Reviewer #2 (Public Review):

This clinical trial is conducted to pursue short course DAA therapy. For an ultra-short course to work, it has to be simple, equally efficacious to established treatments, and requires no additional workup (like genotyping, IL28B, HCV VL determination, etc after initiation of therapy as shown in Liu et al.). This is because our aim is to simplify therapy to treat most people, especially those who are not engaged in care.<br /> This work struggles to achieve these goals, as the to the SVR for short-course therapy is unacceptably low. The authors' conclusion that treat short first and then you can treat those who fail again does not appear to achieve these goals, as realistically, it is difficult to re-engage marginalized population from an elimination perspective. The ideal is to treat them in one attempt.

Reviewer #2 (Public Review):

Despite the fact that Igf2 and H19 are the two best-studied imprinted genes in (mouse) development, substantial gaps in knowledge continue to exist as to their independent functions in normal and pathological situations, including in the imprinting disorders BWS and SRS to which they are linked. Here, using three established mouse models in a clever way, the authors are able to dissect the impact of overexpression or depletion of Igf2 and H19 independently of each other. This sophisticated use of mouse genetic models is a major achievement in trying to discern the precise impact of these genes on the development of particular cell types and tissues.

The authors report the placenta and heart as the two most severely affected organs in these mouse models. What remains unclear is if these phenotypes are causally linked, such that - for example - endothelial cell dysfunction causes the placental phenotypes or, conversely, trophoblast dysfunction causes the heart phenotypes. The "placenta-heart axis" is repeatedly referred to; however, unlike in the use of the term in the current manuscript, this term is more commonly used to describe situations where the placenta is causative of heart phenotypes, as first established in the Pparg mutation. Along these lines, it would be instrumental to establish in at least one of the mouse models under investigation here, if any of the heart or placental defects are secondary to gene function in a cell type outside of the affected organ itself.

The transcriptomic analysis of E12.5 endocardial cushion cells in the various mouse models is informative in the extraction of Igf2- and H19-specific gene functions. This analysis raises a few questions: In Fig. 6D, a huge sex effect is obvious with many more DEGs in female embryos compared to males. How can this be explained given that Igf2/H19 reside on Chr7 and do not primarily affect gene expression on the X chromosome? Is any chromosomal bias observed in the genomic distribution of DEGs? A separate issue is raised by Fig. 6E that shows a most dramatic dysregulation of a single gene in the delta3.8/hIC1 "rescue" model. Interestingly, this gene is Shh. Hence, these embryos should exhibit some dramatic skeletal abnormalities or other defects linked to sonic hedgehog function.

The placental analysis needs to be strengthened. Placentas should be consistently positioned with the decidua facing up, and the chorionic plate down. The placentas in Fig. 3F are sectioned at an angle and the chorionic plate is missing. These images must be replaced with better histological sections. The CD34 staining has not worked and does not show any fetal vasculature, in particular not in the WT sample. The "thrombi" highlighted in Fig. 4E are well within the normal range, to make the point that these are persistent abnormalities more thorough measurements would need to be performed (number, size, etc).

The statement that H19 is disproportionately contributing to the labyrinth phenotype (lines 154/155) is not warranted as Igf2 expression is reduced to virtually nothing in these mice. Even though there is more H19 in the labyrinth than in the junctional zone, the phenotype may still be driven by a loss of Igf2.<br /> Given the quasi Igf2-null situation in +/hIC1 mice, is the glycogen cell type phenotype recapitulated in these mice, and how do glycogen numbers compare in the other mouse models?

How do delta3.8/+ and delta3.8/hIC1 mice with a VSD survive? Is it resolved some time after birth such that heart function is compatible with postnatal viability? And more importantly, do H19 expression levels correlate with phenotype severity on an individual basis?

Reviewer #2 (Public Review):

Copeland et al. set out to assess the impact of HIF1 activation - either through glycolysis or pharmacological inhibition of prolyl hydroxylase (PHD) - in primary fibroblasts and smooth muscle cells. The goal was to compare the metabolic responses between these two states, and with the scores of papers studying metabolic responses to hypoxia in cancer cells. Using a combination of metabolomics, metabolic flux analysis, and gene expression studies, they surprisingly find that hypoxia induces the expected activation of glycolytic genes, but fails to induce some of the classical metabolic responses reported in cancer cells, including glucose uptake and lactate secretion. Lactate secretion is enhanced by PHD expression, but this is reversed by hypoxia. The authors further find that hypoxia induces the expression of MYC, and they use gain and loss of function experiments to show that MYC is involved in the reduction of lactate secretion by hypoxia. The paper's strengths are the detailed and quantitative analysis of metabolic responses in two different models of non-transformed cell proliferation, as well as the combination of gain- and loss-of-function analyses of MYC. Relative limitations include the use of a single chemical compound to activate HIF-1 in normoxia, and the lack of an explanation for why MYC is induced in hypoxia but not in normoxic HIF activation, or why MYC's effects are so different here than what is usually observed in cancer cells. If validated, the findings of the paper would add complexity to the mechanisms by which cells respond to hypoxia; to date, these responses have mostly been studied in cancer cells, and the new data suggest that non-transformed cells respond quite differently. The findings also suggest that MYC's effects on metabolism are determined in part by the cell state, as a large amount of existing data indicates that MYC drives lactate secretion in cancer cells.

Reviewer #2 (Public Review):

The purpose of this study was to evaluate the transcription factor NF-KB, a common transcription factor that is thought to mediate muscle atrophy, in the setting of a rotator cuff injury. Unlike many other models of atrophy such as hind limb suspension, aging, and neurologic injury, the tenotomy model represents a unique mechanical change where the muscle is acutely unloaded from the bone, which is relevant for rotator cuff injuries as well as achilles tendon ruptures.

The premise of the study was that NF-kB, a known central regulator of muscle atrophy, would be a central mediator of this process in a tenotomy model as well. The study hypothesized that NF-kB inhibition would reduce atrophy in a rotator cuff model through atrogene-independent mechanisms, a hypothesis that is well supported by literature in other models.

Using gain of function and loss of function NF-kB inhibitors, the IKKB family, to evaluate this pathway after a tenotomy model. The results were rigorously approached with appropriate timelines and controls, and the analyses were well done. Surprisingly, the study found that NF-kB did not appear to be an important regulator of tenotomy-induced atrophy, which they did an excellent job of exploring in detail with their gain/loss of function mice, and by looking at cellular changes, protein changes, and architectural changes after rotator cuff injury. They did find that autophagy, which was more pronounced in male mice, was a sex-dependent mechanism that seemed to regulate atrophy.

The primary strength of this paper is a rigorous approach to 'negative' data. Did the authors definitively prove that NF-kB has no role in the tenotomy-induced atrophy? Probably not entirely, since there are limitations of the mouse model and the knockdown mice. There cannot be complete elimination of load since mice heal with some scar tissue, and the knockdown is not complete elimination. However, even with these limitations, this presents important findings that tenotomy, which induces mechanical unloading of the muscle-tendon unit, provides a unique biomechanical environment for the muscle to undergo atrophy, which warrants a more in-depth look given that these injuries are unique and extremely common. It must be mentioned that the results are entirely supported by their data and that even though the model is not 'perfect' it truly supports that NF-kB has a limited role in atrophy. The sex-mediated differences based on autophagy are a secondary hypothesis and are interesting but possibly less clinically relevant based on the differences shown.

The important next step for this group and others is to evaluate the 'how and why' of tenotomy atrophy if not through NF-kB. Is it that there are many redundant processes that the muscle may have to circumnavigate the NF-kB pathway given that it is so ubiquitous that the authors didn't see a difference? Could it be differences in axial vs appendicular muscle? Or should there be a closer look at the mechanosensors in the muscle cells to determine if there are other key drivers of atrophy? Regardless, this paper shows that tenotomy-induced muscle atrophy is unique, and supports the conclusion that muscle has many ways to atrophy based on the injury it undergoes.

Reviewer #2 (Public Review):

Does our proprioceptive system try to recognize our own actions?

Proprioception is our sense of the motion and posture of our own body. This sixth sense uses signals from receptors in the joints, tendons, muscles, and skin that measure forces and degrees of extension. These receptors enable us to sense, for example, the posture of our body as we wake from sleep. They also provide feedback signals that help us precisely control our limbs, for example during handwriting.

Feedback is thought to be essential to motor control, enabling the controller in our brains to rapidly adapt to the unexpected. The unexpected may include changes in the environment (like something pushing our hand that we didn't see coming), changes in our bodies (such as muscle fatigue or injury), and shortcomings of the motor program (such as a lack of precision or a badly planned limb trajectory). Feedback can come from vision and even audition, but proprioception provides an essential additional feedback path that informs us directly about the motion and posture of our limbs, and any forces on them.

How does feedback control work in the human motor system? I want to write a 'k', but there are forces on my limbs resulting from the friction of chalk on this particular blackboard. Also, my muscles are recovering from tennis practice this morning, and I haven't used chalk on a blackboard in years.

If the goal is to write a 'k', I have some flexibility. I am committed, not to a precise trajectory, but to a more abstractly defined objective: to write a legible 'k'. This suggests that feedback processing should evaluate to what extent I am succeeding at the action, not at tracing out a particular trajectory. Does what I'm actually doing look like writing a 'k'?

In a new paper, Sandbrink et al. (pp2022) report on simulations of the human musculoskeletal system and neural network models that suggest that the tuning properties of neurons in the somatosensory cortex (S1) can be explained by assuming that the objective of the proprioceptive system is to recognize the action being performed.

They used recorded traces of a person writing lower-case letters to simulate the responses of muscle spindles sensing the lengths and velocities of muscles in the human arm as would be present if the hand was moved passively along these trajectories. The physical simulation uses a 3D model of the human arm with two parameters for the direction of the upper arm and two more for the direction of the lower arm. These four parameters are inferred by inverse kinematics from the hand trajectories tracing each letter in a variety of vertical and horizontal planes. A 3D muscle model then enables the authors to compute the expected spindle responses that reflect the lengths and velocities of 25 relevant upper arm muscles.

The authors then trained neural network models of proprioceptive processing that took the simulated muscle spindle signals as input. The neural net architectures included one that first integrates information over the muscle spindles and then across time ("spatial-temporal"), one that integrated across muscle spindles and time simultaneously ("spatiotemporal") and a recurrent long-short-term-memory model.<br /> Each architecture was trained on two objectives: to decode the trajectory (i.e. the position of the hand tracing a letter as a function of time) or to recognize the action (i.e. the letter being traced). The two objectives correspond to two hypotheses about the function of proprioceptive processing: To inform the feedback controller about either the current position of the hand or the letter being drawn.

The models trained to recognize the action developed tuning more consistent with what is known about the tuning of neurons in the primary somatosensory cortex in primates. In particular, direction tuning with roughly equal numbers of units preferring each direction emerged in the middle layers of the neural network models trained to recognize the action, similar to what has been observed in primate neural recordings. Direction tuning is already present in the muscle-spindle signals, but the spindle signals do not uniformly represent the directions.

The task-optimization approach to neural network modeling is inspired by work in vision, where neural networks trained on the task of image classification explained responses to novel images in populations of neurons in the inferior temporal cortex. This result suggested a tentative answer to the why question: Why do inferior temporal neurons exhibit the response profiles and representational geometry they exhibit? Because their function (or one of their functions) is to recognize the objects in the images. Here, similarly, the authors address a why question with task-optimized neural network models: Why do somatosensory cortical neurons exhibit the types of tuning that have been reported in the literature?

The function of proprioception, of course, is not for the brain to recognize which letter it is trying to write. It already knows that. The function is to sense how the current trajectory - the actual, not the intended one - differs from, say, a legible "k" (if that was the intention), and to map from that difference to a modification vector that will improve the outcome.<br /> Why is action decoding relevant for performing the action? A key reason may be that the goal is not to produce a fixed trajectory, but to produce a legible 'k'. A legible 'k' is not a single trajectory, but a class of trajectories containing an infinity of viable solutions. If someone nudged my arm while writing, adaptive feedback control should not attempt to return me to the originally intended trajectory, but to a new trajectory that traces the most legible 'k' that is still in the cards, which may be a different style of 'k' than I originally intended.

The paper contributes a useful data set for training models and a qualitative comparison of models to real neurons in terms of tuning properties. It would be good, in follow-up studies, to directly test to what extent each of the models can quantitatively predict either single-neuron responses or population representational geometries, as has been done in vision, and to perform statistical comparisons between models.

Importantly, this paper develops the idea of combining simulations body and brain, of the musculoskeletal system, and the processing of control-related signals in the nervous system, which provides a very exciting direction for future research.

Strengths

• The paper introduces a highly original research program that marries simulation of the musculoskeletal system and neural network modelling to predict neural representations in the proprioceptive pathway.<br /> • The authors performed an architecture search and trained multiple instances of different neural network architectures with each of the two objectives.<br /> • The paper includes comprehensive analyses of the proprioceptive representations from the simulated muscle-spindle signals through the layers of the models. These analyses characterize unit tuning, linear decodability, and representational similarity.<br /> • The results suggest an explanation for the direction tuning with a roughly uniform distribution of the units' direction preferences that has been reported previously for neurons in the primate primary somatosensory (S1) cortex.<br /> • If the simulated muscle-spindle data set, models and analysis code were shared along with the published paper, this work could form the basis for quantitative model evaluation and further model development.

Weaknesses

• The models are qualitatively evaluated by comparison of model unit tuning to what is known about the tuning of neurons in the somatosensory cortex. Follow-up studies should quantitatively evaluate the models by inferential analyses of their ability to predict measured responses.<br /> • The two training objectives differ in multiple respects, making it difficult to assess what the necessary requirements are for the emergence of representations similar to primate S1. Decoding the hand position may be too simple, but what about decoding velocity, or trajectory descriptors such as curvature? There may be a middle ground between trajectory decoding and action recognition that also leads to the emergence of tuning properties as found in primate S1.

Reviewer #2 (Public Review):

The authors profiled the transcriptome and proteome of human umbilical vein endothelial cells freshly isolated from in vivo and compared that with the same cells exposed to in vitro culture under different conditions, including static culture, flow, and co-culture with smooth muscle cells. The experiments were properly designed and performed. The authors also provided a reasonable and sound interpretation of their findings. This study provides valuable insights into how the culturing conditions impact on gene expression, encouraging the field to select their in vitro work setting appropriately. Overall, the manuscript is well-written and easy to follow.

Several notable strengths include:

1. Parallel transcriptome- and proteome-wide profiling of endothelial cells enabling the unbiased interrogation of gene expression and a genome-wide view of the impact of in vitro culture on endothelial transcriptome.<br /> 2. The innovative experimental design and comparisons were done with genetically identical ECs (from the same donors) in vivo and in vitro.<br /> 3. The analyses were robust and provided novel information on flow-dependent and cell context-dependent gene regulation, with the native freshly isolated cells as a baseline.<br /> 5. The donor samples used in this study were diverse including Asian, White, Black, Latino, and American Indian samples which reduce racial background bias.

Some points that can strengthen the study:

A clear description of experimental and analytical details (e.g. how the comparisons were made) and more in-depth interpretation and discussion of the results, e.g. the complete genes that are rescued by flow and co-culture and potential synergy of these factors.

Reviewer #2 (Public Review):

This manuscript provides a detailed and useful account of post-infection viral trajectories during the early SARS-CoV-2 Omicron era. Data in these analyses come from a unique cohort individuals from the National Basketball Association including players who, while they may not be representative of the general population, were sampled densely throughout the pandemic. The authors describe the duration of (presumptive) infectiousness, using CT values as a proxy, and explore how time to non-infectiousness differs by immune history and demographics. The authors used logistic regression models to estimate the probability of having a Ct value < 30 by day since detection and various other factors including lineage, age, post-primary vaccination antibody levels and exposure history. They then used previously published semi-mechanistic models to post infection kinetics, allowing for variability in kinetics by similar factors.

The authors make several important observations:

1. That most people continue to have a Ct value < 30 on the 5th day post detection. While not a novel observation, even for Omicron infections, it further adds to the importance of isolation strategies that include a testing component.

2. That rebounds do happen but even with relaxed definitions it is usually less than 1% (as high as perhaps 3%). If these are indeed in individuals that did not take anti-virals, these data are important for quantifying changes in the risk of rebound infections after antiviral treatment.

3. That boosted individuals were less likely to have an Omicron infection but among those that were infected, they were more likely to have a longer period with an elevated viral load. While this may be partly due to an age effect (and other factors), the authors suggest that even after controlling for age, this difference persists. Through looking at post-primary vaccination antibody levels (to the prototypical SARS-CoV-2) in a subset of the cohort, the authors show that this booster effect may be due to the fact that breakthrough infections in boosted individuals tended to occur in those who had a lower initial antibody response.

The authors do a great job of trying to disentangle lineage, age and exposure history, in primary and sensitivity analyses but there is no way to do this perfectly. I believe the conclusions are well justified by the results of the analyses and the authors sufficiently discuss the limitations of the data and results.

Reviewer #2 (Public Review):

In this manuscript, Zhou et al carried out a very thorough spatial and temporal transcriptomic analysis of various cellular responses in the injured sciatic nerve using single-cell RNAseq. As such, it provides a wealth of new information on how cells in the nerve respond to a crush injury, both at the injury site and distally, during the first-week post-crush. The data are technically sound and the authors validated many of the observed expression changes in specific cells using a variety of approaches such as FACS, RNAscope, and immunostaining. They also created a searchable, publicly available tool, iSNAT, that allows the exploration of changes in gene expression in the injured nerve, which will be very valuable for the research community. The authors focus particularly on immune cells in the nerve and reveal a number of interesting findings. For example, they demonstrate that monocytes and macrophages are recruited to the nerve and undergo reprogramming, initially to pro-inflammatory cells relying on glycolysis, then to inflammation-resolving cells that rely on oxidative phosphorylation. In addition, they use sarm1-/- mice, which have very delayed Wallerian degeneration, to demonstrate that independent of Wallerian degeneration, immune cells are recruited to the injury site, but minimally in the distal region. However, they find an increase in monocytes distally, suggesting that these cells fail to differentiate into macrophages in the absence of WD.

Overall, this is a very comprehensive analysis that provides a very useful resource for the field and reveals a number of interesting new insights into the immune response in the injured peripheral nerve. These results have important implications for understanding nerve regeneration and neuropathic pain.

As with any such study, the results are limited by the number of cells that can be analyzed and the number of sequencing reads. The authors were able to obtain a large number of most cells for analysis; however, the number of myelinating Schwann cells was fairly small, due to the need to remove myelin debris. A similar limitation has been encountered by others and does limit the ability to deeply investigate changes in Schwann cells after injury. This is particularly relevant because, as the authors bring up in their discussion, there is considerable evidence indicating that Schwann cells are involved in recruiting immune cells to the injured nerve. Thus, it was somewhat surprising that some of the signaling detected in Fig. 5 was not from Schwann cells, but this may be due to these cells being underrepresented. The authors should consider specifically examining changes in the Schwann cell profiles to determine if there is an increase in the expression of any of the known chemokines.

Among the interesting findings that came out of their analysis was an increase in monocytes in the distal nerve of the sarm1-/- mice, suggesting that these cells are recruited prior to Wallerian degeneration (WD) but in the absence of WD, they fail to differentiate into macrophages. This finding indicates that some aspect of WD promotes the differentiation of these cells. However, the authors should confirm the increase in monocytes prior to WD in the wild-type nerve, for example at 1-day post crush. This could be done by immunostaining or FACS.

The metabolic reprogramming observed after the injury, to a more glycolytic phenotype, is consistent with what has been observed by others for macrophages that are pro-inflammatory. However, the metabolic changes were only noted in the whole nerve at 3 dpc (Fig. 3). The authors should similarly comment on, and provide evidence for, the metabolic phenotype of the macrophages specifically in the distal nerve (Fig. 8). Are these initially pro-inflammatory and then inflammation resolving or are they always largely anti-inflammatory?

Reviewer #2 (Public Review):

Zylbertal and Bianco propose a new model of trial-to-trial neuronal variability that incorporates the spatial distance between neurons. The 7-parameter model is attractive because of its simplicity: A neuron's activity is a function of stimulus drive, neighboring neurons, and global inhibition. A neuroscientist studying almost any brain area in any model organism could make use of this model, provided that they have access to 1) simultaneously-recorded neurons and 2) the spatial locations of those neurons. I could foresee this model being the de-facto model to compare to all future models, as it is easy to code up and interpret. The paper explores the effectiveness of this distance model by modeling neural activity in the zebrafish optic tectum. They find that this distance-based model can capture 1) bursting found in spontaneous activity, 2) ongoing co-fluctuations during stimulus-evoked activity, and 3) adaptation effects during prey-catching behavior.

Strengths:

The main strength of the paper is the interpretability of the distance-based model. This model is agnostic to the brain area from which the population of neurons is recorded, making the model broadly applicable to many neuroscientists. I would certainly use this model for any baseline comparisons of trial-to-trial variability.

The model is assessed in three different contexts, including spontaneous activity and behavior. That the model provides some prediction in all three contexts is a strong indicator that this model will be useful in other contexts, including other model organisms. The model could reasonably be extended to other cognitive states (e.g., spatial attention) or accounting for other neuron properties (such as feature tuning, as mentioned in the manuscript).

The analyses and intuition to show how the distance-based model explains adaptation were insightful and concise.

Weaknesses:

Model evaluation and comparison: The paper does not fully evaluate the model or its assumptions; here, I note details in which evaluation is needed. A key assumption of the model - that correlations fall off in a gaussian manner (Fig. 1C-E - is not supported by Fig. 1C, which appears to have an exponential fall-off. Functions other than gaussian may provide better fits. Furthermore, it is not clear whether the r^2s in Fig. 1E are computed in a held-out manner (more details about what goes into computing r^2 are needed). Assessing the model based on peak location alone (Fig. 1E) is not sufficient, as other smooth monotonically-decreasing functions may perform similarly. Simulating from the model greatly improves the reader's understanding (Fig. 2D), but no explanation is given for why the simulations (Fig. 2D) have almost no background spikes and much fewer, non-co-occurring bursts than those of real data (Fig. 2E). A key assumption of the distance model (Fig. 2A) is that each neuron has the same gaussian fall-off (i.e., sigma_excitation and sigma_inhibition), but it is unclear if the data support this assumption. Although an excitatory and inhibitory gain is assumed (Fig. 2A), it is not clear from the data (Fig. 1C) that an inhibitory gain is needed (no negative correlations are observed in Fig. 1C-D). After optimization (Fig. 3), the model is evaluated on predicting burst properties but not evaluated on predicting held-out responses (R^2s or likelihoods), and no other model (e.g., fitting a GLM or a model with only an excitatory gain) is considered. In particular, one may consider a model in which "assemblies" do exist - does such an assembly model lead to better held-out prediction performance? It is unclear why a genetic algorithm (Fig. 1A-C) is necessary versus a grid search; it appears that solutions in Generation 2 (Fig. 3C, leftmost plot, points close to the origin) are as good as solutions in Generation 30 and that the spreads of points across generations do not shrink (as one would expect from better mutations). Given the small number of parameters (7), a grid search is reasonable, computationally tractable, and easier to understand for all readers (Fig. 3A). It is unclear why the excitatory and inhibitory gains of the temporal profiles (Fig. 3I) appear to be gaussian but are formulated as exponential (formula for I_ij^X in Methods). Overall, comparing this model to other possible (similar) models and reporting held-out prediction performance will support the claim that the distance model is a good explanation for trial-to-trial variability.

