Reviewer #2 (Public Review):

Summary:

This work presents a new machine-learning method, RaSP, to predict changes in protein stability due to point mutations, measured by the change in folding free energy ΔΔG.

The model consists of two coupled neural networks, a 3D self-supervised convolutional neural network that produces a reduced-dimensionality representation of the structural environment of a given residue, and a downstream supervised fully-connected neural network that, using the former network's structural representation as input, predicts the ΔΔG of any given amino-acid mutation. The first network is trained on a large dataset of protein structures, and the second network is trained using a dataset of the ΔΔG values of all mutants of 35 proteins, predicted by the biophysics-based method Rosetta.

The paper shows that RaSP gives good approximations of Rosetta ΔΔG predictions while being several orders of magnitude faster. As compared to experimental data, judging by a comparison made for a few proteins, RaSP and Rosetta predictions perform similarly. In addition, it is shown that both RaSP and Rosetta are robust to variations of input structure, so good predictions are obtained using either structures predicted by homology or structures predicted using AlphaFold2.

Finally, the usefulness of a rapid approach such as RaSP is clearly demonstrated by applying it to calculate ΔΔG values for all mutations of a large dataset of human proteins, for which this method is shown to reproduce previous findings of the overall ΔΔG distribution and the relationship between ΔΔG and the pathological consequences of mutations. The RaSP tool and the dataset of mutations of human proteins are shared.

Strengths:

The single main strength of this work is that the model developed, RaSP, is much faster than Rosetta (5 to 6 dex), and still produces ΔΔG predictions of comparable accuracy (as compared with Rosetta, and with the experiment). The usefulness of such a rapid approach is convincingly demonstrated by its application to predicting the ΔΔG of all single-point mutations of a large dataset of human proteins, for which using this new method they reproduce previous findings on the relationship between stability and disease. Such a large-scale calculation would be prohibitive with Rosetta. Importantly, other researchers will be able to take advantage of the method because the code and data are shared, and a google colab site where RaSP can be easily run has been set up. An additional bonus is that the dataset of human proteins and their RaSP ΔΔG predictions, annotated as beneficial/pathological (according to the ClinVar database) and/or by their allele frequency (from the gnomAD database) are also made available, which may be very useful for further studies.

Weaknesses:

The paper presents a solid case in support of the speed, accuracy, and usefulness of RaSP. However, it does suffer from a few weaknesses.

The main weakness is, in my opinion, that it is not clear where RaSP is positioned in the accuracy-vs-speed landscape of current ΔΔG-prediction methods. The paper does show that RaSP is much faster than Rosetta, and provides evidence that supports that its accuracy is comparable with that of Rosetta, but RaSP is not compared to any other method. For instance, FoldX has been used in large-scale studies of similar size to the one used here to exemplify RaSP. How does RaSP compare with FoldX? Is it more accurate? Is it faster? Also, as the paper mentions in the introduction, several ML methods have been developed recently; how does RaSP compare with them regarding accuracy and CPU time? How RaSP fares in comparison with other fast approaches such as FoldX and/or ML methods will strongly affect the potential usefulness and impact of the present work.

Second, this work being about presenting a new model, a notable weakness is that the model is not sufficiently described. I had to read a previous paper of 2017 on which this work builds to understand the self-supervised CNN used to model the structure, and even so, I still don't know which of 3 different 3D grids used in that original paper is used in the present work.

A third weakness is, I think, that a stronger case needs to be made for fitting RaSP to Rosetta ΔΔG predictions rather than experimental ΔΔGs. The justification put forward by the authors is that the dataset of Rosetta predictions is large and unbiased while the dataset of experimental data is smaller and biased, which may result in overfitting. While I understand that this may be a problem and that, in general, it is better to have a large unbiased dataset in place of a small biassed one, it is not so obvious to me from reading the paper how much of a problem this is, and whether trying to fix it by fitting the model to the predictions of another model rather than to empirical data does not introduce other issues.

Finally, the method is claimed to be "accurate", but it is not clear to me what this means. Accuracy is quantified by the correlation coefficient between Rosetta and RaSP predictions, R = 0.82, and by the Mean Absolute Error, MAE = 0.73 kcal/mol. Also, both RaSP and Rosetta have R ~ 0.7 with experiment for the few cases where they were tested on experimental data. This seems to be a rather modest accuracy; I wouldn't claim that a method that produces this sort of fit is "accurate". I suppose the case is that this may be as accurate as one can hope it to be, given the limitations of current experimental data, Rosetta, RaSP, and other current methods, but if this is the case, it is not clearly discussed in the paper.

Reviewer #2 (Public Review):

The antennae of insects are excellent sensors and are able to distinguish chemicals/compounds using odorant receptor proteins. Though many are promiscuous, several ORs are extremely specific and respond to only one or few related chemicals. In this study, the authors focus on two ORs from southern house mosquito, Culex quinquefasciatus namely OR10 and OR2, which respond to (show high specificity) skatole and indole respectively. Notably, these two compounds differ only by a methyl group raising the question how this is achieved. To address this question, the authors have chosen CquiOR10 (as it is more sensitive) for swapping the transmembrane domains (TMDs) from CquiOR2 and by performing heroic work, arrive at one single residue in one of the TMDs to explain the specificity in these ORs.

The major strengths of the manuscript include the careful design of the many different chimeric receptors (36 in total) and dissecting the importance of each TMD and zeroing on TMD2 as the specificity determinant, followed by zooming to a single residue in TMD2 that can change responsiveness of CquiOR10 to CquiOR2 and vice versa. This residue in TM2 is an alanine in CquiOR10, which when mutated to bulky residue becomes responsive to indole but when mutated to glycine remains specific to skatole and shows higher sensitivity. Similarly, mutating the equivalent residue in CquiOR2, Leucine 74 to a smaller residue makes this receptor now more responsive to skatole and making it more like CquiOR10.

Using RoseTTAfold and AlphaFold, the authors build models of CquiOR10 and CquiOR2, which gives them a platform to observe how ligands can bind using Rosettaligand both in native structures as well as mutants. They further ask how larger ligands or the methyl group at different position in the indole ring affects the response of the receptor, which follow a consistent trend on the key residue of Alanine 73. All these analysis allow authors to propose that the odorants or chemicals are accommodated/restricted due to the volume constraints by residues lining the cavity derived from the TMDs.

Reviewer #2 (Public Review):

In this communication by Motta and colleagues, the authors address the emerging role of the gut microbiome in degrading and detoxifying plant metabolites, using bees as a study system. The experiments are elegantly controlled, spanning in vitro and in vivo work that leverages the increasing tractability of bees and their microbial symbionts. This is evident in the extensive screening of Bifidobacterium, Bombilactobacillus, Lactobacillus, and Gilliamella relative to their susceptibility to amygdalin. This provided a foundation to pinpoint which strains can degrade the cyanogenic glycoside, the potential pathways underlying that process, and the key enzymes involved. The strain Bifidobacterium wkB204 displayed elevated expression of GH3, correlating to the ability of this microbe to degrade amygdalin in vitro. Expression of the GH3 in E. coli corroborated its putative role in the transformation of amygdalin to prunasin, consistent with the single inoculation effects of Bifidobacterium wkB204 into microbiota-deprived bees. These experiments collectively point to the importance of the bee microbiota for the consistent degradation of amygdalin. The findings are nicely contextualized relative to prior work on the gut microbiome and the metabolism of the cyanogenic glycoside, including efforts on bees and rats.

Reviewer #2 (Public Review):

In recent years the activity of cortical VIP+ interneurons in relation to learning and sensory processing has raised great interest and has been intensely investigated. The ability of VIP+ interneurons in the auditory cortex to respond to both reward and punishment was already reported a few years ago by some of the authors (Pi et al., 2013, Nature). However, this work importantly adds to their previous study demonstrating a largely similar and synchronous response of a large fraction of these interneurons across the neocortex to salient stimuli of different valence during the performance of an auditory discrimination task.

An additional strength of this study is the analysis and identification of the general pattern of VIP+ interneuron responses associated to specific behaviors in the different layers of the neocortex depth.

Interestingly, the authors also identified using cluster analysis 5 different classes of VIP+ interneurons, based on the dynamic of their responses, that were unequally distributed in distinct cortical areas.

This is a well performed study that took advantage of a cutting-edge imaging approach with high recording speed and good signal-to-noise ratio. Experiments are well performed and the data are properly analyzed and nicely illustrated. However, one shortcoming of this paper, in my opinion, is the "case report" structure of the data. Essentially for each neocortical area the activity of VIP+ interneurons was analyzed only in one animal. This limits the assessment of the stability of the response/recruitment of these interneurons. I appreciate the high number of recorded VIP+ interneurons per area/animal and I do understand that it would be excessively laborious to perform 3D random-access two-photon microscopy in several mice for each cortical area. On the other hand, it would be important to have some knowledge of the general variability of the responses of these neurons among animals.

In conclusion, despite the findings described in this manuscript being generally sound, additional experiments are recommended to further substantiate the conclusions.

Reviewer #2 (Public Review):

General description:<br /> This study elucidates how Advanced Glycation End-products (AGE), found in processed food and endogenously, drive food intake and cause some of the pathophysiological defects associated with metabolic disease. In their previous C. elegans study, the authors found that glod-4 mutants, animals that lack glyoxalase activity and thus accumulate AGEs, eat more and share some of the pathophysiological effects seen in metabolic disease. In this study, they identify a specific AGE, hydroimidazolone (MG-H1), that is sufficient to increase feeding, similar to what was previously observed in the glod-4 mutants. Gene expression studies then show expression changes in several neurotransmitter and eating genes, including the tyramine decarboxylase gene tdc-1 and its receptor. Measuring eating behaviors in animals carrying mutations in tyramine signalling genes they show that tyramine signaling system is required for the behavioral and pathophysiological effects of MG-H1. Finally, they show that the transcription factor elt-3 controls the expression of tyramine signaling components and thus is also required for the response to MG-H1.

Strength: Strengths of the paper include the elegant approach to study how toxic metabolites affect physiology and behavior in vivo. The logic behind the study is easy to follow and the paper is clearly written.

Weakness: The main weakness is that the genetic studies were generally only carried out with a single mutation that was not rescued. To corroborate the requirement of tdc-1 and elt-3 for the response to MG-H1, the results should be repeated either in a rescue strain or using a different allele. Some of the effects are subtle and there is the danger of them being caused by background mutations.

Impact: The occurrence of metabolites like AGEs in either processed food or endogenously is a topic that is not well investigated despite its general importance. In this study the authors show the functional consequences of a non-enzymatically generated metabolite and how it exerts its toxicity.

Reviewer #2 (Public Review):

This is an important and timely characterization of a diversity of behaviors male and female rats exhibit during the acquisition of Pavlovian fear conditioning in a conditioned suppression procedure. Using hand-scored video analysis and ethogram of nine different behaviors, the authors report that auditory conditioned stimuli that predict shock with high certainty evoke not only freezing, but a variety of other behaviors including locomotion, jumping, and rearing (in addition to suppressing reward-seeking). Auditory stimuli that were followed by shock on only some trials (uncertainty condition), were less likely to evoke freezing and did not lead to a suppression of port/cup-directed behaviors (reward seeking). There were subtle sex differences in the temporal profile of freezing behavior, but not in the properties of the other behaviors under study.

Ultimately, these findings point to the importance of task variables (eg., reward seeking in a conditioned suppression procedure) and shock probability in shaping an animal's defense repertoire under threat.

An important factor that this work does not resolve is how the magnitude of the threat/shock (and presumably the state of fear that it engenders) influences an animal's defensive topography. This report used a modest/weak footshock intensity that supported very low levels of tone-elicited freezing (<20%) - a stark contrast to the extant fear conditioning literature that typically reports much higher levels of freezing behavior.

