Reviewer #3 (Public Review):

The work presented by McKay et al. details the development of a new wireless network-enabled automated feeder system with which diet amount and schedule can be controlled across individually housed killifish. The manuscript describes the characterization of the system and demonstrates the robustness, precision, and high fidelity in feeding control achieved due to modular design.

The technique in principle can be applied to hundreds of tanks and to other species that are reared in similar tank system racks.

Strengths:

- The authors provide a convincing account of the use of automated feeder systems for implementing experiments where diet is controlled precisely. The experimental design allows the authors to clearly demonstrate feeding schedules optimal for killifish growth, reproduction, and longevity. Their characterization and results will be highly valuable for a growing community of researchers who are beginning to use killifish in laboratory settings and can choose the regimen most suited to their research goals. The system presented in this study may also allow for better husbandry practices with the potential to mimic the ephemeral natural habitats of this species more closely in the laboratory.<br /> - The authors also conducted additional experiments comparing restricted food delivery schedules. The conclusion they reach that a time and quantity restricted feeding regimen increases the lifespan of males based on this experiment is well justified from the data presented. The differences between the sexes are interesting to note as the authors observed similar results with two different cohorts, though cohorts can differ in the median and maximum lifespan.

Weaknesses:

- The authors imply the value of automated feeders is in scaling to hundreds of individual animals/tanks. I agree with the author's assessment of this need in research labs, however, it is not easy to infer exactly how many automated feeders were operating simultaneously in this study. Estimates of the costs of building, and operating (maintenance, server use, and cloud computing costs) for conducting 1 experiment (2 conditions, 24 animals per condition) running over 100 days will be valuable for other researchers interested in adapting this resource. A clearer supplementary video 1 that demonstrates the entire feeder properly, in the home tank will also be valuable for the researchers interested in adapting the system.<br /> - The proof of concept experiment showing associative learning is extremely interesting but is quite difficult to assess, based on the detail provided in the results and the method. The rationale behind key considerations for behavioral measures, whether based on previous studies or, due to technical constraints are difficult to judge. This needs a better description. In particular, results mention a "pipeline", but this is obscure, in the methods section. Clearer definitions would also be needed to evaluate if an objective scoring system for was used in measures such as the"startle" response. In principle, as all trajectories are recorded, it should be possible to describe a range of acceleration/velocity changes that quantify most parameters such as startle, unless it was manually scored. As this will be a first, clarity on how "early" and "late" sessions were categorized; exact experimental design on the number of trails that made up a session; whether all animals went through same number of trials in Figure 5, etc. will improve the description and future adaptations of the experimental design.<br /> - One more cautionary note is in the interpretation that young individuals had significantly higher learning index scores than old individuals, as the size of the effects can't be estimated from the type of data provided and the analysis used. Given the fairly small sample size for animals used in learning index calculations (< 15), and as the authors demonstrate in diet restriction experiments there can be cohort-dependent differences as well, I would caution against such an interpretation. The p values reported in Suppl. Figure 4E especially brings home the need to move away from dichotomous thinking of yes/no based on a threshold, without taking into account effect sizes. Please refer to this recent post in eNeuro on the inherent issues with such interpretations, and methods to overcome them (https://doi.org/10.1523/ENEURO.0091-21.2021). The deficiency in "old" may not be as large, and it would be important to interpret this appropriately. Other normalization issues, rather than learning could account for small differences between the young and the old. For instance, a small latency in the average velocity and/or other locomotion kinematics differences between fish categorized as old vs. young could result in the criterion of "3 seconds before the food drops" to meet the "threshold of learning" being unmet. The data available in the paper at present can't be used to evaluate such a point.

Reviewer #3 (Public Review):

In this manuscript, the authors characterize the localization and function of two proteins, BdPOLAR and BdPAN1 in the asymmetric cell divisions required for stomatal patterning in Brachypodium distachyon (Bd). The authors clearly demonstrate these proteins are required for normal stomatal complex formation. Most excitingly, the authors reveal that these proteins occupy two opposing polar domains during stomatal formation, particularly the localization of BdPOLAR defines a novel polar domain that is dependent on BdPAN1 for its unique accumulation. The authors clearly link the functions of these proteins to cell division orientation and division potential and show an impact on stomatal function. The data presented here are clearly described and well documented and the figures are clear and well constructed. Their results support a broadly interesting hypothesis whereby polarization of cell fate-dependent and -independent factors pattern stomata in this grass. It will be very interesting to see how/if similar or other new polarity domains function in other developmental contexts in plants where control of cell division orientation is critical for cell fate and tissue function.

The authors' careful and elegant experiments clearly demonstrate a fascinating new avenue for exploration into plant cell polarity and cell division control. Their results will be of interest to scientists interested in development and cell biology across species, as well as those broadly interested in plant biology topics. Developmental patterning of the stomata in grasses is an elegant system to address important basic biological questions about the regulation of cellular asymmetries, cell division, and cell morphology. Additionally, the function of stomata is critical to the productivity and survival of plants, including in carbon intake (for photosynthesis). Understanding the developmental framework underlying pore formation provides insights into plant patterning processes and, importantly, provides a toolbox from which plant biologists can work to engineer improved crop plant performance in a rapidly changing climate.

Reviewer #3 (Public Review):

In this work the authors identify genes and pathways important for CO2 and thermotolerance in Cryptococcus neoformans. They additionally rule out the contribution of the bicarbonate or cAMP-dependent activation of adenylyl cyclase to this pathway, which is important for CO2 sensing in other fungi, further solidifying the need to characterize CO2 sensing in basidiomycetes. The authors establish the importance of focusing on CO2 tolerance by testing the impact of CO2 on fluconazole susceptibility with varied pH, suggesting the ability of CO2 to sensitize cryptococcal cells to fluconazole. Furthermore, the authors compared the CO2 tolerance of clinical reference strains to environmental isolates. The characterization of the RAM pathway Cbk1 kinase illustrated the integration of multiple stress signaling pathways. By using a series of CBK1OE insertions in strains with deletions in other pathways, the ability of Cbk1 over-expression to rescue several strains from CO2 sensitivity was apparent. Additionally, NanoString expression analysis comparing cbk1∆ to H99 validated the author's screen of CO2-sensitive mutants as 16/57 downregulated genes were found in their screen, further confirming the interconnected nature of these pathways. The importance of the RAM pathway in maintaining CO2 and thermotolerance was also incredibly clear.

Perhaps most interestingly, the authors identify suppressor colonies with distinctive phenotypes that allowed for the characterization of downstream effectors of the RAM pathway. These suppressor colonies were found to have mutations in SSD1 and PSC1 which somewhat restore growth at 37oC with CO2 exposure. Further confirming the importance of the RAM pathway, the cbk1∆ strain had markedly attenuated virulence during infection. Interestingly, the generated suppressor strains had varying impacts on fungal infection in vivo. While the sup1 suppressor was completely cleared from the lungs during both intranasal and IV infection, the sup2 strain, containing mutations in SSD1, maintained a high fungal load in the lungs and was able to disseminate into host tissues during IV infection but not intranasal infection.

The authors make a strong case for the exploration of thermotolerance and CO2 tolerance as contributors to virulence. Through screening and characterization of RAM pathway kinase CBK1's ability to rescue other mutants from CO2 sensitivity, the overlapping contributions of several signaling pathways and the importance of this kinase were revealed. This work is important and will be valuable to the field. However, the cbk1∆ strain does show reduced melanization, urease secretion, and higher sensitivity to cell wall stressor Congo Red in SI Appendix, Figure S4. While the authors make a strong argument that these well-established virulence factors are not perfect predictors of virulence in vivo, the cbk1∆ strain is not an example of such a case as it does have defects in these important factors in addition to thermotolerance and CO2 tolerance. Not acknowledging the changes in these virulence factors in the cbk1∆ and their potential contribution to phenotypes observed is a weakness of the manuscript. Interestingly, the sup1 and sup2 strains also rescue these virulence factors compared to cbk1∆. Additionally, the assertion that "the observation that only sup2 can survive, amplify, and persist in animals stresses the importance of CO2 tolerance in cryptococcal pathogens" due to the sup2's slightly higher CO2 tolerance compared to sup1, could be better supported by the data. These suppressors did not restore transcript abundances of the differentially expressed genes to WT levels, suggesting post-transcriptional regulation. However, there may be differences in the ability of sup2 to resist stress better than sup1 especially given the known Ssd1 repression of transcript translation in S. cerevisiae. Finally, pH appears to impact the sup1 and sup2 strain's sensitivity to CO2 in SI Appendix Figure 4. This could be better explained and interrogated in the manuscript. Finally, this work includes a variety of genes in several signaling pathways. The paper would be greatly clarified by a graphical abstract indicating how CBK1 may be integrating these pathways or by indicating which genes belong to which pathways in the Figure 1 legend to make this figure easier to follow.

Reviewer #3 (Public Review):

This work provides a series of tests of hypothesis, which are not mutually exclusive, on how genomic diversity is structured within human microbiomes and how community diversity may influence the evolution of a focal species.

Strengths:<br /> The paper leverages on existing metagenomic data to look at many focal species at the same time to test for the importance of broad eco-evolutionary hypothesis, which is a novelty in the field.

Weaknesses:<br /> It is not very clear if the existing metagenomic data has sufficient power to test these models.<br /> It is not clear, neither in the introduction nor in the analysis what precise mechanisms are expected to lead to DBD.<br /> The conclusion that data support DBD appears to depend on which statistics to measure of community diversity are used. Also, performing a test to reject a null neutral model would have been welcome either in the results or in the discussion.

Reviewer #3 (Public Review):

To motivate the proposal, Karageorgiou et al. first identify a problem in applying current multivariable MR (MVMR) methods with many correlated exposures. I believe this problem can really be broken into two pieces. The first is that MVMR suffers from weak instrument bias. The second is that some traits may have nearly co-linear genetic associations, making it hard to disentangle which trait is causal. These problems connect in that inclusion of co-linear traits amplifies the problem of weak instrument bias - traits that are nearly co-linear with another trait in the study will have no or very few conditionally strong instruments.<br /> The authors then propose a solution: Apply a dimension reduction technique (PCA or sparse PCA) to the matrix of GWAS effect estimates for the exposures. The identified new components can then be used in MVMR in place of the directly measured exposures.

I think that the identified problem is timely and important. I also like the idea of applying dimension reduction techniques to GWAS effect estimates. However, I don't think that the manuscript in its current form achieves the goals that it has set out. Specifically, I will outline the weaknesses of the work in three categories:<br /> 1. The causal effects measured using this method are poorly defined.<br /> 2. The description of the method lacks important details.<br /> 3. Applied and simulation results are unconvincing.<br /> I will describe each of these in more detail below.

1. To me, the largest weakness of this paper is that it is not clear how to interpret the putatively causal effects being measured. The authors describe the method as measuring "the causal effect of the PC on outcome" but it is not obvious what this means.

One possible implication of this statement is that the PC is a real biological variable (say some hidden regulator) that can be directly intervened on. If this is the intention it should be discussed. However, this situation would imply that there is one correct factorization and there is no guarantee that PCs (or sparse PCs) come close to capturing that.

The counterfactual implied by estimating the effects of PCs in MVMR is that it is possible to intervene on and alter one PC while holding all other PCs constant.<br /> In the introduction, the authors note (and I agree) that one weakness of MR applied to correlated traits is that "MVMR models investigate causal effects for each individual exposure, under the assumption that it is possible to intervene and change each one whilst holding the others fixed." However, it is not obvious that altering one PC while holding the others constant is more reasonable.

2. This section combines a few items that I found unclear in the methods section. The most critical one is the lack of specification on how to select instruments.<br /> For the lipids application, the authors state that instruments were selected from the GLGC results, however, these only include instruments for LDL, HDL, and TG, so 1) it would not be possible to include variants that were independently instruments for one of the component traits alone and 2) there would be no instruments for the amino acids. There is no discussion of how instruments should be selected in general.<br /> This choice could also have a dramatic impact on the PCs estimated. The first PC is optimized to explain the largest amount of variance o of the input data which, in this case, is GWAS effect estimates. This means that the number of instruments for each trait included will drive the resulting PCs. It also means that differences in scaling across traits could influence the resulting PCs.

The other detail that is either missing or which I missed is what is used as the variant-PC association in the MVMR analysis. Specifically, is it the PC loadings or is it a different value? Based on the computation of the F-statistic I suspect the former but it is not clear. If this is the case, what is the effect of using loadings that have been shrunk via one of the sparse methods? It would be nice to see a demonstration of the bias and variance of the resulting method, though it is not clear to me what the "truth" would be.

3. In the lipids application, the fact that M.LDL.PL changes sign in MVMR analysis are offered as evidence of multicollinearity. I would generally associate multicollinearity with large variance and not bias. Perhaps the authors could offer some more insight on how multicollinearity would cause the observation.<br /> A minor point of confusion: I was unable to interpret this pair of sentences "Although the method did not identify any of the exposures as significant at Bonferroni-adjusted significance level, the estimate for M.LDL.PL is still negative but closer to zero and not statistically significant. The only trait that retains statistical significance is ApoB." The first sentence says that none of the exposures were significant while the second sentence says that Apo B is significant. The GRAPPLE results don't seem clearly bad, indeed if only Apo B is significant, wouldn't we conclude that of the 118 exposures, only Apo B is causal for heart disease? It would help to discuss more how the conclusions from the PC-based MVMR analysis compare to the conclusions from GRAPPLE.

It is a bit hard to interpret Table 4. I wasn't able to fully determine what "VLD, LDL significance in MR" means here. From the text, it seems that it means that any PC with a non-zero lodaing on VLDL or LDL traits was significant, however, this seems like a trivial criterion for the PCA method, since all PCs will be dense. This would mean this indicator only tells us whether and PCs were found to "cause" heart disease.

In simulations, I may be missing something about the definition of a true and false positive here. I think this is similar to my confusion in the previous paragraph. Wouldn't the true and false positive rates as computed using these metrics depend strongly on the sparsity of the components? It is not clear to me what ideal behavior would be here. However, it seems from the description that if the truth was as in Fig 7 and two methods each yielded one dense component that was found to be causal for Y, these two methods would get the same "score" for true positive and false positive rate regardless of the distribution of factor loadings. One method could produce a factor that loaded equally on all exposures while the other produced a factor that loaded mostly on X1 and X2 but this difference would not be captured in the results.

Reviewer #3 (Public Review):

Riquelme et al. develop a spiking neural network model based on experimental measurements from ex vivo turtle visual cortex (neuronal parameters, connectivity profiles, synaptic strength distributions). Within the constraints given, the connectivity is random. The analyses in the manuscript are based on multiple instantiations (300) of the network and multiple simulations of each. The principle finding is that, if a randomly selected excitatory neuron is induced to emit an action potential, a reliable sequence of spikes follows (in more than 90% of cases). They then examine the role of connectivity in this phenomenon, including the frequency of specific motifs in the spike cascade and the comparative role of strong and weak connections. In particular, the authors show that rare strong connections are vital for producing (long) reliable sequences. The authors then examine how the sequences can be broken down into sub-sequences that may or may not occur for a given trigger. They show that the sub-sequences are characterized by strong internal connections (compared to those between sub-sequences). Moreover, they show that the spike sequence can be routed by exciting or depressing the 'gate' neurons (i.e. those at the beginning of a particular sub-sequence) raising the intriguing possibility of context-driven routing of activity. Finally, the authors demonstrate that their model has interesting combinatorial properties, as the results of triggering two sequences at once cannot be accounted for in a linear fashion. All in all, this is a solid piece of work with well-thought-through analyses which is an interesting contribution to the fundamental question of how the brain manages reliable computation in a noisy world.

Strengths

"Ensemble approach" I appreciated the approach to generate many networks from the same distributions rather than (as is often the case) basing all their conclusions on one instantiation. In general, the statistical rigour is high.

Well-chosen analyses to tease apart the relationships between structure and dynamics.

Figures (for the most part) clearly support the conclusions of the paper.

Weaknesses

The spontaneous activity of the network is extremely low, with [0.02 0.09] spks/s considered as a high activity range. Granted, this is based on ex vivo measurements. However, if this phenomenon is to be considered computationally relevant, as the authors claim, the paper should have examined the reliability of propagation and routing with in vivo activity levels.

The above weakness is a special case of the issue that the limits of applicability/robustness of results to model assumptions have not been well established. In particular, it is not clear how strong the strongest weights must be whilst still enabling long sequences, and what is the dependence of the results on the parameters of the distance-dependent connectivity.

The figures are too densely packed and many of the elements are too small or too fine to be distinguished, especially if your eyesight is not the greatest. Although many people read online, where zooming is possible, the aim should still be that all elements of the figure can be perceived by a person over 45 who has printed the paper on regular A4 paper.

Reviewer #3 (Public Review):

This manuscript presents a new method to estimate the selective effect of heterozygous loss of function mutations. The authors offer a sequential Monte Carlo algorithm coupled with ABC estimates based on forward population genetics simulations. The method is of obvious interest to the field. The result confirms that DFE distribution for PTVs is broad with the mean and median exceeding 1% and ~20% of genes associated with more than 10% loss in fitness. The new quantitative estimates are likely an improvement over the state-of-the-art. Importantly, the authors include estimates for PTVs on the X chromosome, which are expectedly higher. The authors demonstrate that de novo PTVs leading to a substantial fitness loss are highly enriched in individuals affected by severe complex disorders including neuropsychiatric disorders. They also provide estimates of allelic ages for variants with specific selection coefficients. This work is of interest to both population and medical geneticists.

Reviewer #3 (Public Review):

This is a very interesting study examining for the first time the influence of lateral tilt of the whole body on orientation tuning in macaque IT. They employed two types of displays: one in which the object was embedded in a scene that had a horizon and textured ground surface, and a second one with only the object. For the first type, they examined the orientation tuning with and without tilting the subject. However, the effect of tilt for the scene stimuli is difficult to interpret in terms of gravitational reference frame since varying the orientation of the object relative to the horizon leads to changes in visual features between the horizon and object. If neurons show tolerance for the global orientation of the scene (within the 50{degree sign} manipulation range) then the consistent orientation tuning across tilts may just reflect tuning for the object-horizon features (like the angle between the object and the horizon line/surface) that is tolerant for the orientation of the whole scene. Thus, the effects of tilt can be purely visually-driven in this case and may reflect feature selectivity unrelated to gravitation. The difference between retinal and gravitational effects can just reflect neurons that do not care about the scene/horizon background but only about the object and neurons that respond to the features of the object relative to the background. Thus, I feel that the data using scenes cannot be used unambiguously as evidence for a gravitational reference frame. The authors also tested neurons with an object without a scene, and these data provide evidence for a gravitational reference frame. The authors should concentrate on these data and downplay the difficult-to-interpret results using scenes. Furthermore, the analysis of the single object data should be improved and clarified.

Reviewer #3 (Public Review):

In this paper, Van Eyndhoven et al. use a quantitative and system immunology approach to dissect the factors contributing to the fate of early IFN-I responders. Overall, this manuscript is quite elegant and technically very strong. My questions/comments are limited to (1) the fraction of cells that respond in the absence of Poly(I:C), (2) the source of stimulation for the second responders in this system.

1. For the small fraction of cells that respond in the absence of Poly(I:C), are these cells just showing IRF7 translocation or are they fully responding with IFNB production? Has this been observed in other experimental systems or contexts? Do you also observe secondary responders in the unstimulated samples (as shown in the stimulated in Fig. 2G-I)?

2. Do the second responders only arise through direct IFN-I production by first responders? Is it possible that this response has any relationship with the initial transfection with Poly(I:C)?

Reviewer #3 (Public Review):

To determine how the clinical-stage inhibitor vamifeport interacts with ferroportin, the authors used cryogenic electron microscopy (cryo-EM) to determine several structures of ferroportin in complex with newly isolated sybodies. They found that the highest resolution structure shows an occluded state of the transporter bound to sybody 3 and vamifeport. The inhibitor occupies a small portion of a large occluded cavity, interacts with both the N and the C lobe of the transporter, and overlaps with the binding site for both hepcidin and the iron ion binding site 2. The authors also use binding assays and mutagenesis to confirm that the residues in the vamifeport binding site are important for binding.

As the authors point out, the vamifeport inhibitor can readily be modeled in two orientations. The authors provide a reasonable argument that one orientation provides more specific interactions, but the case would be stronger if the structure had a high enough resolution to distinguish between the two orientations, or if the authors could provide some complementary supporting evidence. Still, the manuscript provides convincing evidence to explain how the compound inhibits ion transport and the similarities and differences between this inhibitor and the endogenous regulatory protein hepcidin.

The authors describe the occluded conformation that they resolve with bound sybody 3 and vamifeport as "on the transport pathway". However, this occluded conformation was captured in the presence of two ligands that are not on-pathway, the inhibitor and the sybody. It seems plausible (maybe even likely?) that the conformation is off-pathway and trapped by these additional ligands. The study would therefore benefit from additional evidence as to whether this conformation is indeed on-pathway.

Reviewer #3 (Public Review):

The manuscript by Vandry et al analyzes the circuitry connecting LEC to MEC, identifying a new connection with potential significance for cortico-hippocampal coding and memory. Using a combination of viral tracing, patch-clamp electrophysiology, and optogenetics, the authors reveal a new excitatory projection from Fan cells of LEC layer 2 to superficial neurons of MEC. Specifically, Fan cells synapse on MEC L2 stellate and pyramidal neurons, as well as layer 1 and layer 2 local interneurons, which provide fast and slow local feedforward inhibition to MEC excitatory neurons. The authors observe substantial cell-to-cell heterogeneity in the excitatory-to-inhibitory ratio, which does not seem to be a result of anatomical location. This heterogeneity is conserved during theta-like stimulation. This new connection allows for a kind of unidirectional "cross-talk", in which LEC can speak to MEC prior to or during communication of both of these regions with the hippocampus.

The results are generally clear and well-contextualized by the text. The authors use multiple complementary anatomical methods to identify the LEC to MEC connection, all of which agree. This is supported by the electrophysiological measurements, which are straightforward and generally convincing. The results provide important data for understanding the previously underappreciated reciprocal circuitry between MEC and LEC, which, as the authors nicely lay out in the introduction, is likely key for understanding the operation of memory networks.

The work described in this manuscript, which is all in vitro, appears nicely conducted and solid and is well presented and analyzed appropriately. However, it is not clear how this information can be used to glean an improved understanding of how LEC and MEC interact in the intact system, which is obviously the big question. In vivo experiments of this kind are quite challenging, but without some observation or perturbation of circuit dynamics in the intact animal, or at the very least a compelling model of how hippocampal/memory information processing is influenced by this new circuit, it may be hard for readers to know what to make of the new data the authors provide.

Reviewer #3 (Public Review):

In this manuscript, Raval et al. investigated the cost and benefit of maintaining seemingly redundant components of the translation machinery in the E. coli genome. They used systematic deletion of different components of the translation machinery including tRNA genes, tRNA modification enzymes, and ribosomal RNA genes to create a collection of mutant strains with reduced redundancy. Then they measured the effect of the reduced redundancy on cellular fitness by measuring the growth rate of each mutant strain in different growth conditions.

This manuscript beautifully shows how maintaining multiple copies of translation machinery genes such as tRNA or ribosomal RNA is beneficial in a nutrient-rich environment, while it is costly in nutrient-poor environments. Similarly, they show how maintaining parallel pathways such as non-target tRNA which directly decodes a codon versus target tRNA plus tRNA modifying enzymes which enable wobble interactions between a tRNA and a codon have a similar effect in terms of cost and benefit.

Further, the authors show the mechanisms that contribute to the increased or reduced fitness following a reduction in gene copy number by measuring tRNA abundance and translation capacity. This enables them to show how on one hand reduced copy numbers of tRNA genes result in lower tRNA abundance in rich growth media, however in nutrient-limiting media higher copy number leads to increased expression cost which does not lead to an increased translation rate.<br /> Overall, this work beautifully demonstrates the cost and benefits of the seemingly redundant translation machinery components in E. coli.

However, in my opinion, this work's conclusion should be that the seeming redundancy of the translation machinery is not redundant after all. As mentioned by the authors, it is known that tRNA gene copy number is associated with tRNA abundance (Dong et al. 1996, doi: 10.1006/jmbi.1996.0428), this effect is also nicely demonstrated by the authors in the section titled "Gene regulation cannot compensate for loss of tRNA gene copies". Moreover, this work demonstrates how the loss of the seeming redundancy is deleterious in a nutrient-rich environment. Therefore, I believe the experiments presented in this work together with previous works should lead to the conclusion that the multiple gene copies and parallel tRNA decoding pathways are not redundant but rather essential for fast growth in rich environments.

Reviewer #3 (Public Review):

In this study, the authors explore an under-studied but widely observed phenomenon that polyA site selection often occurs in clusters leading to the excepted interpretation that cleavage and polyadenylation are imprecise. Here, the authors use 3READS to map polyA sites in yeast and human cells to define trends in intra-cluster polyA site usage as it relates to RNAPII speed. They observe clear trends in cleavage events that correlate with either increased or decreased RNAPII elongation rate and make a further identification that downstream GC content also correlates with these trends. The potential impact of this work is to explain the imprecise behavior of cleavage and Polyadenylation as a component of local elongation rates that are influenced by nucleotide content.

Reviewer #3 (Public Review):

The authors are designing a novel continuous evidence accumulation task to look at neural and behavioral adaptations of continuously changing evidence. They particularly focus on centroparietal EEG potential that has been previously linked with evidence accumulation. This paper provides a novel method and analysis to investigate evidence accumulation in a continuous task set-up.

I am not familiar with either the EEG or evidence accumulation literature, therefore cannot comment on the strength of the findings related to centroparietal EEG in evidence accumulation. I have therefore commented only on the coherence and details of the method and clarity of the argumentation and results.

The main strength is in the task design which is novel and provides an interesting approach to studying continuous evidence accumulation. Because of the continuous nature of the task, the authors design new ways to look at behavioral and neural traces of evidence. The reverse-correlation method looking at the average of past coherence signals enables us to characterize the changes in signal leading to a decision bound and its neural correlate.<br /> By varying the frequency and length of the so-called response period, that the participants have to identify, the method potentially offers rich opportunities to the wider community to look at various aspects of decision-making under sensory uncertainty.

The main weaknesses that I see lie within the description and rigor of the method. The authors refer multiple times to the time constant of the exponential fit to the signal before the decision but do not provide a rigorous method for its calculation and neither a description of the goodness of the fit. The variable names seem to change throughout the text which makes the argumentation confusing to the reader. The figure captions are incomplete and lack clarity.<br /> The authors claim that the method enables continuous analysis of decision-making and evidence accumulation which is true. The analysis of the signals that come prior to the decision provides a rich opportunity to characterize decision bound in this task. The behavioral and neural analyses globally lack clarity and description and thus do not strongly support the claims of the paper. The interpretation of the figures within the figure caption and the lack of a neutral and exhaustive description of what is being shown prevent the claims to be strongly supported.