Data results: Data results were clear and straightforward. However, the explanation was not given for certain results. For example, the relationship between pre-stimulus linear drive and delta R was weak; the examples in Fig. 4C do not appear to be representative of the other sessions. The example sessions in Fig. 4C have R^2=0.17 and 0.19, the two outliers in the R^2 histogram (Fig. 4D). The black trace in Fig. 4D has large variations (e.g., a linear drive of 25 and 30 have a change in delta R of ~0.1 - greater than the overall change of the dashed line at both ends, ~0.08) but the SEMs are very tight. This suggests that either this last fluctuation is real and a major effect of the data (although not present in Fig. 4C) or the SEM is not conservative enough. No null distribution or statistics were computed on the R^2 distribution (Fig. 4C, blue distribution) to confirm the R^2s are statistically significant and not due to random fluctuations. The absence of any background activity in Fig. 6B (e.g., during the rest blocks) is confusing, given that in spontaneous activity many bursts and background activity are present (Fig. 2E). Finally, it appears that the anterior optic tectum contributes to convergent saccades (CS) (Fig. 7E) but no post-saccadic activity is shown to assess how activity changes after the saccade (e.g., plotting activity from 0 to 60). No explanation is given why activity drops ~30 seconds before a convergent saccade (Fig. 7E). No statistical test is performed on the R^2 distribution (Fig. 7H) to confirm the R^2s (with a mean close to R^2=0.01) are meaningful and not due to random fluctuations.

Presentation: A disjointed part of the paper is that for the first part (Figs. 1-3), the focus is on capturing burst activity, but for the second part (Figs. 4-7), the focus is on trial-to-trial variability with no mention of bursts. It is unclear how the reader should relate the two and if bursts serve a purpose for stimulus-evoked activity.

Citations: The manuscript may cite other relevant studies in electrophysiology that have investigated noise correlations, such as:<br /> - Luczak et al., Neuron 2009 (comparing spontaneous and evoked activity).<br /> - Cohen and Kohn, Nat Neuro 2011 (review on noise correlations).<br /> - Smith and Kohn, JNeurosci 2008 (looking at correlations over distance).<br /> - Lin et al., Neuron 2015 (modeling shared variability).<br /> - Goris et al., Nat Neuro 2014 (check out Fig. 4).<br /> - Umakantha et al., Neuron 2021 (links noise correlation and dim reduction; includes other recent references to noise correlations).

Reviewer #2 (Public Review):

The authors use XL-MS and AlphaFold to predict the structure and interactions of the six individual IFT-A proteins of Tetrahymena. As this data set still allows for numerous possible 3D structures of the hexameric complex, the authors fitted their models to the low-resolution 3D structure of the IFT-A densities of Chlamydomonas IFT trains in situ obtain by cryo-EM and image averaging. While not optimal, this cross-species approach is possible as IFT proteins are highly conserved and the identified crosslinks fit the Tetrahymena and Chlamydomonas AlphaFold structures almost equally well. The result is a best-fitting model, which was further "validated" by accounting for previously established interactions between IFT-A proteins (and IFT-A to -B interactions). The manuscript also provides a scholarly comparison of the IFT-A particle and protein structure with other cellular protein of similar domain structure and observe that many such proteins participate in intracellular transport.

The structure of the IFT-A complex presented here is modeled rather than based on direct imaging. In as much, this is probably an intermediate step. However, because the fine structure of the IFT-A particle remains unknown, this indirect approach appears useful and appropriate. The model presented here fits the available data and likely can be tested further in future experiments. Probably, the approach could be also used to predict the structure of other multiprotein complexes. The work elegantly demonstrates how the structures of single proteins provided by AlphaFold can drive structure predictions of protein complexes.

Reviewer #2 (Public Review):

This study was built on the authors' previous publications to visualize angiogenesis and osteogenesis processes at subcritical-sized mouse calvarial defects using multiphoton microscopy. This provides, for the first time, the visible imaging of bone healing and vascularization within the defect after different time points of injury, although the physiological progression of calvarial bone healing was already known. More interestingly, the study used microscopy to visualize the oxygen distribution and energy metabolism within the defects at different time points during the process of bone healing. This allows one to understand the pathophysiological progressions of bone diseases and regeneration. It will also provide critical information to optimize the therapeutic bone healing and regeneration approach for different clinical situations.

Reviewer #2 (Public Review):

Möller and colleagues describe a crucial role for the centrosome in tissue resident macrophages in the brain, termed microglia, in limiting the rate of efferocytosis. They undertake a live cell imaging approach in zebrafish to demonstrate that microglia remove dying neurons mainly by extending long cellular branches - a process, which depends on an intact microtubule cytoskeleton. They further establish a relationship between centrosome movement into microglial branches and successful neuronal engulfment. Artificial doubling of centrosome numbers led to enhanced engulfment and simultaneous removal of two cells, while cells with only one centrosome preferentially phagocytose one neuron at a time. Thus, they propose that centrosome polarization is a critical parameter in regulating the rate of microglial efferocytosis.

This is a very interesting manuscript. The conclusions of the work are well supported by the data. The imaging is beautiful.

Reviewer #2 (Public Review):

Himmel and colleagues study how individual sensory neurons can be tuned to detect noxious vs. gentle touch stimuli. Functional studies of Drosophila class III dendritic arborization neurons characterized roles in gentle touch and identified a receptor, NompC, and other factors that mediate these responses. Subsequent work primarily from the authors of the current study focused on roles for the same sensory neurons in cold nociception. The two proposed sensory inputs lead to quite distinct sets of behaviors, with touch leading to halting, head turning and reverse peristalsis, and noxious cold leading to whole body contraction. How activity of one type of sensory neuron could lead to such different responses remains an outstanding question, both at the levels of reception and circuitry.

The cIII responses to noxious cold and innocuous touch raises questions that the authors address here, proposing that studies of this system could advance the understanding of chronic neuropathic pain. A candidate approach inspired by studies in vertebrate nociceptors led the authors to study anoctamin/TMEM16 channels subdued, and CG15270, termed wwk by the authors. The authors focus on a pathway for gentle touch vs. cold nociception discrimination through anoctamins. Several of the experiments in this manuscript are well done, in particular, the electrophysiological recordings provide a substantial advance. However, the genetic and expression analysis has several gaps and should be strengthened. The data also do not provide strong support for some key aspects of the proposed model, namely the importance of relative levels of Cl co-transporters.

Major comments:

1) Knockout studies are accomplished using two MiMIC insertions whose effects on subdued or CG15270/wwk are not characterized by the authors. This needs to be established. The MiMIC system is also not well explained in the text for readers.

2) Subdued expression is inferred by a Gal4 enhancer trap. This can be a hazardous way of determining expression patterns given the uncertain relevance of the local enhancers driving the expression. According to microarray analysis subdued is strongly expressed in cIII neurons, but c240-Gal4 is barely present compared to nearby neurons, raising questions about whether this line reflects the expression pattern, including levels, even though the authors suggest that the line is previously validated (line 95; it is unclear what previously validated means). Figure 1B should not be labeled "subdued > GFP" since it is not clear that this is the case. Another more direct method of assessing expression in cIII is necessary. Confidence is higher for wwk using a T2A-Gal4 line, however, Figure 1C might be misleading to readers and indicate that wwk-T2A-Gal4 is cIII specific whereas in supplemental data the authors show how it is much more broadly expressed. The expression pattern in the supplemental figures should be moved to the main figures.

3) In figure 8 the authors propose a model in which the relative levels of K-Cl cotransporters Kcc (outward) and Ncc69 (inward) in cIII neurons determine high intracellular Cl- levels and a Cl- dependent depolarizing current in cIII neurons. They test this model using overexpression and loss of function data, but the results do not support their model since for most of the overexpression and LOF of kcc and ncc69 do not significantly affect cold nociception, the exception being ncc69 RNAi. The authors suggest that this could be due to Cl homeostasis regulated by other cotransporters. Nonetheless, it leaves a significant unexplained gap in the model that needs to be addressed.

4) Related to the #3, the authors should verify the microarray data that form the basis for their differential expression model.

Reviewer #2 (Public Review):

Drosophila suzukii prefers to lay eggs on ripe, intact fruit, which contrasts with Drosophila melanogaster, which lays eggs primarily on overripe fruit. The goal of the work by Wang et al. is to decipher the basis for this difference. Part of the explanation is that D. suzukii have a lower preference for sugars, compared to D. melanogaster. Based on electrophysiological recordings, the lower sugar preference in D. suzukii could be due in part to reduced sugar responsiveness of their gustatory receptor neurons in the labella.

The authors then performed transcriptome analyses to analyze the differential expression genes in the tarsi and labella of D. melanogaster and D. suzukii. They found that multiple sugar Grs were reduced in expression in D. suzukii, potentially accounting for the lower sugar responsiveness of D. suzukii.

Ripe fruit is harder than overripe fruit. Therefore, the authors considered whether the differential preferences for D. suzukii and D. melanogaster to lay eggs on ripe and overripe food respectively might be due in part to distinct biases for substrates of different hardness. Indeed, D. suzukii and D. melanogaster preferred harder and softer food, respectively. Moreover, several mechanosensory genes, most notably nompC, were expressed at higher levels in D. suzukii.

The authors also examined combinations of different concentrations of sugars and different levels of food hardness. The results support the conclusion that both food hardness and sugar levels contribute to the distinct preferences for oviposition sites for D. suzukii and D. melanogaster.

This work does an excellent job of employing a diverse combination of approaches (behavioral, electrophysiological and transcriptomics) to interrogate the basis for the differences in oviposition preferences in the two Drosophila species. Moreover, this study raises many new questions concerning the mechanisms contributing to the distinct preferences for ripe and overripe fruit exhibited by D. suzukii and D. melanogaster.

Reviewer #2 (Public Review):

Ishii and colleagues investigated the process of monosaccharide release from algae in low-pH environmental conditions, mimicking the acidic lysosomal-like intracellular compartment where the algae reside symbiotically and transfer nutrients to their hosts, namely corals and other animals. Upon exposure of cultured algae to low pH, subsequent physiological changes as well as the increased presence of glucose and galactose were measured in the surrounding media. Concurrently, photosynthetic activity was decreased, and further experiments employing the photosynthetic inhibitor DCMU to cultures also replicated the increased monosaccharide release. Transcriptomic comparison of algae in low pH to controls showed differential expression in glycolytic pathways and, interestingly, a strong upregulation of signal-peptide-containing isoforms of cellulases. Finally, the elegant use of a cellulase inhibitor on the cultured algae revealed a decrease in monosaccharides in the media. This led the authors to propose a pathway of sugar release in which acidic conditions trigger a cellulase-driven cascade of cell wall degradation in the algae and their consequent release of monosaccharides. These results have interesting implications on the molecular mechanisms of coral-algae symbiosis, contributing to the understanding of how these important symbioses function on the cellular level.

Overall the conclusions of this manuscript are supported by the data presented, but clarification and elaboration are needed to fully justify the proposed mechanisms and better situate the results in a broader context of the field.

Reviewer #2 (Public Review):

Consistent fetal growth trajectories are vital for survival and later life health. The authors utilise an elegant and novel animal model to tease apart the role of Eed protein in the female germline from the role of somatic Eed. The authors were able to experimentally attribute placental overgrowth - particularly of the endocrine region of the placenta - to the function of Eed protein in the oocyte. Loss of Eed protein in the oocyte was also associated with dynamic changes in fetal growth and prolonged gestation. It was not determined whether the reported catch-up growth apparent on the day of birth was due to enhanced fetal growth very late in gestation, a longer gestational time ie the P0 pups are effectively one day "older" compared to the controls, or the pups catching up after birth when consuming maternal milk.

Reviewer #2 (Public Review):

In this report, Hoel and colleagues present evidence that the TMEM87 proteins are members of a larger family of GOLD domain seven-transmembrane helix proteins that consist of a 7 transmembrane helix containing membrane domain and an extracellular / luminal Golgi-dynamics (GOLD) domain. Combining AlphaFold2 modelling with a low-resolution (~4.7) cryo-EM map, the authors were able to build a model of human TMEM87A. Comparisons revealed that TMEM87A is most structurally related to Wntless, including a large membrane accessible cavity on the extracellular / luminal side of the 7 TM domain. A non-protein density was resolved in this cavity in TMEM87A that may correspond to a lipid molecule.

This study represents an important advance of the understanding of this poorly characterized family of proteins. While the structure is of low resolution, it is well interpreted, and authors take good advantage of AlphaFold2 to gain insights into potential function.

Reviewer #2 (Public Review):

The authors report here on the development of an integrated, on-axis fluorescent module as an upgrade to existing FIB-SEMs. The optical axis of the new fluorescent module is designed to be coincident with both SEM and FIB beams, thus allowing imaging of the same spot of the specimen with three beams (i-beam, e-beam, and light beam), all within the chamber of a FIB-SEM and without any stage movement. The authors show a detailed design of the FLM module, together with the complete redesign of the specimen-holding stage. A new specimen stage is needed to accommodate the objective lens of the FLM that must be positioned within a few millimeters from the sample and would not fit into the already crowded upper part of most FIB-SEM chambers. In such a setup, the sample is observed from the top with i-beam and e-beam and with a light beam from underneath.

The design of the piezo positioning stage is well presented together with the results of the stage performance. It has very low repositioning error and resistance to mechanical vibrations. With five degrees of freedom, the sample at this stage can be accurately positioned for specific milling geometry. It is unclear what are the stage limits and if, for example, 90 degrees (orthogonal) FIB-milling is achievable with this stage.

The second part of the paper showcases two results utilizing the coincident beam setup for fluorescence-guided lamellae preparation. The authors describe the successful preparation of several lamellae while guided by the fluorescent signal from the area of thinning. Subsequent TEM data acquisition showed that a) the target of interest was present in the lamella after the final thinning; b) lamellae were sufficiently thin for tomographic data acquisition; c) ice remained vitreous and with minor contamination.

Advantages:<br /> In the described setup, all three beams inside the FIB-SEM chamber are coincident and can be centered on the same area of the specimen given the correct Z-height. This greatly simplifies and accelerates the acquisition of the fluorescent signal that is currently done in either a) an external fluorescent microscope, which involves additional time-consuming sample transfer steps prone to contamination; or b) integrated off-axis FLM, which involves large stage movements with limited precision. Additionally, since there is no need for stage movement, fluorescent data can be acquired without interrupting the milling process, enabling real-time monitoring of the presence of the fluorescent label. Reported Z-resolution for the light microscope module, coupled with the precision of the piezo stage enables accurate positioning of the sample for the targeted milling with 100 nm accuracy in Z using the specimen's fluorescent signal without the need for additional fiducials.<br /> The proposed setup comes as a complete solution: FLM module + custom cryo-cooled piezo stage + modified Quorum sample shuttle transfer + Odemis imaging software to control the microscope as well as all custom components. This setup has the potential to modernize older FIB-SEMs that don't have a cryo stage at all, lack integrated FLM, have stage issues, or run outdated software. However, it is unclear how compatible this system is FIB-SEM manufacturers other than TFS.

Limitations:<br /> The described stage is designed from the ground up to work with the standard TEM AutoGrids, thus limiting the type of the compatible sample to the prepared on-the-grid (i.e. plunge-frozen grids or grids prepared following the waffle method). It looks like the standard SEM stub cannot be used in this system, however, a 3 mm standard HPF type-B can potentially be accommodated (perhaps additional modification is needed). Even if 3 mm HPF hats can be used, positioning of the FLM objective below the specimen makes fluorescent imaging impossible, thus lift-out will rely on external fluorescence imaging.<br /> Another concern is the possibility of automated FIB-milling using Odemis software. Modern proprietary software, such as TFS AutoTEM, Zeiss' SmartFIB, or open-source Autoscript-based solutions such as SerialFIB, offer the GUI-based user-friendly automated milling setup suitable for unsupervised overnight lamellae preparation. It is unclear whether Odemis software would allow a similar level of automation.

Reviewer #2 (Public Review):

The manuscript by Vaisey et al investigates the organization of Piezo1 on the surface of mouse red blood cells. The authors found that Piezo1 prefers to distribute within the concave dimple as compared to the convex rim regions of the RBC. Additionally, Piezo1s form individual trimers that do not show an apparent tendency to cluster or interact with cytoskeletal components.

The manuscript addresses a timely topic regarding the mechanisms underlying the subcellular distribution of Piezo1, a major mammalian mechanosensitive ion channel. The findings regarding the behavior (curvature sensing, lack of clustering) of Piezo1 in live cells potentially have broad implications in biophysics, mechanobiology, and physiology. Overall, I found the manuscript well written. The experimental data collected with super-resolution microscopy and electron microscopy are compelling and of high quality. However, important details of the modeling aspects are unclear and several key control experiments are missing.

Reviewer #2 (Public Review):

In this manuscript, the authors have identified cryptic pockets in the Nsp1 protein of the SARS-CoV-2 virus. The authors used computational methods to identify these pockets and demonstrate drug binding via simulation studies. The authors also show that such cryptic pockets exist in other beta-coronaviruses as well.

The authors carried out fragment-based screening using macromolecular crystallography and confirmed the presence of drug bound in one of the pockets identified. However, the binding assays showed a weak binding with high error. Further, the authors perform Nsp1-mRNA simulation studies to identify how Nsp1 binds to the 5'UTR of SARS-CoV-2 mRNA and mention that targeting the identified pocket in Nsp1-N could disrupt the SARS-CoV-2 Nsp1-mRNA complex. However, there are conflicting reports on direct binding between the SARS-CoV-2 Nsp1-mRNA (See references 17 & 29).

Nsp1 helps establish viral infection in the host, and hence identifying the druggable site in this protein is important. Therefore, this study is important and exciting.

Reviewer #2 (Public Review):

Shi et al present novel genetic tools to carry out conditional reversible genetics. These allow the Cre-dependent inactivation of a given gene of interest, together with reporter expression, and the posterior Flp-recombinase-dependent deletion of the ReCOIN cassette and reactivation of the full gene expression. The process is based on the alternative splicing of an exon containing the ReCOIN cassette in the default mode, followed by sequential recombination/reversion of this cassette by Cre, leading to the expression of a reporter in the cells having a gene deleted. Subsequently, Flp recombinase can be used to delete the ReCOIN cassette, restoring the wild-type gene. The strategy is largely based on the XTR system (Robles-Oteiza et al, 2015) but with the difference that it also allows the targeting of genes composed by a single exon (without introns), as the construct is not targeted to an intron but instead integrated into one of the first exons of the gene of interest.

The authors also design and generate a single genetic construct (CIRKO), that enables doxycycline-inducible expression of Cre and FlpOERT2, followed by tamoxifen activation of the latter. After doxycycline administration, inversion of the ReCOIN allele truncates the gene and fuses it with a reporter and a polyA cassette, and after tamoxifen induction of FlpOERT2, the ReCOIN cassette is deleted, thus restoring the wildtype gene.<br /> Although the authors provide convincing evidence of the sequential recombination process, several aspects of the data analysis need to be improved and controlled.<br /> 1) Authors did not evaluate whether the integration of the ReCOIN construct in the exon of the gene of interest affects the gene's endogenous expression levels. This needs to be carefully assessed as it may generate loss-of-function or hypomorphic alleles, even in the absence of any other manipulation. Data presented in Fig.1-Sup 5 shows that the Cherry levels are much lower in the unrecombined allele containing the ReCOIN than after full recombination and expression of the native wildtype allele, suggesting that the simple integration of the ReCOIN cassette may decrease gene expression.<br /> 2) One of the main problems of this and previous COIN or XTR systems is that the expression of the reporter after flipping the ReCOIN allele (to produce gene knockout) is often too weak because its expression is driven by the endogenous gene promoter and alternative splicing, which for most genes does not allow the clear separation between mutant (reporter+) and wildtype/reversed cells. Another caveat of this system is that wild-type and reversed (gene-reactivated) cells are indistinguishable.<br /> 3) Although the authors use cell lines to demonstrate the expression of the targeted gene of interest before and after each of the sequential recombination events of the allele by immunofluorescence, there is no quantitative data reflecting the efficiency and reliability of each event in the entire cell population. Data is mainly obtained from a few single cell-derived clones, rather than the entire population of transfected cells.<br /> 4) There is no data showing that their system works as predicted in vivo.

Reviewer #2 (Public Review):

Molendijk et al. have performed muscle proteomics on a large population of genetically-variable mice - 161 mice from 73 different inbred strains out of the HMDP set, typically in duplicate but with a handful of strains with 3 or 4 biological replicates. Proteomics was run by TMT-based DDA, with around ~2000 proteins quantified in the entire cohort and an additional ~2000 quantified in at least 30% of the cohort. They have identified a few dozen of genes of interest that were detected through QTL mapping to be possibly associated with muscle phenotypes, of which a couple of dozen were designed to be targeted with AAVs and checked in vitro. Potentially two - and definitely one - of the knockdowns were successful in cell lines, and the successful one, on the gene UFC1, was tested in mice. The knockdown of UFC1 in mice had a very striking phenotypic effect on muscle function, providing new insight into the physiology and opening new avenues of research for how this mechanism may work.

Reviewer #2 (Public Review):

In this manuscript, Long and colleagues explore a very fundamental question regarding the origin and evolution of selfish genetic elements. In particular, they focus their study on the paradoxical abundance of toxin-antidote elements in Caenorhabditis species that reproduce largely by selfing. As a model system, they study the C. elegans peel-1/zeel-1 locus, the first TA to be molecularly dissected in eukaryotes.

Major strengths

1. The manuscript is well-written and easy to follow.<br /> 2. It tackles a very interesting question. Toxin-antidote elements are made of two genes, one coding for a toxin and a second one for its cognate antidote. Although these selfish genes seem relatively simple, there are two paradoxes associated with their evolutionary inception. First, what function evolved first? How can a toxin evolve in the absence of an antidote? Why would an antidote evolve in the absence of a toxin? Second, how does gene drive evolve in selfing nematodes? Toxin-antidote elements thrive under conditions that maximize their dispersal, that is, outcrossing. So, why are toxin-antidote elements so common in nematodes that mainly reproduce by selfing? The main finding of Long and colleagues, namely, that the toxin peel-1 increases the fitness of selfing hermaphrodites, has the potential to change how we think about these ubiquitous selfish elements.