Reviewer #2 (Public Review):

This paper by Sherratt et al. evaluated the performance of real-time predictions for COVID-19 submitted to the European COVID-19 Forecast Hub between March 8 2021 and March 7 2022. This large-scale multi-team multi-county collaboration collected short-term forecasts for COVID-19 from 26 teams generated for 32 countries in Europe, making this dataset one of the largest archives of real-time COVID-19 forecasts. The results indicate that ensemble models combining forecasts from individual models generally performs better than each individual model, and ensemble methods based on medians outperform the ones based on means. The comparison also shows that incident death forecasts are more reliable than incident case forecasts beyond two weeks into the future. The paper further included detailed discussions on several practical considerations in the operational use of forecasting models. These findings provide practical guides for generating real-time forecasts for infectious diseases and novel insights into coordinating international forecasting efforts during a public health emergency.

The conclusions of this paper are well supported by the data and analyses. A few aspects could be further discussed in the manuscript.

1. A parallel effort of real-time COVID-19 forecasting in the US (i.e., the US COVID-19 Forecast Hub) reported similar findings on the use of ensemble models. This study from Europe provides independent validation that shows the robustness of these findings. While both studies followed similar guidelines and used the same evaluation metrics (coverage and WIS), I believe there should be unique challenges associated with forecasting for multiple countries (as opposed to forecasting in a single country). As a result, it might be worthwhile to discuss those challenges and potential solutions to inform similar efforts in the future.

2. WIS is a strictly proper score for evaluating forecast performance; however, it must rely on a reference forecast model. This may create difficulties in interpreting forecast accuracy for the general public who may not understand the concept of WIS. For instance, what is a WIS score good enough to trust? The authors may want to include a simple metric (e.g., mean absolute error) as a supplement even though these metrics have some caveats. I presume the performance should be highly correlated using different evaluation metrics.

3. It might be helpful to elaborate more on the assumptions for near-term predictions in participating models (e.g., status quo, reactive change of transmission, etc.). Essentially all real-time predictions were generated based on assumptions, although sometimes those assumptions were not stated explicitly. For behavior-induced changing points (peaks or troughs), it might be challenging to predict using the status quo without considering a change in model states.

4. Data in the tables and figures were used to compare forecasts. It would be great to have a formal statistical test for comparing model performance, if possible.

Reviewer #2 (Public Review):

It is known that bacterial outer membrane proteins must interact with a variety of cellular factors to reach their final destination safely. There is considerable biochemical evidence in the literature (primarily from crosslinking studies) that these factors interact to promote the movement of client proteins and to prevent their aggregation or misfolding, but the details of the interactions are unknown. The authors showed that they could use a novel virtual screening method together with known crystal structures of individual factors to predict the three-dimensional structures of several pairs or groups of interacting factors (supercomplexes). The predicted supercomplex structures are both fascinating and compelling because they are consistent with the published results and they help to explain the mechanism by which the cellular factors promote outer membrane protein biogenesis. I think that this study will be of interest to a wide audience because it serves as a proof-of-concept that although Alpha Fold is incredibly useful for predicting the structures of protein monomers, more sophisticated applications can be used to successfully predict the structures of protein complexes which are often the workhorses of the cell. I have only two significant concerns. First, the authors focused on high confidence supercomplexes that have known biological significance. Their method also identified other high confidence supercomplexes, but they need to explain how they can distinguish predicted supercomplexes that have potential biological significance from those that are simply "false positives". Second, one of the proposed functional models does not seem to be consistent with the results of a previous study.

Reviewer #2 (Public Review):

Luongo et al. investigated the behavioural ability of 4 different species (macaque, mouse lemur, tree shrew and mouse) to segment figures defined by opponent motion, as well as different visual features from the background. With carefully designed experiments they convincingly make the point that figures that are not defined by textural elements (orientation or phase offsets, thus visible in a still frame) but purely by motion contrast, could not be detected by non-primate species. Interestingly it appears to be particularly motion contrast, since pure motion - figures moving on a static background - could be discriminated better, at least by mice.

This is highly interesting and surprising -- especially for a tree shrew, a diurnal, arboreal mammal, very closely related to primates and with a highly evolved visual system. It is also an important difference to take into account considering the multitude of studies on the mouse visual system in recent years.

The authors additionally present neuronal activity in mice, from three different visual cortical areas recorded with both electrophysiology and imaging. Their conclusions are mostly supported by the data, but some aspects of the recordings and data analysis need to be clarified and extended.

The main issues are outlined below roughly in order of importance:

1. The most worrying aspect is that, if I interpret their figures correctly, their recordings seem not very stable and this may account for many of the differences across the visual conditions. The authors do not report in which order the different stimuli were shown, their supplemental movie, however, makes it seem as though they were not recorded fully interleaved, but potentially in a block design with all cross1 positions recorded first, before switching to cross2 positions and then on to iso... If I interpret Figure 6a correctly, each line is the same neuron and the gray scale shows the average response rate for each condition. Many of these neurons, however, show a large change in activity between the cross1 and the cross2 block. Much larger than the variability within each block that should be due to figure location and orientation tuning. If this interpretation is correct, this would mean that either there were significant brain state changes (they do have the mice on a ball but don't report whether and how much the animals were moving) between the blocks or their recordings could be unstable in time. It would be good to know whether similar dramatic changes in overall activity level occur between the blocks also in their imaging data.

The same might be true for differences in the maps between conditions in figure 4. If indeed the recordings were in blocks and some cells stopped responding, this could explain the low map similarities. For example Cell 1 for the cross stimuli seems to be a simple ON cell, almost like their idealized cell in 3d. However, even though the exact texture in the RF and large parts of the surround for a large part of the locations is exactly identical for Cross1 and Iso2, as well as Cross2 and Iso1, the cells responses for both iso conditions appear to only be noise, or at least extremely noise dominated. Why would the cell not respond in a phase or luminance dependent manner here?

This could either be due to very high surround suppression in the iso condition (which cannot be judged within condition normalization) or because the cell simply responded much weaker due to recording instability or brain state changes. Without any evidence of significant visual responses, enough spikes in each condition and a stable recording across all blocks, this data is not really interpretable. Instability or generally lower firing rates could easily also explain differences in their decoding accuracy.

Similarly, it is very hard to judge the quality of their imaging data. They show no example field of views or calcium response traces and never directly compare this data to their electrophysiology data. It is mentioned that the imaging data is noisy and qualitatively similar, but some quantification could help convince the reader. Even if noisy, it is puzzling that the decoding accuracy should be so much worse with the imaging data: Even with ten times more included neurons, accuracy still does not even reach 30% of that of the ephys data. This could point to very poor data quality.

2. There is no information on the recorded units given. Were they spike sorted? Did they try to distinguish fast spiking and regular spiking units? What layers were they recorded from? It is well known that there are large laminar differences in the strength of figure ground modulation, as well as orientation tuned surround suppression. If most of their data would be from layer 5, perhaps a lack of clear figure modulation might not be that surprising. This could perhaps also be seen when comparing their electrophysiology data to the imaging data which is reportedly from layer 2/3, where most neurons show larger figure modulation/tuned surround suppression effects. There is, however, no report or discussion of differences in modulation between recording modalities.

3. There is an apparent discrepancy between Figure 5d and i. How can their modulation index be around -0.1 for cross (Figure 5d) - which would correspond to on average ~20% weaker responses to a figure than to background, when their PSTH (5i) shows an almost 50% increase of figure over ground. This positive figure modulation has also been widely reported in the literature (Schnabel, Kirchberger, Keller). Are there different populations of cells going into these analyses?

4. In a similar vein, it is not immediately clear why the average map correlation would be bigger for random cell pairs (~0.2, Fig 3g) than for the different conditions of the same cell (~0, Fig 5b). Could this be due to differences in recording modality (imaging in 3g and ephys in 5b)?

5. The maps in Figure 4 should show the location of the RF, because they cannot be interpreted without knowledge of the RF center and size. For example cell 4 in the iso 1 condition could be a border cell, or could respond to the center of the figure. It is impossible to deduce without knowledge of the location of the RF.

6. It could help the reader to discuss the interpretation of the map correlations in Fig 5 a and b in more detail. My guess is that negatively correlated maps (within cross or iso condition) could come from highly orientation tuned neurons, whereas higher correlation values point to more generally figure/contextually modulated cells (within this condition). While the distribution is far from bimodal, this does not rule out a population of nicely figured modulated cells at the high end of the distribution. It might not be necessary at the level of V1 that the figure modulation be consistent across all textures. It would not be surprising, if orientation contrast-defined, phase contrast-defined and motion contrast-defined figures could be signalled to higher areas by discrete populations of V1 or even LM cells.

7. Some of the behavioural results warrant a little more explanation or discussion, as well. In Figure 2h, the mice seem significantly better on the static version of the iso task, than on the moving one. If statistically significant, this should be discussed. Is this because the static frame was maximally phase offset? Then the figure would indeed be better visible better (bigger phase contrast in more frames) than in the moving condition.

Figure 2 and extended Figure 1c: why is the mouse lemur performing so poorly on average? It also appears to have biggest problems with the cross stimulus early on in training.

Tree shrews seem not to be able to memorize the textures as well as the mice do. Is this because of less deprivation/motivation? Or because of the bigger set of textures in training? This would make memorization harder and could thus lower their overall performance. The comparative aspects are very interesting but the absolute differences in performance could be discussed in more detail or explained better.

8. In Figure 7b, why wouldn't the explanation for the linear decodability in cross also hold for iso? There are phase offsets at the borders that simple cells should readily be able to resolve, just as in the case of orientation discontinuities. Could they make a surround phase model, similar to their surround orientation model, that could more readily capture the iso discontinuities?

Reviewer #2 (Public Review):

The authors trained two monkeys to perform a task that involved sequential (blocked) but unsignalled rules for discriminating the colour and shape of visual stimulus, by responding with a saccade to one of four locations. In rules 1 and 3, the monkeys made shape (rule 1) or colour (rule 3) discriminations using the same response targets (upper left / lower right). In rule 2, the monkeys made colour judgments using a unique response axis (lower left/upper right). The authors report behaviour, with a focus on time to relearn the rules after an (unsignalled) switch for each rule, discrimination sensitivity for partially ambiguous stimuli, and the effect of congruency. They compare the ability of models based on Q-learning, Bayesian inference, and a hybrid to capture the results.

The two major behavioural observations are (1) that monkeys re-learn faster following a switch to rule 2 (which occurs on 50% of blocks and involves a unique response axis), and (2) that monkeys are more sensitive to partially ambiguous stimuli when the response axis is unique, even for a matched feature (colour). These data are presented clearly and convincingly and, as far as I can tell, they are analysed appropriately. The former finding is not very surprising as rule 2 occurs most frequently and follows each instance of rule 1 or 3 (which is why the ideal observer model successfully predicts that the monkeys will switch by default to rule 2 following an error on rules 1 or 3) but it is nevertheless reassuring that this behaviour is observed in the animals. It additionally clearly confirms that monkeys track the latent state that denotes an uncued rule.

The latter finding is more interesting and seems to have two potential explanations: (i) sensitivity is enhanced on rule 2 because it is occurs more frequently; (ii) sensitivity is enhanced on rule 2 because it has a unique response axis (and thus involves less resource sharing/conflict in the output pathway).

The authors do not directly distinguish between these hypotheses per se but their modelling exercise shows that both results (and some additional constraints) can be captured by a hybrid model that combines Bayesian inference and Q learning, but not by models based on either principle alone. A Q-learning model fails to capture the latent state inference and/or the rule 2 advantage. The Bayesian inference model captures the rapid switches to rule 2 (which are more probable following errors on rule 1 and rule 3) but predicts matched discrimination performance for partially ambiguous stimuli on colour rules 2 and 3. This is because although knowing the most likely rule increases the probability of a correct response overall it does not increase discriminability and thus boosts the more ambiguous stimuli. I wondered whether it might be possible to explain this result with the addition of an attention-like mechanism that depends on the top-down inference about the rule. For example, greater certainty about the rule might increase the gain of discrimination (psychometric slope) in a more general way.

