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.

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.

Reviewer #3 (Public Review):

The authors have accomplished large amounts of work to prove the role of VPS9D1-AS1 in promoting immune escape from cytotoxic T cells, and the mechanistic exploration is valid enough to support the conclusions, as well as the translational significance of this target through in vivo experiments. However, the logicality of the diagram requires improvement, and several revisions are warranted.

Reviewer #3 (Public Review):

The authors do an excellent job of producing relatively simple model neurons to characterize the key properties of two different cell classes (feedforward Somatostanin, SOM, interneurons and serotonergic, 5-HT, output neurons) in the dorsal raphe nucleus. They make a strong case for the role of an A-type potassium current and strong spike-threshold adaptation in the 5-HT neurons in reproducing the measured single-cell responses to stimuli and characterize well the interplay of these two currents in producing a transient measure of the derivative of inputs under some circumstances. While the demonstration of rapid adaptation leading to an output that resembles a derivative of the input under some circumstances is far from novel, the authors here have succeeded in matching such a computation to known properties of neurons in known circuitry. However, a little more care is needed in how the authors describe the output of serotonergic neurons being linear in the derivative of the inputs, because in general that is not the case. For example, the sustained response of the neurons depends on the net input current when the derivative of the input is zero in all cases. A neuron that really follows a derivative would, for example, respond in a manner independent of baseline current and produce a cosine output to a sinusoidal input. Rather, it is a transient output response that in some limited ranges of constant baseline input and ramping rate of the input, has a peak that is linearly dependent on the ramping rate. Also, only positive slopes were considered. Thus, it seems unlikely that the serotonergic output in the model is very close to the derivative of a general input signal, nor does it appear likely to operate on the long temporal timescale needed for reinforcement learning, as suggested.

Reviewer #3 (Public Review):

In this work, the authors have assessed the bone phenotype of a mouse with targeted ablation of the vacuolar ATPase accessory protein ATP6AP2 in the osteoblast lineage. They observe a clear increase in cortical thickness, but the cortex is highly porous and contains remnant cartilage as well as extensive woven bone. They then follow this by suggesting that one cause of this phenotype may be a change in the surface expression of the protein MMP14, a matrix metalloproteinase, known to be involved in bone matrix degradation, at least in osteoclasts. They provide evidence that this protein may also regulate matrix degradation surrounding osteocytes and an increase in this protein in osteocytes lacking ATP6AP2 may be a cause of the initial phenotype described.

While the phenotype described is very dramatic, the interpretation that it reflects a defect in osteoblast to osteocyte transition is questioned by this reviewer. The phenotype appears to be an osteopetrosis, including a lack of remodelling of the cortex. Cartilage and woven bone are not replaced effectively by lamellar bone. The bone contains ample osteocytes, but they are the osteocytes typical of woven bone, with rounded cell bodies, disordered organisation, low sclerostin expression, and short dendritic processes. The defect in the ATP6AP2 mice is a lack of cortical remodelling during cortical consolidation (for review see PMID: 34196732). Cartilage and woven bone remnants, which are normally remodelled as cortical bone matures, remain in the cortex until adulthood. It is not clear whether this results from reduced or increased remodelling of the cortex, but it is not because the osteoblasts cannot form osteocytes.

Some of the data is very challenging to interpret because of low sample numbers (n=4 for much of the analysis), and lack of detail as to the sex of the animals. Regions used for imaging, histomorphometry, and dynamic histomorphometry all need to be defined throughout the work. Since the cortex differs dramatically by site, and by distance from the growth plate (due to the different stages of maturation) this is critical. Some methods are not defined, although they could be of great use to the field (e.g. the method for assessing bone degradation by MMP14).

Reviewer #3 (Public Review):

The aim of Weber and colleagues' study was to generate arthropod environmental DNA extracted from a unique 30-year time series of deep-frozen leaf material sampled at 24 German sites, that represent four different land use types. Using this dataset, they explore how the arthropod community has changed through time in these sites, using both conventional metabarcoding to reconstruct the OTUs present, and a new qPCR assay developed to estimate the overall arthropod diversity on the collected material. Overall their results show that while no clear changes in alpha diversity are found, the β-diversity dropped significantly over time in many sites, most notable in the beech forests. Overall I believe their data supports these findings, and thus their conclusion that diversity is becoming homogenized through time is valid.

While overall I do not doubt the general findings, I have a number of comments. Firstly while I agree this is a very nice study on a unique dataset - other temporal datasets of insects that were used for eDNA studies do exist, and perhaps it would be relevant to put the findings into context (or even the study design) of other work that has been done on such datasets. One example that jumps to my mind is Thomsen et al. 2015 https://besjournals.onlinelibrary.wiley.com/doi/full/10.1111/1365-2656.12452 but I am sure there are others.

From a technical point of view, the conclusions of course rely on several assumptions, including (1) that the biomass assay is effective and (2) that the reconstructed levels of OTU diversity are accurate,

With regards to biomass although it is stated in the manuscript that "Relative eDNA copy number should be a predictor for relative biomass ", this is in fact only true if one assumes a number of things, e.g. there is a similar copy number of 18s rDNA per species, similar numbers of mtDNA per cell, a similar number of cells per individual species etc. In this regard, on the positive side, it is gratifying to see that the authors perform a validation assay on 7 mock controls, and these seem to indicate the assay works well. Given how critical this is, I recommend discussing the details of this a bit more, and why the authors are convinced the assay is effective in the main text so that the reader is able to fully decide if they are in agreement. However perhaps on the negative side, I am concerned about the strategy taken to perform the qPCR may have not been ideal. Specifically, the assay is based on nested PCR, where the authors first perform a 15cycle amplification, this product is purified, then put into a subsequent qPCR. Given how both PCR is notorious for introducing amplification biases in general (especially when performed on low levels of DNA), and the fact that nested PCRs are notoriously contamination prone - this approach seems to be asking for trouble. This raises the question - why not just do the qPCR directly on the extracts (one can still dilute the plant DNA 100x prior to qPCR if needed). Further, given the qPCRs were run in triplicate I think the full data (Ct values) for this should be released (as opposed to just stating in the paper that the average values were used). In this way, the readers will be able to judge how replicable the assay was - something I think is critical given how noisy the patterns in Fig S10 seem to be.

Next, with regards to the observation that the results reveal an overall decrease in arthropod biomass over time: The authors suggest one alternate to their theory, that the dropping DNA copy number may reflect taxonomic turnover of species with different eDNA shedding rates. Could there be another potential explanation - simply be that leaves are getting denser/larger? Can this be ruled out in some way, e.g. via data on leaf mass through time for these trees? (From this dataset or indeed any other place).

With regards to estimates of OTU/zOTU diversity. The authors state in the manuscript that zOTUs represent individual haplotypes, thus genetic variation within species. This is only true if they do not represent PCR and/or sequencing errors. Perhaps therefore they would be able to elaborate (for the non-computational/eDNA specialist reader) on why their sequence processing methods rule out this possibility? One very good bit of evidence would be that identical haplotypes for the individual species are found in the replicate PCRs. Or even between different extractions at single locations/timepoints.

With regards to the bigger picture, one thing I found very interesting from a technical point of view is that the authors explored how modifying the mass of plant material used in the extraction affects the overall results, and basically find that using more than 200mg provides no real advantage. In this regard, I draw the authors and readers attention to an excellent paper by Mata et al. (https://onlinelibrary.wiley.com/doi/full/10.1111/mec.14779) - where these authors compare the effect of increasing the amount of bat faeces used in a bat diet metabarcoding study, on the OTUs generated. Essentially Mata and colleagues report that as the amount of faeces increases, the rare taxa (e.g. those found at a low level in a single faeces) get lost - they are simply diluted out by the common taxa (e.g those in all faeces). In contrast, increasing biological replicates (in their case more individual faecal samples) increased diversity. I think these results are relevant in the context of the experiment described in this new manuscript, as they seem to show similar results - there is no benefit of considerably increasing the amount of leaf tissue used. And if so, this seems to point to a general principal of relevance to the design of metabarcoding studies, thus of likely wide interest.

Reviewer #3 (Public Review):

Park and Bafna et al. applied a genetics-based epidemiological approach, the Mendelian randomization analysis (MR), to evaluate the potential causal roles of triglycerides across 2,600 disease traits (i.e., the phenome). In a typical two-sample MR framework, they utilized existing genome-wide association study (GWAS) summary statistics from two separate studies. They are Global Lipids Genetics Consortium (GLGC) and UK Biobank in the discovery analysis, and UK Biobank and FinnGen in the replication analysis. This replication design is a great strength of the study, enhancing the robustness and reproducibility of the results. For the candidate pairs of causal associations, the authors further perform multiple sensitivity analyses to evaluate the robustness of the results to possible violations of assumptions in MR. To disentangle the independent effects of triglycerides from other lipid fractions (i.e., LDL-cholesterol and HDL-cholesterol), the authors performed multivariable MR analysis. In the end, possible causal associations were revealed in three tiers, based on statistical significance in the two-stage analysis. The results support the causal effects of triglycerides in increasing the risk of atherosclerotic cardiovascular disease. They also reveal novel conditions, which are either new treatable conditions (e.g., leiomyoma, hypertension, calculus of kidney and ureter) for repurposing of triglycerides-lowering drug, or possible side effects (e.g., alcoholic liver disease) the triglyceride-lowering treatment should pay special attention to.

The analysis approaches in the paper are standard and solid. The discovery-replication study design is a great strength. Correction for multiple testing was implemented in a conservative way. The sensitivity analyses and MVMR strengthen the robustness of the results. The manuscript is very clearly written and pleasant to read. The limitations were well-presented. The conclusions and interpretations are mostly supported by the data, with one major concern as explained below. But overall, in addition to the specific findings, this study could be an exemplar study for the use of phenome-wide MR in identifying treatable conditions and side effects for most existing drugs.

1. My major concern is about reverse causation. For example, having atherosclerotic cardiovascular disease increases circulating triglycerides. Reverse causation can induce false positives in MR analysis. With the existing data in this study, the authors can perform a reverse MR to evaluate the effect of the 19 disease traits on triglycerides. Ruling out the presence of reserve causation is important to make sure that the current findings are not false positives.

Reviewer #3 (Public Review):

This study examines behavior of humans and monkeys in a standard two-player game theory game called Bach or Stravinsky (also known as Battle of the Sexes) and, more technically, the iterated version of this game. This game is less well studied than the Prisoner's Dilemma or the Stag Hunt, but has an interesting twist relative to these - the optimal strategy in an iterated version is one of two options that provide a better reward to one player. For this reason, humans will typically show alternating behavior. This game then lets us ask whether and how well monkeys will also come to the same alternating behavioral pattern.

The study is unique in that it uses a novel format for interaction - the "transparent game" in which subjects press their fingers on a clear glass screen. Among other benefits, this feature allows the experimenters to study dynamics of choice, so that the decision takes on properties of continuousness. That in turn allows for reaction time biases.

In summary, this is an excellent and fascinating study. The authors have asked important and interesting questions, and have done a careful study that provides answers. I anticipate that their "transparent game" technique will become more popular due to its utility.

Another strength of this paper is the combination of human and macaque players (and the return of the macaque to the macaque-macaque group play). The result is exciting and surprising. This is a novel and remarkable element of the study and a source of great strength.

Limitation - lots of conceptual differences, including primary vs secondary reward, expectations, that may be due to learning/socialization. Having said that, these need to be acknowledged, but the study is good anyway. I will also note the authors already include a good and healthy limitation section in the Discussion.

Reviewer #3 (Public review):

This papers builds on a previous publication from the same group that showed compartmentalisation model of beta-cell fuel metabolism in which plasma membrane-localized pyruvate kinase is sufficient to close KATP channels required for insulin secretion. In this current manuscript the authors identified the PK isoforms involved in this process using tissue specific KO mouse models. Using excised patch-clamp experiments, they demonstrated that although redundant in their function both the constitutively active PKm1 and allosterically PKm2 are associated with the PM and locally regulate KATP channel closure. Further, the authors showed that the mitochondrial PEP carboxylase (PCK2) is essential for amino acids to promote an increase in cytosolic ATP/ADP and closure of KATP channels. Therefore, this study very nicely demonstrates that he distinct response of PK isoforms to the mitochondrial and glycolytic sources of PEP impacts beta cell nutrient preference and affects the oscillatory cycle regulating secretion. These findings do provide new mechanistic information about the control of the regulated secretory pathway and will be of interest to broader audience.

Strength<br /> The major strength of the study is the use of tissue/isoform specific KO mouse models. Although limited by constitutive KOs with compensatory increase in other isoforms, the authors have achieved what they were set out to do i.e identify the PK isoform involved in the regulation of PM ATP generation and regulation of KATP channel closure. Their experimental rigorosity including the ability to perform the excised patch clamp experiments and use of PKa to show the specific effect of the allosterically regulated PKM2 are also strength.

Weakness<br /> It is not clear from the manuscript what the "littermate controls" are used in all the experiments. Given the limitations of the cre lox system, it is really important to clearly show what controls have been used and their phenotypes (and the rationale for pooling the different controls if that is what is done here).

The data adds to our understanding of the role of PM localised PK on the regulated exocytosis pathway however the claim that these findings question the canonical mitochondrial ATP coupled to KATP channel closure is not fully supported by the data especially given glucose induced insulin secretion is not affected by any of the KO models.

Reviewer #3 (Public Review):

This manuscript presents a new tool, SIMMER, to predict bacterial enzyme-mediated transformations of compounds, an important and incompletely understood aspect of microbiome drug metabolism. The authors compare their resource to existing resources that allow users to generate hypotheses related to compound toxicity and putative routes of compound metabolism. The authors identify the key innovations of their resource as including full chemical representations of reactions and a novel method to predict an enzyme's EC number (a description of function) from its reaction.

Strengths:

• Generating user-friendly tools to explore existing knowledge of bacterial enzymes and their reactions is important.

• SIMMER is a novel resource where the user provides the substrates and products as input and receives a list of potential microbiome enzymes as output.

• SIMMER includes a novel EC predictor based on reaction rather than based on sequence.

Weaknesses:

• Validation claims are not well supported by the results.

• Need for the user to know both the substrate and the product for a reaction of interest limits the utility of the resource.

• Reliance on homology transfer annotation to predict enzyme function; this approach has important, microbiome-relevant, limitations.

Reviewer #3 (Public Review):

The manuscript by Voufo et al. aims to advance our understanding of the mechanisms responsible for the earliest pattern of spontaneous activity in the mouse retina, stage I retinal waves. These waves occur during embryonic development (E16-18) and are the least known form of activity in the immature retina.

The authors show that stage I waves have broad spatiotemporal features and are mediated by circuitry involving subtypes of nicotinic acetylcholine receptors (nAChRs) and gap junctions. The authors also found that the developmental decrease of intrinsic photoreceptor retinal ganglion cells (ipRGCs) density is preserved between control and ß2-nAChR-KO mice, indicating that processes regulating ipRGC distribution are not influenced by early spontaneous activity.<br /> The quality of the data is excellent, and the conclusions of this paper are mostly well supported by data, but the presentation of the data and the analysis need to be clarified and extended.

Strengths:<br /> The earliest patterns of spontaneous activity are crucial for the correct development of sensory circuits. In the visual system, most studies focus on postnatal activity (stage 2 and 3 retinal waves) overlooking embryonic stages, likely due to challenges related to methods and animal handling. Therefore, in this manuscript, the authors from a laboratory pioneer in studying retinal waves in the mouse, tackle a very relevant subject that has not been explored in detail. The bibliography that encompasses most of the current knowledge about stage 1 retinal waves in mammals is compressed into three fairly dated publications: Galli and Maffei 1988, Bansal et al 2000, and Syed et al 2004. These publications were pioneering attempts to describe early spontaneous activity; however, much work remained to be done regarding the molecular and cellular mechanisms involved. Here, Voufo and colleagues provide additional fundamental details about the properties and components of stage 1 waves. The dataset has excellent quality and plenty of information could be extracted from it. The authors used a macroscope that allows the acquisition of images from the entire retina while preserving a good spatial resolution.

