Reviewer #3 (Public Review):

Han et al. present important insights into the effect of interventions on the regional importation and within-country spread of SARS-CoV-2 variants. The authors combine phylogenetic and epidemiological approaches to study the introductions and spread of SARS-CoV-2 variants in The Netherlands. The manuscript is clear, concise, and well-written.

1. The main focus of the study is on the effect of international travel restrictions, but these restrictions are not well defined. Moreover, the effect of travel restrictions cannot be distinguished from other restrictions and interventions that were enforced. It seems more appropriate to focus the paper on the effect of collective interventions on SARS-CoV-2 introductions and spread, rather than focusing on international travel restrictions.

2. Most introductions originated from other European countries and it would be valuable to perform a more in-depth analysis at the country level to understand patterns of introductions within Europe.

3. The authors conclude that robust surveillance in regions of early spread is important for variant detection and outbreak control. Given the retrospective nature of this study (the studied variants have mostly disappeared after the emergence of omicron), it would be good to further discuss how future outbreak control can be achieved in a timely manner.

Reviewer #3 (Public Review):

The authors describe a new mechanism by which an antimicrobial cytokine, IFNg, restricts the growth of the intracellular pathogen Chlamydia trachomatis. IFNg antagonizes the expression of c-myc, a transcription factor associated with cellular growth and metabolism. The authors previously showed that Chlamydia activates the expression of c-myc to promote bacterial replication. By inhibiting c-myc activity, IFNg limits the ability of Chlamydia to replicate. This constitutes a potentially new mechanism of pathogen restriction by IFNg. Major strengths of the study include the use of engineered cell lines to dissect the role of c-myc in Chlamydia replication and how metabolite restriction in the context of IFNg limits Chlamydia replication. Another strength is the use of Chlamydia mutants to discover that depletion of the amino acid tryptophan, which was believed to be critical to the anti-Chlamydia activity of IFNg, is not. Weaknesses of the study include reliance on quantification methods that may not be as accurate as stated and the tendency of some correlations to be stated as causal.

In general, the study is of high significance especially if some of the findings related to a c-myc-IFN axis and the regulation of metabolism can be extended to the controls of other pathogens.

Reviewer #3 (Public Review):

Understanding the relevance of skewed X-Chromosome Inactivation (XCI) in women disease susceptibility and development is an intriguing open question. In this manuscript entitled "Age acquired skewed X Chromosome Inactivation is associated with adverse health outcomes in humans" Roberts et al. attempt to characterize this relationship by assaying skewed X-Chromosome Inactivation in >1.500 females from the TwinsUK population cohort. The authors reported an association between skewed XCI and increased cardiovascular risk across the tested population. This association is reinforced by a twin study based on age matched twin pairs discordant in their degree of XCI skewing. This approach is indeed powerful as it controls for age in predicting the cardiovascular disease risk score. The authors also found an association between skewed XCI and a haematopoietic bias towards the myeloid lineage. Finally, skewed XCI was shown to be predictive of future cancer incidence.

This area of research is timely and of great interest for the community. However, in my opinion, the conclusions of this manuscript are not fully supported by the presented data and some aspects of the data analysis and results need to be extended.

Reviewer #3 (Public Review):

Ostermeyer et al. investigated the geometry of plasmodesmata in the vasculature of plant roots, and more specifically in the region where phloem is unloaded into the root tip. They developed a protocol based on transmission electron microscopy to image these plasmodesmata in 7 species of flowering plants, 2 monocots and 5 dicots. In all species, they found funnel-shaped plasmodesmata connecting sieve elements to neighbouring cells. They reconstructed typical 3D shapes of plasmodesmata (including the desmotubule) and quantified this geometry and its variations across species. Finally, they modelled diffusion and viscous flow in plasmodesmata with funnel-like geometries, showing that funnel-shaped plasmodesmata have a higher diffusive and viscous conductivity than cylindrical plasmodesmata with a radius equal to the minimal radius of the funnel. This increase in conductivity is much higher for viscous flow than for diffusion, suggesting that phloem unloading mostly occurs by bulk flow. Based on these results, the authors propose that the funnel shape enables plasmodesmata to simultaneously function as sieves and allow fast flow of small molecules.

Strengths. First, Ostermeyer et al. convincingly show the existence of funnel-shaped plasmodesmata in the phloem-unloading region of the root of several species. Second, they use mathematical models to assess the function of these plasmodesmata in movement of solutes between cells. Third, based on these models, they demonstrate that tapering of plasmodesmata is most effective in increasing bulk flow between cells.

Weaknesses. All conclusions on diffusion and bulk flow also apply to plasmodesmata with a central widening (with biconical shape), raising doubts about the function proposed for the funnel shape. This shape may well be the mere result of developmental constraints associated with differentiation of sieve elements. Finally, it is unclear wether the reference geometry in the model should be a cylinder with the minimal radius of the funnel, as assumed by the authors, a cylinder with the average radius of the funnel, or a cylinder with the same volume as the funnel.

Reviewer #3 (Public Review):

In this manuscript, Woike et al, investigated and compared the molecular effects of the several N-terminal (SPN-Ank) missense mutations of SHANK3, mainly using biochemical and biophysical analyses. The authors also identified a novel missense mutation (L270M) in the AnK repeats in patients with an ADHD-like phenotype, further expanding clinical diversity caused by SHANK3 variants.

The major strength of this work is that the authors could identify or dissect two major effects of the N-terminal mutations; interference with the SPN/Ank interaction and reduced binding to catenin. Meanwhile, the major weakness is that most experiments were performed in HEK 293T cells with overexpression of the small fragment, not full-length, of Shank3. Whether the two distinct groups of missense mutations have different synaptic or functional phenotypes in neurons also needs to be further investigated to better support the authors' conclusions.

Reviewer #3 (Public Review):

The article by Garcia et al clearly describes a set of experiments establishing Yap as a novel regulator of DNA replication dynamics. Its characterization as both a RIF1 interaction partner as well as playing its own role in replication initiation will likely have a significant impact on the field, as currently little is known about how DNA replication during early embryonic cell divisions is regulated.

The authors aim to identify a non-transcriptional function of YAP through the use of the Xenopus in vitro replication system and Yap depletion. Strengths of the paper include the particularly appropriate use of the Xenopus in vitro replication system, as well as the combined use of Trim-Away and morpholino oligonucleotides to deplete Yap and Rif1. Moreover, these experiments were elegantly complemented by single-molecule molecular combing and in vivo studies. Identifying Yap as a novel regulator of DNA replication dynamics, the authors achieved their aim. Through characterization of Yap as both playing a role in replication initiation and as a Rif1 interaction partner will likely have a significant impact on the field, as currently little is known about how DNA replication during early embryonic cell divisions is regulated. A weakness of the paper is that some of the representative data does not appear to be very representative of the entire data set.

Reviewer #3 (Public Review):

By means of in vivo electrophysiology within the primary somatosensory hindlimb cortex in a thoracic SCI rat model, the authors tried to identify injury-induced, sensory deprivation-related, and cortical layer-specific changes in neuronal excitability and network plasticity in terms of recorded spontaneous and evoked activity.

Spinal cord transection, peripheral stimulation, electrode placement, and electrophysiological recordings have been performed according to widely established approaches and protocols and appropriate statistical tests were used for the analysis. A strength of the methodological approach is the small-scale assessment of functional cortical reorganization in the somatosensory cortex after SCI, addressing the different functional properties and connections of the distinct cortical layers before and after the injury. Moreover, cortical reorganization has so far mostly been studied in a period ranging from days to months after SCI. Investigating changes already acutely after the injury in this study might allow to predict long-term cortical reorganization and serve as a biomarker for functional recovery or associated sensory pathologies. Another major strength of the study is the consideration and analysis of thalamocortical connections when investigating layer-specific changes in neuronal network properties after sensory deprivation. The results are detailed and very nicely illustrated and presented in the figures.

One weakness of the study might be the missing data in a sub-acute or chronic phase after SCI which might shed light on the absent cortical changes in a subset of the animals. Such data could actually help to understand the truly different findings in the distinct subsets of animals immediately after the injury and therefore improve the generalizability of the results and conclusions.

By observing different neuronal and network properties of each layer being responsible for the specific changes in spontaneous and evoked activity in the somatosensory cortex post-SCI in their results, the authors were able to confirm the hypotheses. Their findings shed first light on layer-specific changes in somatosensory cortex activity induced by sensory deprivation upon complete thoracic spinal cord transection and mediated by corticocortical and thalamocortical connections as well as local hindlimb cortex networks. The presented methods and data might well serve as a fundament for future preclinical research on cortical reorganization already at the acute stage after SCI and the findings might serve as biomarkers of long-term alterations in neuronal network plasticity potentially being involved in the development of sensory pathologies after SCI such as neuropathic pain.

Reviewer #3 (Public Review):

In this paper, the authors considered how prestimulus alpha oscillations affect stimulus processing and perception, a classic problem that has attracted many prior studies. The authors introduced some manipulations to prime the subject and to modulate decision criterion. The main findings are that low prestimulus alpha is better for stimulus processing and perception and prestimulus alpha phase has a role in stimulus processing and perception.

This work, like similar studies in the past, proposes a linear relationship between prestimulus alpha and stimulus processing and perception, namely, lower alpha is better for stimulus processing and perception. There are also reports of a nonlinear relationship. See, for example, Linkenkaer-Hansen K, Nikulin VV, Palva S, Ilmoniemi RJ and Palva J M (2004). Prestimulus oscillations enhance psychophysical performance in humans. Journal of Neuroscience 24:10186-10190. There should be some discussion on which of the two relationships are theoretically more plausible. It could be that detecting the nonlinear relationship is more difficult.

The work is rigorous and elegantly designed but although the authors frame the study in terms of decision making criteria and how these may shift on a trial-by-trial basis, the data analysis do not directly address these claims, instead focussing on the linear relation between alpha and perception. A more detailed analysis of the data centred on the decision variables may yield interesting new insights.

On a technical note, defining alpha range to be 6 - 14 Hz is too wide. Both high theta and low beta is included in this range. This makes the attribution of observed effects exclusively to alpha difficult.

Reviewer #3 (Public Review):

The authors performed several analyses to pinpoint whether and how top-down prediction can modulate performance. They used a combination of pharmacological experiments, high-density EEG, Bayesian modeling, and machine learning. In particular, the behavioural+pharmacological results seem to show strong and consistent effects. To investigate the mechanisms underlying this effect, the authors present several EEG analyses. However, given the analyses' complexity, some of the preprocessing steps and the results' interpretations are not fully clear. For this reason, in its current form, it is difficult to understand the implications of these findings.

Reviewer #3 (Public Review):

The major goal of this study is to distinguish the contributions of two classes of motor cortex neurons to the control of the forelimb during a learned task that requires variable-amplitude movement. The first class are pyramidal tract (PT) neurons that travel down the spinal cord and synapse near the motor neuron pools. The second are intratelencephalic (IT) neurons that project within the brain but do not exit it.

These are, slightly unusually, described by the authors as two different output pathways. PT neurons are obviously an output so that makes sense. IT neurons aren't normally thought of as an output (given that their projections, by definition, stay in the brain) but they project to the striatum. Although the basal ganglia have no spinal projection (a major influence is thought to be back to cortex via the thalamus) they connect to brainstem areas that do. Thus, the authors are comparing the contribution of a direct pathway (traditionally thought to directly drive movement) with that of a more indirect pathway (which could exert its influence both via recurrence onto the first pathway, and via other pathways).

Two major findings are:

1) the impact of PT inactivation is different from than that of IT inactivation. That difference argues that PT neurons are likely not the primary drivers of movement but perform some trajectory shaping function. More broadly it is argued that PT and IT pathways have dissociable impacts on behavior (e.g., one determining direction and the other determining amplitude).

2) PT neurons have responses inconsistent with the view that they are primary drivers of movement.

The first finding leads to the claim that PT and IT neurons have dissociable functions. E.g., controlling movement direction and amplitude. The evidence for a dissociation is present in their data but not necessarily compelling. Both PT inactivation and IT inactivation decrease movement amplitude and both alter the trajectory. The difference is that, with amplitude, the impact of PT inactivation is about 40% as large as that of IT inactivation. With trajectory, the impact of PT inactivation appears cleaner and tighter in time but isn't obviously larger (the claim that there is a differential effect rests on the 'one effect is significant and the other isn't fallacy). The results can certainly support the claim that effects are not the same. They are also surprising in the sense that the PT impact is smaller than expected. But the idea that they are cleanly differentiable (e.g., adjusting orthogonal aspects of movement) goes beyond what the data can support.