The continuous nature of the task and the computation of those evidence kernels are valuable methods to look at evidence accumulation that could be of use within the community. However, due to the lack of rigor in the analysis and description of the method, it is hard to know if the current dataset is under-exploited or whether the choice of the parameters for this set of experiment does not enable stronger claims.

Reviewer #3 (Public Review):

The work provides direct evidence for the coherent activity of head-direction (HD) cells in the anterior thalamus and retrosplenial cortex (RSC). RSC is one of two major direct cortical recipients of the subcortical HD signal, the other being the postsubiculum (POS). While it is established that POS inherits its HD tuning from ADN (Peyrache et al, 2015), it is not known whether HD cells in RSC show similar coordination with ADN. The manuscript employs technically challenging dual electrophysiological recordings from ADN and RSC to establish that the local internal representations of HD encoded in ADN and RSC are coherent during free exploration but also show coordinated realignment after cue rotation as well as coordinated drift in darkness. The work thus provides evidence that HD and RSC assemblies represent the same internal heading direction, at least in the behavioural paradigms tested and at the investigated temporal resolution. The manuscript also makes a claim that the RSC is unlikely to mediate the realignment of the HD signal following cue rotation because the HD signal realigns itself synchronously across the two brain regions. This claim is additionally supported by the sparse anatomical projection and the paucity of putative direct synaptic connections from RSC to ADN.

The manuscript convincingly demonstrates overall ADN-RSC coordination in two different paradigms. While such coordination is expected in instances when HD representations in both areas are precisely aligned with the current HD, it may not be the case in instances of sensory conflict or limited sensory information. The fact that internal HD in both ADN and RSC drifts coherently in darkness provides strong evidence of the tight functional coupling between the two areas. Additionally, while the cue rotation paradigm used in the study often failed to elicit the full realignment of the HD signal, this variability was certainly utilized to the manuscript's advantage as it makes the coupling evident even when the HD signal realigns only partially. The overall conclusions of the manuscript are largely supported by the presented data but the strength of the argument, especially with regard to the zero-lag coupling between ADN and RSC, is somewhat affected by the technical limitations.

1) The manuscript relies heavily on supervised decoding of the internal HD from population activity in RSC and ADN and in turn suffers from relatively low numbers of simultaneously recorded neurons, which is especially evident in the representative images in Figure 2C. The reported average decoding errors are much higher than those reported elsewhere (Peyrache et al, 2015; Viejo et al, 2018; Xu et al, 2019), which may occlude the effects of RSC activity on ADN that are more subtle and/or occur at shorter timescales than the bin size used in the decoding algorithm. The manuscript includes no discussion of how much these factors could contribute to the observed variability in the data.

2) RSC-HD cells recorded in the study are relatively poorly tuned to HD, which is contrary to the reports of HD cells recorded in RSC (Lozano et al, 2017; Javob et al, 2017; Keshavarzi et al, 2021). In fact, the median directional information score for RSC-HD cells is the same as that for non-HD cells in ADN (Supplementary Figure 2B). In fact, due to their relatively low HD modulation, it may be more appropriate to refer to them as 'HD-modulated' cells. While the electrode positions indicate that RSC was sampled across layers and sub-regions so missing the HD cell 'hot spots' like granular RSCb is unlikely, the apparent poor directional tuning of RSC cells could possibly be due to the nature of the recording environment (e.g. low light condition with the LED landmark being the only light source). Importantly, the manuscript lacks a control 'baseline' condition in which HD cells are recorded in a standard, well-lit open field, as well as a discussion of the discrepancy between the observed HD tuning and that reported in the literature.

3) Analysis of decoding error, which features prominently in the manuscript, is critically dependent on the quality of behavioural tracking - errors in tracking could lead to the accumulation of decoding errors and this could dominate decoding error analyses. Indeed, Figure 2A shows many gaps in the tracked HD of the mouse, which may point to the sub-optimal quality of the behavioural tracking. This is especially important for analyses like the one in Figure 2D which shows that internal HD representations in ADN and RSC are coordinated at zero lag (+/- 20ms). The observed zero-lag peak could be instead explained by errors in behavioural tracking dominating the analysis, which would affect both representations simultaneously and show spurious zero-lag positive correlations. As such, the analysis that is missing is the difference between internal HD decoded from ADN and RSC at different time lags, without reference to the HD tracked behaviourally.

4) The work often uses a number of trials as their 'n' sample size for statistical analyses and the methods state that tetrodes were regularly advanced, but there is no indication of whether multiple trials at the same tetrode position were included in the same statistical comparison (except for recordings '4 days apart' for the HD tuning and synaptic connectivity analyses). Multiple trials with a high likelihood of recording the same cell population should not be counted as separate samples when calculating statistical significance.

Reviewer #3 (Public Review):

This manuscript by Takahashi et al., reveals the structure of a chimeric VRAC channel composed by the LRRC8C and a short domain corresponding to the intracellular loop of LRRC8A. Homomeric LRRC8C channels are not functional but this chimera has been shown to "rescue" the functional and pharmacological properties of heteromeric VRAC channels. The authors obtained the Cryo-EM structure for this chimera, which provide some interesting insights about these channels. The major finding of this work is that the channel is asymmetrically formed by 7 protomers, with associated lipid-like densities that are proposed to play a role in gating. Unfortunately, critical domains of this structure could not be solved, which limit the interpretations of this new work. These missing domains include the entire LRRD, the N-terminus and the first intracellular loop containing the 25 amino acids incorporated from the LRRC8A. While this work is very interesting, the data presented are not enough to support the author claims. Particularly, the idea that the 'lipid blocked pore' is associated with gating.

Reviewer #3 (Public Review):

This study investigates the recently published findings by Sugisawa et al that microbial ssRNA40, a known agonist for the immune surveillance system activates the mechanically gated ion channel Piezo1. In addition to providing mechanistic insights into the study, this finding also had much broader implications as it suggested a novel role for the channel as a physiological receptor for ssRNA. Although there is nothing that prevents Piezo1 from carrying out such a role in principle, the finding caused a great deal of interest and professional skepticism among Piezo researchers and, more broadly, in the field of mechanobiology. This manuscript set out to reproduce the main findings of Sugisawa et al using the same approaches, and in addition utilized other techniques to address potential differences in experimental conditions. In summary, the authors failed to reproduce the major Piezo1-related findings reported by Sugisawa et al, while all the new experiments pointed to the absence of a functional interaction between ssRNA40 and Piezo1. The study is well-designed, with appropriate controls and statistical analyses.

Reviewer #3 (Public Review):

Schneggenburger and colleagues set out to reveal roles for D1R+ and Adora+ amygdala-striatal transition zone neurons in fear learning. In the first two experiments, the authors expressed fluorescent calcium indicators in D1R+ or Adora+ neurons, measuring change in fluorescence during habituation, training and testing of tone-shock conditioning. In the next experiments, the authors expressed archeorhodopsin (or a control fluorophore) in D1R+ or Adora+ neurons and illuminated with yellow light just before and after foot shock delivery. Freezing was quantified during training and retrieval. Finally, retrograde tracing was performed to reveal direct synaptic inputs on D1R+ and Adora+ neurons.

The paper is potentially interesting. However, some important weaknesses include: the authors use of only male mice, the lack of validation of the Cre lines used in the study, and the data acquisition pipeline.

Reviewer #3 (Public Review):

Yang and colleagues provide a thorough characterization of a transgenic mouse model expressing fluorescently tagged synaptotagmin. In particular, they present key controls validating this mouse model as a tool, including co-localization of the tagged synaptotagmin with other synaptic markers as well as normalcy of synaptic transmission mediated by synaptic terminals expressing the tagged synaptotagmin. Importantly, the authors present data on the potential use of neuronal cultures obtained from these mice in synaptic co-culture assays. In these assays, synaptic cell adhesion molecules expressed on non-neuronal cell lines such as HEK-293 cells or COS cells are used to test the sufficiency of these molecules to trigger synapse assembly. This mouse model will be a useful addition to existing models expressing fluorescently-tagged synaptic vesicle proteins such as synaptophysin, synaptotagmin as well as synaptobrevin.

Reviewer #3 (Public Review):

This study is primarily a descriptive analysis that provides a clear and accessible account of how screening activity varied across Italy and between groups. While primarily a simple descriptive account such work is important to document what were the impacts of the pandemic on preventative health services and to understand how they differed across groups. The combination of survey responses from regional screening programmes and individuals is a useful use of two data sources. The study is very clearly written and does not over-interpret the presented data.

The methods description states that the analysis presents the "standard months" required for the programmes to recover from the service delays. The subsequent reporting of these delays in the results section did not use the same terminology and I see scope for clarification by using common language regarding this assessment throughout the paper. I see scope for further disaggregation of the regional results within the study but equally I understand why the authors might not wish to report outcomes for specific regions. I see scope for improvement in the figures within the manuscript but this is a relatively presentational matter. I would like to see some further description of the Poisson regression analysis as what is included within the manuscript appears rather brief. There is also one section of the methods that seems as if it would better belong in the introduction, but overall the manuscript was very clearly structured.

The analysis presented achieves the authors' stated aims in my view. I see a useful contribution in documenting the impact of the COVID-19 pandemic on screening in Italy. This may inform further work on assessing the eventual health impact of delays as well as work considering how best to make screening programmes more resilient to such shocks. Ultimately it will take time to observe just how significant the impacts of service interruptions were on cancer prevention. Readers should remember that many screening services may still provide good protection against cancer as long as the interruptions are limited to simply to delays in coverage rather than the longer-term loss of participation, especially for those with incomplete screening histories or of otherwise elevated risk of disease.

Further work may wish to consider how programmes prioritised capacity or what efforts have been made to restart screening. Similarly, there is scope for more detailed disaggregation assessment of who received screening as programmes restarted. Both these issues are beyond the scope of the present study however. The present submission provides a good basis for any further such exploration.

Reviewer #3 (Public Review):

A difficulty with the paper is the different cognitive tests used in the different cohorts; the authors address this at some length in the discussion. However, I am afraid that this matter makes the results hard or impossible to interpret along the lines of their research question. One would need to know that, if these cognitive tests were administered in a single cohort at one time, they would have the same correlation with height.

I judge that the main limitation of the method is the fact that different cognitive tests are used in the different cohorts. The tests in themselves are valid tests of cognitive functions. However, given that the focus of the study is on the change in correlations across time, then it is a worry that the tests are different; that is, the authors have the burden of proving to us that, if the environmental/social changes had NOT been operative across time, then the height-cognitive test correlations would be the same. What can the authors do to prove to us that if, say, all of these different-cohort verbal tests had been given to a single cohort on a single occasion, then they would have the same correlations with height? The same goes for the mathematics based tests. I note the tests' somewhat different distributions in Figure 1, but that is not the only thing that could lead to different correlations with, say, height. I am aware that all cognitive tests tend to correlate positively and that they all have loadings on general intelligence; however, different tests will not necessarily have the same correlations with outside variables (e.g. height). This will depend on things such as their content, their reliability/internal consistency etc.

In the Results the authors state: "Cognitive test scores were strongly-moderately positively correlated with each other, with the size of the correlation weakening across time." That's true, but perhaps, also a major concern for this study. One possible reason for the decline in verbal-maths test correlations across cohorts (old to recent) is that the nature of these tests has changed across time, either/both in terms of content (what capabilities are assessed) or something such as reliability/internal consistency/ceiling-or-floor effects (how well the capabilities are assessed). That is, given that the height-cognitive test correlations show a similarly declining pattern of correlations over cohorts, it could be that the tests' contents (of the different tests) is partly or wholly responsible. I raise that as a possibility only, and I appreciate that it might be correct, as the authors prefer, that there is an inherent lowering of intelligence-height correlations over time, but I do not think that one can rule out-with the present study's design-that it might have been due to the change in tests. For example, a reading-math correlation of 0.74 in 1946 lowered to a correlation of .32 in 2001, in the face of different tests. To show that this is not due to the different tests being used would require more information. If this is a true result, it is big news.

I have a suggestion: if the authors wish to rule out the possibility that the lowering intelligence-height correlations across cohorts are due to different cognitive tests being used, they should take all the cognitive tests used here and apply them cross-sectionally to single-year-born samples (of 11- and 16-year olds) that have also been measured for height. If the cognitive tests all correlate at the same level with height within each of these two samples (they needn't do so across the 11- and 16-year olds), then one could proceed more safely with between-cohorts (1946, 1958, 1970, 2001) comparisons of the correlations.

Reviewer #3 (Public Review):

This manuscript will be of interest primarily to researchers in the field of NADPH oxidases (NOXs) but also to those interested in the wider ferric reductase superfamily, also comprising members of the six-transmembrane epithelial antigen of the prostate enzymes (STEAPs). More limited interest may be expressed by investigators of ferredoxin - NADP reductases, resembling the dehydrogenase region (DH) of NOXs, expressing lesser "visibility" in the structure described in the paper. Considering the fact that NOXs are essentially electron transport machines from NADPH to dioxygen, along a multi-step redox cascade, those interested in hydride and electron transfer, at a more conceptual level, might also want to have a look at the paper. Elucidating structures of NOXs are still rare achievements, with only four published papers, so far (one coming from the group of the present main author) and, thus, any new publication profits from the aura of novelty.

Introduction<br /> This manuscript offers a detailed and in depth description of the structure of the catalytic core of the human phagocyte NADPH oxidase, NOX2, in heterodimeric association with the protein p22phox. The phagocyte NADPH oxidase is responsible for the production of reactive oxygen species (ROS), the primary molecule of which is the superoxide radical (O2.-), derived by the one-electron reduction of molecular oxygen by NADPH. NOX2 belongs to the NOX family, consisting of 7 members (NOX 1-5, and DUOX1 and DUOX2), sharing common structural characteristics but expressing a wide variety of functions. The principal but not the only function of NOX2 is as a source of ROS for the killing of pathogenic microorganisms (bacteria, fungi, protozoa) engulfed by phagocytes in the course of innate and acquired immunity.

The structures of C. stagnale NOX5, and that of murine and human DUOX1 were determined by X-ray crystallography (NOX5) and cryo-EM (DUOX1). As sources of potentially dangerous auto-toxic ROS, NOXs are subject to strict functional regulation. Whereas Nox5 and the DUOXs are regulated by Ca2+, NOXs 1, 2, and 3 are regulated by several cytosolic proteins, that associate with the Nox2-p22phox dimer forming the active O2.-generating complex. The paramount model of cytosolic regulation is Nox2 and the "dream" of structure investigators is to elucidate the structure of NOX2 in both resting and activated states.

Achievements<br /> Note: When this paper was received for review, this reviewer was not aware of any publication dealing with the structure of human Nox2. However, on October 14, 2022 a paper was published on line, dealing with the structure of Nox2 (S. Noreng et al., Structure of the core human NADPH oxidase Nox2, Nature Communications (2022)13:6079). This review will not discuss the present manuscript in relation to the paper by S. Noreng et al.

This manuscript is successful in describing the structure of the NOX2-p22phox heterodimer using cryo-EM methodology. In order to compensate for the small size of the complex, use was made of the Fab of a monoclonal anti-Nox2 antibody binding an anti-light chain tagged nanobody. In order to mimic as much as possible the milieu of NOX2-p22phox in the phagocyte membrane bilayer, the authors reconstitute the quaternary complex in a nanodisc, using soybean phosphatidylcholine (PC) and a membrane scaffold protein (MSP). To the best of my knowledge, this is the first report of studying a NOX in a nanodisc, for both function and structure. Peptidiscs were used in determining the structure of human DUOX1 by a group led by the main author of this paper, but nanodiscs offer the advantage of adding a phospholipid chosen by the investigator. The purified nanodiscs incorporating the quaternary complex led to successful structure determination of the transmembrane domain (TMD), extracellular and intracellular loops, inner and outer hemes, distances between hemes and FAD to inner heme, and a hydrophilic tunnel connecting the exterior of the cell to the oxygen-reducing center of NOX2. The structure of the dehydrogenase region (DH) was less well defined; the FAD-binding domain (FBD) was more visible than the NADPH-binding domain (NBD). The structure of p22phox and the interface between Nox2 and p22phox are well described.

The mutations in NOX2 and p22phox causative of the deficient bactericidal function in Chronic Granulomatous Disease are related in detail to the location and role of the mutated residues as revealed by the solved structure.<br /> The authors make it clear that the structure, as presented, is in the resting state. The distances between hemes are suitable for electron transfer but the distance between FAD, in the FBD, and the inner heme is too large for transfer. The poor quality of the obtained structure of the DH (especially, the NBD), even after local refinement focusing, suggests its flexibility (mobility?) relative to the TMD and that, in NOX2, the DH is "displaced" relative to the TMD, when compared to the situation in the activated (by Ca2+) DUOX1. The mobility of NBD in NOX2 also results in weak interaction with FBD, making hydride transfer from NADPH to FAD inefficient

A major achievement of the work described in this manuscript is what I believe to be the first description of the activation of recombinant NOX2-p22phox in a nanodisc, to generate O2.-, when activated by a trimeric fusion protein (trimera), consisting of the functionally important parts of the three cytosolic components, p47phox, p67phox, and Rac (see Y. Berdichevsky et al., J. Biol. Chem. 282, 22122-22139, 2007). This proves that the resting state structure of NOX2-p22phox has all that is needed to be converted to the activated state. The fact that the nature of the phospholipid in the nanodisc can be varied and that this is known to have a major effect on the affinity of the trimera for NOX2-p22phox, offers additional advantages.

Weaknesses<br /> A weakness of this, otherwise impressive work, is the difficulty for readers who are not sufficiently "structure educated" to fully understand the "displacement" of the DH of NOX2, shown in the NOX2/DUOX1 overlay (Figure 5). The meaning of "centers of mass" of FBD and FAD, in Figures 5C and 5D, respectively, is not properly explained.

Yet another weakness is the much too vague wording of the change in NOX2 conformation from the resting to the activated state by cytosolic factors as "the cytosolic factors might likely stabilize the DH of NOX2 in the "docked" conformation which is similar to that observed in the activated DUOX1 in the high-calcium state". First, the evidence from biochemical studies of NOX2 activation indicates clearly distinct targets of individual cytosolic components and not a "block" action. There is also support for the conformational change being the result of the action of a single cytosolic component (p67phox), with the other cytosolic components acting as carriers or activators of one cytosolic component by another, such as Rac-GTP acting as a carrier and inducer of a conformational change in p67phox (see J. El-Benna and P.M-C. Dang, J. Leukoc. Biol. 110, 213-215, 2021, and E. Bechor et al., J. Leukoc. Biol. 110, 219-237, 2021). Also, the concept of "docking of the DH to the TMD" seems like an oversimplification of the many locations and partners of such "docking" and ignores the possible multiple consequence of such docking. Even before the appearance of structural studies of NOXs, revealing precise distances between redox stations (NADPH-FAD; FAD-inner heme; inner heme - outer heme), as first reported for C. stagnale Nox5, by F. Magnani et al., Proc. Natl. Acad. Sci. U.S.A. 114, 6764-6769, 2017, a shortening of the distance between an electron donor and acceptor at specific locations in the redox cascade was proposed. The most popular was the NADPH - FAD hydride transfer, based on structural work by P.A. Karplus on Ferredoxin - NADP reductases, the accepted model for the DH of NOXs.

An unfair request for an unachieved task<br /> Of course, the dream of those hoping for a structure-based response to solving the molecular mechanism of NOX activation is to see the structure of the activated NOX2 in complex with three cytosolic components. The compelling finding in the present manuscript that a nanodisc-embedded recombinant NOX2-p22phox can be activated to ROS production by the use of a [p47phox-p67phox-Rac] trimera (replacing three cytosolic components) will provoke in all the readers the wish to see the structure of such a complex. The size of the trimera with a GFP tag (108 kDa) might make the use of the anti-Nox2 Fab and anti-light chain nanobody, unnecessary. Prenylation of the trimera at the Rac moiety is bound to markedly enhance its affinity for the phospholipids in the nanodisc and is likely to generate a more stable complex, most suitable for cryo-EM (see A. Mizrahi et al., J. Biol. Chem. 285, 25485-25499, 2010).

Reviewer #3 (Public Review):

The authors present a machine learning method for predicting the effects of mutations on the free energy of protein stability. The method performs similarly to existing methods, but has the advantage that it is faster to run. Overall this is reasonable and a faster method will likely have some potential uses. However, not improving performance beyond the reasonable but not great performance of existing methods of course makes this a less useful advance. The authors provide predictions for a set of human proteins, but the impact of their method would be much greater if they provided predictions for all substitutions in all human proteins, for example. In places the text somewhat overstates the performance of computational methods for predicting free energy changes and is potentially misleading about when ddGs are predicted vs. experimentally measured. In addition, the comparison to existing methods is rather slim and there isn't a formal evaluation of how well RASP discriminates pathological from benign variants.

Reviewer #3 (Public Review):

Franco et al. consider two mosquito olfactory receptors that have different sensitivities to two odorants: CquiOr10 is activated by skatole while CquiOr2 is activated by indole. Starting with chimeric receptors composed of pieces from each receptor, they are ultimately able to identify a single amino acid that, when mutated, switches the specificity of the receptors. When Ala73 is mutated to a Leu in CquiOr10, the mutant receptor now preferentially binds indole, while the counterpart Leu74 to Ala substitution in CquiOr2 creates a receptor that is more sensitive to skatole. To better understand why these substitutions alter ligand-binding specificities, the authors use molecular docking to identify the likely interactions between indole or skatole and the natural or mutant CquiOr10 receptors. They conclude that the size of the amino acid at position 73 affects ligand specificity by altering the amount of space available to bind ligands.

Reviewer #3 (Public Review):

Motta, Erick et al. investigated the role of members of the bacterial gut microbiota of honey and bumble bees in the degradation of amygdalin, a plant cyanogenic glycoside found in almond trees and other plants. The role of the microbiota in contributing to secondary plant compounds in this system is of interest because it has been demonstrated that the genomes of these bees are depauperate in genes of detoxification enzymes relative to other insects. Using in vitro assays across a range of honey and bumble bee-derived strains of the bacterial species Bifidobacterium, Bombilactobacillus, Gilliamella, and Lactobacillus nr. melliventris the authors demonstrate strain-specific growth on amygdalin as a carbon source, clearly showing amygdalin metabolism by particular strains. The data strongly support that amygdalin degradation occurrence is not a pan-species trait, but rather strain-specific, and also that even within a bacterial species the strains metabolizing amygdalin achieve this through different pathways, with some strains producing the metabolite prunasin, but others not. Subsequent proteomics analysis suggests that a glycoside hydrolase family 3 (GH3) is likely responsible for the degradation of amygdalin. The conclusion that this GH3 is at least partially responsible for strain-specific degradation is supported by gene expression analysis of the enzyme and experiments with E. Coli transformed with the gene. Further in vivo studies demonstrate that the honey bee microbiota contributes to amygdalin metabolism, including specific strains of Bifidobacterium, but that the hosts themselves can metabolize amygdalin to prunasin in the absence of gut microbes, but not to the same degree.

The approach and evidence supporting the step-wise conclusions are comprehensive. However, further extension is required to gain a full appreciation for what the importance and relevance of the results for conclusions relating to cooperation between hosts and microbiota and particularly the consequences for host health.

Although the authors rightly do not directly interpret the attributed breakdown of amygdalin and its metabolites by specific bacterial strains as a benefit, this is alluded to in the title and parts of the discussion. Following the degradation of amygdalin through intermediates, hydrogen cyanide is produced. Hydrogen cyanide is generally considered to be detrimental. As such, it could be argued that is not appropriate to consider the production of such a compound as cooperative between host and microbiota, given that cooperation is usually to a beneficial end. Experiments exposing hosts with microbiota absent and present to amygdalin and relevant breakdown products and subsequently measuring relevant health outcomes would be an important step in aiding in the interpretation of the otherwise clear experimental outcomes. Especially given the relatively limited number of strains tested showing the ability to degrade amygdalin, it is possible that there is limited adaptive value, and/or the ability could be due to either chance or selection for the metabolism of other compounds. This is especially relevant when considering further work that may look at how health-related outcomes such as parasite resistance are affected.

This being said, the work adds to demonstrations of different functions of host gut microbiota, how they can mediate the environment encountered by hosts, and the increasing appreciation that effects derived from the microbiota can be not only dependent upon the bacterial species present but frequently the specific strains.

Reviewer #3 (Public Review):

In this study Szadai et al. show reliable, relatively synchronous activation of VIP neurons across different areas of dorsal cortex in response to reward and punishment of mice performing an auditory discrimination task. The authors use both a relatively fast 2 photon imaging, as well as fiber photometry for some deeper areas. They cluster neurons according to their temporal response profiles and show that these profiles differ across areas and cortical depths. Task performance, running behavior and arousal are all related to VIP response magnitude, as has been previously shown.

Methodologically, this paper is strong: the described imaging technique allows for fairly fast sampling rates, they sample VIP cells from many different areas and the analyses are sophisticated and touch on the most relevant points. The figures are of high quality.

However, as the manuscript is now, the presentation could be clearer, the methods more complete and it is not clear whether their conclusions are entirely supported by the data.

The main issue is that reinforcement and arousal are hard to distinguish in this study. It is well known that VIP activity is correlated with arousal. And it is fairly clear that the reinforcement they use in this study - air puffs to the eye, as well as water rewards - cause arousal. It is possible that the reinforcer responses they observe in VIP neurons throughout all areas merely reflect the increases in arousal caused by these behaviorally salient events. They do discuss this caveat (albeit not fully convincingly) and in their abstract even state that the arousal state was not predictive of reinforcer responses. However their data clearly shows the tight relationship of the VIP reinforcer responses to both arousal (as measured by pupil diameter), as well as running speed of the animal. Both of these variables are well known to be tightly coupled to VIP activity.

Although barely mentioned, the authors do appear to sometimes present uncued reward (Figure S2F). If responses were noticeably different from the same events in the task context (as actual reinforcers) this could at least hint towards the reinforcement signal being distinct from mere arousal. However, this data is only mentioned in one supplementary figure in a different context (comparison with PV cells) and neither directly compared to cued reward, nor is this discussed at all. Were uncued air puffs also presented? How do the responses compare to cued air puffs/punishment?