Weakness:<br /> The authors distinguish different subtypes of activity during embryonic stages in the retina of mice. However, they do not provide a detailed characterization that allows a clear definition of these subtypes (and specifically stage 1 waves). Moreover, throughout the manuscript, there are many technical details of the analysis that are missing and preclude a complete understanding of the robustness of the data. The authors have an excellent dataset that needs more analysis and an improvement in the presentation of the results.

Reviewer #3 (Public Review):

This work provides a detailed single-cell transcriptomic analysis of endothelial cell (EC) differentiation from induced pluripotent stem cells in a suspension culture. The data demonstrates that the protocol produces a large number of both endothelial cells and mural cells, which is comparable to a 2D monoculture, with differences observed mainly in the expression of ECM genes. This first part of the study shows that EC differentiation works well both in 2D monolayer and in suspension cultures, with some key differences in their gene expression patterns, which has been shown before. Here, a detailed transcriptomic landscape of single cells during differentiation is provided. The second part of the paper examines how the transcriptome of ECs and pericytes (PC) that are differentiated in a suspension culture is remodeled in a 3D hydrogel environment, when cells sprout and form tubular structures. Based on the single-cell transcriptomes, the present study allows detection of different EC and PC populations during tube formation and cell maturation, and the identification of the genes and transcription factors, which regulate cellular behavior and phenotype, such as coalescing and sprouting ECs.

Strengths:<br /> The study is an extensive description of remodeling of the transcriptomic landscape in endothelial cells and mural cells during differentiation from human induced pluripotent stem cells. It demonstrates the potential of endothelial cell and pericyte differentiation in suspension culture, which allows larger yields compared to 2D monolayer cultures.<br /> The novelty in the study is the detailed transcriptomic characterization of the different cell clusters formed during tube formation and maturation in 3D hydrogel. This provides important information for future experiments studying the mechanisms of vasculogenesis and angiogenesis in vitro and for designing vascularization for organ-on-chip approaches.<br /> The data gathered here provides a great possibility to identify unknown interaction mechanisms between PC and EC during vascular development and maturation. Especially identification of PC subpopulations, which seem to produce both common and individual ligands for ECs is intriguing and creates several new research questions.

Weaknesses:<br /> The paper has a lot of transcriptomic data from different cell culture conditions and time points, making it difficult to identify the key observations and novel findings of the study. The paper provides a resource for future studies, but does not answer a clear biological question or test a hypothesis.<br /> One of the concerns is the large number of mural cells in maturing 3D hydrogel cultures (66-85%), indicating that the protocol directs the cells more towards mesenchymal than endothelial cells or ECs lose their identity over time. It would be important to show the localization and organization of these two cell types in the 3D cultures and to demonstrate if the different EC and pericyte subclusters localize to certain parts of the network.

Reviewer #3 (Public Review):

Using a robust transient transgenic approach in the zebrafish embryonal rhabdomyosarcoma (ERM) model, Chen et. al. identified diverse activities of several disease-relevant TP53 variants in ERM pathogenesis. The useful tools established in this study would allow rapid in vivo assessment of the effect of newly identified TP53 mutations on ERM tumorigenesis.

Strengths:<br /> • It's the first time to dissect the activities of several rare patient-specific TP53 mutations in ERM tumor initiation and progression in vivo.

• This study demonstrates the robustness of transient co-injection transgenic approach for rapid structure function analyses of disease-relevant variants in vivo.

• This study also suggests distinct activities of different TP53 structure variants, such as their potential functions as a hypomorphic allele, a gain-of-function mutation, or a predisposition mutant for the head musculature ERMs.

Weaknesses:<br /> • The role of tp53 loss in promoting the initiation of kRASG12D-driven ERM has been demonstrated previously using a similar strategy by coauthors (Ignatius, M. S., eLife, 2018; Langenau, D. M., Genes Dev, 2007).

• The data from TP53-null SaOS2 osteosarcoma cell line did not consistently support the findings from in vivo zebrafish studies, which is confusing and would need to be addressed.

• It is not clear how overexpression of the TP53P153△ or TP53Y220C mutant induced different effects on the tumor initiation and cell survival of kRASG12D-driven ERM but led to similarly enhanced head ERM development.

• This study mainly applied the overexpression approach to understand the function of TP53 mutants in ERM pathogenesis and demonstrated the distinct effects of their overexpression on kRASG12D-driven tumor initiation, cell survival and proliferation. However, these mutations are not gained or amplified in human ERMs. Hence, overexpression approach could provide some insights of their function, but cannot faithfully mimic the ERM disease situation to uncover the real function of these mutants in ERM pathogenesis.

Reviewer #3 (Public Review):

In their manuscript, McKitterick and Bernhardt perform a screen to determine host factors, such as receptors, which are important for bacterial viruses (phages) to infect Corynebacterium glutamicum., an organism that shares the unique membrane of mycobacteria (mycomembrane), with M. tuberculosis. To do so, they challenge a previously described Tn-seq library with a high MOI of 2 phages - Cgl and Cog. The surviving strains are those in which genes important for phage infection (such as receptors) are disrupted. The authors' screen is successful, and the authors identify and validate several factors important for the infection of each phage, providing the first such screen in Corynebacterium. Moreover, the authors perform a suppressor screen to identify additional factors and experimentally follow up several genes of interest. Finally, the authors use the newly determined host specificity of te phages to implicate new genes in mycolic acid synthesis. As a whole, this is a strong work that paves the way to a deeper understanding of Corynebacterial and (by extension) Mycobacterial phages and should be of broad interest.

Below, we suggest additional analyses, context, and elaboration that will help the ms. elaboration to fully realize its impact.

Major points:

1. Although the authors' experimental design is fundamentally sound, I am worried about the possibility of "jackpotting" in shaping their results, particularly in the uninfected control experiment. If the authors' Tn-seq library is ~200,000 strains, and they don't plate at least 10-100x times that many colonies then any given strain (regardless of its phenotype) may or may not be represented in the output of the experiment, causing false phenotypes to be ascribed to genes based on chance. This is particularly a problem for the uninfected control, where the authors choose to dilute the culture 1000-fold to mimic the number of colonies that survive infection. They may be better served by plating the whole culture on the plates, to ensure adequate representation of the library. Part of the reason for this concern is that an overwhelming majority of statistically significant hits (something like 80-90%) appear to confer susceptibility rather than resistance (source data Fig 2) - something the authors' experimental design should not be able to measure. The lack of accurate representation of distributions of strains in the starting culture also calls into question the quantitative differences they present in the results

a. L138. Where the authors describe their initial experimental design it would be helpful to add more details. What is the size of the Tn library? What is the coverage in their experiment? Approximately how many colonies are recovered on the plates after phage infection and in the uninfected control?

b. it is important to know how the number of colonies on the plates compares to the number of reads in the experiment. In the analysis of most HT screens, one implicitly assumes that each read corresponds to 1 cell, hence each read can be treated as statistically independent. This assumption is critical to the statistical methods used to analyze this data. By scraping a plate of colonies (which may be required for efficient phage infection), the authors potentially violate this assumption (since the number of cells → number of colonies, which are the actual statistically independent entities in the experiment). Does this assumption hold (or approximately hold) for the screen? If not, a different statistical method should be used to determine p-values.

2. The authors' Tn-seq methodology is different from previously published HT-phage screens (e.g. Mutalik et al., 2020 and Rousset et al., 2018). Based on my knowledge of classical phage biology, I agree that plating the infected cells has advantages. However, the rationale will not be clear for most people performing such experiments. Please explain the rationale for the experimental protocol.

a. Why did the authors plate the cultures after initial phage absorption instead of remaining in liquid?

b. How reproducible are the authors' Tn-seq results? The SRA ascension shows multiple replicates but this is not described in the manuscript nor reflected in the supplementary data. Given the potential for bottleneck and jackpotting effects in this assay, some measure of reproducibility is important for interpreting the results (see point 1).

c. L587 "Significant hits with fewer than 10 insertions on each strand were manually removed." Why did the authors choose this criterion? Almost all of the genes they removed have very asymmetric distributions (e.g. in the Cog experiment, cgp3051 has 47853 fwd reads and 6 rev reads. Asymmetric distribution of insertions suggests that overexpression of downstream genes has an important (positive or negative) effect. This is a worthwhile pursuit, and many automated analysis pipelines can disambiguate these effects, including those developed in the Walker Lab (e.g. doi: 10.1038/s41589-018-0041-4). These genes shouldn't be thrown away when they are arguably some of the most informative hits!

3. There is a somewhat extensive phylogeny of M. smegmatis phages (phagesdb.org). Are the phages that the authors work on related to any of these phages? If so, what cluster do they map to? What is the host range of other phages in that cluster? If not, may be worthwhile to mention that these are quite distinct from other studied phages.

4. Given that cgp_0475 was a strong hit in the Tn-seq, why was it not identified in the previous chemical genomics experiments from the lab (https://doi.org/10.7554/eLife.54761) ?

5. Is there any relationship between the growth-rate of the mutants and their phage susceptibility? This can be analyzed using the authors' previous studies of this library.

Reviewer #3 (Public Review):

In this manuscript, the authors Castro, Shortill, Dziurdzik, Cadou and colleagues perform an extensive systematic analysis of six membrane contacts sites (MCSs) to uncover novel proteins required for organelle tethering and modulation of membrane contacts. This work is critical as few if any proteins have been identified to regulate the formation and/or function of these contact sites. The authors identify over 100 new potential contact site proteins and effectors, including identification of proteins associated with the recently discovered plasma membrane-LD (pClip) and Golgi-peroxisome (GoPo) contact sites. This data set alone represents a huge contribution to the MCS field. The authors go on to identify and characterize novel proteins associated with the pClip homologous to known lipid binding proteins which may facilitate the transfer of lipids at membrane contact sites. Finally, the authors investigate the lipid droplet-ER (LiDER) contact site associated protein Lec1, which contains a novel putative lipid binding domain and may facilitate ergosterol transport between the plasma membrane and lipid droplets. The screening approach and experiments characterizing the function of Lec1 are for the most part straight forward, well-controlled, and easy to interpret. However, the authors' conclusions regarding the identified family of VPS13 like proteins role at the pCLIP is not strongly supported. Overall, these findings greatly expand our knowledge of proteins that regulate MCSs and will serve as the foundation for the identification of MCS tethers.

Reviewer #3 (Public Review):

Strege et al. addressed the mechanism underlying the well-known mechanosensitivity of voltage-gated sodium channels. They cleverly bypassed the complexities of working with the mammalian NaV channels using a non-inactivation version of the bacterial homologue NaChBac T220, validating its use as a model for studying mechanical modulation of voltage-gated Na channel gating.

By performing a high-quality single channel recording the authors demonstrated that mechanosensitivity affected the channel Po nor single-channel conductance, and importantly, that the effects of pressure on channel gating were reversible. This is particularly appreciated due to the exceptionally low unitary conductance of the channel makes it exceedingly difficult to obtain this type of result.

The authors performed kinetic modelling over the single channel recording at different voltages and pressures, using linear gating schemes that contrasted two extreme situations: mechanosensitivity was a property of the voltage sensing transitions - mechanosensitive activation (MSA) - or, alternatively, it was a feature of the voltage-independent step that opens the channel pore - mechanosensitive opening (MSO). The MSO performed much better than MSA model, suggesting that mechanosensitivity arises from conformational changes at channel regions different than the VSD during the pore opening. The latter gains further support with the experiments showing that pressure modulates the D93A - a mutation that stabilizes the VSD in its resting state - resembles wt channels, but is ineffective on the I228G channel, a channel with altered closure.

Overall, the manuscript presents a nice experimental approach, making use of kinetic analysis in terms that are very accessible to the reader to shed light on a physiologically relevant question.

Reviewer #3 (Public Review):

In this manuscript from Wang et al., the authors use cryo-EM and complementary functional assays to examine nucleosome recruitment and histone H3 engagement by the S. cerevisiae Yta7 chromatin remodeler. The authors investigate H3 tail engagement by determining two high-resolution structures of Yta7 in different conformations - an ADP-bound conformer absent of H3, and another bound to ATPgS, where the H3 tail is bound in the central channel of AAA1. The most striking finding is an unexpected organization of the N-terminal bromodomains (BRDs) in the ADP-bound conformer. This structure shows that the BRDs oligomerize via a novel interaction with a C-terminal bromo-interaction motif (BIM), which together assemble a spiraling assembly above the AAA1 ring. This structural feature is only observed in conformers that are absent of bound H3 peptide, and since this BRD/BIM organization appears to block entry to the central channel of the AAA1 ring, the authors propose that nucleosome binding by these domains induces a rearrangement that exposes the AAA1 pore loops for H3 tail engagement. The authors provide biochemical evidence that the BRD interacts with the H3 tail, and the role of these domains in nucleosome recruitment is further supported by a low-resolution cryo-EM structure showing that the nucleosomes bind to Yta7 via the BRDs. The cryo-EM studies are performed with expertise, which includes advanced processing methodologies to improve the resolution of the flexible regions such as the BRDs. However, further clarification of the mechanistic details would increase the study's impact in the field, particularly regarding the relationship between nucleotide state and Yta7 conformation, since two conformations are observed in the presence of ATPgS. Also, while BRD & BIM binding to the H3 tail is shown in isolation, it remains to be seen how these domains impact H3 tail binding in the context of the assembled hexamer. Lastly, the low resolution of the nucleosome-bound complex complicates concise mechanistic interpretation of nucleosome binding, which is an important aspect of the study. Overall, the work provides a generalized mechanism of nucleosome recruitment and the rearrangements associated with positioning of a histone tail for subsequent nucleosome disassembly, which will be of broad relevance to the chromatin remodeling field, although there remain ambiguities regarding many of the mechanistic details.

Reviewer #3 (Public Review):

Using experimental evolution with a nematode model system in a novel salt environment, Mallard et al. present a very nice experiment testing whether plasticity aligns with genetic variance and whether phenotypic divergence can be predicted from patterns of genetic variance. They find that although plasticity is not in the direction of genetic variance, estimates of selection that predict divergence are concordant with observed divergence. However, direct selection on a trait not included in the analysis is expected to be the underlying cause of phenotypic evolution. I commend the authors on their experiment and the framing of such a conceptually difficult topic.

Strengths:

Comparing a common ancestor to evolved populations to predict evolution has rarely been achieved, and the authors provide a strong test for predicting evolution in a novel environment.

Weaknesses:

Although a valuable dataset, the framing of the paper needs to be more focused on the question of predicting phenotypic evolution and comparing direct versus indirect selection. There are many details that are missing in the methods, which include the biological importance of the traits that are being studied, and how adaptation to the novel environment has occurred. In the discussion, they reveal that a measured trait that hasn't been included in the analyses is likely responsible for the observed patterns and it is unclear why this trait wasn't included in the analyses. In addition, direct versus indirect selection is not formally compared, which makes it difficult to interpret their results.

Reviewer #3 (Public Review):

The flowering heads of species in the Asteracaeae comprise large number of flowers, and this phenotype is thought to contribute to their reproductive success. The Harmer lab has developed sunflower as an experimental model to investigate the contribution of circadian regulation to the processes of reproduction in the Asteraceae, and this paper presents a new addition to this line of research.

The novelty of the article is that it resolves unanswered questions around the processes that underlie coordinated flowering within the disc structure of the floral capitulum. The authors demonstrate a role for circadian clock in the temporal structuring of this process. They identify a free running rhythm in constant darkness of floral anthesis, and this rhythm has several key characteristics of circadian rhythms. The data collected also indicate that the circadian clock might gate the response of anthesis to darkness.