The claim that PT neurons aren't primary drivers of movement rests upon the second finding: PT neurons become less active around the time of the forelimb movement, and are instead more likely to be active around the time of the subsequent lick. This is clear in the data and is described by the authors as follows: "IT+ neurons, showed greater peri-movement activation than PT neurons, while PT neurons had greater activation timed to reward collection". Why not then conclude that these PT neurons ARE involved in movement, but that the movement in question involves facial movements during reward projection. After all, the authors back-label the PT neurons from the brainstem (where the motoneurons for the face reside) rather than the cord (where the motoneurons for the forelimb reside).

In summary, I found the dissociation between the effects of IT an PT inactivation to be not overly compelling. There are interesting differences, but they seem like ones of degree and not of kind. The argument that PT neurons are not primary drivers of movement is intriguing, and could be right (which would likely indicate a species difference between rodent and primate). But given that the neurons in question seem destined for the brainstem I found this hard to interpret. It seems like the neurons one would want to investigate are PT neurons that project to the cord, or neurons that project to the brainstem (and thence to the cord).

Reviewer #3 (Public Review):

This manuscript aims at better understanding why social groups with mixed or low kin structure occur in some cooperative species. The authors investigate the hypothesis that two alternative strategies of males (stay or disperse) can persist within a population if conditions fluctuate from year-to-year and the relative fitness payoffs of these strategies are environment-dependent. To do so, they use a highly commendable long-term dataset on superb starlings and investigate 1) the relationship between pre- or postnatal environment (group size, sex ratio and rainfall) and the likelihood to disappear from the study population around 1 year of age (assumed to be a dispersal event) 2) whether these presumed dispersal events occur more among individuals that have been seen alloparenting (suggesting that promoting alloparenting may be a way parents incentivize their offspring to stay in their natal group), 3) whether immigrants and residents differ in terms of various fitness measures and 4) whether differences in fitness may be environment dependent. The results suggest that 1) prenatal rainfall is linked to dispersal decisions, 2) alloparents are more likely to stay in their natal groups, 3) immigrants/dispersers and residents have similar fitness and 4) their fitness is condition dependent (fitness of one strategy is higher when prenatal conditions match the condition that favour this strategy). Therefore, this set of results are all in agreement with the initial hypothesis.

The manuscript addresses an important and interesting question, represents an important amount of work (both fieldwork and analysis), is very well written, transparent, easy to follow with well-designed and informative figures. However, as it stands the robustness of some results seem weak due to three major potential issues:

1) One cannot tell whether individuals categorised as "dispersed" did disperse or died. The authors do acknowledge this possibility in the Methods section but state that their result (more likely to disperse after high prenatal rain) is unlikely to be driven by mortality since the opposite relationship would then be expected. However, this is not necessarily true. For example, it has been suggested that females may decrease egg mass in poor environmental conditions if there is a greater positive effect of egg size on offspring survival under adverse conditions, and such patterns have been shown by several experimental studies.

2) Similarly, it is unclear whether individuals categorized as "not alloparenting" were seen but not alloparenting, or were simply not seen (potentially dead). It seems to be the latter, which could easily lead to a spurious relationship between probability to be not seen alloparenting/die and probability to disperse/die. This is particularly problematic since it was apparently particularly common for individuals to be missed for multiple breeding seasons before being seen again, especially if they did not act as alloparents.

3) Several potentially important random effects (season, cohort, or group identity effects) were omitted from most analyses. I understand that this was likely due either to issues with model convergence, and/or to odd error distributions (e.g. lifetime reproductive success, Tuljapurkar et al. 2020) pushing to use non-parametric tests. Nevertheless, ignoring these effects are likely to artificially increase the power of the analyses and therefore likely lead to overconfidence of the estimated effects.

In addition to these methodological issues, there are a couple of potential misinterpretations of the results:

4) The authors found no evidence for differences in lifetime inclusive fitness between Immigrants and Residents, suggesting that dispersers and residents have similar fitness. However, immigrants already dispersed and a cost of dispersal is highly expected. If such costs do exist, as suggested by the authors, then the dispersing strategy may have lower fitness benefits than the philopatric strategy. This needs to be acknowledged.

5) It is suggested that females breeding after low rain indirectly promote philopatry by promoting alloparenting (because of the relationship between these two measures). The authors do have data to investigate this (keeping in mind the above-mentioned potential biases). However, the data do not seem to support this statement (replacing "dispersal/non dispersal" status by the "alloparent/non alloparent" status in the analyses lead to non-significant effects of prenatal rainfall).

Reviewer #3 (Public Review):

Hutchinson et al present the second paper on scRNAseq from tsetse salivary glands to hit the literature over the last year. They use a different technique from Vigneron et al (PNAS 2020), and though the results are generally congruent, they focus on an aspect of biology missed by the previous authors. Specifically, they demonstrate that metacyclic VSGs, which are monoallelically expressed in metacyclics, are in fact co-expressed in pre-metacyclic cells, suggesting that the establishment of monoallelic expression is developmentally regulated (and proposing specific mechanisms as to how, based on that).

Specific comments:

Figure 1 sets up the technique and shows nice separation of cultured BSF, PCF and Ramos B cells. Two questions: is it concerning that VSG transcripts also show up (at lower intensities) in PCFs (and vice versa with EP - Figure 1E)? And a related question: what is the limit of detection (ie how many transcripts are reliably captured per cell and at what depth?) From the figure it would appear that the number of Ramos transcripts per cell is higher than per tryp (naturally) but does this mean better coverage for BSF and PCF transcripts? This is important, as a Ramos "spike" is used throughout to normalize amount of "ambient" RNA in the drop.

Figure 2 shows convincingly the subtypes of trypanosomes present in the salivary glands. And Figure 3 notes transcriptional profiles within those subtypes. Again it would be nice to know the absolute cutoff here (is InDrop capturing 50% of all transcripts? more? it's important, if this is not only meant as a validation pipeline but also a discovery pipeline, including discovery of intra-subtype heterogeneity, as was done in the gamete cluster - Figure 4).

Figure 5 is in many ways the most exciting. It is particularly interesting that the authors reprocess data from a recently published paper (Vigneron, O'Neill et al. (2020)) that uses 10X rather than InDrop, to arrive at the identical finding (missed by Vigneron et al) that the mVSG transcriptional program initiates early and becomes monoallelically restricted only in the final, metacyclic stage (a finding which they then also validate in vitro, with RBP6 over expression - that does not look as stunning, however, mostly due to lack of antibodies against all 6 mVSGs and the inability to do single cell proteomics).

Overall these are well done experiments, and the conclusions are justified. It is an important addition to the literature.

Reviewer #3 (Public Review):

The H3K36 histone methyltransferase MES-4 and the Polycomb system in C. elegans are essential for the viability of germ cells. The cause of inviability has not been determined, but it has been hypothesized that germ cell death in mutants may be due to a need for MES-4 to promote germline gene expression and prevente somatic gene expression. In addition, it had been previously observed that the MES-4/Pc system represses X chromosome gene expression in the adult germ line. Here the authors directly investigate the cause of early germ cell death in mes-4 and Pc mutants by conducting an impressive set of gene expression profiling experiments on dissected primordial germ cells and slightly later germ cells. The found that the most striking defect in primordial germ cells is the upregulation of X-linked gene expression. They demonstrated that decreasing X chromosome dosage in mes-4 and Pc mutants substantially rescues germ cell development, showing that upregulation of X-linked gene expression is a major contributor to germ cell death. These are important findings that further understanding of the roles of the key germline regulation systems in the C. elegans germline. The profiling experiments in primordial germ cells did not show downregulation of germ line gene expression, leading the authors to conclude that MES-4 is not needed for turn on of germline gene expression. However, as it was not clear whether wild-type germ cells had fully activated zygotic gene expression at the early larval time point assayed, and profiling in later germ cells did show reduced germline gene expression, this conclusion needs further support. It would also be of interest to determine whether upregulation of X-linked gene expression in the mutants occurs before or after the major wave of zygotic gene expression activation in germ cells.

Reviewer #3 (Public Review)

In this study, the authors introduce a novel food that requires handling time to five vervet monkey groups, some of which had previous experience with the food. Through the natural dispersal of males in the population, they show that dispersing individuals transmit behavioral innovations between groups and are often also innovators. They also examine muzzle contact initiations and targets within the groups as a way to determine who is seeking social information on the new food source and who is the target of information seeking. The authors show that knowledgeable adults are more often the target of muzzle contacts compared to young individuals and those that are not knowledgeable.

This is a very interesting study that provides some novel insights. The methods employed will be useful to others that are considering an experimental approach to their field research. The data set is good and analyzed appropriately and the conclusions are justified. However, there are several areas where the paper could be improved for readers in terms of its clarity.

1) It wasn't until the Discussion that it became clear to me that the actual physiological and personality traits of dispersers were being linked with innovation. From the Title, Abstract, and Introduction, it seemed as though the focus was on dispersing males bringing their experience with a novel food to a new group to pass it on. I think it needs to be made clear much earlier in the manuscript that the authors are investigating not only the transmission of behavioural adaptation but also how the traits of dispersers might may make them more likely to innovate.

2) Early in the paper on line 28, the authors state that continued initiation of muzzle contacts by adult females could have been an effort to seek social information. This is true but another interpretation is that females were imparting or giving social information. It seems important here and elsewhere (lines 322-323) to consider and report the target of these initiations. If these were directed at more knowledgeable individuals, it supports the idea that this was social information seeking. If muzzle contacts were directed to younger or unknowledgeable individuals, it would imply a form of teaching, which is possible but perhaps unlikely, so I think the authors need to be totally clear here.

3) The argument made on lines 344-350 needs more fleshing out to be convincing or it should be deleted. The link between number of dispersers, social organization, and large geographic range seems a little muddled. There are many dispersing individuals in species that are not typically in large multi-male, multi-female social organizations. Indeed, in many species both sexes disperse. Think of pair living birds where both sexes disperse and geographic range can be enormous. There are also no data or references presented here to show that species in multi-male, multi-female social organizations do have larger geographic ranges than those that are not in these social organizations. It seems to me that, even if this is the case, niche is more important than social organization, for instance not being dependent on forests to constrain much of your range.

Reviewer #3 (Public Review):

In this manuscript, the authors demonstrated that acute and chronic neuroinflammation was attenuated in hIL-37 transgenic mice, suggesting that recombinant IL-37 might be a therapeutic option for neuroinflammation. However, additional mechanistic insights may be required for better elucidating IL-37's effects in neuroinflammation.

1. In vitro phenotypes in Fig.1 were not closely connected with the following in vivo neuroinflammation phenotypes. Although three different neuroinflammation models were used, the detailed mechanisms for how IL-37 reduced neuroinflammation were not well addressed.

1.1 Fig. 3 and Fig. 5 showed reduced pro-inflammatory cytokine production in brain lysates. However, glial cells other than microglia, such as astrocytes and infiltrating leukocytes could be sources of inflammatory cytokines. There was no evidence supporting that reduced cytokine levels were intrinsic to microglia as modelled in the in vitro system. Thus, in vitro and in vivo phenotypes appeared somewhat disconnected.

1.2 In Fig. 5, IL-1β-induced neuroinflammation model was used to demonstrate the beneficial effects of IL-37 on cognition and synaptic function. Could injection of recombinant IL-37 display similar effects for LPS challenge model in Fig. 3 and Fig. 4?

1.3 Reduced IL-37-mediated neuroinflammation was shown in both chronic and acute models in the manuscript. Did microglia or other cells play a major role in those models? The detailed mechanisms regarding cell type contribution were largely missing in the manuscript.

2. Fig. 2 showed differential metabolomic profiling of microglial cells between WT and IL-37tg mice. However, there were no further evidence demonstrating that the metabolic function of microglial cells was indeed altered by IL-37 expression. It would be better to show the results of seahorse or other metabolic functional assays.

3. The models for acute neuroinflammation including LPS and IL-1β challenge were systematic inflammation. It might be reasonable to propose that reduced neuroinflammation was secondary effect to reduced inflammation response in periphery. In addition, in Fig. 3, would injection of LPS twice induce tolerance responses?

4. In Fig. 3 and Fig. 6, CD68 was used as activation marker for microglia. However, CD68 expression by itself is not enough to define microglia to be in the activation state. The phenotypic changes of microglial cells would also depend on specific models used. Additional experimental evidence is needed for defining the reduced activation status of microglia in IL-37tg mice.

Reviewer #3 (Public Review):

This work addressed some of the limitations in the production of the CVS-N2c strain of the rabies virus. CVS-N2c exhibits lower cytotoxicity and more efficient transsynaptic spread than the more widely used SAD-B19 strain, but its use as a circuit tracing tool has been held back by its slow packaging process and low resultant titers. By demonstrating that rabies packaging cell lines do not affect retrograde labelling efficiency and by creating a pseudotyping cell line that can amplify pseudotyped virus from a small amount of starting material, Sumser et. al have achieved an improvement in the speed, titer, and native coat contamination of CVS-N2c preparations whilst generating a new set of viral vectors that will help to implement a range of circuit mapping tools.