Major weaknesses

Although the results presented by the authors are interesting and suggestive, I find the evidence largely insufficient. In particular, a lack of appropriate controls in the following experiments.

1. The main claim of this paper boils down to a single experiment. Figure 3C and 3E. In particular the contrast between N2(marker) worms and N2 (peel-1 null; marker) strains. In essence, the authors show that peel-1 null worms lay 6% fewer embryos than WT and that they are outcompeted by N2 worms when co-cultured. However, I feel this is not properly controlled. Every time one generates a mutant worm by CRISPR (or other means) there is a chance that a secondary non-desired change is introduced. This could be due to the technique itself, for instance, CRISPR gRNA having an off-target or it could be derived from the transgenesis procedure itself. That is, the bottleneck effect associated with injecting worms and picking single progeny to establish mutant lines that could fix random mutations. Since these effects are to some degree unavoidable, careful controls must be provided. First and foremost is the generation of independent alleles. As far as I could tell, the authors only mention and do experiments with a single peel-1 null mutant. There is also no mention of backcrossing strains to the parental strain in the methods section. This is particularly troubling because at the end of the day, the authors based the whole paper on a very modest effect on fitness. Now, such a modest effect, of course, would be sufficient for natural selection to act upon in the wild, but at the same time, it could be perfectly caused by off-targeting or genetic drift of random mutations in the background. If one were to take "N2" reference strains from different labs in the world, I'm pretty sure that we would see differences in fitness, most of them with a larger effect size.

In my opinion, a critical control missing in the study would be a "rescue" experiment by performing CRISPR editing on the peel-1 null mutant line and "fixing" the toxin allele. This should restore the phenotypes back to WT levels and would discard any secondary off-target effects. The authors could claim that the NIL experiment (Figure 2) strengthens their view because they see a similar effect as in the peel-1 null worm. However, as they also point out, these worms have >100kb introgression with multiple genes in there, thus any small effect on fitness could be perfectly due to linkage. For consistency, one would have expected also to make a peel-1 null allele in the NIL background, but that experiment was not provided either.

In summary, there are many trivial ways in which a mutant line will have a slight decrease in fitness and none of these are controlled in this manuscript. Moreover, decreasing fitness is trivial, but increasing it, is not.

2. The authors propose PEEL-1 increases the fitness of hermaphrodites by making them lay more eggs. Now, as far as we know, PEEL-1 is not expressed in the female gonad, only in sperm. Thus, one logical conclusion (or the only simple scenario I can think of) would be that PEEL-1 increases the total number of mature sperm in hermaphrodites. I think that further work characterizing this phenomenon would be fundamental to strengthen the claim made by the authors.

Reviewer #2 (Public Review):

In this paper, Shen and co-workers report the results of experiments using single particle tracking and FRAP combined with modeling and simulation to study the diffusion of molecules in the dense and dilute phases of various kinds of condensates, including those with strong specific interactions as well as weak specific interactions (IDR-driven). Their central finding is that molecules in the dense phase of condensates with strong specific interactions tend to switch between a confined state with low diffusivity and a mobile state with a diffusivity that is comparable to that of molecules in the dilute phase. In doing so, the study provides experimental evidence for the effect of molecular percolation in biomolecular condensates.

Overall, the experiments are remarkably sophisticated and carefully performed, and the work will certainly be a valuable contribution to the literature. The authors' inquiry into single particle diffusivity is useful for understanding the dynamics and exchange of molecules and how they change when the specific interaction is weak or strong. However, there are several concerns regarding the analysis and interpretation of the results that need to be addressed, and some control experiments that are needed for appropriate interpretation of the results, as detailed further below.

(1) The central finding that the molecules tend to experience transiently confined states in the condensed phase is remarkable and important. This finding is reminiscent of transient "caging"/"trapping" dynamics observed in diverse other crowded and confined systems. Given this, it is very surprising to see the authors interpret the single-molecule motion as being 'normal' diffusion (within the context of a two-state diffusion model), instead of analyzing their data within the context of continuous time random walks or anomalous diffusion, which is generally known to arise from transient trapping in crowded/confined systems. It is not clear that interpreting the results within the context of simple diffusion is appropriate, given their general finding of the two confined and mobile states. Such a process of transient trapping/confinement is known to lead to transient subdiffusion at short times and then diffusive behavior at sufficiently long times. There is a hint of this in the inset of Fig 3, but these data need to be shown on log-log axes to be clearly interpreted. I encourage the authors to think more carefully and critically about the nature of the diffusive model to be used to interpret their results.

Even in the context of the 'normal' two-state diffusion model they present, if they wish to stick with that-although it seems inappropriate to do so-can the authors provide some physical intuition for what exactly sets the diffusivities they extract from their data. (0.17 and 0.013 microns squared per second for the mobile and confined states). Can these be understood using e.g., the Stoke-Einstein or Ogston models somehow?

(2) Equation 1 (and hence equation 2) is concerning. Consider a limit when P_m=1, that is, in the condensed phase, there are no confined particles, then the model becomes a diffusion equation with spatially dependent diffusivity, \partial c /\partial t = \nabla * (D(x) \nabla c). The molecules' diffusivity D(x) is D_d in the dilute phase and D_m in the condensed phase. No matter what values D_d and D_m are, at equilibrium the concentration should always be uniform everywhere. According to Equation 1, the concentration ratio will be D_d/D_m, so if D_d/D_m \neq 1, a concentration gradient is generated spontaneously, which violates the second law of thermodynamics. Can the authors please justify the use of this equation?

Indeed, the derivation of Equation 1 appears to be concerning. The flux J is proportional to D * dc/dx (not k*D*c as in the manuscript). At equilibrium dc/dx = 0 on both sides and c is constant everywhere. Can the authors please comment?

So then another question is, why does the Monte Carlo simulation result agree with Equation 1? I suspect this has to do with the behavior of particles crossing the boundary. Consider another limit where D_m = 0, that is, particles freeze in the condensed phase. If once a particle enters the condensed phase, it cannot escape, then eventually all particles will end up in the condensed phase and EF=infty. The authors likely used this scheme. But as mentioned above this appears to violate the second law.

(3) Despite the above two major concerns described in (1) and (2), the enrichment due to the presence of a "confined state", is reasonable. The equilibrium between "confined" and "mobile" states is determined by its interaction with the other proteins and their ratio at equilibrium corresponds to the equilibrium constant. Therefore EF=1/P_m is reasonable and comes solely from thermodynamics. In fact, the equilibrium partition between the dilute and dense phases should solely be a thermodynamic property, and therefore one may expect that it should not have anything to do with diffusivity. Can the authors please comment on this alternative interpretation?

Reviewer #2 (Public Review):

Dr. Kawakami and colleagues investigate that FA is taken up via both apical and basolateral sides of tubular epithelial cells. CD36 is known to be expressed in tubular cells, so it is expected and well known that FA was taken up via CD36 at the basolateral side. However, FA is also taken up at the apical side (primary urinary side) independent of CD36 activity, and albumin reabsorption is an interesting new finding, although the specific mechanism involved in this process is not shown but discussed possible mechanism in a discussion section in the manuscript. Authors provide the evidence of CD36 expression in the basolateral side of tubules, TG contents in kidney tissue, and FA levels in serum and urine utilizing CD36KO mice, PTi (PT specific injury), and megalin KO mice to support the author's hypothesis.

Although it is an interesting study, this study is overall descriptive by performing staining and testing FA levels in serum or urine rather than conducting functional studies in tubule cells. Moreover, the authors exclude the possibility that TG content is associated with TG lipolysis. Cell stores uptake FA as TG in lipid droplets and lipase activity is required to use FA as their energy source especially in tubular cells that are known to use FA as their energy source. Thus, there is a high probability that the balance between FA uptake and TG lipolysis determines TG contents. To exclude the TG lipolysis and ensure the TG contents are highly and solely associated with serum FA, the author should provide lipase activity, level of lipolysis, and TG content in tubular cells in vitro.

Reviewer #2 (Public Review):

In this work, Kotler et al. examine the consequences of SUMOylation in the regulation of cortical pyramidal neurons excitability. The authors take advantage of previous articles from their groups in which they have shown that ion channel activity is regulated by the SUMO pathway. Most of the experiments are whole-cell recordings including purified SUMO1 or SENP peptides in the pipette solution, in order to show the effects of SUMOylation and deSUMOylation, respectively. They study repetitive firing and passive membrane properties of cortical neurons, finding that SUMO1 increases the neuronal excitability and the neuronal gain. Interestingly, deSUMOylation with SENP1 dialysis produces opposite effects, indicating that an endogenous basal level of ion channel SUMOylation is present. Importantly, they generate (using CRISPR/Cas9 technology) a mouse model with a point mutation that renders Nav1.2 channels insensible to SUMOylation (Nav1.2-K38Q). In cortical neurons from this mouse model, SUMOylation affects mainly passive neuronal properties; so, modification of neuronal gain by SUMO1 seems to be mediated by changes in Nav1.2 activity. Next, they make use of voltage-clamp recordings and simultaneous fluorescence imaging of Na+ fluxes to assess changes in the persistent sodium current, one of the main factors that can modify neuronal gain. SUMOylation causes a leftward shift in the activation kinetics of INaP, and that change is not observed in Nav1.2-K38Q mice. Finally, the authors show that SUMOylation of Nav1.2 channels affects EPSPs and, of great relevance, the speed of back-propagating action potentials, without modifying the speed of forward-propagating action potentials.

Overall, the conclusions of this paper are mostly well supported by data, the manuscript is well-written, nicely organized, and breaks new ground in the role of SUMOylation modulating neuronal activity and action potential backpropagation, a key aspect for synaptic plasticity, and should be of broad interest.

However, some aspects need to be clarified.

1) The statistical analysis for the first two figures (lines 156 to 215 in the main text) seems to contain some errors, that could change the interpretation of the results. Or, by the contrary, there are some errors in the data provided (mean +/- S.E., number of replicates). Details on this subject are indicated in "recommendations for the authors". Figure 5b has not included statistical analysis. Mean values plus S.E. and "n" are not indicated in Figure 3 - Figure Supplement 1 and 2, and in Figure 4.

2) The authors mention that the recordings are done in L5 pyramidal neurons, but there are two main classes of these neurons: "thick-tufted" and "slender tufted". These two classes have different morphological and electrophysiological profiles. For example, spiking properties and input resistance can be quite different in both types, as showed by van Aerde & Feldmeyer, 2015 ("Morphological and Physiological Characterization of Pyramidal Neuron Subtypes in Rat Medial Prefrontal Cortex", Cerebral Cortex, 25:788-805). The number of neurons recorded for each condition is around 5-7 for most of the experiments in the article. This number should be increased to avoid bias that could provide differences between different treatments, if the neurons are chosen randomly and not selected by type. The values for input resistance, time constant, etc., should be comparable between conditions just after break-in (before SUMO1 or SENP1 dialysis, and similar to control internal solution). For example, in the case immediately after the break-in, the F-I curves showed in Figure 1 - Figure Supplement 1 should be similar for SUMO1, SENP1 and Control, but they are not.

3) The neurons in the mutant (Nav1.2-Lys38Gln mouse model) are provided with Nav1.2 channels that are constantly deSUMOylated. This condition could likely drive compensatory changes in the expression/activity of different ion channels in the neurons from mutant mice. A more complete characterization of neuronal properties from this mouse model, in control internal solution, should be desirable, and also a comparison with parameters obtained from wild type neurons with SENP1 internal solution (after dialysis).

Reviewer #2 (Public Review):

In this clearly presented study, the authors are assessing the impact of introducing hexamerisation-associated mutations into human monoclonal antibodies that target the capsule of pneumococcus. The impact of these mutations is assessed in in vitro systems using human sera or neutrophils. The second series of studies use mouse models involving the adoptive transfer of antibody and the subsequent challenge of mice.

The major strengths of the study are that the authors are addressing an important area of unmet need, both in terms of alternative treatments for bacterial infections and also in how antibodies function against bacterial pathogens. This is a neglected area, particularly in the context of understanding how antibodies function after binding to bacterial capsules. The results are intriguing, and one consideration is whether enhancing complement activation is beneficial or harmful for a therapeutic antibody. Based on these results is there the possibility of a natural selection against strong levels of complement activation?

The study clearly shows that the introduction of the hexamerisation mutations affects the ability of the antibodies to bind and activate complement. The studies in Fig 2 examining the role of Fc are particularly elegant. One issue is that it is surprising that the WT IgG1 and IgG3 monoclonals have a minimal capacity to fix and activate complement, despite IgG1/3 to other antigens being efficient isotypes at fixing complement. In the absence of data showing whether IgG1/3 from normal human sera against capsule fixes complement then it is difficult to contextualise these results or to assess if other changes, such as in glycosylation, contribute to the results presented. Related to this, there is reasonable evidence that antibodies induced to capsules can be protective yet the data in Fig 5 suggests that without the mutations then the monoclonals are not effective at all for 6B and only effective at the highest concentration for 19A.

The adoptive transfer experiments demonstrate that the antibodies can moderate bacteraemia. The mechanism of this is not explored and the importance of hexamerisation and complement activation not demonstrated, especially as it is not clear if human antibodies and mouse complement are a productive combination in this context.

Reviewer #2 (Public Review):

In this manuscript, Jian et al. reported their biochemical study demonstrating that histone H3 lysine acetylation facilitates H3K4me3 binding by the PHD finger proteins on nucleosome as compared to peptides, and enhances H3K4 methylation by histone methyltransferase MLL1. Histone lysine acetylation and methylation are well known to play an important role in directing gene transcription in chromatin, but how they work in coordination is much less understood. Therefore, this study provides new insights into how histone H3 lysine acetylation promotes gene transcriptional activation through enhancing writing and reading of histone H3K4 methylation, which is recognized as a histone mark for transcriptional activation. While this is an interesting study, there are a number of questions that the authors should address as described below, which would confirm the functional importance and relevance of their results.

Specific Comments:

1. It has been reported that PHD fingers can bind to DNA in addition to lysine-methylated histone H3. Can the authors address whether or not the enhanced selectivity of PHD-nucleosome interactions over PHD-peptide interactions is due to PHD-DNA binding?

2. What's the binding affinities of PHD-nucleosome interactions and PHD-peptide interactions, respectively?

3. Histone H4K5acK8ac is a well-known site-specific histone acetylation mark for gene transcriptional activation, much more so than histone H3 acetylation. Does H4K5K8 acetylation enhance PHD-H4K3me3 binding in nucleosome?

4. The authors provided the data showing cis histone H3 tail lysine acetylation effects on PHD-H4K3me3 binding. What about trans histone H3 lysine acetylation effects?

Reviewer #2 (Public Review):

In addition to the nucleus accumbens, the bed nucleus stria terminalis (BNST; part of the extended amygdala) is also a recipient of dopamine release from VTA and other regions. While nucleus accumbens dopamine signaling has been heavily implicated in individual differences in the attribution of incentive salience towards a reward predictive cue and reward learning, it is still unclear whether dopamine signaling in extended amygdala is involved in this process.

Here, Gyawali et al. use GRABDA sensors to record dopamine signaling in the dorsal BNST (dBNST) during Pavlovian and instrumental cue-evoked reward tasks. During a Pavlovian lever autoshaping task, they observed individual differences in dopamine signaling in response to a reward CS, with sign-tracking rats showing heightened dopamine response compared to goal-tracking rats. dBNST dopamine signaling also bidirectionally encoded violations in reward prediction, as well as outcome-specific satiety. Finally, they show that fentanyl self-administration-associated cues also elevate dBNST dopamine signaling.

The manuscript is very well written, includes appropriate controls, use of statistical analyses, and conclusions were generally justified by their results. In some instances, larger group sizes would allow authors to more powerfully assert their claims. Although causal manipulations would further solidify the necessity of cue-evoked dopamine signaling in the BNST, these are a very interesting and thorough set of experiments that importantly highlight the role of endogenous dopamine dynamics in BNST in cue-related reward motivation. Not only are these findings important in defining a role for BNST in appetitive motivation (in addition to its more famous role in aversive motivation), but they are also likely to impact future important work that causally delineates sources of dopamine to BNST.

Reviewer #2 (Public Review):

The authors designed this study to identify the direct T3 target genes that underlie the T3 actions in the brown adipose tissue (BAT). The unique model used (dominant negative TRa knock-in and a TRb knock-out) allows for the isolation of BAT-specific actions from other well-known systemic effects on thermogenesis, including the central nervous system. The strengths of the study reside in the novel methodological approach. Previous studies of T3 actions in the BAT used animal models that did not allow for full isolation of BAT-specific effects of T3. A limitation however is the combination of TRa knock-in (which causes permanent suppression of TRa-dependent genes) with the TRb knockout, which only prevents T3 induction of TRb-dependent genes. Nonetheless, the results were impressive with the identification of about 1,500 genes differentially expressed in the BAT, among which UCP1 and PGC1a were the two main ones. Although it has been known that both UCP1 and PGC1a are downstream targets of T3, the work establishes through an ingenious approach the critical direct role played by T3 in BAT thermogenesis. In addition, the genetic approach utilized here is of great value and could be easily expanded to other tissues and systems.

Reviewer #2 (Public Review):

Jullie et al addressed the long-standing question of how presynaptic desensitization of opioid receptor signaling can occur on the timescale of hours despite the fact that it does not occur on the timescale of minutes. They also compared the mu and delta opioid receptors in this context and asked whether their desensitization occurs in a homologous or heterologous manner when co-expressed in the same neurons.

A major strength of the work is the use of a relatively high-volume imaging assay of synaptic transmission based on VAMP2-SEP to detect exocytosis of synaptic vesicles and its modulation by heterologously expressed opioid receptors in cultured neurons. This allowed for large data sets to be acquired and analyzed with good statistical power. It also reports on a validated metric of synaptic transmission.

A significant weakness arises from the need to overexpress opioid receptors in cultured striatal neurons in order to conduct the experiments with high reliability. Because the authors did not attempt to address receptor expression levels and relate overexpression to endogenous receptor expression levels in axons, the physiological significance of the findings remains, to some extent, in doubt.

Using heterologously expressed receptors, the primary finding that slow desensitization (of presynaptic suppression of neurotransmission) occurs via endocytosis of membrane-localized opioid receptors, is well supported by multiple lines of evidence. 1) Blocking receptor endocytosis, either via mutation of GRK2/3 phosphorylation sites or pharmacological block with compound 101 prevents slow desensitization of MOR. ) SEP-MOR and SEP-DOR fluorescence (indicative of plasma membrane localization) is reduced by chronic agonist treatment.

The secondary findings that MOR and DOR do not desensitize or undergo endocytosis in a heterologous manner, and that DOR-depletion from the plasma membrane is more facile than MOR and independent of C-terminus phosphorylation, are well supported by the data and analyses.

Despite the reliance on heterologously expressed opioid receptors, the findings are likely to have a high impact on the fields of GPCR trafficking and opioid signaling, as they address a major outstanding question with direct relevance to opioid drug tolerance and may generalize to other GPCRs.

The findings also evoke new questions that will spur further work in the field. For example, just focusing on DOR, by what mechanism does agonist-driven DOR endocytosis occur not via GRK2/3 phosphorylation? By extension, would G protein-biased DOR agonists be expected to produce less tolerance? To be clear these are not to be addressed in this manuscript.

Reviewer #2 (Public Review):

The LGMD for well over 40 years has served as a model for understanding neural computations, and its mechanisms for integrating visual stimuli are thought to be well established (including past work from the authors and other labs). The LGMD has one large dendrite field that renders it selective to dark expanding objects through a combination of retinotopically distributed off inputs and intrinsic conductances. The LGMD has two smaller dendrite fields that receive on (luminance increments) or off (luminance decrements) inhibition. Surprisingly, Dewell et al. find one of the small dendrite fields, previously found to process off inhibition, also responds robustly to expanding white objects (on excitation). Interestingly, its integration strategy differs from how the larger dendrite processes off excitation. Ca2+ activity within this smaller dendrite field shows minimal to no retinotopic arrangement of inputs. Ca2+ responses to white looming stimuli are also maintained as the coherence of the stimulus decreases, suggesting the change in luminance, but not the spatial pattern of change in luminance, underlies the LGMD's response to white expanding objects. Interestingly, the grasshopper takeoff behavior, for which the LGMD is involved, also follows a similar trend. The probability a dark looming stimulus elicits an escape strongly depends on stimulus coherence, while the probability a white looming stimulus elicits an escape does not. Overall, these findings shed light on how feature inputs can be differentially computed within the same neuron and how these computations shape behavioral responses.

Claims:

1. ON excitation occurs on the LGMD dendrite field previously thought to receive only OFF inhibition.<br /> a. The authors provide calcium imaging and local delivery of cholinergic antagonist data to support this claim.

2. ON inputs do not have retinotopic mapping across the dendrite field, unlike OFF inputs dedicated to a different dendritic field<br /> a. Analyzed calcium imaging data support this claim, but analysis methods need to be clarified and relevant anatomy need to be discussed in relation to the columnar structure of the lobula.

3. Lack of retinotopy of ON inputs makes the LGMD insensitive to ON looming stimuli coherence<br /> a. The authors provide calcium imaging data supporting the response within the dendrite receiving ON inputs does not have a strong dependency on the coherence within the looming stimulus.

4. Behavior follows witnessed dendrite integration, with decreasing coherence affecting escapes to dark but not white looms.<br /> a. The provided behavior data support these claims.

5. Limited coherence reduces energetic cost<br /> a. The rationale for this claim and the methods for the modeling experiments that support these claims need to be included/expanded.

Reviewer #2 (Public Review):

The manuscript by Abdel-Haq and colleagues is a descriptive study providing evidence that mice displaying motor impairment related to Parkinson's disease fed with a prebiotic diet show a decrease in the severity of this impairment (some, but not all, of the motor functions tested). Their data indicate that microglial cells are required to mediate the beneficial effect of the prebiotic treatment. Indeed, in the absence of microglial cells, the prebiotic treatment is no longer able to attenuate the motor deficit. This manuscript is of interest to a wide audience as it provides further evidence that links motor impairment related to PD to events occurring in the gut (gut-brain axis). Furthermore, some of the new findings presented in the manuscript highlight the contribution of immune mechanisms as key contributors to the pathophysiological process leading to PD.