I like the presentation of an external coincidence model for the interaction of light and circadian cues in the floral developmental program of the capitulum. However, I wonder whether this is the only potential explanation. The data in Fig. 4C look like classical entrainment responses. Are the authors sure that they are not just seeing an entrainment process within the capitulum, combined with a masking effect of continuous light upon the rhythmic phenotype? I encourage the authors to retain speculation about the coincidence model within the discussion – it's so important for future work – but perhaps consider alternative interpretations of the data also.

Reviewer #3 (Public Review):

The manuscript by Jelen and colleagues aims at investigating the neuronal circuits underlying aversive and appetitive gustatory Pavlovian conditioning in Drosophila. To this end Jelen and colleagues employ an intuitive novel training device that allows for automatic optogenetic activation of neuron populations of choice upon physical contact of freely walking flies with a food source. Whereas olfactory appetitive or aversive conditioning is well established for Drosophila, learning paradigms using other sensory modalities like acoustic or gustatory stimulation are either not yet well established or cumbersome for the broad community. In this concern the advancement existing optogenetic gustatory learning setups to the automated optogenetic learning device "STROBE" finds a remedy to allow for high throughput experiments on gustatory learning in freely walking and behaving animals.

In the first part of their study Jelen and colleagues employ the experimental setup to induce an aversive memory in the fly to low concentrated sugar in combination with activation of bitter neurons optogenetically recapitulating earlier studies where low concentrated sugar was presented in combination with quinine. However, in contrast to those earlier studies where sugar was presented to the tarsi of the fly and quinine to the proboscis, allowing for differentiated detection of taste modalities in the study by Jelen and colleagues the presentation of both taste modalities are synchronous. Sugar and bitter appear to be sensed simultaneously and bitter neurons are globally activated once the fly gets in contact with the sugar solution. In this scenario it is difficult to understand how the bitter neuron activation does not directly interfere with the sensation of sugar changing the perception of the sugar itself instead of being sensed as a punitive stimulus. The strong aversion of the flies towards the sugar during the training phase may reflect indeed such a change in perception mixed with the learning. Further, during the first 5 - 10 min of the test phase for short-term memory the flies appear to show a stronger preference to the aversively conditioned sugar when compared the control gustatory stimulus. According to the theory during this phase short-term memory should be displayed the strongest and decline over time reaching a nearly complete attenuation after one hour. However, the displayed cumulative average preference indices let assume that for the aversive conditioning the memory recall takes place after about 30 minutes when middle-term memory starts to emerge. In this regard it is further worth noting that after an initial increase of the sugar preference to about 0.25 the preference index of the trained flies remain stable whereas the control flies only reach the same level of attraction to the sugar after 12-15 min and only start increasing their sugar preference after about 20 - 25 min. Compared to the dynamics of the cumulative average preference indices of the appetitive gustatory neuron activation and the artificial activation of dopamine neuron subsets the dynamics of the cumulative average preference after the aversive reinforcement through bitter gustatory neuron activation appears drastically different. This may further indicate competing pathways between sensing and conditioning the bitter taste stimulus as well as a delayed memory recall according to the metabolic need of the animal, as again the strongly delayed dynamics of the cumulative preference index indicates.

In the subsequent part Jelen and colleagues investigate the role of different subsets of dopaminergic neurons in the formation of aversive or appetitive gustatory short- and long-term memory. Similar to olfactory memory, gustatory memory relies on mainly two major sets of dopaminergic neurons that drive aversive and appetitive memory, namely the PPL1 and the PAM cluster that innervate different compartments of the mushroom body where they provide aversive or appetitive input to the conditioned stimuli encoded through the sparse activity of mushroom body Kenyon cells. As a consequence, in the following experiments Jelen and colleagues interfere with the hereinafter layer of memory and silence the mushroom body during conditioning while opto-genetically activating the PAM dopaminergic neurons, conceptionally recapitulating earlier studies that demonstrated the role of the mushroom body in gustatory memory earlier. Analogous to earlier findings for olfactory learning Jelen and colleagues use their intuitive setup to functionally subdivide the dopaminergic neurons in functional subunits with different roles in memory formation. These results strongly demonstrate how conserved the value-giving neuronal circuits are independent from their stimulus modality.

Consequently, extrapolating questions on olfactory memory formation on gustatory learning the authors use the STROBE essay to investigate how different nutrients may affect the formation of a long-term memory. In accordance with the findings on olfactory memory formation Jelen and colleagues find that long-term memory formation depends on readily accessible energy sources.

The study is interesting and rigorously conducted and reveals striking similarities between olfactory and gustatory memory formation. However, it appears that the authors have put large focus on the recapitulation of an already demonstrated mode of action of learning circuits using their new technique and many of the parallels between olfactory and gustatory memory formation appear pertinent as e.g., the need of readily accessible energy sources for long-term memory formation. The need for energy to form a long-term memory should not depend on the stimulus modality you learn but on the cellular mechanisms underlying learning itself. The innovative technique Jelen and colleagues present in their manuscript has such a strong potential that to me as a reader it appears a pity that the study did not exploit the possibilities of their technique to investigate virgin soil instead of walking on beaten tracks.

Reviewer #3 (Public Review):

In this article, Juette et al employed single-molecule FRET, cryo-EM, and Hpg incorporation (in cell translation assays) to compare the mechanisms by which Didemnin B and Ternatin-4 inhibit translation elongation. They found that, while binding to the same pocket of eEF1A and blocking accommodation after GTP hydrolysis, Didemnin B had an irreversible effect on protein synthesis, but Ternatin-4, while still a potent inhibitor, allowed more flexibility in complexes (increased disorder of regions in cryo-EM structures) that allowed increased sampling of on-pathway accommodated states (observed by smFRET), and reversibility of effects on protein synthesis in cultured cells (by Hpg incorporation). This is a straightforward study and the conclusions are well-supported by the data using appropriate techniques. The work will be of impact to the ribosome field, which may use these drugs in other mechanistic studies, and researchers wanting to employ the drugs to combat cancer and other diseases.

Reviewer #3 (Public Review):

The manuscript describes a novel and well-designed psychopharmacological investigation of the mechanisms underpinning reward-induced pain relief in healthy humans. In a within-subjects design, ~28 healthy volunteers participated in three sessions (placebo, dopamine enhancement, opioid blockade) assessing subjective and behavioural effects of winning a temporary pain decrease (compared to no change or losing, i.e. a temporary pain increase). The task combines a simple two-choice gambling task with the capsaicin-heat model of pain to uncover the mechanisms involved in endogenous processing of pain reduction. A number of outcomes are presented. Pain ratings are decreased following heat decrease (from winning in the decision-making task) in all conditions, but more strongly so in conditions where the winning cue has the highest informational value, i.e. after active vs passive choice, under unpredictable vs predictable conditions, and after pharmacological enhancement of dopamine. These and several other interesting findings represent a substantial increase in the field's mechanistic understanding of learning and motivation related to pain and pain relief. The manuscript is very well written, the analyses well-reasoned and the results instructive and intriguing.

Reviewer #3 (Public Review):

Wild et al examined several syngeneic breast cancer cell lines using the novel cell tracing technology they have developed. This identified subpopulations of cells that were differentially represented in the in vitro pool and when grown in vivo. This difference extended to immunodeficient as compared to immunocompetent hosts. Treatment altered the dynamics of clonal persistence, allowing the authors to interrogate pathways expressed in cells in response to treatment with a BET inhibitor or docetaxel, revealing the exciting finding that taxane resistant subclones were dependent on NRF2 and emerged with a collateral sensitivity to l-aspariginase, which was demonstrated in vivo, and further observed in human tumor samples.

Overall, this is an exciting, elegant, and rigorously developed strategy, which gives a new level of insight into the tumor biology. The authors make an important discovery regarding dynamic intratumoral response to systemic therapy.

Reviewer #3 (Public Review):

Nuclear shape and size have long been characterized and can indicate and influence cell fates. The present study starts with a knockdown screen of size and shape, adds some information on lamins known to influence size and shape, proceeds to focus on 'subtle' modulators that are often epigenetic factors, then provides in vitro pulldown and array studies that support histone-lamin interactions, and concludes with further such evidence from one small final cell study. Some concerns temper enthusiasm. I found it important that they restricted analyses "To eliminate hits due to cell death or altered cell-cycle behavior, we excluded any hits with a cell number z-score of less than -2." Some mention of this in the abstract seems important. Secondly, The histone-H3 mutation effects on nuclear morphology in Fig.6 are especially important, but it is unclear whether the histone intensities are uniform or enriched in places with LaminA, nor what happens to LaminA levels or localization.

Reviewer #3 (Public Review):

The authors proposed an antibody catenation strategy by fusing a homodimeric protein (catenator) to the C-terminus of IgG heavy chain and hypothesized that the catenated IgGs would enhance their overall antigen-binding strength (avidity) compared to individual IgGs. The thermodynamic simulations supported the hypothesis and indicated that the fold enhancement in antibody-antigen binding depended on the density of the antigen. The authors tested a catenator candidate, stromal cell-derived factor 1α (SDF-1α), on two purposely weakened antibodies, Trastuzumab(N30A/H91A), a weakened variant of the clinically used anti-HER2 antibody Trastuzumab, and glCV30, the germline version of a neutralizing antibody CV30 against SARS-CoV-2. Measured by a binding assay, the catenator-fused antibodies enhanced the two weak antibody-antigen binding by hundreds and thousands of folds, largely through slowing down the dissociation of the antibody-antigen interaction. Thus, the experimental data supported the catenation strategy and provided proof-of-concept for the enhanced overall antibody-antigen binding strength. Depending on specific applications, an enhanced antibody-antigen binding strength may improve an antibody's diagnostic sensitivity or therapeutic efficacy, thus holding clinical potential.

Reviewer #3 (Public Review):

This paper is of interest to scientists within the field of iNKT cells. The authors conducted scRNA-seq to longitudinally profile activated iNKT cells and generated a transcriptomic atlas of iNKT cells at the activation states. The study suggests that transcriptional signatures of activation are highly conserved among heterogeneous iNKT cell populations and that the adipose iNKT cells undergo blunted activation and display constitutive enrichment of memory like population, plus identifying a conserved cMAF- associated network in NKT10 cells. This study provides some new insights into the NKT biology.

Reviewer #3 (Public Review):

This is a computational modeling study to evaluate the merits (likely success) of different 'suppression' gene drive systems. Gene drives offer a possible simple and low-effort means of suppressing or even extinguishing pest populations. Using CRISPR technology, several gene drive systems have been developed in the last decade for key mosquito vector species. As no gene drive has been approved for release in the wild, efforts to evaluate their likely success are limited to cage trials and modeling, the latter as done here. In contrast to some modeling studies, the effort here is to develop and analyze models that match the gene drive and mosquito biology closely. The models are thus parameterized with values representative of what is known about mosquito biology and of the various gene drive constructs that have been developed for lab studies.

In these models, gene drive success or failure in population suppression largely depends on (i) how well the drive spreads throughout the population, and (ii) whether the population persists because of a type of ongoing spatial 'group selection' in which local pockets invaded by the drive die out and are then repopulated by migrants lacking the drive. Formal evolution of functional resistance is not allowed. The numerical results show striking differences in suppression success with different gene drive constructions, and these differences are likely to be of use when designing drives for actual releases.

The basic group selection outcome that allows population persistence amid a suppression gene drive has been shown before, as cited in the ms. The novelty provided by the present study is to tie the models to the biology of known gene drive constructions. Given the high specificity of the models, the audience for this work is likely to be somewhat narrow, confined to those involved in gene drive design. The work is nonetheless significant in view of the strong potential of gene drives in global public health efforts.

The software used to generate the trials is freely available from one of the authors for anyone wishing to repeat the simulations. There is an extensive supplement of results referenced (but not otherwise included) in the main text.

Reviewer #3 (Public Review):

Zhang et al. examined a novel articular cartilage progenitors NFATc1 expressing cells. Through multiple pulse-chase experiments, they found that NFATc1 expressing cells generated most of the articular chondrocytes, but not chondrocytes in the growth plate primordium. In vitro and in vivo transplantation of NFATc1 expressing progenitors demonstrated that these cells exhibit pluripotency to chondrocytes, osteoblasts, and adipocytes. RNA-seq analysis of NFATc1 expressing progenitors demonstrated that these cells are enriched with articular cartilage stem cell markers such as Prg4. Interestingly, NFATc1 expression in chondrocytes diminished as mice aged, suggesting that NFATc1 expressing progenitors are no longer expressing NFATc1. Through CRISPR-mediated knockdown and conditional deletion in Prrx1-cre cells, authors found that NFATc1 negatively regulated chondrocyte differentiation with its putative binding sites on Col2a1 promoter and intron 1. These data support authors' conclusion that NFATc1 negatively regulates chondrocyte differentiation but it also marks chondrocyte progenitors.

The major strengths of the manuscript are the rigorous approach to examine NFATc1 expressing progenitors, including in vivo pulse-chase experiments, in vitro differentiation and in vivo transplantation studies, and transcriptomic profiling. Authors also use multiple approaches to demonstrate functional role of NFATc1, which is negatively regulating chondrocyte differentiation. All these findings generally support authors conclusions. There are some minor weaknesses, such as discordance between NFATc1 expression and NFATc1 expressing cells on articular cartilage, comparison of NFATc1 to the well-known articular chondrocyte progenitors such as GDF5 expressing progenitors, and lack of analyses of in vivo multipotency of NFATc1 expressing progenitors. Nevertheless, authors' findings will substantially advance the field that have long sought to examine the mechanism of joint development by revealing novel population of progenitors and chondrocyte differentiation mechanism. This could ultimately lead to novel treatment strategies for articular cartilage diseases.

Reviewer #3 (Public Review):

Gajwani et al present an intriguing study which concludes that a key aspect of endothelial TNFa-induced inflammation involves mitochondrial clearance (mitophagy) and secretion of formylated peptides that activate neutrophils. TNFa is shown to not only increase mitophagy but also increase the secretion of mitochondrial contents, which are expected to promote enhanced inflammation. A severe mouse lung inflammation model shows that mice with reduced endothelial PINK1, a major driver of mitochondrial damage-induced mitophagy, demonstrated enhanced survival and reduced neutrophil recruitment.

This manuscript is of interest as it proposes a novel mechanism of cell-cell signaling in inflammation, involving the surprising release of mitochondrial proteins via mitophagy. This is potentially an important advance. However, the results are far from conclusive. In addition to specific technical problems, they provide no evidence that this mechanism operates in vivo.

Conceptual issues.

Fig 2. In general, inflammation in endothelial cells is associated with high glycolysis, not high mitochondrial metabolism. Thus, it is important to address the question, how does TNFa trigger increased mitophagy? Is it preceded by elevated mitochondrial oxidative phosphorylation, reactive oxygen production and mitochondrial damage? Or simply mitochondrial depolarization? Is it a consequence of general upregulation of ROS? General upregulation of autophagy? Figure 3 is relevant to this question but does not answer it.

It is not clear what figures 2C,D add to the paper. Why is the occasional contact of mitochondria and mitolysosomes relevant? The absence of controls or quantification further detract from this figure.

Fig 5. What about other leukocyte populations? Is the effect of PINK1 ECKO specific to neutrophils or were they the only cells examined?

Fig 6. These experiments appear to be compromised by the presence of TNFa or FCCP in the EC conditioned medium, which could act directly on the neutrophils. Additionally, the authors provide no evidence that the effect requires PINK1.

Figure 6 also raises an important question of specificity. If the consequence of mitophagy is the release of mitochondrial content and the activation of neutrophils, wouldn't other cell types that have more mitochondrial content and more mito-phagosomal flux contribute more to neutrophil activation? Perhaps the authors could compare to other cell types and test if endothelial cells are more prone to secrete their mitochondrial content. Time courses would also improve this panel.

Reviewer #3 (Public Review):

Li et al. present cryo-EM structures of the insulin receptor (IR) and insulin-like growth factor-1 receptor (IGF1R), exploring the functional roles of the disulfide-linked alphaCT regions in ligand binding and receptor activation.