The authors propose a hybrid model in which there is an implicit assumption that the response axis defines the rule. The model infers the latent state like an ideal observer but learns the stimulus-response mappings by trial and error. This means that the monkeys are obliged to constantly re-learn the response mappings along the shared response axis (for rules 1/3) but they remain fixed for rule 2 because it has a unique response axis. This model can capture the two major effects, and for free captures the relative performance on congruent and incongruent trials (those trials where the required action is the same, or different, for given stimuli across rules) on different blocks.

I found the author's account to be plausible but it seemed like there might be other possible explanations for the findings. In particular, having read the paper I remained unclear as to whether it was the sharing of response axis per se that drove the cost on rule 3 relative to 2, or whether it was only because of the assumption that response axis = rule that was built into the authors' hybrid model. It would have been interesting to know, for example, whether a similar advantage for ambiguous stimuli on rule 2 occurred under circumstances where the rule blocks occured randomly and with equal frequency (i.e. where there was response axis sharing but no higher probability); or even whether, if the rule was explicitly signalled from trial to trial, the rule 2 advantage would persist in the absence of any latent state inference at all (this seems plausible; one pointer for theories of resource sharing is this recent review: https://www.cell.com/trends/cognitive-sciences/fulltext/S1364-6613(21)00148-0?_returnURL=https%3A%2F%2Flinkinghub.elsevier.com%2Fretrieve%2Fpii%2FS1364661321001480%3Fshowall%3Dtrue). No doubt these questions are beyond the scope of the current project but nevertheless it felt to me that the authors' model remained a bit tentative for the moment.

Reviewer #2 (Public Review):

In this manuscript, the authors investigated the mechanism by which glycine prevents cell membrane rupture. They found that deficiency of NINJ1 (the key executioner of plasma membrane rupture by forming oligomers) phenocopies the cytoprotection of glycine during lytic cell death, and glycine treatment inhibits the oligomerization of NINJ1. Based on these observations, they claimed that glycine executes its inhibitory effect on cell lysis by targeting and inactivating NINJ1. This study addresses an important subject, because how glycine prevents cell membrane rupture is not understood and the literature is full of the implausible conclusion that it works as an osmoprotectant and that pyroptotic cell rupture is secondary to osmotic changes in cells undergoing pyroptosis, even though the gasdermin pore is very large and should allow the free passage of ions and many small proteins.

Reviewer #2 (Public Review):

This manuscript provides additional data about how smell is encoded by insects. The study includes both new experimental measurements and simulations. At present, there are questions about whether simulations are appropriately performed to support experimental measurements.

The main experimental finding reported here is that the same olfactory receptor neurons (ORN) can respond with different temporal dynamics to different odorants. This finding is of interest. However, it is very important to discuss whether the differences in temporal dynamics can be explained by differences in how this odorant is carried by air, as has been described here: https://pubmed.ncbi.nlm.nih.gov/23575828/.

There are several questions that need to be addressed regarding the simulations part of the manuscript.

1) There is a mismatch between the number of ORNs used in the model and in the insect system studied.

2) The demonstration in Figure 5 that motif switching improves odor classification includes motif switching for a given odorant, which is not observed experimentally.

3) The methodology for estimating neural temporal dynamics needs to be corrected to apply to the natural stimuli used here.

Reviewer #2 (Public Review):

This fascinating study describes a possible effect of cancer-generated microvesicles on fibroblasts. Microvesicles from a particularly metastatic line promote more contractile and proliferative fibroblasts, and there is a key role for at least one microvesicle factor - the crosslinking enzyme Transglutaminase-2. A wide range of studies help identify and elucidate these effects, but a few aspects remain unclear.

1. MV- has more crosslinking TGM2 but also less MMP14 degradation, and so ECM is more stable either way. The authors should describe any other factors that would give a similar effect as these. The authors should address: do other genes change with TGM2 knockdown; does MMP14 change? If the latter changes, does it have a more important role than TGM2?

2. Perhaps the cleanest and important study of MV effects is in Fig.6j,k, but it shows in vivo differences that are barely significant or not significant, and compares to 'SF' serum free media as a control. Are serum components detected in Mass Spec? If so, wouldn't this suggest a serum supplemented media is a better control? The serum is usually from another species, which is a further (xenogenic) concern that motivates care and discussion about dose -- especially given the high frequency of injection. Also, is there a survival difference for the mice?

Reviewer #2 (Public Review):

The goal of the study by Rutherford and colleagues was to characterize functional, structural, and molecular changes at the highly specialized cochlear inner hair cell (IHC) - spiral ganglion neuron (SGN) ribbon synapse in GluA3 AMPA receptor subunit knockout mice (GluA3KO). Previous work by the authors demonstrated that 2-month-old GluA3KO mice experienced impaired auditory processing and changes in synaptic ultrastructure at the SGN - bushy cell synapse, the next synapse in the auditory pathway.

In the present study, the authors investigated whether GluA3 is required for ribbon synapse formation and physiology in 5-week-old mice using a series of functional and light- and electron microscopy imaging approaches. While deletion of GluA3 AMPAR subunit did not affect hearing sensitivity at this age, the authors reported that cochlear ribbon synapses exhibited changes in the molecular composition of AMPARs and pre- and postsynaptic ultrastructural alterations. Specifically, the authors demonstrated that GluA3KO ribbon synapses exhibit i) a global reduction in postsynaptic AMPARs, which is also reflected by smaller AMPAR arrays, ii) a reduction in GluA2 and an increase in GluA4 protein expression at individual postsynaptic sites, and iii) changes in the dimensions and morphology of the presynaptic specialization ("ribbon") and in the size of synaptic vesicles. These reported structural changes are linked to the side of innervation with respect to the IHC modiolar-pillar axis.

The results presented by the authors are conceptually very interesting as the data support the notion that potentially detrimental changes in the molecular composition of a sensory synapse can be compensated to sustain synaptic function to a certain extent during development. The conclusions of the study are mostly well supported by the data, but some experimental details or control experiments are missing or need to be clarified to allow a full assessment.

1. The authors tested which GluA isoforms are expressed in SGNs of GluA3KO mice and reported that only GluA2 and GluA4, and not GluA1, receptor subunits are present in the cochlear. It is, however, a bit difficult to understand why immunolabelling for GluA1 was only performed on brainstem sections (Fig. 1B right) and not in the cochlear to probe for postsynaptic localization at ribbon synapses as it was done for the other isoforms (Fig. 2 and 6) given that GluA3KO IHCs exhibited a larger number of ribbons that lacked GluA2 and 3 (lone or 'orphaned' ribbons; Fig. 6B). It is also not clear why immunolabelling for GluA2 and 4 was performed to probe for expression of these receptor subunits on SGN cell bodies in the cochlear spiral ganglion. Which neurons are expected to synapse onto these somata?

2. The authors state in the text that GluA3 expression is completely abolished in GluA3KO IHCs, however, there appears to still be a faint punctate immunofluorescence signal visible when an antibody directed against GluA3 was used (Fig. 2C). Providing additional information on the specificity of this (and the other) antibodies used in the study would be helpful.

3. The authors reported changes in the volume of the presynaptic ribbon and postsynaptic density surface area in GluA3KO KO animals. The EM data as presented are however not sufficiently convincing.<br /> i) There appears to be a mismatch between the EM data shown in Fig. 3 and 4 and the information in the text with respect to the number of data points in the plots and the reported number of reconstructed synapses. This raises several questions with respect to the analysis. For instance, it is unclear whether certain synapses were reconstructed but excluded from the analysis. If so, what were the exclusion criteria?<br /> ii) The authors compare PSD surface areas in reconstructions from 3D serial sections, but for some of the shown reconstructions (i.e. Fig. 3A' and B' and 4B'), it appears as if PSDs were only incompletely reconstructed.

4. The immunolabelling experiments shown in Fig. 2 and 6 are of very high quality and the quantitative analysis of the light microscopy data (Fig. 6-9) is clearly very detailed, but slightly difficult to interpret the way it is presented. Specifically, it is unclear how the number of synapses per IHC (Fig. 6B) and the separation into modiolar and pillar side (Fig. 8) was achieved based on the shown images without the outlines of individual cells being visible.

5. Adding more detailed information about important parameters (mean, N/n, SD/SEM) and the statistical tests used for the individual comparisons presented in the Figures would help strengthen the confidence in the presented data.

6. In general, the authors report a series of molecular and structural changes in IHCs and reach the conclusion that GluA3 subunits may have a role in "trans-synaptically" determining or organizing the architecture of both the pre- and post-synapse. However, some of the arguments are very speculative and many of the claims are not supported by experimental data presented in the paper. The authors should consider to also compare their findings to studies that investigated ultrastructural changes of AMPAR subunit knockouts in other synapse types, and discuss alternative interpretations (e.g. homeostatic changes).

Reviewer #2 (Public Review):

The manuscript by Lefebvre et al. investigates how the tissue-scale spatial organization of protein evolves during germ band extension. The key question is whether changes in the localization of important features such as pair-rule gene (PRG) stripes and apical myosin orientation can be explained purely via passive advection without the need for additional regulatory mechanisms. In the case of the PRG, as well as TLRs, their data strongly suggests the answer is yes: the authors show that the deformation of the characteristic stripe pattern closely matches that predicted by advecting the initial pattern in a velocity field extracted from the observed tissue flow. By contrast, the authors find that anisotropic myosin orientation cannot be explained purely in terms of the local velocity field, in particular the fact that myosin remains robustly oriented with the DV axis. This leads the authors to postulate that myosin orientation is continually re-established via a static source aligned with said axis, which dominates over re-orientation due to advection. A simple model of myosin reorientation is developed from this hypothesis, which produces qualitatively similar relationships between orientation and local vorticity to that seen both in WT and in several mutants.

The strongest feature of this paper is illustrated by the results in Figure 2. The result it presents, which the authors summarize as "PRGs flow with tissue while myosin does not," is a very nice application of recent advances in using toto microscopy for embryonic systems to extract and quantify whole embryo expression patterns and flow fields, which are needed information for this kind of result. Tissue flow is a complicated, active process, and identifying which parts of the dynamics can be sufficiently explained by passive transport can tremendously simplify the conceptual challenges of germ band extension and related tissue movements found during neurulation or organogenesis. The resistance the authors found that myosin exhibits to re-orientation is likewise very interesting because it implies that information about global geometry (the direction of the DV axis) is somehow maintained at the cellular level throughout the convergent extension.

The principle weakness in this manuscript is the vagueness of the proposed static source mechanism and the lack of direct evidence for it in experiments. The FRAP experiments performed here suggest that binding/unbinding happens on the right timescale to play a role in anisotropy maintenance, but if the principle question is 'how does myosin remain oriented along the DV axis' then the static source hypothesis just kicks the can down the road to ask 'how does the static source remain oriented along the DV axis'? The minimal model the authors employ has the benefit that it lets them relate angular deviation to vorticity, at the cost that it is agnostic to the form and nature of the source term, so it cannot be used to extract useful constraints. This said the evidence provided regarding the connection between vorticity and binding rates to myosin deflection is sufficient indirect evidence of the hypothesized mechanism that I suspect it will be of interest to a good number of people interested in epithelial morphogenesis.

