereas current injections that included fluctuations resembling synaptic input generated spike trains that were more reliable and reproducible in terms of timing. This study concluded that a low intrinsic noise level in spike generation was essential in generating informative spike sequences. Following this pivotal work, the authors could add noise to their current stimulus and observe the effect on the AP firing patterns. If this is not possible, the authors should at least report the sweep-to-sweep variability for the data shown, e.g., in panels 1A2, 1B2, 1D2, and 1E2.

3) In most of the data presented in this manuscript, Carbachol appears to induce a 3 mV hyperpolarization and increase input resistance. As a result, the amount of current injected during Carbachol is drastically lower than during the controls. This should be emphasized more, and the input resistance should be quantified for each experimental condition. It should also be discussed whether this change in input resistance can account for the changes in the firing pattern observed. Finally, it should be clearly stated how the amount of the current injected was chosen for each cell, and data from a range of injected current ramps should be shown for each cell.

4) It remains unclear how the current result that TRPM4 channels can mediate the firing pattern change relates to the previous finding that the current injection evoked CA1 neuronal firing pattern is due to long-term Na channel inactivation.

5) Figure 8: Panel C is supposed to confirm the prediction from the model that the carbachol-mediated change of firing activity is related to intracellular Ca2+ domains. However, the example cell shown is depolarized to -52 mV, and there is no hyperpolarization following Carbachol. Is this an effect of the high concentration of BAPTA? Again, what was the current injected under this experimental condition?

Reviewer #1 (Public Review):

The manuscript studies the spontaneous contractions (SC) of the Hydra body wall and presents a detailed simple mathematical model of nutrient transport to hypothesize the role of SC on maintaining the microbiota. This work provides valuable insights on the functional im- plications of the SC and the increased nutrient update obtained from mixing the local fluid environment through body wall contractions.

Reviewer #1 (Public Review):

Brunetti et al. investigated the mechanism used by the SARS CoV-2 virus to infect CD4 T cells and the potential impact on the immune system by viral infection. They find that SARS CoV-2 infects CD4 helper T cells and not CD8 T cells present in the blood and bronchoalveolar fluid of infected patients. The ACE-2 receptor expression on T lymphocytes is less compared to epithelial and endothelial cells, but still, the virus is able to establish a productive infection of T lymphocytes by some alternative mechanism. The group also demonstrated that interaction between CD4 and SARS virus spike protein further enhances viral entry and infectivity as CD4 is acting as an auxiliary molecule for viral entry.

By performing a technically impressive analysis of the infectivity of CD4 T cells isolated from healthy donors/controls invitro with the SARS CoV-2 virus and also by testing the infected population of CD4 cells invivo from COVID patients, the authors find that SARS CoV-2 infects CD4 T cells using Insitu Hybridization using probes against viral polymerase (RdRP), immunofluorescence and electron microscopy. Further, the authors also identified the region of SARS CoV-2 spike protein that interacts with CD4 by performing molecular docking analysis and found CD4 NTD interacts directly with the RBD region of the SARS Virus. The specific interaction between CD4 and SARS virus is further demonstrated by the use of anti-CD4 blocking antibodies and cell lines that over-express CD4 molecules. Interestingly by antibody inhibition of ACE-2 and camostat inhibition of TMPRSS2, the authors demonstrated that SARS CoV-2 infection of CD4 T cells requires ACE-2, TMPRSS2, and CD4. The data also show that viral infection of CD4 T cells leads to the expression of cytokines like 1L-10 that may impact cell viability and dampens immune response.

The experiments in the paper are well- performed and the conclusions of this paper are mostly well supported by data, but some aspects of the impact of viral infection of CD4 cells leading to lymphopenia need to be clarified and extended.<br /> A major weakness of the paper is the reference citations. Inconsistency in maintaining the citation style and numbering in the manuscript draft drastically impacts the readability. For example, the use of superscript format references in the introduction and results section and paraphrasing format in materials and methods could not make the readers identify the correct citations.

1. In this paper, the authors describe infection of CD4 T cells may lead to T cell death. Although the extended Fig. 9 suggests the expression of a multitude panel of gene expression, including apoptotic genes, the authors could not provide a piece of direct evidence to show how CD4 T cell death happens. What is the underlying mechanism of cell death? Is it by necrosis or apoptosis or pyroptosis? The exact mechanism of CD4 cell death needs to be discovered and adding control experiments to assess the exact mechanism of cell death would increase confidence in the presented results of the functional interaction of proteins (Ext Fig 9).

2. Some previous studies suggested that lymphocytopenia in COVID infection could be due to impaired T cell proliferation or extravasation of T cells into tissue. The exact mechanism for lymphocytopenia could be addressed by performing an animal experiment, but it would be interesting to see what are the author's opinion about other possibilities of lymphocytopenia.

3. The data on CREB-1 Ser 133 in Figure 4E is not sufficiently convincing. It is difficult to understand what is the difference between every three lanes within mock and SARS CoV-2 infection. There is a pCREB band in lane 5 (2nd lane of CoV-2), but not in the other two. Better data would have helped to substantiate the authors' conclusions.

Reviewer #1 (Public Review):

Guo et al. demonstrate that Metformin, a first-line anti-diabetic drug, significantly improves bone healing in diabetic mice. Mechanistically, they demonstrate that Metformin improves BMSC differentiation in T2D type 2 diabetic mice, potentially through an indirect mechanism. Overall the study is comprehensive and the effects of Metformin on bone healing are demonstrated by overwhelming data. The study further offers important information for management of the complications associated with type-2 diabetes. The weakness of the study is the lack of in-depth understanding of the mechanism underlying Metformin's effects on healing. The writing of the manuscript could also be improved for clarity and accuracy.

Reviewer #1 (Public Review):

In the manuscript the authors identify new players regulating cell state transitions. They show that heme biosynthesis is required for the naïve-to-primed pluripotency transition. In particular, they provide a link between heme biosynthesis inhibition and failure to activate TGFβ and MAPK pathways, two crucial regulators of the exit from the naïve pluripotent state. Heme biosynthesis inhibition increases the percentage of 2CLCs within the mESC population. The authors further show that this increased level of 2CLCs depends on the accumulation of succinate in non-mitochondrial cell compartments as a consequence of heme biosynthesis inhibition. Based on experiments using chemical inhibitors, the authors conclude that succinate acts in a paracrine and autocrine manner to enhance reprogramming of mESC into 2CLCs.

The observations provided by the authors are interesting and potentially relevant in the field of pluripotent cell state transitions. However, in the present state, neither the role of heme biosynthesis on FGF-ERK and TGF beta signalling and exit from naïve pluripotency nor the role of heme biosynthesis in controlling the 2CLC state are clear.

Below I list my main concerns:

1. The authors claim that the heme metabolic pathway is important for the naïve-to-primed transition. Interfering with its proper function appears to have developmental consequences. However, it is unclear how exactly this pathway is regulated during the exit from naïve pluripotency in WT cells. Hence the physiological relevance remains unclear. Are the levels of heme itself, or the mentioned 7 enzymes of the heme pathway regulated during differentiation?

2. The claim that "the roles of this [heme] biosynthetic pathway and this metabolite have never been studied in the context of pluripotency" is a bit misleading. It has been reported that HO1 is regulated by Oct4 (https://doi.org/10.1002/1873-3468.14138).

3. The link between heme biosynthesis and the TGFβ and MAPK pathways remains unclear. Is there any evidence for a direct link, or are these two observations simply linked through an altered cell state? Without further experiments it remains unclear whether the lack of proper Tgf beta and Fgf-ERK signalling activation are cause or consequence of the observed differentiation defects. Results must be discussed with this limitation in mind.

4. The fact that MEKi did not recapitulate the phenotype of SA treatment to prevent EpiLC differentiation should already be clarified in the results section. Moreover, the fact that SMAD inhibition seems to delay downregulation of naïve markers more than SA treatment, and the fact that SMAD inhibition combined with MEK inhibition seems weaker than SMAD inhibition alone seem counterintuitive and needs explanation. Can the authors attempt to titrate pathway inhibition to a similar level as observed in heme pathway deficient ESCs? Furthermore, can the differentiation defect be rescued upon overstimulation of Fgf-ERK and TGF beta to reach WT levels?

5. To better characterize the direct effect of heme biosynthesis inhibition it is necessary to in depth analyse any possible cell proliferation, viability of cell cycle defects after SA (or AA5) treatment. If there is an impact on cellular health, this needs to be reported and taken into careful consideration when interpreting results.

6. The authors claim that SA pre-treatment of mESC is able to enhance their differentiation ability into throphoblast like cells; however, they do not show statistically significant differences in terms of throphoblast expression markers between SA-treated and control cells (Supplemental Fig.2d). Furthermore, the variance of measurements in 2iL are not shown. Expression levels in TS cells or in trophoblast tissue must be used as control to judge the effect size. 2iL cells do also generate cells similar in morphology to 2iL+SA cells (Suppl Fig. 2b). Should these also be giant cells; this would be very surprising. Together, this makes drawing conclusions from these experiments impossible. If the statement that SA treatment expands the lineage potential of ESCs is made, it will need to be supported by appropriate and statistically strong data. A mere increase in some marker genes (which are not really specific for the TE lineage but also expressed in embryonic tissues) and statements based on morphology are no good support for this hypothesis.

7. What is the reason for calling the increase in 2C like cells 2C-like reprogramming? Is there any evidence that this is indeed a reprogramming event? There is no evidence for disruption of heme biosynthesis directly instructing cells to take on a 2CLC state, there is simply an increase in the Zscan4 expressing population, for a reason that remains unclear.

8. Addition of AA5 or SA results in absolutely striking changes in the epigenetic state of ESCs. H3K4me3, H3K27me3 and H3K9me3 together with 5mC levels appear drastically increased based on IF images in Supp. Fig. 5a. I wonder how physiological these levels are. How much data directly meaningful for 'normal' differentiation can be obtained from such a massively perturbed system? This needs to be appropriately acknowledged and discussed.

Reviewer #1 (Public Review):

In this manuscript, Dhekne et al. sought to identify modulators of LRRK2 activity. Thereto, the authors performed a FACS-based genome-wide pooled CRISPR/Cas9 screen in NIH-3T3 cells monitoring phosphorylation of the Rab GTPase Rab10 which is a well-characterized target of LRRK2. Besides Rab10 and LRRK2, this unbiased screen surprisingly uncovered another Rab GTPase, Rab12, as one of the most significant hits. Validation experiments with Rab12 knockout (KO) NIH-3T3 cells, the LRRK2 inhibitors MLi-2, and Rab12 KO mice unanimously confirmed the dependency of Rab10 phosphorylation on Rab12. Conversely, the authors used A549 LRKK2 and PPM1H KO cells to show that overexpression of Rab12 but not Rab29 which is another LRRK2 modulator leads to elevated phospho-Rab10 levels in a manner dependent on LRRK2 but insensitive to pathogenic mutations thereof. Moreover, the authors mapped E240 and S244 of LRRK2 as specific binding sites for Rab12 and showed that these residues are important to sustain full LRKK2 activity towards Rab10. Lastly, the authors showed that RAB12-dependent LRRK2 activation occurs under (patho)physiological stress conditions, namely endolysosomal membrane damage. Together, this comprehensive and elegant study of Dhekne and colleagues reveals exciting new mechanistic insights into the regulation of LRRK2 which have therapeutic potential for Parkinson's disease.

Reviewer #1 (Public Review):

For many years it has been understood that transposable elements (TEs) are an important source of natural variation. This is because, in addition to simple knockouts of genes, TEs carry regulatory sequences that can, and sometimes do, affect the expression of genes near the TEs. However, because TEs can be difficult to map to reference genomes, they have generally not been used for trait mapping. Instead, single nucleotide polymorphisms are widely used because they are easy to detect when using short reads. However, improvements in sequencing technology, as well as an increased appreciation of the importance of TEs to both linked to favorable alleles and are more likely to be causing the changes that make those alleles beneficial in a given environment. Further, because TE activity can occur after bottlenecks, they can provide polymorphisms in the absence of variation in point mutations.

In this manuscript, the authors carefully examine insertion polymorphisms in rice and demonstrate linkage to differences in expression. To do this, they used expression quantitative trait locus (eQTL) GWAS using TIPs as genetic markers to examine variation in 208 rice accessions. Because they chose to focus on genes that were expressed in at least 10% of the accessions, presumably because more rare variants would end up lacking statistical power. This is an understandable decision, but it says that recent insertions, such as the MITE elements detailed in a previous paper, would not be included. Importantly, although TIPs associated with differentially expressed genes are far less common than SNPs' traditional eQTLs, there were a significant number of eQTLs that showed linkage to TIPs but not to QTL.

The authors then show that of the eQTLs associated with both TIPs and SNPs, TIPs are more tightly linked to the eQTL, and are more likely to be associated with a reduction in expression, with variation in the effects of various TEs families supporting that hypothesis. Here and throughout, however, the distance of the TEs could be an important variable. It is also worth noting the relative numbers in order to assess the claim in the title of the paper. The total number of eQTL-TIPs is ten-fold less than the number of eQTL-SNPs, and, of the eQTLs that have both, there are a significant number of eQTL-TIPs that are not more tightly linked to the expression differences than the eQTL.

The authors show that eQTL-TIPs are more likely to be in the promoter-proximal region, but this may be due to insertion bias, which is well documented in DNA-type elements. Here and throughout the authors are careful to state that the data is consistent with the hypothesis that TEs are the cause of the change, but do not claim that the data demonstrate that they are.

Throughout the rest of the manuscript, the authors systematically build the case for a causal role for TEs by showing, for instance, that eQTL-TIPs show much stronger evidence for selection, with increased expression being more likely to be selected than decreased expression. The authors provide examples of genes most likely to have been affected by TE insertions.

Overall, the authors build a convincing case for TEs being an important source of regulatory information. I don't have any issues with the analysis, but I am concerned about the sweeping claims made in the title. Once you get rid of eQTLs that could be altered by either SNPs or TIPs and include only those insertions that show strong evidence of selection, the number of genes is reduced to only 30. And even in those cases, the observed linkage is just that, not definitive evidence for the involvement of TEs. Although clearly beyond the scope of this analysis, transgenic constructs with the TEs present or removed, or even segregating families, would have been far more convincing.

The fact that many of the eQTL-TIPs were relatively old is interesting because it suggests that selection in domesticated rice was on pre-existing variation rather than new insertions. This may strengthen the argument because those older insertions are less likely to be purged due to negative effects on gene expression. Given that the sequence of these TEs is likely to have diverged from others in the same family, it would have been interesting to see if selection in favor of a regulatory function had caused these particular insertions to move away from more typical examples of the family.

Reviewer #1 (Public Review):

In this manuscript the authors proposed a novel system by which they can suppress the expression of any gene of interest precisely and efficiently with a pre-validated, highly specific and efficient synthetic short-hairpin RNA. The idea of identifying potent artificial RNAi (ARTi) triggers is intriguing, and the authors successfully identify six ARTi with robust knockdown efficiency and limited to no off-target effects. As a proof-of-concept, the authors examined three oncology targets for validation, including EGFRdel19 (which already has a clinically approved drug for validation), KRASG12R (for which there are no in vivo compatible inhibitors yet) and STAG1 (which has a synthetic lethal interaction with recurrent loss-of-function mutations of STAG2). The authors demonstrated significant suppression of colony formation and in vivo tumor growth for all three oncology targets.

This novel system could serve as a powerful tool for loss-of-function experiments that are often used to validate a drug target. Not only this tool can be applied in exogenous systems (like EGFRdel19 and KRASG12R in this paper), the authors successfully demonstrated that ARTi can also be used in endogenous systems by CRISPR knocking in the ARTi target sites to the 3'UTR of the gene of interest (like STAG2 in this paper).