Cryo-EM structures of mutants of IGF1R and IR designed to increase the flexibility between disulfide-linked alphaCT regions revealed conformational states that were distinct from those of the wild-type (WT) receptors. Mutant (P673G4) IGF1R displayed conformations in which two IGF1 molecules were bound, rather than the 1:1 ligand:receptor state observed previously for WT IGF1R. Mutant (3CS) IR displayed asymmetric conformations with four insulin molecules bound, as well as the symmetric T conformation with four insulin molecules bound observed previously for WT IR. In each case, the mutant receptor was shown in cells to be poorly activated by its respective ligand.

This study demonstrates the importance of the disulfide-coupled alphaCT regions in the IR and IGF1R for ligand binding and receptor activation. What is not resolved in this study is whether differences in the alphaCT regions of these two highly related receptors contribute to their disparate active states - asymmetric for IGF1R (and 1:1 IGF1:IGF1R) vs. symmetric (T) for IR (and 4:1 insulin:IR).

Reviewer #3 (Public Review):

Zuber et al. investigated various KOW domains and found that the thermodynamically less stable EcRfaH and VcRfaH domains can switch between an all-alpha and all-beta state. This property has been known so far only for the KOW domain of the E. coli orthologue. For the latter, a very detailed thermodynamic and structural biology study could be performed at residue resolution by combining DSC and CD spectroscopy with very sophisticated NMR methods. The latter revealed the role of hydrogen bonds of the switching KOW domains by analyzing long range scalar couplings along hydrogen bonds. The second elegant approach was to use 15N and 13C CEST experiments to characterize the ensemble of conformations forming the unfolded state. This is a very difficult task to do experimentally. The authors can show experimentally at residue resolution that residues forming the alpha helices in the all-alpha switched form have already some alpha helical content in the U ensemble. Requirements for fold switching proteins from published theoretical approaches could be all experimentally confirmed and were convincingly discussed together with the Gibbs free energy landscape of all relevant conformational states. There is only one minor weakness concerning the interpretation of chemical shift changes of U with increasing urea concentrations (Figure 6B -Figure supplement 2).

Reviewer #3 (Public Review):

Shen et al. investigated the relationship between the diagnosis of cardiovascular disease (CVD) and subsequent diagnosis of psychiatric disorders using national databases and health records over a 30-year period in Sweden. They also investigated the association between the diagnosis of psychiatric disorder and subsequent CVD-related mortality. Comparisons were made between participants diagnosed with CVD and siblings without CVD, and between the CVD participants and random age- and sex-matched controls from the general population.

They show that diagnosis of all types of CVD investigated was associated with increased risk of all types of psychiatric disorders considered, both in comparison to non-CVD siblings and general population controls. They also showed that diagnosis of psychiatric diagnosis subsequent to CVD diagnosis was associated with greater CVD-related mortality.

A key strength of this study is the use of national databases and populations, as it has allowed for sufficiently large numbers for important subgroup analyses investigating specific types of CVD and psychiatric disorders. In addition to disease and disorder subtypes, the authors have investigated many other factors that may be important for understanding these relationships, including time of diagnosis during follow-up, year of diagnosis, age of participant, and various comorbidities. The duration of follow-up is another important strength of this study, as is the use of sibling controls to mitigate the potential confounding effect of genetic and early-life environment.

However, while it is acknowledged as a limitation by authors, the lack of lifestyle data is a notable weakness of the study. The authors allude to causal inference in the abstract and discuss controlling for important confounding factors, but this is somewhat undermined by not being able to account for lifestyle factors, particularly since there are shared biological pathways such as inflammation linked to both CVD and many psychiatric disorders. As such, the associations reported in this study are potentially influenced substantially by unmeasured confounding related to lifestyle factors.

Overall, this is important data, and the conclusions around these findings supporting surveillance of psychiatric disorders in individuals diagnosed with CVD due to its association with increased risk of mortality may be of interest to clinical settings.

Reviewer #3 (Public Review):

This work aims at testing hypotheses derived from the field of behavioral economics (Kahneman's theories), related to subjective value perception in ants foraging for food. The work was conceived to test how ants react to a specific feature which is the segregation or the bundling of food resources. Behavioral economics posits that individuals value more segregated resources than the same amount of resources presented in a bundled way. At the same time, if accessing the segregated resources implies an increase in energetic costs to access them (i.e. longer displacements), then costs would be also perceived as higher in the segregated-resource case than in the bundled-resource case.

Whether ants conform or not to this model is an interesting question, and irrespective of the results obtained, the experiments presented by the authors have been conceived to address this model as the experimental parameters varied refer to resource separation (drops of sucrose solution with different degrees of spacing between them) and to walking distances.

Yet, the manuscript suffers from various serious deficits that preclude being enthusiastic with respect to its present form. Various problems are listed below, which reduce the quality of this work. Hopefully, the authors can amend some of these problems to reach a more consistent version.

1) The inconsistent and unjustified "wrapping" with a "wanting vs liking" framework<br /> While it is unquestionable that the question raised by the authors revolves around behavioral-economic hypotheses on value perception and is fully addressed by the experiments performed, the "extra wrapping" of the "wanting/liking" framework added, probably to make the manuscript more attractive, is unjustified and excessively speculative. The use of a "wanting vs liking" interpretation framework is inappropriate as neither the experiments were conceived to address this topic, nor the results allow any robust conclusion on this point. These concepts originate in neuroscience analyses of neural-circuit activation in the mammalian brain upon situations that allow distinguishing several components related to reward: 1) the hedonic effect of pleasure itself (liking); 2) motivation to obtain the reward (wanting or incentive salience); and 3) and reward-related learning(1-3). These components refer to different identified neural circuits and brain areas as wanting for reward is generated by a large and distributed dopaminergic brain system including the frontal cortex, while liking is generated by a smaller set of hedonic hot spots within limbic circuitry and which are not dopamine-dependent.

Clearly, the use of the wanting vs liking terminology requires accuracy and appropriate studies to support it. This is not the case in the present manuscript which was not conceived to tackle this issue. Moreover, inconsistent testing procedures (see below point 3) undermine the use and interpretation of choice data as wanting. The authors have no proof of the involvement of wanting vs. liking systems in their design and even more, cannot disentangle between these components based on their behavioral data. Considering that pheromone deposits after food experience express "liking" can be questioned as it does not dissociate between individual liking and social information transfer (the liking and wanting systems are individually based systems). Moreover, the assignment of a choice in a binary-choice test to a wanting system is also questionable as the experiments cannot disentangle between the eventual individual wanting and the reward-related learning as animals are making choices based on odorant cues they have learned during their previous foraging bouts. In the absence of neurobiological data, the hypotheses of wanting vs. liking remain on a shaky, highly speculative ground.

Thus, the whole "wanting vs liking interpretation" (which attains alarming speculative levels in the Discussion section) should be omitted entirely from the manuscript if the authors want to provide a solid convincing framework articulated exclusively around the bundling vs. the segregation effects, which is precisely what their experiments tested. The rest is speculation in the absence of analyses supporting the wanting vs liking dissociation. An example of the kind of analysis necessary to go in this direction is provided by a recent work in which a dopamine-based wanting system was shown in honey bees(4), a work that the authors did not consider. We are clearly far from this kind of analysis in the present manuscript. As the authors wrote, "the present study is the first to examine bundling vs. segregation in an animal (line 99)", yet not liking vs. wanting.

2) Some experimental assumptions are not substantiated by data<br /> The experimental procedure relies on separating or aggregating reward (drops of sucrose solution) and determining the impact of this variation on pheromone deposition while returning to the nest and subsequent choice in a dual test situation in which two of the three treatments designed - distinguished by the odorant experienced en route to reward - were presented. While the "Segregated All Treatment" (Fig. 2A) managed to space the 0.2 µl reward drops by significant 25-cm segments, thus enhancing potentially both reward appreciation (segregated food drops) and cost appreciation (successive segments to be negotiated), the "Segregate Reward Treatment" (Fig. 2B) raises doubts about its validity.<br /> In this case, three drops were offered at the end of three consecutive 25-cm segments, with the assumption that drops spaced by 5 mm should be perceived as being segregated (two of 0.2 µl and 1 ad libitum). Yet, there is no proof - at least in the manuscript - that spacing two food drops by 0.5 mm induces a segregated perception in ants. The first experience with the first drop may induce both sensitization and a local search that may last until the very close next drop is detected so that for the ant, these drops would be perceived as belonging to the same resource rather than being perceived as segregated resources. The same applies to the vicinity between the 0.2 µl drop and the ad libitum drop.<br /> This raises the question of the real volume of the ad libitum drop, which is not mentioned (it is just described as beings "large"; line 205). One could argue that if drops separated by 5 mm were bound together, the results would be similar to those of the "Bundled Treatment" (Fig. 2C). Strictly speaking, this is not necessarily true if the volume of the large drop was known. If this were the case, the Bundled Treatment offered a volume that was 0.4 µl smaller than the total food provided in the "Segregate Reward Treatment".<br /> Overall, further controls are needed to support the assumptions of the different treatments chosen.

3) Unclear design in the testing procedures<br /> The authors did not specify in the methods if a reward was provided in the tests in which a Y maze was presented to the ants having experienced a succession of short and long segments. This information was provided later, in the Results section (line 309) and, as expected, no reward was provided during the tests, thus raising the question of the necessity of the three consecutive tests, with no refreshment trials in between. This procedure is puzzling because it induces extinction of the odor-length association - as verified by the authors (see lines 306-309) - and makes the design questionable. Only the results of the very first test should be kept and analyzed in the manuscript.<br /> The same remark applies to the three tests performed after comparing the experimental treatments, which - one discovers only in the Results Section - were also performed in the absence of refreshment trials. In fact, the absence of coherence in the results of these tests (e.g. lines 328-332) could be precisely due to a change of strategy between the tests following the absence of reward in the first test. This underlines the necessity of focusing exclusively on the first test and dismissing the data of the 2nd and 3rd tests in which performance may have been affected by extinction and strategy change. This again shows why speaking about "wanting" in this inconsistent framework makes no sense at all.

1 Berridge, K. C. & Robinson, T. E. Am Psychol 71, 670-679. (2016).<br /> 2 Berridge, K. C. & Kringelbach, M. L. Neuron 86, 646-664. (2015).<br /> 3 Berridge, K. C. & Kringelbach, M. L. Curr Opin Neurobiol 23, 294-303. (2013).<br /> 4 Huang, J. et al. Science 376, 508-512. (2022).

Reviewer #3 (Public Review):

This paper provides a novel framework for understanding prediction-based learning rules that are potentially employed by the hippocampus to optimize behavior. Specifically, the authors examined how a cognitive map containing predictive information (termed the successor representation) is computed in the hippocampus with spike-timing-dependent synaptic plasticity (STDP).

Strengths:<br /> By using an ecologically plausible computational model that is embedded with important biological characteristics, the authors propose a novel framework that demonstrates a set of computational principles employed by the hippocampus to achieve successful predictive learning. The paper clearly and thoroughly explains different components of the model with concrete examples and illustrations. Analytical solutions are also provided in addition to narratives to help readers understand the model setup as well as its relevance and connection to biological studies. Among the set of biologically realistic computational dynamics achieved by the modeling framework, the proposed model can elegantly account for both exponential and hyperbolic discounting by demonstrating that exponential discounting is utilized while the animals travel through space, whereas hyperbolic discounting is capitalized while the animals travel through time. Additionally, this paper discusses the model findings in the context of experimental and theoretical work which help readers understand how the proposed framework can be utilized in future work to guide investigations on predictive learning both empirically and computationally.

The proposed model makes connections to other theoretical frameworks

Weaknesses:<br /> While the framework proposed in this paper is potentially powerful in capturing different aspects of hippocampus-based predictive learning, the links between the model results and experimental findings are not sufficiently demonstrated. There are several biological concepts that are discussed in the context of the model. It is, however, unclear if the implementations of these concepts within the model capture the same underlying principles that happen in nature. For example, there is rich literature on hippocampal replays including their heterogeneity across contexts and species. The paper does not provide sufficient information regarding the specific types of replays or the specific aspects of replay dynamics that are observed in the model.

Reviewer #3 (Public Review):

The Nkx3.2 transcription factor is both necessary and sufficient for jaw joint development. In this paper the authors use comparative genomics to identify a conserved Nkx3.2 enhancer that they call a jaw regulatory sequence (JRS). With transgenic zebrafish they show that JRSs from multiple species of gnathostomes, including humans, can drive reporter expression in the larval jaw joint. Transgene expression in each case includes the joint interzone as well as adjacent cartilage and perichondrium. With CRISPR-targeted deletion they show requirements for the zebrafish JRS in larval jaw joint development. Analyses of additional gnathostome genomic sequence near Nkx3.2 suggest that jawless hagfish lack JRS-like sequences, consistent with an important role for this enhancer and Nkx3.2 in the evolutionary origin of jaws. Despite a growing recognition of the importance of enhancers in development and disease, very few have been shown to have essential functions in vivo.

The first part of the paper is straightforward, with clear evidence that the JRSs tested act as functional enhancers, since they drive reporter expression in very specific and similar subsets of skeletal cells. The images are high quality and the cellular resolution is impressive. There are also several moderate/major weaknesses that should be addressed. The experiments showing larval joint defects following JRS deletion in zebrafish are less clear, since the phenotypes are subtle in mutants and later recover to resemble wild type siblings. One major weakness is that there is no evidence that JRS deletion alters Nkx3.2 expression at any stage. In the absence of these data, it is possible that the JRS acts on other nearby genes. Another weakness is a lack of quantification of variation in this phenotype in the JRS mutants. Though the images of larval joint defects in the JRS mutants are clear, they are single examples. Finally, the apparent absence of an obvious JRS in the region adjacent to Nkx3.2 in hagfish is used to argue that it arose together with the origin of jaws during vertebrate evolution. Alternatively, this may instead reflect a unique loss of this element in hagfish.

Reviewer #3 (Public Review):

In this manuscript, the authors present data supporting FOXP2 as an oncogene in PCa. They show that FOXP2 is overexpressed in PCa patient tissue and is necessary and sufficient for PCa transformation/tumorigenesis depending on the model system. Over-expression and knock-down of FOXP2 lead to an increase/decrease in MET/PI3K/AKT transcripts and signaling and sensitizes cells to PI3K/AKT inhibition.

Key strengths of the paper include multiple endpoints and model systems, an over-expression and knock-down approach to address sufficiency and necessity, a new mouse knock-in model, analysis of primary PCa patient tumors, and benchmarking finding against publicly available data. The central discovery that FOXP2 is an oncogene in PCa will be of interest to the field.

However, there are several critically unanswered questions.

• No data are presented for how FOXP2 regulates MET signaling. ChIP would easily address if it is direct regulation of MET and analysis of FOXP2 ChIP-seq could provide insights.<br /> • Beyond the 2 fusions in the 100 PCa patient cohort it is unclear how FOXP2 is overexpressed in PCa. In the discussion and in FS5 some data are presented indicating amplification and CNAs, however, these are not directly linked to FOXP2 expression.<br /> • There are some hints that full-length FOXP2 and the FOXP2-CPED1 function differently. In SF2E the size/number of colonies between full-length FOXP2 and fusion are different. If the assay was run for the same length of time, then it indicates different biologies of the over-expressed FOXP2 and FOXP2-CPED1 fusion. Additionally, in F3E the sensitization is different depending on the transgene.<br /> • The relationship between FOXP2 and AR is not explored, which is important given 1) the critical role of the AR in PCa; and 2) the existing relationship between the AR and FOXP2 and other FOX gene members.

Reviewer #3 (Public Review):

Helsell et al. uses atomistic molecular dynamics simulations to characterize the structural dynamics of the M2 protein together with continuum elastic models to evaluate the energetic cost of the protein-induced bilayer deformations. Using unbiased simulations (without constraints on the protein) they show that the M2 structure is dynamic and that the AH helices are mobile (though they tend to retain their secondary structure), in agreement with experimental observations. Then, using simulations in which the peptide backbone was restrained to the starting structure, they were able to quantitatively characterize the protein-induced bilayer deformations as well as the acyl chain dynamics.