Reviewer #2 (Public Review):

This manuscript by Winter et al represents an analysis of the function of the ATAD1 gene in cancer. At present, the manuscript makes a number of interesting observations, with strong experimental support. First, the authors show that tumors with PTEN deletions frequently have additional mutations in ATAD1, and that prostate tumors with both mutations are associated with a shorter period of survival. Second, tumors lacking ATAD1 are more sensitive to proteotoxic stress, based in part on an increased tendency to apoptosis. Third, the ATAD1 protein interacts with BIM, and interactions with BIM contribute in part to an increased tendency to apoptosis. Fourth, ATAD1 and MARCH5 have at least moderate synthetic sick/lethal interactions; together with other data, this suggests they control the release of BIM from the OMM, contributing to its degradation. Overall, the data suggest that tumors with ATAD1 deletions may be particularly vulnerable to drugs that induce proteotoxic stress, suggesting new potential therapeutic regimens, which would be a valuable contribution to the field. The level of data presented here is already substantial; however, some additional experiments to support the authors' contentions would strengthen the work. Some claims about the mechanism are overstated given the current body of data and should be qualified.

Reviewer #2 (Public Review):

In this work, the authors were trying to prove the model that the fungal pathogen Fusarium oxysporum f. sp. lycopersici (Fol) utilizes the acetyltransferase FolArd1 to induce the acetylation of the K167 residue of the effector protein FolSvp1. This acetylation prevents the K152, K258 and K284 ubiquitination-mediated degradation of FolSvp1 in Fol, and meanwhile inhibits the K167 ubiquitination-mediated degradation of FolSvp1 in tomato plants. In the host plants, FolSvp1 interacts specifically with the apoplastic defense protein SlPR1 and translocates it to the nucleus, which suppresses the SlPR1-derived CAPE1 peptide-induced fungal resistance. Overall, the experiments were well designed and the large amount of data justified most of their conclusions. The work sheds novel insight into the virulence mechanisms of fungal effectors by showing that acetylation modification can stabilize a fungal effector, which is able to mis-localize a key defense protein to dampen the host immunity.

There are two issues that need to be addressed.

1. As far as I know, the apoplastic PR1 proteins may have a fungicide activity. When the authors tested the interaction between FolSvp1 and SlPR1 in Nicotiana benthamiana by BiFC, both apoplastic and nuclear interactions could be detected. Therefore, the authors should discuss the possibilities whether the binding of FolSvp1 to SlPR1 remained in the apoplast can inhibit (i) its anti-Fol activity and (ii) the cleavage of SlPR1 to produce the CAPE1 peptide. In other words, although translocating SlPR1 to the nucleus by FolSvp1 is effective for suppressing CAPE1 production, this may not be the only way.

2. The FolSvp1 produced in N. benthamiana was using the SlPR1 signal peptide and lacked the acetylation modification. It is possible that the acetylation of FolSvp1 can affect the interaction affinity or localization between FolSvp1 and SlPR1. The K167Q mutation of FolSvp1 might not be able to faithfully mimic the K167 acetylation.

Reviewer #2 (Public Review):

This study seeks to determine how neuronal glycolysis is coupled to electrical activity. Previous studies had found that glycolytic enzymes cluster within nerve terminals (in C. elegans) during activity. Furthermore, the glucose transporter GLUT4 is recruited to synaptic surface during activity. The authors previously showed that Ca2+ does not stimulate glycolysis in active neurons. Here, the authors show that the cytosolic Na+, not Ca2+, and the activity of the Na/K pump drive glycolysis. However, it is important to note that in this study, glycolysis was examined in the soma, not nerve terminals, where some of the previous studies were conducted. A few other caveats in the interpretation of the findings are listed below:

1. The NADH/NAD ratio is used throughout as the only measurement reflecting glycolytic flux.<br /> 2. It has been hypothesized that the close association of glycolytic enzymes with ion transporters (such as the Na+/K+ pump) is meant to provide localized ATP to power these pumps. How does bulk glycolysis (monitored with NADH/NAD ratio) relate to localized/compartmentalized glycolysis?<br /> 3. Related to point 2, most of the peredox measurements in the paper have been made at baseline, in the absence of electrical activity. Therefore, it is not clear how the findings relate to activity-driven glycolysis.<br /> 4. The finding that inhibition of SERCA during stimulation actually elevates cytosolic NADH level argues against Na+ being the only ion that regulates glycolysis.<br /> 5. The finding that "SBFI ΔF/F transients were longer in duration than the RCaMP LT transient" does not necessarily mean that Na+ elevation lasts longer than Ca2+ in the cell. This could be an artefact of the SBFI on/off rate relative to RCaMP. In fact, prolonged elevation of cytosolic Na+ would make neurons refractive to depolarization in AP trains.

Reviewer #2 (Public Review):

The authors applied an innovative and very interesting approach based on different -omics platforms to study the biological post-mortem transformations of human bone. Despite the study being a proof-of-concept, because of the small number of collected samples and the lack of external validation, the methodology is promising. The study will have a strong impact on the field and present the state-of-art of -omics sciences.

Reviewer #2 (Public Review):

The goal of this study was to understand the molecular mechanism of how transcription factor DUX4, which has a role in cancer, inhibits the induction of genes stimulated by interferon-gamma. The authors achieved this goal, and their results mostly support their conclusions. They found that DUX4, in their experimental model, interacts with STAT1, thereby decreasing STAT1 and Pol-II recruitment to sites of gene transcription.

The present study has many strengths: The topic is of broad interest, the findings are novel and intriguing, the experiments are well-designed and controlled, the data, with one exception, is carefully interpreted, and the manuscript is very well-written.

Two major weaknesses were identified. One is that all experiments, except Figure 6, rely on one experimental setup, which is a human skeletal muscle cell line with an integrated doxycycline-inducible transgene. The concern is that both the treatment of cells with the drug doxycycline and the fact that signaling pathways could be disrupted in this (immortalized?) cell line could lead to artifacts that skew results. Indeed, results in Figure 4C indicate that total STAT1 is completely localized in the nucleus even prior to interferon stimulation when it should be in the cytoplasm. The other weakness is the use of the DUX4-C-terminal-domain (DUX4-CTD) mutant for the majority of the mechanistic experiments. The concern here is that although the phenotype of ISG repression is observed in this truncated mutant, important regulatory domains could be missing that modulate the interaction with STAT1 or other proteins. Is the NLS added after the flag tag identical to the endogenous NLS? Related, I disagree with the interpretation of Figure 4C that "this interaction happens within the nuclei of DUX4-CTC expressing cells". The interaction could happen prior to STAT1 shuttling to the nucleus.

Reviewer #2 (Public Review):

To test how oxytocin impacts the brain and the psychological, neural, and hormonal response to touch, the authors tested human females during two counterbalanced fMRI sessions wherein females were stroked on the arm or the palm, by a real-world romantic partner or a stranger, while blood levels of oxytocin and cortisol were collected at multiple time points.

This combination of measures, and the number of hypotheses that could be tested with them, is remarkable - virtually unheard of. This impressive, difficult, and more ecological design than is typical for the field is a major strength of the study, which allowed the authors to test many important hypotheses concurrently and to show contextual effects that could not otherwise be observed. The only potential drawback perhaps is that with such a large design, including many measures, the authors produced so many significant interactions and results that it could be hard for the casual reader to appreciate the importance of each.

The authors supported their hypothesis that oxytocin effects are context-sensitive, as they found a key interaction wherein experiencing the partner first increased oxytocin for the partner relative to when they came first the OT levels were low but then increased if they were preceded by the partner (excepting one timepoint). Cortisol responses (which reflect hormonal stress) were also higher when the stranger came first than when he was preceded by the partner). In addition, touch was experienced more positively on the arm than on the palm, supporting the role of c-fibers in conveying specifically felt responses to warm, tender touch.

These data indicate significant context sensitivity with real-world implications. For example, experiencing warm touch on the arm can make us more receptive to other people in subsequent encounters. Conversely, when strangers try to approach and get close to us "out of the blue" people experience this as stressful, which reduces the pleasantness of the interaction and may reduce trust in the moment...perhaps even subsequently.

This research is critical to the basic science of neurohormonal modulation, given that most of this research occurs in rodents or in simplified studies in humans, usually through intranasal oxytocin administration with unclear impacts on circulating levels in the brain and blood. Oxytocin in particular has suffered from oversimplification as the "love drug" - wherein people assume that it always renders people more loving and trusting. The reality is more complex, as they showed, and these demonstrations are needed to clarify for the field and the public that neurohormones adaptively shift with the context, location, and identity of the social partner in an adaptive way. These results also help us understand the many null effects of oxytocin on trusting strangers in human neuroeconomic studies. In a modern world that is characterized by significant loneliness, interactions with strangers and outsiders, and touch-free digital interactions, our ability to understand the human need for genuine social contact and how it impacts our response to outsiders (welcomed in versus a source of stress) is critical to human health and the wellbeing of individuals and society.

Reviewer #2 (Public Review):

In their manuscript, Wiest and colleagues focus on testing two primary hypotheses. The first is that the aperiodic exponent from the intracranial EEG / LFP reflects to population EI balance, and the second is that Parkinson's disease is specifically associated with reduced inhibition-concomitant excessive excitation-in the STN.

To accomplish this, they make use of data from 24 patients with Parkinson's disease who have undergone surgery to implant a deep brain stimulator as part of the treatment of their disease. These patients provide a rare opportunity to record high signal-to-noise EEG/LFP data directly from the human brain. These data are complemented by an additional dataset collected from eight 6-OHDA-lesioned rats, which provide a model of Parkinson's disease. The rat data includes both single-unit spiking activity, which allows Wiest and colleagues to examine periods of relatively high- or low-firing as a proxy for excitatory tone, as well as LFP data which allows them to bridge to the human data and more directly test their first hypothesis that the aperiodic exponent reflects EI balance.

Overall this is a very strong paper. The cross-species approach is especially convincing, and the methods are well-implemented and sound. The authors use appropriate analysis tools and statistical methods, and their inferences are clear, but measured. Their results are convincing, and the potential for aperiodic activity to serve as a potential physiologically interpretable index of Parkinsonian state.

Reviewer #2 (Public Review):

This work presents valuable evidence of the connection between Huntingtin's (HTT) phosphorylation state and the recruitment of Kif1A in the axonal anterograde trafficking of synaptic vesicles precursors (SVPs). In brief, the authors describe how phosphorylation of HTT in Serine 421 determines the recruitment of the anterograde molecular motor Kif1A to SVPs, increasing their rate of transport along the axons to the synapse. This conclusion is substantiated by the measured impact of HTT phosphorylation on motor skills learning ability.

The study presents a variety of investigative angles, combining both ex vitro and in vivo approaches. The use of custom microfluidics chamber to recreate neuronal circuits is a point of strength as it allows for in depth analysis of the transport phenotype. This tool could be a very useful tool for the community to explore for a variety of similar studies. The use of mouse models also adds credibility to the physiological importance of the findings.<br /> The evidence presented supports the claims, though more emphasis could be added to the explanation and mechanisms behind how an increased transport dynamic of SVPs due to HTT phosphorylation, results in a detrimental effect on motor skill learning. This finding is perhaps the most critical as it reiterates the importance of balance in SVPs transport and highlights how the system is finely regulated and sensitive to both down and upregulation. This fine tuning might ensure the presence of the proper quantity of SVs at synapses to guarantee an effective synaptic function.

This works adds an important angle to role of HTT phosphorylation, which could open new avenues of treatment for HTT disease based on the manipulation of HTT phosphorylation state.

Reviewer #2 (Public Review):

Ines Lago-Baldaia et al. investigate the connection between transcriptional and morphological diversity of glial cells. This is an important question to answer in the glial biology field and has been amplified by recent advances in single-cell sequencing. It remains unclear if transcriptional diversity that is often reported in scRNA analysis equates to morphologically distinct glia. To explore the correlation between transcriptome and morphology, the authors utilize the strength of the Drosophila model system to demonstrate that although morphotypes of glia can be identified in the nervous system, the morphotypes do not correlate with a distinct transcriptional profile. Overall, the paper is well written and the conclusion matches the results that are presented. This work will be an important contribution to the glial biology field.