While many of the results from N2c evaluation experiments shown here (including bicistronic rabies usage, time courses of functional characterisation with GCaMP/channelrhodopsin, Cre-OFF labelling) have been previously demonstrated in other N2c and SAD-B19 rabies studies, the suite of vectors described in the manuscript will serve as a useful resource for the community. However, some key aspects of these vectors, specifically the propensity for the starter AAV for off-target labelling, are not characterized.

1. The six DIO AAV vectors described here, and the Flp-dependent AAV-FRT-EF1a-TVA-2A-N2cG do not contain recombinase leak prevention mechanisms such as the "ATG-out" approach, where the initiating codon and Kozak sequence are moved outside of the recombinase recognition sites to reduce inverted ORF expression. Even with these measures in place, DIO constructs are prone to recombinase-independent reversion, proposed to occur during AAV production from spontaneous recombination (Fischer et al., 2019). This presents an issue for the sensitive TVA/EnvA system, where only a small amount of TVA expression can mediate off-target rabies infection in non-Cre expressing cells. The dilution of AAV vectors can have a strong effect on the amount of non-specific labelling (Lavin et al., 2020). As the bicistronic TVA-N2cG vectors used here do not allow for individual dilutions of TVA with respect to N2cG, which is required at higher expression levels for efficient transsynaptic spread, it is especially important to test these vectors for leak expression. A sensitive test for starter leak would be to inject the AAV and rabies virus in WT mice.<br /> 2. The manuscript reports the use of a bicistronic N-P system to express the optogenetic actuator ChIEF together with a fluorescence protein. While the results of the bicistronic experiments show that both proteins are successfully expressed, control experiments using other expression strategies would strengthen the claim that the bicistronic N-P system is superior.

Reviewer #3 (Public Review):

The dogma in the literature is that cohesin's role in chromosome function is completed by the onset of anaphase because the destruction of cohesin activity by artificial cleavage of its kleisin subunit at metaphase induces normal anaphase with proper chromosome segregation. The authors show that artificial cleavage does not destroy all kleisin functions. When the kleisin subunit is completely destroyed, the induction of anaphase in the metaphase arrested cells exhibit chromosome missegregation and chromosome bridges. These results suggest that cohesin has novel functions post metaphase cells. Consistent with this conclusion the authors show that a small fraction of cohesin is present on chromosomes after metaphase. Inactivation of this cohesin fraction in telophase leads to changes in chromosome structure. For the most part, the authors' conclusions are based upon well-designed experiments with clear results. Demonstrating a role for cohesin post metaphase would be an important contribution to the field of mitosis. However currently several important aspects of the work need clarifying, particularly the analysis of the Hi C results.

Reviewer #3 (Public Review):

In patients with AML and FLT2 ITD mutations, inhibiting FLT3 frequently reduces the number of leukemic cells in the peripheral blood, while the number of leukemic blasts in the marrow remains unchanged. The mechanisms behind this differential response remain uncertain.

In this paper, Park et al., show that the mTOR pathway is enriched in AML cells exposed to stroma compared to cells not exposed to the stroma. Combining mTOR and FLT3 inhibitors kills AML cells in the presence of stroma. As such, this paper offers potential mechanisms to explain the clinical problem of why FLT3 inhibitors frequently fail to eradicate marrow blasts while eliminating blasts in the peripheral blood. In addition, this paper provides the rationale for a future clinical trial combining FLT3 and mTOR inhibitors in patients with FLT3 mutated AML.

Strengths of the paper include the use of primary AML samples and extension into mouse models. Chemical and genetic approaches are used to validate some key findings.

Reviewer #3 (Public Review):

The authors use a large neonatal dataset to examine how development may occur differently based on whether on not the neonate spent that time in gestation or out of the womb accruing potentially accruing visual experience. In this manner, the authors hope to tease apart those aspects of development that are biologically programmed versus those that occur in response to experience within the visual cortex. They show structurally that cortical thickness is affected by postnatal experience while cortical myelination is not, and functionally they find regional differentiation present between visual areas at birth and that their connectivity changes with development and postnatal experience. The conclusions seem well supported by the data and analyses and provide some insight into which aspects of brain structure at birth are sculpted more by postnatal experience and which are more determined by endogenous developmental timelines.

The analyses are based on a large sample of infants, and the authors were careful to statistically separate which aspects of an infant's age, gestational or postnatal, are driving brain development, providing a deeper picture of infant brain development than previous publications. Overall, the findings seem well supported by the data as the analyses are relatively straightforward.

- Visualization of the data and findings could be improved, as a few figures are difficult to interpret without having to read the methods.<br /> - The acronyms regarding gestation, postnatal, and post-menstrual time are a little distracting. Please consider explicitly writing "gestational time" etc when referring to these numbers to improve readability.<br /> - Because the cortical ribbon of infants is so thin at birth, there seems to be a possibility that partial-volume effects could be more prevalent in less-developed infants and impact myelin metrics. If not modeled or estimated, it should at least be discussed.<br /> - Structural and functional development could be more formally compared using quantitative models if the authors want those points more strongly related; the two are only qualitatively discussed at present.

Reviewer #3 (Public Review):

In this study, the authors' goal was to accelerate biologically-detailed neuronal network simulations, by leveraging the computational efficiency and amenability to parallelization of modern artificial neural network architectures (ANNs). The general idea is to train an ANN on time series generated by traditional neuronal simulations, then provide new inputs and determine whether the generalization capabilities of the ANN allow it to predict the time series that would be generated by the original model with the same inputs.

Starting with a simple, single-compartment neuron model, the authors trained five different ANNs to reproduce/approximate the behaviour of the model and found that only one of these ANNs, a recurrent model containing convolutional layers, long short-term memory layers, and fully connected layers, which they termed the CNN-LSTM network, was able to satisfactorily reproduce both the subthreshold activity and the spiking activity (91% of spikes predicted correctly).

The authors then added complexity to the model and found that it generalized well to changes in synaptic weight, non-linear synapses, and changes in input patterns. Partial retraining of the upper layers allowed generalization to other changes (steady-state activation of K channels).<br /> Moving from single-compartment to multi-compartment neurons, the CNN-LSTM network was still able to accurately reproduce sub-threshold fluctuations, although the accuracy of spike prediction declined to 67%.

Applying the technique to circuit models, the authors performed a parameter-space mapping in a model of Rett syndrome. In all cases beyond single point-neuron models, the CNN-LSTM network running on a GPU system outperformed the NEURON simulator running on a single CPU. In the case of simulating many identical neurons (but with different inputs), and when simulating the small circuit model of Rett syndrome, the performance gain was of several orders of magnitude.

Strengths:<br /> Although ANNs have been used extensively in approximating the dynamics of neural systems, this has typically been at a much higher level of abstraction and a much coarser anatomical scale. I am not aware of any previous demonstration of using ANNs as a practical tool to approximate the behaviour of individual biological neurons and networks of such neurons (as opposed to using spiking neural networks to perform machine learning tasks, where there is a large literature). This manuscript demonstrates convincingly that this approach is a promising one, and provides a practical starting point for further research on this technique. The comparisons at the single neuron level are particularly thorough and well done, and (i) demonstrate that the trained network displays good generalization, and (ii) provide good evidence that the network has really learned important features of the underlying system.

Weaknesses:<br /> The comparisons at the network level are less convincing than for single neurons.<br /> Although this is not stated clearly, it seems that the simulations with 50 and 5000 neurons were for identical neurons differing only in their inputs. Given the variability of neuronal properties even within a specific cell type and the increasing representation of this variability in computational neuroscience models, it would have been valuable to know what the performance impact of incorporating such variability would be, in both the training and exploitation phases.<br /> Furthermore, much less detailed comparisons between the NEURON simulation results and the ANN results are given for the network models. For the Rett syndrome model, no results from the NEURON simulations are shown, neither at the single neuron level nor at the level of parameter maps, which makes it impossible to determine whether the ANN model is adequately reproducing the correct behaviour.<br /> For very large-scale simulations, spike communication is often the rate-limiting factor in simulations, rather than solving the equations for neuronal dynamics. Cortical pyramidal neurons typically receive around 10000 synapses, a much higher number than appears to have been used here. It remains to be demonstrated whether the advantage in computational speed is still an important factor in systems with very high rates of synaptic events, in particular for models that are too large to fit on a single GPU.<br /> It could be argued that the playing field was tilted towards the ANN, since the NEURON simulator cannot benefit from GPU parallelization at the present time. Using a GPU-capable simulator such as GeNN as an additional point of comparison would give a clearer picture of the strengths and weaknesses of the ANN approach.<br /> The Discussion section does not adequately discuss the limitations of the current study, nor does it sufficiently address the potential weaknesses of the ANN approach, although some potential limitations are mentioned.

In summary, the authors have shown that ANNs are a promising tool for greatly increasing the scope of what can be modelled with generally available computing hardware, reducing the bottleneck of supercomputer availability. Clearly further studies are needed to explore the utility of the approach in a broader range of real-world scenarios, but by providing a good description of the successful CNN-LSTM model, and by providing their source code in a public repository, the authors have provided a strong basis for others to test this approach in their own projects.

This study is likely to have a substantial impact, stimulating further work in (i) understanding the performance-accuracy tradeoffs of this approach in comparison to other GPU-based simulation methods, and (ii) understanding the modelling domain for which this approach gives adequate accuracy (does it break down for networks on the edge of chaos, for example?). Should the method live up to its promise, it could greatly accelerate progress in computational neuroscience.

Reviewer #3 (Public Review):

This interesting paper from Hendi et al. describes a novel mechanism governing synaptic tiling that depends on expression of a gap junction protein at the border between adjacent presynaptic domains of neighboring neurons. The authors define the role of innexin UNC-9 in establishing the spatial arrangement of synapses in adjacent C. elegans GABA motor neurons. They show that axonal tiling is controlled by Wnt signaling. However, synaptic tiling is preserved when axonal tiling is disrupted in egl-20/Wnt mutants. Synaptic and axonal tiling are both disrupted in egl-20;unc-9 double mutants, suggesting these two processes are controlled through distinct molecular mechanisms. The authors find that UNC-9 is localized to the border between axons of adjacent GABA neurons and provide evidence that the function of UNC-9 in tiling does not require its channel function. The experiments are made possible by the development of a new system for labeling adjacent GABA motor neurons that will also be of general use to the field. The studies rule out requirements for either gap junction activity or several other genes previously implicated in gap junction function/localization, but fall short of clearly defining mechanism. Instead, the study provides additional support for channel-independent structural roles of gap junctions in the nervous system.

The study's conclusions are generally well-supported by the data but more clarification is required in some areas:

1. Overlaps between DD5 and DD6 dendrites are not evaluated directly. The authors show the extent of labeling in the DD5 dendrite. This should be clarified.<br /> 2. The authors suggest UNC-9 establishes axonal tiling as early as L2 stage, immediately following DD remodeling. However, no data is shown for UNC-9 localization at this developmental stage. It would also be interesting to know whether UNC-9 performs a similar role prior to remodeling, or if UNC-9 itself undergoes redistribution during the remodeling process.<br /> 3. Based on the representative image, UNC-9 abundance appears reduced in unc-104. The authors should quantify.<br /> 4. The authors show the distribution of muscle NLG-1 mirrors that of RAB-3. While this suggests the altered distribution of RAB-3 reports on synaptic rearrangement, this conclusion would be strengthened by analysis of an active zone marker.

Reviewer #3 (Public Review):

This paper follows other excellent work from the Pincus laboratory detailing the molecular mechanisms of Hsf1 regulation and extending experimental observations into predictive mathematical models. Overall, the work is top-quality, however, the findings are incremental in nature with respect to our understanding of the HSR and refine existing models rather than break new experimental or conceptual ground. Additionally, the relevance of the non-fermentable carbon source growth phenotype for the 2XSUP35pr-SIS1 strain is unclear with respect to HSR regulation.

Reviewer #3 (Public Review)

In this study, the authors investigated cell-cell communication between perimysial cells and skeletal muscle progenitors during soft palate development in the mouse. The authors have previously reported on the development of this structure and here they propose that a TGF-β signaling and Creb5 act to regulate Fgf18, and this pathway regulates pharyngeal muscle development through the indicated cell populations. The study is of high quality, very nicely illustrated, and uses multiple approaches including inferences from single cell transcriptomics, validations on sections, and lineage-specific gene activations. In addition, the authors successfully optimized an organ culture system from thick sections to test locally the role of FGF signaling (bead implantation). The results largely confer with the conclusions and provide a valuable example of how subjacent cell populations cooperate to establish an embryonic structure.