ARTi enables specific, efficient, and inducible suppression of these genes of interest, and can potentially improve therapeutic target validations. However, the system cannot be easily generalized as there are some limitations in this system:

• The authors claim in the introduction that CRISPR/Cas9-based methods are associated with off-target effects, however, the author's system requires the use CRISPR/Cas9 to knock out a given endogenous genes or to knock-in ARTi target sites to the 3' UTR of the gene of interest. Though the authors used a transient CRISPR/Cas9 system to minimize the potential off-target effects, the methods does not, as the authors acknowledge, eliminate the possibility of off-target effects.

• Instead of generating gene-specific loss-of-function triggers for every new candidate gene, the authors identified a universal and potent ARTi to ensure standardized and controllable knockdown efficiency. It seems this would save time and effort in validating each lost-of-function siRNAs/sgRNAs for each gene. However, users will still have to design and validate the best sgRNA to knock out endogenous genes or to knock in ARTi target sites by CRISPR/Cas9. The latter is by no-means trivial. Users will need to design and clone an expression construct for their cDNA replacement construct of interest, which will still be challenging for big proteins. This is not, as the authors point out, a replacement for other LOF methods, and there are other ways to achieve gene-specific regulation via, for example, degrons. However it is an effective orthogonal approach that many users may find compelling for their applications.

Reviewer #1 (Public Review):

This is a carefully performed and well documented study to indicate that the FUS protein interacts with the GGGGCC repeat sequence in Drosophila fly models, and the mechanism appears to include modulating the repeat structure and mitigating RAN translation. They suggest FUS, as well as a number of other G-quadruplex binding RNA proteins, are RNA chaperones, meaning they can alter the structure of the expanded repeat sequence to modulate its biological activities.Overall this is a nicely done study with nice quantitation.

Reviewer #1 (Public Review):

Based on a recent report of spontaneous and reversible remapping of spatial representations in the enthorhinal cortex (Low et al 2021), this study sets out to examine possible mechanisms by which a network can simultaneously represent a positional variable and an uncorrelated binary internal state. To this end, the authors analyse the geometry of activity in recurrent neural networks trained to simultaneously encode an estimate of position in a one-dimensional track and a transiently-cued binary variable. They find that network activity is organised along two separate ring manifolds. The key result is that these two manifolds are significantly more aligned than expected by chance, as previously found in neural recordings. Importantly, the authors show that this is not a direct consequence of the design of the model, and clarify scenarios by which weaker alignment could be achieved. The model is then extended to a two-dimensional track, and to more than two internal variables. The latter case is compared with experimental data that had not been previously analysed.

Strengths:

• rigorous and careful analysis of activity in trained recurrent neural networks

• particular care is taken to show that the obtained results are not a necessary consequence of the design of the model

• the writing is very clear and pleasant to read

• close comparison with experimental data

• extensions beyond the situations studied in experiments (two-dimensional track, more than two internal states)

Weaknesses:

• no major weaknesses

• (minor) the comparison with previous models of remapping could be expanded

Altogether the conclusions claimed by the authors seem to be strongly supported and convincing.

Reviewer #1 (Public Review):

This work by Gonzalez-Segarra et al. greatly extends previous research from the same group that identified ISNs as a key player in balancing nutrition and water ingestion. Using well-balanced sets of exploratory anatomical analyses and rigorous functional experiments, the authors identify and compile various peptidergic circuits that modulate nutrient and/or water ingestion. The findings are convincing and the experiments rigorous.

Strengths:

- The authors complement anatomically-reconstructed and functionally-validated neuronal connectivity with extensive and intensive morphological and synaptic reconstruction.

- Neurons and genes involved in specific components of feeding control are undoubtedly challenging, because numerous neurons and circuits redundantly and reciprocally regulate the same components of feeding behavior. This work dissociates how multiple, parallel and interconnected, peptidergic circuits (dilp3, CCHa2, CCAP) modulate sucrose and water ingestion, in tandem and in parallel.

- The authors address some of the incongruencies / discrepancies in current literature (IPCs) and try to provide explanations, rather than ignoring inconsistent findings.

Weaknesses:

- Is "function" of the ISNs to balance "nutrient need" or osmolarity? Balancing hemolymph osmolarity for physiological homeostasis is conceptually different from balancing thirst and hunger.

- The final schematic nicely sums up how the different peptidergic pathways might work together, but it is unclear which connections are empirically-validated or speculative. It would be informative to show which parts of the model are speculative versus validated. For example, does FAFB volume synapse = functional connectivity and not just anatomical proximity? A bulk of the current manuscript relies on "synapses of relatively high confidence" (according to Materials and methods: line 522). I recommend distinguishing empirically tested & predicted connections in the final schematic, and maybe reword/clarify throughout the manuscript as "predicted synaptic partners"

Reviewer #1 (Public Review):

There are a number of outstanding questions concerning how cohesin turnover on DNA is controlled by various accessory factors and how such turnover is controlled by post-translational modification. In this paper, Nasmyth et al. perform a series of AlphaFold structure predictions that aim to address several of these outstanding questions. Their structure predictions suggest that the release factor WAPL forms a ternary complex with PDS5 and SA/SCC3. This ternary complex appears to be able to bind the N-terminal end of SCC1, suggesting how formation of such a complex could stabilize an open state of the cohesin ring. Additional calculations suggest how the Eco/ESCO acetyltransferases and Sororin engage the SMC3 head domain and thereby protect against WAPL-mediated release.

This work thus demonstrates the power of AF prediction methods and how they can lead to a number of interesting and testable hypotheses that can transform our understanding of cohesin regulation. These findings require orthogonal experimental validation, but the authors argue convincingly that such validation should not be a pre-requisite to publication.

As the authors did not systematically include model confidence scores it is difficult for the reader to evaluate the reliability of the models obtained. The caveat is that many readers will by default assume that the presented models are correct, when in fact, some of them may score poorly and require careful assessment. As numerous readers will not be very familiar with the AF confidence scoring mechanisms, it would be important to include such metrics and indicate what these scores mean for the different interfaces (Acceptable, Medium and High confidence?). pLDDT and PAE plots should be included. When they report on a key interaction (E.g. WAPL-SCC1) they should indicate the key region (SCC1 N-terminus) on the PAE plot. False positives are always possible even with good scores, especially when many protein pairs are tried. It would therefore be important to also include a table showing the global scores for pTM and ipTM to summarise the confidence scores of interfaces.

It is exciting to see AF-multimer predictions being applied to cohesin. As some of the reported interactions are not universally conserved and some involve relatively small interfaces the possibility arises that these interfaces show poor or borderline confidence scores. As some of these interfaces map to mutants that have previously been obtained by hypothesis-free genetic screens and mutational analyses they appear nevertheless valid. Thus, an important point to make is that even interfaces that show modest confidence scores may turn out to be valid while others may be not. The authors therefore should emphasize that the proposed models are just predictions and that additional orthogonal validations are required.

Reviewer #1 (Public Review):

This paper presents extensive numerical simulations using a model that incorporates up to second-order epistasis to study the joint effects of microscopic epistasis and clonal interference on the evolutionary dynamics of a microbial population. Previous works that explicitly modeled microscopic epistasis typically assumed strong selection & weak mutation (SSWM), a condition that is generally not met in real-life evolutionary processes. Alternatively, another class of models coarse-grained the effects of microscopic epistasis into a generic distribution of fitness effects. The framework introduced in this paper represents an important advance with respect to these previous approaches, allowing for the explicit modeling of microscopic epistasis in non-SSWM scenarios. The modeling framework presented promises to be a valuable tool to study microbial evolution in silico.

Reviewer #1 (Public Review):

Ibar and colleagues investigate the function of spectrin in Drosophila wing imaginal discs and its effect on the Hippo pathway and myosin activity. The authors find that both βH-Spec and its canonical binding partner α-Spec reduce junctional localization of the protein Jub and thereby restrict Jub's inhibitory effect on Hippo signaling resulting in activation of the Hippo effector Yorkie regulating tissue shape and organ size. From genetic epistasis analysis and analysis of protein localization, the authors conclude that βH-Spec and α-Spec act independently in this regulation. The major point of this study is that the apical localization of βH-Spec and myosin is mutually exclusive and that the proteins antagonize each other's activity in wing discs. In vitro co-sedimentation assays and in silico structural modeling suggest that this antagonization is due to a competition of βH-Spec and myosin for F-actin binding.

The study's strengths are the genetic perturbation that is the basis for the epistasis analysis which includes specific knockdowns of the genes of interest as well as an elegant CRISPR-based overexpression system with great tissue specificity. The choice of the model for such an in-depth analysis of pathway dependencies in a well-characterized tissue makes it possible to identify and characterize quantitative differences between closely entangled and mutually dependent components. The method of quantifying protein localization and abundance is common for multiple figures which makes it easy to assess differences across experiments. The flow of experiments is logical and in general, the author's conclusions are supported by the presented data. The findings are very well embedded into the context of relevant literature and both confronting and confirming literature are discussed.

The study shows how components of the cytoskeleton are directly involved in the regulation of the mechanosensitive Hippo pathway in vivo and thus ultimately regulate organ size supporting previous data in other contexts. The molecular mechanism regulating myosin activity by out-competing it for F-actin binding has been observed for small actin-binding proteins such as cofilin but is a new mode for such a big, membrane-associated actin-binding protein. This may inspire future experiments in different morphogenetic contexts for the investigation of similar mechanisms. For example, the antagonistic activity of βH-Spec and myosin in this tissue context might help explain phenomena in other systems such as spectrin-dependent ratcheting of apical constriction during mesoderm invagination (as the authors discuss). Against the classical view, the work shows that βH-Spec can act independently of α-Spec. Together the results will be of interest to the cell biology community with a focus on the cytoskeleton and mechanotransduction.

Reviewer #1 (Public Review):

This manuscript by Mahlandt, et al. presents a significant advance in the manipulation of endothelial barriers with spatiotemporal precision, and in the use of optogenetics to manipulate cell signaling in vascular biology more generally. The authors establish the role of Rho-family GTPases in controlling the cytoskeletal-plasma membrane interface as it relates to endothelial barrier integrity and function, and adequately motivate the need for optogenetic tools for global and local signaling manipulation to study endothelial barriers.

Throughout the work, the optogenetic assays are conceptualized, described, and executed with exceptional attention to detail, particularly as it relates to potential confounding factors in data analysis and interpretation. Comparison across experimental setups in optogenetics is notoriously fraught, and the authors' control experiments and measurements to ensure equal light delivery and pathway activation levels across applications is very thorough. In demonstrating how these new opto-GEFs can be used to alter vascular barrier strength, the authors cleverly use fluorescent-labeled dextran polymers of different sizes and ECIS experiments to demonstrate the physiological relevance of BOEC monolayers to in vivo blood vessels. Of particular note, the resiliency of the system to multiple stimulation cycles and longer time course experiments is promising for use in vascular leakage studies.

Given that dozens of Rho GTPase-activating GEFs exist, expanded rationale for the selection of p63, ITSN1, and TIAM1 in the form of discussion and literature citations would be helpful to motivate their selection as protein effectors in the engineered tools. Extensive tool engineering studies demonstrate the superiority of iLID over optogenetic eMags or rapamycin-based chemogenetic tools for these purposes. However, as the utility of iLID and eMags has been demonstrated for manipulation of a variety of signaling pathways, the iSH-Akt demonstration does not seem necessary for these systems.

The demonstration of orthogonality in GTPase- and VE-cadherin-blocking antibody-mediated barrier function decreases and is compelling, even without full elucidation of the role of cell size or overlap in barrier strength. The discussion section presents a mature and thoughtful description of the limitations, remaining questions, and potential opportunities for the tools and technology developed in this work. Importantly, this manuscript demonstrates a commitment to scientific transparency in the ways in which the data are visualized, the methods descriptions, and the reagent and code sharing it presents, allowing others to utilize these tools to their full potential.

Joint Public Review:

In this study, Anthoney and coworkers continue an important, unique, and technologically innovative line of inquiry from the van Swinderen lab aimed at furthering our understanding of the different sleep stages that may exist in Drosophila. Here, they compare the physiological and transcriptional hallmarks of sleep that have been induced by two distinct means, a pharmacological block of GABA signaling and optogenetic activation of dorsal fan-shaped-body neurons. They first employ an incredibly impressive fly-on-the-ball 2-photon functional imaging setup to monitor neural activity during these interventions, and then perform bulk RNA sequencing of fly brains at different stages. These transcriptomic analyses leads them to (a) knocking out nicotinic acetyl-choline receptor subunits and (b) knocking down AkhR throughout the fly brain testing the impact of these genetic interventions on sleep behaviors in flies. Based on this work, the authors present evidence that optogenetically and pharmacologically induced sleep produces highly distinct brain-wide effects on physiology and transcription.

The study is of significant interest, is easy to read, and the figures are mostly informative. However there are features of the experimental design and the interpretation of results that diminish enthusiasm.

a - Conditions under which sleep is induced for behavioral vs neural and transcriptional studies

1) There is a major conceptual concern regarding the relationships between the physiological and transcriptomic effects of optogenetic and pharmacological sleep promotion, and the effects that these manipulations have on sleep behavior. The authors show that these two means of sleep-induction produce remarkably distinct physiological and transcriptional responses, however, they also show that they produce highly similar effects on sleep behavior, causing an increase in sleep through increases in the duration of sleep bouts. If dFB neurons were promoting active sleep, the sleep it produces should be more fragmented than the sleep induced by the drug, because the latter is supposed to produce quiet sleep. Yet both manipulations seem to be biasing behavior toward quiet sleep.

2) The authors show that the pharmacological block of GABA signaling and the optogenetic activation of dorsal fan-shaped-body neurons cause different responses on brain activity. Based on these recordings and the behavioral and brain transcriptomic data they then claim that these responses correspond to different sleep states and are associated with the expression and repression of a different constellation of genes. Nevertheless, neural activity in animals was recorded following short stimulations whereas behavioral and transcriptomic data were obtained following chronic stimulation. In this regard, it would be interesting to determine how the 12-hour pharmacological intervention they employed for their transcriptomic analysis changes neural activity throughout the brain - 12 hours will likely be too long for the open-cuticle preps, but an in-between time-point (e.g. 1h) would probably be equally informative.

b - Efficiency of THIP treatment under different conditions

1) There are no data to quantify how THIP alters food consumption. It is evident that flies consume it otherwise they would not show increased sleep. However, they may consume different amounts of food overall than the minus THIP controls. This might have an influence on the animal's metabolism, which could at least explain the fact that metabolism-related genes are regulated (Figure 5). Therefore, in the current state, it is not possible to be certain that gene regulation events measured in this experiment are solely due to THIP effects on sleep.

2) A similar problem exists in the sleep deprivation experiments. If flies are snapped every 20 seconds, they may not have the freedom to consume appropriate amounts of food, and therefore their consumption of THIP or ATR may be smaller than in non-sleep deprived controls. Thus, it would be crucial to know whether the flies that are sleep-deprived (i.e. shaken every 20 seconds for 12 hours) actually consume comparable amounts of food (and therefore THIP) as those that are undisturbed. If not, then perhaps the transcriptional differences between the two groups are not sleep-specific, but instead reflect varying degrees of exposure to THIP.

3) The authors should further discuss the slow action of THIP perfusion vs dFB activation, especially as flies only seem to fall asleep several minutes after THIP is being washed away. Is it a technical artifact? If not, it may not be unreasonable to hypothesize that THIP, at the concentration used, could prevent flies from falling asleep, and that its removal may lower the concentration to a point that allows its sleep-promoting action. The authors could easily test this by extending THIP treatment for another 4-5 minutes.

c - Comments regarding the behavioral assays

1) L319-322: the authors conclude that dFB stimulation and THIP consumption have similar behavioral effects on sleep. However, this is inaccurate as in Figure S1 they explain that one increases bout number in both day and night and the other one only during the day.