Both the atomistic simulations and the continuum-based determinations of the bilayer deformation energies are of high quality. The authors are careful to note that their unbiased simulations do not reach equilibrium, and the authors' conclusions are well supported by their results, though some issues need to be clarified.

1. P. 7: Choice of lipid composition: POPC:POPG:Cholesterol 0.56:0.14:0.3. This lipid composition (or POPC:POPG 0.8:0.2) has been used in a number of experimental studies that the authors use as reference. It differs, however, substantially from the lipid composition of the influenza membrane (Gerl et al., J Cell Biol, 2012; Ivanova et al., ACS Infect Dis, 2015), which is enriched in cholesterol, has a 2:1 ratio of phosphatidylethanolamine to phosphatidylcholine, and almost no PG. The choice of lipid composition is unlikely to impact the authors' major conclusions, but it should be discussed briefly. As noted by Ivanova et al., the lipids of the influenza membrane are enriched in fusogenic lipids. How will that impact the authors results.

2. The definition of the lipid tilt needs to be revisited. On P. 13 (in the Pdf received for review, the authors do not provide page numbers), the tilt is defined/approximated as "the angle between the presumed membrane normal (aligned with the Z axis of the box) and the vector pointing from each phospholipid's phosphate to the midpoint between the last carbon atoms of the lipid tails." This (equating the normal to the interface with the Z axis of the simulation box) may be an acceptable approximation for the lower leaflet, which is approximately flat, but probably not for the upper leaflet where the interface is curved in the vicinity of the protein. The authors should, at least, discuss the implications of their approximation in terms of their conclusion that there is little lipid tilt in the upper leaflet.

3. P. 14, last paragraph, Figure 5 and 6: The snapshots in Figure 5 are too small to see what the authors refer to when they write "tilt their lipid tails to wrap around the helices." The authors should consider citing the work of H W. Huang, e.g., Huang et al. (PRL, 2004), who introduced the notion of curvature stress induced by antimicrobial peptides, a concept similar to what the present authors propose.

4. P. 17-18, Figure 7: The authors introduce the bilayer midplane, which becomes important for the determination of the deformation energy in the (unnumbered) equation on P. 17, but do not specify how it is determined. This is a non-trivial undertaking, but critical for the evaluation of the deformation energy; please add the necessary details.

5. P. 18-19, Figure 8: The comparison of the MD and continuum membrane deformations is very informative, but the authors should discuss the implications of the increased symmetry further in terms of the estimated deformation energies. (I do not believe the authors really mean that they predicted the energies, they estimated/approximated them.)

Reviewer #3 (Public Review):

This communication describes the molecular design of antinociceptive peptides with the aim to improve the peptide affinity and blocking activity towards Nav1.7. The authors performed in vivo experimental assays of such molecular designed peptides to validate them. The methods incorporate state-of-the-art techniques, and the results are clear and of great quality.

Strengths: Many synthetic variants were generated to accomplish the best antinociceptive peptides including its in vitro and in vivo assays.

Weaknesses: Some of the reasonings for creating some of the in-silico peptide variants were not clear at all even though they were designed based on ligand-receptor models.

Reviewer #3 (Public Review):

The manuscript by Pauli and Chen et al. is a beautiful and much-needed study that characterizes the cell types that make up these nuclei.

The major strength of this study is it combines a profiling approach to identify subtypes (single-cell sequencing) with microscopy that reveals where the cells reside within the PBN (multiplex FISH) and a genetic approach to visualize the projection pattern of these cell types throughout the brain (genetic labeling with Cre-lines). The result is important new insight about the cell types in this structure. Highlights include (1) the identification of two inhibitory neuron subtypes and 19 excitatory cell types, (2) the discovery of two output streams, (3) insight about the ontogeny of cell types and transcription factors that may be involved in fate specification/determination, (4) clarification of which neuropeptides are expressed/not expressed in which cell types, (5) and the identification of suitable genetic markers to target many of the cell types. Importantly, all the data are available for researchers to mine their genes of interest. This information is a major advance in the field.

Reviewer #3 (Public Review):

Grande and colleagues used high-resolution 7 Tesla fMRI to investigate the topographical distribution of functional connectivity, and sensitivity to scene and object stimuli, across subregions of the entorhinal-hippocampal circuitry. They report scene-specific activations in functionally connected voxels of the posterior-medial entorhinal cortex (EC) and the distal subiculum. In contrast, no specific preference for object stimuli was detected.

The authors managed to characterize functional connectivity patterns in the entorhinal-hippocampal circuitry to an impressive level of detail. The division of the subiculum and CA1 region in 5 and 3 segments, respectively, extends the very sparse body of literature on the organization of connectivity across the transversal axis of the hippocampal formation in humans. Notably, the authors replicate findings by Maass et al. 2015 of a dissociation of functional connectivity preference between voxels in anterior-lateral and posterior-medial EC with voxels in proximal and distal subiculum (informed by the well-described connectivity architecture of the hippocampal formation in rodents; see e.g. Witter et al. 2000). In addition, they report the novel finding of specific functional connectivity preference of voxels in anterior-medial and anterior-lateral EC seed regions with distal CA1 (again consistent with previous findings in rodents, as well as diffusion MRI findings in humans; Syversen et al. 2021).

After having established 4 different clusters of entorhinal voxels based on functional connectivity to 4 'source' regions, the authors report specific scene sensitivity in a posterior-medial cluster, partially replicating previous studies. In addition, they describe another novel finding of specific scene sensitivity in the two most distal segments of the subiculum.

I agree with the authors that understanding the connectivity of hippocampal subregions and their functional preferences is an important goal with relevance for many research disciplines, such as on episodic memory, spatial navigation, or Alzheimer's disease.

While the paper makes a number of important contributions to help understand entorhinal-hippocampal function and connectivity, I feel that the premise and research question is somewhat unclear and potentially misleading. Most notably, I don't think the conclusions pertaining to processing of object (item) information are supported by the results. An absence of a difference does not provide evidence for similar levels of processing of object and scene information.

Reviewer #3 (Public Review):

This manuscript, "Variation in Ubiquitin System Genes Creates Substrate-Specific Effects on Proteasomal Protein Degradation" studies the genetic basis of differences in protein degradation. The authors do so by screening natural genetic variation in two yeast strains, finding several genes and often several variants within each gene that can affect protein degradation efficiency by the Ubiquitin-Proteasome system (UPS). Many of these variants have "substrate-specific effects" meaning they only affect the degradation of specific proteins (those with specific degrons). Also, many variants located within the same genes have conflicting effects, some of which are larger than others and can mask others. Overall, this study reveals a complex genetic basis for protein degradation.

Strengths: Revealing the genetic basis for any complex trait, such as protein degradation, is a major goal of biology. The results of this paper make a significant step towards the goal of mapping the genes and variants involved in this specific trait. Fine mapping methods are used to home in on the specific variants involved and to measure their effects. This is very nicely done and provides a detailed view of the genetic basis of protein degradation. Further, the GFP/RFP system used to quantify the efficiency of the protein degradation system is a very elegant system. Also, the completeness of the analysis, meaning that all 20 N-degrons were studied, is impressive and leads to very detailed findings. It is interesting that some genetic variants have larger and opposite effects on the degradation of different N-degrons.

Weaknesses: Some of the results discussed in this paper are not surprising. For example, the finding that both large effect and small effect genetic variants contribute to this complex trait is not at all surprising. This is true of many complex traits. The discussion of human disease is also a bit extensive given this study was performed on yeast. It might be more productive to use these findings to understand the UPS better on a mechanistic level. Why does the same genetic variant have opposite effects on the degradation of different degrons, even in cases where those degrons are of the same type?

Overall, this manuscript excels at mapping the genetic basis of variation in the UPS system. It demonstrates a very complex mapping from genotype to phenotype that begs for further mechanistic explanation. These results are important to the UPS field because they may help researchers interrogate this highly conserved essential system. The manuscript is weaker when it comes to the broader conclusions drawn about the relative importance of large vs. small effects variants on complex traits, the amount of heritability explained, and the effects of genetic variation on protein abundance vs transcript abundance. Though in the case of protein vs transcript, I feel the cursory examination of the trends is perhaps at an appropriate level for the study, as it is mainly meant to show these things differ rather than to show exactly how and why they differ.

Reviewer #3 (Public Review):

This paper reports the humoral (neutralizing antibody concentrations from serum) and cellular (cytopathic effect on Vero cells) immune responses of volunteers enrolled in a randomized clinical trial for the CoronaVac® SARS-CoV-2 vaccine. The findings are useful and, through solid reporting, discussion, and statistical analyses, provide context for the efficaciousness of the 0-14 day and 0-28 day dosing schedules of CoronaVac®. The results show that these two dosing schedules are similar across most metrics. Furthermore, the findings pave the way for key future work, including reporting and understanding the clinically relevant protective effects, and how long they last, of CoronaVac® against the emerging variants/subvariants.

Reviewer #3 (Public Review):

Meissner et al. employ stochastic Gal4 labeling with MCFO to ease the identification candidate lines for split-Gal4 line generation to genetically target neurons of interest identified in EM traces. Data basis for the approach is a novel resource of 74k MCFO images aligned to the JRC18 template allowing the matching between EM and LM traces of single neurons. The resource is released in combination with data processing and query tools. In addition, an open web-based data portal to the released data collection and data mining tools is made available. This will allow broad access to this novel resource with the potential to create high impact in the community.

Strength:

The possibility to bridge between EM neuron traces and expression patterns in LM images is a key method to achieve and accelerate genetic access to individual neurons. The proposed resource and tools contribute to this effort and provide open and easy access to it. This also includes the possibility to upload and analyze own data using the provided infrastructure, which is a great asset.

Weaknesses:

While the generation and analysis of the MCFO data is described in great detail and the overall technical approach seems feasible, the description of the technical part and its evaluation are lacking important implementation details and scientific rigor. Although this is primarily a life science paper introducing a new data resource it's the mining capability making this resource really valuable. The provided evaluation of the image mining capabilities however is currently insufficient to support the very general claims on effectivity and speed of the method.

Reviewer #3 (Public Review):

Noel et al provide a neural representational account of three brain areas in a virtual, visual navigation task paradigm especially designed to achieve a closed action-perception loop closely resembling natural behaviour. The authors recorded hundreds of neurons from three monkeys while the animals were engaged in the task where latent cognitive variables like distance travelled and distance to target continuously changed. The authors build on their previous work where they robustly characterized animal behaviour on this task paradigm. Here, they aim to find neural codes of dynamic, latent variables and report a mixed and heterogeneous profile of task variable coding distributed across the two brain areas in the parietal cortex (MSTd and area 7a) and one in the prefrontal cortex (dlPFC).

Major strength: Multi-area recording and the close-loop behavioural paradigm are major strengths of this study. The robust model-based analysis of neural data strengthens the paper even more. The correlation of coupling between MSTd and dlPFC and behaviour, albeit in a coarse time scale (of sessions), is particularly interesting and makes the paper strong by quantitatively relating behaviour to neural activity.

Major weakness: The paper mainly gives a long list of what task variables the three brain areas code for along with measures of connectivity between areas. Although this is a valuable contribution to the field, the study is not designed to test predictions of specific computational hypotheses. Towards the end of the paper, the authors bring up the two alternate mechanisms: vector-coding vs distance-coding, but only as a speculation. These two hypotheses could have been developed further at the outset to make specific predictions for neural dynamics and subsequently be tested in their data. This will likely lead to richer findings going beyond representations of task variables. Nevertheless, the findings presented in the paper are surely novel and exciting.

Impact: The main impact of the paper is neurophysiology under a novel, naturalistic behavioral paradigm. The data, both behavioral and neurophysiological, is rich and has potential to test predictions of more fine-grained computational hypotheses. However, the observation that MSTd codes for latent variables is not as surprising as the authors claim. Given the recent observations of heterogeneous variables represented in brain areas traditionally thought to be highly specific (e.g. locomotion variables in V1, mixed coding in EC etc.), it is not surprising to find latent variables in a 'traditionally' sensory area, especially in a continual behavioral paradigm where many variables are changing and are correlated.

Based on their previous work and this work, the authors mention multiple times the task strategy and its embodied nature. While the authors conclusively show the involvement of eye movement in solving the task, it is difficult to imagine a concrete definition of an embodied task strategy without clear alternate hypotheses. How would the animals behave if their eye movements were prevented? Worse performance (like humans did in their previous paper) or unable to perform (akin to a bird unable to fly without wings) or a different strategy? What should we predict based on the neural observation reported here? The impact of this paper would be greater if the authors bring up these questions and provide some speculations rooted in neurophysiological observations.

Reviewer #3 (Public Review):

This manuscript should be of broad interest to readers not only in the field of gap junction (GJ) mediated cell-to-cell communication but also to scientists and clinicians working on the function of mitochondria and metabolism. Their data elucidates a new function of Cx43 in regulating the energy (ATP) generation of mitochondria, e.g., under oxidative stress.

The canonical function of gap junctions is in direct cell-to-cell communication by forming plasma membrane traversing channels that electrically and chemically connect the cytoplasms of adjacent cells. These channels are assembled from connexin proteins, connexin 43 (Cx43). However, more recently new, non-canonical cellular locations and functions of Cx43 have been discovered, e.g. mitochondrial Cx43 (mtCx43). However, very little is known about where Cx43 transported into mitochondria is derived from, how Cx43 is transported into mitochondria, where it is located in mitochondria, in which form Cx43 is present in mitochondria, (polypeptides, hemi-channels (HCs), complete GJ channels), and what the function of mtCx43 is. The authors addressed the latter question. The authors provide convincing evidence that mtCx43 modulates mitochondrial homeostasis and function in bone osteocytes under oxidative stress. Together, their study suggests that mtCx43 hemi-channels regulate mitochondrial ATP generation by mediating K+, H+, and ATP transfer across the mitochondrial inner membrane by directly interacting with mitochondrial ATP synthase (ATP5J2), leading to an enhanced protection of osteocytes against oxidative insult. These findings provide important information of a role of Cx43 functioning directly in mitochondria and not at the canonical location in the plasma membrane. While most of the functional assays presented in Figures 2-8 appear solid, the mitochondrial localization of Cx43, its translocation into mitochondria under oxidative stress, and its configuration as hemi-channels (Figure 1) is less convincing. I have five general comments that should be addressed:

1) This study was performed in MLO-Y4 osteocyte cells. Is the H2O2 induced increase of mitochondrial Cx43 MLO-Y4 cell type or osteocyte specific, or is Cx43 playing a more general role in mitochondrial function, e.g. under oxidative stress? Osteoblasts such as MC3T3-E1 and MG63, and many other cell types endogenously express Cx43, and oxidative stress is a general physiological stressor, not only for osteocytes and bone cells. Attending to this question would address the generality of the findings for mitochondrial function.

2) The images of MLO-Y4 cells (Figure 1A) and the primary osteocytes isolated from Csf-1+/- and control mice (Figure 8) do not show visible gap junctions. I guess this is due to the fact that slides were stained with the Cx43(E2) antibody. I feel, staining of these cells in addition with the Cx43(CT) antibody would be helpful to get a better understanding on the distribution of Cx43 in gap junctions and undocked/un-oligomerized Cx43 in these cells.

3) The images of cells presented in Figure 1A are quite fussy. No mitochondria are visible, and the Cx43 staining is hazy and does not localize to any subcellular structures. Also, it is not clear if the higher resolution image presented in Figure 1C actually represents a mitochondrion. A good DIC image, or co-staining with another mitochondrial marker such as MitoTracker (as shown in Figure 4-S1) would make the localization and translocation of Cx43 into mitochondria upon oxidative stress more convincing. This is especially important as the translocation, although statistically significant, increases only by about 10% or less (Figure 1B). Such a small difference (also represented in the Western analyses presented in Figure 1D) could easily be artefactual, depending on how the correlation coefficient was generated. Of note in this respect is that control cells in Figure 1A appear larger (compare the size of the nuclei) and are spread out more than the H2O2 treated cells. Better, more clear images would make the mitochondrial localization/translocation more convincing.