Reviewer #2 (Public Review):

What the authors were trying to explore is very interesting with translational potential toward glaucoma treatment. They used a topical dexamethasone (dex) induced mouse model showing ocular hypertension and a culture model using human TM cells treated with tBHP to induce TM oxidative stress. Their results suggested that metformin protected TM cells from cytoskeletal destruction by enhancing the integrin/ROCK pathway and alleviated elevated IOP in the mouse model. However, the provided simulative results were vague and the research needs extra experimental data to support its conclusion.

Reviewer #2 (Public Review):

The work of Iyer et al. uses a computational approach to investigate how cells using multiple tiers of processing and multiple parallel receptor types allow more accurate reading of position from a noisy signal. Authors find that combining signaling and non-signaling types of receptors together with additional feedback increases the accuracy of positional readout against extrinsic noise that is conveyed in the morphogen signal. Further, extending the number of layers of signal processing counteracts the intrinsic stochasticity of the signal reading and processing steps. The mathematical formulation of the model is general but comprehensive in the way it handles the difference between branches and tiers for the processing of channels with feedbacks. The results of the model are presented from simple one-branch and one-tier architecture to two-branch and two-tier architecture with feedbacks. Interestingly authors find that adding more tiers results in only very small improvements in the accuracy of positional readout. The model is tested against a perturbation experiment that impairs one of the signaling branches in the Drosophila wing disc, but the comparison is only qualitative as further experiment-oriented work is planned in a separate paper.

Strengths

There is a clear statement of objectives, model, and how the model is evaluated. In particular, the objective is to find what number of receptor types and their concentrations for a given number of tiers and feedback types is resulting in the most accurate positional readout. The employed optimization procedure is capable to find signalling architectures that result in one cell diameter positional precision for most of the tissue with 3-4 cells at the tissue end that is most distant to the morphogen source. This demonstrates that employing additional complexity in signal processing results in a very accurate positional readout, which is comparable with estimates of positional precision obtained in other developmental systems (Petkova et al., Cell 2019, Zagorski et al., Science 2017).

The optimal signalling architectures indicate that both signalling (specific) and non-signalling (non-specific) receptors affect the precision of positional readout, but the contributions of each type of these receptors are qualitatively different. Even slight perturbation of signalling receptors drives the system out of optimum, resulting in a decrease in positional precision. In contrast, the non-signalling receptors could accommodate much larger perturbations. This observation could provide a biophysical explanation for how cross-talk between different morphogen species could be realized in a way that positional precision is kept at the optimum when morphogen signaling undergoes extrinsic and intrinsic perturbations.

Last, the model formulation allows to specifically address perturbations of signalling and feedbacks, that could be explored to validate model predictions experimentally in Drosophila wing disc, but also in other developmental tissues. The authors present a proof-of-concept by obtaining consistent results of variation of output profiles in two-tier two-branch architectures with non-signaling branch removed and intensity profiles of Wg in wing disc where the CLIC/GEEC endocytic pathway was perturbed.

Weaknesses

The list of model parameters is long including more than 20 entries for two-tier two-branch architectures. This is expected, as the aim of the model is to describe the sophisticated signalling architecture mimicking the biological system. However, this also makes it very challenging or impossible to provide guiding principles or understanding of the system behaviour for the complete space of signalling architectures that optimize positional readout. Although, the employed optimization procedure finds solutions that exhibit very high positional accuracy, there is only very limited notion how these solutions depend on variation of different parameters. The authors do not address the following question, whether these solutions correspond to broad global optima in the space of all solutions, or were rather fine-tuned by the optimization procedure and are quite rare.

It is unclear how contributions from the intrinsic noise affect the system behaviour compared to contributions from extrinsic noise. In principle, the two-branch one-tier architecture results in an already very accurate positional readout across the tissue. The adding of another tier seems to provide only a very weak improvement over a one-tier solution. It is possible that contributions from intrinsic noise for the investigated signalling architectures are only mildly affecting the system compared with contributions from extrinsic noise. Hence, it is difficult to assess whether the claim of reducing intrinsic noise by adding another tier is supported by the presented data, as the contributions from intrinsic noise could overall very weakly affect the positional readout.

Reviewer #2 (Public Review):

Hwang et al take an unconventional approach to address a longstanding problem in the field of Wnt signaling and cancer: the mechanism of beta-catenin nuclear import. The authors introduce expression of Xenopus beta-catenin in budding yeast, a heterologous model system that does not harbor any known Wnt signaling components but carries highly conserved nuclear transport machinery. They find that GFP-tagged beta-catenin is actively transported to the yeast nucleus in a Ran-GEF-dependent process, indicating NTR-dependent transport. An elegant rapamycin treatment-dependent Anchor-Away method is applied to systematically inhibit 10 budding yeast NTRs, for which orthologues exist in human cells. Significant and specific inhibition of beta-catenin nuclear import is identified when Kap104 (orthologue of Kapbeta2/Transportin-1 (TNPO1) was anchored to the plasma membrane. Furthermore, nuclear import depends on a PY-like NLS sequence in the beta-catenin C-terminus, which was shown to mediate a direct interaction with TNPO1. A role of the vertebrate paralogs tnpo1/2 and the PY-like NLS was confirmed in Xenopus, using double axis formation assays, and in mouse and human cell lines, combining tnpo1/2 depletion with nuclear localization and reporters for beta-catenin dependent transcription. Finally, the authors provide proof that responses of MEF cells to Wnt3a or human beta-catenin overexpression can be inhibited by treatment with a TNPO1/2 blocking peptide (M9M).

Overall, the results of this study provide a valuable addition to the longstanding and ongoing discussions on the mechanisms of beta-catenin nuclear import. The conclusions are based on a well-focused and solid set of experiments and are confirmed across species in a diverse set of model systems, and findings are discussed against the state of the field. Although the identified TNPO1/2-dependent beta-catenin nuclear import pathway was shown to be a target for peptide-based inhibitory strategies, these findings remain to be confirmed in relevant (colorectal) cancer model systems in which levels of beta-catenin are inappropriately enhanced and inhibition of its nuclear entry is most warranted.

Reviewer #2 (Public Review):

This manuscript puts forward a new idea that topography in neural networks helps to remove noise from inputs. The neural network consists of multiple stages. At each stage, the network is structured to be balanced in terms of the strength of inhibitory and excitatory signals. Because of topography, the networks become "dis-balanced" and receive more recurrent excitatory signals locally for those regions that receive strong initial inputs. This leads to error correction. The main weakness in the manuscript is that the approach will only work for inputs that are constant-in-time. It is important to acknowledge this limitation in both the title and throughout the manuscript.

Reviewer #2 (Public Review):

Overall, I think that the screen or mutants in the Arabidopsis flowering pathway and its outcome are biologically interesting and important. The authors show that FIO1 methylates U6 snRNA and not (or rarely) mRNA. However, subsequent to this, the results are entirely from bioinformatics of RNAseq data from the derived mutants; there are no further experiments performed, either to confirm or test newly-derived hypotheses. Furthermore, the main hypothesis, that 5'SS pos.+4 identity is critical for sensitivity to U6 N6-methylation, was already described in yeast S. pombe, based on data from mutants in the pombe ortholog Mtl16. Minimally, the conclusions based on bioinformatics should be confirmed with experimental data. In addition, there are examples throughout the manuscript where the authors state results or conclusions without providing any data; this is not acceptable and data supporting these assertions must be included.

Reviewer #2 (Public Review):

Pulmonary neuroendocrine cells (PNECs) are known to monitor oxygen levels in the airway and can serve as stem cells that repair the lung epithelium after injury. Due to their rarity, however, their functions are still poorly understood. To identify potential sensory functions of PNECs, the authors have used single-cell RNA-sequencing (scRNA-seq) to profile hundreds of mouse and human PNECs. They report that PNECs express over 40 distinct peptidergic genes, and over 150 distinct combinations of these genes can be detected. Receptors for these neuropeptides and peptide hormones are expressed in a wide range of lung cell types, suggesting that PNECs may have mechanical, thermal, acid, and oxygen sensory roles, among others. However, since some of these cognate receptors are not expressed in the lung, PNECs may also have systemic endocrine functions. Although these data are largely descriptive, the results represent a significant resource for understanding the potential roles of PNECs in normal biology as well as in pulmonary diseases and cancer and are likely to be relevant for understanding neuroendocrine cells in other tissue contexts.

However, there are several aspects of the data analysis that are unclear and require clarification, most notably the definition of a neuroendocrine cell (points #1 and #2 below).

1. Figure S1 shows the sorting strategy used for isolation of putative PNECs from Ascl1CreER/+; Rosa26ZsGreen/+ mice, and distinguishes neuroendocrine cells defined as ZsGreen+ EpCAM+ and "neural" cells defined as ZsGreen+ EpCAM-; the figure legend also refers to the ZsGreen+ EpCAM- cells as "control" cells. However, the table shown in panel D indicates that the NE population combines 112 ZsGreen+ EpCAM+ cells together with 64 ZsGreen+ EpCAM- cells to generate the 176 cells used for subsequent analyses. Why are these ZsGreen+ EpCAM- cells initially labeled as neural or control, but are then defined as neuroendocrine? If these do not express an epithelial marker, can they be rigorously considered as neuroendocrine?

2. Similarly, in the human scRNA-seq analysis, how were PNECs defined? The methods description states that these cells were identified by their expression of CALCA and ASCL1, but does not indicate whether they also expressed epithelial markers.

3. The presentation of sensitivity and specificity in Figure 1 is confusing and potentially misleading. According to Figure 1B, Psck1 and Nov are two of the top-ranked differentially expressed genes in PNECs with respect to both sensitivity and specificity. However, the specificity of these two genes appears to be lower than that of Scg5, Chgb, and several other genes, as suggested in Figure 1C and Figure S1E. In contrast, Chgb appears to have higher specificity and sensitivity than Psck1 in Figures 1C and E but is not shown in the list of markers in Figure 1B.

4. The expression of serotonin biosynthetic genes in mouse versus human PNECs deserves some comment. The authors fail to detect the expression of Tph1 and Tph2 in any of the mouse PNECs analyzed, but TPH1 is expressed in 76% of the human PNECs (Table S8). Is it possible that Tph1 and Tph2 are not detected in the mouse scRNA-seq data due to gene drop-out? If serotonin signaling by mouse PNECs is due to protein reuptake, as implied on p. 5, is there a discrepancy between serotonin expression as detected by smFISH versus immunostaining?

5. The smFISH and immunostaining analyses are often presented without any indication of the number of independent replicate samples analyzed (e.g., Figure 2B, Figure 3F, G).

6. It would be helpful to provide a statistical analysis of the similarities and differences shown in the graphs in Figures 1E and G.

Reviewer #2 (Public Review):

The authors introduce a model based on textual data for predicting odor properties of a mixture of chemicals. Modelling approach is relevant to olfactory scientists and experimental neuro-scientists.

Work is relevant because it unifies and studies multiple mixture odor datasets, achieving satisfactory results. Work is novel because modelling for mixture datasets is scarce, this work introduces a grounded approach for modeling such data. Model is directly interpretable since it relies on a linear model (lasso) to build mapping between features (metric learning).

The authors's evidence supports most of the conclusions of the work with some room for improvement.

This work can be of the many in the future trying to further modelling approaches for mixture data.

Reviewer #2 (Public Review):

The study by Sommer et al. applies alphafold to the CHESS selection of transcripts with the goal of generating predicted 3D protein structures and a quality measure of folding, the pLDDT score. From these data, the authors build up a database for result exploration. In addition, they provide examples to underline this approach. Examples include proteins, where the authors propose the pLDDT score as a measure of presumed superior biological functionality over other isoforms. The authors also use the generated data to propose novel functionally relevant isoforms, e.g. in the mouse.