2) The behavioral definitions used for active and quiet sleep do not fit well with strong evidence that deep sleep (defined by lowered metabolic rates) is probably most closely associated with bouts of inactivity that are much longer than the >5min duration used here, i.e., probably 30min and longer (Stahl et al. 2017 Sleep 40: zsx084). Given that the authors are providing evidence that quiet sleep is correlated with changes in the expression of metabolism related genes, they should at least discuss the fact that reductions in metabolism have been shown to occur after relatively long bouts of inactivity and might reconsider their behavioral sleep analysis (i.e., their criteria for sleep state) with this in mind.

d - Comments regarding the recordings of neuronal activity

1) There is an additional concern regarding the proposed active and quiet sleep states that rest at the heart of this study. Here these two states in the fly are compared to the REM and NREM sleep states observed in mammals and the parallels between active fly sleep and REM and quiet fly sleep and NREM provide the framework for the study. The establishment of such parallel sleep states in the fly is highly significant and identifying the physiological and molecular correlates of distinct sleep stages in the fly is of critical importance to the field. However, the proposal that the dorsal fan shaped body (dFB) neurons promote active sleep runs counter to the prevailing model that these neurons act as a major site of sleep homeostasis. If quiet sleep were akin to NREM, wouldn't we expect the major site of sleep homeostasis in the brain to promote it? Furthermore, the authors state that the effects of dFB neuron excitation on transcription have "almost no overlap" (line 500) with the transcriptomic effects of sleep deprivation (Supplementary Table 3), which is not what would be expected if dFB neurons are tracking sleep pressure and promoting sleep, as suggested by a growing body of convergent work summarized on page four of the manuscript. Wouldn't the 10h excitation of the dFB neurons be predicted to mimic the effects of sleep deprivation if these neurons "...serve as the discharge circuit for the insect's sleep homeostat..." (line 60)? Shouldn't their prolonged excitation produce an artificial increase in sleep drive (even during sleep) that would favor deep, restorative sleep? How do the authors interpret their results with regard to the current prevailing model that dFB neurons act as a major site of sleep homeostasis? This study could be seen as evidence against it, but the authors do not discuss this in their Discussion.

2) Regarding the physiological effects of Gaboxadol, to what extent is the quieting induced by this drug reminiscent of physiology of the brains of flies spontaneously meeting the behavioral criterion for quiet sleep? Given the relatively high dose of the drug being delivered to the de-sheathed brain in the imaging experiments (at least when compared to the dose used in the fly food), one worries that the authors may be inducing a highly abnormal brain state that might bear very little resemblance to the deeply sleeping brain under normal conditions. As the authors acknowledge, it is difficult to compare these two situations. Comparing the physiological state of brains put to sleep by Gaboxadol and brains that have spontaneously entered a deep sleep state therefore seems critical.

3) There are some issues with Figure 3, in particular 3C-D. It is not clear whether these panels show representative traces or an average, however both the baseline activity and fluorescence are different between C and D, in particular in their amplitude. Therefore, it is difficult to attribute the differences between C and D to the stimulation itself or to the previously different baseline. In addition, 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, however it is not being discussed. Finally, the authors claim that the flies eventually wake up from THIP-induced sleep (L360-361), however there are no data to support this statement.

4) In Figure 4C, it is strange that the SEM is always exactly the same across the whole experiment. Readers should be aware that there might have been an issue when plotting the figure.

e - Comments regarding the transcript analyses

1) General comment: the title of this manuscript is inaccurate - the "transcriptome" commonly refers to the entirety of all transcripts in a cell/tissue/organ/animal (including genes that are not differentially expressed following their interventions), and it is therefore impossible to "engage two non-overlapping transcriptomes" in the same tissue. Perhaps the word "transcriptional programs" or transcriptional profiles" would be more accurate here?

2) Given the sensitivity of transcriptomic methods, there is a significant concern that the optogenetic experiments are not as well controlled as they could be. Given the need for supplemental all-trans retinal (ATR) for functional light gating of channelrhodopsins in the fly, it is convenient to use flies with Gal4-driven opsin that have not been given supplemental ATR as a negative control, particularly as a control for the effects of light. However, there is another critical control to do here. Flies bearing the UAS-opsin responder element but lacking the GAL4 driver and that have been fed ATR are critical for confirming that the observed effects of optogenetic stimulation are indeed caused by the specific excitation of the targeted neurons and not due to leaky opsin expression, or the effect of ATR feeding under light stimulation or some combination of these factors. Given the sensitivity of transcriptomic methods, it would be good to see that the candidate transcripts identified by comparing ATR+ and ATR- R23E10GAL4/UAS-Chrimson flies are also apparent when comparing R23E10GAL4/UAS-Chrimson (ATR+) with UAS-Chrimson (ATR+) alone.

3) Figures about qPCR experiments (5G and 6G) are problematic. First, whereas the authors seem satisfied with the 'good correspondence' between their RNA-seq and qPCR results, this is true for only ~9/19 genes in 5G and 2/6 genes in 6G. Whereas discrepancies are not rare between RNA-seq and qPCR, the text in L460-461 and 540-541 is misleading. In addition, it is unclear whether the n=19 in L458 refers to the number of genes tested or the number of replicates. If the qPCR includes replicates, this should be more clearly mentioned, and error bars should be added to the corresponding figures.

4) There is a lack of error bars for all their RNAseq and qPCR comparisons, which is particularly surprising because the authors went to great lengths and analyzed an applaudably large amount of independent biological replicates, yet the variability observed in the corresponding molecular data is not reported.

Reviewer #1 (Public Review):

Ritvo and colleagues present an impressive suite of simulations that can account for three findings of differentiation in the literature. This is important because differentiation-in which items that have some features in common, or share a common associate are less similar to one another than are unrelated items-is difficult to explain with classic supervised learning models, as these predict the opposite (i.e., an increase in similarity). A few of their key findings are that differentiation requires a high learning rate and low inhibitory oscillations, and is virtually always asymmetric in nature.

This paper was very clear and thoughtful-an absolute joy to read. The model is simple and elegant, and powerful enough to re-create many aspects of existing differentiation findings. The interrogation of the model and presentation of the findings were both extremely thorough. The potential for this model to be used to drive future work is huge. I have only a few comments for the authors, all of which are relatively minor.

1. I was struck by the fact that the "zone" of repulsion is quite narrow, compared with the zone of attraction. This was most notable in the modeling of Chanales et al. (i.e., just one of the six similarity levels yielded differentiation). Do the authors think this is a generalizable property of the model or phenomenon, or something idiosyncratic to do with the current investigation? It seems curious that differentiation findings (e.g., in hippocampus) are so robustly observed in the literature despite the mechanism seemingly requiring a very particular set of circumstances. I wonder if the authors could speculate on this point a bit-for example, might the differentiation zone be wider when competitor "pop up" is low (i.e., low inhibitory oscillations), which could help explain why it's often observed in hippocampus? This seems related a bit to the question about what makes something "moderately" active, or how could one ensure "moderate" activation if they were, say, designing an experiment looking at differentiation.

2. With real fMRI data we know that the actual correlation value doesn't matter all that much, and anti-correlations can be induced by things like preprocessing decisions. I am wondering if the important criterion in the model is that the correlations (e.g., as shown in Figure 6) go down from pre to post, versus that they are negative in sign during the post learning period. I would think that here, similar to in neural data, a decrease in correlation would be sufficient to conclude differentiation, but would love the authors' thoughts on that.

3. For the modeling of the Favila et al. study, the authors state that a high learning rate is required for differentiation of the same-face pairs. This made me wonder what happens in the low learning rate simulations. Does integration occur? This paradigm has a lot of overlap with acquired equivalence, and so I am thinking about whether these are the sorts of small differences (e.g., same-category scenes and perhaps a high learning rate) that bias the system to differentiate instead of integrate.

4. For the simulations of the Schlichting et al. study, the A and B appear to have overlap in the hidden layer based on Figure 9, despite there being no similarity between the A and B items in the study (in contrast to Favila et al., in which they were similar kinds of scenes, and Chanales et al., in which they were similar colors). Why was this decision made? Do the effects depend on some overlap within the hidden layer? (This doesn't seem to be explained in the paper that I saw though, so maybe just it's a visualization error?)

5. It seems as though there were no conditions under which the simulations produced differentiation in both the blocked and intermixed conditions, which Schlichting et al. observed in many regions (as the present authors note). Is there any way to reconcile this difference?

6. A general question about differentiation/repulsion and how it affects the hidden layer representation in the model: Is it the case that the representation is actually "shifted" or repelled over so it is no longer overlapping? Or do the shared connections just get pruned, such that the item that has more "movement" in representational space is represented by fewer units on the hidden layer (i.e., is reduced in size)? I think, if I understand correctly, that whether it gets shifted vs. reduce would depend on the strength of connections along the hidden layer, which would in turn depend on whether it represents some meaningful continuous dimension (like color) or not. But, if the connections within the hidden layer are relatively weak and it is the case that representations become reduced in size, would there be any anticipated consequences of this (e.g., cognitively/behaviorally)?

Reviewer #1 (Public Review):

The authors present a carefully controlled set of experiments that demonstrate an additional complexity for GPCR signalling in that endosomal signalling make 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.

Reviewer #1 (Public Review):

In their research article, Sapiro et al. overcome the technical burden of low B. burgdorferi numbers during infection by physically enriching for spirochetes prior to RNA-sequencing/mass spectrometry. This technology, which has potential broad applications, was applied to B. burgdorferi-infected ticks, generating datasets for future studies.

Sapiro et al. addressed many of the reviewers' comments including the addition of experimental details, comparisons to other studies and some caveats to their approach. The manuscript has been significantly improved and I appreciate the efforts to address our critiques. There are a few remaining comments that the authors should consider before creating the final Version of Record.

The authors sought to develop technology for a transcriptomic analysis of B. burgdorferi directly from infected ticks. The methodology has exciting implications to better understand pathogen RNA profiles during specific infection timepoints, even beyond the Lyme spirochete. The authors demonstrate successful sequencing of the B. burgdorferi transcriptome from ticks and perform mass spectrometry to identify possible tick proteins that interact with B. burgdorferi. This technology and first dataset will be useful for the field. The study is limited in that no transcripts/proteins are followed-up by additional experiments and no biological interactions/infectious-processes are investigated.

Remaining critiques:

Experimental data regarding the sensitivity of this approach are missing. What is the limit of detection for this protocol? While the authors have stated that they were unable to sequence B. burgdorferi from unfed nymphs, the number of bacteria needed for antibody enrichment are not tested. The starting CFU in their infected nymphal ticks was also not reported (the authors only report reisolation data from 12 ticks). Page 18, line 458 the authors claim their approach "captured the vast majority" of Bb inside of the tick. Data are missing to demonstrate this. Understanding the limits of this approach will be critical for future applications, especially when using B. burgdorferi infected material with low bacterial burden.

The authors should clarify the term "genes" in the abstract and throughout the manuscript. I think they actually mean "open reading frames" or "annotated mRNAs".

More information regarding the efficacy of RNA-seq coverage is still warranted and lacking from the results, especially on page 6. The authors skip right to differential expression analysis without fully examining sequencing effectiveness. This is especially important given their development of a new technique. What was the numbers of detected genes for each sample? How is this affected by bacterial burden of the sample? What is the distribution of reads among tRNAs, mRNAs, UTRs, and sRNAs? How reproducible is the coverage for one gene across replicates? A few browser images of RNA-seq data (ex. of BAM files) across different genes would be useful to visualize the read coverage per gene.

Downregulated genes are largely ignored and should be commented on further.

Page 11, line 258-260: authors state Rpos, Rrp1, and RelBbu are the "three main Bb regulatory programs active in the tick." Yes, these three regulons have been well studied but there could be other uncharacterized regulatory programs. Please consider changing the language.

Reviewer #1 (Public Review):

This paper looks at nutrient-responsive Ca++ flux in islet cells of eight genetically diverse mouse strains. The investigators correlate Ca++ flux with insulin secretory capacity, demonstrating that calcium parameters in response to different nutrients are a better predictor of insulin secretory capacity than average calcium. They also correlate Ca++ flux with previously collected islet protein abundance followed by integration with human genome-wide association studies. This integration allows them to identify a sub-set of proteins that are both relevant to human islet function and that may play a causal role in regulating islet Ca++ oscillations. All data have been deposited in a searchable public database. There are many strengths to this paper. To my knowledge, this is the first work to assess the genetics of nutrient-responsive Ca++ flux in islets. Given the importance of Ca++ for beta cell insulin secretion, this work is of high importance. Investigators also use the founders of two powerful genetic mouse models: the diversity outbred and collaborative, opening up several avenues of future research into the genetics of Ca++ flux. By looking at multiple parameters of Ca++ flux, investigators are able to start to understand which parameters may be driving low or high insulin secretion. Integration with protein abundance and human GWAS has allowed identification of proteins with known roles in insulin secretory capacity, as well as several novel regulators, again opening up several avenues of future research. Finally, the public database is likely to be useful to multiple investigators interested in following up specific protein targets or in conducting future genetic studies. I found only minor weaknesses in this paper, mainly regarding clarity in certain areas. One specific area to be improved is Figure 4A, B where in addition to the heat maps, it would be useful to see regression plots that show the differences per sex and strain for the insulin secretion vs Ca++ parameters.

Reviewer #1 (Public Review):

The authors take on the challenge of defining the core nucleus for amyloid formation by polyglutamine tracts. This rests on the assertion that polyQ forms amyloid structures to the exclusion of all other forms of solids. Using their unique assay, deployed in yeast, the authors attempt to infer the size of the nucleus that templates amyloid formation by polyQ. Further, through a series of sequence titrations, all studied using a single type of assay, the authors converge on an assertion stating that a single polyQ molecule is the nucleus for amyloid formation, that 12-residues make up the core of the nucleus, that it takes ca. 60 Qs in a row to unmask this nucleation potential, and that polyQ amyloid formation belongs to the same universality class as self-poisoned crystallization, which is the hallmark of crystallization from polymer melts formed by large, high molecular weight synthetic polymers. Unfortunately, the authors have decided to lean in hard on their assertions without a critical assessment of whether their findings stand up to scrutiny. If their findings are truly an intrinsic property of polyQ molecules, then their findings should be reconstituted in vitro. Unfortunately, careful and rigorous experiments in vitro show that there is a threshold concentration for forming fibrillar solids. This threshold concentration depends on the flanking sequence context on temperature and on solution conditions. The existence of a threshold concentration defies the expectation of a monomer nucleus. The findings disagree with in vitro data presented by Crick et al., and ignored by the authors. Please see: https://doi.org/10.1073/pnas.1320626110. These reports present data from very different assays, the importance of which was underscored first by Regina Murphy and colleagues. The work of Crick et al., provides a detailed thermodynamic framework - see the SI Appendix. This framework dove tails with theory and simulations of Zhang and Muthukumar, which explains exactly how a system like polyQ might work (https://doi.org/10.1063/1.3050295). The picture one paints is radically different from what the authors converge upon. One is inclined to lean toward data that are gleaned using multiple methods in vitro because the test tube does not have all the confounding effects of a cellular milieu, especially when it comes to focusing on sequence-intrinsic conformational transitions of a protein. In addition to concerns about the limitations of the DAmFRET method, which based on the work of the authors in their collaborative paper by Posey et al., are being stretched to the limit, there is the real possibility that the cellular milieu, unique to the system being studied, is enabling transitions that are not necessarily intrinsic to the sequence alone. A nod in this direction is the work of Marc Diamond, which showed that having stabilized the amyloid form of Tau through coacervation, there is a large barrier that limits the loss of amyloid-like structure for Tau. There may well be something similar going on with the polyQ system. If the authors could show that their data are achievable in vitro without anything but physiological buffers one would have more confidence in a model that appears to contradict basic physical principles of how homopolymers self-assemble. Absent such additional evidence, numerous statements seem to be too strong. There are also several claims that are difficult to understand or appreciate.