This is an interesting study showing for the first time that the beneficial effect of a prebiotic treatment in the context of motor impairment related to PD is mediated by microglial cells. Since these cells are of macrophagic origin, their data support the concept that the immune system plays a role along the gut-brain axis during the pathophysiological process leading to PD. The sequencing data may be of additional value to some. Considering that the authors had a model system where clear beneficial motor impairment was observed, it is surprising that they did not investigate further whether the dopaminergic system in the SN and STR was modified in relation to the prebiotic treatment and microglial depletion.

Reviewer #2 (Public Review):

This manuscript describes a web-based tool (Taxonium) for interactively visualizing large trees that can be annotated with metadata. Having worked on similar problems in the analysis and visualization of enormous SARS-CoV-2 data sets, I am quite impressed with the performance and "look and feel" of the Taxonium-powered cov2tree web interface, particularly its speed at rendering trees (or at least a subgraph of the tree).

The manuscript is written well, although it uses some technical "web 2.0" terminology that may not be accessible to a general scientific readership, e.g., "protobuf" (presumably protocol buffer) and "autoscaling Kubernetes cluster". The latter is like referring to a piece of lab equipment, so the author should provide some sort of reference to the manufacturer, i.e., https://kubernetes.io/. In other respects, the manuscript lacks some methodological details, such as exactly how the tree is "sparsified" to reduce the number of branches being displayed for a given range of coordinates. Some statements are inaccurate or not supported by current knowledge in the field. For instance, it is not true that the phylogeny "closely approximates" the transmission tree for RNA viruses. Mutations are not associated with a "point in the phylogeny", but rather the branch that is associated with that internal node.

A major limitation of displaying a single phylogenetic tree (albeit an enormous one) is that the uncertainty in reconstructing specific branches is not readily communicated to the user. This problem is exacerbated for large trees where the number of observations far exceeds the amount of data (alignment length). Hence, it would be very helpful to have some means of annotating the tree display with levels of uncertainty, e.g., "we actually have no idea if this is the correct subtree". DensiTree endeavours to do this by drawing a joint representation of a posterior sample of trees, but it would be onerous to map a user interface to this display. I'm raising this point as something for the developers to consider as a feature addition, and not a required revision for this manuscript.

The authors make multiple claims of novelty - e.g., "[...] existing web-based tools [...] do not scale to the size of data sets now available for SARS-CoV-2" and "Taxonium is the only tool that readily displays the number of independent times a given mutation has occurred [...]" - that are not entirely accurate. For example, RASCL (https://observablehq.com/@aglucaci/rascl) allows users to annotate phylogenies to examine the repeated occurrence of specific mutations.<br /> Our own system, CoVizu, also enables users to visualize and explore the evolutionary relationships among millions of SARS-CoV-2 genomes, although it takes a very different approach from Taxonium. Taxonium is an excellent and innovative tool, and it should not be necessary to claim priority.

Although the source code (largely JavaScript with some Python) is quite clean and has a consistent style, there is a surprising lack of documentation in the code. This makes me concerned about whether Taxonium can be a maintainable and extensible open-source project since this complex system has been almost entirely written by a single developer. For example, `usher_to_taxonium.py` has a single inline comment (a command-line example) and no docstring for the main function. `JBrowsePanel.jsx` has a single inline comment for 293 lines of code. There is some external documentation (e.g., `DEVELOPMENT.md`) that provides instructions for installing a development build, but it would be very helpful to extend this documentation to describe the relationships among the different files and their specific roles. Again, this is something for the developers to consider for future work and not the current manuscript.

Reviewer #2 (Public Review):

The way a child sleeps is much different than the way an adolescent or an adult sleeps. One difference concerns the time spent in active sleep (AS, also called paradoxical or REM sleep), which is very high in early stages of development and thought to favor brain plasticity that is relevant for circuit development. This study is a step forward to understand the neuronal activity patterns by which REMS promotes this plasticity.

The study addresses this question in particular for higher-order cortical areas. It finds that activity in M2 and mPFC is greater for AS than for wakefulness. Within AS, activity is further elevated in relation to spontaneous limb movements that are characteristic for this state. At P8 but less so at P12, both M2 and mPFC also respond to external sensory stimulation. Therefore, the authors have identified the time window over which these higher cortical areas are sensory responsive yet decline to do so over a period of four days. Through contrasting their results with naturally sleeping with the ones of urethane-anesthetized pups, they further support the unique status of AS in the regulation of neuronal activity and sensory responsiveness that is critical for development. This will enable precise further manipulation to study the anatomo-functional basis of this sensory responsiveness and its role for the development of the sensorimotor system.

Reviewer #2 (Public Review):

Farrell et al. investigated the effect of FABP5 inhibition in myeloma, demonstrating a reduction in tumour burden. They present extensive data to demonstrate that FABP5 inhibition, either by CRISPR-Cas9 or pharmacologically, reduces myeloma cell growth. Transcriptomic and proteomic profiling reveals a wealth of gene and protein sets that are altered in response to FABP5 inhibition, the most notable of which are the UPR and MYC. Two preclinical murine models of myeloma are employed, with a significant reduction in tumour burden and increase in survival observed in response to FABP5 inhibition, providing strong support for the translational potential of this approach in myeloma. Supporting in silico analysis of patient datasets demonstrates associations between FABP5 expression and myeloma survival, providing a strong clinical correlate. The conclusions of the paper are well supported by the data.

Strengths

To the best of my knowledge, this is a novel finding in myeloma, revealing a new therapeutic approach which appears to be highly effective in reducing tumour burden. The work is comprehensive, using a panel of myeloma cell lines and a multitude of in vitro approaches to determine response to FABP inhibition.

Weaknesses

FABP inhibition is known to be effective in other cancers, therefore it is not surprising that it is also effective in myeloma. Mechanism is eluded to following the transcriptomic and proteomic analysis, however, this is not explored in a conclusive manner. Myeloma is a cancer of the bone marrow associated with osteolysis, however, no analysis of the effect of FABP inhibition on myeloma bone disease is presented.

Reviewer #2 (Public Review):

The authors have tried to provide a molecular mechanism for the observation that the lack of DCX increases run lengths of retrogradely moving cargo. The authors show a direct interaction of DCX with Dynein and that this direct interaction is the key means by which to regulate dynein-dependent retrograde run lengths of cargo. DCX seems to have a dual role - on microtubules where it appears to prevent attachment of dynein to microtubules. DCX also appears to reduce JIP3 binding to dynein.

A major strength is that they have used a combination of approaches including in vitro motility assays to support their arguments.

Reviewer #2 (Public Review):

In this paper, eGFP: LlamaTag-Runt was inserted into Drosophila embryo cells by CRISPR-Cas9 technology, and quantitative gene expression and time-lapse measurements were performed. The molecular mechanism was modeled and analyzed by thermodynamic model, the experimental data were fitted by MCMC, and the necessity of cooperation was given.

Reviewer #2 (Public Review):

This is an interesting study investigating the effects of sensory conflict on rhythmic behaviour and gene expression in the sea anemone Nematostella vectensis. Sensory conflict can arise when two environmental inputs (Zeitgeber) that usually act cooperatively to synchronize circadian clocks and behaviour, are presented out of phase. The clock system then needs to somehow cope with this challenge, for example by prioritising one cue and ignoring the other. While the daily light dark cycle is usually considered the more reliable and potent Zeitgeber, under some conditions, daily temperature cycles appear to be more prominent, and a certain offset between light and temperature cycles can even lead to a breakdown of the circadian clock and normal daily behavioural rhythms. Understanding the weighting and integration of different environmental cues is important for proper synchronization to daily environmental cycles, because organisms need to distinguish between 'environmental noise' (e.g., cloudy weather and/or sudden, within day/night temperature changes) and regular daily changes of light and temperature. In this study, a systematic analysis of different offsets between light and temperature cycles on behavioural activity was conducted. The results indicated that several degrees of chronic offset results in the disruption of rhythmic behaviour. In the 2nd part of the study the authors determine the effect of sensory conflict (12 hr offset that leads to robust disruption of rhythmic behaviour) on overall gene expression rhythms. They observe substantial differences between aligned and offset conditions and conclude a major role for temperature cycles in setting transcriptional phase. While the study is thoroughly conducted and represents and impressive amount of experimental and analytical work, there are several issues, which I think question the main conclusions. The main issue being that temperature cycles by themselves do not seem to fulfil the criteria for being considered a true Zeitgeber for the circadian clock of Nematostella.

Major points:

Line 53: 'However, many of these studies did not compare more than two possible phase relationships.....'. Harper et al. (2016) did perform a comprehensive comparison of different phase relationships between light and temperature Zeitgebers (1 hr steps between 2 and 10 hr offsets), similar to the one conducted here. I think this previous study is highly relevant for the current manuscript and -- although cited -- should be discussed in more detail. For example, Harper et al. show that during smaller offsets temperature is the dominant Zeitgeber, and during larger sensory conflict light becomes the dominant Zeitgeber for behavioural synchronization. Only during a small offset window (5-7 hr) behavioural synchronization becomes highly aberrant, presumably because of a near breakdown of the molecular clock, caused by sensory conflict. Do the authors see something similar in Nematostella? Figure 3 suggests otherwise, at least under entrainment conditions, where behaviour becomes desynchronized only at 10 and 12 hr offset conditions. But in free-run conditions behaviour appears largely AR already at 6 hr offset, but not so much at 4 and 8 hr offsets (Table 2). So there seems to be at least some similarity to the situation in Drosophila during sensory conflict, which I think is worth mentioning and discussing.

Line 111: The authors state that 14-26C temperature cycle is 'well within the daily temperature range experienced by the source population'. Too me this is surprising, as I was not expecting that water temperature changes that much on a daily basis. Is this because Nematostella live near the water surface, and/or do they show vertical daily migration? Also, I do not understand what is meant by '...range of in situ diel variation (of temperature)'. I think a few explanatory words would be helpful here for the reader not familiar with this organism.

Lines 114-117: I was surprised that clock genes can basically not be synchronized by temperature cycles alone. Only cry2 cycled during temperature cycles but not in free-run, so the cry2 cycling during temperature cycles could just be masking (response to temperature). Later the authors show robust molecular cycling during combined LD and temperature cycles (both aligned and out of phase), indicating that LD cycles are required to synchronize the molecular clock. Moreover, a previous study has demonstrated that LD cycles alone (i.e., at constant temperature) are able to induce rhythmic molecular clock gene expression (Oren et al. 2015). Similarly, the free running behaviour after temperature cycles does not look rhythmic to me. In Figure 2A, 14-26C there is at best one peak visible on the first day of DD, and even that shows a ~6 phase delay compared to the entrained condition. After the larger amplitude temperature cycle (8:32C) behaviour looks completely AR and peak activity phases in free-run appear desynchronized as well (Fig. 2B). Overall, I think the authors present data demonstrating that temperature cycles alone are not sufficient to synchronize the circadian clock of Nematostella. One way to proof if the clock can be entrained is to perform T-cycle experiments, so changing the thermoperiod away from 24 hr (e.g., 10 h warm : 10 h cold). If in a series of different T-cycles the peak activity always matches the transition from warm to cold (as in 12:12 T-cycles shown in Fig. 1A) this would speak against entrainment and vice versa.

Lines 210-226: As mentioned above, I think it is not clear that temperature alone can synchronize the Nematostella clock and it is therefore problematic to call it a Zeitgeber. Nevertheless, Figure 3A, B, D show that certain offsets of the temperature cycle relative to the LD cycle do influence rhythmicity and phase in constant conditions. This is most likely due to a direct effect of temperature cycles on the endogenous circadian clock, which only becomes visible (measureable) when the animals are also exposed to certain offset LD cycles. My interpretation of the combined results would be that temperature cycles play only are very minor role in synchronizing the Nematostella clock (after all, LD and temperature cycles are not offset in nature), perhaps mainly supporting entrainment by the prominent LD cycles.

Gene expression part: The authors performed an extensive temporal transcriptomic analysis and comparison of gene expression between animals kept in aligned LD and temperature cycles and those maintained in a 12 hr offset. While this was a tremendous amount of experimental work that was followed by sophisticated mathematical analysis, I think that the conclusions that can be drawn from the data are rather limited. First of all, it is known from other organisms that temperature cycles alone have drastic effects on overall gene expression and importantly in a clock independent manner (e.g., Boothroyd et al. 2007). Temperature therefore seems to have a substantially larger effect on gene expression levels compared to light (Boothroyd et al. 2007). In the current study, except for a few clock gene candidates (Figure 2C), the effects of temperature cycles alone on overall gene expression have not been determined. Instead the authors analysed gene expression during aligned and 12 h offset conditions making it difficult to judge which of the observed differences are due to clock independent and clock dependent temperature effects on gene expression. This is further complicated by the lack of expression data in constant conditions. I think the authors need to address these limitations of their study and tone down their interpretations of 'temperature being the most important driver of rhythmic gene expression' (e.g., line 401). At least they need to acknowledge that they cannot distinguish between clock independent, driven gene expression and potential influences of temperature on clock-dependent gene expression rhythms. Moreover, in their comparison between their own data and LD data obtained at constant temperature (taken from Oren et al. 2015), they show that temperature has only a very limited effect (if any) on core clock gene expression, further questioning the role of temperature cycles in synchronising the Nematostella clock. Nevertheless, I noted in Table 3 that there is a 1.5 to 3 hr delay when comparing the phase of eight potential key clock genes between the current study (temperature and LD cycles aligned) and LD constant temperature (determined by Oren et al.). To me, this is the strongest argument that temperature cycles at least affect the phase of clock gene expression, but the authors do not comment on this phase difference.

Network analysis: This last section of the results was very difficult to read and follow (at least for me). For example, do the colours in Figure 6A correspond to those in Figure 6B, C? A legend for each colour, i.e., which GO terms are included in each colour would perhaps be helpful. As mentioned above, I also do not think we can learn a lot from this analysis, since we do not know the effects of temperature cycles alone and we have no free-run data to judge potential influence on clock controlled gene expression. Under aligned conditions genes are expressed at a certain phase during the daily cycle (either morning to midday, or evening to midnight), which interestingly, is very similar to temperature cycle-only driven genes in Drosophila (Boothroyd et al. 2007). Inverting the temperature cycle has drastic effects on the peak phases of gene expression, but not so much on overall rhythmicity. But since no free-run data are available, we do not know to what extend these (expected) phase changes reflect temperature-driven responses, or are a result of alterations in the endogenous circadian clock.

Reviewer #2 (Public Review):

Schaefer and Hummer have performed all-atom molecular dynamics (MD) simulations to study the mechanism of GSDMDNT assembly in membranes closely resembling human plasma membranes. Poses of GSDMDNT-lipid interaction were analyzed. Comparing the assemblies of different GSDMDNT oligomeric states reveals key steps in the membrane pore formation by GSDMDNT, resulting in a model with two GSDMDNT concentration-dependent pathways. That is, low concentration favors monomer insertion followed by assembly in the membranes, whereas high concentration promotes prepore formation at the membrane surface followed by membrane insertion to mature into pore. This model is valuable since it reconciles different experiments that cast doubt on the exact order and mechanism with which GSDMDNT binds the plasma membranes. With comparisons against the existing studies, this paper has provided a better understanding of how various factors such as GSDMDNT concentration and, in particular, the membrane composition may influence the process. The study was well carried out. Given the system size, complexity of the membrane composition, and abundance of cholesterol, the simulations were conducted with strong physical rigor (e.g., long all-atom equilibration with tensionless membranes and with cholesterol flip-flop in equilibrium). The paper was well-organized and nicely written.

Reviewer #2 (Public Review):

It is established that different histone chaperones not only facilitate the assembly of DNA into nucleosomes following DNA replication and transcription but also are essential to stem cell maintenance and differentiation. Here the authors Xiaowei Xu et al. propose a novel role for Mcm2 DNA helicase, a subunit of the origin licensing complex Mcm2-7 in stem cell differentiation in addition to or in connection to maintaining genomic integrity in DNA replication. This study is a continuation of the authors' previously published work implicating Mcm2-Ctf4-Polα axis in the parental histone H3-H4 transfer to lagging strands. The present study is elegantly executed with a systemic analysis of the role of Mcm2 in the ES differentiation to neuronal lineage.

Major questions<br /> 1. Mouse ES cells with a mutation at the histone binding motif of Mcm2 (Mcm2-2A) grew normally, but exhibited defects in differentiation. Also, the Mcm2-2A mutation linked global changes in gene expression, chromatin accessibility and histone modifications were not apparent to the similar degree in mouse ES cells compared to NPCs.<br /> The authors suggest that the excessive amount of Mcm2 in ES cells, similar to DNA replication, safeguards the chromatin accessibility and gene expression in mouse ES cells resulting in Mcm2-2A mutant ES cells being able to restore the symmetric distribution of parental histones before cell division.<br /> What is underlying the mechanism of this difference since overabundant Mcm2 is present in both ES cells and NPCs?

2. CAF-1, Asf1a, and Mcm2 partake in similar or redundant chromatin regulation during differentiation with silencing of pluripotent genes and induction of lineage-specific genes. These processes were found commonly dysregulated in both Mcm2-2A cells and Asf1a KO ES cells, albeit with varying degrees.<br /> How can authors exclude the possibility of Mcm2 affecting the differentiation via Asf1 with which it forms a complex, as a potentially redundant mechanism in the deposition of newly synthesized or recycled histones?<br /> Can authors test potential redundancy between Mcm2 and other histone chaperones and modifiers? Can the authors rescue the NPC phenotype induced by Mcm2 -2A mutant? Can the authors rescue the Mcm2-2A phenotype by overexpression of another histone chaperone or modifier?

3. Authors argue that Mcm2 may regulate the deposition of newly synthesized or recycled histones via the ability to recycle 1. parental H3.1 and H3.3, 2. via binding directly H3-H4, and/or via 3. Pol II transcription. Which of these mechanisms may be more unique to Mcm2 compared to the other histone chaperones and modifiers?

4. Authors observed that in the ES cells the majority of Mcm2 CUT&RUN peaks were enriched with H3K4me3 CUT&RUN signals and ATAC-seq peaks and a small fraction of Mcm2 CUT&RUN peaks were engaged at the bivalent chromatin domains (H3K4me3+ and H3K27me3+). In contrast, in wild-type NPCs all the Mcm2 peaks co-localized with H3K4me3 and ATAC-seq peaks (H3K4me3+, H3K27me3-). The authors thus argued that Mcm2 binding to chromatin is rewired during differentiation citing this differential engagement of Mcm2 with the bivalent chromatin domains in ES and NPCs. What is the mechanism of Mcm2 differential engagement with the bivalent chromatin domains?

5. Authors indicated that in mouse ES cells Mcm2 CUT&RUN peaks exhibited low densities at the origins. DNA replication origins are licensed by the MCM2-7 complexes, with most of them remaining dormant. Dormant origins rescue replication fork stalling in S phase and ensure genome integrity. It is reported that ESs contain more dormant origins than progenitor cells such as NPCs and that may prevent the replication stress. Also, partial depletion of dormant origins does not affect ECs self-renewal but impairs their differentiation, including toward the neural lineage. Moreover, reduction of dormant origins in NPCs impairs their self-renewal due to accumulation of DNA damage and apoptosis.<br /> Can authors exclude the role of reduced dormant origins reflected in the reduced density of Mcm2 at the origins in the differentiation to neuronal lineages?

Reviewer #2 (Public Review):

In this study, Servello and the colleagues characterize how a temperature sensing neuron AFD regulates increased resistance to hydrogen peroxide in worms cultivated at a higher temperature. They show that loss of AFD and the insulin-like peptide INS-39 produced by AFD increase H2O2 resistance similarly as high temperature growth. To understand the molecular basis, they use mRNA-seq and analysis of gene expression at the whole-genome scale and transgenic lines to show that AFD ablation and high cultivation temperature generate overlapping changes in gene expression via the function of the FOXO transcription factor DAF-16 in the intestine.

This study is built on their previous work that established C. elegans as a model to study mechanisms for sensing and resistance of H2O2, an important environmental chemical threat for living organisms. Here, the authors uncover the neuronal and molecular basis for H2O2 resistance induced by high cultivation temperature. The authors use multiple approaches, including genetics, transgenics, whole-genome gene expression analysis, to characterize "enhancer sensing" that they discovered in this study. The experiments are well designed with appropriate controls. The data analysis is comprehensive and revealing. The findings are novel and explain a common and interesting phenomenon. The new understanding generated in this study will appeal to the readers in the fields of sensory biology, signaling transduction and physiology. The implications or conclusions of a few results presented here could be further discussed or clarified in the context of several previous studies.

Reviewer #2 (Public Review):

The cartilaginous fish Leucoraja erinacea (little skate) exhibits core features of tetrapod locomotion, thus it is a key species to study conserved principles of tetrapod motor neuron development. Baek et al. provide a new and improved version of the little skate genome, which will be of great interest to the field of comparative genomics and evolutionary biology. In addition, the manuscript uses already published RNA-seq data from skate, mouse and chicken, as well as newly generated ATAC-seq data in little skate to try to reach a better understanding of the regulatory networks underlying motor neuron specification in these different species. While the question is of key importance, the bioinformatics comparisons followed by the authors seem inadequate and deeply biased. All comparative analyses are performed with lists of genes that for each species are selected following different criteria or compared with different neuronal populations, introducing important biases that will later limit the conclusions driven by the authors. Moreover, additional key aspects of evolution, such as paralog substitution or expression of species-specific genes should also be studied. Finally, the lack of experimental validations also reduces the impact of the conclusions, which at this point are highly speculative.

Reviewer #2 (Public Review):

In the submitted article, Xu and co-workers have explored the alternative splicing of CD44 and NUMB isoforms responsible for promoting epithelial-to-mesenchymal transition in quasi-mesenchymal and highly metastatic subtype of colon cancer. In this regard, the authors have performed numerous RNA-seq and Gene Ontology analyses to identify differentially expressed RNA binding proteins and their associated pathways to understand the related alternative splicing events. CD44s and NUMB2/4 spliced isoforms have been identified as promoting the invasive and metastatic properties while negatively affecting the proliferation of the HCT116 and SW480 cells in Zeb1-ESPR1-dependent manner. Unfortunately, there exists discrepancy and inconsistency at a large extent in the experimental data, along with lack of novel findings as CD44 and NUMB alternative splicing is well investigated in other types of cancers.

Reviewer #2 (Public Review):

This study examines the encoding of distinct visual features during self-motion and reveals distinct mechanisms that contribute to the suppression of features that may be corrupted during self-motion - one based on motor output and one based on the resulting visual input. The authors develop an imaging approach to measure neural activity across many glomeruli, which enables analysis in terms of population codes. They first demonstrate that even though movement strongly alters the response in individual glomeruli, a population-based readout is still able to decode stimulus identity. They then demonstrate that this modulation is primarily suppression of glomeruli that respond to local features, while global features (i.e. looming) are unaltered. Finally, through a combination of visual stimulus manipulations that mimic the effect of movement and analysis of responses relative to behavioral epochs, they show that both the visual input and a motor signal contribute to this suppression.