4) How pure are the mitochondria that were probed for Cx43 by Western shown in Figure 1D? The preparation method described is relatively simple, collecting the 10,000xg supernatant (here 9,000xg supernatant) as mitochondrial fraction. Is it possible that the Cx43 signal, at least in part, is derived from other, contaminating membranes, such as PM, Golgi, or ER? Testing the mitochondrial preparation by Western with marker proteins specific for these compartments would strengthen the author's results.

5) The authors rely on previous studies to postulate that Cx43 in mitochondria forms hemichannels in their system, is localized in the inner membrane, and is oriented with the Cx43 C-termini facing the inter-membrane space (as schemed in Figure 8C). The authors use lucifer yellow (LY) dye transfer and carbenoxolone, but both are not hemi-channel specific probes. They are transferred by, and block GJ channels as well. Experiments, using hemi-channel specific probes would be more convincing. This is important, as the information cited is based on only two references (Boengler et al., 2009; Miro-Casas et al., 2009), and it still is highly unclear how a membrane protein that is co-translationally inserted into the ER membrane, then traffics through the Golgi to be inserted into the plasma membrane is actually imported into mitochondria and in which state (monomeric, hexameric). Why the Cx43(CT) specific antibody traverses the outer mitochondrial membrane and reaches the Cx43CT while the Cx43(E2) specific antibody is not described and clear either. Where are these mitochondria permeabilized with Triton X-100 as described in M&M?

Reviewer #3 (Public Review):

With resting-state fMRI data, recent work has mapped the organisation of the cortex along a continuous gradient, and regions that share similar patterns of functional connectivity are located at similar points on the gradient (Margulies et al., 2016). In the current study, the authors investigate how this dimension of connectivity changes during conceptual retrieval with different levels of semantic association strength. Specifically, they perform gradient analysis on task-fMRI informational connectivity data and reveal a similar principal gradient to the previous study, which captures the separation of heteromodal memory regions from the unimodal cortex. More importantly, by comparing the gradient generated with data from different experimental conditions (i.e., strong vs. weak association), the authors find the separation of the regions at the two ends of the gradient can be regulated by the association strength, with more separation for stronger association. They also examine the relationships between the gradient values and dimensionality and brain-semantic alignment measures, to explore the nature of this shifting gradient as well as the corresponding brain areas.

Strengths:<br /> 1. The aim of this study is clear and the relevant background literature is covered at an appropriate level of detail. With the cortical gradient analysis approach, this study has the potential to make a contribution to the understanding of the topographical neural basis of semantics in a fine-grained manner.<br /> 2. The methodology in the current study is novel. This study validates the feasibility of performing gradient analysis on task-fMRI data, which is enlightening for future research. Using the number of PCs generated by PCA as a measure of dimensionality is also an interesting approach.<br /> 3. The authors have conducted multiple control analyses, which tested the validity of their results. Specifically, a control task without engaging semantic processing was built in the experimental design (i.e., the chevron task), and the authors conducted multiple parallel control analyses with the data from this control task as a comparison with their main results. Other control analyses were also performed to validate the robustness of their methodological choices. For example, varied thresholds were used during the calculation of dimensionality and similar results were obtained.

Weaknesses:<br /> 1. As a major manipulation in the experiment, it is not very clear how the authors split/define their stimuli into strong and weak semantic association conditions. If I understood correctly, word2vec was used to measure the association strength in each pair of words. Then the authors grouped the top 1/3 association strength trials as a "strong association" condition and the bottom 1/3 as "weak association" (Line 689), and all analyses comparing the effect of "strong vs. weak association" were conducted with data from these two subsets of stimuli. However, in multiple places, the authors indicate the association strength of their stimuli ranges from completely unrelated to weakly related to highly related (Line 612, Line 147, Line 690, and the examples in Figure 1B). This makes me wonder if the trials with bottom 1/3 association strength (i.e., those were used in the current study) are actually "unrelated/no association" trials (more like a baseline condition), instead of "weak association" trials as the authors claimed. These two situations could be different regarding how they engage semantic knowledge and control processing. Besides, I am very interested in what will the authors find if they compare all three conditions (i.e., unrelated vs. weak association vs. strong association).<br /> 2. Following the previous point, because the comparison between weak vs. strong association conditions is the key of the current study, I feel it might be better to introduce more about the stimuli in these two conditions. Specifically, the authors only suggested the word pairs fell in these two conditions varied in their association strength, but how about other psycholinguistic properties that could potentially confound their manipulation? For example, words with higher frequency and concreteness may engage more automatic/richer long-term semantic information and words with lower frequency and concreteness need more semantic control. I feel there may be a possibility that the effect of semantic association was partly driven by the differences in these measures in different conditions.<br /> 3. The dimensionality analysis in the current study is novel and interesting. In this section, the authors linked decreasing dimensionality with more abstract and less variable representations. However, most results here were built based on the comparison between the dimensionality effects for strong and weak association conditions. I wonder if these conclusions can be generalised to results within each condition and across different regions (i.e., regions having lower dimensionality are doing more abstract and cross-modal processing). If so, I am curious why the ATL (a semantic "hub") in Figure 3A has higher dimensionality than the sensory-motor cortices (quite experiences related) and AG (another semantic "hub").<br /> 4. I am not sure about the meaning/representational content underlying the semantic similarity matrix in the semantic-brain alignment analysis. According to the authors, this matrix was built based on the correlation of participants' ratings of associative strength (0, no link; 1~4, weak to strong) across trials. The authors indicate that this matrix reflects the global similarity of semantic knowledge between participants (Line 403). However, even though two participants share very similar ratings of association strength across trials, they could still interpret the meaning/knowledge underlying the associations very differently. For example, one participant may interpret the link between "man" and "car" as a man owns a car but another participant may interpret it as a man is hit by a car, although both associations could be rated as strong for this trial. This situation may be even more obvious for those pairs with weak association. Therefore, I am not confident this is a measure of similarity of semantic knowledge.

Reviewer #3 (Public Review):

Higher BMI in childhood is correlated with behavioral problems (e.g. depression and ADHD) and some studies have shown that this relationship may be causal using Mendelian Randomization (MR). However, traditional MR is susceptible to bias due to population stratification, assortative mating, and indirect effects (dynastic effects). To address this issue, Hughes et al. use within-family MR, which should be immune to the above-listed problems. They were unable to find a causal relationship between children's BMI and depression, anxiety, or ADHD. They do, however, report a causal effect of mother's BMI on depression in their children. They conclude that the causal effect of children's BMI on behavioral phenotypes such as depression and anxiety, if present, is very small, and may have been overestimated in previous studies. The analyses have been carried out carefully in a large sample and the paper is presented clearly. Overall, their assertions are justified but given that the conclusions mostly rest on an absence of an effect, I would like to see more discussion on statistical power.

1) The authors show that the estimates of within-family MR are imprecise. It would be helpful to know how much power they have for estimating effect sizes reported previously given their sample size.

2) They used the correlation between PGS and BMI to support the assertion that the former is a strong instrument. Were the reported correlations calculated across all individuals? Since we know that stratification, assortative mating, and indirect effects can inflate these correlations, perhaps a more unbiased estimate would be the proportion of childrens' BMI variance explained by their PGS conditioned on the parents' PGS. This should also be the estimate used in power calculations.

3) In testing the association of mothers' and fathers' BMI with children's symptoms, the authors used a multivariable linear regression conditioning on the child's own BMI. Was the other parent's BMI (either by itself or using the polygenic score) included as a covariate in the multivariable and MR models? This was not entirely clear from the text or from Fig. 2. I suspect that if there were assortative mating on BMI in the parent's generation, the effect of any one parent's BMI on the child's symptoms might be inflated unless the other parent's BMI was included as a covariate (assuming both mother's and father's BMI affect the child's symptoms).

4) They report no evidence of cross-trait assortative mating in the parents generation. The power to detect cross-trait assortative mating in the parents' generation using PGS would depend on the actual strength of assortative mating and the respective proportions of trait variance explained by PGS. Could the authors provide an estimate of the power for this test in their sample?

5) Are the actual phenotypes (BMI, depression or ADHD) correlated between the parents? If so, would this not suffice as evidence of cross-trait assortative mating? It is known that the genetic correlation between parents as a result of assortative mating is a function of the correlation in their phenotypes and the heritabilities underlying the two traits (e.g., see Yengo and Visscher 2018). An alternative way to estimate the genetic correlation between parents without using PGS (which is noisy and therefore underpowered) would be to use the phenotypic correlation and heritability estimated using GREML or LDSC. Perhaps this is outside the scope of the paper but I would like to hear the author's thoughts on this.

6) It would be helpful to include power calculations for the MR-Egger intercept estimates.

7) Finally, what is the correlation between PGS and genetic PCs/geography in their sample? A correlation might provide evidence to support the point that classic MR effects are inflated due to stratification.

Reviewer #3 (Public Review):

This study outlines the role of osteoclast-mediated resorption in integrating the skeletal elements during limb regeneration, using axolotls that can regenerate the entire limb upon amputation. Using calcium-binding vital dyes (calcein and alizarin red), the authors first demonstrated that a large portion of amputated skeletal elements is resorbed prior to blastema formation. They further show that 1) inhibiting bone resorption by zoledronic acid impairs proper integration of the pre-existing and regenerating skeletal elements, 2) removing the wound epithelium using the full skin flap surgery inhibits bone resorption, and 3) bone resorption and blastema formation are correlated. The authors reached the major conclusion that bone resorption is essential for successful skeletal regeneration. Notably, this study applies a well-established and elegant axolotl limb regeneration model and transgenic reporter strains to reveal the potential roles of resorption in limb regeneration.

Strengths:<br /> 1. The authors utilized a well-established axolotl limb regeneration model and applied elegant vital mineral dyes and transgenic reporter lines for sequential in vivo imaging. The authors also provided quantitative assessment by examining multiple animals, particularly in the early sections, ensuring the rigor and the reproducibility of the study.<br /> 2. The authors further performed important interventions that can impinge upon successful limb regeneration, including inhibition of bone resorption by zoledronic acid and impairment of the wound epithelium by full skin flap surgery. These procedures gave rise to useful insights into the relationship between bone resorption and successful limb regeneration.<br /> 3. The imaging presented in this manuscript is of exceptionally high quality.

Weaknesses:<br /> 1. Despite the high quality of the work, many analyses in this study are incomplete, making it insufficient to support the major conclusion. For example, in Figure 4, the authors did not provide any quantitative assessment to show how zol affects the integration of the skeletal elements (angulation?), which seems to be essential for supporting the conclusion. Likewise in Figure 7, the analyses of EdU+ cells and Sox9 reporter expression were not included in zol-treated animals. Similarly in Figure 5, quantification of osteoclasts was not performed with the full skin flap surgery group. Analyses of only normally regenerated animals are not sufficient to support many of the conclusions.<br /> 2. The phenotype of zol-treated animals in limb regeneration is somewhat disappointing. Although zol-treated animals show decreased blastema formation and unresorbed pre-existing skeletal elements, limb regeneration still occurs and the only phenotype is a relatively minor defect in skeletal integration. It is possible that zol-induced defect in blastema formation is not directly linked to the failure of integration at a later stage.<br /> 3. As an integration failure of the newly formed skeleton still occurs in untreated animals, it is not entirely clear how the authors can attribute this defect to a lack of bone resorption. More quantitative analyses would be necessary to demonstrate the correlation between zol treatment and lack of integration.

Reviewer #3 (Public Review):

In this work, Dumrongprechachan et al. impressively expanded their earlier work on the identification of cell type-specific subcellular proteomes from mouse brain by APEX2 proximity labeling. Instead of using viral expression of APEX2, the authors now created a Cre-dependent APEX2 reporter mouse line using CRISPR knock-in, which can be combined with multiple Cre-driver lines for proteomic applications. Using this novel tool in combination with sophisticated mass spectrometry and elegant bioinformatics, they mapped the temporal dynamics of the axonal proteome in corticostriatal projections (instead of only identifying a static cell type- and compartment-specific proteome) together with its phosphorylation status (instead of only looking at protein abundance). The data will provide a valuable resource on developmental trajectories at the proteomic and phosphoproteomic level, and will allow for pathway- and phosphosite-centric systems-level analyses as exemplified by the identification of proline-directed protein kinases as major regulators of corticostriatal projection development.

Strengths:<br /> The key tool developed in this work is the APEX2 reporter mouse line as it enables capturing of early postnatal time points, which was not possible before due to the time window of 2-4 weeks required for viral APEX expression. Thus, this tool puts the authors into position to access the temporal dynamics of the developing axon at time points spanning from neonate (as early as P5) to young adult (P50). Within this complex experimental design, the authors even managed to introduce a crucial compartment control at least for the time point P18, in which APEX expression is restricted to nucleus and soma upon viral expression. The resulting resource will be of high value as the data are derived from advanced mass spectrometric methods and stringent data handling. Examples of this high level of scrutiny include the use of MS3 methodology for the acquisition of TMT data to address the ratio distortion issues typically seen with isobaric labeling and thereby increase the quantification accuracy and the limitation to proteins quantified in all biological replicates.

Weaknesses:<br /> As to sample preparation for mass spectrometry, the authors follow the interesting concept of first enriching the phosphopeptides from the pool of TMT-labeled tryptic peptides and then using the unbound fraction from that step for further peptide fractionation, followed by mass spectrometric protein quantification. While this strategy sounds very straightforward in principle, one would expect that the phosphopeptide enrichment comes with an unspecific loss of other peptides in general, and with a semi-specific loss of acidic peptides in particular. Was this potential issue investigated by comparison with samples that were fractionated directly without prior phosphopeptide enrichment? Or with other words: the rationale for this sequential procedure is compelling - quantification of both protein and phosphopeptide abundance from the same (limited) sample, but what is the price for it as to peptide loss?

The APEX2 reporter mouse line is a novel tool with broad applicability for proximity labeling approaches and, understandably, the authors advertise its advantages, mainly via the suitability for short temporal windows. However, the discussion on the limitations of the approach falls short. The authors should make clear that the APEX method in general is limited to ex vivo approaches such as the acute brain slices used here due to the limitation that potentially toxic reagents (i.e. low membrane-permeable biotin-phenol and H2O2) have to be delivered to the target tissue. Although treatment with H2O2 is rather short, undesired oxidative stress signaling may have to be taken into account, particularly when protein phosphorylation rather than protein abundance is assessed. It would also be interesting to discuss the pros and cons of perfusing the mice prior to preparation of brain slices; e.g., in the context of removal of catalases/endogenous peroxidases or potential for substrate delivery (like recently shown in heart, doi: 10.1038/s41586-020-1947-z). Another issue with the Discussion is that the authors do not properly reflect the involvement of proline-directed kinases in the development of corticostriatal projections, which stands in contrast to the fact that they sell this as one of their major findings throughout the manuscript, including the Abstract.

Reviewer #3 (Public Review):

This study investigates the neuronal correlates of low-frequency changes in respiration volume per unit time (RVT). The authors report distributed patterns of correlations between RVT and fMRI that may represent a respiration-driven brain network. The ability to demonstrate that this pattern has neuronal origins would make an important contribution to the fMRI field, especially as physiological signals are typically treated as artifacts in fMRI analysis.

A major strength of this paper is the use of concurrent fMRI, physiological monitoring, and invasive electrophysiology (electrode in the anterior cingulate cortex; ACC) in the anesthetized rat, which allows for directly measuring local neuronal activity associated with changes in respiration. A second strength is that the authors demonstrate coherence between respiration (the raw signal as well as RVT) and gamma-band power in the ACC, and furthermore replicate prior findings of a close link between gamma-band power and the BOLD fMRI signal. The authors also take care to ensure that the pattern of correlation between RVT and fMRI is distinct from artifacts resulting from breathing-induced static field changes as well as from CO2-related effects of breathing on the BOLD signal. The findings are clearly presented throughout the paper.