The imaging method appears well suited for their task, however the improvements listed in table S1 make the method appear far superior to existing methods in many aspects. Published or preprinted papers with 2 photon imaging of VIP populations (eg. from Scanziani lab (Keller et al.), Carandini lab (Dipoppa et al.), deVries lab (Millman et al.), Adesnik lab (Mossing et al.), which use the much more common resonant scanning, seem to be able to image 4-7 layers at 4-8Hz with a good enough SNR and potentially bigger neuronal yield of approximately 100-200 VIP cells, depending on the field of view. While not every single cell in a volume would be captured by these studies, the only main advantage of the here-used technique appears to be the superior temporal resolution.

Even though this is not mentioned at all, it certainly appears possible, that the accousto-optical scanning emits audible noise. In this case it would be good to know the frequency range and level of this background noise, whether there are auditory responses to the scanning itself and if it interferes with the performance of the animals in the auditory task in any way. If this is not the case, this should probably simply be mentioned for non-experts.

The authors show a strong correlation between task performance (hit rate) and the response to the auditory cue on hit trials. Was there any other significant correlations of VIP cells' responses to other trial types? Was reinforcer response correlated to behavioral variables at all?

Reviewer #3 (Public Review):

This manuscript studied an interesting topic: the maillard reaction, catalyzed by glyoxalases, converts α-dicarbonyl compounds to Advanced Glycation End-products (AGEs). glod-4 is one of the glyoxalases and MG-H1 is one of AGEs which is converted from methylglyoxal (MGO). The authors discovered that both glyoxalase glod-4 KO and supplementation of MG-H1 increased pumping rates in C. elegans. MG-H1 mediated pumping rates increase is dependent on glod-4. The authors further found that tyramine synthease tdc-1 and two of the tyramine receptors ser-2 and tyra-2 are required for the increased pumping by glod-4 knockout or MG-H1. They also found the transcriptional factor elt-3 is required for pumping increase by MG-H1 and glod-4 KO, and also regulates tdc-1 transcriptional level. Lastly, they found that tdc-1 and the two tyramine receptors mutants rescue the shorter lifespan of glod-4 and neuronal loss in glod-4.

The topic is interesting, and it is a good design to show mechanistic function of neurotransmitter in regulating tasty AGEs in a model organism. Most of the results are supported by the data.

Reviewer #3 (Public Review):

The authors' goal was to explore if there were fear behaviors expressed to a conditioned fear cue other than freezing and how the timing of these behaviors may change across a discrete conditioned cue. Three separate cues representing danger (1.0 footshock probability), safety (0 footshock probability), and uncertainty (0.25 footshock probability) were used against a backdrop of operant nosepoke responding for reward in male and female Long Evans rats. All behaviors were recorded with a frame capture of 5 frames per second and manually scored afterwards blindly for one of ten behaviors.

Analyzing the repertoire of possible behaviors, beyond freezing, across a 10s conditioned cue that may be perceived as dangerous, uncertain, or safe is a strength of the study. Displaying the possible behaviors stacked across the 10s, second by second, instead of a bulk 10s average of each type of behavior highlights the dynamic nature of the defensive behaviors expressed across time. It is unclear though why the 2s post-cue were not included since the footshock was not administered until 2s after cue offset. Given their argument of defensive behaviors being adjusted as the threat becomes more imminent, this 2s period would appear to be a valuable interval for their analyses and argument.

The authors emphasize the ethology of their findings but they also acknowledge that their findings do not agree with the majority of rodent fear conditioning papers reporting upwards of 80% freezing across a cue. Since these differences could be due to a myriad of experimental differences such as cue length, cue modality, number and strength of shocks, etc, it is difficult to extrapolate and apply the reported findings to potentially broader conditions; e.g. cues that are not 10s, non-rodent species, food-restricted vs not food-restricted, an environment that is not a small, enclosed box, etc. In the end, while additional defensive behaviors were reported in response to a danger cue, the predominant behavior still appeared to be freezing, although there were interesting differences noted between males and females in that females appeared to display most of their freezing early in the cue while males express a more sustained freezing response across the cue.

This work could certainly inspire other labs to approach their video analyses in a similar fashion and, although not discussed in the paper, could potentially be interesting to also look at individual differences across ethograms, instead of the grouped data presented across the 12 males versus 12 females as shown here. These could then be used before or after a manipulation and used to try to predict how an animal may respond to a certain event or manipulation.

Reviewer #3 (Public Review):

In this paper, the authors apply AlphaFold2 to predict the structure of membrane protein complexes in E.Coli. They scan ~1500 membrane proteins starting with one protein to predict the interactions. They present the results for four proteins and analyse them carefully to propose novel models for complexes.

The main problem with the manuscript is that the authors first claim that the method is highly specific but then cherry-pick a subset of interactions that they believe are correct (most likely they are). But the authors do not discuss the other high-scoring predictions. Are these false positives (in which case the method has very limited value) or novel interactions (which would be really interesting but needs further examination)?

Reviewer #3 (Public Review):

This manuscript investigates the basis for the cytoprotective effect of exogenous glycine, which has been known to limit cell lysis in response to various stimuli. The authors propose Ninjurin 1 (NINJ1) as a possible regulator or target of glycine-induced blockade of cell lysis, which is an attractive model, given the recently-described role of NINJ1 in inducing membrane rupture downstream of gasdermin cleavage in response to apoptotic and pyroptotic stimuli. The data that support the conclusion are that the authors report that glycine treatment phenocopies NINJ1 deficiency. They go on to conclude, using both native gel western electrophoresis and fluorescence microscopy to assay NINJ1 aggregation, that glycine treatment prevents higher order NINJ1 oligomerization. The authors test these observations in primary human and mouse cells as well as in human and murine macrophage cell lines. The analysis of the role of glycine in both human and murine cells is a strength of the work. This topic is of broad importance, as the mechanism and manner by which cells die impacts host defense against infection, cancer, and autoinflammatory disease. The mechanisms of terminal cell lysis remain surprisingly unclear as recent studies have found that gasdermin cleavage and oligomerization are not sufficient to mediate cell lysis and that cells can survive in the presence of functional gasdermin D pores. Previous studies have reported that glycine treatment limits the release of some cytoplasmic contents during the activation of pyroptosis, but does not affect the secretion of IL-1 cytokines. This property of glycine phenocopies NINJ1 deficiency, suggesting a possible link between the two. This work, therefore, has the potential to shed further light on the regulation of cell lysis, if the studies can be made more definitive with better quantification and more robust controls, which are currently missing for a large portion of the data.

Overall, the area and topic being investigated are of broad interest. While the manuscript attempts to make inroads into how glycine functions as a cytoprotectant, in its current form, the manuscript does not provide definitive evidence that glycine functions through NINJ1, and the data that are currently provided require substantial development, including the addition of key controls and better quantification of microscopy in order for the authors to robustly make the conclusions that they would like to make.

Reviewer #3 (Public Review):

In this contribution, the authors align an extensive analysis of in vivo recordings of olfactory receptor neuron (ORN) responses to odors in the locust with a data-driven mathematical model of ORN population coding. Their results provide novel insights into the temporal dynamics of peripheral encoding of time-varying and naturalistic olfactory input.

The manuscript presents three central experimental results: 1) ORNs odor responses can be grouped into 4 distinct response motifs (response profiles). This has partly been known with respect to the typical excitatory phasic-tonic motif and odor offset responses. The exhaustive data set here is however unprecedented. 2) Individual ORNs can switch their response motif, e.g. from excitatory to inhibitory responses. To my knowledge, this is entirely new, highly interesting, and has strong implications. For one it implies an increased coding space and odor separability, which is supported by the authors' model study. It also bears implications for our understanding of processing in the antennal lobe where projection neurons were shown to exhibit property but this has largely been attributed to network processing within the AL. The authors discuss ephaptic interactions as a possible underlying mechanism. 3) ORNs not only show classical within and across pulse adaptation where the response amplitude reduces, but also the novel result that the offset response can increase across repeated pulses with short inter-stimulus intervals. The data-driven model reproduces the experimental observations and a population model that confirms the assumed increase in coding space. In the temporal domain, the authors then perform simulations that mimic realistic stimulus statistics with stochastic arrival of odor packets of variably short duration. The model with a trained linear filter and a non-linear transfer function faithfully predicts the experimental firing rates.

These results, based on an exhaustive set of experimental data, provide a novel view of peripheral odor coding in insects and they will have a particularly strong impact on biologically realistic computational (spiking) circuit models of sensory processing and sensory-to-motor transformations during odor source navigation in naturalistic simulated odor environments where conclusive data and analysis of ORN signaling has thus far been lacking.

Reviewer #3 (Public Review):

Lefevbre et al combine in toto imaging with "tissue cartography" to investigate the respective roles of pair-rule (PR) and toll-like receptor (TLR) gene expression, and embryo geometry, in shaping anisotropic distributions of myosin II during germband elongation (GBE) in Drosophila embryos. The authors find that the simple dependence of Myosin II on PR and TLR expression gradients cannot explain observed global patterns of myosin II. PR and TLR expression patterns evolve continuously as expressing cells are advected by tissue flow during GBE, while myosin II anisotropies remain roughly stationary even as myosin-rich junctions are advected and reoriented by tissue flows. The authors show that the observed spatiotemporal evolution of myosin II anisotropies in wild-type and certain mutant embryos can instead be explained by a simple model in which a geometric cue promotes myosin II accumulation of vertically oriented junctions, flows advect myosin-rich junctions, and myosin II turns over on a ~5-minute timescale.

The core findings are well-supported by rigorous quantitative analysis and modeling; they provide a fresh perspective on the role of geometry in the dynamic control of myosin II anisotropies. Thus they are likely to stimulate further experimental work to identify and characterize the underlying basis for this geometric control.

Key strengths

A key strength is the use of in toto light sheet imaging and tissue cartography, plus the high stereotypy of early Drosophila development, which allows the authors to assimilate data across multiple embryos to extract robust quantitative signatures of gene expression, protein localization, and tissue flows that allows robust analysis of relationships between these different factors in the wild type and across different mutants.

A second strength is the introduction of a very simple model for the evolution of myosin II anisotropy driven by local tissue rotation and myosin turnover which allows decomposing of their respective contributions.

Weaknesses

The power of the model is tested only by its sufficiency to reproduce observed features of myosin II anisotropy over time. There is no direct test/verification of a core model assumption - that the local binding of myosin II is biased with respect to a static geometric signal. Similarly, the inference from the model fits that myosin binding times are reduced in eve mutants has not been confirmed (e.g. by FRAP experiments).

There are a number of (clearly fixable) issues with the clarity of presentation - especially if the authors wish to make their work accessible to a broad audience. The comparison of model predictions and experimental observations is presented in a somewhat confusing way. Ditto for the analysis of mutant phenotypes and the conclusions drawn from this analysis. Some key information about the choices made to justify a very simple model (i.e. why alternative hypotheses and/or additional complexity in the junctional dynamics can be ignored) is presented only in the Supplementary text and should be summarized in the main text.

Reviewer #3 (Public Review):

Meyer et al have studied the mechanisms of glycolysis activation in the hippocampus during neuronal activity. The study is logically laid out, uses sophisticated fluorescence lifetime imaging technology and smart experimental designs. The support for intracellular [Na+] vs [Ca2+] rise driving glycolysis is strong. The evidence for the direct involvement of the Na+/K+ pump is based only on pharmacology using ouabain but the Na+/K+ pump is admittedly not an easy subject for specific perturbations. I still think that the Authors should strengthen the support for the pathway.

Also, there is a long list of publications on the connection between the Na+/K+ pump and glycolysis. It might be useful to highlight the role of the NCX- Na+/K+ pump coupling in the activation of glycolysis in the title.

Reviewer #3 (Public Review):

In this paper, for the first time, metabolomics, proteomics, and lipidomics are combined to multi-dimensionally obtain more objective and scientific clues about early and advanced PMI, compared to the traditional methods of PMI estimation that relies on the subjective judgment of morphology. The "ForensOMICS" pipeline establishes a multi-omics analysis pipeline based on the LC-MS platform, which will bring influence and inspiration to the related research of PMI estimation based on molecular biological markers in the foreseeable future. However, due to the limitation of the availability of bone samples and metadata (which might contain covariates with latent influences on the PMI estimation), the current research is still a proof-of-concept study which is incomplete for the "ForensOMICS" approach to be applied in court.

Strengths:

Combing multiple omics and bioinformatics, as claimed by the authors, the "ForensOMICS" approach is more accurate and precise than the conventional morphological methods and molecular biological methods using single omics. Moreover, the research does not stop at developing time-dependent models using several omics biomarkers but carries on the enrichment analysis of relevant markers to further explore the pathophysiology mechanism behind the great changes in the internal environment after death, so as to provide meaningful reference data for the basic forensic research of death.

Data Integration Analysis for Biomarker discovery using Latent variable approaches for Omics studies (DIABLO) method and multiple features selecting tools are used in the bioinformatic process to analyze multiple omics data, and PMI classification model constructed based on PLS-DA, with parameters optimized by 3-fold/100 repeats cross-validation. The overall analysis process is relatively complete, and the data and classification model provided have scientific values for reference.

The "ForensOMICS" workflow in principle is compatible across metabolomics, proteomics, and lipidomics data obtained in different domains of proof-of-concept studies focusing on forensic-related time estimation (e.g. post-mortem submersion interval and time since deposit), for offering relatively complete analysis process.

Weaknesses:

Although the paper does have strengths in principle, the limitation of the availability of bone samples and metadata leads to the major weaknesses of the paper. Therein, age bias samples with single bone type and lack of analysis for environmental factors are the major weaknesses that argue against the key claims in the manuscript by the data presented.

The mean age of body donors is 74 years with {plus minus}11.6 years of standard deviation, while there was only one type of bone tissue (left anterior midshaft tibia). Different structures and locations of the sampled bone tissue as well as metabolic changes and bone degeneration caused by aging may lead to significant discrepancies in different multi-omics data. Moreover, most of the dead found at crime scenes are in the prime of life, and in addition to the tibia, other skeletal remains found at the scenes are commonly skull, ribs, upper limb bones, and teeth. Therefore, the relevant conclusions obtained from the research based on the limited bone samples cannot meet the actual needs for estimating the PMI of skeletal remains. As mentioned by the authors in the discussion, due to the difficulty in acquiring human remain samples with definite post-mortem intervals, this study is still proof-of-concept. If possible, the authors can focus on a larger sample set of different bone remains in younger age groups in future studies.

It is suggested that metadata which may be influence factors of PMI such as temperature, humidity, UV-exposure, and deposition context (which is already recorded) should be recorded and statistically analyzed, so as to further optimize the "ForensOMICS" classification model by considering these possible environmental covariates. In addition, according to the No Free Lunch theorem, PLS-DA is very likely not to be the optimal solution for all the above-mentioned PMI classification tasks based on multi-omics data under different environmental conditions. It is recommended to develop and compare more different classification models for improving the generalization performance of the "ForensOMICS" approach.

Due to the limitation of sample size and the discrete-time gradients, the omics data obtained in the paper could only be applied to build a classification model rather than the regression model. Since such a model does not give a specific predicted PMI with MSE and RMSE indicating its performance, and the current "ForensOMICS" approach failed to distinguish different samples of late PMI (219-834 days), there is still a distance for "ForensOMICS" approach to apply in the actual forensic practice.

Reviewer #3 (Public Review):

In an ambitious, multimodal effort, Handlin, Novembre et al. investigated how the endogenous release of oxytocin and cortisol as well as functional brain activity are modulated by social touch under different contextual circumstances (e.g. palm vs. arm touch, stranger vs. partner touch) in neurotypical female participants.

Using serial sampling of plasma hormone levels in blood during concurrent functional MRI neuroimaging, the authors show that the familiarity of the interactant during social touch not only impacts current hormonal levels but also subsequent hormonal responses in a successive touch interaction. Specifically, endogenous oxytocin levels are significantly heightened (and cortisol levels dampened) during touch from a romantic partner compared to touch from an unfamiliar stranger, at least during the first touch interaction. During the second touch interaction, however, oxytocin levels plummeted when being touched by a stranger following partner touch (although a recovery was made), whereas the normally elevated oxytocin responses to partner touch were dampened when following stranger touch. These results are paralleled by similar familiarity- and order-related effects in neural regions involving the hypothalamus, dorsal raphe, and precuneus.

However, an important distinction to be made is that, although a significant main effect of familiarity was encountered in several brain regions when taking peak plasma oxytocin levels into account, subsequent t-tests showed no activation differences in the BOLD response between partner and stranger touch within the same subjects. Significant interaction maps seem thus mainly driven by between-subject effects at the different time points, which is arguably due to differences between subjects in their initial calibration of neural/hormonal responses, and not session-to-session changes within the same subjects.<br /> A similar comment can be made for the reported covariance between (changes in) maximal oxytocin levels and (changes in) BOLD activity for the hypothalamus.

In an effort to delineate the complex cascade of responses induced by afferent tactile stimulation, the authors report an exploratory regression analysis to identify BOLD activation that precedes the pattern of serial plasma changes in oxytocin levels (looking backwards; i.e. implying changes in brain activation drive changes in hormonal plasma levels). Although the authors are appropriately modest about the significance of the encountered effects, additional control analyses could bring further clarifications about the temporal (e.g., can similar covariations also be found when looking forward) and hormonal specificity (e.g. can similar findings be found for cortisol-variations) of the encountered results. Nevertheless, despite the 'dynamically' covarying relationships between BOLD and max plasma oxytocin levels (i.e. dynamic as in the sense across conditions, not across timepoints), claims about the directionality of this effect (i.e. 'hormonal neuromodulation' vs. 'neural modulation of hormonal levels') remain speculative.

A particular strength of this study is the employment of a "female-first" strategy since experimental data concerning endogenous oxytocin levels in women are sparse. Adequate control analyses are reported to take potential variability due to differences in contraception and phase in the hormonal cycle into account.

Reviewer #3 (Public Review):

In their manuscript Christoph Wiest and colleagues tested the recently established excitation/inhibition (E/I) hypothesis in data from both patients suffering from Parkinson's disease (PD) and a PD rodent model. In particular, they study activity from the basal ganglia, primarily the subthalamic nucleus (STN). It is a thoughtful work which uses sound methods and is well-written and well-structured. The figures are strikingly good.

The authors demonstrate that the aperiodic exponents and power at 30-100 Hz in such data reflect changes in basal ganglia network activity.

Strengths:<br /> - The clear aim and the rare and valuable rodent and patient data under study.<br /> - The cross-species approach.<br /> - Clear perspective towards adaptive deep brain stimulation application.<br /> - Excellent integration in the existing body of knowledge.

Weaknesses:<br /> - No clear link between findings and symptom severity.<br /> - Relatively low number of animals/patients.<br /> - Limited consistency of results across individual data set.<br /> - In parts weak correlations.

All in all, the present manuscript provides initial evidence that the E/I hypothesis is also valid for neurophysiological data from the STN in PD patients and corresponding rodent models.<br /> This is an important finding which will strengthen the idea of the E/I hypothesis in general and also further substantiates our knowledge about neurophysiological activity of the STN.

Reviewer #3 (Public Review):

Our brain is comprised of both electrically active neurons that transmit information and an equal number of a set of cells called glial cells, which are actually comprised of many different cell types with a variety of functions. Compared to neurons, we know much less about glia and therefore need model systems in which they can be studied.

This study reports the generation of an atlas of glial cells in the Drosophila fly model. Drosophila glia have many similarities with those of vertebrates and are a useful model system for the interrogation of glia due to their simplicity and ease of genetic manipulation to better understand glial cell biology. This study catalogued the morphology of various types of glia in different areas of both the developing and adult fly, building a repository that will be of immense value to researchers. The study also aimed to determine how the shape of different glia, or even within glia of the same type related to the genes that they expressed, and their molecular state. The study found that while cell morphology was tightly linked to gene expression state in some cases, in others it was not, meaning that cells with very different shapes had very similar gene expression profiles/ molecular states. This latter finding suggests that at least some glial cells' shapes are more likely controlled by their interactions with the environment or molecular events that are independent of gene expression per se. The study is very impressive in its depth of characterisation and will come to represent a very useful resource for the community of biologists who employ Drosophila to understand glial cell biology.

Reviewer #3 (Public Review):

The authors present a comprehensive profile of signals and sensors expressed in mouse and human PNECs by single-cell RNA sequencing. Analyses revealed a myriad combination of neuropeptide, neurotransmitter, receptor, and channel genes in PNECs. A diverse transcript combination is further enriched by alternative posttranscriptional and posttranslational processing. The authors also surveyed cognate receptors expressed in epithelial cells, endothelial cells, stromal cells, immune cells, and pulmonary sensory neurons, identifying potential local targets for the PNECs signals. The scRNA-seq profile from lung carcinoid tumors suggests that selected PNECs are susceptible to carcinoid transformation. Together, these data indicate that PNECs serve as sentinels to perceive multiple airway stimuli and express a variety of signals that either act locally or potentially through circulation to regulate homeostasis.

Reviewer #3 (Public Review):

It has been difficult to predict perceptual quality of odor mixtures. In this study, Dhurandhar and colleagues developed a computational method to predict perceptual discriminability of odor mixtures. The authors previously developed a method to predict natural language descriptors from chemical structures of monomolecular odorants (Gutierrez et al., Nat. Commun. 2018). In the new model developed in the present study, the authors used these predicted natural language descriptors to predict the discriminability of odor mixtures. This was done by first averaging the values of natural language descriptors across component odorants in a mixture. The authors then used a Lasso regression to predict the fraction of subjects that correctly discriminated these odors from the Mahalanobis distance between the average descriptors of two odorants. The performance of the model was compared against a "Direct model" in which chemical structures were used directly to compute the vector angles based on the cosine similarity metric.

The authors address an important question and the model that the authors propose is potentially interesting to the community. The method is relatively simple and the manuscript was written relatively clearly. However, I have some concerns on the approach or methods used.

Major concerns

1. The authors compare the new model against the Direct model. The performance was compared based on the root mean squared errors (RMSE). While the result indicates statistically significant improvement, the models differ in multiple ways, and it is unclear what components in the new model contributed to the improvement. The authors should compare a model in which discrimination performance was predicted based on chemical structures using a Lasso regression. Comparison to this model would be necessary to demonstrate that transforming to the natural language descriptors was critical for the improvement, and not due to just the use of Lasso.

2. The authors should compare their model against other classes of model proposed before.

Frederic L. Paxson kept a collection of 3 x 5 " slips of thin paper that filled eighty wooden file drawers which he organized using topical-chronologic headings spanning 4639 BCE to 1949.

Reviewer #3 (Public Review):

Hinnekens et al. examined the development of humans' leg movements as they learn to step, kick, and independently walk during infancy. An established theory argues that motor movements can be composed of a finite set of building blocks ("motor primitives"), just like any word can be composed of a finite set of letters. In their paper, Hinnekens et al. follow up this theory by longitudinally recording muscle activations of infants using EMG (at three time points: a few days after birth, at 3 months, and shortly after they learned to walk independently). The authors examined two modules that underlie the infants' stepping and two modules that underlie toddler walking, all based on previous literature. The authors also examined different modules that underlie infants' upright stepping and supine kicking. The authors used supervised machine learning (an advanced version of factor analysis) to identify the modules and to track their change at the different developmental time points. The authors found that trial-to-trial variability in the structure of primitives reduces from newborns to toddlers, even though the number of primitives increased. The authors relate these findings to motor exploration by arguing that newborns generate high variability with a low number of primitives.

The paper has one clear strength - its longitudinal recordings. Unlike most papers in this area of research, the authors follow the same individuals from birth until they learn to walk and the comparison between the use of primitives is done on the same infants. This is certainly novel.

That said, the contribution of the paper to the literature is unclear and it suffers from some critical weaknesses that challenge the current conclusions in the paper, based on the existing data.

1. Although the data is based on longitudinal recordings, and this is certainly desirable, the paper is based only on 10 infants. Moreover, only seven infants contributed supine data at the first time points and only six infants contributed upright data at the different time points. The paper would benefit from a more reliable dataset that includes more infants and time points to compare. To conclude the authors' conclusions, much richer data is required.

2. Relatedly, although the strength of longitudinal data is compared between individuals and has significant insights into individual differences in development, this was not clearly (sometimes not at all) discussed in the paper. The work would benefit from more focus on individual differences and a clear explanation of its contribution to the field from that aspect. The key arguments in the paper focus on the ratio between the number of primitives and the variability in each time point, but none of this from the lens of individual differences. This is challenging to do because there are not many individuals who contribute to the dataset but otherwise, it is not clear what the paper contributes to previous work and more critically.

3. The motivation for the paper is unclear. Why did the authors do what they did? Why is this important to do it the way they did? In the current manuscript, it is not clear why they used this design to get those conclusions.

4. The data selection process is also not clear. At each time point and from each infant, the authors examined 5 cycles from the same leg. The definition of a cycle was hip-flexion onset to another hip-flexion onset on one side of hip extension. It is not clear what variability (measured by % of the cycle in flexion and extension) means in this case because infants hold their legs in one position for a long time. What are those 5 cycles? Why five? A lot of information is missing there about the arbitrary selection of analytic parameters. In addition, the authors argue they performed the same analyses with different parameters and that they got similar results. However, those results are not given in detail and it is hard to compare them with the authors' report.

5. The recording times are not common across individuals. One newborn was recorded after 1 day and the other after 21 days. Not sure this is comparable, especially if the main contribution of the paper is the longitudinal data. Moreover, the second recording was conducted between 74 days to 122 days. This range is too broad. Same for the third time point - one walk onset is not reported, some infants were recorded at <380 days and some >500 days. This difference challenges the reliability of the data.

6. Conceptually, I'm not sure I understand why the authors selected leg alternation (and not other types of movements) as their modules. I was not convinced that leg alternations reflect their real-life locomotor experience (e.g., short bouts in all directions), and therefore the variability measured in this work does not reflect the variability of infants' natural locomotor behaviour.

7. There is not enough rationale for why the specific measurements (IEV, VAF, IRV, etc.) were used and why those are the appropriate ones for the address the questions in the paper. What is the justification for using those measurements?

8. Some of the conclusions, especially those that relate to motor exploration, are not based on sufficient data. Motor exploration was not explicitly measured in this study, and how motor exploration is reflected by the current data and analyses is not clear.

Reviewer #3 (Public Review):

The authors showed that SLC38A5, in the retina, was primarily expressed in the vasculature, and its expression is under the direct control of Wnt/beta-catenin signaling. The deficiency of SLC38A5 resulted in delayed retinal vascular growth and reduces neovascularization in OIR model. Additionally, the authors addressed the mechanisms of Slc38a5 as a glutamine transporter regulating retinal vascular development through VEGF receptors.