The study is based on the elegant idea to aid genome annotation through 3D structure prediction. This is a very powerful approach that allows large-scale data generation for functional interpretation. This approach appears technically sound and well executed (although I may miss details not being a protein expert). However, in my opinion, the authors could make more use of the potential of their approach. From the big-data start, they seem to directly restrict themselves to interesting examples. I am missing a global analysis that shows the bigger picture of their results. Given that they have generated structures from 90,415 isoforms, each associated with a pLDDT score, conservation scores, length, expression levels and other quantifiable data listed on page 18. I would wish for a comprehensive analysis of these data and their potential before applying the focus on a few (admittedly very nice) examples.

Furthermore, one of the weak spots of such an analysis is the relationship between foldability and functional relevance. Disordered regions would imply reduced relevance due to poor pLDDT scores, which may be a misleading conclusion. While this may be a problem difficult to solve with their approach, I think this still needs to be addressed and discussed throughout the paper and particularly as part of the global analysis, not just in the context of examples.

As a minor point, I would like to motivate the authors to be more explicit with some quantifications. For example, when focusing on proteins < 500 aa long, what does this mean in relation to what they are not representing in their analysis? How many isoforms will they miss? Is there going to be a bias (e.g. against scaffolding proteins, kinases like ATM, etc.)?

Overall, I consider the idea of the paper very elegant and well executed, yet focusing too much on trees, while I, as a reader, would like to know more about the forest.

Reviewer #2 (Public Review):

In this work, Li, Dorajoo, and colleagues use national Singaporean data to demonstrate the associations of previously published polygenic risk scores (PRS) for 4 cancers (breast, prostate, colorectal, and lung) with incident cases over 20 years of follow-up. Using available PRS for the four cancers from the Polygenic Score Catalog, they used recommended metrics to evaluate the distribution, discrimination, risk association, and calibration of the PRS. Although the PRS were derived from predominantly European populations, the authors confirmed all PRS-disease associations in this ethnic Chinese population, with per-standard deviation effect sizes ranging from hazard ratio 1.17 for lung cancer to 1.73 for prostate cancer.

The strengths of this work include the use of an apparently unbiased national population with 20 years of follow-up and near-complete outcomes ascertainment. The authors use state-of-the-art methods for genotyping, imputation, and PRS construction, and they use recently published PRS reporting standards to evaluate the PRS and organize the presentation of their work. Although the authors used an unbiased approach to their initial selection of PRS to evaluate (all 1,706 entries with <10,000 predictors in the PGS Catalog at the time), a significant weakness is the lack of detail in how the final 110 cancer PRS were selected for evaluation. Notable absences from these 110 are the PRS from the largest prostate cancer GWAS to date (PGS000662) and a Chinese-specific lung cancer GWAS (PGS000070). The latter absence is particularly notable as the authors report poorest performance of the lung cancer PRS they did evaluate.

Nonetheless, this work confirms prior observations of imperfect portability of PRS derived in one population to another, particularly of different genetic ancestry. The practical consequences of this performance differential will depend on the proposed use of the PRS. One important distinction the authors rightly point out is whether a PRS is intended for individual- or population-level application. The authors do not quantify the potential consequences of applying these PRS to the Singaporean population in different use cases (e.g., screening programs based on PRS), but interested readers will be able to use these findings to make such projections on their own.

Reviewer #2 (Public Review):

In this work, the authors attempt to resolve an apparent paradox in human locomotor development. Previous works have reported that neonates exhibit highly variable movement, which is believed to be important for driving exploration-based motor skill learning. Yet, other recent studies have also demonstrated that locomotor behaviors of newborn babies are generated by a very small number of invariant motor primitives that may underpin stereotypical innate motor behaviors. Indeed, as infants acquire the ability to walk independently, the number of motor primitives tends to increase while the overall motor variability decreases. Hinnekens et al. propose that this apparent paradox can be explained by following the variability of the activations of the motor primitives (or motor modules) as the locomotor behaviors of infants mature. The authors collected bilateral EMGs from infants longitudinally at 3 time points (from ~4 days old to walking onset) and used a well-known machine learning algorithm (non-negative matrix factorization) to extract both spatial and temporal motor modules, along with their activations, from the EMGs. They found that at birth, the cycle-to-cycle activations of the small number of modules were highly variable. But as the infants developed into toddlers, while the number of motor modules increased, their activations across cycles also became less variable. The authors conclude that early motor exploration is driven by the variable activation of a small number of motor modules, which would later fractionate into more modules that are more stably recruited across step cycles.

STRENGTHS:

Overall, this work is a valuable addition to the growing literature on the development of motor modules. It not only emphasizes how motor variability is a hallmark of typical motor development, but also suggests the relatively new concept that development-related motor variability originates from the variable activations of early motor modules. Indeed, recent works have proposed that in human adults, the motor variability that drives early motor skill learning may likewise originate from the variable recruitment of motor modules. With this work, it may become possible to conceptually unite the provenance of motor variability that drives both early development and adult learning under the modularity framework. The authors are also commended for their huge effort in collecting this very valuable data from newborn infants and following them with multiple recording sessions till their walking onset. The demonstration of the same longitudinal trend in variability and modules in two different motor behaviors (stepping and kicking) is also highly appreciated.

WEAKNESSES:

The analysis of EMGs relies on a model of motor modules that assumes that multi-muscle activities across step cycles are generated by the variable activations of fixed spatial modules and fixed temporal modules (line 511); thus, by design, after the identification of the spatial (w_j in equation 511) and temporal (w_i(t) in equation 511) modules, the only variable that is adjustable for explaining motor variability is the modules' activation coefficient (a_ijs in equation 511). But it is possible that the observed EMG or kinematic variability may be equally, if not better, accounted for by the cycle-to-cycle variation of the spatial and/or the temporal modules themselves. In fact, the variances of any combination of w_j, w_i(t), and a_ijs may all contribute to EMG variability, even though with the present model, the variance of w_j and w_i(t) are not considered. Therefore, the conclusion that motor variability is generated by variable activations of fixed modules can only be argued based on how well a single model (i.e., line 511) describes the data, rather than by excluding other alternatives (but equally legitimate a priori) models with perhaps less explanatory power. Notably, recent works (e.g., Cheung et al., 2020, IEEE-OJEMB; Berger, d'Avella et al., 2022, JNP) have shown or implied that the variability of the spatial/temporal modules themselves, in addition to their activation coefficients, may be a source of learning-related motor variability.

Reviewer #2 (Public Review):

Anti-VEGF treatment is currently used to treat patients with pathological retinal angiogenesis, but finding the underlying cause of increased VEGF is a challenge for the field. Wang and colleagues determined the role played by the amino acid transporter, SLC38A5, in retinal angiogenesis. They showed that Slc38a5 mRNA was enriched in retinal blood vessels versus neural retina, supporting previous single cell data that they reanalyzed here. In mouse models of human Retinopathy of Prematurity (ROP; Lrp5-/- and Ndp-/-) with decreased blood vessels, they showed a decrease in SLC38A5 protein. As both LRP5 and NDP encode proteins that work through the Wnt signaling pathway, the authors showed that both Slc38a5 mRNA and protein levels are controlled by Wnt agonists and antagonists in human endothelial cell cultures. They further showed that Slc38a5 transcription is affected by Wnt signaling by performing luciferase assays on putative Wnt binding regions that they identified 5' of the Slc38a5 gene. To further characterize the role of SLC38A5 in vivo, they injected a validated si-RNA into mouse eyes and found that formation of retinal vasculature layers was significantly impaired, which they also showed in Slc38a5 knockout mice. Using another mouse model of Retinopathy of Prematurity (oxygen-induced retinopathy), they find that Slc38a5 is required during pathological angiogenesis, and using in vitro cell culture studies show that it is required for endothelial cell viability, migration and tubular formation via its role in transporting glutamine. In part, they find that this may be through the regulation of angiogenesis-promoting receptor, VEGFR2. The authors performed an impressive series of experiments both in vitro and in vivo in studying the role of SLC38A5 in retinal angiogenesis. Their final model also does a nice job of summarizing their manuscript.

While the overall conclusions are supported by the data, some aspects of image acquisition and data analysis need to be clarified and extended.

Reviewer #2 (Public Review):

The present manuscript revisits the perennial (and important) question of which role the right IFG (rIFG) plays exactly in response inhibition. It does so using a stop-signal task in a patient group with lesions focused on rIFG, as well as a matched healthy control group, along with a group of control patients with lesions outside of the rIFG, and again a matched healthy control group. The behavioral data are analyzed with a novel parametric modeling approach that allows characterizing the distributions of Go RTs as well as the stop-signal reaction time (SSRT). Crucially, in the present form, it also accounts for so-called trigger failures, a long-known (but nearly equally long mostly ignored) phenomenon describing the failure to even initiate an inhibitory process (rather than the latency of this process being too long to succeed). Not accounting for trigger failures is known to inflate SSRT, and conceptually, they have been linked more to attentional processes than specifically to response inhibition. Here it is shown that behavioral deficits in rIFG patients are more strongly related to trigger failures than to the SSRT. This is elegantly complemented by the EEG data, where it is shown that mid-frontal beta bursts are strongly reduced in the rIFG group, but not in the others. Finally, it is shown that these mid-frontal beta bursts lead to corresponding beta bursts over the motor cortex. Importantly, this is also still the case for the rIFG patient group on successful stop trials where such mid-frontal beta bursts happened.

The present work has many strong elements. The use of a targeted patient group, with additional control groups, gets this research closer to causality than e.g. a pure EEG study could. The employed methods (computational modeling, beta bursts) are all cutting-edge and very appropriate, and the results form a coherent story, which is interpreted appropriately. The manuscript is also clear, yet very succinct, which at times might come at some cost towards following the details of the analysis and results, in particular, and some additional analyses might further strengthen the authors' claims. For example, there seems to be no reference to a traditional, non-parametric SSRT estimate, the size of the reduction of which by accounting for trigger failures might be a better metric of how central accounting for trigger failures is, rather than the five-fold TF increase in this group over the others (all of which have very low percentages, which put also a manifold increase into perspective). Maybe also more generally, the conceptual distinction between initiation and actual implementation of inhibition could be further sharpened, including with reference to the residual SSRT group effect from the parametric analysis, which is still quite sizable.

Given its innovative approach and important findings, the present results will undoubtedly have a major impact on the field of response inhibition, which is also relevant to the clinical domain.

Reviewer #2 (Public Review):

This report highlights the unexpected off-target presence of Cre in the mouse epididymis under conditions where specific Cre activity was only expected in the brain or adipose tissue. The use of a modified CLARITY protocol to provide visual demonstration of Cre in the caput epididymis was complemented and strengthened by supplementary data from fluorescent microscopy. However, the apparent '2-phase' expression between the distal and proximal portions of the caput was not further elaborated upon.

Through a series of technically challenging studies involving parabiosis and serum/exosome transfer experiments, there was some evidence that off-target expression involved the circulatory system. However, the lack of consistent outcomes suggests that this is not a robust effector process, so the precise reasons for the off-target expression remain unknown.

This study raises more questions than uncovered answers, and the conclusions are somewhat speculative (and correctly so). We are not closer to understanding why there is off-target Cre expression, nor why it is limited to the epididymis. It is not apparent how, and if, this unexpected observation holds any implications on past research reliant on Cre-recombination if those studies do not focus on the male reproductive tract, or the animal's health/behaviour is not affected. However, there is initial evidence (albeit less robust than desired) to support the authors' claim of distal organ-to-organ signalling, consistent with previous reports. Overall, this study currently speaks more so to the technology, rather than systems biology.