Reviewer #3 (Public Review):

The authors characterize the role of voltage-gated sodium channel Nav1.1 expression in proprioceptors in the peripheral nervous system. They use genetically modified mice, pharmacological blockers, and electrophysiological methods to support their claims. Albeit it was known for a long time that Nav1.1 is expressed in the peripheral nervous system, Espino et al. here present a thorough characterization of its role in proprioception and show its importance for motor behaviour, proprioceptor function, and synaptic transmission in the spinal cord. Characterizing the sodium channel subtype's function is crucial for our understanding of the function and dysfunction of the nervous system and to potentially develop new therapeutic approaches.

Reviewer #3 (Public Review):

The manuscript reports that in vitro fertilization (including in vitro culture) of mouse embryos seemingly originates metabolic alterations probably caused by enhanced oxidative stress compared to in vivo development. Such alterations apparently increase anaerobic glycolysis, as evidenced by altered pH and lactate levels, and remain after birth, as evidenced by altered protein abundance of MCT1 and LDHB.

The manuscript concludes that IVF alters embryo metabolism, increasing oxidative damage and glycolytic activity. The topic is interesting but I consider that the conclusions are not well supported by the experiments:

1) In vivo generated blastocysts are analyzed at a more advanced developmental stage than their in vitro counterparts as evidenced by their increased cell number (70 vs. 50 cells). In this regard, the developmental timing when in vitro generated blastocysts are collected is undisclosed in the Materials and methods. This has an obvious effect on all experiments as the differences observed may be stage-specific rather than IVF vs. in vivo.

2) Several methods are not reliable to quantify the parameters analyzed. For instance, determining protein content by immunofluorescence has been largely shown to be misleading as immunofluorescence can be affected by multiple parameters. Intracellular pH was also analyzed by an assay also based on immunofluorescence, which can also be affected by embryo size (the blastocoel is a call-devoid cavity). These analyses are not reliable.

3) Identifying proteins and metabolites in such small samples is technically difficult and error-prone, requiring validation by alternative techniques.

4) Given the small size of these embryos (~80 µm diameter), it is unclear how they can alter significantly the composition of 500 µl of medium (106 their own volume).

5) The metabolic changes observed in the offspring lack a mechanistic explanation.

Reviewer #3 (Public Review):

This manuscript develops new approaches to species assignment in Anopheles, using kmer-based similarity metrics and a variant auto-encoder (VAE) to overcome ambiguity in sequence alignment between divergent lineages and the complex relationships between lineages in this group. Overall this manuscript is well written and its claims are well supported - I feel it will be of substantial utility to the mosquito research and broader ecology and evolutionary biology communities.

The authors demonstrate that applying kmer-based similarity with nearest-neighbor based assignment across their amplicon panel can successfully assign samples to coarse-grained taxa, but that this approach has difficulty differentiating more subtly differentiated groups like the Anopheles gambiae complex. They subsequently develop a VAE that successfully differentiates most samples in this complex, and assign new samples within the convex hull defined by the samples of a given group. This approach is successfully applied to a large number of samples from Burkina Faso and Gabon, assigning most samples unambiguously and flagging outlier samples for further genome-wide analysis. These case studies demonstrate the utility and scale-ability of this approach.

It's not entirely clear from the manuscript how much better (in quantitative terms) this approach is compared to the earlier non-kmer approach or possible alternatives, though it certainly seems to perform much better than the previously published method (Makunin 2022). The approach itself is clearly explained and transparently documented, and it appears to be well suited to the goal of assigning very large numbers of samples accurately at a low cost.

Reviewer #3 (Public Review):

The finding that wg is regulated in muscle by diet and that a wnt signaling pathway in neurons is involved in diet-induced branching is intriguing. However, the link between these players and Akt is quite tenuous, and not convincing. In order to build on the nice finding that wg from muscle impacts neuron branching through Ror, it will be critical to analyze the downstream components of the pathway more critically.

Reviewer #3 (Public Review):

This work examines the interaction(s) between several cell shape determining proteins and a bactofilin protein (CcmA) in the spiral-shaped bacterium Helicobacter pylori. The study utilizes a nice mix of methodologies and an overall reductionist approach to examine how various domains of CcmA influence its ability to affect cell wall curvature, homo- and hetero- protein interactions, and the regulation of a cell wall hydrolase enzyme. The localization study along with an in-depth analysis of the Gaussian curvature intrinsic to the various mutant strains examined is particularly stunning, and this approach would be very beneficial to others in the field.

Strengths:

- The author's approach to investigating the contributions of various domains of CcmA to their previously identified phenotypes is excellent.<br /> - The TEM filament phenotypes are striking and convincing.<br /> - The use of co-IP was utilized well by the authors throughout the study and was beneficial in moving the study forward.<br /> - The cell curvature analysis and circular dichroism studies were well-conducted.<br /> - The computational rigor present in the CcmA localization study is commendable, and the data convincing.

Weaknesses/Concerns:

- In their model, the authors do not comment on how the N-terminal domain might effectively regulate the interactions of CcmA and Csd7 (assuming direct interactions exist).<br /> o Despite generating a nice assortment of N-terminal truncation mutants, the authors do not utilize them to demonstrate that the N-terminus is actually important for what they define it as (ie. MM, membrane-binding motif); this is a notable omission, given the authors' hypothetical model. Membrane association may potentially underlie the mechanism by which CcmA occludes the protein's association with Csd7.<br /> o Assuming that the N-terminal domain regulates the interaction between CcmA and Csd7, the authors do not address whether this regulation is transient or permanent.

- Despite significantly contributing relevant data to the study, there is some concern that the authors rely too heavily on their co-IP studies when developing their model. The co-IPs appear to have been designed to exclusively utilize a CcmA polyclonal antibody. This effectively prevents the investigation of the interactions of hypothetical complex members (specifically Csd5-Csd7) in the absence of CcmA. Given that the N-terminal truncation mutant shares the cell shape characteristics of a ccmA deletion mutant (curved rod; Yang 2019) rather than that of the csd5 deletion mutant (straight rod), this is a potential concern.<br /> o Additional controls or alternative approaches are needed to support the authors' overall conclusions that the interactions they describe are occurring directly between CcmA and Csd7/Csd5 (specifically investigating potential interactions between Csd5 and Csd7 in the absence of CcmA).

Are the authors' conclusions justified?<br /> The following conclusions appear to be well-supported by the authors' observations:

1) Both the bactofilin domain and N-terminal region of CcmA are required for helical cell shape.<br /> 2) The bactofilin domain of CcmA is sufficient for polymerization.<br /> 3) Deleting the N-terminal region of CcmA destabilizes the peptidoglycan-hydrolase Csd1.<br /> 4) Csd5 recruits CcmA to the cell envelope and promotes CcmA enrichment at the major helical axis.

The following conclusions require clarification, modification, or additional experimental support:

i) The bactofilin domain of CcmA is sufficient for interactions with Csd5 and Csd7.<br /> o The evidence presented indicates that the bactofilin domain is sufficient for all three proteins to associate together. It does not demonstrate that CcmA interacts directly with either Csd5 or Csd7.

ii) CcmA's N-terminal region inhibits interaction with Csd7.<br /> iii) Deleting the N-terminal region of CcmA increases CcmA-Csd7 interactions.<br /> o The evidence presented indicates that the three proteins only associate together when the N-terminal portion of CcmA is removed.<br /> o The competing hypothesis, that CcmA only associates with Csd5 and that it effectively prevents the association of Csd5 with Csd7 (likely via its N-terminal domain) is not acknowledged or disproven.

Reviewer #3 (Public Review):

In this manuscript, the authors have attempted to determine the molecular mechanisms underlying the resistance of an insect fungal pathogen Bauveria barbicans to cyclosporine A (CsA) and tacrolimus (FK506), known antifungal secondary metabolites that are also used extensively as immunosuppressing agents in medicine. By screening the random insertion mutant library of this pathogen, they identified the gene responsible for conferring resistance to CsA and FK506. The amino acid sequence of the gene identified it to be P4-ATPase, designated BbCrpa, which was hypothesized to be involved in vesicle-mediated transport. The identity of this gene as a CsA resistance gene was confirmed by demonstrating that disruption of this gene in B. barbicans confers susceptibility to CsA and FK506 and the expression of the wild-type gene in the BbCRPA knockout strain restores resistance to these compounds. In addition, expression of this gene in a plant pathogen Verticillium dahliae confers resistance to CsA and FK506.

The authors hypothesized that CsA/FK506 detoxification in the resistant B. barbiana strain used is through the BbCRPA-mediated vesicle transport process transporting these toxic metabolites to vacuoles through trans-Golgi (TGN)-early endosome (EE)-late endosomes (LE) pathway. To test this hypothesis, they employed a dual labeling system using 5-carboxyfluorescein fluorescently labeled CsA and FK506 and fusions of red fluorescent proteins (RFP) with BbRab5 GTPase (a marker for early endosomes), BbRab7 GTPase (a marker for late endosome) and pleckstrin homology domain of human oxysterol binding protein (PHOSBP) (a marker for trans-Golgi). By looking at the distribution of fluorescein-labeled CsA and FK506 in the wild-type and ΔBbCRPA cells using confocal microscopy, the authors have provided compelling evidence that these metabolites are transported to the vacuole. The co-localization of CsA with endocytic marker proteins also appears to be convincing for the most part. The co-localization of CsA with mRFP:: PHOSBP as shown in Fig. 2D seems less compelling. Also, in the confocal micrographs presented in Fig. 2, the distinction between early and late endosomes seems less convincing. It seems that there is significant heterogeneity in the early endosome and late endosome populations in the fungal cells.

The authors addressed the question of whether BbCrpa acts as a component involved in vesicle trafficking through the trans-Golgi-endosomes to vacuoles. Ten different eGFP-BbCrpa fusion proteins were constructed and shown to provide detoxification of CsA and FK506. The BbCrpa is localized to the apical plasma membrane and spitzenkorper region of the germ tube. The evidence for localization of BbCrpa in trans-Golgi and vacuole is clear. However, the experimental data shown in Fig. 3D-F claiming localization of BbCrpa in EEs and LEs are somewhat difficult for this reviewer to interpret. It is also not clear to this reviewer why the two FM4-64 staining patterns in Fig. 3C and Fig. 3F are strikingly different. The evidence for co-localization of the fluorescein-labeled CsA or FK506 with RFP-labeled BbCrpa in vacuoles (Fig.3 H and J) is convincing. Figs. 3L-M depicting dynamic trafficking of BbCrpa from TGN to vacuoles using time-lapse microscopy is interesting. In Fig. 3M, eGFP should be labeled eGFP::Drs2p. The authors have identified the N-terminal vacuole targeting motif in BbCrpa and shown that the C-terminal sequence from aa1326 to aa1359 is important for detoxification of CsA and FK506 in B. barbiana. In particular, the importance of three Tyr residues located in the C-terminal domain of the enzyme for CsA resistance is interesting.

Finally, the authors overexpressed BbCrpa gene in transgenic Arabidopsis and cotton plants to show that transgenic plants expressing this enzyme are protected from the toxic effects of the toxin cinnamyl acetate (CA) produced by the fungal pathogen Verticillium dahlia which causes vascular wilt disease in these plants. The data reported in Fig. 5A show that the transgenic Arabidopsis seed is able to germinate in presence of CA, whereas the nontransgenic control seed is not able to germinate. Evidence is presented that CA accumulates in the vacuole in transgenic Arabidopsis. However, the seedlings emerging from transgenic seeds are only partially protected from CA (Fig. 5A). It is also clear from the data presented in Figs. 5B-G that expression of the BbCrpa gene in transgenic Arabidopsis and cotton affords protection from infection by V. dahlia although no evidence for the expression of this gene at the protein level is presented. However, it seems likely that the transgenic lines only show delayed disease symptoms and are not truly resistant to this pathogen. The authors did not state clearly if Verticillium wilt disease resistance assays were performed on homozygous transgenic plants and their corresponding null segregants as negative controls. They also fail to provide evidence that the transgenic Arabidopsis and cotton challenged with the pathogen are able to grow to maturity and set viable seeds.

Reviewer #3 (Public Review):

Strength/novelty: The manuscript is overall well written and the authors have convincingly supported their main conclusion that Wnt3, which was previously shown to be upregulated in several GCs, is present on cytonemes produced by these cells. While Wnt proteins are known to utilise diverse modes and extracellular carriers for their secretion, this work indicates that cytonemes might be the preferential mode for the dispersion of Wnt3 by GC cells. This observation has important implications on our understanding of Wnt3-mediated activation of canonical signaling. Furthermore, they show that Flot2 levels are also higher in the GC, which further enhances Wnt3 levels on the cytonemes and consequently proliferation of the GC cells.