Reviewer #3 (Public Review):

The Cre-Loxp leakage phenomenon in the transgenic mice have been noticed for year. The current study systematically applied multiple "tissue-specific" Cre mouse lines and found that the mouse epididymis is a hot spot for the Cre-Lopx off-target effect. The authors try to demonstrate that the off-target effect in the epididymis could be mediated by the transfer of Cre mRNA/protein molecules from the original Cre-expressing tissue (e.g. brain). Their conclusions are partially supported by the serum/exosome transfer experiment and parabiotic pair experiments (only 1 parabiotic pairs shows positive result, while others didn't).

Overall, these experiments involve lots of works and should be appreciated by the field. However, the paper didn't stringently test the other possibility of Cre-Loxp leakage phenomenon, which is due to transcriptional leakage of the Cre system in the epididymal tissue. Also, the inconsistent of result from parabiosis within limited animal replicates, the questionable quality of PCR results in multiple figures has led to an uncertainty of the conclusion.

Reviewer #3 (Public Review):

This is a remarkable paper which was a pleasure to read. It documents the ability of Type II Topoisomerases of yeast and human to undergo liquid-liquid phase separation, describes the basis for this process in protein structure, and reveals its modulation by DNA and post-translational modifications. Each finding is supported by rigorous, well-controlled and carefully executed and interpreted experiments. The conclusions are clear and unavoidable. The Discussion presents knowledgeable evaluations both of the mechanistic bases for the observed effects and the likely general (and some specific) implications of the findings for context-specific moduation of Topoisomerase II activity. I have no suggestions for improvement. This paper is a classic in this already sophisticated field. The authors present important new and interesting observations while, at the same time, providing the general reader with a beautiful, well-referenced overview of the intricacies of Type II Topoisomerases.

Reviewer #3 (Public Review):

This paper uses a large (638 species representing 598 genera in 138 families) extant sample of osteologically adult mammals to address the question of proximodistal patterns of cross-taxonomic diversity in forelimb bony elements. The paper concludes, based on a solid phylogenetically controlled multivariate analysis of liner measurements, that proximal forelimb elements are less morphologically diverse and evolutionarily flexible than distal forelimb elements, which the paper concludes is consistent with a developmental constraint axis tied to limb bud growth and development. This paper is of interest to researchers working on macroevolutionary patterns and sources of morphological diversity.

Methodological review

Strengths:

The taxonomic dataset is very comprehensive for this sort of study and the authors have given consideration to how to identify bony elements present in all mammalian taxa (no small task with this level of taxonomic breadth). Multivariate approaches as used in this study are the gold standard for addressing questions of morphological variations.<br /> The authors give consideration to two significant confounders of analyses operating at this scale: phylogeny and body size. The methods they use to address these are appropriate, although as I note below body size itself may merit more consideration.

Weaknesses:

The authors assume a lot of knowledge on the part of the reader regarding their methods. Given that one of their key metrics (stationary variance) is largely a property as I understand it of OU models, more explanation on the authors' biological interpretation of stationary variance would help assess the strength of their conclusions, especially as OU models are not as straightforward as they first appear in their biological interpretation (Cooper et al., 2016).<br /> It is unclear what the authors mean when they say they "simulated the trait evolution under OU processes on 100 datasets". Are the 100 datasets 100 different tree topologies (as seems to be the case later "we replicated the body mass linear regressions with 100 trees from Upham et al (2019)." If that is so, what is the rationale for choosing 100 topologies and what criteria were used to select the 100 topologies?<br /> The way the authors approach body mass and allometry, while mathematically correct, ignores the potential contribution of body mass to the questions the authors are interested in. Jenkins (1974) for example argued that small mammals would converge on similar body posture and functional morphology because, at small sizes, all mammals are scansorial if they are not volant. Similarly, Biewener (1989) argued that many traits we view as cursorial adaptations are actually necessary for stability at large body sizes. Thus size may actually be important in determining patterns of variation in limb bone morphology.

Review of interpretation.

The authors conclude that their result, in showing a proximo-distal gradient of increasing disparity and stationary variance in forelimb bone morphology, supports the idea that proximo-distal patterning of limb bone development constrains the range of morphological diversity of the proximal limb elements. However, this correlation ignores two important considerations. The first is that the stylopod connects to the pectoral girdle and the axial skeleton, and so is feasibly more constrained functionally, not developmentally in its morphological evolution. The second, related, issue arises from the authors' study itself, which shows that the lowest morphological integration is found in the stylopod and zeugopod, whereas the autopod elements are highly integrated. This suggests a greater tendency towards modularity in the stylopod and zeugopod, which is itself a measure of evolutionary lability (Klingenberg, 2008). And indeed the mammalian stylopod is developmentally comprised of multiple elements (the epiphyses and diaphysis) that are responding to very different developmental and biomechanical signals. Thus, for example, the functional signal in stylopod (Gould, 2016) and zeugopod (MacLeod and Rose, 1993) articular surface specifically is very high. What is missing to fully resolve the question posed by the authors is developmental data indicating whether or not the degree of morphological disparity in the hard tissues of the forelimb change over the course of ontogeny throughout the mammalian tree, and whether changing functional constraints over ontogeny (as is the case in marsupials) affect these patterns.

References

Biewener, A. A. (1989). Scaling body support in mammals: limb posture and muscle mechanics. Science, 245(4913), 45-48.<br /> Cooper, N., Thomas, G.H. and FitzJohn, R.G. (2016), Shedding light on the 'dark side' of phylogenetic comparative methods. Methods Ecol Evol, 7: 693-699. https://doi.org/10.1111/2041-210X.12533<br /> Gould, F.D.H. (2107), Testing the Role of Cursorial Specializations as Adaptive Key Innovations in Paleocene-Eocene Ungulates of North America. J Mammal Evol 24, 453-463. https://doi.org/10.1007/s10914-016-9359-4<br /> Jenkins, F. A. (1974). Tree shrew locomotion and the origins of primate arborealism. In F. A. Jenkins (Ed.), Primate locomotion. New York: Academic Press.<br /> Klingenberg, C. P. (2008). Morphological Integration and Developmental Modularity. Annual Review of Ecology, Evolution, and Systematics, 39, 115-132. http://www.jstor.org/stable/30245156<br /> MacLeod, N., & Rose, K. D. (1993). Inferring locomotor behavior in Paleogene mammals via eigenshape analysis. Am J Sci, 293(A), 300-355.

Reviewer #3 (Public Review):

Here, Saito et al. studied the mechanism underlying Seipinopathy, a dominant motor neuron neurodegenerative disease, showing that non-glycosylated Seipin dominantly inactivates ER calcium pump SERCA2b and subsequently causes ER stress and apoptosis. Seipin is a key regulator of lipid metabolism and involves in the biogenesis of lipid droplets. This manuscript provides valuable insights into the role of non-glycosylated Seipin in ER calcium homeostasis and ER stress-induced apoptosis, which is important for a better understanding of the pathogenesis of Seipinopathy and the role of ER calcium in neurodegenerative diseases.

1. The biochemical and genetic evidence from HCT116 cells showed in this manuscript strongly supports the function of non-glycosylated Seipin in ER stress and cell apoptosis by disrupting ER calcium homeostasis. However, a concern about this study is the colorectal carcinoma cell line HCT116 used. Neuron cell expresses a much higher level of Seipin than HCT116 cells. Although a higher level of non-glycosylated Seipin was expressed in HCT116 Seipin knockout cells to mimic the physiological level in neuron cells, whether non-glycosylated Seipin exhibits the same mechanism in neuron cells is still unclear. Further studies in neurons or cell lines with comparable Seipin level will help to understand its actual role in neurodegenerative disease.<br /> 2. A higher level of non-glycosylated Seipin expression causes aggregates/clusters of Seipin on ER, as shown in Figure 1C. Since non-glycosylated Seipin physically interacts with SERCA2, it is important to know whether non-glycosylated Seipin expression changes the localization of calcium pump SERCA2b on ER.

Reviewer #3 (Public Review):

Dingus et al. have developed an innovative and powerful approach for improving the intracellular stability of nanobodies. Nanobodies are single chain antibodies that are typically generated in select species such as llamas or alpacas. Because nanobodies are secreted and are present in general in the extracellular environment, they often become unstable when expressed in the reduced intracellular environment. Dingus et al. investigated 75 nanobodies from the Protein Data Bank and found that 42 were unstable when expressed intracellularly. In order to improve stability of these nanobodies, they first determined consensus residues that were present within the framework region, which does not include the CDR regions, in over 80% of the stable nanobodies. Mutating residues within the framework of unstable nanobodies to match consensus residues in the stable nanobodies stabilized 26 of 42 nanobodies. Mutating consensus unstable residues stabilized another 11. Thus 37/42 unstable nanobodies were stabilized using this mutational approach. Further experiments provided evidence that some of the stabilized nanobodies still had some affinity for their targets. Furthermore, one stabilized nanobody was stable when expressed in the retina in vivo and 3 of 5 were stable when expressed in bacteria.

1. This study provides a straightforward approach to improving the intracellular stability of nanobodies that could prove to be very useful for solving a common and vexing problem.

2. From the data provided, it was difficult to determine whether the binding affinity of the mutated nanobodies had been diminished by the mutations that increased stability, and if so, by how much. Furthermore, target binding affinity was assessed for just 5 nanobodies, which calls into question whether this strategy will be useful.

3. Ultimately, the goal of expressing most nanobodies intracellularly is to bind to endogenous targets. It is difficult to assess how useful the stabilization strategy will be since it was not determined whether any of the stabilized nanobodies could bind their endogenous targets intracellularly.

Reviewer #3 (Public Review):

The authors have obtained beautiful structures of the OB-fold of RPA70 and peptides of interacting partners. This is accompanied by biochemical assays to show binding.

What is absent is a clear comparison of the binding sites, peptide orientations (in schematic format) and implications for regulation of ssDNA binding (by RPA70 and partner) as well as regulation of activity.

The impact of the paper in its current format is limited and can do with significant improvement.

Reviewer #3 (Public Review):

In this work, Ramaprasad et al. aimed to investigate the roles of a glycerophosphodiesterase (PfGDPD) in blood stage malaria parasites. to determine its role, they generated a conditional disruption parasites line of PfGDPD using the DiCre system. RAP-induced DiCre-mediated excision results in removal of the catalytic domain of this protein. Loss of this domain leads to a significant reduction of parasite survival, specifically affecting trophozoite stages. They suggest that there is an invasion defect when this protein domain is deleted. They additionally show the introduction of an episomal expression of PfGDPD can rescue the activity of the protein and restore parasite development. Interestingly, exogenous choline can rescue the effects of the loss of PfGPDP. Lipidomic analyses with labelled LPC show that choline release from LPC is severely reduced upon protein ablation and in turn prevents de novo PC synthesis. These experiments also show increase in DAG levels suggesting a defect in the Kennedy pathway. The authors purified PfGDPD and enzymatically show that this protein facilitates the release of choline from GPC. Additionally, the paper also briefly looks at the effects of the protein during sexual blood stages and show this is unlikely to be involved in sexual differentiation.

This paper is of interest to the community since the breakthrough paper of Brancucci et al. (2017), which showed us that decreased LPC levels induce sexual differentiation. This work brings novel insight into a GDPD responsible for the release of choline from GPC which actual seems more relevant to asexual stages and not sexual stage parasites. The authors have been extremely thorough in their experimentations on parasite viability and the exact role of this protein.

Reviewer #3 (Public Review):

The described work is about assessing Drosophila midgut histopathology upon consumption of an entomopathogenic strain of B. thuringiensis and its Cry1A toxins, which are lethal to lepidoptera, but non-lethal to Drosophila. Thus, Drosophila is characterized a non-susceptible organism. The authors tested if this "non-susceptible host" is nevertheless histopathologically susceptible. They convincingly show that it is, because the mechanism of action of the Cry1A toxins on progenitor cell E-Cadherin is functionally (but not biochemically) revealed in flies and in Drosophila S2 cells.

Strengths: The thorough cell fate analysis based on reporter genes and the alternative methods tested e.g. the wild type vs. mutant bacterial strains and purified active and inactive versions of Cry toxins.

Weakness: The heavy reliance on reporter transgenes, instead of staining of endogenous proteins and the lack of clonal analysis. Despite this the main conclusions are sufficiently supported.

Reviewer #3 (Public Review):

Sherpa, Müller et al. utilize temporal global proteome analysis of human erythropoiesis models to identify dynamic differential expression of RANBP9 and RANBP10, two homologous subunits of the multi-subunit ubiquitin E3 ligase CTLH. Through elegant biochemical and structural approaches, the authors provide compelling evidence that RANBP9 and RANBP10 form distinct, but structurally similar, catalytically competent CTLH E3 ligase complexes, that are differentially enriched in different stages of erythrocyte differentiation. Using CRISPR/Cas mediated knock outs, the authors inactivate the catalytic subunit of the CTLH E3 ligase, MAEA, or its cognate E2 enzyme UBE2H and show that this leads to spontaneous differentiation in erythrocyte progenitors under maintenance conditions and provide evidence that loss of these two proteins also accelerates differentiation. Interestingly, in these experiments the authors find that loss of MAEA leads to proteasomal degradation of UBE2H, which can be rescued by wildtype, but not catalytically inactive MAEA, demonstrating that UBE2H stability is coupled to cognate E3 ligase activity.

Strength:<br /> This study confirms previously known transcriptional regulation and functions of UBE2H and CTHL E3 ligase components during erythrocyte differentiation and identifies a previously unrecognized role for CTHL E3s during erythrocyte progenitor maintenance. In addition, the authors identify two new regulatory mechanisms impinging on the UBE2H-CTLH E3 that might be important for erythrocyte differentiation: differentiation stage-specific assembly of RANBP9-CTHL and RANBP10-CTHL complexes and coupling of UBE2H stability to catalytic activity of the CTLH E3 ligase.

Weaknesses:<br /> While the newly identified regulatory mechanisms are interesting, the major weakness of the study is that there is no evidence that these regulatory processes are functionally relevant for erythrocyte differentiation. In addition, the described phenotypes of UBE2H and MAEA deletion on erythrocyte differentiation could be analyzed in more detail, in particular addressing whether the accelerated differentiation reported is yielding functional progeny. Also the study could be strengthened by more quantitative assessment of the differentiation stage-dependent RANBP9-CTLH and RANBP9-CTLH E3 ligase complexes.

Reviewer #3 (Public Review):

This manuscript by Schueder et al. provides new insight into an important question in muscle biology: how can the smaller titin-like molecules of the much larger sarcomeres of invertebrate muscle perform the same function as the larger titin of vertebrate muscles which have smaller sarcomeres? These functions include the assembly, stability and elasticity of the sarcomere. Using two state of the art methods--nanobodies and DNA-PAINT super-resolution microscopy, the authors definitively show that in the highly ordered indirect flight muscle of Drosophila, the elongated proteins Sallimus and Projectin are arranged such that the N-terminus of Sallimus is embedded in the Z-disk, and the C-terminus is embedded in the outer portion of the A-band, and that in this outer portion of the A-band is also embedded the C-terminus of Projectin; thus, if the C-terminus of Sallimus can bind to thick filaments, and/or these overlapping portions of Sallimus and Projectin interact, there would be a linkage of the Z-disk and/or thin filament to the thick filaments to help determine the length and stability of the sarcomere.

The strengths of this paper include the implementation of nanobody and DNA-PAINT super-resolution microscopy for the first time for muscle. The extraordinary 5-10 nm resolution of this method allows imaging for definitive localization of the termini of these elongated proteins in the Drosophila flight muscle sarcomere. In addition, the manuscript is well written with sufficient background information and rationale presented, is easy to read, complex new methods are well-described, the figures are of high quality, and the conclusions are well-justified. A minor weakness is that despite the authors demonstrating that the C-terminus of Sallimus is located at the outer edge of the A-band, and that the N-terminus of Projectin is located also in the outer edge of the A-band, the authors provide no data to show whether, for example, these portions of these titin-like molecules interact, or whether Sallimus might interact with thick filaments. Such data would be required to prove their model. However, I can understand that this would require extensive additional study, and the authors have already provided a tremendous amount of data for this first step in supporting the model. Nevertheless, the authors should cite a relevant previous study on the Sallimus homolog in C. elegans called TTN-1, which is also a 2 MDa polypeptide of similar domain organization to at least the large isoforms of Salliums found in fly synchronous muscles. In the study by Forbes et al. (2010), immunostaining, albeit not to the impressive resolution achieved in the present paper, showed that TTN-1 was also localized to the I-band with extension into the outer edge of the A-band. More importantly, that study also showed that "fragment 11/12", Ig38-40, which is located fairly close to the C-terminus of TTN-1 binds to myosin with nanomolar affinity (Kd= 1.5 nM), making plausible the idea that TTN-1 may bind to the thick filament in vivo.

Reviewer #3 (Public Review):

This prospective study evaluated the utility of D2 VL determination for response-guided ultra-short (4w) sofosbuvir + daclatasvir treatment of chronic HCV patients (with mild disease) with G1+6. Shortening therapy duration reduces DAA use with a cure rate of 75% overall upon first-line treatment and 100% among retreated patients. In contrast to a previous report in G1b patients that showed a 100% success rate with D2-based 3-week triple therapy, the present study fails to show a good enough yield for a 4w sofosbuvir + daclatasvir regimen among G1+6 patients. Given the small number of patients, additional studies should determine whether a different time point and/or a different viral threshold could be more appropriate indicators to allow a 4-week duration of dual therapy (without a protease inhibitor).

Strengths:<br /> A. An important study that is a nice addition to previous reports evaluating the utility of response-guided therapy for shortening the duration of HCV treatment. Given the disease burden and the high costs of treatment, especially in low-income countries, this is a major goal that was also advocated by the WHO.<br /> B. This study investigates an ultra-short protease-inhibitor-free regimen and therefore complements a previous (positive) RGT study of a 3-week triple regimen.<br /> C. This study is prospective with careful analyses of ample data, including the evaluation of RAS by gene sequencing. The follow-up was long enough and analyses of viral kinetics were performed. In addition, a detailed analysis of re-treatment outcomes and viral mutations in this population was performed<br /> D. Although the main objective (shortening therapy to 4 weeks) was not adequately achieved (<90% success rate), the study's results may suggest that re-treatment in case of failure is safe and efficient, although further studies with a higher number of patients are needed for confirmation.

Limitations:<br /> A. Relatively small study cohort. Overall, only 34 patients were treated with a 4-week regimen. However, given the results, it seems that this number of patients who achieved only a 75% cure rate, is enough to exclude the use of a D2 RGUT, at least in G1+6 patients treated with sofosbuvir + daclatasvir. On the other hand, even 100% of success rate on 8-week treatment among 17 patients is not really enough to draw firm conclusions on the adequacy of this short regimen among this group of patients. A higher number of patients could better validate this positive result.<br /> B. The values chosen for the RGT are arbitrary. The relatively small number of patients could not allow for a more detailed analysis of more appropriate time points and/or viral load thresholds to determine the adequacy of a 4-week of therapy in individual patients. The D2 500IU/ML threshold is based on a small previous phase 2 study on G1b patients treated with a triple-drug regimen, which does not necessarily imply dual therapy (w/o a protease inhibitor) involving patients with a different subtype of the virus. In this context, a control group treated with triple combination therapy (with a protease inhibitor) could be very helpful to the study.<br /> C. Is there a particular pattern of viral kinetics to 4w cured patients Vs. failures? Fig 1 (Appendix 1) only shows the means of viral load and the general kinetics for the whole population, but individual plots of viral kinetics are not presented although could potentially be useful. Also, according to the presented data, day 7 VL D. According to Table 3, no significant differences in the host or viral factors were detected between cured or failures of the 4w regimen. However, the low number of patients makes it very difficult to interpret these data and might miss potential differences between these two groups of patients, emphasizing again the difficulty in drawing firm conclusions from this study. In this context, I wonder whether a regression analysis would better define either viral (subtype, RAS) or host factors that are implicated in a 4w duration success.

Reviewer #3 (Public Review):

The study conducted by Chang and colleagues elegantly describes the significance of appropriate H19 and Igf2 gene expression control in the formation of the fetal heart and placenta. They used established and newly developed genetic models in mice, histological analyses, and transcriptomic assessments to assess the contribution of H19 and Igf2 to the defects observed. On a whole the paper is very well written, the experimental design is sound, the results compelling, and the conclusions supported. I only have minor suggested edits/comments.

Reviewer #3 (Public Review):

Via a study of metabolic flux of proliferating human primary cells (lung fibroblasts and PASMCs) in vitro, the authors primarily find that MYC uncouples an increase in HIF-dependent glycolytic gene transcription from the glycolytic flux in hypoxia. This finding is surprising and significant, given that prior work in cancer cell lines has indicated that glycolysis is uniformly increased under hypoxic stress. Strengths of the study include the comprehensive rigor of the approach to reach this conclusion, the accounting of multiple confounding variables, and the well-written presentation of the findings. These findings will be of use to the general scientific community, particularly the atlas of molecular alterations seen with their flux analyses. The surprising findings will set the stage for additional work on MYC's role in primary vs. transformed cells, the mode of regulation of MYC in primary cells, and the relevance of this mechanism in in vivo contexts of health and disease. A weakness of the study that can be improved upon in future work includes confirmation of findings in more physiologically relevant contexts of primary tissue in the body.

Reviewer #3 (Public Review):

This study is designed to test the mechanistic role of NF-kB signaling in muscle atrophy following rotator cuff injury. The authors utilized a genetic gain-of-function and loss-of-function model to manipulate NF-kB activation and how this alters muscle plasticity following rotator cuff tendon transection.

The authors provided thorough analyses of muscle morphology, biochemistry, and function, which is a major strength of the study. However, there are some key confounding variables authors failed to address. For example, the difference in the estrous cycle in female animals was not controlled. The study could have been significantly improved by controlling sex hormone levels or at least testing differences in response to injury. Furthermore, more data are needed to link NFkB signaling and autophagy to make any kind of conclusions.

Overall, in the current form of the manuscript, the presented data seem underdeveloped, and the addition of more supporting data could significantly improve the quality of the manuscript and enhance our understanding of NFkB signaling and muscle wasting in rotator cuff injury.

Reviewer #3 (Public Review):

Afshar et al. performed RNA-seq and LC-MS of in vivo and in vitro HUVECs to identify the role of culture conditions on gene expression. Given the widespread use of HUVECs to study EC biology, these findings are interesting and can help design better in vitro experiments. There have been previous papers that compared in vivo and in vitro HUVECs, however, the depth of sequencing and analysis in this manuscript identifies some novel effects which should be accounted for in future in vitro experiments using ECs.

Strengths:<br /> 1. Major findings of distinct pathways affected by cell culture are novel and interesting. The authors identify major effects on TGFb and ECM gene expression. They also corroborate previous findings of flow response pathways, namely KLF2/4 and Notch pathway regulation.<br /> 2. Use of multiple genomic methods to profile effects of culture conditions. The LC-MS data showed a significant correlation with RNA-seq, however, the data were not as strong so not used for subsequent analyses.<br /> 3. Use of scRNA-seq to show the dynamic effects of co-culture and shear stress on ECs is very novel. However, the heterogeneity in the EC populations is not discussed in this manuscript.

Weaknesses:<br /> 1. The physiological relevance of these changes in gene expression is not demonstrated in the manuscript. The authors claim the significance of their data is to improve in vitro culture to better represent in vivo biology. Is this the case with orbital shear stress? Do they rescue some functional effects in ECs with long-term shear stress? An angiogenesis, barrier function, or migration assay for HUVECs exposed to different conditions would help answer this question. A similar assay for cells after EC-VSMC co-culture would validate the importance of these stimuli.<br /> 2. One explanation for the increased expression of ECM genes in vivo is that these cells are contaminated with VSMCs/fibroblasts. This could be very likely given that cells were not sorted or purified upon isolation. Expression of other VSMC or fibroblast-specific markers (i.e. CNN1, MYH11, SMTN, DCN, FBLN1) would help determine if there is some level of non-EC contamination.<br /> 3. The use of scRNA-seq in Figure 4 is interesting. There appear to be 2 distinct EC populations in the co-cultured ECs. What are the marker genes for the 2 populations?<br /> 4. The modest shifts in gene expression with shear stress and co-culture could be attributed to the batch effect. The authors describe 1 batch correction method (ComBat) in the bulk RNA-seq, but no mention of batch correction was noted in the scRNA-seq methods. The authors should ensure that batch effect correction in all data is adequate, and these results should be added to the manuscript.<br /> 5. Table 1 shows ATAC-seq was done, however, no data from these experiments are provided in the manuscript.<br /> 6. Shear stress was achieved with an orbital shaker, which the accompanying citation states introduces significant heterogeneity in the ECs. This is based on the location of the culture dish. Was this heterogeneity seen in the scRNA-seq data?<br /> 7. It would be important to know whether the authors reproduce the findings from other papers that CD34 expression is reduced in cultured HUVECs:

Muller AM, Cronen C, Muller KM, Kirkpatrick CJ: Comparative analysis of the reactivity of human umbilical vein endothelial cells in organ and monolayer culture. Pathobiology 1999;67:99-107.

Delia D, Lampugnani MG, Resnati M, Dejana E, Aiello A, Fontanella E, Soligo D, Pierotti MA, Greaves MF: Cd34 expression is regulated reciprocally with adhesion molecules in vascular endothelial cells in vitro. Blood 1993;81:1001-1008.

Reviewer 3 (Public Review):

The authors are to be commended on their clear presentation of the animals and time points (in table 1), their validation with ELISA, and the insightful follow-up experiments and validation. This is an important study that will be of broad interest to the field.

However, there are key issues that must be addressed, mostly relating to a lack of basic explorative analyses on the core scRNAseq datasets found in the paper.

Reviewer #3 (Public Review):

In this manuscript, authors present very exciting findings on the cranial bone defect repair using cutting-edge multiphoton imaging to study the role of different vessel subtypes and related oxygen and metabolic microenvironments. The authors used transgenic reporter mouse models to label and track blood vessel subtypes at the site of repair. They demonstrate the role of capillary subtypes at the repair sites in skull bone and provide evidence for the existence of specialized metabolic environments for coupling angiogenesis and osteogenesis. The study provides important insights into the dynamics and role of blood vessel subtypes in cranial bone defect repair.

Reviewer #3 (Public Review):

In this manuscript Moller et al., perform a lovely characterization of how centrosome movements synchronize with phagocytic cup formation during microglial efferocytosis of neuronal corpses in the larval zebrafish. Using a combination of elegant imaging and reporters tools the authors characterize two modes of phagosome formation, one involving process formation. They describe movements of the actin cytoskeleton, microtubules, and the centrosome in this process, and find that targeted migration of the centrosome into one branch is predictive of 'successful' engulfment, and increasing the number of centrosomes increases microglial engulfment capacity, suggesting it is a rate limiting factor. Finally, they use pharmacology to link this to DAG signaling. Although as the authors note, this process has been previously linked to phagocytosis in other cell types and the molecular regulators are well known, the beautiful imaging and the focus on microglia makes this a welcome addition to the field. I have no major concerns.