Reviewer #2 (Public Review):

Despite the long history of the study of topo II, the role of its long CTD in vitro and in vivo has remained poorly understood. The current manuscript provides solid lines of evidence that the intrinsically disordered CTD modulates topo II's enzymatic activities through LLPS. The experiments reported here were properly performed, and the conclusions are largely supported by the data presented, thereby making them an excellent contribution to the field. The current manuscript contains some weaknesses, though. The phosphatase treatment experiments are weak (Figure 4), and the role of phosphorylation on topo II-mediated LLPS remains unclear. The experiments using human topo IIs are also weak (Figure 6): the potential differences between topo IIa and topo IIb have not been rigorously tested or properly discussed. Most importantly, the difference in the catalytic mode between the full-length and CTD-lacking topo II needs to be tested and described more convincingly along with quantitative data (Figure 5).

Reviewer #2 (Public Review):

Congratulations on producing a very nice study. Your study aims to examine the morphological diversity of different mammalian limb elements, with the ultimate goal seemingly to test expectations based on the different timing of development of the limb bones. There's a lot to like: the sample size is impressive, the methods seem appropriate and sound, the results are interesting, the figures are clear, and the paper is very well written. You find greater diversity and integration in distal limb segments compared to proximal elements, and this may be due to the developmental timing and/or functional specialization of the limb segments. These are interesting results and conclusions that will be of interest to a broad readership. And the large dataset will likely be valuable to future researchers who are interested in mammalian limb morphology and evolution. I have one major concern with how you frame your discussion and conclusions, which I explain below. But I think you can address this issue with some text edits.

Major concern - is developmental timing the best hypothesis?

You discuss two potential drivers for the relatively greater diversity in distal elements: 1) later development and 2) greater functional specialization. Your data doesn't allow you to fully test these two hypotheses (e.g. you don't have detailed evo-devo data to infer developmental constraints), and I think you realize this - you use phrases like "consistent with the hypothesis that ...". You seem to compromise and conclude that both factors (development + function) are likely driving greater autopod diversity (e.g. Lines 302-306). Being unable to fully test these hypotheses weakens the impact of your conclusions, making them a bit more speculative, but otherwise, it isn't a critical issue.

But my concern is that you seem to favor developmental factors over functional factors as the primary drivers of your results, and that seems backwards to me. For instance, early in the Abstract (Line 32) and early in the Discussion (Line 201) you mention that your results are consistent with the developmental timing hypothesis, but it's not until later in the Abstract or Discussion that you mention the role of functional diversity/specialization/selection. The problem with favoring the development hypothesis is that your integration results seem to contradict that hypothesis, at least based on your prediction in the Introduction (Line 126; although you spend some of the Discussion trying to make them compatible). Later in the paper, you acknowledge that functional specialization (rather than developmental factors) might be a better explanation for the integration results (Lines 282-284, 345-347), but, again, this is only after discussions about developmental factors.

When you first start discussing functional diversity, you say, "high integration in the phalanx and metacarpus, possibly favoured the evolution of functionally specialized autopod structures, contributing to the high variation observed in mammalian hand bones." (Line 282). This implies that integration led to functional diversity in the autopod. But I'd flip that: I think the functional specialization of the hand led to greater integration. Integration does not result solely from genetic/developmental factors. It can also result from traits evolving together because they are linked to the same function. From Zelditch & Goswami (2021, Evol. & Dev.): "Within individuals, integration is customarily ascribed to developmental and/or functional interdependencies among traits (Bissell & Diggle, 2010; Cheverud, 1982; Wagner, 1996) and modularity is thus due to their developmental and/or functional independence."

In sum, I think your results capture evidence of greater functional specialization in hands relative to other segments. You're seeing greater 1) disparity and 2) integration in hands, and both of those are expected outcomes of greater functional specialization. In contrast, I think it's harder to fit your results to the developmental timing hypothesis. Thus, I recommend that throughout the paper (Abstract, Intro, Discussion) you flip your discussion of the two hypotheses and start with a discussion on how functional specialization is likely driving your results, and then you can also note that some results are consistent with the development hypothesis. You could maintain most of your current text, but I'd simply rearrange it, and maybe add more discussion on functional diversity to the Intro.

Or, if you disagree and think that there's more support for the development hypothesis, then you need to make a better case for it in the paper. Right now, it feels like you're trying to force a conclusion about development without much evidence to back it up.

Limitations of the dataset

Using linear measurements is fine, but they mainly just capture simple aspects of the elements (lengths and widths). You should acknowledge in your paper the limitations of that type of data. For example, the deltoid tuberosity of the humerus can vary considerably in size and shape among mammals, but you don't measure that structure. The autopod elements don't have a comparable process, meaning that if you were to measure the deltoid tuberosity then you'd likely see a relative increase in humerus disparity (although my guess is that it'd still be well below that of the autopod). And you omit the ulna from your study, and its olecranon process varies considerably among taxa and its length is a very strong correlate of locomotor mode. In other words, your finding of the greatest disparity in the hand might be due in part to your choice of measurements and the omission of measurements of specific processes/elements. I recommend that you add to your paper a brief discussion of the limitations of using linear measurements and how you might expect the results to change if you were to include more detailed measurements and/or more elements.

Reviewer #2 (Public Review):

The authors have recorded the activity of neurons in the rat substancia nigra pars reticulata (SNr) while animals performed a version of a stop-signal task. The goal of this study is to investigate and describe the contribution of SNr in proactive inhibitory control. By examining single-cell responses as well as population activity, the authors show that increasing the probability of stop signal trials induces several changes in SNr responses. First, specific populations of SNr neurons increase their activity during proactive, direction-specific inhibition. At the population level, neurons are biased away from the side of the movement that has to be potentially inhibited. Second, during proactive inhibition, neuron activity is more variable, both at the single-cell and population levels. Finally, the authors show that animals' outcome history influences both firing rates and variability of neuron responses in the current trial. Especially, neural variability is increased following a failure to inhibit a movement.

Strengths<br /> The manuscript provides an interesting and timely insight into the role of the basal ganglia output nucleus in movement initiation control. The paper is often clearly and concisely written (although see one issue related to this below). One of the main strengths of the work is to allow an interesting comparison with recent work by the same team, aimed at investigating the responses of another basal ganglia nucleus (GPe) in the same task, using similar analyses (this comparison is not extensively exploited in the discussion section though). Another potential strength is the use of different analysis scales. The authors investigated single-unit responses as well as population "trajectories" in the neural state space. This is an interesting option that could have been better motivated, given that the two approaches assume quite different brain operations.

Weaknesses<br /> The analyses and results sometimes lack clarity and details. For instance, and unless I missed the information, it is not clearly stated whether "maybe-stop" trial analyses only include Go trials or if (failed) Stop trials are also considered. Moreover, quite complicated figures are often described very briefly in the main text. Methods are also often too succinctly described, and sometimes refer to a previous publication (Gu et al., 2020) that readers did not necessarily read.<br /> There are some points that the authors might need to discuss more. Especially, a global picture of the role of the different basal ganglia nuclei during movement control would have been appreciated. Also, the authors monitored the activity of the rat basal ganglia output. We would have appreciated more information regarding the impact of this output activity on SNr target areas, as compared to their previous work that focused on GPe for instance. Another example concerns the observation that SNr activity is elevated during active inhibition regardless of the firing rate pattern before movement (increase or decrease). As noted by the authors themselves, this is inconsistent with the classical role assigned to the basal ganglia output nucleus (i.e. a decrease in activity promotes movement). Despite that this observation is of potential interest to readers working on the basal ganglia, it is not discussed.

Reviewer #2 (Public Review):

This manuscript by Einarsson and colleagues in the Andersson lab examined how genetic variability across a population impacts both gene expression and promoter architecture in a human population. The authors generate new CAGE data in 108 lymphoblastoid cell lines (LCLs). The authors' analysis is focused on defining how DNA sequence and promoter architecture correlate with population-variation in expression across this cohort. In general, there is a lot that I like about this manuscript: The dataset will be an extremely valuable resource for the genomics community. Furthermore, the biological findings are often thoughtful and potentially interesting and significant for the community. The analysis is generally very strong and is clearly conducted by a lab that has a lot of expertise in this area. My main concerns are centered around the often unwarranted implication that DNA sequence or promoter features cause differences in variation at different genes.

Reviewer #2 (Public Review):

The authors used a cell based system to investigate how expression of disease-associated Seipin glycosylation mutants (ngSeipin) impact on endoplasmic reticulum (ER) homeostasis. In particular, they focus their attention on SERCA, previously shown to interact genetically and biochemically with Seipin. They show that endogenous SERCA interacts with both overexpressed WT and ngSeipin. Using reporters monitoring calcium levels in the cytosol and ER lumen, it is shown that overexpression of ngSeipin (but not WT seipin) results in lower ER calcium levels, increase ER stress and eventually apoptosis. Based on the analysis of several Seipin mutants, the authors conclude that the toxicity of ngSeipin requires oligomerization (via the luminal domain) and the presence of its C-terminal domain. It is proposed that low ER calcium resulting from inhibition of SERCA by ngSeipin is a key event in Seipinopathies.

Despite the large amount of data presented, these not always lay support to the main conclusions of the study. Critical flaws are:

1- All conclusions are based on experiments where Seipin is overexpressed to levels are are unlikely to be physiological, even in the disease context. Importantly, as shown at several points (for example Figure 3), the effects of ngSeipin are drastically different depending of the expression levels.

2- The conclusions about ngSeipin aggregation are unjustified. The PLA assay is not suitable to assess protein aggregation or to distinguish between aggregation and oligomerization.

3- The effects of ngSeipin on UPR activation or calcium levels are modest, in particular considering that the levels to which it is overexpressed in relation to endogenous Seipin (see for example Figure 1Ec or 3Ac).

Reviewer #2 (Public Review):

This study investigated a substantial set of camelid nanobodies for their characteristics when expressed in mammalian cells as intrabodies. Intrabodies have a variety of important research, diagnostic and therapeutic uses, and nanobodies have several inherent characteristics that make them amenable for use as intrabodies. While a substantial number of nanobodies have been developed that are effective as intrabodies, a systematic study of the suitability of a set of otherwise unrelated nanobodies for this purpose has not been performed. As such, the molecular characteristics of what may make an nanobody suitable for use as an intrabody have not been defined. This study addresses this gap in knowledge by FP-tagging a set of 75 nanobodies selected from among those whose structure has been solved. The study uses live cell imaging to evaluate expression of these nanobodies when expressed in mammalian cells. These results are used in bioinformatics analyses to define key amino acids positions in the nanobodies that distinguish those that have high level expression in diffuse cytoplasmic pattern that is consistent with expression in a stable, soluble form. These analyses inform mutagenesis to phenoconvert poorly expressed nanobodies into those with improved expression. The outcome is a set of rules that can be used by investigators to predict the likely characteristics of a nanobody with a given sequence when expressed in cells as an intrabody. The strengths of the study is the elegant and rational manner it is pursued by the iterative application of bioinformatics analyses of nanobody sequences, cell biological assays of expression as intrabodies and mutagenesis. This study has great value to the field as nanobodies gain increased use as intrabodies. The weakness is the lack of a quantitative analysis of expression levels and solubility, with all of the results based on a subjective visual determination of the appearance of the FP-tagged nanobody in expressing cells. Moreover, steady-state appearance is used to infer active processes of aggregation and clearance. Another weakness is that the study presumes that the steady-state expression levels of FP-tagged nanobodies are determined solely by posttranslational stability/solubility, and not by differences in transfection levels, transcription, and translation. Lastly, the study implies that the set studied here is representative of nanobodies in general and the results are transferable across all nanobodies. While the study still has substantial value in spite of these weaknesses, the manuscript would be greatly improved by explicitly stating these limitations of the study.