Scope for improvement: 1) Whether Flot2 manipulation specifically affects Wnts on cytonemes, or it could have a more general effect should also be considered. 2) Statistical analysis should be done more consistently. It is either missing for some samples or the comparison between samples is not given. 3) The part of the manuscript related to the analysis of Ror2 and Flot2 in cytoneme formation and PCP pathway could be better connected with the main theme of the work/title, which is mainly on canonical signaling by Wnt3. Perhaps, directly analyzing the effect of Ror2 manipulation on Wnt3 levels on the cytoneme could be useful.

Reviewer #3 (Public Review):

The neural correlates of voluntary action is one of the most intriguing questions in neuroscience. But, at the same time, studying it at laboratory settings is incredibly difficult. Here, Mitelut et al., have used an impressive range of methods and analyses approaches in mice to investigate the neural activity preceding voluntary action in mice. They showed that: 1) Self-initiated behaviour could be decoded up to 10s in advance. 2) Decoding works best when using information from across the cortex rather than any specific region of interest. 3) The neural dynamics become increasingly stereotyped prior to movement. 4) This latter effect becomes stronger with weeks of increased task performance. 5) Single trial variance decreases prior to movement, another sign of increase stereotypy in neural dynamics. 6) Random body movements do not influence the findings. 7) The pre-movement neural dynamics are in slow frequency range. The authors then went on to conclude that neural mechanisms underlying self-initiated voluntary action is preserved between mice and humans and suggested that mice could be an adequate model for studying the neural correlates of self-initiated action.

Using widefield calcium imaging in mice to study volition is novel and welcome but the great strength of this paper is its wide range of analyses approaches. Importantly, all these approaches, more or less, point to same direction: neural dynamics become increasingly stereotyped prior to movement. However, I am not convinced the findings reveal any specific property of 'voluntary action', nor it is clear what the authors refer to by 'voluntary action'. The authors first define volition as "the sense of control or agency over one's voluntary actions", but then they do not clarify how one can measure this 'sense of control' in rodents. They acknowledge, however, that it is not possible to measure the subjective experience of intention in mice but then insist on referring to the behaviour under study as "voluntary behaviour", even when it does not correspond with their own definition of volition. The question then is what makes the behaviour that is under study 'voluntary'. It is perhaps 'voluntary' because it is goal-directed. If this is the case, then the neural signal should be compared with another condition where action is not goal-directed but is habitual. This is, however, unlikely to be the case, because even though the authors did not directly examine the goal-directedness of the behaviour (by violating the value of the outcome or action-outcome contingency), they showed that the signal gets stronger with weeks of increased task performance, which actually makes the behaviour more stereotypical and habitual. Alternatively, the behaviour is perhaps 'voluntary' because it is self-initiated as opposed to externally triggered. If this is the case, then neural signal should be compared with another condition where action is performed in response to an external cue. The authors then should show that increase stereotypy in neural dynamics is only observed prior to self-initiated but not externally triggered actions. Only then one can conclude the presence of a specific signal preceding voluntary action.

Importantly, it is not enough to compare data with a random section of the neural activity. Therefore, to make any conclusion regarding the specificity of the neural signal to 'voluntary' behaviour, the activity needs to be compared with a similar condition where behaviour is not 'voluntary', whatever its definition is.

Reviewer #3 (Public Review):

The ATPase p97 (Cdc48 in yeast) unfolds ubiquitinated substrates with the help of its heterodimeric cofactor UFD1-NPL4 (U-N). Using the previously established CMG helicase complex as model substrate in a fully reconstituted biochemical assay, Fujisawa and Labib show that p97-U-N can efficiently disassemble the helicase complex only when it is modified with multiple, long ubiquitin chains. This is in contrast to the yeast Cdc48-U-N complex, which disassembles helicase complexes carrying long or short (6-10 ubiquitin moieties) chains with similar efficiency. The authors demonstrate that the requirement of p97-U-N for long chains can be overcome by the presence of p97 cofactors of the UBA-UBX type, including UBXN7, FAF1, FAF2 and (much less so) UBXN1. They show that this reduction in the 'ubiquitin threshold' of p97-U-N by UBXN7, FAF1 and FAF2 requires their UBX domain mediating p97 binding. They further show that the UBA and UIM domains of UBXN7 contribute to its activity in the assay, whereas the UBA domain of FAF1 and FAF2 is dispensable. Instead, a coiled-coil domain preceding the UBX domain of FAF1 and FAF2 is required for their activity, and both the coiled-coil-UBX domain organization and its activity are conserved in the worm homologue UBXN-3. Using UBXN7 and FAF1 knockout cells, Fujisawa and Labib then demonstrate that UBXN7 is required for efficient CMG helicase disassembly during S phase, with a minor contribution of FAF1, whereas both cofactors possess redundant roles in mitotic CMG helicase disassembly. Finally, the authors show that UBXN7 and FAF1 double knockout cells are hypersensitive to the NEDDylation inhibitor MLN4924 and suggest that this reflects their importance for p97-U-N unfoldase activity under conditions of restricted ubiquitination activity.

This manuscript describes the intriguing observation that the yeast and mammalian Cdc48/p97-U-N complexes have distinct requirements, at least in the in vitro assay used, with respect to the substrate´s ubiquitination state and to the presence of additional cofactors. While the concept of UBA-UBX cofactors assisting/stimulating Cdc48/p97-U-N activity is well-established, their link to ubiquitin chain length is novel and unexpected. The experiments are performed to a high technical standard, and the conclusions are mostly supported by the data. However, a shortcoming of the paper is that it remains entirely descriptive regarding the effect of the UBX proteins on the ubiquitin threshold, without providing mechanistic insights into their function or the molecular basis underlying the distinct thresholds.

1. It remains unclear if the failure of p97-U-N to disassemble the helicase complex carrying short ubiquitin chains reflects impaired binding, priming or translocation of the substrate. It should be straightforward to test if the UBA-UBX cofactors simply stabilize the p97-U-N-substrate complex.<br /> The distinct domain requirements for UBXN7 (UBA, UIM, UBX) and FAF1/FAF2 (coiled-coil-UBX) suggest different mechanisms of stimulation, which should be discussed in more detail. The additive defects of the UBXN7 and FAF1 double knockout cells could indicate either redundant functions (as the authors propose) or synergistic function of both cofactors. To that end, the authors could test if UBXN7 and FAF1 can bind simultaneously to the same p97-U-N-substrate complex and if they act synergistically in helicase disassembly, e.g. at limiting cofactor concentrations.

2. Having all purified proteins at hand, the authors should test which component of the system causes the elevated ubiquitin threshold of mammalian p97-U-N, by combining yeast Cdc48 with mammalian U-N and vice versa, etc. Can yeast Ubx5, which is a clear homologue of UBXN7, substitute for the mammalian UBA-UBX cofactors?

3. The authors emphasize that mammalian p97-U-N in the absence of UBA-UBX cofactors requires long ubiquitin chains for activity. However, they should consider the possibility that the critical property is chain topology, rather than chain length. There is evidence that p97-U-N prefers substrates with branched chains (see PMIDs 28512218, 29033132), and multiple ubiquitin chains on the helicase substrate may mimic those.<br /> It appears that worm CDC48-U-N in the absence of UBXN-3 cannot efficiently disassemble substrate carrying even long chains (Fig. 3 - supplement 2). The authors should discuss this finding in the context of their ubiquitin threshold model.

Reviewer #3 (Public Review):

Jacques et al. aim to assess properties of low and high-frequency signal content in intracranial stereo encephalography data in the human associative cortex using a frequency tagging paradigm using face stimuli. In the results, a high correspondence between high- and low-frequency content in terms of concordant dynamics is highlighted. The major critique is that the assessment in the way it was performed is not valid to disambiguate neural dynamics of responses in low- and high-frequency frequency bands and to make general claims about their selectivity and interplay.

The periodic visual stimulation induces a sharp non-sinusoidal transient impulse response with power across all frequencies (see Fig. 1D time-frequency representation). The calculated mean high-frequency amplitude envelope will therefore be dependent on properties of the used time-frequency calculation as well as noise level (e.g. 1/f contributions) in the chosen frequency band, but it will not reflect intrinsic high-frequency physiology or dynamics as it reflects spectral leakage of the transient response amplitude envelope. For instance, one can generate a synthetic non-sinusoidal signal (e.g., as a sum of sine + a number of harmonics) and apply the processing pipeline to generate the LF and HF components as illustrated in Fig. 1. This will yield two signals which will be highly similar regardless of how the LF component manifests. The fact that the two low and high-frequency measures closely track each other in spatial specificity and amplitudes/onset times and selectivity is due to the fact that they reflect exactly the same signal content. It is not possible with the measures as they have been calculated here to disambiguate physiological low- and high-frequency responses in a general way, e.g., in the absence of such a strong input drive. The connection of the calculated measures to ERPs for the low-frequency and population activity for the high-frequency measures for their frequency tagging paradigm is not clear and not validated, but throughout the text they are equated, starting from the introduction.

Reviewer #3 (Public Review):

Animals are able to detect an almost unlimited number of odorants or pheromones in the environment for their survival and reproduction. This extraordinary capacity is achieved by hundreds to thousands of olfactory receptors from different receptor families expressed in olfactory sensory neurons. Accordingly, the olfactory sensory neurons can be classified into distinct neuronal subpopulations based on the expressed receptors. How the distinct neuronal subpopulations are generated and maintained is still a mystery. Here, Lin et al. utilized the vomeronasal system, a specialized olfactory subsystem in rodents, as a model to answer this important question. The vomeronasal epithelium are composed of two main types of vomeronasal sensory neurons (VSNs), apical and basal VSNs, which express V1Rs and V2Rs, respectively. In their previous studies (Lin et al., Developmental Biology, 2018), the authors find that a transcription factor, AP-2e, is specifically expressed in maturing and mature V2R-expressing basal VSNs. They also show that deletion of AP-2e results in a reduced number of basal VSNs. In the current study, they further ask if AP-2e functions as a master regulator that is able to fully reprogram differentiated neurons or a terminal selector that maintains the fate of basal VSNs. They generated a knock-in mouse line to ectopically express AP-2e in apical VSNs, and investigated the transcriptomic changes in apical VSNs. By combining histological imaging and single-cell RNA sequencing (scRNA-seq) analysis, they find that ectopic expression of AP-2e in apical VSNs only induces expression of some basal VSN-specific genes but is unable to fully transform apical VSNs into basal VSNs. Thus, they conclude that AP-2e likely acts as a terminal selector for the identity of basal VSN subpopulations by activating some basally enriched genes while suppressing apically enriched genes. They further show that detection of conspecific odorants is impaired in mice with AP-2e ectopic expression.

The conclusions of this paper are mostly supported by data, but some key aspects of gene expression analysis need to be strengthened.

Strengths:<br /> The authors provide genetic tools to rescue or ectopically express AP-2e, which enables precise manipulation of AP-2e in vivo. In addition, the scRNA-seq results nicely reconstruct the process of apical-basal VSN differentiation dichotomy and correlate well with the histological results.

Weaknesses:<br /> Although the present data mainly support the authors' hypothesis, a few aspects of data analysis are required to reinforce their conclusions. The changes of V1Rs and V2Rs after ectopic AP-2e expression are lacking. Direct histological evidence for basally enriched genes in apical VSNs is also needed. Further, more detailed analyses on VSN activation challenged by conspecific odorants could consolidate the molecular and behavioral findings.

Reviewer #3 (Public Review):

This work provides a new statistical framework to model the sharing of antigen receptors among different individuals in healthy or diseased states. The model incorporates probabilities in convergent recombination and convergent selection to fit the complicated process in V(D)J -recombination and clonal selection. In this work, the statistical model has been used in predicting TCR/BCR sharing in healthy individuals and COVID-19 donors and also linked TCR sharing with CMV disease status and ageing.

This paper will be of interest to the large class of immunologists who are interested in antigen receptor sharing and their functions in infectious diseases and autoimmune diseases.

Reviewer #3 (Public Review):

Summary:<br /> In this manuscript Nunes Santos et al. use a combination of computation and experimental methods to identify and characterize a cis-regulatory element that mediates expression of the quail Slow Myosin Heavy Chain III (SMyHC III) gene in the heart (specifically in the atria). Previous studies had identified a cis-regulatory element that can drive expression of SMyHC III in the heart, but not specifically (solely) in the atria, suggesting additional regulatory elements are responsible for the specific expression of SMyHC III in the atria as opposed to other elements of the heart. To identify these elements Nunes Santos et al. first used a bioinformatic approach to identify potentially functional nuclear receptor binding sites ("Hexads") in the SMyHC III promoter; previous studies had already shown that two of these Hexads are important for SMyHC III promoter function. They identified a previously unknown third Hexad within the promoter, and propose that the combination of these three (called the complex Nuclear Receptor Element or cNRE) is necessary and sufficient for specific atrial expression of SMyHC III. Next, they use experimental methods to functionally characterize the cNRE including showing that the quail SMyHC III promoter can drive green fluorescent protein (GFP) expression the atrium of developing zebrafish embryos and that the cNRE is necessary to drive the expression of the human alkaline phosphatase reporter gene (HAP) in transgenic mouse atria. Additional experiments show that the cNRE is portable regulatory element that can drive atrial expression and demonstrate the importance of the three Hexad parts. These data demonstrating that the cNRE mediates atrial-specific expression is well-done and convincing. The authors also note the possibility that the cNRE might be derived from an endogenous viral element but further data are needed to support the hypothesis that the cNRE is of viral origin.