Reviewer #1 (Public Review):

Secondary cell walls support vascular plants and conduct water throughout the plant body, but are also important resources for lignocellulosic feedstocks. Secondary cell wall synthesis is under complex transcriptional control, presumably because it must only be initiated after cell growth is complete. Here, the authors found that two Musashi-type RNA-binding proteins, MSIL2 and MSIL4 are redundantly required for secondary cell wall development in Arabidopsis. The plant phenotypes could be complemented by the wild-type version of either protein, but not by a MSIL4 version that carries mutations in the conserved RNA-binding domains, and the authors localized MSIL2 & 4 to stress granules, implicating the RNA-binding function of MSIL4 in the cell wall phenotype. Upon closer inspection, the secondary cell wall phenotypes included changes in vasculature morphology, and minor changes to lignin and hemicellulose (glucuronoxylan). While there were no changes to likely cell wall target genes in the transcriptome of msil2msil4 plants, proteomics experiments found glucuronoxylan biosynthesis components were upregulated in the mutants, and they detected an increase in substituted xylan via several methods. Finally, they documented MSIL4 binding to RNA encoding one of these targets, suggesting that MSIL2 and MSIL4 act to post-transcriptionally regulate glucuronoxylan modification. Altogether, this is a new mechanism by which cell wall composition could be regulated.

Overall, the manuscript is well-written, the data are generally high-quality, and the authors typically use several independent methods to support each claim. However, several important questions remain unanswered by this work in its current state and the model presented in Figure 7 is quite speculative. For example, the link between the striking plant phenotype and GXM misregulation is unclear since GXM overexpression doesn't alter plant phenotypes or lignin content (Yuan et al 2014 Plant Science), so misregulation of GXMs in msil2msil4 mutants clearly is not the whole story. It also remains to be determined why one particular secondary cell wall synthesis enzyme is regulated likely post-transcriptionally, while so much of the pathway is regulated at the transcriptional level. There are likely other targets for MSIL2- and MSIL4-mediated regulation since it seems that MSIL2 and MSIL4 are expressed in tissues that are not synthesizing secondary cell walls.

Reviewer #1 (Public Review):

This study presents a conceptual and analytical framework for tracking the impacts of human activities on freshwater ecosystems over time. It demonstrates the application of the framework to a 100-year record of community-level biodiversity, climate change, and chemical pollution from sediments cores of Lake Ring, Denmark. By reconstructing biodiversity using environmental DNA (eDNA) and pollutant inputs using mass spectrometry, the authors identify the taxonomic groups responding positively and negatively in different phases of the lake's environmental history. Furthermore, they identify the independent and additive effects of climate variables and pollutants on biodiversity throughout the 100-year record.

Strengths:

The advances in paired molecular and machine learning analyses are an important step towards a better understanding of 20th/21st century trajectories of biodiversity and pollution.

The finding that taxonomic groups so central to ecosystem assessment in Europe (i.e., diatoms) do not appear to respond to degradation or amelioration - providing at least a partial explanation as to why "ecological status" (as defined under the EU Water Framework Directive) has proved so difficult to improve.

The framework shows how both taxonomic and functional indicators can be used to better understand ecological degradation and recovery.

The identification of individual biocides and climate variables driving observed changes is a particular strength.

Limitations:

The analytical framework is not sufficiently explained in the main text.

The significance of findings in relation to functional changes is not clear. What are the consequences of enrichment of RNA transport or ribosome biogenesis pathways between pesticides and recovery stages, for example?

The impact of individual biocides and climate variables, and their additive effects, are assessed but there is no information offered on non-additive interactions (e.g., synergistic, antagonistic).

The level of confidence associated with results is not made explicit. The reader is given no information on the amount of variability involved in the observations, or the level of uncertainty associated with model estimates.

The major implications of the findings for regulatory ecological assessment are missed. Regulators may not be primarily interested in identifying past "ecosystem shifts". What they need are approaches which give greater confidence in monitoring outcomes by better reflecting the ecological impact of contemporary environmental change and ecosystem management. The real value of the work in this regard is that: (1) it shows that current approaches are inappropriate due to the relatively stable nature of the indicators used by regulators, despite large changes in pollutant inputs; (2) it presents some better alternatives, including both taxonomic and functional indicators; and (3) it provides a new reference (or baseline) for regulators by characterizing "semi-pristine" conditions.

Reviewer #1 (Public Review):

In this study, the authors use prospective sorting and microarray analyses, extended by single-cell RNA sequencing, in the neural stem cell niche of the subventricular zone (SVZ) to identify and refine a series of states along the continuum from quiescent neural stem cells to mature progeny. Of note, changes in the levels and subgroups of RNA splicing regulators are detailed across this continuum. Using in vitro proliferation and differentiation assays, coupled with in vivo engraftment of some prospectively sorted subsets, the authors argue that a stage they define as immature neuroblasts (iNBs) retain proliferative and multilineage differentiation capacity that is not seen in the mature neuroblast population, and is unexpected based on prior models for lineage progression in this system. This iNB stage is accompanied by a change in RNA splicing regulator expression, which is of interest due to the emerging roles for RNA processing and preferential translation within this niche.

These data complement several additional sc-RNAseq studies of this stem cell niche, and use a different, but similar, sorting strategy to isolate and profile subpopulations of stem/progenitor cells and neuroblast progeny. The claim that immature neuroblasts retain multipotency - the ability to generate glia and neurons - is surprising and somewhat controversial given that this has largely not been reported before under homeostatic conditions. Some factors to consider when interpreting these data are that the "immature neuroblast" populations are studied in some experiments using a transcriptional signature and a functional assay, namely the timing of reappearance of these cells after use of agents that kill rapidly dividing cells (in this case, radiation), leading to reconstitution of the lineage by previously quiescent stem cells. In a separate set of experiments, a tamoxifen-inducible labeling system is used in combination with cell-surface markers to prospectively isolate and study the differentiation potential of neuroblast populations that are assumed to be equivalent to those found in transcriptional experiments. It would be of interest in future to confirm that the exact sorted populations (using CD24/EGFR/DCX-CreERT2::CAG) have the same transcriptional profile as those studied in earlier experiments within the paper, and to confirm the purity of the sorted populations. Finally, while elegant use is made of engraftment of the sorted populations to study the differentiation and lineage potential of these immature neuroblasts, a remaining question is the relative abundance of each lineage (neurons/astrocytes/oligodendrocytes) produced by the engrafted cells - is production of glia rare, or common? Could this be due to factors such as alteration of lineage potential due to culture conditions, a disconnect between transcript expression and protein expression, or an incompletely purified starter population?

Overall, this manuscript presents an intriguing possible refinement of models for SVZ neurogenesis, and highlights the role of RNA splicing at specific stages in the lineage. It will be of interest to see if additional groups confirm these findings and whether multiplexed immunostaining, highly multiplexed flow cytometry, or other approaches focused at the proteomic level confirm and extend these findings, particularly given recent data in the developing brain that suggest transcript and protein levels are relatively poorly correlated in stem/progenitor populations.

A final point on terminology: "iNB", "A cells", and "D1/D2 cells" are all used in the manuscript to denote different stages along the continuum from TAP/C cells to mature neuroblasts; however, historically "D cells" refers to neuroblasts in the dentate gyrus, not those derived from the SVZ. In this case, the authors are exclusively studying SVZ-derived neuroblasts.

Reviewer #1 (Public Review):

This study by Park et al. describes an interesting approach to disentangle gene-environment pathways to cognitive development and psychotic-like experiences in children. They have used data from the ABCD study and have included PGS of EA and cognition, environmental exposure data, cognitive performance data and self-reported PLEs. Although the study has several strengths, including its large sample size, interesting approach and comprehensive statistical model, I have several concerns:

- The authors have included follow-up data from the ABCD Study. However, it is not very clear from the beginning that longitudinal paths are being explored. It would be very helpful if the authors would make their (analysis) approach clearer from the introduction. Now, they describe many different things, which makes the paper more difficult to read. It would be of great help to see the proposed path model in a Figure and refer to that in the Method.

- There is quite a lot of causal language in the paper, particularly in the Discussion. My advice would be to tone this down.

- It's a bit unclear to me why the authors chose the PEs phenotype as their outcome of interest. They mainly speak about child development and mental health in general in the Introduction, so why focus on PLE? Aren't genes and environments also relevant for other types of mental health problems? Relatedly, in the Discussion the authors seem to conflate PLEs with psychosis. There is a large body of literature highlighting the differences between PLEs and psychosis, and this should - in my opinion - be adjusted throughout the introduction and discussion.

- I feel that the limitation section is a bit brief, and can be developed further.

- I like that the assessment of CP and self-reports PEs is of good quality. However, I was wondering which 4 items from the parent-reported CBCL were used and how did they correlate with the child-reported PEs? And how was distress taken into account in the child self-reported PEs measurement? Which PEs measures were used?

- What was the correlation between CP and EA PGSs?

- Regarding the PGS: why focus on cognitive performance and EA? It should be made clearer from the introduction that EA is not only measuring cognitive ability, but is also a (genetic) marker of social factors/inequalities. I'm guessing this is one of the reasons why the EA PGS was so much more strongly correlated with PEs than the CP PGS. See the work bij Abdellaoui and the work by Nivard.

- Considering previous work on this topic, including analyses in the ABCD Study, I'm not surprised that the correlation was not very high. Therefore, I don't think it makes a whole of sense to adjust for the schizophrenia PGS in the sensitivity analyses, in other words, it's not really 'a more direct genetic predictor of PLEs'.

- How did the FDR correction for multiple testing affect the results?

Overall, I feel that this paper has the potential to present some very interesting findings. However, at the moment the paper misses direction and a clear focus. It would be a great improvement if the readers would be guided through the steps and approach, as I think the authors have undertaken important work and conducted relevant analyses.

Reviewer #1 (Public Review):

Park et al demonstrate that cells on either side of a BM-BM linkage strengthen their adhesion to that matrix using a positive feedback mechanism involving a discoidin domain receptor (DDR-2) and integrin (INA-1 + PAT-3). In response to its extracellular ligand (Collagen IV/EMB-9), DDR-2 is endocytosed and initiates signaling that in turn stabilizes integrin at the membrane. DDR-2 signaling operates via Ras/LET-60. This work's strength lies in its excellent in vivo imaging, especially of endogenously tagged proteins. For example, tagged DDR-2:mNG could be seen relocating from seam cell membranes to endosomes. I also think a second strength of this system is the ability to chart the development of BM-BM linkage over time based on the stages of worm larval development. This allows the authors to show DDR signaling is needed to establish linkage, rather than maintain it. It likely is relevant to many types of cells that use integrin to adhere to BM and left me pondering a number of interesting questions. For example: (1) Does DDR-2 activation require integrin? Perhaps integrin gets the process started and DDR-2 positively reinforces that (conversely is DDR-2 at the top of a linear pathway)? (2) In ddr-2(qy64) mutants, projections seem to form from the central portion of the utse cell. Does this reveal a second function for DDR-2, regulating perhaps the cytoskeleton? And (3) can you use the forward genetic tools available in C. elegans to find new genes connecting DDR-2 and integrin? The authors discuss these ideas in their response to the reviews, and I look forward to hearing about their future work on these questions.

I do see two areas where the manuscript could be improved. First, the authors rely on imprecise genetic methods to reach their conclusions (i.e. systemic RNAi, or expression of dominant negative constructs.) I think their conclusion would be stronger if they used tissue specific degradation to block ddr-2 function specifically in the utse or seam cells. Methods to do this are now regularly used in C. elegans and the authors have already developed the necessary tissue-specific promoters. Second, the manuscript is presented in the introduction as a study on formation and function of BM-BM linkage. However, their results actually demonstrate a mechanism by which cells adhere to BM. Since ddr-2 appears to function equally in both utse + seam cells (based on their dominant negative data), there are likely three layers of adhesion (utse-BM, BM-BM, BM-seam) and if any of those break down, you get a partially penetrant rupture phenotype. I pointed this out in my initial review, and after reading the revised manuscript, I do still feel the authors' introduction presents the paper as dealing with how basement membranes link together. But, I wonder if this might this be a question of terminology/language use? Maybe I am operating on a strict definition of linkage, and the authors use it more inclusively. What term(s) should we use to differentiate two basement membranes that are linked together, versus tissues that are connected through a basement membrane linkage? This is something that could be clarified in future publications.

These concerns do not undercut the significance of this work, which identifies an interesting mechanism cells use to strengthen adhesion during BM linkage formation. In fact, I am excited to read future papers detailing the connection between DDR-2 and integrin. But before undertaking those experiments the authors should be certain which cells require DDR-2 activity, and that should not be determined based solely on mis expression of a dominant negative.

Reviewer #1 (Public Review):

The authors examine signaling factors that differentiate parallel routes to activating phosphoinositide 3-kinase gamma (PI3Kγ). Dissecting the convergent pathways that control PI3Kγ activity is critical because PI3Kγ is a therapeutic target for treating inflammatory disease and cancer. Here, the authors employ a multipronged approach to reveal new aspects for how p84 and p101 pair with p110γ to activate the PI3Kγ heterodimer. The key instigator to this study is a previously reported inhibitory Nanobody, NB7. The hypothesized mechanism for NB7 allosteric inhibition of p84- p110γ was previously proposed to involve blockage of the Ras-binding domain. The authors revise the allosteric inhibition model based on meticulous profiling of various PI3Kγ complex interactions with NB7. In parallel, a cryo-EM-derived model of NB7 bound to the p110γ subunit convincingly reveals a Nanobody interaction pocket involving the helical domain and regulatory motifs of the kinase domain. This revelation shifts the focus to the helical domain, a known target of PKC phosphorylation. While the connections between NB7 interactions and the effects of PKC phosphorylation are sometimes tenuous, it could be argued that the Nanobody served as a tool to reveal the importance of the helical domain to p110γ regulation.

The sites of PKC-mediated p110γ helical domain phosphorylation were unexpectedly inaccessible in the available structural models. Nevertheless, mass spectrometry (MS)-based phosphorylation profiling indicates that PKC can phosphorylate the helical domain of p110γ and p84/p110γ (but not p101/p110γ) in vitro. The authors hypothesize that helical domain dynamics dictate susceptibility to PKC phosphorylation. To explore this notion, carefully executed, rigorous H/D exchange MS (HDX-MS) experiments were performed comparing phosphorylated vs. unphosphorylated p110γ. Notably, this design reveals more about the consequences of p110γ phosphorylation, rather than the mechanisms of p84/p101 promoting/resisting phosphorylation. Nevertheless, HDX-MS is very well suited to exploring secondary structure dynamics, and helical domain phosphorylation strikingly increases dynamics consistent with increased regional accessibility. The increased dynamics also nicely map to the pocket enveloped by the inhibitory NB7 Nanobody.

Ultimately, this study reveals an unexpected p110γ pocket that allows an engineered Nanobody to allosterically inhibit PI3Kγ complexes. The cryo-EM characterization of the interaction inspired an HDX-MS investigation of known sites of phosphorylation in the region. These insights could be linked to differences/convergences of p84 and p101 complex formation and activation of PI3Kγ, and future work may clarify these mechanisms further. The data presented herein will also be useful for broadening the target surface for future therapeutic developments. New allosteric connections between effector binding sites and post-translational modifications are always welcome.

Reviewer #1 (Public Review):

The authors sought to understand the neurocomputational mechanisms of how acute stress impacts human effortful prosocial behavior. Functional neuroimaging during an effort-based decision task and computational modeling were employed. Two major results are reported: 1) Compared to controls, participants who experienced acute stress were less willing to exert effort for others, with a more prominent effect for those who were more selfish; 2) More stressed participants exhibited an increase in activation in the dorsal anterior cingulate cortex and anterior insula that are critical for self-benefiting behaviour. The authors conclude that their findings have important insights into how acute stress affects prosociality and its associated neural mechanisms.