Reviewer #2 (Public Review):

RPA is a ssDNA binding protein that functions as a hub protein to recruit more than three dozen enzyme onto DNA to coordinate almost all DNA metabolic roles. There are two specific protein interaction domain OB-F and the wh domain. NMR and crystallographic studies have solved the structure of OB-F bound to peptides from various target interactors. Nevertheless, these cognate binding sequences are not conserved. To decipher if there are unique binding modes during such interactions, Wu et. al., use a strategy to tether these target peptides to OB-F using a flexible linker and have solved the structure of complexes with peptides from HelB, ATRIP, RMI1, WRN and BLM. The high-resolution structures presented by Wu et. al. showcase key interactions between RPA70N and peptides of binding partners. These findings add to similar knowledge from several other such structures that have been previously reported. The authors also suggest multivalency where multiple OB-F domains can be bound by a single peptide or a cluster of peptides. This leads to a model where such an interaction can stabilize RPA nucleoprotein filaments and better recruit interacting partners. However, such a model assumes that OB-F is floating around freely accessible to interact with other proteins. This assumption is incorrect as in most of these cases (like in Rad52-RPA interaction) there are other inter and intra protein interactions that need to be accounted for and are ignored here. Ideally, interaction studies with full length proteins provide better mechanistic understanding of interactions between proteins.

Reviewer #2 (Public Review):

The work presented in this manuscript details an analysis of the partitioning of low copy plasmids under the control of the ParABS system in bacteria. Using a high throughput imaging set up they were able to track the dynamics of the partition complex of one to a few plasmids over many cell cycles. The work provides an impressive amount of quantitative data for this chemo-mechanical system. Using this data, the paper sought to clarify whether the dynamics of plasmids is due to regular positioning or noisy oscillations around a mean position. They supplement their experimental work with an intuitive model that combines elements of previous modelling efforts. Their model relies on diffusion of the ParA substrate on the nucleoid with the dynamics of the ParB partition complex being driven by the underlying elastic force due to the nucleoid on which the substrate is tethered. Their model dynamics depend on two parameters, the ratio of the length over which the substrate can explore to the characteristic length of the space and the ratio of stimulated to non-stimulated hydrolysis rates of the substrate. If the length ratio is large, ParA can fully explore the space before interacting with the ParB complex leading to balanced fluxes and regular positioning. If it gets reduced, for example by lengthening the cell, oscillations can emerge as fluxes of substrates become imbalanced and a net force can pull the partition complex.

Strengths:<br /> Given the large amount of data, the observations unambiguously show that one particular ParABS system under the conditions studied is carrying out regular positioning of plasmids. The model synthesizes prior work into a nice intuitive picture. These model parameters can be fit to the data leading to estimates of molecular kinetic parameters that are reasonable and in line with other observations. Lining up the experimental observations with the phase space of the model suggests that the system is poised on the edge of oscillations, allowing for the system to have regular positioning with low resource consumption.

Weaknesses:

However, despite the correspondence of the simulated results with the experimental findings, other explanations are not completely ruled out. The paper emphasizes that ParA diffusion/hopping on the nucleoid is essential for the establishment of regular positioning and that without it, only oscillations were possible. Prior simulation efforts, that the paper cites, which include ParA diffusion and mixing in the cytosol but no diffusion on the nucleoid have shown that regular positioning is possible and that oscillations could get triggered as the system lengthened. Thus ParA hopping is not a necessity for regular positioning (as claimed in the paper), but very well might be needed for the given kinetic parameters of the system studied here.

The paper also presents experimental results for a second ParABS system (pB171) that is more likely to show oscillations. They attribute the greater likelihood of oscillations for pB1717 being due to ParA exploring a smaller space than the F plasmid system that showed regular positioning. This is pure conjecture and the paper does not provide any evidence that this is the reason. Thus it is hard to conclude if oscillations may not be due to other factors.

Reviewer #2 (Public Review):

The authors use a conditional Lox/Cre knock-out system to test and confirm the essentiality of glycerophosphodiester phosphodiesterase (GDPD) for blood-stage parasites and a key role in mobilizing choline from precursor lysophosphocholine (LPC) for parasite phospholipid synthesis. Prior works had identified serum LPC as the key choline source for parasites, localized this enzyme in parasites, and suggested an essential function in releasing choline, but this key function had remained untested in parasites. This manuscript critically advances mechanistic understanding of parasite phospholipid metabolism and its essentiality for blood-stage Plasmodium and identifies a potential new drug target.

Overall, this study is well constructed and rigorously performed, and the data provide strong support for the central conclusions about GDPD essentiality and functional contribution to parasite phosphocholine metabolism. The observation that exogenous choline largely rescues parasites from lethal deletion of GDPD is especially compelling evidence for a critical and dominant role in choline mobilization. A few aspects of the paper, however, are not fully supported by the current data and/or need clarification.

1. GDPD localization<br /> a) The authors conclude that GDPD is localized to the parasitophorous vacuole (PV) and parasite cytoplasm (lines 114-115), which is consistent with the prior 2012 Klemba paper. However, the data in the present paper (Figures 2A and 2E) only seem to support cytoplasmic localization but don't obviously suggest a population in the PV, in part because no co-staining with a PV marker is shown. The legend for Fig. 2E indicates staining with the PV marker, SERA5, but such co-stain is not shown in the figures or figure supplements. This data should ideally be included and described.

b) How do the authors explain cytoplasmic localization for GDPD? This protein contains an N-terminal signal peptide, which can account for secretion to the PV but would contradict a cytoplasmic population. The 2012 Klemba paper suggested that internal Met19 might provide an alternate site for translation initiation without a signal peptide and thus result in cytoplasmic localization. Some discussion of this ambiguity, its relation to understanding GDPD function, and a possible path to resolve experimentally seem necessary, especially as the authors suggest from data in Fig. 7 that this enzyme may have functions beyond choline mobilization, which may relate to distinct forms in different sub-cellular compartments.

2. The phenotypes depicted by representative microscopy images in panel 4E (especially for choline rescue) should be supported by population-level analysis by flow cytometry or microscopy of many parasites to establish generality.

3. The analysis in the last results section (starting on line 296) seems preliminary.<br /> a) For panel 7B, a population analysis of many parasites, with appropriate statistics, is important to establish a generalizable defect beyond the single image currently provided.

b) The data here would seem to be equally explained by an alternative model that GDPD∆ parasites are competent to form gametocytes but their developmental stall (due to choline deficiency) prevents progression to gametocytogenesis. The authors speculate that GDPD may play other roles in phospholipid metabolism beyond choline mobilization that are essential for gametocytogenesis. Their model, if correct, predicts that a GDPD deletion clone from +RAP treatment that is rescued by exogenous choline should not form gametocytes. Testing this prediction would be important to strongly support the conclusion of broader roles for GDPD in sexual development beyond choline mobilization.

Reviewer #2 (Public Review):

The authors have used well-characterized Drosophila intestinal epithelium as a model to investigate the potentially harmful effect of Btk Cry toxins on organisms that are not susceptible to the toxins. The experiments are well-designed, precisely performed, and appropriately assessed. Therefore, the presented results are in support of the authors' claims and conclusions. Additionally, the manuscript is written well to convey the message to a wide audience.

Reviewer #2 (Public Review):

In this study, the authors investigate the ubiquitin-mediated mechanisms underlying erythroid maturation. They first investigated proteome changes of CD34+ cells and HUDEP2 cells (an immortalized CD34+-derived line) which can be induced to undergo differentiation into different erythroblast stages. They identified that protein members of the E3 ubiquitin ligase complex called CTLH complex were globally increased during differentiation. They also found that the expression of several E2 enzymes including UBE2H, which partners with the CTLH complex, increase in later stages of erythroid maturation. Interestingly, they found that the 2 subunits of the CTLH complex, RanBP9 and RanBP10 which are structurally very similar, display opposite changes of expression, with RanBP9 decreasing and RanBP10 increasing during differentiation. They then show that both RanBP9 and RanBP10 can support complex formation in vitro and result in ubiquitin transfer competent complexes using ubiquitination with a model substrate peptide in vitro.

In the second part of the study, they created CRISPR-Cas9 knock out of UBE2H and the CTLH complex subunit MAEA in HUDEP2 cells to investigate the effect on proteome changes and erythroid cell differentiation. They found that both UBE2H and MAEA knockout cells display pronounced proteome-wide changes in erythroid-specific factors. They also show that the knockout of UBE2H and MAEA cause aberrant differentiation, with accelerated maturation, altogether suggesting that these 2 factors are required to maintain cells in progenitor state. Finally, they identify that MAEA expression is required to maintain UBE2H expression and that this regulation occurs at the post-translational level.

The authors clearly demonstrate that the CTLH complex and its associated E2 enzyme play important roles in erythroid differentiation. They also generated a wealth of data that document erythroid differentiation and point out very interesting co-regulatory mechanisms regarding ubiquitin machineries underlying this process. Notably, the authors identify an intriguing regulation of two CTLH complex members, RanBP9 and RanBP10 during erythroid maturation that correlates with, and suggests that the replacement of RanBP9 and RanBP10 during the process may be involved in regulating pathways that lead to erythroid maturation.

Unfortunately, while the above-mentioned regulation of the two CTLH complex members, RanBP9 and RanBP10 is suggested to play a role in erythroid maturation, it is not investigated further. It is genuinely surprising that the authors did not investigate the proteome of the RanBP9 and RanBP10 knockout HUDEP2 cells they generated, to figure out the effect the differential expression of these factors on erythrocyte development.

Instead, the study changes direction to focus on another CTLH complex subunit, MAEA, and how that subunit may function to regulate the expression of UBE2H, the E2 enzyme associated with the CTLH complex, in a manner seemingly independent of the other complex members. Overall, the work is interesting and advance our knowledge of the erythroid differentiation process, but there are some main issues including over-interpretation of data and experimental issues limiting data interpretation that would need to be addressed or the authors would need to revise their conclusions since as it stands now, some of the conclusions are not supported by the data.

Reviewer #2 (Public Review):

The authors aim to analyze and describe the neuroanatomy of the Late Jurassic sauropod Europasaurus holgeri. This is done by scanning with microCT both adult and juvenile specimens.

The authors successfully report in detail the overall anatomy of the Europasaurus braincase, as well as morphological characteristics so far undescribed in this taxon. Precociality in juveniles is suggested and also well-supported. Comparisons made with other sauropods are considered appropriate and clear.

Aspects of reproductive and social behavior in this taxon are deduced from the estimated auditory capabilities. They are not investigated in detail and more details regarding these aspects would be welcomed in the discussion.

Images in the manuscript are well-presented and clear, supporting adequately the description. Slicing of the CT data is sufficiently clear although a "polishing" of the final renders in some cases would be appreciated. Again, it is not necessary, since images are clear enough, but only suggested.

Reviewer #2 (Public Review):

Taking advantage of the high molecular order of the Drosophila flight muscle, Schueder, Mangeol et al. leverage small (<4 nm) original nanobodies, tailored coupling to fluorophores, and DNA-PAINT resolution capabilities, to map the nanoarchitecture of two titin homologs, Sallismus and Projectin.

Using a toolkit of nanobodies designed to bind to specific domains of the two proteins (described in the companion article "A nanobody toolbox to investigate localisation and dynamics of Drosophila titins" ), Schueder, Mangeol et al position these domains within the sarcomere with <5nm resolution, and demonstrate that the N-ter of Sallismus overlaps with the C-ter of Projectin at the A-band/I-band interface. They propose this architecture may help to anchor Sallismus to the muscle, thus supporting flight muscle function while ensuring muscle integrity.

This study nicely extends previous work by Szikora et al, and precisely dissect the the sarcomeric geography of Sallismus and Projectin. From these results, the authors formulate specific functional hypotheses regarding the organization of flight muscles and how these are tuned to the mechanical constraints they undergo.

Although they remain descriptive in essence, the conclusions of the paper are well supported by the experimental results.

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.