I believe that additional information would help to more strongly support the main claim, i.e., that the reported RVT-fMRI correlation pattern is of neuronal origin. One analysis that supports this claim is that in the lightly anesthetized state, regressing out the gamma-band power signal considerably reduced correlations between RVT and fMRI. However, the more direct test of this possibility involves the experiment in which neural activity across the brain is silenced (isoelectric state) while respiration is artificially maintained. The resulting disappearance of correlation between RVT and fMRI data points to the neuronal nature of RVT-fMRI correlation. Yet, since the amount of temporal variation in RVT during the iso-electric state was not reported, it was not clear whether RVT itself also exhibited less temporal variation in the isoelectric state. Since respiration was maintained by a ventilator in the isoelectric state, I wondered if the respiration depth and volume was more constant compared to in the lightly anesthetized state, in which it is mentioned that spontaneous respiration occurred. Importantly, the authors do mention that the respiration patterns were visually similar between these conditions (Fig. 5C and line 219), which is very promising, but quantification of RVT properties would be important to provide as well.

Reviewer #3 (Public Review):

The study presents interesting new data on the role of the PTPRK D2 pseudophosphatase domain recruiting determining substrate specificity. The paper also demonstrates the utility of predicted structural models, an aspect that has been nicely integrated into this study. However, many open questions remain and additional experimental data should be provided to experimentally confirm the proposed substrate recognition model.

In particular:<br /> 1) Validation of reagents: The authors generated a pY1230 Afadin antibody claiming that (page 6) "this new antibody is specific to tyrosine phosphorylated Afadin, and that pY1230 is targeted for dephosphorylation by PTPRK, in a D2-domain dependent manner". The WB in Fig 1B shows a lot of background, two main bands are visible which both diminish in intensity in ICT WT pervanadate-treated MCF10A cell lysates. The claim that the developed peptide antibody is selective for pY1230 in Afadin would need to be substantiated, for instance by pull down studies analysed by pY-MS to substantiate a claim of antibody specificity for this site. However, for the current study it would be sufficient to demonstrate that pY1230 is indeed the dephosphorylated site. I suggest therefore including a site directed mutant (Y1230F) that would confirm dephosphorylation at this site and the ability of the pY antibody recognizing the phosphorylation state at this position.<br /> 2) The authors claim that a short, 63-residue predicted coiled coil (CC) region, is both necessary and sufficient for binding to the PTPRK-ICD. The region is predicted to have alpha-helical structure and as a consequence, a helical structure has been used in the docking model. Considering that the authors recombinantly expressed this region in bacteria, it would be experimentally simple confirming the alpha-helical structure of the segment by CD or NMR spectroscopy.<br /> 3) Only two mutants have been introduced into PTPRK-ICD to map the Afadin interaction site. One of the mutations changes a possibly structurally important residues (glycine) into a histidine. Even though this residue is present in PTPRM, it does not exclude that the D2 domain no longer functionally folds. Also the second mutation represents a large change in chemical properties and the other 2 predicted residues have not been investigated.<br /> 4) The interface on the Afadin substrate has not been investigated apart from deleting the entire CC or a central charge cluster. Based on the docking model the authors must have identified key positions of this interaction that could be mutated to confirm the proposed interaction site.<br /> 5) A minor point is that ITC experiments have not been run long enough to determine the baseline of interaction heats. In addition, as large and polar proteins were used in this experiment, a blank titration would be required to rule out that dilution heats effect the determined affinities.

Reviewer #3 (Public Review):

This paper uses transient dark exposure to induce plasticity in the adult visual cortex. It shows that transient dark exposure in the adult mice has opposing effects at the single neuronal level versus the population level. At the population level, the stimulus representation is degraded following dark exposure but rebounds back to normal within 8 days of light re-introduction. Thus, dark exposure does not have a lasting negative impact on the visual cortex. Unexpectedly, at the single neuronal level, following dark exposure a fraction of neurons show more stable responses and higher correlations among pairs of neurons. It is inspiring to hypothesize that this fraction of neurons may form a plastic substrate for representation of complex natural scenes.

Strengths:

The paper uses a combination of single neuron and population analyses to identify the effects of transient dark exposure on visual responses in the adult mouse visual cortex. It succeeds in identifying degradation of stimulus representation at the population level following dark exposure, and stabilization of visual stimulus preference at the single neuron level as well as stabilization of stimulus correlations among pairs of neurons. This success is in part due to an impressively large set of simple visual stimuli used (180 different stimuli). This large set allows the authors to identify even small changes in stimulus preferences at the single neuronal level.<br /> This paper uses transient dark exposure to induce plasticity. An alternative and commonly used method to induce plasticity is monocular deprivation. This paper shows that at the single neuron level, the effects of transient dark exposure are different from the previously reported effects of monocular deprivation. This is an important finding for the field.

Weaknesses:

The analysis methods used are thoughtful and complementary. The statistical tests are mostly performed on visual responses pooled across 6 mice. These statistical tests support the claims of the paper. However, we are left wondering whether the effects identified would also be significant for visual responses of each individual mouse.

Reviewer #3 (Public Review):

In a previous study, the authors screened a genome-wide CRISPRi library for sensitivity to a panel of antibiotics. One of the hits on this screen was found to be an essential adenylate cyclase, Rv3645. Rv3645 is a multidomain adenylate cyclase (AC), membrane-associated, and carries a HAMP domain (often associated with two-component signal transduction pathways). Surprisingly, Rv3645 was the only AC exhibiting this broad sensitivity to antibiotics. These observations were validated using a knock-down strategy and were also shown to be complemented by expressing a CRISPRi-resistant allele. To confirm that the sensitivity is not due to weakened cell walls or increased permeability of the cell to antibiotics, they measured the uptake of vancomycin using fluorescently conjugated vancomycin and by mass-spec. Interestingly, the essentiality and drug sensitivity of rv3645KD was found to be dependent on long-chain fatty acids. When Mtb was cultured in absence of fatty acids, rv3645 was no longer essential which allowed them to construct an rv3645 deletion strain. To determine the role of AC in lipid metabolism, the authors carried out a suppressor screen to identify mutants that reversed the fatty-acid phenotype. Mutants were identified in fatty acid transporter genes and in a cAMP phosphodiesterase gene, rv1339. The role of cAMP levels in mediating fatty acid metabolism and antibiotic resistance was further confirmed through the measurement of cAMP levels using mass spectrometry and expression of an enzymatically inactive mutant of rv3645.

Overall, this is a very elegant study that uses cutting-edge bacterial genetics to address the role of cAMP in mycobacterial pathogenesis. All the experiments have been well designed with the necessary controls and rigor. The studies clearly establish the role of cAMP, especially mediated through rv3645 in fatty acid metabolism and resistance against different classes of antibiotics. While the upstream signal is still unknown this can be for future follow-up studies.

Reviewer #3 (Public Review):

This paper presents a rigorously performed series of studies to improve the ability of the PhIP-seq method to discover autoantibodies against peptide antigens that span the whole peptidome at scale, and increase the ease of validation and definition of disease specificity. The paper is an extension of a recent paper from the DeRisi and Anderson groups done on APS1 patients, which defined and validated a novel series of tissue-specific autoantigens in APS1. The current studies show that the authors can find the antibodies they previously defined, and using larger numbers of disease and control samples, can expand some what they detect. They then use the new method to look at multiple additional processes in which autoimmunity has been demonstrated/postulated.

The dataset may be of use to others interested in defining novel autoantibodies. The findings really did not share significant new insights into the processes they studied,. As the authors note, they were unable to detect the antibodies (~10% of patients) recognizing type I IFNs in severe COVID-19, where these had been demonstrated effectively using ELISA previously. Unlike APS1, where their findings about uncommon tissue specific autoantibody responses across a population with known genetic deficiency and heterogeneous phenotypes could really illustrate the power of the method and approach, that elegance and powerful and novel conclusion is not as evident here.

Reviewer #3 (Public Review):

The authors have determined a range of conformations of the high-affinity prokaryotic K+ uptake system KdpFABC, and demonstrate at least two novel states that shed further light on the structure and function of these elusive protein complexes.

The manuscript is well-written and easy to follow. The introduction puts the work in a proper context and highlights gaps in the field. I am however missing an overview of the currently available structures/states of KdpFABC. This could also be implemented in Fig. 6 (highlighting new vs available data). This is also connected to one of my main remarks - the lack of comparisons and RMSD estimates to available structures. Similarity/resemblance to available structures is indicated several times throughout the manuscript, but this is not quantified or shown in detail, and hence it is difficult for the reader to grasp how unique or alike the structures are. Linked to this, I am somewhat surprised by the lack of considerable changes within the TM domain and the overlapping connectivity of the K indicated in Table 1 - Figure Supplement 1. According to Fig. 6 the uptake pathway should be open in early E1 states, but not in E2 states, contrasting to the Table 1 - Figure Supplement 1, which show connectivity in all structures? Furthermore, the release pathway (to the inside) should be open in the E2-P conformation, but no release pathway is shown as K ions in any of the structures in Table 1 - Figure Supplement 1. Overall, it seems as if rather small shifts in-between the shown structures (are the structures changing from closed to inward-open)? Or is it only KdpA that is shown?

My second key remark concerns the "E1-P tight is the consequence of an impaired E1-P/E2-P transition" section, and the associated discussion, which is very interesting. I am not convinced though that the nucleotide and phosphate mimic-stabilized states (such as E1-P:ADP) represent the high-energy E1P state, as I believe is indicated in the text. Supportive of this, in SERCA, the shifts from the E1:ATP to the E1P:ADP structures are modest, while the following high-energy Ca-bound E1P and E2P states remain elusive (see Fig. 1 in PMID: 32219166, from 3N8G to 3BA6). Or maybe this is not what the authors claim, or the situation is different for KdpFABC? Associated, while I agree with the statement in rows 234-237 (that the authors likely have caught an off-cycle state), I wonder if the tight E1-P configuration could relate to the elusive high-energy states (although initially counter-intuitive as it has been caught in the structure)? The claims on rows 358-360 and 420-422 are not in conflict with such an idea, and the authors touch on this subject on rows 436-450. Can it be excluded that it is the proper elusive E1P state? If the state is related to the E1P conformation it may well have bearing also on other P-type ATPases and this could be expanded upon.

Reviewer #3 (Public Review):

The authors introduce an integrative platform for identifying small molecule ligands that can disrupt RNA-protein interactions (RPIs) in vitro and in cells. The screening assay is based on prior work establishing the MT bench assay (Boca et al. 2015) for evaluating protein-protein interactions in cells by utilizing microtubules as a platform to recruit and detect PPIs in cells. In the current manuscript, the authors adapted this methodology to evaluate small molecules targeting RNA-binding protein (RBPs) interactions with mRNA in cells. By combining the MT bench assay with computational docking/screening and ligand-binding evaluations by NMR, the authors discover inhibitors of the RBP YB-1, which included FDA-approved PARP-1 inhibitors. The impact of this work could be high given the critical roles of RNA-binding proteins in regulating the function and fate of coding and non-coding RNA. While the presented data are promising, the ability to generally apply this method beyond YB-1 and to RBPs in general remains to be addressed.

Reviewer #3 (Public Review):

Gicking et al. analyze the relation between DDB (retrograde motor complex) and different members of kinesin family. The authors directly linked DDB and kinesin-1, -2 and -3 using DNA linker. Consistent with previous force measurement, kinesin-1 can dominate DDB. On the other hand, it is an unexpected and interesting observation that kinesin-2 and -3 can withstand loads by DDB because these motors are sensitive to load and easily detach from microtubules under loaded conditions in optical tweezers experiments. The authors performed computer simulations and suggested that fast detachment in kinesin-2 and -3 can be antagonized by fast reattachment. The work will impact thinking about physical properties of kinesins under loaded conditions.

Weaknesses:<br /> (1) To show DDB-kinesin-2 relation, the authors analyzed KIF3A/KIF3A homodimers. This reviewer does not think KIF3A/KIF3A homodimers represent kinesin-2. The motor domain of kinesin-2 is a heterodimer composed of KIF3A and KIF3B in the cell. The authors have previously shown that properties of KIF3A/KIF3A homodimer are different from those of KIF3A/KIF3B (Andreasson et al., Curr Biol., 2015).

(2) While these in vitro results are interesting, physiological meaning of these findings is not very clear.

Reviewer #3 (Public Review):

The manuscript provides evidence that larvae are capable of operant, as opposed to classical, conditioning: optogenetic activation of serotonergic neurons after a larva bends in one direction increases the likelihood that it will bend in that direction again.

Furthermore, the manuscript shows that serotonergic neurons located in the ventral nerve cord are capable of inducing this associative conditioning. While dopaminergic and serotonergic neurons, notably the dopaminergic PAM cluster in the Mushroom Bodies, have previously been implicated in classical conditioning, where different subsets apply positive or negative valence, these data suggest that specific serotonergic neurons may also contribute to learned behaviors. Although the cellular and circuit mechanisms remain unclear, and the consequences of silencing these neurons in contexts where the larva might more naturally employ operant learning are not tested, this research suggests new areas for exploring the adaptive capacity of a powerful model organism.

Reviewer #3 (Public Review):

The authors demonstrate in mice that the amount of sleep is related to stress resilience, and specifically that increased sleep after stress exposure supports resilient behavior. The aims are achieved through an array of methodologies, which highly strengthens the conclusions of the work. The question of whether sleep is related to stress resilience is highly significant and in the current research, the authors tackle the questions by evaluating differences in sleep homeostasis in stress-resilient compared to stress-susceptible mice. To induce more stress-susceptibility, the authors challenge the mice with sleep restriction, and to induce more stress-resilience, the authors chemogenetically induce increased NREM sleep. Mechanistically the authors demonstrate that cortically mediated NREM sleep is sufficient to promote resilience. Despite the challenging nature of the technological approaches at hand, particularly in mice, the experiments are well-designed and the authors are commended for the execution of these studies.

It is very difficult to separate sleep loss and stress responses, as losing sleep is inevitably a stressful experience. The authors attempt to quell the notion that sleep restriction was also stressful to the animals by measuring fecal corticosterone, however, the measurements were from fecal pellets collected during an entire 24 hr period raises concerns that acute changes in HPA response may not be evident through this measurement. This is a challenging notion to tackle and deserves a bit more consideration.

Chemogenetic experiments induce a beautiful increase in NREM sleep at the expense of REM loss, yet all the animals treated with chemogenetic agents are resilient to social defeat stress. The authors conclude that because sleep restriction also reduced REM, yet opposite effects occur on social interaction, REM sleep is unlikely to be related to resilience. In this context, it would be beneficial to discuss the theories of sleeping to forget and sleeping to remember, and supporting literature that REM sleep is critical to the consolidation of memories, particularly upon stressful experiences.

Reviewer #3 (Public Review):

This study addresses fundamental aspects of the eco-evolutionary dynamics of highly fecund organisms experiencing huge mortality rates during early life stages. In such species, a mechanism called "sweepstakes reproductive success" (Hedgecock, 1994) has been proposed to understand the dynamics of recruitment, in which individual reproductive success shows high variance and skewed distribution. Sweepstakes reproductive success can be either neutral due to random environmental variation influencing the recruitment of reproducing offspring, or selective because genetic variation at particular loci influences the likelihood of successful recruitment. Unfortunately, empirical tests of sweepstakes reproduction remain scarce due to the difficulty of studying individual reproductive success directly, particularly in highly fecund marine organisms.

By analysing genome-wide genetic diversity data under different coalescent models representing alternative recruitment dynamics, this pioneering study specifically tests whether random or selective sweepstakes reproduction occurs in the highly fecund Atlantic cod. Using the classical Kingman coalescent and two multiple-merger coalescent models approximating random sweepstakes (the Xi-Beta-coalescent model) and selective sweepstakes (the Durrett-Schweinsberg model), the authors show that genetic diversity in the Atlantic cod genome is most likely shaped by pervasive selective sweeps of new beneficial mutations. The best-fit selective sweepstakes model is able to reproduce the main characteristics of the allele frequency spectrum of each Atlantic cod population, while alternative models include either random sweepstakes or other biologically plausible scenarios (i.e. historical demographic changes, cryptic breeding structure, and background selection) show a much poorer fit.