Overall, there are several strengths in this well-written manuscript. The experimental design along with acute stress induction procedures were well controlled, the data analyses were reasonable and informative, and the results from the computational modeling provide important insights (e.g., subjective values). Despite these strengths, there were some weaknesses regarding potential confounding factors in both the experimental design and methodological approach, including selective reporting of only some aspects of this complex dataset, and the interpretation of the observations. These detract from from the overall impact of the manuscript. In particular, the stress manipulation and pro-social task are both effortful, raising the possibility that stressed participants were more fatigued. Other concerns include the opportunity for social dynamics or cues during task administration, the baseline social value orientation (SVO) in each group, and the possibility of a different SVO in individuals with selfish tendencies. Finally, Figure 4 should specify whether the depicted prosocial choices include all five levels of effort.

Reviewer #1 (Public Review):

Wang et al., present a paper aiming to identify NALCN and TRPC6 channels as key mechanisms regulating VTA dopaminergic neuron spontaneous firing and investigating whether these mechanisms are disrupted in a chronic unpredictable stress model mouse.

Major strengths:

-This paper uses multiple approaches to investigate the role of NALCN and TRPC6 channels in VTA dopaminergic neurons.

Major weaknesses:

-The pharmacological tools used in this study are highly non-selective. Gd3+, used here to block NALCN is actually more commonly used to block TRP channels. 2-APB inhibits not only TRPC channels, but also TRPM and IP3 receptors while stimulating TRPV channels (Bon and Beech, 2013), while FFA actually stimulates TRPC6 channels while inhibiting other TRPCs (Foster et al., 2009).

Are the author's claims supported by the data?

-The multimodal approach including shRNA knockdown experiments alleviates much of the concern about the non-specific pharmacological agents. Therefore, the author's claim that NALCN is involved in VTA dopaminergic neuron pacemaking is well-supported.

-However, the claim that TRPC6 is the key TRPC channel in VTA spontaneous firing is somewhat, but not completely supported. As with NALCN above, the pharmacology alone is much too non-specific to support the claim that TRPC6 is the TRP channel responsible for pacemaking. However, unlike the NALCN condition, there is an issue with interpreting the shRNA knockdown experiments. The issue is that TRPC channels often form heteromers with TRPC channels of other types (Goel, Sinkins and Schilling, 2002; Strübing et al., 2003). Therefore, it is possible that knocking down TRPC6 is interfering with the normal function of another TRPC channel, such as TRPC7 or TRPC4.

-The claim that TRPC6 channels in the VTA are involved in the depressive-like symptoms of CMUS is supported.

- However, the connection between the mPFC-projecting VTA neurons, TRPC6 channels, and the chronic unpredictable stress model (CMUS) of depression is not well supported. In Figure 2, it appears that the mPFC-projecting VTA neurons have very low TRPC6 expression compared to VTA neurons projecting to other targets. However, in figure 6, the authors focus on the mPFC-projecting neurons in their CMUS model and show that it is these neurons that are no longer sensitive to pharmacological agents non-specifically blocking TRPC channels (2-APB, see above comment). Finally, in figure 7, the authors show that shRNA knockdown of TRPC6 channels (in all VTA dopaminergic neurons) results in depressive-like symptoms in CMUS mice. Due to the low expression of TRPC6 in mPFC-projecting VTA neurons, the author's claims of "broad and strong expression of TRPC6 channels across VTA DA neurons" is not fully supported. Because of the messy pharmacological tools used, it cannot be clamed that TRPC6 in the mPFC-projecting VTA neurons is altered after CMUS. And because the knockdown experiments are not specific to mPFC-projecting VTA neurons, it cannot be claimed that reducing TRPC6 in these specific neurons is causing depressive symptoms.

Impact:

It is valuable to compare pacemaking mechanisms in VTA and SNc neurons and this paper convincingly shows that NALCN contributes to VTA pacemaking, as it is known to contribute to SNc pacemaking. It also shows that TRPC6 channels in VTA dopamine neurons contribute to the depressive-like symptoms associated with CMUS.

It is important to note that the experiments presented in Figure 1 have all been previously performed in VTA dopaminergic neurons (Khaliq and Bean, 2010) including showing that low calcium increases VTA neuron spontaneous firing frequency and that replacement of sodium with NMDG hyperpolarizes the membrane potential.

Additional context:

-The authors explanation for the increase in firing frequency in 0 calcium conditions is that calcium-activated potassium channels would no longer be activated. However, there is a highly relevant finding that low calcium enhances the NALCN conductance through the calcium sensing receptor from Dejian Ren's lab (Lu et al., 2010) which is not cited in this paper. This increase in NALCN conductance with low calcium has been shown in SNc dopaminergic neurons (Philippart and Khaliq, 2018), and is likely a factor contributing to the low-calcium-mediated increase in spontaneous VTA neuron firing.

-One of the only demonstrations of the expression and physiological significance of TRPCs in VTA DA neurons was published by (Rasmus et al., 2011; Klipec et al., 2016) which are not cited in this paper. In their study, TRPC4 expression was detected in a uniformly distributed subset of VTA DA neurons, and TRPC4 KO rats showed decreased VTA DA neuron tonic firing and deficits in cocaine reward and social behaviors.

- Out of all seven TRPCs, TRPC5 is the only one reported to have basal/constitutive activity in heterologous expression systems (Schaefer et al., 2000; Jeon et al., 2012). Others TRPCs such as TRPC6 are typically activated by Gq-coupled GPCRs. Why would TRPC6 be spontaneously/constitutively active in VTA DA neurons?

-A new paper from the group of Myoung Kyu Park (Hahn et al., 2023) shows in great detail the interactions between NALCN and TRPC3 channels in pacemaking of SNc DA neurons.

References

Bon, R.S. and Beech, D.J. (2013) 'In pursuit of small molecule chemistry for calcium-permeable non-selective TRPC channels -- mirage or pot of gold?', British Journal of Pharmacology, 170(3), pp. 459-474. Available at: https://doi.org/10.1111/bph.12274.

Foster, R.R. et al. (2009) 'Flufenamic acid is a tool for investigating TRPC6-mediated calcium signalling in human conditionally immortalised podocytes and HEK293 cells', Cell Calcium, 45(4), pp. 384-390. Available at: https://doi.org/10.1016/j.ceca.2009.01.003.

Goel, M., Sinkins, W.G. and Schilling, W.P. (2002) 'Selective association of TRPC channel subunits in rat brain synaptosomes', The Journal of Biological Chemistry, 277(50), pp. 48303-48310. Available at: https://doi.org/10.1074/jbc.M207882200.

Hahn, S. et al. (2023) 'Proximal dendritic localization of NALCN channels underlies tonic and burst firing in nigral dopaminergic neurons', The Journal of Physiology, 601(1), pp. 171-193. Available at: https://doi.org/10.1113/JP283716.

Jeon, J.-P. et al. (2012) 'Selective Gαi subunits as novel direct activators of transient receptor potential canonical (TRPC)4 and TRPC5 channels', The Journal of Biological Chemistry, 287(21), pp. 17029-17039. Available at: https://doi.org/10.1074/jbc.M111.326553.

Khaliq, Z.M. and Bean, B.P. (2010) 'Pacemaking in dopaminergic ventral tegmental area neurons: depolarizing drive from background and voltage-dependent sodium conductances', The Journal of Neuroscience: The Official Journal of the Society for Neuroscience, 30(21), pp. 7401-7413. Available at: https://doi.org/10.1523/JNEUROSCI.0143-10.2010.

Klipec, W.D. et al. (2016) 'Loss of the trpc4 gene is associated with a reduction in cocaine self-administration and reduced spontaneous ventral tegmental area dopamine neuronal activity, without deficits in learning for natural rewards', Behavioural Brain Research, 306, pp. 117-127. Available at: https://doi.org/10.1016/j.bbr.2016.03.027.

Lu, B. et al. (2010) 'Extracellular calcium controls background current and neuronal excitability via an UNC79-UNC80-NALCN cation channel complex', Neuron, 68(3), pp. 488-499. Available at: https://doi.org/10.1016/j.neuron.2010.09.014.

Philippart, F. and Khaliq, Z.M. (2018) 'Gi/o protein-coupled receptors in dopamine neurons inhibit the sodium leak channel NALCN', eLife, 7. Available at: https://doi.org/10.7554/eLife.40984.

Rasmus, K. et al. (2011) 'Sociability is decreased following deletion of the trpc4 gene', Nature Precedings, pp. 1-1. Available at: https://doi.org/10.1038/npre.2011.6367.1.

Schaefer, M. et al. (2000) 'Receptor-mediated regulation of the nonselective cation channels TRPC4 and TRPC5', The Journal of Biological Chemistry, 275(23), pp. 17517-17526. Available at: https://doi.org/10.1074/jbc.275.23.17517.

Strübing, C. et al. (2003) 'Formation of novel TRPC channels by complex subunit interactions in embryonic brain', The Journal of Biological Chemistry, 278(40), pp. 39014-39019. Available at: https://doi.org/10.1074/jbc.M306705200.

Reviewer #1 (Public Review):

Li et al report that upon traumatic brain injury (TBI), Pvr signalling in astrocytes activates the JNK pathway and up-regulates the expression of the well-known JNK target MMP1. The FACS sort astrocytes, and carry out RNAseq analysis, which identifies pvr as well as genes of the JNK pathway as particularly up-regulated after TBI. They use conventional genetics loss of function, gain of function and epistasis analysis with and without TBI to verify the involvement of the Pvr-JNK-MMP1 signalling pathway.

The strengths are that multiple experiments are used to demonstrate that TBI in their hands damaged the BBB, induced apoptosis and increased MMP1 levels. The RNAseq analysis on FACS sorted astrocytes is nice and will be valuable to scientists beyond the confines of this paper. The functional genetic analysis is conventional, yet sound, and supports claims of JNK and MMP1 functioning downstream of Pvr in the TBI context.

However, the weaknesses are that novelty and insight are both rather limited, some data are incomplete and other data do not support some claims. Some approaches used lacked resolution and some experiments lacked rigour. The authors may wish to improve some of their data as this would make their case more convincing. Alternatively, they should remove unsupported claims.

Novelty and insight:<br /> Others had previously published that both JNK signalling and MMP1 were activated upon injury, in multiple contexts (as well as the articles cited by the authors, they should also see Losada-Perez et al 2021). That Pvr can regulate JNK signalling was also known (Ishimaru et al 2004). And it was also known that astrocytes can respond to injury by proliferating, both in larval ventral nerve cords and adult brains (Kato et al 2011; Losada-Perez et al 2016; Harrison et al 2021; Simoes et al 2022). The authors argue that the novelty of the work is the investigation of the response of astrocytes to TBI. However, this is of somewhat limited scope. The authors mention that Mmp1 regulates tissue remodelling, the inflammatory process and cancer. Exploring these functions further would have been an interesting addition, but the authors do not investigate what consequences the up-regulation of Mmp1 after injury has in repair or regeneration processes.

Incomplete or unconvincing data:<br /> The authors failed to detect PCNA-GFP and pH3 in brains after TBI and conclude that that TBI does not induce astrocyte proliferation. However, this is a surprising claim, as it would be rather different from all previous prevalent observations of cell proliferation induced by injury. Cell proliferation can be notoriously difficult to detect (ie due to timing and sample size), thus instead this raises doubts on the experimental protocol or execution.<br /> Others have previously reported: cells in S- phase using PCNA-GFP and other reporters (eg BrdU, EdU, FUCCI) in the intact adult brain (Kato et al 2009; Foo et al 2017; Li et al 2020; Fernandez-Hernandez et al 2013; Simoes et al 2022); that injury to the adult brain and VNC induces cell proliferation that can be detected with cell proliferation markers like BrdU, Myc, FUCCI and the mitotic marker pH3 (Kato et al 2009; Fernandez-Hernandez et al 2013; Losada-Perez et al 2021; Simoes et al 2022); and that injury to the brain and CNS induces glial proliferation in adult and larval brains/CNS, specifically of astrocytes (Kato et al 2011; Losada-Perez et al 2016; Fernandez-Hernandez et al 2013; Simoes et al 2022). Thus, the fact that they did not observe PCNAGFP+ cells in control, intact adult brains nor after TBI could suggest that they had technical, experimental difficulties. Detecting mitotic cells with anti-pH3 is difficult because M phase is very brief, but others have succeeded (Simoes et al 2022). Given that in all previous reports mentioned above cells were seen to proliferate after injury in the CNS, it would be rather surprising if no cell proliferation occurred after TBI. Resolving this conflicting result is important, as it could imply that TBI induces very different cellular responses from various other lesions or injury types. It is conceivably not impossible, but the most parsimonious start point would be that multiple injury types could cause equivalent responses in cells. Thus, the authors ought to consider whether technical or experimental design problems affected their experimental outcome instead.

Other claims not supported by data:<br /> (1) astrocyte hypertrophy, as the tools used do not have the resolution to support this claim;

(2) localisation of anti-Pvr to specific cells, as the images show uniform signal or background instead;

(3) astrocytes do not engulf cell debris after TBI, as the tools and images do not have the resolution to make this claim.

The authors could improve these data with alternative experiments to maintain the claims; alternatively, these unsupported claims should be removed.

Statistical analysis:<br /> The statistical analysis needs revising as it is wrong in multiple places. Revising the statistics will also require revision of the validity of the claims and adjusting interpretations accordingly.

Altogether, this is an interesting and valuable addition to the repertoire of articles investigating neuron-glia communication and glial responses to injury in the Drosophila central nervous system (CNS). It is good and important to see this research area in Drosophila grow. This community together is building a compelling case for using Drosophila and its unparalleled powerful genetics to investigate nervous system injury, regeneration and repair, with important implications. Thus, this paper will be of interest to scientists investigating injury responses in the CNS using Drosophila, other model organisms (eg mice, fish) and humans.

Reviewer #1 (Public Review):

Jackson and Giacomassi et al. investigated the impact of repeated topical application of the TLR-7/8 agonist R848, mimicking single-stranded RNA viral infection, on circulating monocytes. Interestingly in this murine model of skin inflammation, they find that there is a striking increase in vascular patrolling (Ly6Clow) monocytes in the blood. In the majority of inflammatory settings so far described, it is the classical (Ly6Chi) monocyte population that is augmented. They found that this Ly6Clow monocyte expansion occurred in response to stimulation by R848 at epithelial barrier surfaces (skin and gut) and not following systemic administration of R848. Of note, the Ly6Clow increase was not dependent on type I or type II IFNs or CCR2, all factors that are important for Ly6Chi monocyte expansion in response to life-threatening infections, such as Toxoplasma gondii. Positive factors driving Ly6Clow augmentation are not identified. Alterations to circulating monocytes may have implications for secondary infection as R848-treated animals were less susceptible to flu infection. This research furthers our understanding of how tissues and organs have distinct mechanisms of communication in response to inflammatory and infectious stimuli and the implications this can have on circulating immune populations.

The conclusions of this paper are generally well supported by the data presented, however, some aspects of the study need to be clarified or extended. Additionally, some of the findings could be better discussed in the context of the current literature.

1) CSF-1 availability is described, initially by Yona et al. (DOI: 10.1016/j.immuni.2012.12.001), as an important factor extending the half-life of Ly6Clow monocytes in circulation. Given the expansion of Ly6Clow monocytes and their upregulation of CD115 in circulation, it would have been relevant to measure CSF-1 to assess whether this may be a candidate factor for the phenotype observed.

2) The conclusion that the altered monocyte compartment enhances protection against secondary infection is underdeveloped. The key experiment presented involves treating animals with R848 and demonstrating that they have an altered response to flu infection. This approach does not specifically assess the importance of monocytes. From these studies, it is only possible to conclude there is an association between monocyte alterations and secondary infection.

Reviewer #1 (Public Review):

This study examines the effects of Ca2+ and NHE1 peptide binding on the conformation of CHP3, one of three related calcineurin-homologous proteins. One question that is addressed is whether Ca2+ binding triggers membrane association of the myristoyl group, a so-called "Ca2+-myristoyl switch". This is convincingly demonstrated to not be the case by the experiment in Figure 6B: unlike myristoylated recoverin, mCHP3 does not show enhanced association with liposomes. In the presence of a target peptide, however, myristoylation enhances membrane association. Curiously, this interaction is not Ca2+ dependent, but the membrane association of the non-myristoylated CHP3 is Ca2+-dependent.