Reviewer #3 (Public Review):

1. The described studies seek to test a plausible hypothesis having important biological implications: that Ca2+ coming through TRP channels and/or from intracellular stores during cold stimulation activates anoctamin Cl- channels, which further depolarize the CIII neuron via inward Cl- current (outward Cl- diffusion) resulting from high intracellular Cl- concentration caused by high expression of the outwardly directed Cl- transporter ncc69, thereby driving the intense electrical activity in CIII neurons needed to trigger cold-specific behavioral responses.

2. Elegant phylogenetic analysis is provided to show that Drosophila subdued and white walker are orthologous to human TMEM16/anoctamins ANO1/2 and ANO8, respectively, to go along with ncc69 already known to be orthologous to human NKCC1.

3. Strong genetic and behavioral evidence shows that knocking down the expression of subdued or white walker globally or selectively in CIII neurons reduces the incidence and magnitude of a cold-specific contraction response ("CT") to 5 degree C stimulation but not responses to gentle touch.

4. These knock-downs also reduce electrical activity recorded in cell bodies of CIII neurons induced by cooling to 15 or 10 degrees C in a semi-intact ("fillet") preparation.

5. CIII-specific knock-down of ncc69 reduces CT responses while overexpression of kcc (which should have the opposite effect on intracellular Cl- concentration) also tends to reduce these responses, indicating that the balance of Cl- pump activity in these neurons favors excitation when Cl- channels are opened (e.g., during cold stimulation).

6. Optogenetic activation of an exogenously expressed Cl- channel (Aurora) in CIII neurons evokes CT responses, showing that Cl- currents are sufficient to produce these responses, presumably by strongly activating the CIII neurons.

7. Reducing extracellular Cl- enhances ongoing electrical activity of CIII neurons, strengthening the conclusion that opening Cl- channels excites these neurons.

8. Overexpressing ncc69 in CIII neurons enhances basal and evoked electrical activity, and sensitizes larvae CT responses to cooling to 10 degrees C, further strengthening the conclusion that opening Cl- channels excites CIII neurons and suggesting that this specific genetic manipulation could provide a model in Drosophila for detailed investigations into a potentially general mechanism contributing to neuropathic sensitization and pain.

9. The authors integrate findings from the present study with those from their recent cold acclimation paper to make the speculative but interesting suggestion that mechanisms selected during evolution to enable cold acclimation might also be recruited in neuropathic contexts to produce maladaptive sensitization.

There are also several modest weaknesses in the paper:

1. A notable gap remains in the evidence for the hypothesized mechanisms that enhance electrical activity during cold stimulation and the proposed role of anoctamins (Fig. 8) - the lack of evidence for Ca2+-dependent activation of Cl- current. The recording methods used in the fillet preparation should enable direct tests of this important part of the model.

2. The behavioral and electrophysiological consequences of knocking down either of the two anoctamins are incomplete (Fig.2), raising the significant question of whether combined knock-down of both anoctamins in the CIII neurons would largely eliminate the cold-specific responses.

3. Blind procedures were not used to minimize unconscious bias in the analyses of video-recorded behavior, although some of the analyses were partially automated.

4. The term "hypersensitization" is confusing. Pain physiologists typically use "sensitization" when behavioral or neural responses are increased from normal. In the case of increased neuronal sensitivity, if the mechanism involves an increase in responsiveness to depolarizing inputs or an increased probability of spontaneous discharge, the term "hyperexcitability" is appropriate. Hypersensitization connotes an extreme sensitization state compared to a known normal sensitization state (which already signifies increased sensitivity). In contrast, the effects of ncc69 overexpression in this manuscript are best described simply as sensitization (increased reflexive and neuronal sensitivity to cooling) and hyperexcitability (expressed as increased spontaneous activity at room temperature).

Reviewer #3 (Public Review):

This paper focuses on characterizing differences between D. suzukii and D. melanogaster preferences for laying eggs on substrates of varying sugar content and stiffness. The authors demonstrate that D. suzukii show a weaker preference for multiple sugars in oviposition choice assays, that D. suzukii show a loss of sugar responsiveness in some labellar sensilla, and that some GR-encoding genes are expressed at much lower levels compared to. D. melanogaster in the legs and labellum. Intriguingly, a number of mechanosensory channel genes are upregulated In D. suzukii legs and labellum. The authors show that D. suzukii females prefer stiffer oviposition substrates compared to D. melanogaster and the balance of sweetness/texture preference differs between the two species. This is consistent with their ecological niches, with D. suzukii generally preferring to lay eggs in ripe fruit and D. melanogaster generally preferring overripe fruit.

This paper builds on previous work from this group (Dweck et al., 2021) and others (Karageorgi et al., 2017 and others) that previously demonstrated that D. suzukii prefer to lay eggs on stiffer substrates compared to D. melanogaster, will tolerate more bitter substrates and show reduced expression of several bitter GR genes. This manuscript appropriately acknowledges this work and the findings are consistent with these studies.

The manuscript is well-written, the experiments are well-controlled, the figures clearly convey the experimental findings, the data support the authors conclusions, and the statistical analysis is appropriate.

The weakest point of the paper is the lack of connection drawn between the sequencing, electrophysiological, and behavioral data. For example, the electrophysiological responses to glucose appear to be the same in both species in Figure 3 but the behavioral responses in Figure 2 are different between the two species. The authors do not provide any speculation as to what could account for this seeming discrepancy. Additionally, although Gr64d transcript is almost completely absent in D. suzukii leg RNA seq data in Figure 4B, there are no differences in the electrophysiological responses in leg sensilla in Figure 3. This seems to imply that, although there are differences gene expression of some Grs that this does not necessarily lead to a functional difference.

The authors identify mechanosensory genes that are upregulated in D. suzukii compared to D. melanogaster and suggest that these changes underlie the difference in substrate stiffness. However, it is not immediately clear that high levels of these mechanosensors would impart a new oviposition preference. Although the authors acknowledge that there are likely circuit-level differences between the two species, they do not directly test the role of any of these mechanosensors in oviposition preference in either species.

In Figure 3 there are clear differences in some of labellar responses but the leg responses look similar overall. This suggests that the labellum is playing a special role in oviposition evaluation. The paper would be strengthened by providing more insight into which tissues (labellum, legs, wings, ovipositor, etc...) are likely used to sample potential egg laying substrates.

Reviewer #3 (Public Review):

My understanding of the main claims of the paper, and how they are justified by the data are discussed below:<br /> Overall, loss of PRC2 function in the developing oocyte and early embryo causes:

1) Growth restriction from at least the blastocyst stage with low cell counts and midgestational developmental delay.

Strengths:

• Live embryo imaging added an important dimension to this study. The authors were able to confirm an unquantified finding from a previous lab (reduced time to 2-cell stage in oocyte-deletion Eed offspring, Inoue 2018, PMID: 30463900) as well as identify developmental delay and mortality at the blastocyst-hatching transition.<br /> • For the weight and morphological analysis the authors are careful to provide isogenic controls for most of the experiments presented. This means that any phenotypes can be attributed to the oocyte genotype rather than any confounding effects of maternal or paternal genotype.<br /> • Overall, there is good evidence that oocyte deletion of Eed results in early embryonic growth restriction, consistent with previous observations (Inoue 2018, PMID: 30463900).

Weaknesses:

Gaps in the reporting of specific features of the methodology make it difficult to interpret/understand some of the results.

2) Placental hyperplasia with disproportionate overgrowth of the junctional trophoblast especially the glycogen trophoblast (GlyT) cells.

Strengths:

• The authors provide a comprehensive description of how placental and embryo weight is affected by the oocyte-Eed deletion through mid-to-late gestation development. The case for placentomegaly is clear.<br /> Weaknesses:<br /> • The placental efficiency data presented in Figure 3G-I is incorrect. Placental efficiency is calculated as embryo mass/placental mass, and it increases over the late gestation period. For e14.5 for example (Fig3G), WT-wt embryo mass = ~0.3g, placenta mass = 0.11g (from Fig 3D) = placental efficiency 2.7; HET-hom = 0.25/0.12 = 2.1. The paper gives values: WT-wt 0.5, HET-hom 0.7. Have the authors perhaps divided placenta weight by embryo mass? This would explain why the E17.5 efficiencies are so low (WT-wt 0.11 rather than a more usual figure of 8.88. If this is the case then the authors' conclusion that placental efficiency is improved by oocyte deletion of Eed is wrong - in fact, placental efficiency is severely compromised.<br /> • The authors have performed cell type counting on histological sections obtained from placentas to discover which cells are contributing to the placentomegaly. This data is presented as %cell type area in the main figure, though the untransformed cross-sectional area for each cell type is shown in the supplementary data. This presentation of the data, as well as the description of it, is misleading because, while it emphasises the proportional increase in the endocrine compartment of the placenta it downplays the fact that the exchange area of the mutant placentas is vastly expanded. This is important for two reasons. Firstly, the whole placenta is increased in size suggesting that the mechanism is not placental lineage-specific and instead acting on the whole organ. Secondly in relation to embryonic growth, generally speaking, genetic manipulations that modify labyrinthine volume tend to have a positive correlation with fetal mass whereas the relationship between junctional zone volume and embryonic mass is more complex (discussed in Watson PMID: 15888575, for example). The authors should reconsider how they present this data in light of the previous point.<br /> • Again, some of the methods are not clearly reported making interpretation difficult - especially how they have estimated their GlyT number.

3) Perinatal embryonic/pup overgrowth.

Strengths:

• The overgrowth exhibited by the oocyte-Eed-deleted pups is striking and confirms the previous work by this group (Prokopuk, 2018). This is an important finding, especially in the context of understanding how PRC2-group gene mutations in humans cause overgrowth syndromes. It is also intriguing because it indicates that genetic/environmental insults in the mother that affect her gamete development can have long-term consequences on offspring physiology.

Weaknesses:

• Is the overgrowth intrauterine or is it caused by the increase in gestation length? The way the data is reported makes it impossible to work this out. The authors show that gestation time is consistently lengthened for mothers incubating oocyte-Eed-deleted pups by 1-2 days. In the supplementary material, the mutant embryos are not larger than WT at e19.5, the usual day of birth. Postnatal data is presented as day post-parturition. It would probably be clearer to present the embryonic and postnatal data as days post coitum. In this way, it will be obvious in which period the growth enhancement is taking place. This is information really important to determine whether the increased growth of the mutants is due to a direct effect of the intrauterine environment, or perhaps a more persistent hormonal change in the mother that can continue to promote growth beyond the gestation period.

4) "fetal growth restriction followed by placental hyperplasia, .. drives catch-up growth that ultimately results in perinatal offspring overgrowth".

Here the authors try to link their observations, suggesting that i) the increased perinatal growth rate is a consequence of placentomegaly, and ii) the placentomegaly/increased fetal growth is an adaptive consequence of the early growth restriction. This is an interesting idea and suggests that there is a degree of developmental plasticity that is operating to repair the early consequences of transient loss of Eed function.

Strengths:

• Discrepancies between earlier studies are reconciled. Here the authors show that in oocyte-Eed-deleted embryos growth is initially restricted and then the growth rate increases in late gestation with increased perinatal mass.

Weaknesses:

• Regarding the dependence of fetal growth increase on placental size increase, this link is far from clear since placental efficiency is in fact decreased in the mutants (see above).<br /> • "Catch-up growth" suggests that a higher growth rate is driven by an earlier growth restriction in order to restore homeostasis. There is no direct evidence for such a mechanism here. The loss of Eed expression in the oocyte and early embryo could have an independent impact on more than one phase of development. Firstly, there is growth restriction in the early phase of cell divisions. Potentially this could be due to depression of genes that restrain cell division on autosomes, or suppression of X-linked gene expression (as has been previously reported, Inoue, 2018 PMID: 30463900). The placentomegaly is explained by the misregulation of non-canonically imprinted genes, as the authors report (and in agreement with other studies, e.g. Inoue, 2020. PMID: 32358519).<br /> • Explaining the perinatal phase of growth enhancement is more difficult. I think it is unlikely to be due to placentomegaly. Multiple studies have shown that placentomegaly following somatic cell nuclear transfer (SCNT) is caused by non-canonically imprinted genes, and can be rescued by reducing their expression dosage. However, SCNT causes placentomegaly with normal or reduced embryonic mass (for example -Xie 2022, PMID: 35196486), not growth enhancement. Moreover, since (to my knowledge) single loss of imprinting models of non-canonically imprinted genes do not exist, it is not possible to understand if their increased expression dosage can drive perinatal overgrowth, and if this is preceded by growth restriction and thus constitutes 'catch up growth'.

Reviewer #3 (Public Review):

In this well-written manuscript, Hoel et al., determine the 4.7 Å cryo-EM structure of TMEM87A - a protein of unknown function but proposed to have roles in protein transport to and from the Golgi, mechanosensitive ion channels, and in developmental signaling. The team perform an electrophysiological assay to demonstrate that under their experimental conditions the protein is not a mechanosensitive channel, and compare their structures to other structures and Alphafold models to place this protein in a newly defined protein family which they suggest may have roles in trafficking membrane-associated cargo.

Given that the only data provided in this manuscript (aside from a single electrophysiological assay) is a low resolution cryo-EM map this manuscript has really on reached the hypothesis generating stage. No experiments to demonstrate what the role of this protein is have been performed.

Reviewer #3 (Public Review):

Vaisey et al., 2022 utilize super-resolution and electron microscopy techniques to characterize the distribution of Piezo1 ion channels in red blood cells. Prior theoretical research has proposed that the highly curved Piezo1 conformation may bias the channel localization in cell membranes through a mechanism of curvature coupling (Haselwandter and Mackinnon, 2018). Vaisey et al., 2022 find that Piezo1 channels diffuse in the membrane, are not clustered and that their localization is biased to the highly curved RBC dimple, thus matching the hypothesis of curvature coupling. The findings in this paper advance our understanding of how Piezo1 channel conformation affects its localization. With some exceptions the experiments and analyses are performed carefully and rigorously, and the numbers of biological replicates are sufficient. I find this manuscript exciting.

Reviewer #3 (Public Review):

In this manuscript, Borsatto et. al. have attempted to identify druggable cryptic pockets in the Non-structural protein 1 (Nsp1) of SARS-CoV-2. The authors analyzed analyzed molecular dynamics simulations of Non-structural protein 1 (Nsp1) of SARS-CoV-2 to search for potential drug binding pockets. The authors analyzed potential drug binding pocket volumes in unbiased simulations and utilized a Hamiltonian replica exchange scheme called SWISH to search for additional cryptic binding sites. The authors utilized conformations from their simulations to conduct a computational screen of potential drug fragments, and experimentally tested their predictions by soaking Nsp1 crystals with predicted fragment hits, and found that 1 of 60 predicted hits binds in a predicted pocket with mM binding affinity, and identified crystal packing contacts that may have prevented additional fragment hit binding. Finally, they ran simulations of Nsp1 in complex with RNA which suggest that ligand binding in pocket 1 may hinder RNA complex formation and run simulations of homologous Nsp1 in additional CoV genera to determine if the identified pockets are conserved.

The authors utilized two approaches for identifying potential drug binding pockets: unbiased MD simulations and the SWISH hamiltonian replica exchange that scales water protein interactions to explore the opening of more hydrophobic binding cavities, which can be stabilized by cosolvent benzene molecules. The authors identify 2 potential pockets (pockets 1 and 2) from unbiased simulations, and identify an additional 2-pockets (pockets 3 and 4) from SWISH simulations. Pockets 2-4 are connected by a shallow groove identified on the x-ray structure, but are substantially deeper than this groove. The authors proceed to use the FTDyn and FTMap programs to search for potential fragment binding spots, and identified that pocket 1 contained the largest number binding hotspots (~50%), and that many predicted binding hotspots were found in the cryptic pockets discovered by SWISH.

The authors proceeded to test their predictions by soaking 60 fragment hits obtained by FTMap and FTDyn, identified a single fragment that binds in Fragment 1, and solved the X-ray structure of this bound fragment. They also utilized microscale thermophoresis and thermal shift assays to measure a Kd value of 2.74 + 2.63mM. The authors then proceeded to analyze crystal packing contacts and identify packing contacts that may have prevented additional fragment hit binding. Finally, they ran simulations of Nsp1 in complex with RNA which suggest that ligand binding in pocket 1 may hinder RNA complex formation and run simulations of homologous Nsp1 in additional CoV genera to determine if the identified pockets are conserved.

The authors were successful in identifying an experimentally verifying a druggable pocket in Nsp1. It is unclear to me however, to what extent the features of the this pocket are cryptic, and if the fragment that was found to bind could have been discovered using only the crystal structure, as this ligand appears to bind to a cavity identified by the Fpocket software from a crystal structure. In a sense the authors have computationally identified and experimentally verified a druggable pocket, and have proposed the presence of 3 additional potentially druggable cryptic pockets with strong computational evidence, but have not experimentally verified the druggablity of the proposed cryptic pockets.

This manuscript represents an excellent demonstration of a state-of-the-art MD based computational methods for druggable pocket discovery on an important drug target. The experimental verification fragment binding to one of the identified sites, and the identification of putative additional sites, provide a foundation for future rational drug discovery campaigns of SARS-CoV-2 and other CoVs.

Reviewer #3 (Public Review):

In the research described in this manuscript, Shi and colleagues were attempting to develop a versatile and flexible method for generating conditional and reversible gene knockouts. They wanted their method to be widely applicable and easily adapted to any target gene of interest. In addition, they wanted to demonstrate the use of their new method in several different experimental contexts, reinforcing their conclusions about its value. In pursuit of these goals, the authors modified a method (COIN) in which an artificial intron containing a Cre-dependent gene-trap cassette is inserted into an exon of the target gene. In the modified ReCOIN method, the gene trap cassette is flanked by target sites of Flp recombinase. Cre recombination inverts the gene trap cassette, resulting in the disruption of the targeted gene. Subsequent Flp recombination deletes the gene trap cassette, restoring the expression of the targeted gene. The authors also devised a strategy (CIRKO) to permit rapid, non-invasive control of the ReCOIN system. In general, the authors have achieved their goals. The experiments in the manuscript are well-designed and clearly described, and they highlight the strengths of the strategy. However, a few limitations of the strategy and the experimental analyses are also clear:

1. The ReCoin module retains an antibiotic resistance cassette driven by the PGK promoter, which is a powerful ubiquitous promoter with bidirectional activity. In the original COIN module, the resistance cassette is deleted by Flp recombinase, but this is not possible in ReCOIN where Flp has been co-opted for gene regulation. In a variety of contexts, retained PGK-driven antibiotic cassettes have been shown to have unpredictable effects on the expression of surrounding genes. It would perhaps have been better if the ReCOIN module had been designed so that the resistance cassette was deleted by a third recombinase such as VCre or PhiC31. The possibility of ectopic gene expression or downregulation driven by the PGK promoter should be kept in mind when characterizing new ReCOIN alleles.

2. Somewhat related to point 1, the authors performed an experiment in transiently transfected cells to demonstrate that insertion of the ReCOIN module does not affect the expression levels of an mCherry reporter. However, the metric they reported, % mCherry+ cells, speaks more to transfection efficiency than expression levels. Mean fluorescence intensity might have been more informative.

3. In the section describing Cas9-ReCOIN, the authors mention the need to temporally control Cas9 expression, because persistent Cas9 expression can result in genomic instability. However, it is not clear that ReCOIN offers any advantage over the original COIN module in this context. In experiments where a Cas9 plasmid is transfected, Cre recombination allows the Cas9 to be switched off, but Flp recombination, turning Cas9 back on permanently, would seem to have no experimental value. Alternatively, in a cell line with Cas9 stably integrated into Rosa26 or a similar safe harbor locus, it would be desirable to have Cas9 temporarily turned on (Off-On-Off). Unfortunately, reCOIN seems to offer the ability to temporarily turn Cas9 off (On-Off-On).

4. Although live pigs containing a ReCOIN allele of TP53 were generated, experiments showing recombination of ReCOIN alleles were all performed in cultured cells or pre-implantation embryos. As yet, the ReCOIN/CIRKO strategy has not been fully validated in postnatal animals.

5. The CIRKO strategy allows rapid control of ReCOIN to turn gene expression off and on via dosing with doxycycline and tamoxifen. This non-invasive temporal control of gene expression has obvious value in both cultured cells and model organisms. However, as currently described CIRKO cannot be used for cell type-specific knockouts, because Cre and Flp expression is regulated by ubiquitous (though chemically inducible) promoters.

Reviewer #3 (Public Review):

This paper aimed to understand how toxin-antidote (TA) elements are spread and maintained in species, especially in species where outcrossing is infrequent and the selfish gene drive of TA elements is limited. The paper focuses on the possible fitness costs and benefits of the peel-1/zeel-1 element in the nematode C. elegans. A combination of mathematical modeling and experimental tests of fitness are presented. The authors make a surprising finding: the toxin gene peel-1 provides a fitness advantage to the host. This is a very interesting finding that challenges how we think about selfish genetic elements, demonstrating that they may not be wholly "selfish" in order to spread in a population.

Strengths<br /> 1. The authors support results found with a zeel-1 peel-1 introgressed strain by using CRISPR/Cas9 genetic engineering to precise knock-out the genes of interest. They were careful to ensure the loss-of-function of these generated alleles by using genetic crosses.

2. Similarly, the authors are careful with controls, ensuring that genetic markers used in the fitness assays did not affect the fitness of the strain. This ensures that the genes of interest are causative for any source of fitness differences between strains, therefore making the data reliable and easily interpretable.

3. A powerful assay for directly measuring the relative fitness of two strains is used.

4. The authors support relative fitness data with direct measurements of fitness proximal traits such as body size (a proxy for growth rate) and fecundity, providing further support for the conclusion that peel-1 increases fitness.

Weaknesses<br /> 1. One major conclusion is that peel-1 increases fitness independent of zeel-1, but this claim is not well supported by the data. The data presented show that the presence of zeel-1 does not provide a fitness benefit to a peel-1(null) worm. But the experiment does not test whether zeel-1 is required for the increased fitness conferred by the presence of peel-1. Ideally, one would test whether a zeel-1(null);peel-1(+) strain is as fit as a zeel-1(+);peel-1(+) strain, but this experiment may be infeasible since a zeel-1(null);peel-1(+) strain is inviable.

2. The CRISPR-generated peel-1 allele in the N2 background only accounts for 32% of the fitness difference of the introgressed strain. Thus, the effect of peel-1 alone on fitness appears to be rather small. Additionally, this effect of peel-1 shows only weak statistical significance (and see point 5 below). Given that this is the key experiment in the paper, the major conclusion of the paper that the presence of peel-1 provides a fitness benefit is supported only weakly. For example, it is possible that other mutations caused by off-target effects of CRISPR in this strain may contribute to its decreased fitness. It would be valuable to point out the caveats to this conclusion, or back it up more strongly with additional experiments such as rescuing the peel-1(null) fitness defect with a wild-type peel-1 allele or determining if the introduction of wild-type peel-1 into the introgressed strain is sufficient to confer a fitness benefit.

3. The strain that introgresses the zeel-1 peel-1 region from CB4856 into the N2 background was made by a different lab. Given that N2 strains from different labs can vary considerably, it is unclear whether this introgressed strain is indeed isogenic to the N2 strain it is competing against, or whether other background mutations outside the introgressed region may contribute to the observed fitness differences.

4. Though the CRISPR-generated null allele of peel-1 only accounts for 32% of the fitness difference of the zeel-1 peel-1 introgressed strain, these two strains have very similar fecundity and growth rates. Thus, it is unclear why this mutant does not more fully account for the fitness differences.

5. Improper statistical tests are used. All comparisons use a t-test, but this test is inappropriate when multiple comparisons are made. Importantly, correction for multiple comparisons may decrease the already weak statistical significance of the fitness costs of the peel-1 CRISPR allele (Fig 3E), which is the key result in the paper.

6. N2 fecundity and growth rate measurements from Fig 2B&C are reused in Fig 3C&D. This should be explicitly stated. It should also be stated whether all three strains (N2, the zeel-1 peel-1 introgressed strain, and the peel-1 CRISPR mutant) were assayed in parallel as they should be. If so, a statistical test that corrects for multiple comparisons should also be used.

7. It appears that the same data for the controls for the fitness experiments (i.e. N2 vs. marker & N2 vs. introgressed npr-1; glb-5) may be reused in Fig 2A and 3E. If so, this should be stated. It should also be stated whether all the experiments in these panels were performed in parallel. If so, this may affect the statistical significance when correcting for multiple comparisons.

Reviewer #3 (Public Review):

The authors of this study were trying to determine the mechanisms of of fatty acid uptake and accumulation in the kidney. Their work identified clear evidence for both basolateral (CD36-dependent) and apical uptake of fatty acids in the kidney. The apical uptake of fatty acids is independent of megalin. Interestingly there is absence of fatty acids in the urine even in subjects with significant proteinuria indicating that fatty acids in the urine are completely taken up by the renal tubules.

Reviewer #3 (Public Review):

SUMOylation of sodium channels has been implicated as a substantial modulator of current properties. However, prior studies have been limited as they have not examined the impact of SUMOylation in developed neurons. Here the investigators made a mouse with the key SUMOylation site (K38) in Nav1.2 mutated to prevent SUMOylation (K38Q). They characterize modulation of cortical pyramidal neuron firing while manipulating SUMOylation using recombinant proteins in wild-type and SCN2A-K38Q mouse neurons. SUMOylation modulates sodium currents elicited with ramp depolarizations and alters back-propagation of action potentials and thus impacts excitatory post-synaptic potentials. The K38Q mutation prevents these effects on neuronal sodium currents. The work does indeed suggest that SUMOylation modulates specific ionic currents in neurons and that SUMOylation of Nav1.2 may play a role in synaptic integration.

While the work is interesting, it is limited in several aspects. First, previous studies have reported that SUMOylation modulates the voltage-dependence of Nav1.2 activation and steady-state inactivation. Perhaps because of the difficulties associated with voltage clamping neurons in slice, the current work focuses on ramp currents. While the study states that SUMOylation "exclusively controls InaP generation", this can be misleading as other sodium current properties were not examined in the neurons. Alterations in the voltage-dependence of activation could contribute to the observed changes in ramp currents which are characterized as persistent currents in this study. Second, the study does not examine the impact of the K38Q mutation on behavior. It will be very interesting to see how this mutation impacts learning and memory in the mice.