Strengths:<br /> 1) The experimental work demonstrating that the cNRE is a regulatory element that can mediate the atrial-specific expression of SMyHC III.

Weaknesses:<br /> 1) Justification for use of different regulatory elements in the zebrafish (32 bp cNRE) and the mouse transgenic assays (72 bp cNRE), and discussion of the impact of this difference on the results/interpretation.

2) Is the cNRE really "necessary and sufficient"? I define necessary and sufficient in this context as a regulatory element that fully recapitulates the expression of the target gene, so if the cNRE was "necessary and sufficient" to direct the appropriate expression of SMyHC III it should be able to drive expression of a reporter gene solely in the atria. While deletion of the cNRE does reduce expression of the reporter gene in atria it is not completely lost nor converted from atrial to ventricular expression (as I understand the study design would suggest should be the effect), similarly fusion of 5 repeats of the cNRE induces expression of a ventricular gene in the atria but also does not convert expression from ventricle to atria. This doesn't seem to satisfy the requirements of a "necessary and sufficient" condition. Perhaps a discussion of why the expectations for "necessary and sufficient" are not met but are still consistent would be beneficial here.

3) The claim that the cNRE is derived from a viral integration is not supported by the data. Specifically, the cNRE has sequence similarity to some viral genomes, but this need not be because of homology and can also be because of chance or convergence. Indeed, the region of the chicken genome with the cNRE does have repetitive elements but these are simple sequence repeats, such as (CTCTATGGGG)n and (ACCCATAGAG)n, and a G-rich low complexity region, rather than viral elements; The same is true for the truly genome. These data indicate that the cNRE is not derived from a endogenous virus but is a repetitive and low complexity region, these regions are expected to occur more frequently than expected for larger and more complex regions which would cause the BLAST E value to decrease and appear "significant" but this is entirely expected because short alignments can have high E values by chance. (Also note that E values do not indicate statistical significance, rather they are the number of hits one can "expect" to see by chance when searching specific database.)

Reviewer #3 (Public Review):

Lang et al. provide high-quality data about the maintenance and functioning of the fruit fly nephrocyte slit diaphragm, a topic that has not been investigated formerly with such sophisticated (live) imaging techniques in an in vivo model. Images and videos provided by the authors give spectacular insights into the subcellular events that contribute to diaphragm maintenance and dynamics. They created genetic tools that are suitable for monitoring the trafficking of Sns, the Drosophila ortholog of Nephrin, one of the main components of the mammalian slit diaphragm. Using nephrin-GFP, the authors are the first to follow the stability and turnover rate of this slit diaphragm component. With a variety of labeling experiments (including live imaging of diaphragm proteins, channel diffusion assay, and a live labeling pulse/chase approach, which are all more or less novel in the field) the authors dissected the role of different endocytosis routes in slit diaphragm maintenance. They show that dynamin-mediated and lipid raft-mediated endocytosis pathways cooperate to establish the strictly organized pattern of diaphragm proteins while also enabling their dynamic turnover and filter cleaning. In their model, dynamin-mediated endocytosis restricts lateral diffusion of diaphragm proteins thus maintaining the strictly organized peripheral fingerprint-like distribution of diaphragm units, this way excluding them from the labyrinthine channels. Block of dynamin-mediated endocytosis leads to the formation of ectopic diaphragm protein deposits, while the turnover of diaphragm proteins is not inhibited based on live labeling experiments. In contrast, perturbing raft-mediated endocytosis via either cyclodextrin treatment or flotillin2 knockdown inhibits nephrin turnover without ectopic diaphragm deposits. Based on the decreased uptake of a larger endocytic tracer compared to a smaller one in the case of defective raft-mediated endocytosis, the authors propose that impaired nephrin turnover leads to clogging of the filter. They claim that this might be caused by defects in the shedding of nephrin-bound molecules that would normally occur in the endosomal system right before recycling. The model established by the authors is convincingly supported by their data. Discriminating between the lateral mobility and turnover/cleaning of diaphragm proteins is the major strength of the study, representing a conceptual novelty in the field. In addition, the new methods and tools also serve as useful resources for researchers with similar interests.

Reviewer #3 (Public Review):

This manuscript by Stemm-Wolf and colleagues examines the role of the SON RNA-splicing factor in the transcriptional regulation of centriole assembly and centrosome protein composition. The authors use a combination of siRNA-mediated gene silencing, RNA sequencing/in silico analyses, super-resolution microcopy and electron tomography to investigate how loss of SON function impacts centriole duplication, centrosome protein composition, satellite-mediated protein trafficking and microtubule organization in cultured cells in vitro. The authors demonstrate that although depletion of SON is dispensable for early steps of procentriole assembly, loss of SON causes a bock in the completion of centriologenesis. Using RNA sequencing of SON-depleted cells, they identify a role for SON in splicing of genes encoding components of centriolar satellites, as well as a number of microtubule and centrosome associated proteins. Using immunofluorescence imaging, they confirm that SON loss alters the distribution of centrosomal and centriolar satellite proteins in cells, as well as microtubule nucleation at centrosomes.

Overall, the manuscript is well written and the data are well presented. The strengths of the study are the use of advanced imaging techniques to visualize and quantify centrioles and centrosomes upon SON manipulation, and the RNA sequencing data that points to roles for SON in these processes. However, what remains unclear is the connection between SON-related splicing defects and the mechanisms that result in the observed centriole assembly defects. Loss of SON appears to impact a large number of genes (over 4000). With regards to centrosome biology, it appears to impact a variety of different centriolar, PCM, and satellite proteins, all of which could somehow cause these phenotypes. Thus, it was difficult for this reviewer to understand how exactly loss of SON causes dysregulation of procentriole growth, other than concluding that it impacts the function of numerous genes/proteins simultaneously. As the authors themselves note "the mechanism of SON-based regulation of centriole assembly is multifactorial". Although this makes sense to a certain degree, the same might be said of a number of splicing factors that control expression/splicing of large sets of genes. As such, the only conclusion I left with was that defects in gene splicing of centriolar/centrosomal genes can disrupt procentriole growth, which is not that surprising considering mis-splicing of even a single gene essential for procentriole assembly would result in this phenotype.

Reviewer #3 (Public Review):

This work addresses the question that how likely a mutant takes over in a spatially structured population. It is known that some spatial structures, compared to a well-mixed population, may increase or decrease the fixation probability of a mutant and in this way amplify or suppress the effect of natural selection. This article provides new insights in the understanding of the situations where such amplification or suppression of the effect of natural selection occurs.

The paper studies a model of structured populations on graphs, where each node of the graph contains a well-mixed deme. The model considers birth, death and migration events which arise independently of each other. The birth rate includes a local density dependent competition term at each deme. The authors consider an initial state where all the demes are at their demographic equilibrium size and that all the individuals are of wild type. The objective is to compute the fixation probability of a mutant introduced in this population. Considering different forms of graphs, they investigate which spatial structures amplify or suppress the effect of natural selection.

The authors consider a particular framework where the migration rate is small so that migration is a rare event compared to birth and death events. They also consider large carrying capacities in the demes, so that in the time period of the study no deme extinction occurs and the deme sizes fluctuate around their deterministic steady values.

In this regime they propose a coarse-grained model that approximates the original model. In this model, as a consequence of scarcity of migration events, each deme is of mutant or wild type and the evolution of the population can then be described as a Markov process where elementary steps are migration events, which change the state of the system if fixation occurs. The advantage of such coarse-grained model is that one can compute analytically the fixation probability of a mutant, as a function of the structure of the graph. Note however that in this analytical computation, the fixation probability of the mutant in a single deme is estimated by the fixation probability given by the Moran process, where the size of the population in a deme is considered constant. Numerical simulations, considering small migration rates, confirm that the fixation probabilities estimated by this method approach well the fixation probabilities in the original model. It is not investigated though how small the migration rate should be for the analytical results to remain valid.

The fixation probabilities for different graph structures are computed via this method, identifying situations where amplification or suppression of the effect of natural selection occurs depending on the spatial structure and the asymmetry of migration rates.

The results are compared to previous related works, using population models on graphs, but where each node corresponds whether to an individual or to a deme with constant population size. In these previous works usually the evolutionary outcome depends on the order of birth and death events, while in the present work these events are considered independent. Note however that in those previous works the migration, birth and death events occur at the same time scale, while here the migration events are assumed to be rare compared to the birth/death events. For this reason, I would not consider this work as a universal analysis of the problem but as a complementary one to the previous literature.

Reviewer #3 (Public Review):

In this paper, the authors use gene editing techniques to examine the potential relationship between telomeric binding protein TRF2 and the origin recognition complex protein ORC1. Studies about TRF2 are often difficult, as changes in TRF2 often lead to genome instability. The authors circumvent this through the generation of separation-of-function mutations, to only disrupt TRF2-ORC1 binding. They then demonstrate that overexpression of an ORC1 fragment that binds to TRF2 can lead to replication stress.

Concerns:

• The foremost challenge for this paper is the interpretation of the presented comparisons, due to lack of an effective control. The authors chose to make a separation of function mutation specifically because TRF2 expression level has a direct impact on cell viability. However, their wt control and mutants have decreased TRF2 expression. Moreover, the decreases are not similar between the experimental and the wild type. Concerningly, the differences in the parental line and the wt control clone are quite striking in many of the panels. This becomes quite clear in Figure 2 Supplement 1, Figure 2E, and Figure 3B; it seems that most of the effect is driven by a clonal difference in the wt cell clone rather than a true biological difference. Due to this, it is difficult to tease apart which phenotypes are actually as a result of their mutations, or if it's an issue of TRF2 expression, or preexisting genomic differences in cancer cells that surface when cells are subcloned during the targeting process.

• In addition, a major conclusion of the paper is derived from Figure 6. The authors argue that the separation of function mutants only reveals telomere damage when treated with HU. In Figure 6, it is not clear yet if there is damage at the telomere specifically, or there is damage everywhere including telomeres. This seems to be a central point that would need to be directly shown.

Reviewer #3 (Public Review):

The authors demonstrated the novel mechanism of angiogenesis in human non-small cell lung cancer (NSCLC). They showed that downregulation of miR-22 in endothelial cells in NSCLC (TEC) compared to normal endothelial cells after isolation by laser microdissection method. They demonstrated that NSCLC secreted tumor necrosis factor (TNF)-α and interleukin (IL)-1β cause downregulation of miR-22 in endothelial cells. Endothelial miR-22 inhibits sirtuin (SIRT) 1and fibroblast growth factor receptor (FGFR) 1, leading to inactivation of AKT/mammalian target of rapamycin (mTOR) signaling, suggesting that miR-22 is a potent angiogenic inhibitor. Since current angiogenic inhibitors which target VEGF signaling sometimes cause adverse effects via damage in normal endothelial cells (NECs), the strategy to recruit the molecules which are specifically downregulated sounds promising.

However, the reviewer considers the authors should show additional data to help readers interpret the authors' conclusion; the potential of this miR-22 strategy as angiogenic inhibitors. Also, this reviewer is concerned about several experimental methods which the authors required in this study. In vivo anti-angiogenic effects of miR-22 expressing endothelial cells, 10 times more endothelial cells were mixed with tumor cells for implantation, although endothelial cells are much less numerous than tumor cells in real tumors. For the isolation of endothelial cells by laser microdissection, it is not clear whether endothelial cells were dissected. Did they perform CD31 staining before dissection? It would strengthen the significance of miR-22 if the authors tried to direct miR-22 to in vivo tumors besides co-implantation of endothelial cells.

Reviewer #3 (Public Review):

Yanowski et al present an interesting piece of work that identifies an important and under-investigated field of islet research; regeneration of the pancreas with a focus on non-beta-cells. They identify beta-delta cell pairs in their analysis. They confirm that these pairs are genuine pairs rather than an artefact. They then demonstrate that these pairs exhibit some beneficial properties (transcriptionally) in Ppx pancreas.

I am not experienced in MARS-seq or PIC. Whilst these technologies look interesting and the data are exciting, I hope the other reviewers can comment on the validity of these data.