My concerns with this study relate to physiological relevance. First, it is unclear if Ca2+ binding has a regulatory function in any of the CHP proteins. The authors state that CHP1 and CHP2 have Ca2+ binding affinities <100 nM, so these proteins are likely saturated with Ca2+ under all physiological conditions. On the other hand, CHP3 binds Ca2+ with a Kd of 8 micromolar (in the presence of physiological concentrations of Mg2+) so it will be largely unbound under most normal cellular concentrations of Ca2+ which are in the submicromolar range. Free Ca2+ rarely reaches 1 micromolar under non-pathological concentrations, and if it does, the fraction of CHP3 bound to Ca2+ should be estimated for context. Given these caveats, I am not convinced that experiments done with millimolar concentrations of Ca2+ (e.g., Figures 2, 3, 6) are physiologically informative.

Reviewer #1 (Public Review):

This is the most complete genomic overview of the epidemiology of Salmonella enterica serovar Typhi including close to 13,000 genmoes from multiple countries, clearly demonstrating the geographical differences in molecular epidemiology and antibiotic resistance traits. This database could serve as the global reference for the future with constant addition of new information.

This is a descriptive study, not providing fundamentally new mechanistic insights of the disease, but providing an overview of the global epidemiology of this bacterium.<br /> Open-ended questions remain the generalizability of the findings, which is linked to the completeness of the surveillance systems, as well as the linkage of genotypes to clinical disease presentation (severity) and of linkage of local antibiotic use and the prevalence of the different resistance traits.

Publication of these data will be very helpful for all those interested in the molecular epidemiology of Salmonella and may stimulate not-yet participating institutes to add information for future analyses. It may also stimulate investigators to use the data for deriving more insights in clinical disease presentations, associations with antibiotic use and input for mathematical modelling.

The (lenghty) introduction is textbook epidemiology of the emergence of antimicrobial resistance in Typhi.

Reviewer #1 (Public Review):

The manuscript by Mau et al. describes a sophisticated method to follow enhancer activity in both live embryos and fixed embryos in Tribolium. The authors identified putative enhancers via comparative ATAC-seq of embryos divided into different regions and at different developmental time points. As an experimental piece of work, this is excellent. However, the framing and presentation in this manuscript would need to be improved to avoid misrepresentation of existing ideas and over-interpretation of results. The manuscript would require significant re-writing. This can be done without additional data or analyses, but simply more careful writing.

The Introduction starts by setting up a straw-man argument, claiming that the assumption is that gene expression is set up as stable expression domains that undergo little or no subsequent change. I don't think that any current developmental biologist thinks this is true. The references used to support this claim are from the 1990s up to the early 2000s. There are numerous examples since then that show that developmental gene expression is dynamic as a rule.

The Introduction then continues as a rather detailed review of enhancers, Tribolium methodology, tools for identifying enhancers, and more. The Introduction cites 99 references, which seems excessive for what is essentially an experimental paper. Significant parts of the Introduction can be trimmed or removed. There is no need to mention all the tools available for Tribolium if they are not used in the described experiments. A thorough analysis of the advantages and disadvantages of different modes of ATAC-seq is also beyond the scope of the Introduction. The authors should explain why they chose the tools they chose without excessive background. Having said that, the Introduction actually overlooks a lot of significant work that is relevant to the subject of the paper. Specifically, the authors completely ignore all of the work on development in hemimetabolous insects such as Oncopeltus and Gryllus - the omission is glaring. There has been a lot of relevant work on dynamic gene expression patterns coming out of these species.

The experimental setup involves cutting embryos into three sections at two time points. The results then discuss differences in "space" and "time" but there is no discussion of the embryological meaning of these terms. What is happening at the two time points from a developmental perspective? What is the difference between the three sections? There is a lot of relevant development going on at these stages and important regional differences, which have been well-studied in Tribolium and in other insects but are not even mentioned.

In the preliminary results of the ATAC-seq analysis, it is clear that there are significant differences between the sections, which should come as no surprise, but fairly minor differences between the same section at the two time points. This could be because the two time points are pretty close together at a stage when there is a lot of repetitive patterning going on. A possible interpretation, which the authors don't mention because it goes against their main thesis, is that maybe most of the processes that are taking place at this stage are not dynamic enough to show up at the temporal resolution they have applied. This is worth at least a mention.

The authors link each accessible site to the nearest gene when looking at putative enhancer function. This is a risky assumption since there are many examples of enhancer sites that are far upstream or downstream of the target gene and often closer to an unrelated gene than to the target gene. The authors should at least acknowledge this problem with their functional annotation.

In the Discussion, the authors claim that contrary to how it may seem, the question they are addressing is not a "fringe problem". Once again, I think this is a straw man. No active researcher thinks that the question of dynamic regulation of gene expression during development is a fringe problem. On the contrary, most researchers will accept that this is one of the most interesting and important questions in current developmental biology.<br /> Perhaps the most significant problem with the manuscript is that it is all built around the premise of enhancer switching between dynamic enhancers and static enhancers. The authors find one site that is consistent with their prediction for a dynamic enhancer and one site - regulating a different gene - that is consistent with their prediction for a static enhancer and claim that they have provided support for their model. I think this claim is grossly exaggerated. They present data that can be seen as consistent with their model but are a long way from providing evidence for it.<br /> Like the Introduction, the Discussion includes long paragraphs (lines 450-480) that are more suitable for a review/hypothesis paper. The data presented in this manuscript has little relevance to the question of kinematic vs. trigger waves, and therefore there is no real reason for the question to be discussed here.

Reviewer #1 (Public Review):

This is a very interesting and timely manuscript investigating the roles of root-emitted secondary metabolites in mediating plant-soil feedback in a realistic and agricultural context (maize - wheat rotation). I find this article to be an important contribution to the field as the roles played by soil chemical legacies in mediating plant-soil feedbacks have been largely overlooked so far, particularly in the field. I found this manuscript to be extremely well-written and clear. I was impressed by the number of response variables measured by the authors to characterise how wheat plants responded to the soil legacies created by different maize genotypes.<br /> The article presents the results of a plant-soil feedback experiment in which two maize genotypes (wild type or benzoxazinoid-deficient bx1 mutant plant) conditioned field soil for one growing season. Monocultures of each genotype occupied alternate strips in the field. At the end of this conditioning phase, the authors analysed benzoxazinoids in the soil and found that the soil conditioned by WT maize was characterized by greater concentrations of several benzoxazinoids. In fact, most benzoxazinoids were below the detection limit for soil conditioned by bx1 mutant plants. These differences in soil chemical legacies were associated with differences in bacterial and fungal communities in the roots and rhizosphere of maize. Soon after the maize harvest, monocultures of three wheat varieties were grown in soil that was conditioned by either WT or bx1 maize plants. This factorial design allowed the authors to study the response of different wheat varieties to soil legacies created by maize genotypes that differ in their ability to produce and release benzoxazinoids into the soil. Although root and rhizosphere microbial communities were mainly driven by wheat genotype (and not by maize soil conditioning), soil conditioning effects on benzoxazinoid concentrations were still visible at the end of the feedback phase, but only for specific compounds (e.g. AMPO). In comparison to wheat grown on bx1-conditioned soil, the authors found that wheat plants grown on benzoxazinoid-conditioned soil had better emergence and were taller and more productive. In addition, benzoxazinoid soil conditioning reduced infestation by the cereal leaf beetle Oulema melanopus (particularly in one wheat variety) but did not affect weed pressure. The authors also found that wheat grown on benzoxazinoid-conditioned soil had more reproductive tillers, which led to greater grain yield (+4-5%). Grain quality, however, was not affected by maize soil conditioning.

I appreciated that the authors carefully interpreted the results of their experiment, although data analysis could be improved to take repeated measures within a plot into account. Overall, this is a compelling study, with rigorous and numerous measurements and state-of-the-art methods in plant/soil ecology. This study is unique in that it demonstrates the important role that soil chemical legacies can play in mediating plant-soil interactions and influencing the fitness of the following crop in a realistic agricultural setting. Therefore, I believe that this work will be of broad interest to plant and soil ecologists, as well as to agronomists.

Reviewer #1 (Public Review):

In this work Indurthy and Auerbach investigate the fundamental concept of how the energy of agonist binding is converted into the energy of the conformational change that opens the pore of the nicotinic acetylcholine receptor (nAChR). The conclusions are based on a very large pool of experimental data that are interpreted with great mechanistic insight.

Specifically, the authors define "efficacy" (eta) of a ligand as the fractional change in binding free energy between the open and the closed states of the channel. They construct a log-log scatter plot of efficacy vs. affinity which represents 23 different agonists acting on the WT receptor, plus a subset of the same agonists acting on various nAChR mutants. They go on to show that these largely scattered dots can be partitioned into 5 distinct clusters ("eta-classes") within which the dots are linearly arranged. They interpret these clusters in terms of a mechanistic gating model (the "catch&hold LFER model"), and suggest that a different model accounts for each different eta-class. Put in simple terms, the interpretation is that 5 different subtypes of gating isomerization exist for the nAChR, the choice among which depends on the agonist used.

These types of study are necessary to advance conceptual understanding in biophysics. I have some reservations regarding the mechanistic interpretation of the data set and the uniqueness of the proposed model.

1. One concern regards the clustering of the data sets in Fig. 5 into exactly 5 eta-classes. First, two clusters contain only two data points each. Second, the proposed "catch&hold LFER model" (Fig. 2) does not predict the existence of a discrete number of such eta-classes. How strong is the evidence that there are exactly 5 classes as opposed to a continuum of possible eta values.

2. The authors do not discuss the uniqueness of the proposed model. In fact, it seems to me that the existence of eta-classes might be explained just as well by an alternative model which assumes a single gating mechanism for the receptor, but distinct patterns of ligand-protein interactions for the different agonists. The pore opening-associated increase in agonist affinity is typically caused by a tightening of the substrate binding site (often called clamshell closure) which brings further protein side chains into the vicinity of the ligand, thereby allowing further ligand-protein interactions to form (or further strengthening interactions that exist also in the closed-pore state). Thus, at a first approximation, the ratio between binding free energies in the open- and closed-pore states reflects the ratio of the numbers (and strengths) of ligand-protein bonds in those two states.

As an illustration, consider the following simplified model for a channel and a given ligand. In the open-pore state the number of ligand-protein interactions is n(o), and all those interactions are comparably strong. Out of those interactions only a subset is formed in the closed-pore state, their number is n(c) (where n(c)<br /> The maximal possible values of n(c) and n(o) are determined by the number and spatial arrangement of protein chemical groups that surround the substrate binding site. On the other hand, depending on the number and arrangement of matching chemical groups on the ligand, different ligands will be able to "exploit" different subsets of these possible ligand-protein interactions, resulting in different values of eta. Furthermore, ligands for which the absolute values of n(o) are different, but the ratio n(c)/n(o) is similar, will form apparent "eta-classes", i.e., will be arranged on a "eta-plot" along a straight line. (See attached image file for a graphical representation of the model.)

This model would suggest that there is a single gating mechanism (i.e., the actual protein conformational change is similar regardless of which agonist is bound), but the relative stabilities of the ligand-bound closed and open states are agonist-dependent. Wouldn't such a mechanism equally well explain all the data shown? The authors should either acknowledge this possibility or discuss available structural or functional evidence to exclude it.

Reviewer #1 (Public Review):

This manuscript will interest cognitive scientists, neuroimaging researchers, and neuroscientists interested in the systems-level organization of brain activity. The authors describe four brain states that are present across a wide range of cognitive tasks and determine that the relative distribution of the brain states shows both commonalities and differences across task conditions.

The authors characterized the low-dimensional latent space that has been shown to capture the major features of intrinsic brain activity using four states obtained with a Hidden Markov Model. They related the four states to previously-described functional gradients in the brain and examined the relative contribution of each state under different cognitive conditions. They showed that states related to the measured behavior for each condition differed, but that a common state appears to reflect disengagement across conditions. The authors bring together a state-of-the-art analysis of systems-level brain dynamics and cognitive neuroscience, bridging a gap that has long needed to be bridged.

The strongest aspect of the study is its rigor. The authors use appropriate null models and examine multiple datasets (not used in the original analysis) to demonstrate that their findings replicate. Their thorough analysis convincingly supports their assertion that common states are present across a variety of conditions, but that different states may predict behavioural measures for different conditions. However, the authors could have better situated their work within the existing literature. It is not that a more exhaustive literature review is needed-it is that some of their results are unsurprising given the work reported in other manuscripts; some of their work reinforces or is reinforced by prior studies; and some of their work is not compared to similar findings obtained with other analysis approaches. While space is not unlimited, some of these gaps are important enough that they are worth addressing:

1. The authors' own prior work on functional connectivity signatures of attention is not discussed in comparison to the latest work. Neither is work from other groups showing signatures of arousal that change over time, particularly in resting state scans. Attention and arousal are not the same things, but they are intertwined, and both have been linked to large-scale changes in brain activity that should be captured in the HMM latent states. The authors should discuss how the current work fits with existing studies.<br /> 2. The 'base state' has been described in a number of prior papers (for one early example, see https://pubmed.ncbi.nlm.nih.gov/27008543). The idea that it might serve as a hub or intermediary for other states has been raised in other studies, and discussion of the similarity or differences between those studies and this one would provide better context for the interpretation of the current work. One of the intriguing findings of the current study is that the incidence of this base state increases during sitcom watching, the strongest evidence to date is that it has a cognitive role and is not merely a configuration of activity that the brain must pass through when making a transition.<br /> 3. The link between latent states and functional connectivity gradients should be considered in the context of prior work showing that the spatiotemporal patterns of intrinsic activity that account for most of the structure in resting state fMRI also sweep across functional connectivity gradients (https://pubmed.ncbi.nlm.nih.gov/33549755/ ). In fact, the spatiotemporal dynamics may give rise to the functional connectivity gradients (https://pubmed.ncbi.nlm.nih.gov/35902649/ ). HMM states bear a marked resemblance to the high-activity phases of these patterns and are likely to be closely linked to them. The spatiotemporal patterns are typically obtained during rest, but they have been reported during task performance (https://pubmed.ncbi.nlm.nih.gov/30753928/ ) which further suggests a link to the current work. Similar patterns have been observed in anesthetized animals, which also reinforces the conclusion of the current work that the states are fundamental aspects of the brain's functional organization.

Reviewer #1 (Public Review):

The goal of this study is to identify transcription factors that mediate stem cell transitions during differentiation. To achieve this, the authors examine the type II Drosophila neuroblast lineage, using single-cell RNA sequencing to examine all cell types in the type II lineage. There are known patterns of expression for neurons in this lineage, so they can identify clusters in their data set that are in the developmental state of transitioning from neuroblast to immature intermediate neuronal progenitor. They have outlined a set of expression criteria for transcription factors that are candidates for fine-tuning stem cell fate. They find that an isoform of Fruitless, called FruC, is a candidate transcription factor. Using microscopy and several genetic perturbation conditions the authors find that FruC is expressed in neuroblasts and can alter the number of cells in the lineage. To determine the mechanism that FruC uses to influence stemness the authors examine genomic occupancy of FruC, changes in histone modifications in FruC loss-of-function studies, and examination of DNA occupancy of proteins that function in chromatin modification. The authors argue that FruC functions to promote low-level H3K27me3 enrichment to maintain stemness based on comparisons across these data sets. The identification of transcription factors and the mechanisms used to maintain or differentiate stem cells is an important goal and is still a fundamental question in biology. The Drosophila model is poised for this type of analysis, given the knowledge of gene expression across cell fate that the authors use in this study.