Reviewer #3 (Public Review):

Gyawali et al. make use of fiber photometry methods with a dopamine biosensor to monitor dopamine signaling in the BNST, where it has received much less attention compared to striatal regions. They use a Pavlovian conditioned approach paradigm to assess the encoding of associative learning, finding that, similar to the striatum, BNST dopamine responds to violations of expectation. Further, BNST dopamine responses to Pavlovian cues and outcomes vary according to individual differences in conditioned approach behaviors. In other studies, they demonstrate that BNST dopamine tracks sensory-specific satiety, and is amplified following fentanyl self-administration. Overall these are interesting and well done studies that make great use of new sensor technology. This work represents a foray into monitoring learning-related dopamine signals in non-striatal areas. A primary critique pertains to the analysis and interpretations of the reward prediction error manipulations, which I do not think bidirectional reward prediction error encoding is definitely demonstrated.

Reviewer #3 (Public Review):

This paper details the importance of thyroid hormone signaling in BAT in response to environmental and nutritional stress. The authors utilize a novel genetic model to specifically target BAT and impair thyroid hormone signaling. The physiologic insight is of great interest. The role of the sympathetic nervous system in the BAT response is not fully addressed but it appears that cell-autonomous signaling mediates TH signaling in response to hyperthyroidism. The link cistromically between the TR and PGC1 is also novel and of interest.

Reviewer #3 (Public Review):

The studies in the manuscript "Endocytic trafficking determines cellular tolerance of presynaptic opioid signaling" use a novel approach to assess the signaling of presynaptic opioid receptors that inhibit the release of neurotransmitters. Historically, studies have used whole-cell patch-clamp electrophysiology studies of spontaneous and evoked neurotransmitter release to measure the presynaptic effects of opioid receptors. Since the recordings were made in postsynaptic cells that expressed receptors for the released neurotransmitter, the electrophysiological measurements are indirect with respect to the presynaptic receptors under study. The technique used in this manuscript is based on a pHlorin-based unquenching assay that is a measure of synaptic vesicle exocytosis. In this case, the super-ecliptic pHluorin (SEP) is a pH-sensitive GFP that increases fluorescence as the synaptic vesicle protein that it is attached to (VAMP2-SEP) relocates from the acidic synaptic vesicle to the plasma membrane. Opioid agonists inhibit this activity with acute administration and this inhibition is reduced with prolonged, or chronic administration (hours), demonstrating tolerance. The SEP protein can also be conjugated to opioid receptors and used to measure the proportion of receptors on the plasma membrane compared to internalized receptors. The studies show that agonist activation of mu-opioid receptors (MORs) induces endocytosis that is dependent on phosphorylation of the C-terminus and that the development of tolerance is correlated with the loss of MORs at the surface. The results are different for the delta-opioid receptor (DOR) which is also internalized with acute agonist administration but that loss of receptors on the membrane occurs more rapidly and is not dependent on phosphorylation of the C-terminus.

The results in the studies are clearly presented and clearly substantiate the prior work using electrophysiology to show the late development of tolerance of presynaptic opioid receptor signaling. The studies extend prior work by showing that endocytosis of both MOR and DOR occurs in presynaptic locations but that the cellular mechanisms underlying the maintenance of these receptors on the plasma membrane differ. The imaging results show convincing effect sizes, even with genetic and pharmacological manipulations, that will allow for even further investigation into the cellular mechanisms underlying the development of tolerance. Since these studies transfected the cultured striatal neurons with both the opioid receptors and the VAMP2-SEP, one question that remains is whether imaging of the VAMP2-SEP has the resolution to detect inhibition of endocytosis by endogenous opioid receptors. Since the authors make the point that this technique has advantages over traditional electrophysiological approaches, it is important that the technique allows for the measurement of endogenous levels of receptors. There are minor questions about the statistics used in some of the graphs, and the utility of the presentation of p values on the right-hand axis but these concerns do not alter the overall significance of the studies, which are high impact.

Reviewer #3 (Public Review):

This work investigates how looming stimuli that increase in luminance are processed by the lobula giant movement detector (LGMD) neuron in grasshoppers. The manuscript starts by arguing that real life approaching predators are likely to generate a mixture of looming stimuli that increase (ON) and decrease (OFF) in luminance. Previous work has characterised well the behavioural and neurophysiological responses to OFF looms, showing that they efficiently evoke escape responses in grasshoppers and that they are mapped in a retinotopic manner to the A dendritic field for LGMD, a property important for computing that spatial coherence of the stimulus. In this manuscript, behavioural experiments show that ON looms are as efficient as OFF stimuli in eliciting escape, but that surprisingly the behaviour is independent of spatial coherence. Calcium imaging experiments show that in ON stimuli activate the C field of the LGMD neuron, suggesting a strong segregation at the cellular level between the ON and OFF pathways. Further analysis of these data show that in contrast with the OFF pathways, there appears to be no retinotopic organization of the inputs onto the dendritic tree and instead, the distribution is random. Electrophysiological recordings then reveal a progressive increase in firing rate as the ON looming stimulus approaches, with a profile that is independent of the spatial coherence of the stimulus, in agreement with the behaviour. The manuscript ends by demonstrating that mixed ON and OFF looming stimuli activate both the C and A dendritic fields and retain sensitivity to spatial coherence, and a biophysical model is shown to reproduce the experimental findings.

The overall conclusion from this work is that the visual system of the grasshopper is sensitive to ON approaching stimuli, but it is unable to discriminate their spatial coherence because of the random distribution of ON inputs onto the LGMD dendritic tree. The authors further argue that this organization allows grasshoppers to be sensitive to these stimuli while reducing the number of synapses require to reach AP threshold, thereby conserving energy. I think that the experiments are very nicely done, well designed, the data are of great quality and support the main arguments. The greatest strength of this work, and indeed of the model system, is the ability to link behaviour, sensory processing, and cellular physiology with biophysical detail in a single piece of work. I believe that this is a valuable contribution to all these fields. I have a couple of main comments for the authors to consider.

1 - This work focuses exclusively on excitatory input. However, as the authors mention, LGMD neurons also receive inhibitory inputs, and these inputs also appear to segregate to different areas of the dendritic tree depending on the pathway. The contribution of inhibition is mostly ignored throughout the manuscript, but I think that it would be beneficial to discuss how inhibitory inputs fit into the story. For example, if OFF inhibition maps onto the C field, then presumably when there is mixed ON/OFF stimulation there is inhibition of the ON excitation onto the C field? If so, how much excitation of the C field is left? How much does the retainment of spatial coherence sensitivity with mixed stimuli arise from the fact that OFF excitation might dominate because it inhibits the C field? I don't think that additional experiments are needed, but a discussion would be useful. Related, does the model include inhibitory synapses?

2 - The argument that the cellular organization found here is good because it allows grasshoppers to be sensitive to white approaching stimuli while disregarding spatial coherence and saving energy seems plausible. But it's not clear to me why this is 'optimal' (from the title - 'optimizes neuronal computation'). What exactly is being optimized here? And why is it good that grasshoppers can't discriminate the spatial coherence of ON looming stimuli? Is everything that approaches a grasshopper fast and white always a bad thing, but not the case if the approaching thing is black? Some further placement of these findings into an ecological setting might be helpful here.

Reviewer #3 (Public Review):

Abdel-Haq presents a comprehensive analysis of the impact of dietary fiber on the ASO mouse model. They describe diet-induced changes in the gut microbiota, microbial metabolites, host gene expression, microglial activation, and motor deficits. Pharmacological inhibition of microglia highlights the importance of these cells for the impact of prebiotics, raising intriguing hypotheses for future studies.

Strengths include the rigor and reproducibility of these studies, the clarity of the presentation, and the timely focus on microglial interactions with the gut microbiome.

The major weakness is the descriptive nature of these studies and the lack of reduction in the mechanism. Only a single model is used and there is no attempt to test the translational relevance of these findings in humans. The putative pathway (fiber→bacteria→SCFA→microglia) has already been reported, so the data is largely confirmatory in nature.

Despite these concerns, this work adds to the growing literature on the gut-brain axis and will be helpful for motivating continued studies in mice and human cohorts. However, caution should be advised for using these results to motivate specific dietary recommendations to patients.

Reviewer #3 (Public Review):

The paper succinctly provides an overview of the current approaches to generating and displaying super-large phylogenies (>10,000 tips). The results presented here provide a comprehensive set of tools to address the display and exploration of such phylogenies. The tools are well-described and comprehensive, and additional online documentation is welcome.

The technical work to display such large datasets in a responsive fashion is impressive and this is aptly described in the paper. The author rightly decides that displaying large phylogenies is not simply a matter of rendering "more nodes", and so in my eyes, the major advancement is the approach used to downsample trees on-the-fly so that the number of nodes displayed at one time is manageable. This is detailed only briefly (Results section, 1st paragraph, 2 sentences). I would like to see more discussion about the details of this approach. Examples that came up while exploring the tool: the (well implemented) search functionality reports results from the entire tree (e.g. in Figure 4, the number of red circles is not a function of zoom level), how does this interact with a tree showing only a subset of nodes? How is the node order chosen with regards to "nodes that would be hidden by other nodes are excluded" and could this affect interpretations depending on the colouring used?

Taxonium takes the approach of displaying all available data (sparsification of nodes notwithstanding). Biases in the generation of sequences, especially geographical, will therefore be present (especially so in the two main datasets discussed here - SARS-CoV-2 and monkeypox). This caveat should be made explicit. Has the author considered choosing which nodes to exclude for sparsified trees in such a way as to minimise known sampling biases?

Interoperability between different software tools is discussed in a technical sense but not as it pertains to discovering the questions to ask of the data. As an example, spotting the specific mutations shown in figure 3 + 4 is not feasible by checking every position iteratively; instead, the ability to have mutations flagged elsewhere and then seamlessly explore them in Taxonium is a much more powerful workflow. This kind of interoperability (which Taxonium supports) enhances the claim of "providing insights into the evolution of the virus".

Taxonium has been a fantastic resource for the analysis of SARS-CoV-2 and this paper fluently presents the tool in the context of the wider ecosystem of bioinformatic tools in use today, with the interoperability of the different pieces being a welcome direction.

Reviewer #3 (Public Review):

In this manuscript, Farrell and colleagues investigated the role of FABP genes in multiple myeloma progression using a combination of in vitro, in vivo, and in silico approaches, as well as genetic and pharmacologic interventions. They report that FABP genes are expressed in myeloma cells and show that genetic inhibition of FABP5 or pharmacologic inhibition of several FABP genes decreases myeloma cell number in vitro and in vivo. The decrease in cell number correlates with cell cycle arrest and a modest increase in apoptosis. By performing a comprehensive transcriptomic, proteomic, and metabolomic analysis, the authors find that inhibition of FABP genes reduces MYC gene expression and UPR genes, and decreases mitochondrial respiration, and blocks. Consistent with their in vitro and in vivo data, the authors show that FAPB5 expression in patients negatively correlates with survival. Overall, the data is interesting and provides new therapeutic targets to combat the growth of myeloma cells in the bone marrow. The conclusions are mostly supported by the data; however some mechanistic aspects of the studies need to be clarified and extended.

Strengths:<br /> 1) The use of genetic (CRISPR) and pharmacologic (BMS309403 and SBFI-26) and in vitro and in vivo models adds scientific rigor to the findings presented and increase their clinical relevance.<br /> 2) The authors perform a highly comprehensive analysis of the consequences of FABP inhibition in myeloma cells using transcriptomic, proteomic or metabolic analysis. The bioinformatic analysis of these data is well done and rendered additional potential targets (genes or pathways) mediating FABP effects on myeloma cells.<br /> 3) The addition of in silico analysis of patient databases adds translational value to their findings.

Weaknesses:<br /> 1) Despite the comprehensive bioinformatic analysis performed by the authors, the mechanisms by which inhibition of members of the FABP family decreases tumor progression are not investigated. Several potential mechanisms are inferred (i.e., MYC, DNA methylation, UPR genes, mitochondrial respiration) but no experiments are performed to demonstrate their involvement in the response to FABP inhibitors.<br /> 2) The authors indicate FABP inhibitors are safe, but their toxicity analysis is limited to body weight, which might not be a good indicator of toxicities.<br /> 3) FABP inhibitors have systemic effects that could contribute to the decreased tumor burden. This is not considered in the interpretation of the in vivo results.

Reviewer #3 (Public Review):

The authors have presented results from carefully planned and executed experiments that probe enhancer-drive expression patterns in varying cellular conditions (of the early Drosophila embryo) and test whether standard models of cis-regulatory encoding suffice to explain the data. They show that this is not the case, and propose a mechanistic aspect (higher order cooperativity) that ought to be explored more carefully in future studies. The presentation (especially the figures and schematics) are excellent, and the narrative is crisp and well organized. The work is significant because it challenges our current understanding of how enhancers encode the combinatorial action of multiple transcription factors through multiple binding sites. The work will motivate additional modeling of the presented data, and experimental follow-up studies to explore the proposed mechanisms of higher order cooperativity. The work is an excellent example of iterative experimentation and quantitative modeling in the context of cis-regulatory grammar. At the same time, the work as it stands currently raises some doubts regarding the statistical interpretation of results and modeling, as outlined below.

The results presented in Figure 5 are used to claim that the data support (i) an unchanging K_R regardless of the position of the Runt site in the enhancer and (ii) an \omega_RP that decreases as the site goes further away from the promoter, as might be expected from a direct repression model. This claim is based on only testing the specific model that the authors hypothesize and no alternative model is compared. For instance, are the fits significantly worse if \omega_RP is kept constant and the K_R allowed to vary across the three sites. If different placements of the Runt site can result in puzzling differences in RNAP-promoter interaction, it seems entirely possible that the different site placements might result in different K_R, perhaps due to unmodeled interference from bicoid binding. Due to these considerations, it is not clear if the data indeed argue for a fixed K_R and distance-dependent \omega_RP.

Results presented in Figure 6 make the case that higher order cooperativity involving two DNA-bound molecules of Runt and the RNAP is sufficient to explain the data. The trained values of such cooperativity in the three tested enhancers appear orders of magnitude different. As a result, it is hard to assess the evidence (from model fits) in a statistical sense. Indeed, if all of the assumptions of the model are correct, then using the high-order cooperativity is better than not using it. To some extent, this sounds statistically uninteresting (one additional parameter, better fits). It is not the case that the new parameter explains the data perfectly, so some form of statistical assessment is essential. Moreover, it is not the case that the model structure being tested is the only obvious biophysics-driven choice: since this is the first time that such higher order effects are being tested, one has to be careful about testing alternative model structures, e.g., repression models that go beyond direct repression and pairwise cooperativity that goes beyond the traditional approach of a single (pseudo)energy term.

The general theme seen in Figure 6 is seen again in Figure 7, when a 3-site construct is tested: model complexities inferred from all of the previous analyses are insufficient at explaining the new data, and new parameters have to be trained to explain the results. The authors do not seem to claim that the higher order cooperativity terms (two parameters) explain the data, rather that such terms may be useful.

Reviewer #3 (Public Review):

This article reflects a significant effort by the authors and the results are interesting.

For the third set of experiments, are temperature and light really out of synch? While peak in temperature no longer occurs along with lights on, we do still have two 24 hour cycles where changes in the environmental cues still occur simultaneously (lights on with peak in temperature, lights off with min in temperature). I wonder what would happen if light remained at a 24 hour cycle and temperature became either sporadic (randomly changing cycles) or was placed on a longer cycle altogether (temperature taking 20 hours to increase from min to max, and then another 20 hours to go from max to min).

An area that could significantly benefit a broader readership would be to improve overall clarity of figures and rethink if all the results are necessary to convert the key findings of the paper. As written, the results sections is somewhat confusing.

Reviewer #3 (Public Review):

This paper offers novel mechanistic insights into how pre-exposure to warm temperature increases the resistance of C. elegans to peroxides, which are more toxic at warmer temperature. The temperature range tested in this study lies within the animal's living conditions and is much lower than that of heat shock. Therefore, this study expands our understanding of how past thermosensory experience shapes physiological fitness under chemical stress. The paper is technically sound with most experiments or analyses carried out rigorously, and therefore the conclusions are solid. However, it challenges our current understanding of the role of the C. elegans thermosensory system in coping with stress. The traditional view is that the AFD thermosensory neuron is activated upon sensing temperature rise, and that temperature sensation through AFD positively regulates systemic heat shock response and promotes longevity in C. elegans. Thus, it is quite unexpected that AFD ablation activates DAF-16 and improves peroxide resistance. It also appears counterintuitive that genes upregulated at 25 degrees overlap extensively with those upregulated by AFD ablation at 20 degrees. I feel that it is premature to coin the term "enhancer sensing" for such a phenomenon, as their work does not rule out the possibility that AFD ablation increases resistance to other stresses that are independent of temperature regarding their toxicity or magnitude of hazard. Additional work is necessary to clarify these issues.

1. Whether the role of AFD in inhibiting peroxide resistance is related to AFD activity needs further clarification. AFD activity depends on the animal's thermosensory experience. As animals in this study are maintained at 20 degrees unless indicated specifically, the AFD displays activities starting around 17 degrees and peaks around 20 degrees. Under such condition, the AFD displays little or no activity to thermal stimuli around 15 degrees. It will be important to test whether cultivation of animals at 20 degrees improves peroxide resistance at 15 degrees, compared to 15 degrees-cultivation/15 degrees peroxide testing. The authors should also test whether AFD ablation further improves survival under peroxides at 15 degrees for animals grown at 20 degrees, whose AFD should show little or no activities at 15 degrees.

2. The importance of the thermosensory function of AFD should be verified. In the current study, the tax-4 mutation was used to infer AFD activity, but tax-4 is expressed in sensory neurons other than AFD. In addition to AFD, AWC can sense temperature and it also expresses tax-4. Therefore, influence on AFD from other tax-4-expressing neurons cannot be excluded. On the other hand, ablation of AFD removes all AFD functions, including those that are constitutive and temperature-independent. Therefore, the authors should test the gcy-18 gcy-8 gcy-23 triple mutant, in which the AFD neurons are fully differentiated but completely insensitive to thermal stimuli. These three thermosensor genes are exclusively expressed in AFD. Compared to the tax-4 mutant that is broadly defective in multiple sensory modalities, this triple gcy mutant shows defects specifically in thermosensation. They should see whether results obtained from the AFD ablated animals could be reproduced by experiments using the gcy-18 gcy-8 gcy-23 triple mutant. The authors are also recommended to investigate ins-39 expression in AFD and profile gene expression patterns in the gcy-18 gcy-8 gcy-23 triple mutant.

3. The literature suggests that AFD promotes longevity likely in part through daf-16 (Chen at al., 2016) or independent of daf-16 (Lee & Kenyon, 2009). Whatever it is, various studies show that activation of AFD and daf-16 promote a normal lifespan at higher temperature, and AFD ablation shortens lifespan at either 20 or 25 degrees. Therefore, the finding that DAF-16-upregulated genes overlap extensively with those upregulated by AFD ablation is quite unexpected (Figure 5B). The authors should perform further gene ontology (GO) analysis to identify subsets of genes co-regulated by DAF-16 and AFD ablation, whether these genes are reported to be involved in longevity regulation, immunity, stress response, etc.

4. I feel that "enhancer sensing" is an overstatement, or at least a premature term that is not sufficiently supported without further investigations. The authors should explore whether AFD ablation or pre-exposure to warm temperature specifically enhances resistance to a stressor the toxicity of which is increased at higher temperature, but does not affect the resistance to other temperature-insensitive threats.

Reviewer #3 (Public Review):

Yoo et al. present a greatly improved assembly and annotation of the little skate genome. Using this new assembly and annotation, the authors re-analyze previously published gene expression data from little skate motor neurons, which were initially analyzed using instead zebrafish gene models. New in this paper is the ATAC-seq showing regions of chromatin accessibility, which was made possible by the improved assembly. Finally, the authors search for predicted transcription factor binding motifs in the vicinity of little skate motor neuron-specific genes to arrive at a model for gene regulatory networks operating in this species. They compare this gene expression and accessibility data and predicted network connections to those observed or predicted in other vertebrates (i.e. tetrapods).

The improved assembly and reanalysis of gene expression are of great use for the study of vertebrate motor neuron development and evolution. The ATAC-seq data are new and highly valuable. The thorough analysis of predicted binding sites is impressive and hints at differences in gene regulatory network architecture between cartilaginous fish and tetrapods.

A major weakness of this paper is the fact that the transcription factor binding site analysis is entirely dependent on bioinformatic predictions, as pointed out by the paper's limitations statement. The authors recognize that there is no actual binding site data obtained using little skate proteins, cells, or DNA (e.g. no ChIP-seq, no knockdowns, no cis-regulatory DNA reporters or mutations, etc). Unfortunately, this results in several unsubstantiated claims made throughout the paper, in which the presence of predicted binding sites is taken as a regulatory connection between genes.

Reviewer #3 (Public Review):

This manuscript presents a nice approach for performing population recordings from the optic glomeruli of Drosophila, allowing for explorations of how visual stimuli are encoded at a population level. The authors use a combination of behavioral recordings and visual perturbations to identify two mechanisms that contribute to the suppression of visual responses during body saccades: one motor-related and one visual. Overall this study presents a nice combination of imaging and analysis to determine mechanisms by which the visual system tunes out signals associated with self-movement to produce a reliable encoding of the visual world. I do have some concerns about the sources of the gain modulation that they describe across the population, and was confused by some aspects of the framing in terms of self-motion and visual feature decoding.

Reviewer #3 (Public Review):

Zydrski et al. describe the generation and characterization of multiple adult tissues from canines. While canine derived organoids could potentially be advantageous over murine and human organoids, the novelty of generation and characterization is limited, as organoid systems are now being rapidly genetically editing using CRISPR technologies and modeled within immunocompetent environments. Certain points limit my enthusiasm.

First, the authors do not support the use of serum (FBS) in their media and why they include the same growth and differentiation factors across all tissue types.

Second, while bulk RNA sequencing data shows similarity per certain genes to the corresponding tissue, there is a lack of detailed characterization of what passage these organoids were harvested and how they change over time. Do they become more stem like and are they genetically stable?

Third, it would be important to demonstrate that these organoids can be genetically manipulated or be exposed to drugs and how they might be beneficial over murine and human organoids.

Fourth, the organoid complexity is not clear and cannot be ascertained from bulk RNA sequencing- for example, do kidney organoids recapitulate canonical markers at the protein level of proximal tubules, distal convoluted tubules, etc. Are different lung cells represented (AT1/AT2/club) and what is the composition of these cells? Why are these cells selected for?

Fifth, as the authors note, methodically these canine organoids have been developed before from other tissues. For these reasons, my enthusiasm is diminished and unfortunately many of the necessary experiments for further consideration appear out of the scope of the study.

Reviewer #3 (Public Review):

This research contributes to optimizing the amber stop-codon suppression protocol for voltage-clamp fluorometry (VCF) experiments using Xenopus oocyte heterologous expression system. By in vitro RNA synthesizing the tRNA and tRNA synthetases, combined with the dominant-negative release factor initially developed by Jason Chin's lab, L-Anap can be site-specifically labeled to proteins by a single microinjection of a mixture of molecular components into the cytoplasm of oocytes. Although it avoids nuclear microinjection to oocytes, it adds more RNA synthesis steps. This strategy of using eRF dominant negative variant (eRF1-E55D), was previously applied to the Anap incorporation system using mammalian cell lines and model proteins (Gordon et al, eLife, 2018). In this previous 2018 paper, with eRF1-E55D, the percentage of full-length protein expression increased substantially. Using oocytes in this paper, this percentage apparently did not increase significantly as shown in Fig. 1D, different from the previous paper. Nevertheless, the overall expression level increased successfully by this method, which could facilitate macroscopic fluorescence measurements, especially considering that L-Anap is relatively dim as a fluorophore.

Anap fluorescence change was measured mostly using its environmental sensitivity, which has limited information in interpreting structural changes. The structural mechanisms proposed could be potentially strengthened and the conclusions could be further validated by combining FRET or other distance ruler experiments with the VCF method. The engineered CaM-M13 FRET experiments mostly report the calcium entry, not measuring the rearrangements of P2X7 directly. In addition, results of ATP dose-response relationship for channel activation correlated with ATP dose-dependent Anap fluorescence change, especially for sites showing a large percentage of ATP-induced change in fluorescence, would provide more insights regarding the allosteric mechanism of the channel.

Reviewer #3 (Public Review):

In the submitted manuscript, Eliazer et. al. conclude that Dll4 and Mib present on myofibers maintain a continuum of SC fates providing SCs capable of regenerating muscle and repopulatin the SC niche. The data provide new insights into the maintenance of SCs, demonstrating niche-derived factors are responsible for regulating SC behavior. Loss of either Dll4 or Mib from the myofiber reduces SC numbers and impairs muscle regeneration. Overall the data provide compelling evidence that niche-derived Dll4 and Mib regulate SC fate, however, whether the interaction maintains a continuum of SC fates as concluded by the authors is insufficiently supported by the data provided.

One significant issue with the manuscript is the "discovery" of an SC continuum related to the relative levels of Pax7 expression. A similar continuum was established nearly a decade ago by Zammit et al., 2004 and Olguin et al., 2004 and thus, is not new. The authors need to reference the work and discuss the prior published data with regard to the observations in the current manuscript. The data establishing a continuum of SCs and the relationship to Pax7 protein levels can largely be eliminated and referenced by the two former manuscripts. For example, these manuscripts establish that elevated Pax7 levels drive quiescence and low Pax7 levels correlate with differentiation. The data from these manuscripts establish that SCs with modest Pax7 protein levels can acquire quiescence accompanied by increases in Pax7 protein

The data relating the level of Pax7 expression with Dll4a and Mib are intriguing but the authors do not establish a direct relationship, demonstrating that Dll4 or Mib regulate Pax7 levels. An alternative explanation is that Dll4 and Mib inhibit differentiation and thus promote SC quiescence indirectly. This is a critical distinction, as the authors could be correct and Dll4 via Mib regulate SC fate.<br /> It is unclear that the loss of Dll4 or Mib reduce diversity of SCs. If these repress differentiation then their loss would be expected to enhance differentiation and reduce SC numbers, which is what the data demonstrate. No direct experiments demonstrate that Dll4 regulates the levels of Pax7 protein, the data provided show a correlation of higher Pax7 protein if Dll4 is present.

Finally, the injury data provided are for 4d post injury and thus, the data may represent a delay in regeneration as opposed to a failure to regenerate. At 30 d post injury regeneration is typically considered complete. How do wild type and Dll null as well as Mib null muscle compare at 30d post injury.

In summary, the data are intruiguing and suggest that Dll4 regulates satellite cell fate and maintains quiescence of satellite cells or inhibits their differentiation. Some additional data will resolve which of these outcomes is likely.