Together, this provides an elegant explanation of how different signals combine to adapt sensory processing to ongoing behavior. The experiments are cleverly designed and the results are clearly presented, with few technical concerns. The only significant concern entails how well their imaging isolated the visual projection neurons they were targeting.

This study is likely to have a significant impact as it provides a new view on a timely question in visual neuroscience. The study also opens up clear future directions to determine how these two signals are generated and integrated into visual processing, at the neural circuit level. Finally, it provides intriguing parallels to the impact of eye movements on the mammalian visual system.

Reviewer #2 (Public Review):

Zydryski et al. develop a comprehensive toolbox of organ-specific canine organoids. Building on previous work on kidney, urinary bladder, and liver organoids, they now report on lung, endometrium, and pancreatic organoids; all six organoid lines are derived from two canines. The authors attempt to benchmark these organoids via histological, transcriptomic, and immunofluorescence characterization to their cognate organs. These efforts are a welcome development for the organoid field, broaden the scope of use to studies with canine models, and seek to establish robust standards. The organ specific RNAseq dataset is also likely to be useful to other researchers working with the canine model.

A key methodological advance would appear to be that the authors culture these organ-specific organoids using a common cell culture media. This is not the typical protocol in the organoid field; however, the authors do not provide enough information in the manuscript to evaluate if this is a good choice. Furthermore, it is likely that the authors were successful because they included additional tissue components in the co-culture for the organoids which might have provided the necessary tissue specific cues, but the methodological details to reproduce this and the technical evaluation of this approach are missing.

The authors also directly compare the transcriptional responses of the organoids with the organs, but this is a challenging enterprise given that the organoid models do not incorporate resident immune cells and typically are composed only of epithelial cells. This lack of an 'apples to apples' comparison might explain why in many cases the organoids and organs are highly divergent; however, it could also be that the common cell culture media did not lead to specific maturation of cell types.

Reviewer #2 (Public Review):

The authors aimed to elucidate the structural rearrangements and activation mechanisms of P2X7 upon ATP application by voltage clamp fluorometry (VCF) using fluorescent unnatural amino acid (fUAA) and other fluorophores. They improved the fUAA methodology and detected ATP binding evoked changes in the ATP binding region and other regions. They also observed facilitation of fluorescence (F) changes by repeated application of ATP associated with gating. The F change in the cytoplasmic ballast region was minor, and with their experimental data, they discussed this region is involved in activation by other cytoplasmic factors, such as Ca2+.

The strengths of the study are as follows.<br /> (1) fUAA methodology was improved to enable experiments by one time injection to oocytes (Figs. 1 and Suppl).<br /> (2) They performed intensive mutagenesis study of as many as 61 mutants (Figs. 3, 4, 5).<br /> (3) A careful evaluation of the successful Anap incorporation and formation of full length proteins was performed by western blot analysis (Fig. 2).<br /> (4) By three wave lengths F recording, they obtained better information, i.e. they classified the interpretation of F changes to, quenching, dequenching, increase in polarity and decrease in polarity (Fig. 3E).<br /> (5) They detected F changes upon ATP application in various regions of P2X7, but not many in the ballast region, showing that the ballast region is not well involved in the ATP evoked gating.<br /> (6) They analyzed the kinetics of F and current and their changes upon repeated ATP application to approach the known facilitation mechanisms. The data are very interesting. They concluded that it is intrinsic to the P2X7 molecule and that it is associated not with the ATP binding but with the gating process (Figs. 3F, 4D, 6A).<br /> (7) They performed interesting analysis to clarify the mechanisms of activation by cytoplasmic factors, especially Ca2+ entered via P2X7 (Fig. 6).

The weaknesses of the study are as follows.<br /> (1) As both structures of P2X in the open and closed states are already solved, and the ATP binding evoked structural rearrangements from the ATP binding site to the gate are already known in detail. The structural rearrangements detected in the extracellular region (Fig. 3) and TM region (Fig. 4) upon ATP application are just as expected. The impact and scientific merits of this part are rather limited.<br /> (2) The facilitation mechanism is of high interest. The authors showed it is intrinsic to P2X2 and associated with the gating rather than ATP binding. However, this reviewer cannot have better understanding about the actual mechanism. (a) What is the mechanistic trigger of facilitation? Possibilities are discussed, but it appears there is no clear answer with experimental evidences yet. (b) How is the memory of the 1st ATP application stored in the molecule, i.e. how does the P2X7 structure just before the 1st application differ from that just before the 2nd application of ATP?<br /> (3) The structural rearrangement of the CaM-M13 region (Fig. 6B, C) attached at the C-terminus by Ca2+ influx through P2X7 upon ATP application is natural due course and not very surprising. Also, it is not accepted as an evidence proving that Ca2+ is the mediator of facilitation.<br /> (4) As to the ballast region, data showed its limited involvement in the ATP-induced structural rearrangements. The function of the ballast region is not clear yet. A possible involvement in GDP binding and/ or metabolism is discussed, but there is no clear experimental evidence.

Reviewer #2 (Public Review):

This paper builds on recent work showing that honeybee queens can change the size of the eggs they lay over the course of their life. Here the authors identified an environmental condition that reversibly causes queens to change their egg sizes: namely, being in a relatively small or large colony context. Recently published work demonstrated the existence of this egg size plasticity, but it was completely unknown what signaled to the queen. In a series of simple and elegant experiments they confirmed the existence of this egg size plasticity, and narrowed down the set of environmental inputs to the queen that could be responsible for signaling the change in the environment. They also began the work of identifying genes and proteins that might be involved in controlling egg size. They did a comparative proteomic analysis between small-egg-laying ovaries and large-egg-laying ovaries, and then selected one candidate gene (Rho1). They showed that it is expressed during oogenesis, and that when it is knocked down, eggs get smaller.

The experiments on honeybee colonies are well-designed, and they provide fairly strong evidence that the queens are reversibly changing egg size and that it is (at least some component of) their perception of colony size that is the signal. One minor but unavoidable weakness is that experiments on honeybees are necessarily done with small sample sizes. The authors were clear about this, however, and it was very effective that they showed all individual data points. Alongside the previous work on which this paper builds, I found their core results to be rather convincing and important.

I found the parts of the paper on oogenesis to be useful, but overall less informative in answering the questions that the authors set out for those sections. On balance, I think the best way to interpret the oogenesis results is as "suggestive and exploratory". For instance, the experiment aimed at understanding the relationship between egg-laying rate and egg size does not include a direct measurement of egg-laying rate, but instead puts queens in a place with no suitable oviposition sites. The proteomic analysis was fine, but since they were using whole ovaries, with tissue pooled across all stages of oogenesis including mature oocytes, I would be cautious in interpreting the results to mean that they had identified proteins involved in making larger eggs. These proteins might just as easily be the proteins that are put into larger eggs. In fact, for the one candidate gene that is examined, its transcripts seem as though they are predominantly in the oocyte cell itself rather than in the supporting cells that actually control the egg size (although it is hard to tell from the micrographs without a label for cell interfaces).

On that note, with the caveat that the sample sizes are quite small, I agree that there is some evidence that Rho1 is involved in honeybee oogenesis. If this was the only gene they knocked down, and given that it results in a small size change with such a small sample size, it strikes me as a bit of a stretch to say that these results are evidence that Rho1 plays an important role in egg size determination. It is essential to know if this is a generic result of inhibiting cytoskeletal function or a specific function of Rho1. That is beyond the scope of this study, but until those experiments are done, it is hard to know how to interpret these results. For context, in Drosophila, there are lots and lots of genes such that if you knock them down, you get a smaller or differently shaped egg, including genes involved in planar polarity, cytoskeleton, basement membrane, protrusion/motility, septate junctions, intercellular signaling and their signal transduction components, muscle functions, insect hormones, vitellogenesis, etc. This is helpful, perhaps, for thinking about how to interpret the knockdown of just one gene.

Overall, I found the results to be technically sound, and there are several clever manipulations on honeybee colonies that will doubtless be repeated and elaborated in the future to great effect. The core result-that queens can change the size of their eggs quickly and reversibly, in response to some perceived signal-was honestly pretty astonishing to me, and it reveals that there are non-nutritive plastic mechanisms in insect oogenesis that we had no idea existed. I look forward to follow-up studies with interest.

Reviewer #2 (Public Review):

The work by Eliazer et al investigates the role of Dll4 spatial heterogeneity on myofibers in maintaining MuSC diversity. The authors show on isolated myofibers that individual MuSC exhibit different intensities, by immunofluorescence analysis, of Pax7 and Ddx6, expressed in quiescent MuSC, and that there is a positive correlation between the intensities of the two quiescence markers. They further isolated MuSC high, medium and low Pax7 from the Pax7-nGFP transgenic mouse and validated in vitro that that the Pax7 high are slower in entering the cell cycle and expressing myogenin. To understand whether diversity of factor on myofibers could regulate this spatial diversity, the authors focused on Notch signaling. By comparing by microarray data Notch ligands during postnatal muscle growth, they show that Dll4 showed the most enrichment as cells transition to quiescence. By immunofluorescence on isolated myofibers, the authors show heterogeneity of Dll4 localization across the myofiber, with enriched clusters around MuSC. The authors monitored along individual myofibers the distribution of Dll4 and found no correlation with the distance from the NMJ. Upon myofiber specific deletion of Dll4, the authors show that MuSC exhibit downregulation of Pax7 and Ddx6, as well as a reduced number of MuSC in tissues and increased expression of MyoD and myogenin. Upon injury, mice in which Dll4 was deleted in myofibers exhibited reduced myofiber cross-sectional area, indicating a defect in the repair process. By using mice in which Mib1, an activator of Notch signaling, is deleted in myofibers, the authors show reduced Dll4 intensity and reduced diversity of Pax7 expression in MuSC as well as impaired regeneration. Understanding how the microenvironment regulate MuSC diversity is relevant to dissect their heterogeneity. The findings are interesting and novel and the manuscript is well written. However, while the authors report diversity of Dll4 and Mib1 in myofibers, the approach of genetic deletion complete ablates gene expression, and it does not necessarily modulate spatial distribution. Thus, additional experiments are required in order to fully support the authors' interpretation.

Reviewer #2 (Public Review):

The authors capitalised on the opportunity to obtain functional brain imaging data and cognitive performance from a group of oldest old with normative cognitive ability and no severe neurophysiological disorders, arguing that these individuals would be most qualified as having accomplished 'healthy ageing'. Combined with the derivation of a cohort-specific brain parcellation atlas, the authors demonstrated the importance of maintaining brain network segregation for normative cognition ability, especially processing speed, even at such late stage of life. In particular, segregation of the frontoparietal network (FPN) was found to be the key network property.

These results bolstered the findings from studies using younger old participants and are in agreement with the current understanding of the connectomme-cognition relationship. The inclusion of a modest sample size, power analysis, cohort-specific atlas, and a pretty comprehension neuropsychological assessment battery provides optimism that the observed importance of FPN segregation would be a robust and generalisable finding at least in future cross-sectional studies. The fact that FPN segregation is relatively more important to cognition than other associative networks also provides novel insight about the possible 'hierarchy' between age-related neural and cognitive changes, regardless of what mechanisms lead to such segregation at such an advanced age. it is also interesting that processing speed remains to be the 'hallmark' metric of age-related cognitive changes, indirectly speaking to its long assumption fundamental impact on overall cognition.

As laid out by the authors, if network differentiation is key to normative cognitive ability at old age, intervention and stimulation programs that could maintain or boost network segregation would have high translational value. With advent in mobile self-administrable devices that target behavioural and neural modifications, this potential would have increasing appeal.

However, I feel that a few things have prevented the manuscript to be a simple yet impactful submission<br /> 1) Interpretation and the major theme of discussion. While the authors attempted to discuss their findings with respect to both the compensatory and network dedifferentiation hypotheses, the results and their interpretation do not readily provide any resolution or reconciliation between the two, a common challenge in many ageing research. The authors did not further elaborate how the special cohort they had may provide further insights to this.

While the results certainly are in line with the dedifferentiation hypothesis, why 'this finding does not exclude the compensation hypothesis' (Discussion) was not elaborated enough. In particular, the authors seemed to suggest that maintained network specialisation may be in such a role, but the results and interpretations regarding network specialisation were not particularly focused on throughout the manuscript. In addition, both up regulation within a network and cross-network recruitment can both be potential compensatory strategies (Cabeza et al 2018, Rev Nat Neurosci). Without longitudinal data or other designs (e.g. task) it is quite difficult to evaluate the involvement of compensation. For instance, as rightly suggested by the authors, the two phenomena may not be mutually exclusive (e.g., maintenance of the FPN differentiation at such old age could be a result of 'compensation' that started when the participants were younger).

2) Some further clarity about the data and statistical analyses would be desirable. First, since scan length determines the stability of functional connectivity, how long was the resting-state scan? Second, what is the purpose of using both hierarchical regression and partial correlation? While they do consider different variances in the dataset, they are quite similar and the decision looks quite redundant to me as not much further insights have been gained.

Reviewer #2 (Public Review):

Zhukin et al., present the structure of the central scaffold component of the NuA4 complex. They hypothesise how the nucleosome interacting modules not present in the structure could be arranged, based on Alphafold modelling, and comparison of their structure to other complexes that use the same subunits. They show some interesting -albeit fairly preliminary - biochemistry on the binding of the flexible modules, suggesting a role for acetylation affecting H3K4me3 reading.

While the work builds upon previous structural studies on the Tra1 subunit in isolation and a previous 4.7A resolution structure from another group, there are clear differences and novel findings in this study. The data is presented beautifully and nicely annotated figures make following the many subunits and interactions therein simple. What could have been a very complex manuscript is easy to digest. Some of the figures could do with a couple of additional labels and detailed figure legends to make things a little clearer.

Overall, a nice study and a wonderfully detailed structure of a large multi-subunit assembly but we would recommend some further experimentation validation to bolster their findings.

Major comments

1) All 13 subunits of NuA4 are present by mass spec, however, based on the SDS-page gel (Fig1-1) components of the TINTIN sub-complex seem less than stoichiometric, with Eaf7 and Eaf3 certainly much weaker stained. This is particularly important with reference to Figure 3 and the discussion in the text which assumes the nucleosome interacting modules are all present equally, but too flexible to be observed in the structure.

Simple peptide numbers from mass spec cannot be used as a measure of protein abundance as this is sensitive to multiple confounding factors.

2) A major novel biological finding and conclusion from the abstract concerns the binding to modified nucleosomes. However, this seemed somewhat preliminary, especially considering the discussion around the role of acetylation affecting binding to H3K4me3 nucleosomes based solely on the dCypher screen used.

The discussion on the role of HAT module binding preferential to acetylated and methylated tails concludes that the acetylation liberates the H3 tail from DNA interaction, making H3K4me3 more available for binding by the PHD domain. This is an interesting hypothesis but is stated as fact with very little evidence to make this assertion.

Whilst others have seen similar results (cited in the paper), no data is presented to disregard an alternative hypothesis that there is some additional acetyl-binding activity in the complex. Indeed, in one of the references they cite the authors do show a direct reading of acetylation as well as methylation.

TINTIN binding is subject to high background and a fairly minor effect. The biological relevance to these observations while intriguing needs to be proved further.

3) There is a large focus on the cross-linking mass spec study from another group and the previously published structure of the NuA4 complex. The authors are fairly aggressive in suggesting the other structure from Wang et al., is incorrect. It is very nice that their built structure shows a better interpretation of previous XL-MS data, but still many of the crosslinks are outside of the modelled density. One possibility that should be entertained is that the two studies are comparing different structures/states of NuA4. The authors of the Wang et al., paper indeed comment that Swc4 and Yaf9 are missing from their purified complex. It is of course possible that both structures are correct as they appear to be biochemically different, with the crosslinking in the Setiaputra paper better reflecting the complex presented here.

Reviewer #2 (Public Review):

In this manuscript, Gomez et al. study the role of substrate stiffness in the first steps of biofilm formation of the versatile pathogen Pseudomonas aeruginosa. In a very thorough experimental set-up, the authors demonstrate that the early colonization of surfaces by Pseudomonas aeruginosa depends on the surface stiffness, irrespective of the chemical nature of the surface. At low stiffness, the bacteria form dense microcolonies, move slowly, do not explore most of the surface, and excrete minimal amounts of extracellular matrix. On the other hand, at high stiffness, the bacteria cover most of the available surface more uniformly, move rapidly, and excrete large amounts of extracellular matrix polymers. The surface stiffness doesn't affect the division time, but the residence time of bacteria in the constant flow configuration used in the paper is longer on stiffer substrates. Ultimately, the substrate stiffness differences lead to differences in gene expression. The carefully executed experiments are interpreted in the light of interesting simple models that help illuminate the wealth of information presented. The overall subject of the role of rigidity in bacterial physiology is topical and should be of interest to many scientists. The fact that a model without any explicit mechanosensing via Type IV pili can still account for the substrate stiffness phenotypic differences in colonization is a superb addition to the field and is fully supported by the data presented. Yet, some additional explanations will help even strengthen the work.

1) One of the difficulties in navigating the paper as it stands is the definition of many parameters in a global manner as fits from derived equations whose assumptions are not always fully validated. For instance, Equation (1) assumes no new addition because of the flushing of the channel with the clean medium. Yet the first peak of residence time on 2.7 kPa gels is around 5 minutes per Fig. S7 whereas the calculation of Vg is done over 100 minutes which should leave plenty of time for detachment and reattachment of bacteria upstream of the recording field of view, no? Similarly, the definition of Vcm is not easy to follow or apprehend. Is it that the general averages of the velocities are too noisy?

2) While the simple kinetic model presented does encapsulate many of the aspects of the data in an understandable way, some of the assumptions should be discussed further. Nowhere is it more important than in the assumption that pili only binds with its tips. While this assumption allows many simplifications in the model, type IV pili can potentially bind throughout their length, and as they can be microns in length, so can the binding region. The Koch et al 2021b does go over the reasoning but having a small discussion earlier in the paper would be great.

3) One of the very interesting characteristics of the models put forth is that they do not rely on direct mechanosensing from the bacterial side but rather are an indirect consequence of substrate rigidity and pili dynamics. The authors mention that the Pil-Chp and Wsp systems are the only ones found so far in Pseudomonas, but this doesn't mean that there is not another system in place. Making clear that they do not fully rule out the possibility of mechanosensing would be interesting.

Reviewer #2 (Public Review):

This study tests the capacity of single glabrous skin human tactile afferent to discriminate the orientation of edges scanned over their receptive fields (RF) at different speeds spanning 2.5 to 180 mm/s. Raised bars of different orientations (-10,-5,5,10 degrees) were glued on a rotating drum that contacted the skin and rotated at different speeds. Afferent recordings were obtained using microneurography. Both the intensity of the response (i.e. firing rate) and the response profile (precise spike timing) were used as input for discrimination. Indeed, tactile RFs have multiple sensitive zones or hotspots, and different edge orientations will activate those hotspots with a slightly different sequence.

It is found that using intensity measures, discrimination is possible within but not across speeds. Discrimination performance is, as expected, better using the temporal spiking profile, and is also possible across speed, if the spike trains are represented in the spatial domain, that is if the spike trains are compressed or expanded according to the scanning speed. Furthermore, it is found that filtering the spike trains with a spatial Gaussian of approx. 60-70 um SD optimizes discrimination performance. Contrary to previous reports, it is found the FA-I afferent have better discrimination performance than SA-I afferents.

This study is mainly a follow-up of a previous report (Pruszynski et al., 2014) that showed the capacity of tactile afferents to signal orientation thanks to their complex RF profiles. It uses the same procedures and analyses but tests smaller orientation differences and a much wider range of different speeds. The dataset is rich and unique, the analyses are straightforward but rigorously carried out and the conclusions are well supported but the results.

Reviewer #2 (Public Review):

This paper is of broad interest to scientists in the fields of cell growth, cell division, and cell-cycle control. Its main contribution is to provide a method to restrict the space of potential cell-cycle models using observed correlations in inter-division times of cells across their lineage tree. This method is validated on several data sets of bacterial and mammalian cells and is used to determine what additional measurements are required to distinguish the set of competing models consistent with a given correlation pattern.

The patterns of correlations in the division times of cells within their lineage tree contain information about the inheritable factors controlling cell cycles. In general, it is difficult to extract such information without a detailed model of cell cycle control. In this manuscript, the authors have provided a Bayesian inference framework to determine what classes of models are consistent with a given set of observations of division time correlations, and what additional observations are needed to distinguish between such models. This method is applied to data sets of division times for various types of bacterial and mammalian cells including cells known to exhibit circadian oscillations.

The manuscript is well-written, the analyses are thorough, and the authors have provided beautiful visualizations of how alternative models can be consistent with a finite set of observed correlations, and where and how extra measurements can distinguish between such models. Known models of growth rate correlations, cell-size regulation, and cell cycle control are analyzed within this framework in the Supplemental Information. A major advantage of the proposed method is that it provides a non-invasive framework to study the mechanism of cell-cycle control.

Reviewer #2 (Public Review):

The authors present a novel method to induce electrical signaling through an artificial chemical circuit in yeast which is an unconventional approach that could enable extremely interesting, future experiments. I appreciate that the authors created a computer model that mathematically predicts how the relationship between their two chemical stimulants interact with their two chosen receptors, IacR/MarR, could produce such effects. Their experimental validations clearly demonstrated control over phase that is directly related to the chemical stimulation. In addition, in the three scenarios in which they tested their circuit showed clear promise as the phase difference between spatially distant yeast communities was ~10%. Interestingly, indirect TOK1 expression through K1 toxin gives a nice example of inter-strain coupling, although the synchronization was weaker than in the other cases. Overall, the method is sound as a way to chemically stimulate yeast cultures to produce synchronous electrical activity. However, it is important to point out that this synchronicity is not produced by colony-colony communication (i.e., self-organized), but by a global chemical drive of the constructed gene-expression circuit.

The greatest limitation of the study lies in the presentation (not the science). There are two significant examples of this. First, the authors state this study 'provides a robust synthetic transcriptional toolbox' towards chemo-electrical coupling. In order to be a toolbox, more effort needs to be put into helping others use this approach. However little detail is given about methodological choices, circuit mechanisms in relation to the rest of the cell, nor how this method would be used outside of the demonstrated use case. Second, the authors stress that this method is 'non-invasive', but I fail to see how the presented methodology could be considered non-invasive, in in-vivo applications, as gene circuits are edited and a reliable way to chemically stimulate a large population of cells would be needed. It may be that I misunderstood their claim as the presentation of method and data were not done in a way that led to easy comprehension, but this needs to be addressed specifically and described.