Comments the authors should address:<br /> This is a valuable study that relies on several state-of-the-art genomic data sets to examine the mechanism that drives stemness. However, the authors should be using statical approaches to support their major conclusion regarding FruC and the role of H3K27me3. The study presents peak data in genome browser tracks of a handful of loci in the Notch pathway that show the pattern of reduced HK3K27me3 and not the other modifications they examine. However, it is not clear if the majority of FruC target genes in the genomic analyses have this pattern, though they argue they do. The major conclusion that FruC promotes a stem cell fate is based on the overlap between the list of genes they identify bound by FruC and the lists of genes that have changes in histone modifications (H3K27ac, H3K4me3, and H3K27me3). The limited use of statistical approaches to draw these conclusions is a weakness of the study. The authors do not use statistics to find changes in chromatin modification at loci, instead relying on 2-fold change calculations. Furthermore, the authors don't indicate if the genes with altered histone modification/binding peaks are significantly enriched (or not) with FruC targets, with no quantitative assessments of these data. The data in Figures 5,6 and S4 should have statistics/quantification to support the major conclusions of their study that FruC targets differ in H3K27me3, but not H3K27ac, and H3K4me3.

Reviewer #1 (Public Review):

This study optimized a protocol for analyzing microplastics (MPs) in bovine and human follicular fluid. The authors identified the most common plastic polymers in the follicular fluid and assessed the impact of polystyrene beads on bovine oocyte maturation based on the concentration of MPs in follicular fluid. The authors found a decrease in maturation rate in the presence of polystyrene beads and conducted proteomic analysis of oocytes treated with and without MPs, revealing protein alterations.

Strengths:<br /> • The optimization of the protocol for analyzing MPs in follicular fluid, which is important for future research in this area.<br /> • Investigating the effects of MPs on oocyte maturation and proteomic profiles is significant.

Weaknesses:<br /> • The effects of polystyrene beads on oocyte maturation and proteomic profiles are not directly demonstrated, and insufficient analysis is performed to support the claims made in the manuscript.<br /> • The use of polystyrene beads does not fully mimic the concentration and interaction of MPs in follicular fluid, which warrants careful interpretation and discussion.<br /> • A major weakness is the lack of mechanism. Determining the cause of meiotic arrest (decreased maturation rate) would be needed to strengthen the paper. Are spindle morphology, chromosome morphology/alignment and/or spindle assembly checkpoint mechanism perturbed in MPs-treated oocytes?<br /> • Functional assays to validate one or more of the pathways suggested by the proteomic analysis would be necessary to strengthen the paper.<br /> • The analysis of broken zona pellucida is not sufficiently convincing. Definitely the breakage of zona pellucida is most likely a result of oocyte denudation. However, this may indicate increased fragility of polystyrene beads-treated oocytes. Investigating cytoskeletal components in oocytes treated with or without polystyrene beads would strengthen this paper.<br /> • The percentage of degenerated oocytes in control group is abnormally high which raises concern that the oocytes are not healthy.<br /> • The small font size of the figures (such as Fig. 1C) affects the quality of the manuscript.<br /> • Finally, the authors should cite previous publications on the effects of MPs on female reproduction, as this is not a novel area of research, despite the use of different concentrations. For example, "Polystyrene microplastics lead to pyroptosis and apoptosis of ovarian granulosa cells via NLRP3/Caspase-1 signaling pathway in rats (DOI: 10.1016/j.ecoenv.2021.112012)".

Reviewer #1 (Public Review):

The authors performed a meta-analysis of GC concentrations and metabolic rates in birds and mammals. They found close associations for all studies showing a positive association between these two traits. As GCs have been viewed with close links to "stress," authors suggest that this overlooks the importance of metabolism and perhaps GC variation does not relate to "stress" per se but an increase in metabolism instead.

This is an important meta-analysis, as most researchers acknowledge the link between GCs and metabolism, metabolism is often overlooked in studies. The field of conservation physiology is especially focused on GCs being a "stress" hormone, which overlooks the importance of GCs in mediating energy balance, i.e., an animal that has high GC concentrations may not be doing that poorly compared to an animal with low GC concentrations, it might just be expending more energy, e.g., caring for young. The results, with overwhelming directionality and strong effect sizes, support the link for a positive association with these two variables.

My main concern lies in that most of the studies come from a few labs, therefore there may be limited data to test this relationship. I would include lab as a random effect to see how strong this effect might be. Furthermore, I would like to see a test of the directionality of the two variables. Authors suggest that changes in metabolism affect GC levels but likely changes in GC levels would affect metabolism. Why not look into studies that have altered GC levels experimentally and see the effect on metabolism? Based on the close link, authors suggest that GCs may not play a role outside of "stress" beyond the stressor's effect on metabolic rate. However, if they were to investigate manipulations of GCs on metabolic rate, the link may or may not be there, which would be interesting to look at. I firmly believe that GCs are tightly linked to metabolism; however, I also think that GCs have a range of effects outside of metabolism as well, depending on the course and strength of the stressor.

This work helps in the thinking that GCs are not the same as a "stress" hormone or labelling hormones with only one function. As hormones are naturally pleiotropic, the view of any one hormone being X is overly simplistic.

Reviewer #1 (Public Review):

The paper titled 'A dual function of the IDA peptide in regulating cell separation and modulating plant immunity at the molecular level' by Olsson Lalun et al., 2023 aims to understand how IDA-HAE/HSL2 signalling modulates immunity, a pathway that has previously been implicated in development. This is a timely question to address as conflicting reports exist within the field. IDL6/7 have previously been shown to negatively regulate immune signalling, disease resistance and stress responses in leaf tissue, however IDA has been shown to positively regulate immunity through the shedding of infected tissues. Moreover, recently the related receptor NUT/HSL3 has been shown to positively regulate immune signalling and disease resistance. This work has the potential to bring clarity to this field, however the manuscript requires some additional work to address these questions. This is especially the case as it contracts some previous work with IDL peptides which are perceived by the same receptor complexes.

Can IDA induce pathogen resistance? Does the infiltration of IDA into leaf tissue enhance or reduce pathogen growth? Previously it has been shown that IDL6 makes plants more susceptible. Is this also true for IDA? Currently cytoplasmic calcium influx and apoplastic ROS as overinterpreted as immune responses - these can also be induced by many developmental cue e.g. CLE40 induced calcium transients. Whilst gene expression is more specific is also true that treatment with synthetic peptides, which are recognised by LRR-RKs, can induce immune gene expression, especially in the short term, even when that is not there in vivo function e.g. doi.org/10.15252/embj.2019103894.

This paper shows that receptors other than hae/hsl2 are genetically required to induce defense gene expression, it would have been interesting to see what phenotype would be associated with higher order mutants of closely related haesa/haesa-like receptors. Indeed recently HSL1 has been shown to function as a receptor for IDA/IDL peptides. Could the triple mutant suppress all response? Could the different receptors have distinct outputs? For example for FRK1 gene expression the hae hsl2 mutant has an enhanced response. Could defence gene expression be primarily mediated by HSL1 with subfunctionalisation within this clade?

One striking finding of the study is the strong additive interaction between IDA and flg22 treatment on gene expression. Do the authors also see this for co-treatment of different peptides with flg22, or is this unique function of IDA? Is this receptor dependent (HAE/HSL1/HSL2)?

It is interesting how tissue specific calcium responses are in response to IDA and flg22, suggesting the cellular distribution of their cognate receptors. However, one striking observation made by the authors as well, is that the expression of promoter seems to be broader than the calcium response. Indicating that additional factors are required for the observed calcium response. Could diffusion of the peptide be a contributing factor, or are only some cells competent to induce a calcium response?<br /> It is interesting that the authors look for floral abscission phenotypes in cngc and rbohd/f mutants to conclude for genetic requirement of these in floral abscission. Do the authors have a hypothesis for why they failed to see a phenotype for the rbohd/f mutant as was published previously? Do you think there might be additional players redundantly mediating these processes?

Can you observe callose deposition in the cotyledons of the 35S::HAE line? Are the receptors expressed in native cotyledons? This is the only phenotype tested in the cotyledons.

Are flg22-induced calcium responses affected in hae hsl2?

Reviewer #1 (Public Review):

In this study, Le Moigne and coworkers shed light on the structural details of the Sedoheptulose-1,7-Bisphosphatase (SBPase) from the green algae Chlamydomonas reinhardtii. The SBPase is part of the Calvin cycle and catalyzes the dephosphorylation of sedoheptulose-1,7-bisphosphate (SBP), which is a crucial step in the regeneration of ribulose-1,5-bisphosphate (RuBP), the substrate for Rubisco. The authors determine the crystal structure of the CrSBPase in an oxidized state. Based on this structure, potential active site residues and sites of post-translational modifications are identified. Furthermore, the authors determine the CrSBPase structure in a reduced state revealing the disruption of a disulfide bond in close proximity to the dimer interface. The authors then use molecular dynamics (MD) to gain insights into the redox-controlled dynamics of the CrSBPase and investigate the oligomerization of the protein using small-angle X-ray scattering (SAXS) and size-exclusion chromatography. Despite the difference in oligomerization, disruption of this disulfide bond did not impact the activity of CrSBPase, suggesting additional thiol-dependent regulatory mechanisms modulating the activity of the CrSBPase.

The authors provide interesting new findings on a redox-mechanism that modulates the oligomeric behavior of the SBPase, however without investigating this potential mechanism in more detail. The conclusions of this manuscript are mostly supported by the data, but they should be more carefully evaluated in respect to what is known from other systems as e.g. the moss Physcomitrella patens. This is especially of interest, as SBPase was previously reported to be dimeric, whereas for FBPase a dimer/tetramer equilibrium has been observed.

1.) Given that PpSBPase has been already characterized in detail, the authors should provide a more rigorous comparison to the existing data on SBPases. This includes a more conclusive structural comparison but also the enzymatic assays should be compared to the findings from P. patens. Do the authors observe differences between the moss and the chlorophyte systems, maybe even in regard to the oligomerization of the SBPase?

2.) The authors should include the control experiments (untreated SBPase) and the assays performed with mutant versions of the SBPase, which are currently only mentioned in the text or not shown at all.

3.) The representation of the structure in figures (especially Figures 1 and 3) should be adjusted to match the author's statements. In Figure 1, the angle from which the structure is displayed changes over the entire figure making it difficult to follow especially as a non-structural biologist. Furthermore, important aspects of the structure mentioned in the text are not labeled and should be highlighted, by e.g. a close-up. Same holds true for Figure 3 that currently mostly shows redundant information.

4.) The authors state that mutation of C115 and C120 to serine destabilize the dimer formation, while more tetramer and monomer is formed. As the tetramer is essentially a dimer of dimers, the authors should elaborate how this might work mechanistically. In my opinion, dimer formation is a prerequisite for tetramer formation and the two mutations rather stabilize the tetramer instead of destabilizing the dimer.

Reviewer #1 (Public Review):

The authors used mathematical models to explore the mechanism(s) underlying the process of polar tube extrusion and the transport of the sporoplasm and nucleus through this structure. They combined this with experimental observations of the structure of the tube during extrusion using serial block face EM providing 3 dimensional data on this process. They also examined the effect of hyperosmolar media on this process to evaluate which model fit the predicted observed behavior of the polar tube in these various media solutions. Overall, this work resulted in the authors arriving at a model of this process that fit the data (model 5, E-OE-PTPV-ExP). This model is consistent with other data in the literature and provides support for the concept that the polar tube functions by eversion (unfolding like a finger of a glove) and that the expanding polar vacuole is part of this process. Finally, the authors provide important new insights into the bucking of the spore wall (and possible cavitation) as providing force for the nucleus to be transported via the polar tube. This is an important observation that has not been in previous models of this process.

Reviewer #1 (Public Review):

The goal of the authors is to use whole-exome sequencing to identify genomic factors contributing to asthenoteratozoospermia and male infertility. Using whole-exome sequencing, they discovered homozygous ZMYND12 variants in four unrelated patients. They examined the localization of key sperm tail components in sperm from the patients. To validate the findings, they knocked down the ortholog in Trypanosoma brucei. They further dissected the complex using co-immunoprecipitation and comparative proteomics with samples from Trypanosoma and Ttc29 KO mice. They concluded that ZMYND12 is a new asthenoteratozoospermia-associated gene, bi-allelic variants of which cause severe flagellum malformations and primary male infertility.

The major strengths are that the authors used the cutting-edge technique, whole-exome sequencing, to identify genes associated with male infertility, and used a new model organism, Trypanosoma brucei to validate the findings; together with other high-throughput tools, including comparative proteomics to dissect the protein complex essential for normal sperm formation/function. The major weakness is that limited samples could be collected from the patients for further characterization by other approaches, including western blotting and TEM.

In general, the authors achieved their goal and the conclusion is supported by their results. The findings not only provide another genetic marker for the diagnosis of asthenoteratozoospermia but also enrich the knowledge in cilia/flagella.

Joint Public Review:

In this study, Porter et al report on outcomes from a small, open-label, pilot randomized clinical trial comparing dornase-alfa to the best available care in patients hospitalized with COVID-19 pneumonia. As the number of randomized participants is small, investigators describe also a contemporary cohort of controls and the study concludes about a decrease of inflammation (reflected by CRP levels) after 7 days of treatment but no other statistically significant clinical benefit.

Suggestions to the authors:<br /> • The RCT does not follow CONSORT statement and reporting guidelines<br /> • The authors have chosen a primary outcome that cannot be at least considered as clinically relevant or interesting. After 3 years of the pandemic with so much research, why investigate if a drug reduces CRP levels as we already have marketed drugs that provide beneficial clinical outcomes such as dexamethasone, anakinra, tocilizumab and baricitinib.<br /> • Please provide in Methods the timeframe for the investigation of the primary endpoint<br /> • Why day 35 was chosen for the read-out of the endpoint?<br /> • The authors performed an RCT but in parallel chose to compare also controls. They should explain their rationale as this is not usual. I am not very enthusiastic to see mixed results like Figures 2c and 2d.<br /> • Analysis is performed in mITT; this is a major limitation. The authors should provide at least ITT results. And they should describe in the main manuscript why they chose mITT analysis.<br /> • It is also not usual to exclude patients from analysis because investigators just do not have serial measurements. This is lost to follow up and investigators should have pre-decided what to do with lost-to-follow-up.<br /> • In Table 1 I would like to see all randomized patients (n=39), which is missing. There are also baseline characteristics that are missing, like which other treatments as BAT received by those patients except for dexamethasone.<br /> • In the first paragraph of clinical outcomes, the authors refer to a cohort that is not previously introduced in the manuscript. This is confusing. And I do not understand why this analysis is performed in the context of this RCT although I understand its pilot nature.<br /> • Propensity-score selected contemporary controls may introduce bias in favor of the primary study analysis, since controls are already adjusted for age, sex and comorbidities.<br /> • The authors do not clearly present numerically survivors and non-survivors at day 34, even though this is one of the main secondary outcomes.<br /> • It is unclear why another cohort (Berlin) was used to associate CRP with mortality. CRP association with mortality should (also) be performed within the current study.

Reviewer #1 (Public Review):

Levy and Hasselmo investigated the representational codes of dorsal hippocampus neurons in episodic memory and spatial navigation. Specifically, how new learning affects previously acquired spatial memory. They asked if the hippocampal representational codes evolve in a different manner when two tasks governed by different rules are learnt in a single environment vs. when each rule is learnt in a separate environment. The two rules they used were based on the classical Packard & McGaugh (1996) experiment. In the original 1996 experiment there was a striatal-dependent response-based task vs. a hippocampal-dependent map-based task. In the current paper they either trained the two types of rules (response vs. map based) in two different contexts (Two-Maze), or in a single context (One-Maze). They found that the remapping of the second time in the response-based rule task was greater in the One-Maze variant of the experiment than in the Two-Maze variant, and they interpreted this by suggesting that in the One-Maze variant, the different intermediate map-based task interfered with the representation in the second response-based task, while in the Two-Maze variant no such interference occurred, and thus the hippocampal map remained more stable.

The results of this paper are well supported by data; however, we believe the conclusion of paper should be different than the conclusion the authors have arrived at.