These findings have a broad impact on evolutionary genomics since they provide a new and exciting perspective on the choice of appropriate coalescent models for the study of highly fecund organisms that may experience high rates of selective mortality during early life stages. The low-fecundity low-variance reproductive success model classically used in evolutionary genetics may simply not apply in highly fecund organisms with skewed offspring distribution.

By confronting different alternative models of coalescence with genome-scale genetic diversity data, this work provides a roadmap for exploring fundamental processes at the crossroads between ecology and evolution. It highlights the importance of (i) understanding the potential impact of species-specific biological characteristics when inferring demography and selection from molecular data, and (ii) being aware of the potentially significant effects of unaccounted aspects of the data (e.g. variant misorientation, past admixture) on the interpretation of results.

Reviewer #3 (Public Review):

The manuscript by Ronzano et al presents a rigorous neuroanatomical study to convincingly demonstrate that there is no difference in the medio-lateral organization of flexor and extensor premotor interneurons. The study uses monosynaptic restricted transsynaptic tracing from ankle flexor and extensor muscles with several (4) strategies for delivery of the G protein complement and delta G Rabies virus, and additional (2) variations that consider titer and cre line. The authors went to great lengths here in attempt to replicate prior studies for which they had initial conflicting findings. Further, the experiments are performed in laboratories in four different locations. The variations on the Rabies and complement delivery, regardless of lab performing the experiment and analysis, all converge on the same conclusion. Aside from the primary conclusion, the paper can be used as a manual for anyone considering transsynaptic tracing as it details the benefits and caveats of each strategy with examples.

The initial conflicting results put the onus on the authors to demonstrate where the divergence occurred. The authors took a highly comprehensive approach, which is a clear strength of the paper. All of the data is fully and transparently presented. Standardizations and differences between experiments run or analyzed in each lab are well laid out. Figure 1 and Table 2 provide a great summary of the techniques and their limitations. These are also well thought out and discussed within each section of results.

The only thing missing is a likely explanation for the differences seen. Although the authors made several attempts to provide such explanation, the question remains - how did two groups who published independent studies using different strategies demonstrate flexor and extensor separation in the dorsal horn, when this study, using several strategies in multiple labs, show that the premotor neurons are in complete overlap? Additional small differences in methodologies could be identified which are not discussed and may provide potential explanations, but only for discrepancies in results of single techniques, not for all of the strategies used. The lack of reason for the discrepancy with prior studies despite the extensive efforts is unsatisfying, but, most importantly, the experiments were rigorously performed and the data support the conclusions presented.

Reviewer #3 (Public Review):

Zhao and Sharpe identified telocytes in the periodontium. To address their contribution to periodontal diseases, they conducted scRNA-seq analysis and lineage tracing in mice. They demonstrated that telocytes are activated in periodontitis. The activated telocytes send HGF signals to surrounding macrophages, converting M2 to M1/M2 hybrid status. The study implies that targeting telocytes and HGF signal for the potential treatment of periodontitis.

The significance of the study could be improved by authors testing if targeting telocytes or HGF signals could ameliorate periodontitis in the mouse model. The current form of the manuscript lacks the data that demonstrate the actual contribution of telocytes in the homeostasis of periodontium or progression of periodontitis.

Major comments:

1) I see the genetic validation of the role of telocytes or HGF signals are crucial to assure the significance of this manuscript. I recommend either of two experiments. a. testing the role of HGF signals by deleting the Hgf gene in telocytes. Using Wnt11-Cre; Hgf f/f mice, the authors could address the role of HGF signals in periodontitis. CX3CR1-Cre; cMet f/f mice will delete HGF signals in monocyte-derived macrophages. This will be another verification, but not sure if the PDL macrophages are derived from yolk sac or monocytes. b. measuring the contribution of telocytes in the homeostasis or disease progression. The mouse model could be challenging though, the system if achieved will be very informative. The authors could first check the expression of telocyte enriched genes, such as Lgr5 or Foxl1 reported previously in other tissue telocytes. Delete those genes under the Wnt1-Cre driver and check if telocyte lineage is removed. The system would be very useful for next-level study. DTA model could be an alternative, but Wnt1-Cre is vastly expressed in neural crest lienage.

2) This paper points out that the M1/M2 hybrid state of macrophages appears upon periodontitis. The authors could further characterize the hybrid macrophages by the expression of more markers, production of cytokines, and morphology. Need to clarify if this means some macrophages are in M1 state and others are in M2 state, or one macrophage possesses both M1 and M2 phenotype. Please conduct either FACS or immunofluorescence to demonstrate if one macrophage expresses both markers. Please introduce more information about the M1/M2 hybrid state of macrophage based on other present literature.

3) In the introduction part, the author lists several markers that can be used for telocyte identification, such as CD34+CD31-, CD34+c-Kit+, CD34+Vim+, CD34+PDGFRα+. Could authors explain why they chose CD34 CD31, but not other markers?

4) In figure 5g, I don't think the yellow color cell shows the reduction trend in the Tivantinib treatment group compared with a control group. Please validate the observation by gene expression analysis, WB, etc. In addition, please show c-Met+ cells level in the Tivantinib treatment group and control group.

Reviewer #3 (Public Review):

In this MS by Sefton et al., the authors investigate the role of HGF/MET pathway, as well as the cellular source of these molecules, during diaphragm development. In particular, the authors address the function of this pathway on muscle progenitors and phrenic nerve. They further provide evidence for the expression of HGF in pleuroperitoneal folds and for its requirement for muscle progenitor recruitment and maintenance during diaphragm muscle formation. This study is interesting and in general the results support the conclusions. The work could be improved by (1) providing appropriate controls for the role of HGF in the connective tissue and (2) linking the muscleless diaphragms and HGF to the hernia phenotype.

Reviewer #3 (Public Review):

This manuscript aimed to reveal the difference and similarity of sharp-wave ripples in awake vs. sleeping mice. To do this, the authors used wide-view voltage and glutamate activity imaging in awake head-fixed mice. The two-photon Ca imaging was applied to examine the spiking activity of the retrosplenial cortex.

They showed that the mean membrane potential and glutamatergic transmission of the neocortex's superficial layers were suppressed and enhanced, respectively, just after the sharp-wave ripples in awake mice, contrary to the same authors' previous findings in urethane-anesthetized and naturally sleeping mice. The retrosplenial cortex was most strongly modulated in membrane potential and glutamatergic transmission by awake sharp-wave ripples. The authors also found two groups of retrosplenial cortical neurons, whose spiking activity, measured by Ca dynamics, was suppressed and enhanced by awake sharp-wave ripples. These findings revealed the critical difference between sharp-wave ripples during waking vs. sleep, which would impact the field of memory research.

This manuscript's strength is that it compares the dynamics of membrane potential and glutamate transmission using wide view imaging. Both experimental and analytical methods were appropriate and supported their main conclusions.

Reviewer #3 (Public Review):

The authors attempted to, and succeeded at, testing the recent hypothesis that the large theropod dinosaurs Spinosaurus was a fast and capable swimmer and diver.

The strengths of the paper are the extent of the analyses which address and test numerous aspects of the 'aquatic hypothesis' with some depth, though in places this needs a better explanation of the details of the process and organisation. The results do support the conclusions, though these should be more specific and clear and relate to the recent literature on Spinosaurus habits to say what is, and is not, possible/plausible based on their analyses.

Overall this is likely to be a major step forwards in resolving the biology of this animal (and kin) and add considerable depth to the discussion by adding new data and results.

Reviewer #3 (Public Review):

In the manuscript by Bosada et al, the authors present work identifying and interrogating two cis-regulatory elements at TBX5 associated with atrial fibrillation. Mouse models lacking both copies of either element, but particularly one in the last intron of the Tbx5 gene, referred to as RE(int), results in increased Tbx5 expression, changes to cardiac electrophysiology, and downstream gene expression changes. Of interest, RE(int) induces expression of Prrx1, also associated with atrial fibrillation, and compound mutants partially rescues the RE(int) phenotype. Overall, this paper is of interest and advances our understanding of TBX5 in atrial fibrillation risk in humans. Critically, this study focuses on the impact of risk SNPs, which increase TBX5 expression in patients, while previous studies involving TBX5 in atrial fibrillation have focused on decreased expression.

The authors' work presents the following major claims:<br /> Figure 1. Identification of two, independently segregating risk regions at TBX5, which are conserved in humans and mice with predicted cis-regulatory functions termed RE(int) and RE(down).<br /> Figure 2. Homozygous RE(int) and RE(down) mutants, with a particular focus on RE(int) mutants, resulting in increased expression of Tbx5. The functional SNP appears to be rs7312625 A>G.<br /> Figure 3. Homozygous RE(int) mutants demonstrate several cardiac electrophysiological changes consistent with increased atrial fibrillation risk in humans.<br /> Figure 4. Cellular electrophysiology of RE(int) mutant cardiomyocytes demonstrates additional supportive changes to explain whole organ phenotypes presented in Figure 3.<br /> Figure 5. Transcriptional profiling of RE(int) and RE(down) homozygous mutants demonstrates many significant differences from control samples that are suggestive of specific mechanisms disrupting cardiac electrophysiology, including calcium and potassium regulators and gap junctions.<br /> Figure 6. Homozygous RE(int) and Prrx1(enh) mutant alleles genetically interact and result in partial rescue of phenotypes from RE(int) alone.

Reviewer #3 (Public Review):

Experimental and computational works have proposed that neurons in the hippocampus represent a predictive map of the environment called the successor representation. This theoretical study examines how plasticity in a model network of recurrently connected neurons can lead to such a representation. The main conclusion is that any plasticity rule that encodes transition probabilities in synaptic weights gives rise to the successor representation at the level of neural activity. This fundamental theoretical insight gives additional credibility to the idea that the hippocampus can implement the successor representation.

Strengths:<br /> - elegantly designed theoretical study<br /> - very clear writing that progressively introduces the main result and argues for its generality<br /> - comparison of the model with data in a random foraging task

Weaknesses:<br /> - certain technical choices need additional motivation

Reviewer #3 (Public Review):

The present study is a comparison of brain magnetic resonance imaging (MRI) of mice who developed in an enriched environment laboratory environment, in which some mice become habituated while other mice maintain active exploration of the environment over time. Between these groups, differences are shown in the pattern of correlations between brain regions of interindividual variability, which may indicate differences in brain connectivity or other shared maturational processes between regions. Because the mice are genetically inbred and have the same shared environment, these differences are attributed to individual-level differences in environment and behavior, which are extremely difficult to isolate in non-laboratory settings.

My comments are focused on aspects of the paper that overlap with my area of expertise which is human brain MRI methods. The strengths of the paper include the unique environmental paradigm that provides support for important hypotheses about individual-level variation. The imaging methods are rigorous and sound, and there is a nice convergence with human work. The application of structural covariance is interesting. The weaknesseses of the paper are that the writing could be clearer. Alternative explanations for structural covariance and alterations in "down roamers" should be more fully considered. The statistical approaches could be more rigorous in places. The areas of novelty relative to past work should be more explicitly articulated.

Reviewer #3 (Public Review):

Lillvis et al present a new method for quick targeted analysis of neural circuits through a combination of tissue expansion and (lattice) light sheet microscopy. Three color labeling is available which allows to label neurons of a molecularly specific type, presynaptic and/or post-synaptic sites.

Strengths:<br /> - The experimental technique can provide much higher throughput than EM<br /> - All source code has been made available<br /> - Manual correction of automatic segmentations has been implemented, allowing for an efficient semi-automatic workflow<br /> - Very different kinds of analyses have been demonstrated<br /> - Inclusion of electrical connections is really exciting, what a great complement to the existing EM volumes!

Weaknesses:<br /> - Limitations of the method are not really discussed. While the approach is simpler and cheaper than EM, it's still important to give the readers a clear picture of the use cases where it's not expected to work before they embark on the journey of acquiring tens of terabytes of data. Here are just a few examples of the questions I would have if I wanted to implement the method myself - I am a computational person and can easily imagine my "wet lab" colleagues would have even more to ask about the experimental side:

-- It is not very clear to me if the resolution of the method is sufficient to disentangle individual neurons of the same type. It has been demonstrated for a few examples in the paper, but is it generally the case? Are there examples of brain regions/neuron types where it wouldn't be possible? If another column was added to the table in Figure 1, e.g. "individual neuron connectivity", EM would be "+", LM "-", what would ExLLSM be?<br /> -- Similarly, the procedures for filling gaps in the signal could result in falsely merged neurons. Does it ever happen in practice?<br /> -- How long does semi-manual analysis take in person-hours/days for a new biological question similar in scope to the ones demonstrated in the paper?<br /> -- How robust are the networks for synaptic "blob" detection? The authors have shown they work for different reporters, when are they expected to break? Would you recommend to retrain for every new dataset? How would you recommend to validate the results if no EM data is available?

Reviewer #3 (Public Review):

Almedia and Macklin sought to characterize oligodendrocyte behavior at the earliest onset of myelination in the central nervous system. By sparsely labeling oligodendrocytes with transgenic fluorescent reporters in zebrafish larvae, they show that oligodendrocytes in the dorsal and ventral spinal cord have characteristic numbers of sheaths and sheath lengths. With impressive and technically laborious time-lapse imaging, they demonstrate that oligodendrocytes repetitively sample axon segments before stabilizing a nascent sheath, with most (~90%) immature sheaths failing to stabilize. They quantify differences in dorsal and ventral oligodendrocyte sampling and convincingly show that dorsal oligodendrocytes form more sheaths due to increased sampling relative to ventral, with similar rates of sheath retraction. Finally, the authors conclude that Rab5 and Rab11 promote myelination locally and cell-autonomously in oligodendrocytes, showing specifically that Rab5 is critical for stabilization of nascent sheaths but is dispensable for sampling. Altogether, the authors provide novel and detailed visualization of early myelin sheath development by oligodendrocytes.

Strengths:<br /> This is one of the first studies to closely examine early oligodendrocyte behavior at high resolution and adds to a body of work showing how oligodendrocytes initiate and maintain myelin sheaths. The authors find that sampling is widespread while most immature sheaths destabilize. This is an intriguing finding, as sampling is likely an energetically intensive process, but its prevalence in wild-type animals suggests that it is an important part of development.

The authors' claims are substantiated by technically challenging mosaic labeling experiments that monitored individual oligodendrocyte development over the course of days. Importantly, the authors have measured the sheath accumulation and loss phase for a single oligodendrocyte over the course of several days, and can pinpoint when individual sheaths are maintained with high resolution.

The imaging acquisition and data analyses are thorough, and the labor-intensive nature of the experiments is commendable. The authors carefully use statistics to validate their conclusions, including power analyses to determine appropriate sample size.

Limitations:<br /> Prior studies suggested that the potential for each oligodendrocyte to produce myelin sheaths is at least partially dependent upon the diameter of axons enwrapped. While axons are labeled to demonstrate ensheathment, axonal diameter is not measured, and it is unclear whether dorsal and ventral oligodendrocytes behavior could be explained by regional or individual differences in axon caliber.

Other recent studies suggest that early myelination is driven by axonal factors as much as oligodendrocyte-intrinsic factors. For instance, neuronal activity stabilizes myelination for a subset of early-born neuronal types. Because of the sparse labeling techniques and focus on oligodendrocyte behavior, it is unknown how or whether axonal subtypes and activity influence early oligodendrocyte sheath sampling and stabilization.

While the authors provide a tantalizing suggestion that early oligodendrocyte sampling primes axonal segments for myelination, it is not tested directly here. Thus, the paper does not address why, or even if, repeated sampling is important in development.