In terms of classifying the synchronicity, while phase difference among communities was the key indicator of synchronization, there were little data exploring other aspects of coupled waveforms, nor a discussion into potential drawbacks. For example, phase may be aligned while other properties such as amplitude and typical wave-shape measures may differ. As this is presented as a method meant for adoption in other labs, a more rigorous analytical approach was expected.

Reviewer #2 (Public Review):

Barnes et al. follow individual spines on L5 PC distal tufts in mouse V1 before and after contralateral enucleation. At baseline, some spines show activity driven by visual simulation, others are correlated with network activity (average Ca signal in all other spines). After sensory deprivation (12 h), strongly 'visual' spines had smaller Ca transients while previously weakly 'visual' spines had larger transients, indicating homeostatic boosting. These boosted spines are the ones that were correlated with network activity at baseline. Similar results were obtained in the retrosplenial cortex 48 h after auditory or visual deprivation. As previously described for homeostatic plasticity, a block of TNF-a blocked deprivation-induced boosting of spine responses. Somewhat paradoxically, dendritic sensory-evoked responses did increase after sensory deprivation.

The study is well designed and provides exciting new insights into the plasticity of intracortical connections, (over-)compensating for the partial loss of thalamic inputs. To optically resolve the activity of single synapses in vivo during sensory stimulation is technically very challenging. It would be helpful to know whether the recordings were made in the binocular or monocular region of V1. The results argue against a generalized multiplicative upscaling of all inputs and suggest selective boosting of synapses that are part of sensory-driven subnetworks. However, it is not clear whether homeostatic plasticity occurred at the observed spines themselves or on the level of presynaptic neurons, which could then e.g. fire more bursts, leading to larger postsynaptic Ca transients. The possibility that thalamic inputs from the intact eye in layer 4 could be potentiated should be discussed. It would probably help to explain to the reader the layer-specific connectivity of V1 in the introduction, and why thalamic input synapses themselves were not optically monitored (may require adaptive optics). Technical limitations are a main reason why the conclusions are somewhat vague at this point ("... regulation of global responses"), this could be spelled out better.

Reviewer #2 (Public Review):

In this work, the authors analyze the mechanism through which the fluctuations of the Ecdysone hormone modulate the passage from a third instar larva to a pupa, during the process of metamorphosis. They focus on the imaginal wing disc in which initially the levels of Ecdysone fluctuate and in the later phase when the levels of this hormone increase significantly. This entire process depends on the Ecdysone hormone receptor (EcR) and the interaction it has with co-repressors and co-activators. Using as a tool a dominant negative form that does not have the receptor DNA binding site, but does have the hormone binding site as well as regions with which the receptor interacts with co-repressors and co-activators, they show that genes which are repressed early in the wing disc, are de-repressed if this dominant negative is present. Even more, they manage to demonstrate that at the genetic level, one of the co-repressors that acts together with the EcR in the repression of these genes is Smrter/NCoR1. The strategy used is based on the use of genetic tools that are unique to Drosophila, which allows them to carry out a very precise analysis of the expression of the reporter and endogenous genes in a very fine way and allows them to obtain very robust in vivo results. On the other hand, the work can be reinforced using biochemical techniques that may allow showing the direct interactions of the different players studied in this work. Nuclear receptors that respond to steroid hormones are present in all metazoa. Therefore, this work is useful not only to understand the mechanisms of how nuclear receptors modulate gene expression in flies but also in mammals.

Reviewer #2 (Public Review):

San Martin et al utilize an extensive set of genomic and bioinformatics tools to perform a comprehensive analysis of the transcriptional status of HGPS fibroblast cell lines, which suggests dysregulation of pathways critical for the development and maintenance of mesenchymal tissues affected in this disorder. The authors conclude, based on transcriptional profiling of these cells, that mesenchymal stem cell depletion exacerbated by defective tissue repair responses results in the HGPS bone phenotype. An important strength of this manuscript is the comparison of HGPS cells not only to age-matched controls but to healthy old adults as well, leading this reviewer to question the validity of describing HGPS as a premature aging disorder. A major shortcoming of this work is the drawing of conclusions on pathomechanisms of HGPS in multiple mesenchyme-derived tissues based on fibroblast transcriptional and epigenetic profiles which are, however, acknowledged by the authors.

Reviewer #2 (Public Review):

Okuma, Hidehiko et al. investigated the role of dystroglycan N-terminus (alpha-DGN) in matriglycan synthesis and how the resultant shorter matriglycan affects muscle function and anatomy, and neuromuscular junction formation. Using transgenic mice with muscle-specific loss of alpha-DGN, and DAG1 KO mice exogenously expressing alpha-DGN-deficient DG, they found in both types of mice that less and a shorter form of matriglycan was made. The shorter matriglycan is capable of binding laminin. Additional analyses revealed that the alpha-DGN deficient mice have abnormal neuromuscular synapses and reduced lengthening contraction-induced force. Interestingly, exogenous expression of alpha-DGN or LARGE1 overexpression does not restore the full-length matriglycan or rescue the phenotypes. The authors also compared three transgenic mouse models with different matriglycan lengths and found correlations between matriglycan length and eccentric contraction force, centrally located nuclei (inverse correlation), and laminin binding. These data provide additional insights into the mechanisms underlying matriglycan synthesis and dystroglycanopathies.

The main conclusion of this paper, which is that synthesis of full-length matriglycan requires alpha-DGN, is well supported by data. However, the lack of phenotypic rescue by exogenous alpha-DGN expression makes it difficult to draw a more generalized cause-and-effect conclusion between alpha-DGN, matriglycan length, and pathologies.

Reviewer #2 (Public Review):

Horton et al combined computational and functional approaches to identify a role for a mouse transposable element (TE) family in the transcriptional response to interferon gamma (IFNG, also known as type II interferon). This paper builds on previous work, some of which was done by the corresponding author, in which TE families have been shown to contribute transcription factor binding sites to genes in a species-specific manner. In the current work, the authors analyzed datasets from mouse primary macrophages that had been stimulated by IFNG to identify TEs that might contribute to the transcriptional response to IFNG treatment. In addition to previously identified endogenous retrovirus subfamilies, the authors identified sites from another TE family, B2_Mm2, that they found contained STAT1 transcription factor binding sites and whose binding by STAT1 was induced following IFNG stimulation. To test the hypothesis that a B2_Mm2 element was providing IFNG-inducibility to an associated gene, the authors chose one of the 699 mouse genes that had nearby (<50 kb) B2_Mm2 elements and was upregulated upon IFNG treatment in previous datasets. The gene they chose was Dicer1, which also is upregulated by IFNG in mouse macrophages but not in human primary macrophages, furthering the hypothesis that the presence of B2_Mm2 in mouse cells may provide IFNG-inducibility to Dicer1. Following KO of a ~500 bp region in two separate clones of immortalized mouse macrophages, the authors show a decrease in basal as well as IFNG-induced expression of Dicer1, providing support for their conclusion that a B2_Mm2 is important for IFNG-inducibility. The authors further show that two nearby genes that are also upregulated by IFNG, Serpina3f and Serpina3g, are also reduced at basal conditions as well as when stimulated with IFNG. The authors use these data to suggest that additional elements in the B2_Mm2 element in the Dicer1 gene, possibly CTCF elements, are have long distance effects on transcription of nearby genes.

Overall, this is an interesting and well written manuscript. The computational conclusions are supported by their data and add to the growing field of TEs and their role in transcription regulatory network evolution. While the authors do a good job of experimentally validating one example, inclusion of additional data, all of which they already have, as detailed below would substantially increase the applicability of their work and strengthen their conclusions about the broad role of TEs in the IFNG response in mice versus other species.

1) Following their genome-wide comparisons, the authors hone in on Dicer1 as an interesting example in which they hypothesize that a B2_Mm2 element near the Dicer1 gene could be contributing to the fact that this gene is upregulated by IFNG in mouse cells but not human cells. What would be very useful to the readers of this paper is knowing how many other examples there might be like this one. Adding DEseq values from human RNAseq data the authors already use (current references 10 and/or 37) for identifiable human orthologs to Table S7 would thus strengthen their conclusions. If Dicer1 is unique in this aspect of having (a) a nearby B2_Mm2 element and (b) a binary difference between inducibility in mouse versus human cells, that is interesting. If Dicer1 is not unique, that strengthens the authors' assertion that B2_Mm2 insertions have altered the transcriptional network in a host-specific manner. Either way, the answer is interesting, but without including this analysis, the authors leave out an important aspect of their work and it remains unclear how generalizable their conclusions are.

2) The results with Serpina3g and Serpina3F gene expression in the authors' knockout cells are very interesting. However, the authors focus almost exclusively on Serpina3g and Serpina3F, which makes it difficult to understand what is happening genome wide. Are other IFNG-induced genes (including those not on chromosome 12) similarly affected at the level of basal or induced transcription? How many genes are different in WT versus KO cells, both at basal and induced states? Does this correlate with their CUT&TAG data shown in Fig. 5? By focusing only on nearby genes (Serpina3g and Serpina3F), the authors hint that this may be a long range regulatory effect, "potentially mediated by the CTCF binding activity of the element" that they removed. But by only focusing on two nearby IFNG-induced genes, their data do not rule out the (also potentially quite interesting) possibility that there may be a more indirect role for this TE site or Dicer1 in basal transcription of IFNG-induced genes or IFNG-mediated gene expression. Providing more data on other genes throughout the genome in WT and KO cells, which the authors have generated but do not include in the manuscript, would help distinguish between these models. While a broader effect of these KOs on IFNG expression, or gene expression in general, would not fit as neatly with their model for local gene regulation, these analyses are needed to understand the effects of TE insertion on gene regulation.

Reviewer #2 (Public Review):

In this manuscript, the authors are proposing a generalizable solution to masking brains from medical images from multiple species. This is done via a deep learning architecture, where the key innovation is to incorporate domain transfer techniques that should allow the trained networks to work out of the box on new data or, more likely, need only a limited training set of a few segmented brains in order to become successful.

The authors show applications of their algorithm to mice, rats, marmosets, and humans. In all cases, they were able to obtain high Dice scores (>0.95) with only a very small number of labelled datasets. Moreover, being deep-learning-based segmentation once a network has been trained is very fast.

The promise of this work is twofold: to allow for the easy creation of brain masking pipelines in species or modalities where no such algorithms exist, and secondly to provide higher accuracy or robustness of brain masking compared to existing methods.

I believe that the authors overstate the importance of generalizability somewhat, as masking brains is something that we can by and large do well across multiple species. This often uses specialized tools for human brains that the authors acknowledge work well, and in the usually simpler non-human (i.e. lissencephalic rodent) brains also work well using image registration or multi-atlas segmentation style techniques. So generalizability adds definite convenience but is not a game-changer.

The key to the proposed algorithm is thus that it works better than, or at least as well as, existing tools. The authors show multiple convincing examples that this is the case even after retraining with only a few samples. Yet in those examples, the authors proposed retraining the network on even subtle acquisition changes, such as moving in field strength from 7 to 9.4T. I tried it on some T2 weighted ex-vivo and T1 weighted manganese enhanced in-vivo mouse data and found that the trained brain extraction net does not generalize well. None of the pre-trained networks provided by the authors produced reasonable masks on my data. Using their domain adaptation retraining algorithm on ~20 brains each resulted in, as promised, excellent brain segmentations. Yet even subtle changes to out-of-sample inputs degraded performance significantly. For example, one set of data with a slight intensity drop-off due to a misplaced sat band created masks that incorrectly excluded those lower intensity voxels. Similarly, training on normal brains and applying the trained algorithm to brains with stroke-induced lesions caused the lesions to be incorrectly masked. BEN thus seems to be in need of regular retraining to very precisely matched inputs. In both those examples, the usual image registration/multi-atlas segmentation approach we use for brain masking worked without needing any adaptation.

Overall, this paper is filled with excellent ideas for a generalized brain extraction deep learning algorithm that features domain adaptation to allow easy retraining to meet different inputs, be they species or sequence types. The authors are to be highly commended for their work. Yet it appears to at the moment produce overtrained networks that are challenged by even subtle shifts in inputs, something I believe needs to be addressed for BEN to truly meet its promised potential.

Reviewer #2 (Public Review):

This is the first report that establishes gamma-TuNA as an activator of gamma-TuRC-dependent microtubule-nucleation, using purified components. This is an in-depth study that establishes experimental conditions under which gamma-TuNA can function as an activator (dimerization of gamma-TuNA, appropriately sized N-terminal tag) and clarifies why similar attempts to study gamma-TuNA have failed in the past. I think that the information in this manuscript will be of immense value to the scientific community, as it resolves a long-standing mystery concerning the function of gamma-TuNA. A key question that still remains unanswered is whether the gamma-TuNA-dependent activation mechanism involves a conformational change of the gamma-TuRC, from an asymmetric to a ring-shaped template structure, but this may be beyond the scope of the present submission.

Reviewer #2 (Public Review):

Here, the authors aim to address the role of R loops in CSR. Though implicated in CSR since decades, R loops remain enigmatic regarding their true function at the Igh locus during CSR. In particular, its role in AID targeting to S regions remains debated with no direct evidence supporting this claim. In this study, the Barlow lab sheds interesting new light on what R loops may be doing during CSR. They study the response to elevated R loop levels which they achieve using single or double KO of SETX (a helicase that can unwind R loops) and RNaseH2 (which can cleave R loops). In this system, R loop removal is deficient and the effect on CSR and genome instability can be assessed. This is a fresh approach which allows the authors to draw new insights into R loop biology. Overall, the results support the conclusions that the timely removal of R loops is not necessary for optimal CSR but is necessary to maintain genome stability. But there are some experiments that need to be done to solidify this conclusion.

The major findings are that the increase in steady-state R loops in dKO cells does not appear to affect CSR frequency although small increase in mutation is observed. However, in dKO cells, there is a significant increase in gross chromosomal aberrations (translocations and fusions) as well as increased usage of alternative end-joining during CSR. Thus, surprisingly, increased DNA damage and increased reliance on alternative end-joining do not appear to reduce CSR which would have been expected based on many previous studies. Thus, they conclude that R loop removal by SETX and RNaseH2 is necessary to enhance the usage of classical end-joining repair pathways that are more efficient and less prone to genome instability.

The major weakness here is the lack of a proper characterization of B cell development in the mice. They use Cd19-cre which acts earlier in B cell development in the bone marrow and hence it is important to know whether B cell populations were skewed or otherwise influenced by the early KO of Setx and Rnaseh2. Along these lines, gene expression analysis is necessary to know whether the single and double KO (both naïve and activated) splenic B cells have undergone differential expression in DNA repair pathways or other pathways that could impinge upon CSR and contribute to the DNA repair phenotype they observe.

There is no western blot analysis to show how well RNASEH2 is depleted. Cd19-cre is known to have variable effects hence it is unclear whether efficient deletion was obtained in mature B cells.

One puzzling finding is that R loops were increased only in the S-mu but not the S-gamma1 region although both form R loops. Some thoughts on this would be useful for the readers since this implies that R loop resolution at S-gamma1 is independent of both enzymes.

Reviewer #2 (Public Review):

Canetta et al. investigated the time-dependent effects of inhibition of parvalbumin-positive interneurons in the mouse prefrontal cortex on task learning and cognition. The authors have used electrophysiology, optogenetics, behavioral paradigms, and histology. This study provides an interesting angle to understand cell behavior in the mouse prefrontal cortex, which eventually may help in therapies against schizophrenia.

Reviewer #2 (Public Review):

This is an original and carefully argued study on a key question of immunology. The authors detect statistical differences in the TRA and TRB repertoires between negatively selected thymocytes and mature T cells. Discrimination does not work for individual T cell receptor chains, but starts to become reasonably sensitive and specific for quora of 30 alpha chains (I did not find ROCs for beta chains; see below). These results, including the more detailed statistics based on CDR3 sequence, are technically sound and make a unique conceptual contribution to quantitative immunology.

In terms of interpretation, the premise of the paper - that negatively selected and peripheral T cell repertoires should systematically differ in some characteristics because thymocytes could scan only a tiny fraction of self-peptides - is not based on experimental evidence. Experimental data allow for the possibility that a thymocyte scans a much larger fraction of self-peptides than the number given by the authors. Hence this point cannot be maintained as a premise, while the underlying question is key and worth discussing. In this context, I also recommend that the title give a factual account of the main finding, rather than propose a particular hypothetical interpretation (hypotheses will be better placed in the Discussion, possibly the Abstract). These suggested edits do not impact the originality and importance of the experiments and computational results; these will be of wide interest.

Reviewer #2 (Public Review):

The authors hypothesized that T-cells capable of recognizing SARS-CoV-2 specific antigens might be present within the pre-existing CMV specific T cell memory pool in CMV+ individuals. In order to test this hypothesis, the authors used a collection of pre-pandemic samples from CMV+ and CMV- donors. Using the approach described in the manuscript, the authors were able to demonstrate the existence of CMV specific T cells capable of crossreacting with SARS-CoV-2 antigens. In addition they were able to show that this crossreactivity can be mediated by a public TCR. The findings warrant additional studies in larger cohorts of acutely infected individuals. This important finding expands our knowledge of T-cell crossreactivity and heterologous immunity. In addition, this study provides useful information regarding the origin of T-cells that crossreact with SARS-CoV-2.

Reviewer #2 (Public Review):

This manuscript addresses the function of osteocytes that are not well understood. These cells are embedded in the largest organ, bone. In addition to mechanosensing, the concept that bone, and that of the osteocytes can act as endocrine cells, communicating with other organs with soluble factors is beginning to take shape. In addressing the function of osteocytes in mice, the authors specially remove/reduce the number of osteocytes using genetic tools to conditionally activate the expression of diphtheria toxin (DTA) in osteocytes that are expressing the DMP1, thus, killing these cells. The impact on the skeletal system in development and ageing were studied, as well as cells in the bone marrow.

Mice with completed removal of DMP1-expressing osteocytes die before birth. However, mice with partially reduced osteocytes survive with reduced life span associated with severe osteoporosis, kyphosis and sarcopenia, conditions that are age-related, and the authors claimed an association with prematured ageing. The authors showed changes in the balance between the osteoblast, osteoclast and adipocyte lineages as possible mechanisms.

A relationship between bone and muscle is known, especially the contractile muscles. Their finding that there is a continuing body and muscle weight lost substantiates this relationship with focal muscle atrophy and sarcopenia.

Using a similar genetic approach, a previous study by Asada et al (2013) has shown that osteocytes regulate mobilization of haematopoietic stem/progenitor cells in mice. This manuscript extends this relationship in an assessment of the bone marrow cells using single cell RNA sequencing (scRNA-seq), revealing an alteration of the haematopoietic lineage commitment, with a shift from lymphopoiesis to myelopoiesis.

The most novel and interesting finding is the association with senescence. However, this is also perhaps the weakest link in the manuscript, as it presents a big jump in the hypothesis from the single cell data. The hypothesis was substantiated from an assessment of a senescence associated secretory phenotype (ASAP) score from the scRNA-seq data, which was not well explained. Nevertheless, circulatory SASP were elevated in osteocyte compromised mice, and concluded that osteocyte reduction induced senescence in osteoprogenitors and myeloid lineage cells.

Overall, the manuscript was logically presented, and the data in most parts supported the conclusion. The relationship however was mostly through descriptive morphological and biochemical analyses of the mutant mice. While there are weaker areas that need to be further strengthened, there are novel findings providing further insights into the biology of osteocytes and reaffirms the concept of bone as an endocrine organ.

Reviewer #2 (Public Review):

Wang et al. present a detailed description and analysis of the previously reported cranial remains of enantiornithine bird Yuanchuavis. The authors use X-ray CT scan data to reconstruct the cranial elements and retro-deform the facial and palatal skeleton. The authors also use principle component analysis with geometric morphometrics data to investigate where Yuanchuavis falls in palatine phylomorphospace. The authors use these data to make inferences about the kinetics of the Yuanchuavis skull as well as the evolution of cranial kinesis across birds.

Generally, I find the authors' direct interpretation of their anatomical and PCA data to be convincing and compelling. The anatomical description is thorough and accurate. The methods used for the geometrics morphometrics and PC analyses are appropriate. I find compelling the authors' interpretations that Yuanchuavis largely retained the ancestral non-avialan akinetic skull.

One of the greatest strengths of this paper are the extremely attractive figures. In particular, I find figure 4 to be exceptionally useful - this is easily the most effective illustration I have yet seen of avian cranial kinesis and the shifts in cranial morphology that underlie its evolution. I applaud whoever designed this figure.

My one major concern with this paper's methodology is that the palatine used for Ichthyornis is incorrect. Torres et al. (2021) published the correct palatines, which were very different from those incorrectly (but understandably) identified in Field et al. (2018) and used here. I strongly urge the authors to rerun their GMM analysis with corrected data.

The remaining weaknesses I find in this paper are not major but are worth addressing, and generally pertain to the broader discussion of significance of the authors' more direct interpretations of their data. The authors' suggestion that reduction/loss of the jugal process of the palatine was an early step towards the modern kinetic avian skull is logical, but I don't think the GMM analysis presented here demonstrates that (contrary to lines 390-392). The GMM analysis can only help identify such morphological shifts, not connect them to functional shifts. Rather, I think this analysis helps refine when this shift occurred - indicating that, if there is such a functional link, the earliest steps towards the modern kinetic skull occurred early in avialan evolution.

I find the discussion of the evolution of cranial kinesis as exaptation (lines 427-441) confusing, distracting and largely unnecessary. Has anyone previously suggested that avian cranial kinesis is an example of preadaptation?

I am similarly confused by the connection made by the authors of evolutionary modularity, the akinetic skull of enantiornithines and patterns of avialan diversification (lines 442-464). Specifically, I do not understand how the dominance of enantiornithine clade in the Cretaceous is "counterintuitive" (line 452), nor do I understand how this pattern is explained by evolutionary modularity.

Reviewer #2 (Public Review):

In this article entitled "The missing link between genetic association and regulatory function", Connally and colleagues attempted to quantify the extent to which genetic variants affect complex traits by altering the expression levels of putative causal genes. They focused on nine complex traits (including four common diseases) for which large-scale GWAS data were available. They curated 143 candidate genes (127 unique genes) for Mendelian forms of the traits under the assumption that genes causing the Mendelian form of the complex traits should also be the genes influencing complex trait variation in the general population. They found enrichment of the candidate genes in the GWAS regions (+/- 1Mb of a genome-wide significant signal) for all the complex traits but height and breast cancer. They then investigated the proportion of the candidate genes whose eQTL signals are colocalized with the GWAS signals for the nine traits, the proportion of the genes in close physical proximity with the fine-mapped GWAS variants, and the proportion of genes whose functionally active regions annotated using chromatin modification and activity data are overlapped the fine-mapped GWAS variants. All the proportions appeared to be small.