Major issue:<br /> 1. The main claim is that a new behavioral rule in a familiar environment leads to an increase in the level of remapping of hippocampal activity when returning to the original rule in the same environment. However, we are worried that the result is not due to the interference by a different task in the same environment, but rather by the fact that the mouse spent more time in the environment, causing a larger representational drift. Consider, for example, the change in correlation in Figure 4E over days. In all cases, there is a maze-dependent reduction in correlation from day to day. This reduction continues in the One-maze case also when changing the rule, suggesting that what determined the larger reduction is the time spent in each context, and not the actual change of rule or behavior. Thus it is probable that the fact that the mouse was longer in the first maze in the one-maze variant was enough to create a difference in correlation. See also Khatib et al., bioRxiv, 2022 on the issue of context-dependent drift. To actually control for that, we suggest that the mouse spends twice the time in the first maze during the first Turn-Right session, in the Two-maze variant, and then the comparison will be more valid, by equating the amount of time spent in the first maze in-between comparisons, in the two types of experiments.

Additional points:<br /> 2. Figure 1.d: While behaving differently, is there a difference in the representation? (e.g. mouse 2 on 7th day showed in the beginning very bad behavior). What is the relation between the reduction in performance and the change in representation?<br /> 3. Figure 3.c: We suggest to get a better estimate of the significance of the effect here using shuffling. Specifically, it could be a good idea to distinguish between signal correlations (derived from the overlapping spatial fields) vs. noise correlations. To what extent are the correlations dependent on spatial overlap? It could be worthwhile to determine the type of correlation: Is it due to the fact that the maps are similar for overlapping place cells, or is there noise correlations between these cells?<br /> 4. Figure 4.a: What is the explanation for the reduction in correlation between days 5 and 6?<br /> 5. Supplementary Figure 2: Higher correlations in all arms - note the higher correlation in all arms in the Two-Maze vs. the One-Maze, suggesting again that the effect is related to the longer time in the context, and not so much to the rule-change.<br /> 6. Methods: the researchers note that the animals were previously used in a different study. This should be stated clearly also in the results.

Reviewer #1 (Public Review):

In their manuscript, Brischigliaro et al. show that the disruption of respiratory complex assembly results in Drosophila melanogaster results in the accumulation of respiratory supercomplexes. Further, they show that the change in the supercomplex abundance does not impact respiratory function suggesting that the main role of supercomplex formation is structural. Overall, the manuscript is well written and the results and conclusion are supported. The D. melanogaster system, in which the abundance of supercomplexes can be altered through the genetic disruption of the assembly of the individual complexes, will be important for the field to discover the role of the supercomplexes. This manuscript will be of broad interest to the field of mitochondrial bioenergetics. The findings are valuable and the evidence is convincing.

Strengths:

The system developed in which the relative levels of SCs can be varied will be extremely useful for studying SC physiology.

The experiments are clearly described and interpreted.

Weaknesses:

The statement in the abstract regarding low amounts of SCs in "insect tissues" needs further support or should be narrowed. I am only aware of detailed characterization of the mitochondrial SC composition from D. melanogaster, which is insufficient to make a broad statement about the large and diverse category of insects. This should be rewritten.

In the introduction (line 76) and discussion (line 283), the authors reference the CoQ binding sites in CI and CIII2 being "too far apart" to allow for substrate channeling. The distance between the active sites, though significant, is insufficient to rule out substrate channeling. A stronger argument arises from the fact that the CoQ sites of both CI and CIII2 are open to the membrane and that there are no clear barriers for the free exchange of CoQ with the membrane pool.

Line 195, the slight elevation in CI amounts referred to here, does not appear to be statistically significant and therefore should not be discussed a being altered relative to the control.

Figure 4H, the assignments of the observed larger bands seem incorrect. The largest band (currently assigned as SC I1+III2+IV1) represents too large of a shift for only the addition of CIV and the band currently assigned at SC I1+III2 appears to also contain CIV. The identity of these bands should be reevaluated and additional experiments are needed to definitively prove their identity. This uncertainty should be addressed experimentally or made more explicit in the text.

Line 302, the authors state that the structural basis for less SC in D. melanogaster is "due to a more stable association of the NDUFA11 subunit..." However, this would not result is a less stable SC association and only explains why NDUFA11 is more stably associated with CI in the absence of CIII2. The more likely structural reason for the observation of less SC in D. melanogaster is the N-terminal truncation of Dm-NDUFB4 relative to mammalian NDUFB4. This truncation results in the loss of a major SC interaction site between CI and CIII2 in the matrix.

Reviewer #1 (Public Review):

This article describes the development and refinement of an open-source software framework that is used to track how the COVID-19 pandemic impacted healthcare use in England over a range of key healthcare use indicators.

Important strengths of this study include the high coverage of 99% of practices in England, the development of health care indicators with the input of a clinical advisory group, extensive online documentation, and rigorous safeguards for the protection of patient confidentiality.

Perhaps the largest limitation is that only high-level descriptive data on the monthly volume of health outcomes are presented. It is not clear whether the system could be used to generate more fine-grained or stratified information, ex. weekly or daily data, or data stratified by important characteristics of practices or of patient characteristics. As such, the utility of the system for answering new scientific questions is unclear, and also what the utility and long-term potential uses of this system will be past the COVID-19 pandemic.

Reviewer #1 (Public Review):

In this study, Fang H et al. describe a potential pathway, ITGB4-TNFAIP2-IQGAP1-Rac1, that may involve in the drug resistance in triple negative breast cancer (TNBC). Mechanistically, it was demonstrated that TNFAIP2 bind with IQGAP1 and ITGB4 activating Rac1 and the following drug resistance. The present study focused on breast cancer cell lines with supporting data from mouse model and patient breast cancer tissues. The study is interesting. The experiments were well controlled and carefully carried out. The conclusion is supported by strong evidence provided in the manuscript. The authors may want to discuss the link between ITGB4 and Rac 1, between IQGAP1 and Rac1, and between TNFAIP2 and Rac1 as compared with the current results obtained. This is important considering some recent publications in this area (Cancer Sci 2021, J Biol Chem 2008, Cancer Res 2023).

Reviewer #1 (Public Review):

Current experimental work reveals that brain areas implicated in episodic and spatial memory have a dynamic code, in which activity representing familiar events/locations changes over time. This paper shows that such reconfiguration is consistent with underlying changes in the excitability of cells in the population, which ties these observations to a physiological mechanism.

Delamare et al. use a recurrent network model to consider the hypothesis that slow fluctuations in intrinsic excitability, together with spontaneous reactivations of ensembles, may cause the structure of the ensemble to change, consistent with the phenomenon of representational drift. The paper focuses on three main findings from their model: (1) fluctuations in intrinsic excitability lead to drift, (2) this drift has a temporal structure, and (3) a readout neuron can track the drift and continue to decode the memory. This paper is relevant and timely, and the work addresses questions of both a potential mechanism (fluctuations in intrinsic excitability) and purpose (time-stamping memories) of drift.

The model used in this study consists of a pool of 50 all-to-all recurrently connected excitatory neurons with weights changing according to a Hebbian rule. All neurons receive the same input during stimulation, as well as global inhibition. The population has heterogeneous excitability, and each neuron's excitability is constant over time apart from a transient increase on a single day. The neurons are divided into ensembles of 10 neurons each, and on each day, a different ensemble receives a transient increase in the excitability of each of its neurons, with each neuron experiencing the same amplitude of increase. Each day for four days, repetitions of a binary stimulus pulse are applied to every neuron.

The modeling choices focus in on the parameter of interest-the excitability-and other details are generally kept as straightforward as possible. That said, I wonder if certain aspects may be overly simple. The extent of the work already performed, however, does serve the intended purpose, and so I think it would be sufficient for the authors to comment on these choices rather than to take more space in this paper to actually implement these choices. What might happen were more complex modeling choices made? What is the justification for the choices that are made in the present work?

The two specific modeling choices I question are (1) the excitability dynamics and (2) the input stimulus. The ensemble-wide synchronous and constant-amplitude excitability increase, followed by a return to baseline, seems to be a very simplified picture of the dynamics of intrinsic excitability. At the very least, justification for this simplified picture would benefit the reader, and I would be interested in the authors' speculation about how a more complex and biologically realistic dynamics model might impact the drift in their network model. Similarly, the input stimulus being binary means that, on the single-neuron level, the only type of drift that can occur is a sort of drop-in/drop-out drift; this choice excludes the possibility of a neuron maintaining significant tuning to a stimulus but changing its preferred value. How would the use of a continuous input variable influence the results.

Result (1): Fluctuations in intrinsic excitability induce drift<br /> The two choices highlighted above appear to lead to representations that never recruit the neurons in the population with the lowest baseline excitability (Figure 1b: it appears that only 10 neurons ever show high firing rates) and produce networks with very strong bidirectional coupling between this subset of neurons and weak coupling elsewhere (Figure 1d). This low recruitment rate need may not necessarily be problematic, but it stands out as a point that should at least be commented on. The fact that only 10 neurons (20% of the population) are ever recruited in a representation also raises the question of what would happen if the model were scaled up to include more neurons.

Result (2): The observed drift has a temporal structure<br /> The authors then demonstrate that the drift has a temporal structure (i.e., that activity is informative about the day on which it occurs), with methods inspired by Rubin et al. (2015). Rubin et al. (2015) compare single-trial activity patterns on a given session with full-session activity patterns from each session. In contrast, Delamare et al. here compare full-session patterns with baseline excitability (E = 0) patterns. This point of difference should be motivated. What does a comparison to this baseline excitability activity pattern tell us? The ordinal decoder, which decodes the session order, gives very interesting results: that an intermediate amplitude E of excitability increase maximizes this decoder's performance. This point is also discussed well by the authors. As a potential point of further exploration, the use of baseline excitability patterns in the day decoder had me wondering how the ordinal decoder would perform with these baseline patterns.

Result (3): A readout neuron can track drift<br /> The authors conclude their work by connecting a readout neuron to the population with plastic weights evolving via a Hebbian rule. They show that this neuron can track the drifting ensemble by adjusting its weights. These results are shown very neatly and effectively and corroborate existing work that they cite very clearly.

Overall, this paper is well-organized, offers a straightforward model of dynamic intrinsic excitability, and provides relevant results with appropriate interpretations. The methods could benefit from more justification of certain modeling choices, and/or an exploration (either speculative or via implementation) of what would happen with more complex choices. This modeling work paves the way for further explorations of how intrinsic excitability fluctuations influence drifting representations.

Reviewer #1 (Public Review):

Qin et al. set out to investigate the role of mechanosensory feedback during swallowing and identify neural circuits that generate ingestion rhythms. They use Drosophila melanogaster swallowing as a model system, focusing their study on the neural mechanisms that control cibarium filling and emptying in vivo. They find that pump frequency is decreased in mutants of three mechanotransduction genes (nompC, piezo, and Tmc), and conclude that mechanosensation mainly contributes to the emptying phase of swallowing. Furthermore, they find that double mutants of nompC and Tmc have more pronounced cibarium pumping defects than either single mutants or Tmc/piezo double mutants. They discover that the expression patterns of nompC and Tmc overlap in two classes of neurons, md-C and md-L neurons. The dendrites of md-C neurons warp the cibarium and project their axons to the subesophageal zone of the brain. Silencing neurons that express both nompC and Tmc leads to severe ingestion defects, with decreased cibarium emptying. Optogenetic activation of the same population of neurons inhibited filling of the cibarium and accelerated cibarium emptying. In the brain, the axons of nompC∩Tmc cell types respond during ingestion of sugar but do not respond when the entire fly head is passively exposed to sucrose. Finally, the authors show that nompC∩Tmc cell types arborize close to the dendrites of motor neurons that are required for swallowing, and that swallowing motor neurons respond to the activation of the entire Tmc-GAL4 pattern.

Strengths:<br /> -The authors rigorously quantify ingestion behavior to convincingly demonstrate the importance of mechanosensory genes in the control of swallowing rhythms and cibarium filling and emptying<br /> -The authors demonstrate that a small population of neurons that express both nompC and Tmc oppositely regulate cibarium emptying and filling when inhibited or activated, respectively<br /> -They provide evidence that the action of multiple mechanotransduction genes may converge in common cell types

Weaknesses:<br /> -A major weakness of the paper is that the authors use reagents that are expressed in both md-C and md-L but describe the results as though only md-C is manipulated<br /> -Severing the labellum will not prevent optogenetic activation of md-L from triggering neural responses downstream of md-L. Optogenetic activation is strong enough to trigger action potentials in the remaining axons. Therefore, Qin et al. do not present convincing evidence that the defects they see in pumping can be specifically attributed to md-C.<br /> -GRASP is known to be non-specific and prone to false positives when neurons are in close proximity but not synaptically connected. A positive GRASP signal supports but does not confirm direct synaptic connectivity between md-C/md-L axons and MN11/MN12.<br /> -As seen in Figure Supplement 2, the expression pattern of Tmc-GAL4 is broader than md-C alone. Therefore, the functional connectivity the authors observe between Tmc expressing neurons and MN11 and 12 cannot be traced to md-C alone

Overall, this work convincingly shows that swallowing and swallowing rhythms are dependent on several mechanosensory genes. Qin et al. also characterize a candidate neuron, md-C, that is likely to provide mechanosensory feedback to pumping motor neurons, but the results they present here are not sufficient to assign this function to md-C alone. This work will have a positive impact on the field by demonstrating the importance of mechanosensory feedback to swallowing rhythms and providing a potential entry point for future investigation of the identity and mechanisms of swallowing central pattern generators.

Reviewer #1 (Public Review):

In this preprint, Zhang et al. describe a new tool for mapping the connectivity of mouse neurons. Essentially, the tool leverages the known peculiar infection capabilities of Rabies virus: once injected into a specific site in the brain, this virus has the capability to "walk upstream" the neural circuits, both within cells and across cells: on one hand, the virus can enter from a nerve terminal and infect retrogradely the cell body of the same cell (retrograde transport). On the other hand, the virus can also spread to the presynaptic partners of the initial target cells, via retrograde viral transmission.

Similarly to previously published approaches with other viruses, the authors engineer a complex library of viral variants, each carrying a unique sequence ('barcode'), so they can uniquely label and distinguish independent infection events and their specific presynaptic connections, and show that it is possible to read these barcodes in-situ, producing spatial connectivity maps. They also show that it is possible to read these barcodes together with endogenous mRNAs, and that this allows spatial mapping of cell types together with anatomical connectivity.

The main novelty of this work lies in the combined use of rabies virus for retrograde labeling together with barcoding and in-situ readout. Previous studies had used rabies virus for retrograde labeling, albeit with low multiplexing capabilities, so only a handful of circuits could be traced at the same time. Other studies had instead used barcoded viral libraries for connectivity mapping, but mostly focused on the use of different viruses for labeling individual projections (anterograde tracing) and never used a retrograde-infective virus.

The authors creatively merge these two bits of technology into a powerful genetic tool, and extensively and convincingly validate its performance against known anatomical knowledge. The authors also do a very good job at highlighting and discussing potential points of failure in the methods.

Unresolved questions, which more broadly affect also other viral-labeling methods, are for example how to deal with uneven tropism (ie. if the virus is unable or inefficient in infecting some specific parts of the brain), or how to prevent the cytotoxicity induced by the high levels of viral replication and expression, which will tend to produce "no source networks", neural circuits whose initial cell can't be identified because it's dead. This last point is particularly relevant for in-situ based approaches: while high expression levels are desirable for the particular barcode detection chemistry the authors chose to use (gap-filling), they are also potentially detrimental for cell survival, and risk producing extensive cell death (which indeed the authors single out as a detectable pitfall in their analysis). This is likely to be one of the major optimisation challenges for future implementations of these types of barcoding approaches.

Overall the paper is well balanced, the data are well presented and the conclusions are strongly supported by the data. Impact-wise, the method is definitely going to be useful for the neurobiology research community.