Reviewer #3 (Public Review):

In this manuscript, the authors describe experiments that were performed to investigate the peripheral neural mechanism of geometric feature extraction in human glabrous skin. The cutaneous sensory space of fast-adapting type 1 (FA-1) and slow-adapting type 1 (SA-1) afferents comprises multiple sensitive zones (subfields) spanning several fingerprint ridges, and the authors had earlier shown that subfield layout and edge orientation sensitivity are linked. In that study, the authors used edges with large orientation differences. Here they examine the signaling mechanism for fine edge orientation differences and the role of the scanning speed. They find that the same mechanism extends to the signaling of fine edge orientation differences and that it is maintained across a broad range of scanning speeds. Both FA-1 and SA-1 afferents perform well, albeit the former better than the latter, in signaling fine edge orientation differences when the sequential structure of their spiking response is considered. Further, the edge orientation sensitivity is tuned to natural scanning speeds with both afferent types showing speed-invariant orientation signaling when spike trains are represented in the spatial domain. These findings advance the idea that the subfield layout/terminal organization of primary tactile afferents in human glabrous skin is important for the early processing of geometric features.

Reviewer #3 (Public Review):

We are enthusiastic about this paper. It demonstrates controlled expression of ion channels, which itself is impressive. Going a step further, the authors show that through their control over ion channel expression, they can dynamically manipulate membrane potential in yeast. This chemical to electrophysiological conversion opens up new opportunities for synthetic biology, for example development of synthetic signaling systems or biological electrochemical interfaces. We believe that control of ion channel expression and hence membrane potential through external stimuli can be emphasized more strongly in the report. The experimental time-lapse data were also high quality. We have two major critiques on the paper, which we will discuss below.

First, we do not believe the analyses used supports the authors' claims that chemical or electrical signals are propagating from cell-to-cell. The text makes this claim indirectly and directly. For example, in lines 139-141, the authors describe the observed membrane potential dynamics as "indicative of the effective communication of electrical messages within the populations". There are similar remarks in lines 144 and 154-156. The claim of electrical communication is further established by Figure 2 supplement 3, which is a spatial signal propagation model. As far as we can tell, this model describes a system different from the one implemented in the paper.

Second, it is not clear why the excitable dynamics of the circuit are so important or if the circuit constructed does in fact exhibit excitable dynamics. Certainly, the mathematical model has excitable dynamics. However, not enough data demonstrates that the biological implementation is in an excitable regime. For example, where in the parameter space of Figure 1 supplement 1 does the biological circuit lie? If the circuit has excitable dynamics, then the authors should observe something like Figure 1 supplement 1B in response to a non-oscillating input. Do they observe that? Do they observe a refractory period? Even if the circuit as constructed is not excitable, we don't think that's a major problem because it is not central to what we believe is the most important part of this work - controlling ion channel expression and hence membrane potential with external chemical stimuli.

Reviewer #3 (Public Review):

In this work, the authors address the question of whether sensory deprivation drives homeostatic responses in all dendritic spines (the standard model/status quo) or is restricted to a functional subset of spines. The key claims of the manuscript are well supported by the data, the writing is clear, and the conclusions are both thoughtful and restrained. The contrast/comparison of the current results to prior work, specifically the difference between homeostatic responses in adult versus critical period animals, should be presented early and often.

Strengths:<br /> This manuscript builds on prior work from the authors that seek to understand compensatory plasticity in cortical circuits in the intact animal. Here, the authors present clear evidence that, instead of a global homeostatic response, circuit rebalancing may be the result of a selective strengthening of intra-network connections. Crucially, this rebalancing via network tuning does not involve homeostatic adjustment of sensory-related spines. More specifically, by tracking the same spines over 3 d, the authors reveal a functional separation between those spines that faithfully respond to sensory input and those spines that are network-correlated. The amplitude of calcium transients in network-correlated spines is increased following enucleation, which the authors suggest forms the basis of the global (network-wide) sensory-evoked responses. This is quite interesting as it is somewhat counterintuitive; absent these data, it would be reasonable to assume that increased network responses are reflective of homeostatic processes in the sensory-related spines and synapses. To reach these conclusions, the authors employ GCaMP6s-based calcium imaging of L5 pyramidal neurons in visual and retrosplenial cortices prior to and during sensory deprivation (enucleation or ear-plugging).<br /> This manuscript is well written. It is clear and not overstated. The work is presented in a linear and approachable style that should be accessible to readers outside of the field. These findings are a meaningful advance for the field and raise foundational questions about the neurobiology of the cortex. Specifically, homeostatic regulation of neuronal activity may be constrained to a subset of processes, or alternatively, adult sensory processes are somehow shielded from the impact of homeostatic change.

Weaknesses:<br /> Weaknesses are largely restricted to suggested changes to the writing - specifically, there are additional explanations of the data whose discussion may strengthen the long-term impact of the manuscript.<br /> 1. Most importantly, the hypothesis at the heart of this work (subset versus global processes) is framed as orthogonal to the status quo model of homeostatic processes (global). I suspect that adherents to the global argument would quickly point out that the current work is conducted in adult animals, and the majority of the homeostatic plasticity research (which forms the basis of the global model) is conducted in juvenile animals. This is an important distinction because the visual system is enriched in plasticity mechanisms during the ocular dominance critical period. Since Hubel and Wiesel at least, there is extensive evidence to suggest that sensory systems take advantage of critical periods to set themselves up in accordance with the statistics of the world in which they are embedded. The flip side of this is that sensory systems are far less readily influenced by experience once the critical period is closed (Vital-Durand et al., 1978, LeVay et al., 1980; Daw et al., 1992, Antonini et al., 1999, Guire et al., 1999, Lehmann and Lowel, 2008). Through this lens, one might predict that a key feature of the adult cortex is that sensory spines could benefit by being selectively protected from what would otherwise be global homeostatic processes. Either way, the manuscript can be read as if it is framing a show-down between the classical model and a newer, higher-resolution model. I worry that this will be interpreted as misleading without careful presentation/contextualization of the role of development in the introduction and a thorough dissection in the discussion. Currently, the first occurrence of the word, "adult", occurs in the methods, on page 27, line 512. "Juvenile" and "critical period" are not in the manuscript. The age of the animals in this study isn't mentioned until the methods (between P88 and P148 at the time of imaging).<br /> 2. Goel and Lee (2007) seem quite pertinent here: they show that L2/3 neurons give rise to homeostatic regulation of mEPSCs in both juvenile and adult animals, but that the process is no longer multiplicative in nature once the animal is post-critical period. Multiplicity has been the basis of the argument for global change since Turrigiano 1998. Thus, the Goel and Lee finding seems to really bolster the current findings - and also perhaps reconcile the likelihood of a mechanistic difference between CP and adult homeostatic plasticity.

Reviewer #3 (Public Review):

First of all, I enjoyed the manuscript by Horton et al. In the manuscript, they first re-analyzed published ChIP-seq data for STAT1 binding in INF-activated macrophages and found that a fourth of the >20,000 STAT1 binding sites were in transposable elements. Especially, about 10% of the total STAT1 binding sites were in B2_Mm2, a murine-specific SINE. They showed that these B2 elements are associated with H3K27ac signal upon INF treatment, thus likely serve as an INF-inducible enhancer through STAT1 binding. The authors then focus on the STAT1-bound B2_Mm2 in the Dicer1 gene (designated as B2_Mm2.Dicer1), and demonstrated that deletion of this B2 in a macrophage-like murine cell line resulted in loss of STAT1 binding, H3K27ac, and Dicer1 upregulation upon INF treatment. Their findings suggest that B2 transposition events has altered the transcriptional regulatory network in the innate immune response in the mouse.

The manuscript is well organized, and the findings are potentially interesting in terms of the evolution of species-specific regulatory networks of the innate immune response. But, I am not convinced with the enhancer role of the B2_Mm2.Dicer1 copy for the Dicer1 expression (see below).

Major Comments:

(1) In Fig. 4, the degree of Dicer1 induction by INF was small (1.2-fold or so), and accordingly the effect of the B2 deletion on the Dicer1 induction was also small. In addition, this B2 binds to CTCF, and its deletion should also eliminate CTCF binding. Therefore, it is difficult to conclude from the presented data that this B2 serve as an enhancer for Dicer1. The B2 may increase the frequency of transcription (as suggested by the authors), may serve as an obstacle for transcriptional elongation (via binding to CTCF), or may regulate the splicing efficiency. In Fig.5C, promoter acetylation level does not seem to be affected in KO1. Pol II either does not seem to be affected if the Pol II peak is compared to the background level. Taken together, the enhancer role is not supported by strong evidence.

(2) On the other hand, the authors discovered that the B2 deletion resulted in the decrease of Serpina3h, Serpina3g, Serpina3i and Serpina3f by >100-fold, which are 500 kb apart from the B2 locus. This is also interesting, and could be evidence for the B2 enhancer. Given that this B2 binds to both STAT1 and CTCF, the locus could interact with the Serpina3 locus to act as an enhancer. Were there STAT1 CUT&TAG peaks around the Serpina3 genes? Did H3K27ac and Pol II ChIP peaks in the Serpina3 promoters disappear in the KO cells? It would be interesting to see the IGV snapshots for H3K27ac, POLR2A and STAT1 ChIP-seq data around Serpina3 genes. In addition, HiC data for activated macrophages, if available, could be supportive evidence for the interaction between B2_Mm2.Dicer1 and the Serpina3 locus.

Minor Comments:

(3) Regarding Fig.1C, the authors calculated the B2 expression levels by mRNA-seq and DESeq2 analysis. But it does not accurately give the B2 transcription level, because the method does not discriminate B2 RNAs and B2-containing mRNA (and lncRNA as well). I wonder that the apparent upregulation of STAT1-binding B2 loci is due to the increase of Pol II transcription around the loci, rather than Pol III-mediated B2 transcription. This possibility should be discussed in page 6 after "Taken together, these data indicate that thousands of B2_Mm2 elements show epigenetic and transcriptional evidence of IFNG-inducible regulatory activity in primary murine bone marrow derived macrophages."

(4) Fig. 2B shows that about 70-80% of B2_Mm2 loci carry the STAT1 motif, whereas only a limited number (2-3%) of B2_Mm2 bind to STAT1. Is this because of differences in their motif sequences, in genomic locations, or in epigenomic environments? For example, do these STAT1-binding loci have a C-to-A mutation at the second last position in the GAS motif (TTCNNGGAA), like B2_Mm2.Dicer1 (shown in Fig. S4)? Can the authors discuss about it? In addition, although the consensus sequence of B2_mm2 has a GAS motif with only a single mismatch, the presence of the STAT1 motif in >70% of B2_Mm2 is surprising, given that their average divergence to the consensus sequence is about 10% (ref. 26 of the manuscript). Is the binding site significantly conserved in compare to the other regions of the B2 sequence?

Reviewer #3 (Public Review):

Rale et. al. convincingly establish the regulatory role of the γ-TuNA motif in microtubule nucleation and settle the conflicting results in the literature. They show that γ-TuNA binds to and activates γ-TuRC-based microtubule nucleation both in Xenopus extracts and in vitro. The authors use real-time imaging of the nucleating microtubules in vitro to show that γ-TuNA activates microtubule nucleation by ~20 fold. They further go on to show that γ-TuNA exists as a dimer and propose that its dimeric state is important for the activating function.

Reviewer #3 (Public Review):

Zhao et al. investigate how RNA:DNA hybrids/R loops that are generated during class switch recombination (CSR) due to the transcription activity at the switch regions in the IgH locus affect the outcome of CSR. Specifically, the authors used primary B cells lacking the helicase senataxin and RNaseH2 to interrogate the changes of R loop levels in the switch regions during CSR. Consistent with the known activities of these two proteins in R loop resolution, the authors find increased R loop formation in the double deficient cells. The effect of senataxin and RNaseH2 double deficiency on R loop processing appear to be restricted to the donor switch region Sm but not the acceptor switch regions. Importantly, senataxin and RNaseH2 function redundantly in resolving R loops in activated B cells as inactivation of individual genes does not affect R loop levels. Aberrant R loop resolution has been implicated in defected DNA double strand break (DSB) repair and productive CSR involves the generation and repair of DSBs between the recombining switch regions. Surprisingly, CSR to several Ig isotypes is not affected in Setx-/-, RNaseH2b-/- and the double knockout cells when compared to WT cells. The double knockout cells, in contrast to Setx-/-, RNaseH2b-/- and WT cells, do accumulate more chromosomal abnormalities, including AID-dependent IgH DSBs. The authors went on to conduct a series to show that in the activated double knockout primary B cells, cell proliferation, germline transcription, AID expression, the association of activated RNA pol II and AID with switch chromatin all appear comparable to WT or single deficient cells; therefore ruling out that the defects in these events cause chromosomal abnormalities observed in the activated Setx-/-: RNaseH2b-/- primary B cells and consistent with normal CSR in these cells. Lastly, the authors determine the switch junction sequences and found that in the activated Setx-/-: RNaseH2b-/- primary B cells, insertions and C to T mismatches are increased, suggesting a deviation from normal DSB processing in these cells that eventually lead to increased usage of alternative end joining during CSR.

The experiments conducted are well done and support the conclusion that the loss of senataxin and RNaseH2 leads to an increase in genome stability in the setting of IgH class switch recombination. The aberrant accumulation of R loops is very subtle at the switch region in the activated Setx-/-: RNaseH2b-/- primary B cells. Could this be due to RNaseH1 activity? How do the authors reconcile the increase in un-repaired switch DSBs without an impact on IgH CSR?

Reviewer #3 (Public Review):

Macaisne et al., use C. elegans oocytes to investigate the function of the kinetochore localised BHC module composed of BUB-1 (homologue of mammalian Bub1), HCP1/2 (homologue of mammalian CENP-F) and CLS-2 (homologue of mammalian CLASP) in meiotic spindle regulation. Since defects in meiotic spindle assembly would lead to defective meiotic chromosome segregation, known to give rise to birth defects, this is an important area of research. In the first part of the paper, the authors determine the domains of the BHC module and outer kinetochore components that are involved in localising the complex to kinetochores or ring domains of meiotic bivalent chromosomes. The functional consequences of BHC module mis-localisation are then assessed by live cell imaging. The authors find that a correctly assembled BHC module is indispensable for correct chromosome segregation during meiosis. Using recombinantly expressed proteins, the authors then show that in vitro the components of the BHC module synergistically regulate microtubule behaviour. In particular, the incidence of pausing during microtubule growth was significantly increased by the addition of all three BHC components. This is interesting because BUB-1 by itself did not influence microtubule growth properties hence only seems to exert its influence in a complex with HCP1/2 and CLS-2.

Strengths:

The data presented in the manuscript are generally of very high quality and very nicely presented, and the effects observed are convincing and confirm the statements in the manuscript text. The analysis of the purified proteins of interest in an in vitro setting adds an extra dimension to the study and is highly informative since it shows that the combined actions of the BHC proteins results in the strong promotion of microtubule growth pausing.

Weaknesses:

While the combination of live cell imaging and in vitro essays with purified proteins is one of the strengths of the manuscripts, it also highlights a gap in the understanding of the function of the BHC module. How does the ability of this complex to induce pausing in microtubule growth relate to the observed defects in chromosome segregation in oocytes expressing defective BHC components? What are the precise molecular deficiencies causing the mis-segregation? Could the authors investigate this more directly than by just measuring spindle microtubule density? Is the spindle assembly checkpoint activated by the BHC module modifications that the authors test? Some of the conditions seem to result in delayed timing of meiosis consistent with this idea.

Although the analysis of the process of meiosis in C. elegans oocytes has interesting implications for mitosis and meiosis in other systems, it is a very specialised system, that not all readers may be entirely familiar with. A more extensive discussion, comparing systems and highlighting points of diversion would therefore be useful for many readers.

Reviewer #3 (Public Review):

Canetta et al have characterized the developmental regulation of PV neurons in PFC. The experiments have been carefully conducted and even though this is an area of broad scientific interest, there are several issues that require consideration.

1) The dosing regime of the CNO that has been employed will not provide persistent inhibition. Inhibition will operate on a 16 hr on/ 8 hr off cycle. Under such circumstances, it will be very difficult to rule out interspersed inhibition-related artifacts.

2) The second major issue with the dosing regime is that it is long (35 days). Realizing that the development of PFC circuitry is complex but at P90, the animals will have been dosed for more than a third of their lives. How can the authors rule out compensatory changes that do not have anything to do with critical periods?<br /> To this point, in the discussion first para line 8 - please change "transient" to something more suitable to reflect the duration of treatment.

Reviewer #3 (Public Review):

The authors aimed to quantify changes in the (CDR3beta) T cell receptor (TCR) repertoire as the cells go through the successive stages of thymic selection. To this end, they used Nur77 reporter mice and Annexin V to detect activated and/or dying cells, allowing them to some extent to identify cells that had undergone positive and/or negative selection. The authors appear to set out to prove the absence of major sequence-specific differences between these repertoires to support a stochastic model of thymic selection, in which T cells experience mild sequence-specific biases rather than being strongly pushed towards a specific fate. Indeed, since the ground-breaking results by Davis et al (https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4455602/), such a stochastic model is now commonly assumed rather than the older "text-book view" of thymic selection removing most or all auto-reactive cells; as such, it is a reasonable starting point.

The dataset generated for this paper is very interesting and will no doubt be useful to the wider community. To my knowledge, this is the first time this combination of Nur77 and Annexin V was used to aim to pinpoint cells that were deleted. The authors use state-of-the-art generative statistical models for TCR repertoires to conduct their analyses. The initial analyses shown in Figure 2 are promising in that they indicate that there are indeed visible systematic differences between these subsets, even if they might be small.

A limitation of the Annexin V based approach is that the fraction of cells expressing Annexin V is small, and there appears to be no clear "cutoff" value separating negative from positive cells. This means that the negatively selected subpopulations, probably the most interesting ones for this study, are also the smallest at 1000 cells or less per sample. This limits the ability to detect specific "signatures" of detection. Indeed, from the initial analyses (Figure 2), it appears that the difference between Annexin V+ and Annexin V- populations is just barely detectable. Unfortunately, this means that not too much can be concluded from the absence of clear signals when comparing these subpopulations, as there may simply not be enough statistical power. This would make it very important for the authors to state clearly which signals they can and cannot expect to detect in datasets of this size. For instance, it may well be that some of the TCRs that are specific for a small number of ubiquitously expressed proteins (such as beta-Actin) are reliably removed during negative selection, but these TCRs may be a small minority of the overall pool, and they may not share common sequence features as there would presumably be many different peptides that they could respond to. As such, this kind of sequence-specific selection would likely go undetected by the analyses shown in this paper.

The authors show in Figure 3 that while individual TCRs coming from the different populations cannot be distinguished reliably, we can still distinguish these populations if we instead look at larger groups of TCRs. The authors interpret this as evidence for the idea that T cells collectively distinguish self from nonself by quorum sensing. However, the fact that several noisy predictions of a class can be combined to obtain a better prediction is not specifically related to TCR sequences, and a similar phenomenon would appear in any classification task (in machine learning, this phenomenon is known as "boosting"). It is a consequence of the law of large numbers -- an average taken from several values (TCR sequence predictions in this case) will be closer to the true population average than one taken from few values. Thus, as soon as there is *any* difference between the mean predicted class probabilities for the two classes, then this phenomenon will occur.<br /> The authors do not clearly explain how this basic fact substantiates the idea of quorum sensing, which is a phenomenon involving several T cells that are specific to the same antigen.

In Figure 4, the authors show that there are differences in amino acid usage between the populations (further detailed in supplementary figures) and that similarity in amino acid usage corresponds to closeness in the lineage. This is an interesting observation, which raises the question whether it is really necessary to look at 3-mers to get this result or whether simple 1-mers (i.e., simply the usage of amino acids without considering contiguity of positions) would already be sufficient. Several results show that differences do exist at the 1-mer level already, so it remains unclear whether going to k-mers is really necessary.

In Figure 5, the authors argue that the data are inconsistent with a model in which two fates for the same T cell receptor are mutually exclusive (or at least sufficiently strongly biased towards mutual exclusion), as they would expect a negative correlation between the class probabilities of these two fates in this case. However, the scenario shown in Figure 5A is not comparable to the data. For example, even if the CD4SP and CD8SP fates were mutually exclusive, we might still not expect a negative correlations between the quantities E_CD4SP-E_DPPRE and E_CD8SP-E_DPPRE because the cells need to reach the DPPOS stage first. Therefore, E_DPPOS would be a common cause of E_CD4SP and E_CD8SP, inducing a positive correlation which may well be stronger than the expected negative correlation.

Overall, this is a relevant paper based on an interesting dataset and sophisticated methodology. However, I was not convinced of some of the authors' conclusions due to the aforementioned issues in the methodology. Generally speaking, the paper is also still rather difficult to parse since it is not always clear what exactly the authors are trying to achieve with their quite sophisticated analyses, and simpler baselines are not considered to show that these complex analyses are truly necessary; certainly for the analyses shown in Figure 3B and Figure 5A, it was not entirely clear why these were performed and what we might conclude from them. Therefore, in its current state, I worry that the paper might not yet be very accessible to the broader community and that the motivation behind its methodology might remain somewhat obscure to many readers.

Reviewer #3 (Public Review):

Authors identified that HCMV specific T cells cross-react to SARS-CoV-2 epitopes. These cross-reactive CD4+ and CD8+ cells were identified in pre-pandemic healthy donors by stimulating with SARS-CoV-2 and HCMV protein peptide pools. The manuscript convincingly showed that HCMV specific T cells cross-react to SARS-CoV-2 peptides, which explains the detection of SARS-CoV-2 specific T cells in pre-pandemic PBMC samples. This highlights that T cells primed by highly prevalent pathogens, in addition to highly similar coronaviruses, are a potential source of cross-reactive T cells. Although these T cells showed relatively low affinity to SARS-CoV-2 epitopes, they showed potential to control SARS-CoV-2 replication in vitro. The detection of these T cells was limited to a small cohort of individuals with severe SARS-CoV-2. These initial observations from this study support the claim that cross-reactive T cells recognize the coronavirus epitopes, but detection in severe COVID-19 cohorts might point to the limited role of these cells in control of the SARS-CoV-2 infection, especially in the light of previous studies that report HCMV positivity as a potential risk factor for severe COVID-19 disease. Future studies should focus on explaining if other high prevalence virus, such as EBV or Influenza, specific T cell responses can also cross-react with SARS-CoV-2.

Reviewer #3 (Public Review):

The authors studied the impact of partial ablation of osteocytes on the changes of musculoskeletal system. Using a mouse model of partial osteocyte deletion by the expression of DTA in DMP-1-positive osteocytes (DTRhet), the authors demonstrated an interesting phenotype with multi-organ deficits. Particularly, the authors found that DTRhet mice have severe osteoporosis, kyphosis, sarcopenia with shorter life span. By assessing the cellular changes in bone/bone marrow, the authors showed that partial osteocyte ablation increased adipogenesis, impaired osteogenesis and promoted osteoclastogenesis. They went on to show that osteocyte ablation altered hematopoietic lineage, characterized by the shift from lymphopoiesis to myelopoiesis. Finally, they conducted scRNA-seq and found that total bone marrow from DTRhet mice (vs. WT mice) had increased senescence featured by higher SASP score. The authors reach the major conclusion that osteocytes play critical roles in regulating lineage cell specifications in bone and bone marrow by inducing organismal senescence. This is a very interesting set of studies, in which most of the authors' conclusions are supported by well-established mouse genetic conditional approaches and skeletal phenotypic analyses.

I have the following points for the authors to address:

1. The finding that osteocyte reduction induced senescence in osteoprogenitors and myeloid lineage cells is intriguing. However, further validation of cellular senescence in bone/bone marrow is lacking. Additional approaches, such as immunostaining of key senescence markers in bone tissue sections, are needed to validate the phenotype.<br /> 2. It is interesting that partial osteocyte ablation alters mesenchymal lineage commitment, i.e. increased adipogenesis and impaired osteogenesis. The authors should perform further analysis of their scRNA-Seq data and conduct trajectory analysis to confirm the phenomenon. Additional functional assays of bone marrow mesenchymal stem/progenitor cells, such as CFU-F and tri-lineage differentiation assays, are needed to claim the lineage commitment change of the cells.<br /> 3. The mechanism why osteocyte reduction causes cellular senescence of the surrounding cells is an interesting question. It would be helpful if the authors provide evidence or give an explanation on this point. Does the phenotype recapitulate age-associated bone impairment? The laboratories of Sundeep Khosla (Mayo Clinic) and Maria Almeida (University of Arkansas for Medical Sciences) reported that osteocytes are a major cell type in bone that become senescent during aging. Although most of osteocytes were eliminated in the mouse model used in this study, were the rest osteocytes undergoing cellular senescence?

Reviewer #3 (Public Review):

Connally et al investigated a central question in complex trait genomics - what's the main mechanism that mediates the effects of trait-associated variants in non-coding regions, which harbour most of the signals identified by genome-wide association studies (GWAS). It is widely perceived that these variants affect trait phenotypes by regulating expression of genes in cis that are functionally relevant to the trait. The authors argue that this is not true because they find limited evidence of linking the trait-associated non-coding variants to a set of putatively causative genes that are known to cause the severe form of the complex trait. The authors discussed four possible explanations to their observations. They argue that incorrect assumptions and lack of statistical power are not likely to be critical, withhold their judgment on the biological context, and claim that the most convincible explanation is the existence of alternative regulatory mechanisms. This conclusion is very important and sobering if it is true because it will inform where to invest the most efforts in the future GWAS.

It is an interesting idea of using genes of known roles in the "Mendelian forms" of the cognate complex traits as true positives to investigate the biology of non-coding variants. The analyses are done carefully. The discussion of the results is sharp, stands high, and provides lots of food for thought. My major comments lie in the strength of support of their results for the conclusion of "missing regulation" likely attributed to alternative regulatory mechanisms. The results presented seem to also support the biological context hypothesis that non-coding variants regulate gene expression in a tissue or cell type-specific manner.

Major comments:

The positive results are substantially reduced when restricting the analyses to a set of selected tissues of relevance to the trait. Isn't it implicated that the selection of relevant tissues in this study is not comprehensive, and further, tissue specificity is common in mediating genetic effects by gene expression?<br /> First, it seems some apparently relevant tissues are not selected (Table 2), such as bone for height (Finucane et al. 2015 NG). One approach to assess the relevant tissues for the predefined set of putatively causative genes is to see if these genes are enriched in the differentially expressed gene sets for those tissues. Second, among 84 putatively causative genes overlapped with GWAS signals, they identified 39 genes by TWAS, 11 genes by fine mapping with linear distance to chromatin modification features, and 41 genes by fine mapping with ChromHMM enhancer annotations, but these numbers reduced substantially to 9, 5 and 27 when restricting the same analysis to the selected tissues for each trait. If genes function only in the relevant tissues, I think using bulk expression data would lose power but is unlikely to give false positives. Thus, it is possible that for the traits analysed, not all relevant tissues are selected so that only a fraction of genes identified in bulk expression analysis can be replicated in the tissue-specific analysis. This appears to me a notable piece of evidence to support the hypothesis of biological context that the authors tend to have reservations in discussion.