Major comments

The hypothesis that the genes responsible for the Mendelian traits are also the causal genes for the cognate complex traits does not seem to hold, given the prior work and the data shown in the study. For example, if this hypothesis is true, it is unexplained why the candidate genes were not even enriched in the GWAS regions for height and breast cancer.

The only evidence supporting their hypothesis appears to be the enrichment of the candidate genes in the GWAS regions for seven out of the nine traits. However, significant enrichment of the candidate genes in the GWAS regions does not necessarily mean that a large proportion of the candidate genes are the causal genes responsible for the GWAS signals. Analogously, we cannot use the strong enrichment of eQTLs in GWAS regions as evidence to claim that a large proportion of the GWAS signals are driven by eQTLs.

Considering the large numbers of GWAS signals, we would expect a substantial number of genes in the GWAS regions by chance. It would be interesting to quantify the number of genes in the GWAS regions if the 143 genes are randomly selected. Correcting the observed number of genes for that expected by chance (e.g., subtracting the observed number by that expected by chance), the proportion of the candidate genes in the GWAS regions would be small.

The proportion of the candidate genes whose eQTL signals were colocalized with the GWAS signals or in close physical proximity with the fine-mapped GWAS hits was small. However, I would not be surprised if they are significantly enriched, compared with that expected by chance (e.g., quantified by repeated sampling of the 143 genes at random).

It is unclear how the authors selected the breast cancer genes. If the genes were selected based on tumor somatic mutations, it is a problem because there is no evidence supporting that somatic mutation target genes are also cancer germline risk genes.

The authors observed no enrichment of the candidate genes in height and breast cancer GWAS regions. In this case, should these traits and the corresponding genes be removed from the subsequent analyses?

Reviewer #2 (Public Review):

General linear modelling (GLM) forms a cornerstone in analyses of task-based functional magnetic resonance imaging (fMRI) data. Obtaining robust single-trial fMRI beta estimates is a difficult problem given the relatively high levels of noise in the fMRI data. The introduced toolbox significantly improves such estimates using a few key features: 1) estimating a separate hemodynamic response function (HRF) for each voxel, 2) including noise regressors to improve reliability of the betas across repetitions, using a cross-validated approach, 3) using ridge regression on the beta estimates. The authors explain these steps well and compare the results obtained on subsequent metrics when choosing the include, or not, the different features along this procedure. They also compare their new approach to the Least-Squares Separate technique for beta estimation. For their demonstrations, they use two condition-rich datasets (NSD and BOLD5000) to show the improvements that different components of GLMsingle afford.<br /> The metrics used for comparisons are well chosen and relevant. Especially the test-retest reliability of GLM beta profiles is often a prerequisite for most subsequent analyses. Additionally, they consider temporal autocorrelation between beta estimates of neighbouring trials, and a few potential downstream analyses, looking at representational similarity analysis and condition classification. Thus, they really consider a range of possible applications and provide the reader with useful pointers to inspect what is most relevant for a given application.<br /> This manuscript and toolbox present a major advancement for the field of neuroimaging and should be of interest to essentially any researcher working with task-based fMRI data.

The strengths of the manuscript and toolbox are numerous, and presented results are convincing. To further the impact of the toolbox and paper, the authors could provide more guidelines on implementation for various common uses of the toolbox and/or factors to consider when deciding which steps to implement in one's analysis pipeline (FitHRF, DenoiseGLM, RR).

Additionally, there are a few considerations that could be addressed directly:<br /> 1) The authors use crossvalidation to determine the number of nuisance regressors to add in the model. Thus, any variability in responses to a single condition is considered to be 'noise'. How might this influence a potential use of single-trial estimates to assess brain-behaviour correlations (e.g. differences in behavioural responses to a single condition), or within-session learning conditions? For such uses, would the authors suggest to instead use LSS or a subset of their features in GLMsingle (i.e. not using GLMdenoise)?<br /> 2) In the results, using a fixed HRF leads to drastically lower performance on a variety of subsequent measures compared to fitting an HRF to each voxel, especially as regards to beta map test-retest reliability (Fig. 2-3). Have the authors ensured that the HRF chosen is the most appropriate one for the region of interest? In other words, is the chosen HRF also the one that most voxels are fitted in the flexible option?

Reviewer #2 (Public Review):

'Hairlessness' has convergently evolved numerous times in mammals. In this paper the authors look for patterns in the rate of DNA sequence evolution across the mammalian phylogeny to identify regions of the genome that are independently evolving at similar rates in hairless mammals. The authors find that signatures of convergent accelerated sequence evolution in hairless mammals is biased towards coding and gene regulatory regions known to be involved in hair biology, likely reflecting genetic drift following hair reduction. This bias toward hair-relevant genomic regions also highlights the utility of this approach to identify new candidate regions of the genome that haven't previously been implicated in hair biology and the authors describe several intriguing coding and non-coding candidates. Authors further find that genes and putative gene-regulatory regions have non-random patterns of drift, with mutations in coding regions biased toward proteins that compose physical aspects of the hair sheath.

The analysis in this paper is centered on the RERconverge tool. Importantly, the authors have taken numerous steps to address potential issues with such an approach. One issue with RERconverge is the need to include/exclude ancestral branches as having a trait, which introduces assumptions about ancestral states. The authors controlled for this by running multiple variations of RERconverge with and without ancestral states as being 'hairless' with no major impact on results. The authors also controlled for whether certain lineages are driving the correlation signal, and found that removal of any given lineage does not impact skin or hair follicle enrichments. Finally, the authors have adequately distinguished whether other common phenotypes in hairless mammals (e.g. marine lifestyle or body size) drive the convergent signals in the dataset and found the reported genetic signatures are best explained by hair loss compared to these other traits.

The paper should be of interest to a broad selection of biologists interested in evolution, development and phylogenomic methods. The candidate genes identified in this paper provide a compelling launching point for future experimental studies into the genetic basis of hair.

Reviewer #2 (Public Review):

This work follows up on an earlier publication that showed PNPase and RNase J2 play important roles in CRISPR RNA processing (doi: 10.7554/eLife.45393). Here, the authors show that RNase R also plays a critical role in CRISPR RNA maturation. In addition, they show that RNase R and PNPase are both recruited to the type III CRISPR complex (Cas10-Csm) via direct interactions with the Cmr5 subunit and that deletion of an intrinsically disordered region (IDR2) on Cmr5 selectively inhibits PNPase recruitment but not RNase R. The authors show unquantified stimulation of PNPase nuclease activity by Cmr5. Phage challenge assays are performed to test the impact of PNPase and RNase R deletion mutations on CRISPR-Cas mediated phage defense. Contrary to expectation, over-expression of the CRISPR system in cells that contain a deletion of PNPase and/or RNase R, maintain robust anti-phage immunity. The interpretation of this experiment is that RNase R and PNPase may be dispensable in an over-expression system that produces high (non-natural) concentrations of the Csm complex. They test this idea using a system that expresses the CRISPR-Cas components off of a chromosomally encoded locus (strain RP62a) and challenge these cells using a plasmid conjugation assay. In this iteration, deletion of PNPase has no impact on CRISPR performance, while deletion of RNase R "exhibited a moderate" attenuation of the immune response. In contrast, to either single gene deletion, the PNPase and RNase R double mutant showed a near complete loss of immunity.

Overall, the paper provides convincing evidence that PNPase and RNase R are involved in crRNA processing, and that they are recruited to the type III complex via Cmr5. The work on RNase R is entirely new and the role of PNPase is expanded. The role of cellular RNases in CRISPR RNA biogenesis is important, though some of the results are subtle and some of the biochemistry would benefit from a more quantitative analysis.

Reviewer #2 (Public Review):

Mounting evidence demonstrates that reversible methylation of mRNA (m6A) is a ubiquitous regulator of mRNA splicing, stability, and translation. The biology of m6A involves writer proteins that add a methyl group to mRNA, reader proteins that mediate the function of the methylated mRNA, and eraser proteins that remove the methyl group upon accomplishing the goal. This manuscript reports a key role of the m6A reader protein YTHDC1 in regulating the function of skeletal muscle stem cells that are crucial for postnatal muscle growth and regeneration.

The strengths of the manuscript include using several tour-de-force techniques to examine m6A and the biological consequence in satellite cells. A large amount of data supports the conclusion. Combining conditional knockout animal models and molecular tools to dissect in vivo functions of YTHDC1 and molecular mechanisms underlying the function.

There are only a few minor weaknesses. The main body is lengthy, and some content can be reduced or condensed. For example, RNA-seq was used to determine gene expression in WT and cKO cells, but the purpose of this is not well justified given that YTHDC1 mainly functions to regulate splicing and nuclear expert of mRNA rather than controlling their expression levels. Does the RNA-seq data suggest that YTHDC1 may also regulate gene expression independent of m6A reader function?

Reviewer #2 (Public Review):

The present study proposes a novel methodology for genetic labeling and manipulation of cerebrospinal fluid-contacting neurons (CSF-cNs). This is based on an impressive quantity of nice images of very high quality, results being obtained both in classical confocal microscopy and electronic microscopy and an advanced images analysis procedure. Anatomical findings are put in a more functional aspect with investigations of neuronal properties and motor function using in vitro and in vivo approaches examining functional consequences of perturbation of CSF-cNs' activity. Conclusions are strongly supported by the data. Nevertheless, it could be important to describe a bit more how the quantity of virus injected can be controlled, to increase the size of the sample for the collection of in vivo data (n=4 presently) and eventually discuss these new anatomical data with the presence of locomotor central pattern generators known to be located in restricted regions of the spinal cord (is there any relation or not). Overall this new method should be of great interest for researchers investigating the anatomy and the role of these still enigmatic cells.

Reviewer #2 (Public Review):

Macaisne and colleagues investigate the assembly and function of a protein module consisting of the kinase BUB-1 and the microtubule binding proteins HCP-1/CENP-F and CLS-2/CLASP, which function at kinetochores during cell division. By replacing endogenous proteins with RNAi-resistant transgenic mutants that are expressed at endogenous levels, the authors screen for protein domains involved in recruitment of the module to meiotic kinetochores in oocytes. This tour de force clarifies the connectivity among the components of the module and confirms a linear assembly hierarchy in which the outer kinetochore protein KNL-1 recruits BUB-1 (surprisingly independently of its binding partner BUB-3), BUB-1 recruits HCP-1, and HCP-1 recruits CLS-2. Having identified deletion mutants that perturb specific interactions among module components, the authors use these separation-of-function mutants to investigate how the module contributes to female meiotic divisions using live cell imaging. The results allow the authors to conclude that the module has both kinetochore-dependent and kinetochore-independent functions and that module integrity is important for spindle assembly and chromosome segregation. In an elegant domain-swapping experiment the authors target CLS-2 directly to BUB-1 so that HCP-1 is no longer necessary for CLS-2 recruitment. Depletion of HCP-1 in this background reveals that HCP-1's role goes beyond that of a CLS-2 recruitment factor. Finally, an in-depth mutational analysis of CLS-2's microtubule binding region shows that only one of the two TOG-like domains is essential for CLS-2 function, consistent with the absence of critical residues in the second TOG-like domain. The extensive in vivo analysis of module mutants is complemented by in vitro assays that directly assess the effect of module components on microtubule dynamics. This confirms CLS-2's role as a microtubule stabilizer but also reveals that addition of the other two components modulates this effect.

The experiments presented in this paper are rigorous and succeed in elucidating the functional relevance of the interactions among BUB-1, HCP-1, and CLS-2. The main conclusion of the paper, namely that these components work as a unit, is well supported by the in vivo evidence. What is less clear is whether the effects observed in vitro reflect the activity of the intact module. This part of the paper would profit from analysis of binding-defective mutants. Specifically, including HCP-1 mutants defective in CLS-2 and/or BUB-1 binding would help determine whether the enhancement of microtubule pausing that is observed in the presence of all three components requires assembly of the module.

Reviewer #2 (Public Review):

In this work Kado and colleagues analyzed cell membrane partitioning in Mycobacterium smegmatis. Based on the membrane fluidizing effect of benzyl alcohol they did a transposon sequencing that are sensitive to the treatment. Among a group of genes that code for antiporter, they identify the bifunctional PBP PonA2 to be involved in benzyl alcohol sensitivity. Membrane partitioning in domains with higher and lower fluidity seems to depend on the peptidoglycan cell wall. In particular, de novo partitioning depends on preexisting cell wall, but not on the active synthesis. The authors use a variety of techniques to support their claims.

The authors claim that the membrane in Msmeg is partitioned in IMDs (intracellular membrane domain) and a PM-CW (apparently a more rigid membrane domain). I know that the term IMD has been used before, but I find this misleading. Intracellular means that something is within the cell. Here we are talking about different fluidities within the 2D space of the membrane. I do not think that this term is meaningful and should be used.

The authors suggest that PonA2 regulates the density (or heterogeneity - I assume the authors mean degree of crosslinking?) of the peptidoglycan, thereby influencing membrane partitioning (lines 371-372). This claim would require a PG analysis and a comparison of the cross-linking degree. The influence of PonA2 on membrane partitioning remains somewhat unclear. While the authors claim that PonA2 was also shown to provide a protective effect against other stresses, such as heat, it is not certain that this has to do with membrane partitioning. Although increase in temperature has certainly an effect on membrane dynamics, heat also triggers unfolded protein response. Bacteria furthermore adapt their membranes quickly to changes in temperature and likely adaption also takes place when other stressors influence membrane fluidity. Also, only the TG defective PonA2 led to the phenotype and not the TP mutation, which would argue against a change in crosslinking.

Reviewer #2 (Public Review):

In this study, the authors set out to decode the latency of position representations of static and moving stimuli using EEG multivariate pattern analysis. Linear classifiers were trained on the positions of static stimuli and then generalized to the positions of moving objects in a time-resolved manner. The authors find that the early neural representations of the position of moving stimuli are close to positions in the real world. As neural delays from the retina to the early visual cortex should theoretically induce a latency of ~70 ms their findings suggest that these delays are compensated very early in the visual hierarchy. Furthermore, they find that delays that are accumulated during subsequent processing stages of the visual hierarchy are not compensated, supporting the interpretation of an early compensation mechanism.

I congratulate the authors on this excellent scientific work. I believe its major strength lies in the successful attempt to generalize neural representations of static objects to moving objects. This is made possible due to the large amount of collected EEG data as well as smart task design. Effectively this allows the authors to track which location is currently represented in the brain and how this compares to the actual physical location, all in a time-resolved manner. The approach is remarkably robust against biases due to its relative simplicity, both in task design and analysis.

One of the few limitations of the study is their inability to generalize very early location signals from static to moving objects. This might be indicative of differences in neural codes/mechanisms and in turn, limits the interpretation of which stages of the visual hierarchy are involved in motion extrapolation. That being said, I agree with the authors that this is a fundamentally difficult problem to solve, and importantly it does not negatively impact the main conclusions of this paper.

The current work provides significant methodological and theoretical utility. I am certain that the classification method and principal task design will be used by future studies investigating motion perception due to their effectiveness in tracking internally represented locations. On a theoretical level, the authors' results provide strong evidence that motion compensation processes occur very early in the visual hierarchy. There has been an ongoing debate about how and where this is achieved in the visual system and fMRI studies have only provided limited evidence to solve this issue due to the sluggish nature of the BOLD signal. In addition, the present results challenge previous theories on the role of feed-forward and feedback signals in neural delay compensation and provide concrete directions for future research.

Reviewer #2 (Public Review):

Aims:

This paper asks whether a risk score integrating the impact of common genetic variants across the genome (polygenic risk score) on Type II Diabetes is also to any degree predictive of diabetes in pregnancy (Gestational Diabetes Mellitus or GDM). A number of quantitative endpoints relevant to the risk of GDM are also evaluated. The authors also test for any evidence of statistical interaction between the GDM polygenic risk score and some predictive risk factors - asking if a high polygenic risk score has a more (or less) powerful effect on GDM risk in certain strata of BMI and diet quality. They find no evidence of such interaction.

Strengths/Weaknesses:

The cohorts are strong for the investigation of this question. The paper integrates data from well phenotyped pregnant South Asian women participants on two continents - 837 participants from the Canadian START study and 4372 participants from the UK Born in Bradford cohort. Among these, 734 women had GDM.

There are some differences between the cohorts - for example the occurrence of GDM was about 25% in the START study participants and only around half that in the BIB study, there were differences in the specific origins of the two cohorts within South Asia, and there were life course and lifestyle differences. Appropriate caveats are made by the authors.

The T2D PRS used was derived from previously published data in which only 18% of the population was of South Asian ethnic origins. This could lead to some inaccuracy when applied to an entirely South Asian population, which the authors acknowledge. It seems the "best available" approach to the problem.

Regarding the analyses for interaction, even these cohorts seem likely underpowered to detect this.

Aims achieved?

The authors achieved their aims and showed that the PRS for T2D had small magnitude, but highly significant, association with fasting plasma glucose, two hour post OGTT glucose, and the risk of GDM (47% increase in risk overall). They calculated the population attributable fraction of being in the top tertile of PRS compared with the bottom two tertiles. They did not find any evidence of interactions.

Likely impact:

This paper adds to the literature supporting the hypothesis that genetic factors predisposing to T2D and GDM substantially overlap.

Reviewer #2 (Public Review):

The authors use Jurkat CD4 T-cells stimulated with either antigen (via B-cells or immobilised) or using ionomycin and PMA to broadly stimulate as a model for T-cell activation. They have previously used this system to show that nuclear actin controls expression of some cytokines during T-cell activation. They describe a burst of actin assembly in the nucleus, followed by cytoplasmic actin assembly and organisation into an actin ring synapse in the case of the B-cell stimulation. The main novel observation is that knockdown of either ARPC5 or ARPC5L subunits of the Arp2/3 complex give different impairment of nuclear vs cytoplasmic actin assembly depending on the stimulus. The data are mostly clear and convincing and seem to be appropriately analysed. This study raises the interesting point that signal-induced actin assembly might use different isoforms of Arp2/3 complex depending on the context. These observations are of interest and reveal potential signal-dependent functions of the Arp2/3 subunits, but the study doesn't reveal a biological importance of these differences (e.g. consequences for gene expression or signaling) or explain how/why the different ARPC5 subunits can have different functions.

Reviewer #2 (Public Review):

In this manuscript, Marti-Solans et al., investigate how ASICs have been employed during early bilaterian evolution. Using thorough phylogenetic investigation of transcriptomes of metazoan DEG/ENaC genes, they identify ASICs through the Bilateria. ASIC genes are present in 3 major bilaterian groups, and absent from all other lineages. With the help of in situ hybridization and electrophysiology they demonstrate anatomical expression and functional properties of diverse ASICs from each major bilaterian lineage. They find that ASIC expression is broader than expected and is present centrally and peripherally, suggesting integrative and sensory roles. By heterologous expression of the ASIC channels of interest in oocytes, they characterize electrophysiological currents to expose that proton activation properties, Na/K permeability, and inactivation kinetics are diverse across the different lineages. The manuscript is well written, and the results support their conclusions. The results from this study aid the authors in hypothesizing that ASICS were a bilaterian innovation, and, perhaps they were first expressed in the periphery before being incorporated into the brain.

Reviewer #2 (Public Review):

As the first comprehensive integrative analysis on TCR convergence, this study provided several interesting insights:

1) Convergence might be induced by an ongoing immune response against viral infection or tumor; 2) in the tumor, there is a positive association between TCR convergence and tumor mutation load, and neoantigen-specific T cells are enriched for convergent TCRs, both observations further supporting the tumor-reactive hypothesis; 3) a potentially new diagnostic predictor for ICB treatment. Given these strengths, this work is of general interest to a broad audience.

Reviewer #2 (Public Review):

This theoretical study looks at individuals' strategies to acquire information before and after the introduction of pathogens into the system. The manuscript is well-written and gives a good summary of the previous literature. I enjoyed reading it and the authors present several interesting findings about the development of social movement strategies. The authors successfully present a model to look at the costs and benefits of sociality.

I have a couple of major comments about the work in its current form that I think are very important for the authors to address. That said, I think this is a promising start and that with some revisions, this could be a valuable contribution to the literature on behavioral ecology.

Before starting, I would like to be precise that, given the scope of the models and the number of parameter choices that were necessary, I am going to avoid criticisms of the decisions made when designing the models. However, there are a few assumptions I rather find problematic and would like to give proper attention to.

The first regards social vs. personal information. Most of the model argumentation is based on the reliance on social information (considering four, but to me overlapping, social strategies that are somehow static and heritable) but in fact, individuals may oscillate between relying on their personal information and/or on social information -- which may depend on the availability of resources, population density, stochastic factors, among others (Dall et al. 2005 Trends Ecol. Evol., Duboscq et al. 2016 Frontiers in Psychology). In my opinion, ignoring the influence of personal and social information decreases the significance of this work. I am aware that the authors consider the detection of food present in the model, but this is considered to a much smaller extent (as seen in their weight on individual decisions) than the social information cues.

Critically, it is also unclear how, if at all, the information and pathogen traits are related to each other. If a handler gets sick, how does this affect its foraging activity (does it stop foraging, slow its activities, or does it show signs of sickness)? Perhaps this model is attempting to explore the emergence of social movement strategies only, but how they disentangle an individual's sickness status and behavioral response is unclear.

Very little is presented about the virulence of the pathogens and how they could affect the emergence of social strategies. The authors keep their main argumentation based on the introduction of novel pathogens (without distinctions on their pathogenicity), but a behavioral response is rather influenced by how fast individuals are infected and which are their chances of recovering. Besides, they consider that only one or two social interactions would be enough for pathogen transmission to occur.

Another important component is that individuals do not die, and it seems that they always have a chance (even if it is small) to reproduce. So, how the authors consider unsuccessful strategies in the model outputs or how these social strategies would be potentially "dismissed" by natural selection are not considered.

Reviewer #2 (Public Review):

In this paper, Yang et al. seek to show the importance of the lncRNA VPS9D1-AS1 in the biology and pathology of colorectal cancer (CRC). Starting with the analysis of patient data, and proceeding to cellular and animal cancer models.