Here are my main comments:

1) Introduction: The introduction is lacking a clear introduction into Sox9. This is very important because in the results you suddenly 'jump' to looking at Sox9-CreER:TdTomato pancreas's (Figure 1) without any explanation as to why. You then (Figure 2) discuss Ppx in Sox9-CreER:TdTomato pancreas's, and again it is not clear why. What led you to consider this model in this context? You could consider reframing the results to give this narrative. Did you use Sox9 as a proxy for Sst? Why not just use Sst-ER:tDtomato mice?

2) Line 155-157: This is a discussion point. Also, how can you, at this point at least, claim that there are beta-delta pairs in Figure 3. Could they, at this point, still be considered an artefact or cell dissociation or leaky RNA?

3) How would your findings fit into the context of (or be explained through the lens of) what is currently known about interactions between beta and delta-cells? There are a few recent studies that have investigated the interaction between beta- and delta-cells, notably PMC5767697, PMC5026721. These should be cited as you have only given one reference which does not investigate beta-delta interactions, and a review.

Reviewer #3 (Public Review):

In yeasts Saccharomyces cerevisiae and Schizosaccharomyces pombe, the Smc5/6 complex is associated with regulatory subunits Nse5 and Nse6, which have been shown to be required for chromatin association and also inhibit ATP turnover. In addition, the recruitment to sites of DNA damage requires a multi-BRCT domain protein, Brc1/Rtt107. The identity of the Nse5 and Nse6 regulatory subunits of the human SMC5/6 complex has long been a mystery. The Nse6 homologue, SLF2, was identified in a proteomics screen for factors required for bypass of DNA crosslinks. SLF1, the other recruiter identified in the same screen, showed very limited homology to Nse6 and also contained BRCT domains and ANKRIN repeats suggesting that it provided an analogous mechanism of recruitment of SMC5/6 to sites of DNA damage to that of Brc1/Rtt107. The questions of whether SLF1 was the functional homologue of Nse5 in humans and whether it was required for all chromatin association remained.

The authors address this in their analysis. They used proteomics to identify SMC5/6 interactors in PML nuclear bodies. They identified a novel regulator, SIMC1, that contains SUMO interacting motifs (SIMs) and an Nse5-like domain. They showed that SIMC1 and SLF2 interact through the Nse5 domain and form an alternative complex to SLF1/SLF2. They then suggested that the SIMC1/SLF2 complex is specific for recruitment of SMC5/6 for restriction of viral replication/transcription as the SIM and Nse5 domains were found to be important to localise SMC5/6 to polyomavirus replication centres at PML bodies, which are enriched for SUMO, and for recruitment of SMC5/6 to SV40 replication centres. Since PML bodies are associated with alternative lengthening of telomeres (ALT) and SMC5/6 has roles in telomere maintenance it would be interesting to know whether the SIMC1/SLF2 complex was also required in these circumstances but this is a question for a future study.

Overall, the manuscript is well written and the data is of high quality. It makes a step advance in terms of our understanding of how the Smc5/6 complex functions.

Reviewer #3 (Public Review):

This study provides a comprehensive analysis of the brown fat adipogenesis process in vivo. The study addresses several outstanding questions in the field regarding the identity of brown fat progenitors, and mechanisms for activation. The extensive scRNAseq studies under basal conditions and during BAT proliferation upon cold exposure, CL316 treatment, and in response to Adrb1 deletion provide a complete view of the adipogenesis process. This analysis leads the authors to identify PDGFRa+ cells (ASC1) as the direct upstream progenitor cell for new brown adipocytes, and identifies Nnat as a novel maker of newly differentiating cells. These findings are reinforced by lineage tracing and in situ hybridization analyses. Additionally, the paper shows that the metabolic activation of mature brown adipocytes provides key signal(s) for progenitor differentiation and that the direct activation of progenitors by norepinephrine is dispensable.

This paper provides an excellent foundation for future work identifying key components of the progenitor niche, and the pathways that regulate progenitor differentiation.

Reviewer #3 (Public Review):

DNA methylation inheritance through the UHRF1-DNMT1 signaling axis is becoming increasingly appreciated as a ubiquitin-regulated process. Mechanistic studies of the enzymes and ubiquitin-dependent interactions that facilitate the association of DNMT1 with replicating chromatin are of fundamental importance for the epigenetics field and have potential to reveal mechanisms of dysregulation that are associated with abnormal DNA methylation patterning in human cancers. This study builds on the observation that UHRF1 multi mono-ubiquitinates the PCNA-associated protein PAF15, and that, similarly to H3 substrates, these mono-ubiquitin sites are bound by DNMT1 and may contribute to its S-phase chromatin association. The authors focus on players involved in ubiquitin removal and PAF15 release from chromatin. They identify the deubiquitinase USP7 and the DNA replication regulator ATAD5 as important to this termination process. However, while manipulation of these factors shows quite striking effects on DNMT1 chromatin association using Xenopus egg extracts as a model system for the process of DNA methylation maintenance, effects on DNA methylation are minimal, which brings to question the importance and potential impact of this pathway involving PAF15 and its role in regulating DNA methylation inheritance through mitotic cell divisions. This and other major concerns that limit my enthusiasm for this study in its current form are bulleted below.

Major concerns:

• Intro paragraph beginning on Line 86 needs more detail to support 1) a role for PAF15 in DNA methylation maintenance; 2) the contribution of PAF15Ub2 to DNMT1 chromatin association; and 3) the association of inefficient termination of PAF15Ub signaling with abnormal DNA methylation maintenance in human cancers. The way this paragraph is written makes it unclear whether these are speculations or whether there is strong data to support these claims.<br /> • To what extent does inhibition of PAF15Ub2 support DNA methylation maintenance? Is it an absolute requirement, or only a partial contributor? And how does PAF15 compete with H3 (which is presumably in vast excess) for these Ub ligase, DUB, and reader activities? These questions are important to consider for framing the potential impact of this study that focuses on the PAF15-Ub regulatory mechanism. Does it really matter in a physiologic context and for the faithful propagation of DNA methylation patterns through mitotic cell divisions?<br /> • (Line 116) Again, the way this is written 'to understand how the termination of DNA methylation maintenance is regulated' implies that PAF15Ub2 is a major regulator of DNMT1 function. I'm not so sure the data strongly support this claim.<br /> • Fig 1A - with only a chromatin fraction shown, the authors cannot claim that 'PAF15 underwent dual mono-ub on chromatin and then dissociated from chromatin.' More generally, the paper relies exclusively on this chromatin association western blotting assay for querying chromatin interaction dynamics. Orthogonal approaches should be considered to strengthen conclusions being drawn.<br /> • The extent to which these findings in Xenopus extracts translate to mammalian regulation of DNA methylation maintenance is unclear.<br /> • Do the mutations that disrupt PAF15-USP7 interaction also affect PAF15-PCNA interaction?<br /> • It is difficult to interpret Fig 3C since USP7 is also elevated in the 90 and 120 min time points in the presence of the inhibitor.<br /> • For such striking effects of these perturbations on DNMT1 chromatin association, it is surprising that the effects on DNA methylation are subtle. This comes back to my comment above regarding whether this mechanism that has now been elegantly dissected actually matters for DNA methylation maintenance. To strengthen the impact of this work, it will be important to expand DNA methylation analyses and extend these findings to mammalian cells.<br /> • (Line 517) How can the authors claim - or even suggest - that 'PAF15 Ub signaling is the primary pathway to maintain DNA methylation during S-phase' when there is limited evidence to support significant effects on DNA methylation maintenance in the absence of these factors?

Reviewer #3 (Public Review):

The authors nicely demonstrate tissue-specific differences in lipogenesis and insulin sensitivity which are differentially regulated in a diet-dependent manner. With the addition of comprehensive lipidomic phenotyping, their data implicate the differential regulation of ceramide accumulation as a likely mediator of insulin resistance in muscle and liver. Studies of dietary regulation of metabolic dysfunction, like this, are essential for understanding the etiology of metabolic diseases and efforts to combat them. With the exhaustive metabolic and lipidomic analyses, this paper can be a great resource for the field.

Reviewer #3 (Public Review):

Here, Johnstone et al. developed novel tools to study endogenous and tissue-specific circadian clocks, which control gene expression oscillation over a 24-hour period. They find that these genetically encoded luciferase-based tools, which they call LABL (Locally Activatable BioLuminiscence). Other known techniques monitor downstream products of circadian clock gene activity (ie, neuronal calcium imaging) or utilize terminal assays such as qRT-PCR or require removal of organs for ex vivo monitoring. The authors show that their LABL technique faithfully mimics the oscillations of gene expression seen with other techniques for broad circadian expression drivers and for neuronally specific expression drivers but show different patterns for non-neuronal, so-called peripheral clocks. These results suggest that the canonical hierarchy of central clocks regulating peripheral clocks may need closer re-examination.

The conclusions of this paper are mostly well supported but three specific aspects need to be clarified or tested.

1) Figures 5A, 6B, and 6E are critical for the conclusions of this paper and from what this reviewer can tell, they support these conclusions but the overlay of mutant and wild type on the same graphs obscures both. This reviewer would suggest including split graphs with wild type and mutant alone for independent evaluation.

2) Luminescence is a well-established, high-resolution real-time monitor; at the same time, my one concern is that luminescence via luciferase and feeding of luciferin substrate might be dependent on the host animal's feeding patterns. How do we know that the peaks and troughs of luminescence are not due to peaks and troughs of feeding and metabolism rather than peaks and troughs of circadian clock gene expression? Can the authors offer evidence to support the latter?

3) While the comparison of wild type to arrhythmic mutants is consistent with current data and seems to reflect faithful monitoring of tissue-specific circadian clock activity, the classic technique for demonstrating faithful monitoring of clock activity is to slow down or speed up the clock. The authors have themselves used this technique in previous publications, including using phosphosite-specific mutants of clock components and flies containing constitutively active or kinase-inactive regulators of clock activity. Another classic technique is to use short or long period mutants. Use of any of these types of mutants showing that they shift the luminescence rhythms generated by LABL would provide further evidence that LABL reflects endogenous, tissue-specific clock activity. Alternatively, monitoring the rhythm of a clock thought to be independent of central clock activity such as that in the antennae or Malpighian tubules and showing that this is not disrupted by central clock disruption would provide such support as well.

Reviewer #3 (Public Review):

The authors propose that the DNA methylation signature of tumor aggressiveness would be independent of the physiological context: starting from a human tumor, shared signatures relevant to aggressiveness should emerge independent of whether this trait was acquired in humans or whether cells have been implanted into rats or mice. In a multi-step selection process, they identified hypermethylated sites common to the most aggressive melanoma forms, analyzed the distribution of these sites in the genome, and validated these methylation peaks in cell lines and patient samples.

The weakness is related to the use of murine cells and also to The Functional annotation and pathway analysis. The list of hypermethylated genes was imported into QIAGEN's Ingenuity® Pathway Analysis (IPA®, QIAGEN Redwood City, www.qiagen.com/ingenuity). I am wondering if it would be more appropriate to use other platforms to explore the data.

The strengths are related to the main strategy that identified a DNA methylation signature of five CpG sites in four gene promoters in primary tumors that could predict the overall survival of the patients and thus has potential diagnostic application. This strategy, which overcomes heterogeneity in tumors due to the environment, can potentially be generalized to other cancers involving DNA methylation alterations

The authors combined analysis of the DNA methylome with the chromosomal location. The multistep strategy developed and used to identify differentially methylated genes predicting aggressiveness is originally identified as a common pattern or a specific signature of melanoma aggressiveness. The unique approach used in this study yielded a potential DNA methylation signature that correlates with outcomes.

The description of a novel multistep approach allowed identifying a methylation signature of five CpGs in primary melanoma tissues that has the potential to predict survival outcomes in cutaneous melanoma patients. This integrated approach can be applied not only to other cancer types but also to other diseases or biological processes such as aging and development.

Reviewer #3 (Public Review):

This is an important paper querying odor responses in the olfactory bulb at low concentrations. The main conclusion is that at low concentrations the dimensionality of odor responses rises, and responses become very sparse, sparse enough that a subset of odors can be identified that deterministically activate a single characteristic glomerulus. The observation that one can precisely match odors to specific glomeruli - as has been done previously in flies - has substantial implications for the ways in which receptors interact with odors, and for future experiments querying odor coding. From a technical perspective this paper is excellent, but the ways in which glomerular responses are computed, some of the conclusions related to sparseness and dimensionality, and the model used to compare descriptions of odor space require some revision. My sense is that after appropriate revision this will be an excellent addition to the literature, and provide us with valuable tools for querying odor processing.

Reviewer #3 (Public Review):

Sensory preconditioning (SPC) refers to a conceptually important, higher-order form of Pavlovian conditioning. It involves two training phases and a final test. In the first, pre-conditioning training phase two 'neutral' stimuli are presented together (S1, S2). In the second training phase, one of them is paired with for example a punishment (S1+). In the final test conditioned response to the respective other stimulus is assessed (S2).