How much do both LD differences between GWAS and eQTL samples and the presence of allelic heterogeneity contribute to the observed low colocalization rate?<br /> One of their main findings is the low colocalization between trait-associated variants and eQTL in non-coding regions, which accounts for only 7% of the putatively causative genes. In discussion, the authors believe that this finding cannot be explained by lack of statistical power and is directly supported by a Bayesian analysis which reported high posterior probabilities of distinct signals for GWAS and eQTL. I agree that power is probably not a big issue. However, my concern is that given the large difference in sample size between GWAS and GTEx datasets, any small differences in LD between the two samples might cause a statistical separation of the signals even when trait phenotype and gene expression truly share a causal variant. Moreover, the presence of more than one causal variant with allelic heterogeneity in the locus may also play a part in the failure of colocalization. Consider two causal variants for the complex trait, one regulating the target gene and the other regulating another gene in co-expression. Potentially, the presence of the second causal variant would diminish the colocalization probability at the target gene.

Perhaps the authors can perform some simulations to quantify the influence of tissue-specific expression effects, LD differences between eQTL and well-powered GWAS, and allelic heterogeneity, as discussed above, on their analyses. I understand that the authors may not be willing to do as it would involve a lot of work. But I'd like to see at least some discussion on how these questions can be better addressed in the future research.

It looks quite striking that only 6% of the putatively causative genes are identified by TWAS with the correct effect direction. But I think this number is slightly misleading as one may interpret it as only 6% of the functionally relevant genes are regulated by trait-associated variants. In fact, 46% of the genes are detected by TWAS but only 11% are confirmed in their selected tissues, among which about half (5/9) have correct effect direction. First, the result could be limited by the selection of relevant tissues, as discussed above. Second, the fact that half of the genes do not show correct effect direction may reflect a nonlinear relationship between expression and trait, or the presence of cell-type heterogeneity within a tissue. These may not necessarily overturn the assumption that these genes are regulated by trait-associated variants in the causal tissues or cell types.

While they highlight the roles of alternative regulatory mechanisms, few testable hypotheses are put forward for the field, which is somewhat disappointing but understandable given how little we know about the human genome at the mechanistic level.

Reviewer #3 (Public Review):

Prince et al set out to develop and demonstrate a toolbox for application to fMRI data collected in condition-rich designs, which are characterized by having a large number of conditions, each with a fairly small number of instances within a single participant. This describes a fairly small minority of all fMRI studies conducted currently, but is nonetheless an active area of research, which the GLMsingle toolbox has the potential to benefit. Because these designs benefit less from the trial-averaging approach of the standard GLM, any step that can increase SNR will have an outsized influence on the quality of the results.

The description of the logic and basic steps instantiated in the toolbox is clear and easy to understand for any researcher with background in this area. Likewise, the analyses the authors conduct to validate the toolbox are reasonable, and are described fairly clearly. If I were conducting a study using this sort of design, I would certainly try out the off-the-shelf version of the toolbox, although I would also do so provisionally, and run my own checks to ensure that the results were not biased or degraded by the toolbox.

Overall, there are few weaknesses in the methods or results presented in this article, if it is taken as a description of a specific approach developed and employed elsewhere, rather than as a comprehensive test of possible approaches to solve the problems the authors outline. In other words, the article begs the question of what the effect would be of substituting the vast majority of specific choices made in this toolbox, in terms of the algorithms used, the order in which they are applied, and so forth. Given that this is an initial introduction of the toolbox, and the complexity of the article as it is, it is more than reasonable for the authors to forego this kind of comprehensive comparison, but readers may nonetheless be left wondering about the optimality of this specific implementation. Likewise, given the small number of datasets that have the necessary characteristics, it is not surprising that the validation of the toolbox relies on (a large amount of data from) N=8. The authors' point that the two datasets chosen differ in a number of ways is well taken, but it is nonetheless an open question to what extent the results presented here will generalize to other datasets.

As for strengths, the sheer number of different metrics the authors used to validate the toolbox, covering intra- and inter-subject measures, and distinct analysis methods including RSA and MVPA, was impressive, as was the care in thinking about important issues such as voxel selection. Overall, the methods and results reflect a high degree of expertise and hard work on the part of the authors.

My overall assessment is that the utility of the toolbox itself may be limited, given the relative rarity of this type of design, at least at present. However, in pointing out new avenues for scholars working in this general area to pursue-for instance, exploring the impact of voxel-specific HRFs or regularization-this paper may have a larger influence, insofar as some of the techniques employed here may eventually find use in other, more widespread use cases.

Reviewer #3 (Public Review):

The authors present a phylogenetic analysis of evolutionary rates as they correlate with independently derived "hairlessness" across mammals. This is a very good paper, well written and very carefully analyzed. This paper makes a number of interesting biological insights, including the identification of protein coding as well as noncoding regions that appear to evolve in correlated fashion with hairlessness.

I have several recommendations:

1) The main assumption behind this experiment is that species "use" the same genes to accomplish hairlessness. Only then would one predict correlated rate shifts along hairless lineages. If, on the other hand, each hairless species used a unique gene to accomplish hairlessness, then one might only see a rate shift on that species' lineage. Therefore, a complementary approach might be to i) define all genes with known involvement in hair morphology (i.e., genes in the categories listed in Fig. 1C). ii) test how many of those genes show a significant rate shift in **at least one hairless lineage**. iii) test whether hair genes are more likely to show at least one rate shift compared to genomic background. This complementary analysis would relax the assumption that all hairless species show similar rate shifts compared to haired species.

2) It would be interesting to break up noncoding into additional strata. For example, one might predict that rate shifts in predicted transcription factor binding sites would have a larger functional impact than rate shifts in noncoding regions with no function. Or... that rate shifts in highly conserved noncoding regions vs. less conserved noncoding regions.

3) Why is aardvark considered a haired species? Aardvarks have as much (or as little) hair as pigs.

4) The primary goal of the paper is to identify coding/noncoding regions that show shifts in evolutionary that are correlated on hairless vs. haired lineages. I was left wondering... when these correlations are found, how often is it due to the same mutations hitting the regions vs. mutations randomly hitting the same regions. If the former, this would suggest some limited way that species can achieve "hairlessness".

Reviewer #3 (Public Review):

YTHDC1 has recently been reported as an epigenetic regulator of chromatin. In addition, this protein is known to regulate RNA splicing and export. This manuscript is trying to understand the RNA regulatory mechanism of YTHDC1 in skeleton muscle activation and proliferation. Inactivating YTHDC1 by inducible knockout and protein degradation demonstrates YTHDC1's role in skeleton muscle regulation. Further, the authors applied their LACE-seq, a house-made pipeline suitable for small cell numbers (e.g., activated skeleton muscle stem cells). Together with meRIP, they identified YTHDC1's potential targets in the skeleton muscle stem cells. Moreover, authors have attempted to investigate YTHDC1's RNA splicing and export targets in regulating skeleton muscle regeneration and proliferation. They also discussed the functional specificity of YTHDC1 by identifying its binding partners. These preliminary analyses provide a valuable foundation for further mechanistic investigation. The identification of YTHDC1 as a regulator in skeleton muscle development would be beneficial for the field of muscle injury and regeneration.

Reviewer #3 (Public Review):

The work by Nakamura and Colleagues describes a new method that allows, for the first time in mammals, to specifically target cerebrospinal fluid-contacting neurons (CSF-cNs) in the spinal cord with an adeno-associated virus. The role of these neurons still remains largely unknown. The new method allows to introduce a gene into these neurons in order to label them for anatomical investigations, to activate them to decipher the microcircuitry they form with other cells, or to silence them to investigate their function during behavior. The authors were successful to specifically target cerebrospinal fluid-contacting neurons located in the ventral part of the central canal leading to an exceptional amount of anatomical data (including at the ultrastructural level). The material provided (figures and videos) is qualitatively and quantitatively tremendously valuable. The observation of synaptic contacts allows the authors to make assumptions on the microcircuitry they form with other neurons. Importantly, optogenetic stimulation combined with electrophysiological recording allows the authors to fully demonstrate that each CSF-cN establishes functional inhibitory connections with other CSF-cN located more rostrally. However, the connectivity with axial motor neurons, V0c and V2a interneurons only relies on the anatomical study. We may wonder whether a more solid and full demonstration could be provided using again optogenetics tools and electrophysiological recordings in complement to the anatomical data? Finally, the authors report the interesting observation that mice with inactivated CSF-cNs cannot run on a treadmill at a speed faster than 15 m/s in sharp contrast with mice with functional CSF-cNs.

yellow journalism in this manner did not have good effects. It was spreading misinformation to audiences.

This is what yellow journalism was- getting the information they needed at whatever cost, eve if it meant making up some facts in order to attract readers.

Reviewer #3 (Public Review):

This manuscript aims to address whether age-related iron status influences differential effects of estrogen replacement on atherogenesis in postmenopausal females. Specifically, whether age-related iron accumulation reduces estrogen signaling through ERa receptor in relevant cell types (endothelial cells and macrophages). They test this fairly rigorously using in vitro, preclinical and clinical data, and the data is presented logically.

First, the authors demonstrate that in postmenopausal women, there is an inverse correlation between age-related increase in iron levels and age-related decrease in ERa levels in atherosclerotic plaques. This is consistent with the fact that, in ovariectomized (OVX) pro-atherogenic (ApoE KO) mice, there is differential effect of estrogen (E2) on ERa, atherosclerosis, lipid profiles and key biomarkers (ABCA1, eNOS, etc) based on age (early: 16 weeks, versus late: 40 weeks), with young mice responding favorably while older mice responding negatively. Consistent with this, in the older OVX ApoE KO mice, E2 treatment worsened atherosclerosis. Importantly, they point this differential effect of E2 to iron overload in macrophages using ApoE KO; LysM-macrophage-specific Fpn1 KO as E2 now has deleterious effects regardless of age. Next, using ferric ammonium citrate (FAC) to supplement iron and deferiprone (DFP) to chelate iron, they show that iron manipulation impacts the E2 response appropriately in the relevant cell types (endothelial cells and macrophage cells). Then they provide evidence for the MdM2-mediated post-translational regulation of ERa as a mechanism by which iron status impacts differential E2 has differential effects on ERa. Finally, they test the impact of systemic iron chelation on the OVX ApoE KO mice model of atherosclerosis and show that iron chelation attenuates the deleterious effects of E2 in late postmenopausal mice.

Overall the evidence is solid and logically laid out. Given that serum iron levels do not correlate with the rest of the story, inclusion of LysM-macrophage-specific Fpn1 KO provides the key evidence that iron loading in macrophages drives differential effects to E2 in postmenopausal mice. The paper provides evidence that iron levels influence how the relevant cells respond to E2 with clinical implications to hormonal replacement therapy in younger and older postmenopausal women. While this study is limited by a small clinical sample size, it provides an important framework for future studies on the impact of age-related iron status on the response to hormonal replacement therapy.

Reviewer #3 (Public Review):

This paper demonstrates neural mechanisms important for the representation of moving stimuli. Specifically, using EEG, the authors investigated the temporal profiles of visual activities that correspond to changes in positions of moving stimuli.

Strengths:<br /> The authors examined an interesting question of how moving stimuli can be smoothly represented and perceived by using a neural recording modality with high temporal resolution. To my knowledge, the temporal dynamics of the neural correlates of successful motion perception are not well understood, and the study provides evidence for a plausible mechanism for this process. Additionally, the paper is well-written where the results are clearly communicated, and the figures are clearly presented.

Weaknesses:<br /> The findings reported are derived from a specific case of motion perception which may not reflect the general mechanisms optimized for motion perception. The limitations related to task designs and the neural readouts should be discussed as they affect the way that the reported results will be interpreted.

Reviewer #3 (Public Review):

This study provides interesting new information on the cellular roles of different Arp2/3 complex isoforms. Previous studies have shown that T cell receptor signaling induces both cytoplasmic and nuclear actin filament assembly, which are dependent on the Arp2/3 complex. Moreover, nuclear actin filament assembly can be induced in response to DNA damage by the Arp2/3 complex. However, the possible roles of different Arp2/3 isoforms in these processes have not been reported. Here, Sadhu et al. demonstrate that the two isoforms of the ARPC5 subunit (ARPC5 and ARPC5L) have specific functions in these processes. By using knockdown and knockout cells, they provide evidence that cytoplasmic actin polymerization induced by T cell receptor activation is dependent on the ARPC5 isoform, whereas consequent nuclear actin filament assembly relies on the ARPC5L isoform. Interestingly, the nuclear actin polymerization induced by DNA replication stress is dependent on ARPC5. The authors also examined the upstream signaling pathways, and provide evidence that nuclear calcium-calmodulin signaling and N-WASP are specific activators of ARPC5L containing complexes.

Majority of the data presented in the manuscript appear of good technical quality, and the study provides interesting new insights into specific cellular roles of different Arp2/3 isoforms in T lymphocytes.

Reviewer #3 (Public Review):

The article reports the functional analysis of one of the critical genetic determinants for bacterial infection, the RHIZOBIUM-DIRECTED POLAR GROWTH protein (RPG). The evolutionary pattern linking RPG to the Transcription Factor and the LysM receptor-like kinase, and the ability to form root nodule symbiosis makes it one of the prime candidates for engineering symbiotic nitrogen fixation in cereals. Therefore this analysis is timely and of huge importance. In a previous study, an EMS-induced mutant of M. truncatula (rpg-1 allele) was reported to have aberrant infection threads and poorly colonized nodules, the RPG expression was strongly associated with rhizobial infection, and the RPG protein showed nuclear localization when heterologously overexpressed in N. benthamiana (Arrighi et al., 2008), but its cellular and molecular function in M. truncatula have not been understood in detail.

In the present manuscript, the authors showed that RPG is a crucial component of the infectosome machinery. They conclude that RPG sustains polar growth of intracellular infection threads, being necessary to recruit via protein-protein interactions the VAPYRIN.<br /> In absence of rpg, the authors reported : (i) the absence of membrane polarization at the advancing tip of the infection thread using phosphoinositide reporters, (ii) the lack of connectivity between the infection thread tip and the nucleus via the microtubules cytoskeleton, (iii) the loss of polar secretion of the cell wall modifying enzyme NODULE PECTATE LYASE (NPL).

These results confirmed that RPG is part of the core host machinery required to support symbiont accommodation. Furthermore, this work shows that multimeric host factors work as a module committed to endosymbiosis to sustain the infection thread-mediated bacterial infection.

This paper is well written, with extremely beautiful cell biology and the conclusions are clearly connected to the data presented. However, in my view, some critical points need to be further addressed:

1. The author showed that RPG co-purified with EXO70H4 and VPY indicating that these proteins indeed belong to the same complex. Moreover, they showed that the major molecular determinant conferring functionality to RPG resides in its coiled-coil domain with structure-function analysis. Does the CC domain important for RPG interaction with "the multimeric host factors" described here? What is the localization of the truncated form of RPG (reported in this paper for rpg1 complementation analysis) in the WT plant?

2. It is not clear to me how RPG acts on the membrane polarity: Is there a direct role of RPG in the PIP2 polarization, and cytoskeleton arrangement in the exocytosis of cell wall modifying enzyme NODULE PECTATE LYASE (NPL)? Did the authors observe an increase in membrane polarity when RPG is overexpressed? The authors might consider adding a model for the different molecular components they studied in the context of this biotic interaction.

Reviewer #3 (Public Review):

In this manuscript, the authors propose that there is a special, previously unrecognized, high-frequency population of a/b TCRs that are shared between people, have high generation probabilities, and react to many unrelated viral epitopes. Here is the main flow of the results, with comments on the strengths of the conclusions:

"Thymopoiesis selects a large and diverse set of clustered CDR3s with high generation probabilities" – this seems correct and has been noted in earlier work by Mora and Walczak and others. Selection leads to a focusing of the CDR3 length which likely increases the degree of clustering and increases Pgen.

"Clustered CDR3s are enriched for publicness" This also seems correct and again it makes sense: publicness is equivalent to having been independently rearranged (and sequenced) in another individual, which is determined by Pgen, and clustering is also determined to a large extent by Pgen (the factors that contribute to Pgen, shorter CDR3s for example, are largely shared between neighbor TCRs).

"Clustered public CDR3s are enriched in viral specificities" – This claim is not justified by the data, which comes from sequence matching against literature-derived databases. Rather, what is true is that "Clustered public CDR3s are enriched in public viral specificities". But this might be a simple consequence of the previous observation, that "clustered CDR3s are enriched for publicness". One would need experimental specificity data on the very same datasets to make a conclusion about viral specificities in general.

"Identification of polyspecific TCRs" – In this section, the authors report that some of the CDR3 clusters contain CDR3 sequences from literature-derived TCRs with multiple specificities. They conclude that these must represent polyspecific TCRs. The problem with this conclusion is that even having the same CDR3beta, let alone similar CDR3beta sequences, does not imply the same specificity. One can see the problem if one imagines a very deeply sequenced dataset, and focuses on a short CDR3 length with high frequency. WIth sufficient sampling, one will be able to navigate from nearly any single CDR3beta to any other CDR3beta of the same or similar length by jumping between single-mismatch variants. But this doesn't imply that all the TCRs from which these CDR3s were sampled, which likely have many different Vbeta genes and completely different TCRalpha sequences, must all bind the same thing.

"Binding properties of polyspecific TCRs" – Here the authors look to validate these results with paired TCR sequences. They analyze a public dataset made available by 10X genomics, featuring single-cell gene expression, TCR sequencing, and dextramer UMI counts for ~150,000 T cells. This is an amazing dataset with lots of interesting features, but, like any large high-throughput dataset, it needs to be analyzed with care. The authors claim to see evidence for large-scale cross-reactivity. This comes mainly from a set of dextramers for A*03 and A*11-restricted peptides. But these dextramers appear to be binding in a uniquely non-specific manner (by comparison with the other dextramers) and non-TCR-dependent manner in this experiment. One can see this, for example, by comparing the consistency of binding within expanded clonotypes: for a specific dextramer like A*02-GIL(Flu), positive binding for one cell in a clonotype greatly increases the likelihood of binding for other cells in the clonotype, suggesting that the binding is mediated by the TCR. This is not true for the A*03 and A*11 dextramers (except for a few expanded clonotypes in an A*11 donor). TCR sequence doesn't appear to be the determining factor for binding to these dextramers; rather it may be expression of KIR genes or other surface proteins that can interact with MHC.

"Polyspecific T cells are activated in vitro by multiple viral peptides" Here the authors explore polyspecificity experimentally. First they report that polyclonal populations of T cells, sorted for binding to one dextramer, can also produce IFNgamma upon stimulation with a distinct peptide, albeit more weakly than for the cognate peptide. But it's not clear that the concentrations of the peptides are appropriate for stringently detecting cross-reactivity. Then the authors actually synthesize and characterize individual TCRs. Here what is seen is consistent with expectation and does not seem to support the idea of substantial fuzzy cross-reactivity: binding to the cognate peptide is 3-4 orders of magnitude stronger than to the alternative peptides. The only exception is the GAD 114-122 TCR, where the different peptides appear to be closer in binding strength. But in this case, the authors state that they "analyzed their response to a set of peptides comprising their cognate peptide and peptides with no significant structural commonalities, selected by testing combinatorial peptide libraries". If the competitor peptides came from peptide library screening then the observation of strong binding to alternative peptides does not seem as surprising as a TCR that binds well to a Flu peptide, say, and also a CMV peptide, selected from a smallish set of possibilities.

It is pretty well established that TCRs are cross-reactive, both for nearby peptides and also for sequence-dissimilar peptides. The question is whether widespread, functionally relevant (not just dextramer binding at some concentration) poly-reactivity to diverse viral peptides is a defining feature of a large fraction of the TCR repertoire. The paper does not appear to present sufficiently strong evidence to support this claim.

Reviewer #3 (Public Review):

This manuscript explores the concept of TCR convergence, defined here as the presence of TCRs with the same amino acid sequence but distinct nucleotide sequences. The central premise is that TCR convergence is a sign of antigen-driven selection. TCR convergence as a biomarker for immune checkpoint blockade (ICB) response is also investigated. Although both these ideas have been put forward in the literature, this manuscript provides some new analyses and a new perspective on these topics.

Main results:

- TCR convergence is different from publicity: The authors look at CDR3 sequence features of convergent TCRs in the large Emerson CMV cohort. Amino usage does not perfectly correlate with codon degeneracy, for example, arginine (which has 6 codons) is less common in convergent TCRs, whereas leucine and serine are elevated. It's argued that there's more to convergence than just recombination biases, which makes sense. (I wonder if the trends for charged amino acids could be explained by the enrichment of convergent TCRs in CD8 T cells, which tend to have more acidic CDR3 loops). There's also a claim that the overlap between convergent and public TCRs is lower in tumors with a high mutational burden (TMB), but this part is sketchy: the definition of public TCRs is murky and hard to interpret, and the correlation between TMB and convergence-publicity overlap is modest (two cohorts with low TMB have higher overlap, and the other three have lower, but there is no association over those three, if anything the trend is in the other direction). It's also not clear why the overlap between COVID19 cohort convergent TCRs and public TCRs defined by the pre-2019 Emerson cohort should be high. A confounder here is the potential association between convergence and clonal expansion since expanded clonotypes can spawn apparently convergent TCRs due to sequencing errors. The paper "TCR Convergence in Individuals Treated With Immune Checkpoint Inhibition for Cancer" (Ref#5 here) gives evidence that sequencing errors may be inflating convergence in this specific dataset.

- Convergent TCRs are more likely to be antigen-specific: This is nicely shown on two datasets: the large dextramer dataset from 10x genomics, and the COVID19 datasets from Adaptive biotech. But given previous work on TCR convergence, for example, the Pogorelyy ALICE paper, and many others, this is also not super-surprising.

- Convergent T cells exhibit a CD8+ cytotoxic gene signature: This is based on a nice analysis of mouse and human single-cell datasets. One striking finding is that convergent TCRs are WAY more common in CD8+ T cells than in CD4+ T cells. It would be interesting to know how much of this could be explained by greater clonal expansion of CD8+ T cells, together with sequencing errors. A subtle point here is that some of the P values are probably inflated by the presence of expanded clonotypes: a group of cells belonging to the same expanded clonotype will tend to have similar gene expression (and therefore similar cluster membership), and will necessarily all be either convergent or not convergent collectively since they share the same TCR. So it's probably not quite right to treat them as independent for the purposes of assessing associations between gene expression clusters and convergence (or any other TCR-defined feature). You can see evidence for clonal expansion in Figure 3C, where TRAV genes are among the most enriched, suggesting that Cluster 04 may contain expanded clones.

- TCR convergence is associated with the clinical outcome of ICB treatment: The associations for the first analysis are described as significant in the text, and they are, but just barely (0.045 and 0.047, but you have to check the figure to see that).

- Introduction/Discussion: Overall, the authors could do a better job citing previous work on convergence, for example, papers from Venturi on convergent recombination and the work from Mora and Walczak (ALICE, another recombination modeling). They also present the use of convergence as an ICB biomarker as a novel finding, but Ref 5 introduces this concept and validates it in another cohort. Ref 5 also has a careful analysis of the link between sequencing errors and convergence, which could have been more carefully considered here.

Reviewer #3 (Public Review):

Gupte and colleagues develop an individual-based model to examine how the introduction of a novel pathogen influences the evolution of social cue use in a population of agents for which social cues can both facilitate more efficient foraging, but also expose individuals to infection. In their simulations, individuals move across a landscape in search of food, and their movements are guided by a combination of cues related to food patches, individuals that are currently handling food items, and individuals that are not actively handling food. The latter two cues can provide indirect information about the likely presence of food due to the patchiness of food across the landscape.

The authors find that prior to introducing the novel pathogen, selection favors strategies that home in on agents, regardless of whether those agents are currently handling food items. The overall contribution of these social cues to movement decisions, however, tends to be relatively small. After pathogen introduction, agents evolve to rely more heavily on social information and to either be more selective in their use of it (attending to other agents that are currently handling food and avoiding non-handlers) or avoiding other agents altogether. Gupte and colleagues further examine the ecological consequences of these shifts in social decision-making in terms of individuals' overall movement, food consumption, and infection risk. Relative to pre-introduction conditions, individuals move more, consume less food, and are less likely to be infected due to reduced contact with others. Epidemiological models on emergent social networks confirm that evolved behavioral changes generate networks that impede the spread of disease.

The introduction of novel pathogens into wild populations is expected to be increasingly common due to climate change and increasing global connectedness. The approach taken here by the authors is a potentially worthwhile avenue to explore the potential eco-evolutionary consequences of such introductions. A major strength of this study is how it couples ecological and evolutionary timescales. Dominant behavioral strategies evolve over time in response to changing environmental conditions and impact social, foraging, and epidemiological dynamics within generations. I imagine there are many further questions that could be fruitfully explored using the authors' framework. There are, however, important caveats that impact the interpretation of the authors' findings.

First, reproduction bears no cost in this model. Individuals produce offspring in proportion to their lifetime net energy intake, which is increased by consuming food and decreased by a set amount per turn once infected. However, prior to reproduction, net energy intake is normalized (0-1) according to the lowest individual value within the generation. This means that individuals need not maintain a positive energy balance nor even consume food at all to successfully reproduce, so long as they perform reasonably well relative to other members of the population. Since consuming food is not necessary to reproduce, declining per capita intake due to evolved social avoidance (Fig. 1d) likely decreases the importance of food to an individual's reproductive success relative to simply avoiding infection. This dynamic could explain the delayed emergence of the 'agent avoiding' strategy (Fig. 1a), as this strategy potentially is only viable once per capita intake reaches a sufficiently low level across the population (Fig. 1d). I am curious to know what the results would be if reproduction required some minimal positive net energy, such that individuals must risk food patches in order to reproduce. It would also be useful for the authors to provide information on how net energy intake changes across generations, as well as whether (and if so, how) attraction to the food itself may change over time.

A second important caveat is that the evolutionary responses observed in the model only appear when novel pathogen introductions are extremely frequent. The model assumes no pathogen co-evolution, but rather that the same (or a functionally identical) pathogen is re-introduced every generation (spillover rate = 1.0). When the authors considered whether evolutionary responses were robust to less frequent introductions, however, they found that even with a per-generation spillover rate of 0.5, there was no impact on social movement strategies. The authors do discuss this caveat, but it is worth highlighting here as it bears on how general the study's conclusions may be.