Specifically, the authors report higher VPS9D1-AS1 levels in tumor tissues in two independent cohorts of CRC patients. There was a positive association between VPS9D1-AS1 levels and molecules involved in TGFb signaling, yet a negative association between VPS9D1-AS1 levels and levels of tumor-infiltrating CD8+ T cells (and a negative correlation of these levels of tumor-infiltrating CD8+ T cells and protein expression of molecules involved in TGFb signaling). Cell line studies revealed a positive feedback loop between VPS9D1-AS1 and TGFb signaling molecules, with a cell-intrinsic, pro-proliferative, and pro-survival effect of VPS9D1-AS1 on CRC cancer cells. VPS9D1-AS1 also controls the expression of several genes in the IFN pathway, in particular the ISGs IFI27 and OAS1. In addition, IFI27 and OAS1 expression are controlled by TGFb, TGFBR1, and SMAD1, and the promoter of OAS1 is targeted by SMAD4 (but also TGFb), which binds to it. VPS9D1-AS1 expression in tumor cells promotes PD1 expression and negatively affects IFNAR1 on T cells to reduce their effector functions. In vivo, MC38 CRC cells overexpressing VPS9D1-AS1 show increased tumor growth in mice, and animals with transgenic VPS9D1-AS1 expression in the intestine develop larger CRC lesions upon AOM/DSS treatment. Finally, in vivo targeting of VPS9D1-AS1 using anti-sense oligo reduced tumor size. The data indicate a series of intricate molecular and cellular interactions and suggest that VPS9D1-AS1 can help with patient stratification, improving prognostic prediction and allowing for personalized treatment.<br /> Taken together, there is a multitude of datasets and several complementary experiments using patient-derived samples, genetically engineered cell lines, and mouse models. Definitely, the paper includes many avenues of inquiry that cover the broad field of cancer molecular biology, biochemistry, and pathogenesis. However, this broad approach renders the paper difficult to follow at times and also leads to numerous typographical and interpretive (but, largely, not methodological), mistakes. In addition, the quality of some of the figures needs to be improved before they can be properly evaluated.

In methodology, the authors are largely successful, and I would not recommend major changes to the work, other than to recommend a "focusing" of the manuscript objectives, or a paring of the data to better convey the desired story.

The experiments presented herein, particularly those that test the efficacy of the lncRNA as cancer therapeutics are important for the field, and should be of high import to other cancer biologists.

Reviewer #2 (Public Review):

This study used electrophysiological data acquired from neurons in the dorsal raphe to model 5-HT output in response to extrinsic excitatory inputs based on the intrinsic properties of 5-HT neurons and local network connectivity with GABAergic neurons. Specifically, general and modified integrate-and-fire single cell models, together with local network models among 5-HT neurons and local GABAergic neurons providing feedforward inhibition (FFI), are used to simulate the firing output of 5-HT neurons in response to transient and prolonged depolarizations. The conclusions are as follows. 1) 5-HT neurons display prominent spike frequency adaptation, resulting from afterhyperpolarization potentials and change in firing threshold, and inactivating K current characteristic of A-type K current (I-A). These two features cause the rapid decline in firing responses at the onset of depolarizing input. 2) Heterogeneous FFI due to heterogeneous electrophysiological properties of local GABA neurons lead to divisive inhibition of 5HT neuron firing (i.e., change in the slope of input-output function) in the network model. 3) Using a ramp depolarization, the authors found that 5-HT neurons encode the temporal derivative of depolarization, i.e., the slope of ramp depolarization. This property can be ascribed to the prominent spike-frequency adaptation observed in 5-HT neurons. Overall, this study provides new insights into the control of 5-HT output by single cell and network mechanisms.

The conclusions are well supported by combination of rigorous brain slice electrophysiological recordings of the two types of neurons in the dorsal raphe, i.e., 5-HT neurons and somatostatin-positive GABA neurons, which are identified by the usage of transgenic mice where these neurons are fluorescently labeled, and the application of single cell and network models.

As the authors state, the most striking finding of this study is that 5-HT neurons encode temporal derivative of excitatory inputs, as it may relate to reinforcement learning models. Here, this feature is captured using a ramp depolarization and is solely modeled with intrinsic property of 5-HT neurons, i.e., spike-frequency adaptation. Instead of using a ramp depolarization, using repetitive brief depolarizations with changing intervals/frequency will be more informative. Further, incorporating the network model with FFI, in particular the delay in inhibition following excitation associated with FFI when same inputs (single and repetitive) feed into 5-HT neurons and GABA neurons, may be more relevant to the reinforcement learning algorithms (e.g., see Fig. 6a in J. Neurosci. 2008, 28: 9619-9631).

Reviewer #2 (Public Review):

The authors examine the effects of depletion of an accessory subunit of the V-ATPase, ATP6AP2, using recombination of a floxed gene with osteocalcin promoter cre recombinase. Major findings are that defects and death in osteocytes occur, with mass spectrometry sequencing showing that matrix metalloproteinase, MMP14, which is involved in collagen remodeling in a number of other contexts, regulates bone matrix remodeling and osteocyte differentiation downstream of ATP6AP2. Further, ATP6AP2 depletion was counteracted in part by direct expression of MMP14 in ATP6AP2 depleted osteoblast-lineage cells.

Major strengths of the work include a clear description of methods and most results, as well as a concise and clear discussion.<br /> - There is an extensive description of the bone with a detailed discussion of micro computed tomography and staining results.<br /> - Interesting findings include retention of woven bone, and labeling for secondary indicators including cleaved caspase 3, RunX2, and sclerostin.<br /> - Osteocyte tomato labeling of the ATP6AP2Ocn-cre animals is a very good confirmation of the histomorphometric analysis.<br /> - The KI67 labeling of proliferative cells is very interesting but should be introduced more clearly. Similarly, cleaved caspase 3 is very useful but a sentence stating why this was done would be useful for clarity.<br /> - Interaction of ATP6AP2 directly with MMP14 is very interesting and useful in wrapping up the paper.

Weaknesses include:<br /> - When introducing assays, a brief description of why this is done would make the paper more accessible.<br /> - The reviewer would like to see a clearer description of the depletion of ATP6AP2 by cre-lox recombination.<br /> - Results showing calcein deposition, not on the surface of the cortical bone requires more data to strengthen this finding.<br /> - Retention of woven bone suggests a defect in resorption, but a clear description of the resorbed area is not seen.

The reviewer is enthusiastic about the manuscript.

Reviewer #2 (Public Review):

The authors use microfluidic devices to follow single swimmers for long periods, measuring their movement in detail and allowing detailed statistics at a level that has never been possible before and machine learning.

Its strength is the extraordinary detail and the doors opened by the quality of the resultant data. As such it makes a substantial contribution to a narrow field and adds slightly more subtly to an important field of full mathematically accessible descriptions of migration phenotypes.

Its weakness is that these tools are not yet used for any particularly enlightening tests. The directed probability fluxes are interesting, but not surprising. The strength of this paper is in the method, the analysis, and the ability to generate rigorous datasets.

Reviewer #2 (Public Review):

Krehenwinkel et al. investigated the long-term temporal dynamics of arthropod communities using environmental DNA (eDNA) remained in archived leave samples. The authors first developed a method to recover arthropod eDNA from archived leave samples and carefully tested whether the developed method could reasonably reveal the dynamics of arthropod communities where the leave samples originated. Then, using the eDNA method, the authors analyzed 30-year-long well-archived tree leaf samples in Germany and reconstructed the long-term temporal dynamics of arthropod communities associated with the tree species. The reconstructed time series includes several thousand arthropod species belonging to 23 orders, and the authors found interesting patterns in the time series. Contrary to some previous studies, the authors did not find widespread temporal α-diversity (OTU richness and haplotype diversity) declines. Instead, β-diversity among study sites gradually decreased, suggesting that the arthropod communities are more spatially homogenized in recent years. Overall, the authors suggested that the temporal dynamics of arthropod communities may be complex and involve changes in α- and β-diversity and demonstrated the usefulness of their unique eDNA-based approach.

Strengths:<br /> The authors' idea that using eDNA remained in archived leave samples is unique and potentially applicable to other systems. For example, different types of specimens archived in museums may be utilized for reconstructing long-term community dynamics of other organisms, which would be beneficial for understanding and predicting ecosystem dynamics.

A great strength of this work is that the authors very carefully tested their method. For example, the authors tested the effects of powdered leaves input weights, sampling methods, storing methods, PCR primers, and days from last precipitation to sampling on the eDNA metabarcoding results. The results showed that the tested variables did not significantly impact the eDNA metabarcoding results, which convinced me that the proposed method reasonably recovers arthropod eDNA from the archived leaf samples. Furthermore, the authors developed a method that can separately quantify 18S DNA copy numbers of arthropods and plants, which enables the estimations of relative arthropod eDNA copy numbers. While most eDNA studies provide relative abundance only, the DNA copy numbers measured in this study provide valuable information on arthropod community dynamics.

Overall, the authors' idea is excellent, and I believe that the developed eDNA methodology reasonably reconstructed the long-term temporal dynamics of the target organisms, which are major strengths of this study.

Weaknesses:<br /> Although this work has major strengths in the eDNA experimental part, there are concerns in DNA sequence processing and statistical analyses.

Statistical methods to analyze the temporal trend are too simplistic. The methods used in the study did not consider possible autocorrelation and other structures that the eDNA time series might have. It is well known that the applications of simple linear models to time series with autocorrelation structure incorrectly detect a "significant" temporal trend. For example, a linear model can often detect a significant trend even in a random walk time series.

Also, there are some issues regarding the DNA sequence analysis and the subsequent use of the results. For example, read abundance was used in the statistical model, but the read abundance cannot be a proxy for species abundance/biomass. Because the total 18S DNA copy numbers of arthropods were quantified in the study, multiplying the sequence-based relative abundance by the total 18S DNA copy numbers may produce a better proxy of the abundance of arthropods, and the use of such a better proxy would be more appropriate here. In addition, a coverage-based rarefaction enables a more rigorous comparison of diversity (OTU diversity or haplotype diversity) than the read-based rarefaction does.

These points may significantly impact the conclusions of this work.

Reviewer #2 (Public Review):

In this article, the authors leveraged patterns on the empirical genomic data and the power of simulations and statistical inferences and aimed to address a few biologically and culturally relevant questions about Cabo Verde population's admixture history during the TAST era. Specifically, the authors provided evidence on which specific African and European populations contributed to the population per island if the genetic admixture history parallels language evolution, and the best-fitting admixture scenario that answers questions on when and which continental populations admixed on which island, and how that influenced the island population dynamics since then.

Strengths:

1) This study sets a great example of studying population history through the lens of genetics and linguistics, jointly. Historically most of the genetic studies of population history either ignored the sociocultural aspects of the evidence or poorly (or wrongly) correlated that with genetic inference. This study identified components in language that are informative about cultural mixture (strictly African-origin words versus shared European-African words), and carefully examined the statistical correlation between genetic and linguistic variation that occurred through admixture, providing a complete picture of genetic and sociocultural transformation in the Cabo Verde islands during TAST.

2) The statistical analyses are carefully designed and rigorously done. I especially appreciate the careful goodness-of-fit checking and parameter error rates estimation in the ABC part, making the inference results more convincing.

Weaknesses

1) Most of the methods in the main analyses here were previously developed (eg. MDS, MetHis, RF/NN-ABC). However, when being introduced and applied here, the authors didn't reinstate the necessary background (strength and weakness, limitations and usage) of these methods to make them justifiable over other methods. For example, why ADS-MDS is used here to examine the genetic relationship between Cabo Verde populations and other worldwide populations, rather than classic PCA and F-statistics?

2) The senior author of this paper has an earlier published article (Verdu et al. 2017 Current Biology) on the same population, using a similar set of methods and drew similar conclusions on the source of genetic and linguistic variation in Cabo Verde. Although additional samples on island levels are added here and additional analyses on admixture history were performed, half of the main messages from this paper don't seem to provide new knowledge than what we already learned from the 2017 paper.

3) Furthermore, there are a few essential factors that could confound different aspects of the major analyses in this article that I believe should be taken into account and discussed. Such factors include the demographic history of source populations prior to admixture, different scenarios of the recipient population size changes, differences in recombination rates across the genome and between African and European populations, etc.

Overall, the paper is of interest to the field of human evolutionary genetics - that not only does it tell the story of a historically important population, but also the methodology behind this paper sets a great example for future research to study genetic and sociocultural transformations under the same framework.

Reviewer #2 (Public Review):

This work conducted a Mendelian randomization analysis between TG and a large number of disease traits in biobanks. They leverage the publicly available summary statistics from the European samples from the UK Biobank and FinnGen. A solid but routine standard summary-statistics based MR study is conducted. Several significant causal associations from TG to phenotypes are called by setting p-value cutoff with some Bonferroni correction. Sensitivity statistical analyses are conducted which generate largely consistent results. The research problem is important and relevant for public health as well we drug development. Overall this is a solid execution of current methods over appropriate data source and yields a convincing result. The interpretation of the results in discussion is also well-balanced.

While the paper does have strengths in principle, a few technical weaknesses are observed.

They used UK Biobank as the discovery and FinnGen as the replication. But the two cohorts are rather used symmetrically. Especially for the Tier 3 (NB), it seems to be an attempt of reusing the replication cohort as the discovery. I wonder if that would create additional multiple testing burden as a greater number of hypotheses are considered.

The replication p-value cutoff is a bit statistically lenient. In a typical discovery-replication setting the two stages are conducted sequentially and replication should go through the Bonferroni adjustment on the number of significant signals from discovery that is tested in the replication. For example, in this case, in tier 2, the cutoff should be 0.05/39. This may make the association of leiomyoma of the uterus slightly non-significant though. Similar cutoff should be applied to tier 3 as well.

The causal effect of TG to leiomyoma of the uterus is weak, as indicated by both the sub-significant in the replication and the non-significant of MR-PRESSO. Similarly, I would recommend more caution on the weak statistical rigor when interpreting Tier 2 and Tier 3 results.

Another methodological choice that might need justification is the use of UKB TG GWAS loci (1,248 SNPs) are the instrument for FinnGen. This may create some subtle interference with the use of UKB as outcomes in the discovery analysis. It may be minor but some justification or at least some discussions of potential limitations should be mentioned. What about the alternative of using GLGC as instruments in replication?

For disease outcomes (line 188), UKB European sample size is ~400,000 rather than ~500,000. Can the author clarify the sample size they used?

It would be reassuring to the reader if the TG measurements were measured in a treatment-naïve manner.

"Phenome-wide MR is a high-throughput extension of MR that, under specific assumptions, estimates the causal effects of an exposure on multiple outcomes simultaneously." - I guess it is more informative to mention the specific assumptions, at least briefly, in the introduction so it is easier for the reader to interpret the results.

Reviewer #2 (Public Review):

In this new exciting manuscript, Möller and colleagues studied different behavioral patterns of human and non-human primate subjects in a transparent social coordination game. In the task, two subjects chose between two visible options, in which each subject preferred a different option. Critically, the reward level also varied based on a payoff matrix. Choosing the non-preferred options by both subjects resulted in the lowest rewards, whereas choosing the preferred options by both resulted in medium-sized rewards for both. However, when both subjects chose the same option (i.e., coordinated), which was preferred by one subject but not preferred by the other subject, both received the highest rewards, with the subject who indicated the preferred option receiving a higher reward than the other. Therefore, the optimal strategy would be a dynamic turn-taking strategy in which both subjects choose the same option while taking turns over time. The authors found that about half of the human pairs adopted the turn-taking strategy. On the other hand, monkeys performed the task mostly in a selfish manner - both monkeys tended to choose their preferred options. Interestingly, in the human-monkey pairing, the monkeys could learn the turn-taking patterns. Furthermore, a detailed examination showed that turn-taking patterns in humans indicated a prosocial strategy, while turn-taking patterns in monkeys reflected a competitive strategy, where a slow-responding monkey followed the option of the fast-responding monkey. Together, the results convincingly demonstrate very interesting similarities and differences between humans and monkeys in carrying out social coordination.

Strength: This study provides convincing results with good sample size and rigorous data analyses. The transparent task design uniquely allowed the authors to examine the visual social aspects underlying social coordination. The direct comparison between human and monkey subjects, as well as examining human-monkey pairs were important and informative. Overall, the results provide novel insights into other studies in non-human primates that aim to understand the common social decision-making mechanism of both human and non-human primates.

Weakness: In the situation when the human subjects were paired with monkey subjects, it was unclear what detailed aspects of this experience directly led to the increase in the turn-taking behavior in the monkey subjects. About half of the human subjects behaved more like the monkey subjects by not exhibiting the dynamic turn-taking behavior, yet the reasons behind this within-group difference were unclear.

Reviewer #2 (Public review):

The present studies by Foster and colleagues use mouse genetics to show that pyruvate kinase 1 and 2 (PKM1 and PKM2) regulate ATP-sensitive K+ channel activity (KATP channel) through mitochondrial PEP-dependent cytoplasmic ATP/ADP increases, leading to first phase insulin secretion. During the second phase of insulin secretion, when ATP hydrolysis is maximal, oxidative phosphorylation is engaged to sustain ATP/ADP ratios and KATP channel closure. As such, the work challenges the consensus view of KATP channel activity, which states that ATP derived from oxidative phosphorylation in the mitochondrial matrix increases cytoplasmic ATP/ADP ratio, thus closing KATP channels and increasing Ca2+ fluxes.

Strengths of the study include: 1) careful experimental design and execution; 2) use of comprehensive mouse genetics to pinpoint roles of PKM1, PKM2 and phosphoenolpyruvate carboxykinase 2 (which produces PEP from oxoaloacetic acid); and 3) multiple lines of corroboratory evidence that the PEP-PKM1/2 system influences KATP channel activity and downstream signaling, via changes in non-mitochondrial ATP/ADP.

Weaknesses include: 1) lack of in vivo data to support a role of PKM1/PKM2 in determining glucose levels; and 2) over-reliance on mouse models, meaning that translational relevance to human biology is unclear.

Nonetheless, on balance, the authors have achieved their aims of showing that PEP and PKM1/PKM2 are critical regulators of KATP channel activity, Ca2+ fluxes and insulin secretion.

Overall, this is a potentially important study, which updates the textbook view of KATP-channel regulation, the major signaling mechanism through which pancreatic beta cells couple blood glucose levels to insulin release.

In the article, "The New Normative: Queer Politics in The Outs," author John Sherman, a freelance writer from Brooklyn, implores reader's to give credit to show's casually- revolutionary representation of queer characters. Sherman indicates to reader's that this is a rarely great representation for its time (2012) because it gives gay characters a non-stereotypical story line. It allows it's characters to be people who just happen to be gay. In just the pilot episode, it's not hard to see this truth. With the first four queer male characters being introduced, they all have different characteristics, priorities, and dynamics with eachother that don't center around their gayness. This gives a depth to the queer character being represented without relying on the fact that their gay to do so. I think that the positive reaction to this show bodes very well for the style of queer representation being presented and will hopefully inspire more writing and content making of this kind which non-chalently gives a voice gay to story lines in a relatable- human way instead of a stereotypical and tokenising way.

I believe that Shitt's Creek also does this fairly well. Although I've only seen a couple episodes myself- I saw the character of David as a complete person and story line not defined by his gayness or partner choices although it is an obvious part of his identity.

Comparaciones part 2

DOI: 10.1016/j.isci.2022.105028

Resource: RRID:ZFIN_ZDB-ALT-150213-2

Curator: @evieth

SciCrunch record: RRID:ZFIN_ZDB-ALT-150213-2

What is this?

DOI: 10.1016/j.isci.2022.105028

Resource: RRID:ZFIN_ZDB-ALT-150311-2

Curator: @evieth

SciCrunch record: RRID:ZFIN_ZDB-ALT-150311-2

What is this?

魂牵梦萦

Eric Topol. (2021, October 27). 'The benefit of a third [booster] dose in reducing transmission is sizeable and increases with vaccine coverage and contact rates among individuals." https://medrxiv.org/content/10.1101/2021.10.25.21265500v1 by @billygardner and @DiseaseEcology https://t.co/zAZcJgF1nJ [Tweet]. @EricTopol. https://twitter.com/EricTopol/status/1453153076165021696

• When considering the work of social reproduction in the University, I suggest we focus our attention to two interrelated and crucial sites: the reproduction of the University itself, and the reproduction of the subject (the student-self).
• Mapping social reproduction in the University uncovers the racism and patriarchy at work. As the neoliberal university seeks to market its diversity and multiculturalism, it requires those diverse bodies to do the work of reproducing the university as such.

DOI: 10.1242/dev.198408

Resource: (ZFIN Cat# ZDB-GENO-071003-2,RRID:ZFIN_ZDB-GENO-071003-2)

Curator: @scibot

SciCrunch record: RRID:ZFIN_ZDB-GENO-071003-2

What is this?

You need a space to gather, make sense of, and share your answers as they develop.

Once the government has control of the economy, will they ever let go?

5.1 Recognize that 1) the biggest threat to good decision making is harmful emotions, and 2) decision making is a two-step process (first learning and then deciding).

2 Use the 5-Step Process to Get What You Want Out of Life

Definition DIrect 2 Mobile

Bold

Process flow: Worldview, purpose, metric and finally design

Paper 1: Worldview and purpose Paper 2: practical implementation Paper 3: Design

DOI: 10.7554/eLife.76640

Resource: (ZFIN Cat# ZDB-ALT-120103-2,RRID:ZFIN_ZDB-ALT-120103-2)

Curator: @scibot

SciCrunch record: RRID:ZFIN_ZDB-ALT-120103-2

What is this?

DOI: 10.7554/eLife.76640

Resource: (ZFIN Cat# ZDB-ALT-110721-2,RRID:ZFIN_ZDB-ALT-110721-2)

Curator: @scibot

SciCrunch record: RRID:ZFIN_ZDB-ALT-110721-2

What is this?

Referencing Intersections and Dialectics, McNally identifies in liberalism what he calls "social Newtonianism." In this theoretical framework, "things—be they entities, processes, or relations—can thus only be understood as utterly discrete atomic bits whose identities exclude the co-constituting effects of others," a critique which he applies doubly to liberalism and strains of intersectional theory. (McNally, 97) In a much less radical sense, we can understand social Newtonianism to be constitutive of the faux-progressivism of the neoliberal university, in which Contemporary DEI is an entrenched set of discourses. The production of social atomism amongst the working class is essential to ruling class ideology, and by extension managerial rationality. Managerial rationalities within the university are thus accepting of a watered-down form of intersectionality--one that leans into social Newtonianism and discourages class analysis. For this reason, among others, SRT provides resources to combat institutional discourse on race, gender and inequality.

In the residential university, the economic and social life of the student are collapsed. In this sense, the labor of being surveilled and the production of human capital through all social processes inherent to regimes of managerial rationality, is panoptical, totalizing.

Part 2 aims to supplement the economic questions addressed in Part 1 to fully account for the vast unpaid and uncompensated labor that constitutes the labor process within the university. A proper analysis of labor in the university, and Contemporary DEI's role in facilitating it is to understand the socio-economic productive and reproductive forces that produce student, faculty and administrative subjectivity.

In the racialized and gendered capitalism that produces DEI, embodying diversity and similar forms of labor are made invisible as such.