The conclusion that sensory preconditioning does indeed occur requires showing that i) conditioned responding is observed for S2 but not for other, not pre-conditioned stimuli (S3); ii) that conditioned responding to S2 depends on the jointness of presentation of S1 and S2; iii) that conditioned responding to S2 depends on S1 indeed being paired with punishment. It is a strength of the current paper that these requirements are met and that this is the case both at the behavioural level and for a plausible stand-in at the physiological level.

A weakness is that key data belonging together are not shown and analysed together.

Reviewer #3 (Public Review):

This article describes a series of experiments aimed at mapping in a comprehensive way the possible avenues by which E. coli is able to assimilate nitrogen into its metabolism. In particular, the authors address the question of whether (and how efficiently) different amino acids can be used by E. coli as a nitrogen source. The paper portrays an interesting and complex global picture of how the nitrogen assimilation capabilities are shaped by the connectivity of metabolism and the promiscuity of some enzymes. The results are based on a combination of genetic modifications and laboratory evolution experiments, all carefully planned to dive deep into each individual case. The evidence presented, in the form of growth curves and isotope labelling patterns, seems very solid and clear. In fact, I find it truly beautiful that one can still learn so much about metabolism by measuring growth under cleverly chosen combinations of environmental conditions. I think that this manuscript is important, and will have a strong impact, both in terms of fundamental understanding of metabolism, and as a starting point for future metabolic engineering applications. The article is well written, and inspiring, as it makes the readers realize how the amination network is at the same time concentrated around a few key pathways, but also substantially diverse in its interactions with other cellular processes.

Reviewer #3 (Public Review):

Schwartz et al investigate how mitochondria genomes (mtDNA) are inherited in C. elegans. They identify two distinct processes in primordial germ cells (PGCs) that shape mtDNA inheritance -one that influences the quantity of mtDNA, and the other the quality.

In the first part of the study, they show that during PGC development mtDNA is reduced two-fold. Building and extending on their previous work, they find that both cannibalization and autophagy contribute to this reduction. The authors show that this does not preferentially eliminate mutant mtDNA and they do not report any long-term, overt phenotypic consequences to inhibit this decrease in mtDNA. Indeed, they find that copy number returns to control levels when PGCs become germline stem cells, even though excess mtDNA is present at the onset of germline expansion. The authors do not explore to what degree this reduction in mtDNA contributes to the stochastic drift of mtDNA variants in the germ line.

In the second part of the study, they identify a stage of PGC development during which mtDNA quality is assessed. They find that the proportion of mutant mtDNA in PGCs decreases by approximately 5 percent in L1 PGCs, but increases again at later stages of development, presumably due to the selfish nature of the mutant genome they are using. Interestingly, they find that this selective decrease in mutant mtDNA requires the mitochondrial stress kinase PINK1, independent of its canonical role in mitochondrial autophagy.

Strengths: This study is well conducted with rigorous experimentation and thoughtful data interpretation. The authors make substantial gains in understanding mtDNA inheritance in the C. elegans germline. To my knowledge, they are the first to define a precise developmental stage during which mtDNA purifying selection occurs in the C. elegans germline and to implicate a non-canonical role for PINK1 in this process in C. elegans.

Weaknesses: The main weakness of this study, in my opinion, is that the authors do not directly measure mtDNA replication in PGCs, which has important implications with respect to how they interpret their data. Based on ddPCR and counting TFAM positive nucleoids, they conclude that little mtDNA replication likely occurs until after PGCs differentiate into GSCs and selection has taken place. While this may be true, it is hard to be certain without directly measuring mtDNA replication.

Reviewer #3 (Public Review):

The authors observe that unsaturated fatty acids in combination with vancomycin have a pronounced bactericidal effect on S. aureus with a >4 orders of magnitude greater killing at 20-fold MIC compared to vancomycin alone, which is specific to unsaturated fatty acids among several membrane-active agents. They observe that this phenomenon is not general to all antibiotics that target peptidoglycan pathways, but is only observed with vancomycin and bacitracin among the antibiotics they tested. The authors also observe that the combined treatment does not depolarize the bacterial cell membrane, but causes ATP levels to rise in the growth media. Microscopically, the combination results in membrane abnormalities not seen with either compound alone, which include foci that stain intensely with a dye that preferentially labels disordered regions of lipid bilayers. Furthermore, distorted septa and membrane defects are visible by EM only in cells treated with the vancomycin-palmitoleic acid combination; similar defects are also observed by light microscopy in the pattern of divisome protein localization and peptidoglycan biosynthesis. Finally, the authors demonstrate that the combination is active against vancomycin-resistant forms of several Gram-positive pathogens.

The paper reports at least three remarkable observations. However, they are not pursued to a definitive resolution, which makes it difficult to determine what to conclude from the reported findings.

One of the interesting observations made by the authors is the bactericidal effect of the combination of vancomycin and palmitoleic acid against apparent persisters (Fig. 1), which is a topic of considerable interest to the field of antibacterial therapy. This prompts the question of what the basis of this anti-persister activity is. The combination treatment could theoretically induce persisters to exit the persister state and thereby render them susceptible to killing by antibacterial mechanisms effective against the non-persister state. Alternatively, the combination may kill persister bacteria while they are still in the persister state. The combination might kill the persister and non-persister bacteria by the same mechanism, or the combination might act by multiple distinct mechanisms simultaneously, one or more of which is active against the persister state. Without further investigation, it is unclear what implications the anti-persister activity of vancomycin in combination with palmitoleic acid has for the general study of the phenomenon of bacterial persistence outside the specific context in which it was observed in this work.

Another interesting observation is that palmitoleic acid only potentiates vancomycin and bacitracin among the several antibiotics that target peptidoglycan pathways (Fig.2 b-f), which argues against the potentiation resulting from compounded simultaneous generalized damage to the peptidoglycan (by an antibiotic) and to the cell membrane (by palmitoleic acid). The specificity of the effect is intriguing; however, it is not pursued to its resolution elsewhere in the manuscript: for example, it's not shown what phenotypes the peptidoglycan-targeting antibiotics other than vancomycin have in combination with palmitoleic acid in the experiments of figures 4-6 and if the phenotypes shown there are as distinct as in Fig. 2.

Related to the above observation, the authors also invoke accumulation of lipid II as the mechanistic basis for the potentiation that they observe. Although it is a sound hypothesis, it is surprising that the authors limit themselves to inferring lipid II accumulation from effects observed under similar treatment conditions by other groups, without apparently assessing it directly and independently in their own experimental system under treatment, even though lipid II accumulation appears in the title of the manuscript. While it is highly likely that accumulation of lipid II will be observed in these experiments, the more important question is its causality in the antibacterial effects of vancomycin combined with palmitoleic acid. The killing of bacteria with antibiotics will produce a cascade of propagating effects in a cell, including membrane disturbances of various extent and types, and only further investigation can settle whether lipid II buildup is sufficient or incidental to achieve the antibacterial effects reported here. Assessing lipid II buildup under treatment conditions employed and correlating the degree of lipid II buildup to the extent of killing could at least strengthen the case for its role in the phenomenon if the degree of lipid II accumulation predicts the strength of the effect observed.

Yet another set of curious observations are the microscopic abnormalities induced by the combination treatment that are not apparently caused by either of the agents alone (Figs 4-6). While these findings argue for a unique mechanism of bacterial killing by the vancomycin-palmitoleic acid combination, it is difficult to relate them to the other results reported in the manuscript and to the mechanistic hypothesis of lipid II buildup. It is unclear if the authors believe that DiI-C12 foci observed under vancomycin-palmitoleic acid treatment represent an enhanced extent of lipid II buildup under the combination treatment relative to vancomycin alone, or if the foci are a different phenomenon that is downstream from lipid II buildup, in other words, what the lipid composition of those membrane regions is exactly. It could be imagined that palmitoleic acid induces the confluence of accumulated lipid II into fewer regions of larger size or that it triggers a chain of events that result in a very different membrane composition. It is also unclear if any other antibiotics that target peptidoglycan produce similar foci in combination with palmitoleic acid, which would be important to know in order to assess their relevance to the unique effectiveness of the vancomycin-palmitoleic acid combination observed earlier. This consideration also applies to the EM images in Fig. 5 and the localization images in Fig. 6. In addition, it is unclear how observations in Fig. 6 relate to those in Fig. 4: aberrant PG incorporation could happen preferentially at the regions of increased fluidity that the authors observe, or peptidoglycan synthesis machinery could actively shun those regions. It seems important to connect those findings to each other in order to construct a mechanistic model. It is also unclear whether divisome proteins like EzrA gravitate towards domains of increased fluidity or if they are excluded from them. More generally, it remains unclear if there is a direct mechanistic connection between regions observed in Fig. 6 and those observed in Fig. 4, or if they are different consequences of the same upstream disturbance, and their locations are unrelated. For example, delocalizing the divisome by other means may or may not enhance vancomycin killing in the manner that the authors see, which would have implications for the mechanism of killing. Also, it is unclear if these disturbances appear in a defined time sequence or simultaneously or haphazardly, which can also shed light on their mechanistic relevance.

Finally, the paper concludes by showing that this combination is effective in a range of Gram-positive species and is also active against vancomycin-resistant strains. This is potentially a very valuable finding because of the breadth of the spectrum and problems posed by antibiotic resistance; however how it relates to the preceding mechanistic hypotheses is left unexplored. As above, it would be useful to check that this new effect is still specific to palmitoleic acid and not observed with other membrane-active agents when combined with vancomycin. Importantly, vancomycin resistance seems to offer a unique opportunity to probe the mechanism of the bactericidal effect of the vancomycin-palmitoleic acid combination, both in terms of the dependence of the effect on the composition of the lipid II pool in the cell, how the effect changes under pretreatment with either agent alone, and the effect on phenotypes observed in Figs 4-6, but neither is pursued. It is not clear if the effectiveness of the combination depends on outpacing the modification of lipid II, or if it overcomes that mechanism of resistance, such as if palmitoleic acid somehow inhibits proteins that confer vancomycin resistance. Without knowing more about what underlies this activity against vancomycin-resistant bacteria and what its limitations are, it is difficult to assess its implications for the problem of vancomycin resistance in general.

Methodological weaknesses:<br /> The implications of some of the reported results are difficult to interpret because of a lack of relevant comparisons. In Fig. 3, the performance of known peptidoglycan-targeting agents is not available as a benchmark to calibrate the performance of the vancomycin-palmitoleic acid treatment. The inclusion of gramicidin in Fig. 3a is helpful to indicate what signal a high degree of membrane depolarization would produce in this assay, and that does make the point that membrane depolarization is not the primary mechanism of killing by the combination treatment. However, it is unclear how the more relevant negative controls such as membrane-active agents of Fig. 1a or other peptidoglycan-targeting antibiotics perform in this assay, nor how degrees of depolarization (as assessed by this assay) correlate with bacterial viability. This would be needed in order to try to make inferences about its role in the effectiveness of the combination and how these data relate to the distinctions made elsewhere in the manuscript. Similar considerations apply to data in Fig. 3b. It indicates some amount of ATP leakage under combination treatment, but it is not clear how one can calibrate these values to the fitness of bacteria or how the relevant comparison treatments with established mechanisms of action against the cell envelope would perform in that assay. Literature reports of increased membrane permeability by other membrane-active agents are alluded to in the discussion, but it is unclear what signal they would produce in the specific assay the authors use to assess it in this manuscript. Similar points apply to Fig. 4 d-f.

Some of the points made in the discussion section would require additional work to substantiate. The very specific statement that "the insertion of palmitoleic acid into the microenvironment around lipid II increases the disordered phospholipid environment surrounding the peptidoglycan monomer, subsequently creating large RIFs", would require something like a demonstration in an in vitro bilayer reconstituted from purified components (such as in ref 76, "Ca(2+)-Daptomycin targets cell wall biosynthesis by forming a tripartite complex with undecaprenyl-coupled intermediates and membrane lipids.") because there are far too many components in a cell to rule out all of them as contributors. The authors speculate that the vancomycin-palmitoleic acid combination acts too rapidly to allow enough time for the induction of vancomycin resistance in vancomycin-resistant strains. This could be tested by using strains with constitutive vancomycin resistance or by pre-exposure to vancomycin. The authors conclude by speculating about the possibility of using the vancomycin-palmitoleic acid combination in antibacterial therapy. However, several difficulties involved in doing so would be worth addressing. Getting two compounds with such different physical properties to distribute similarly in order for both to reach efficacious concentrations at infection sites is likely to be challenging. Also, how to deploy it in the types of infections where Gram-positive organisms are most dangerous: bacteremia, endocarditis, abdominal infections, and UTIs is far from straightforward. Thus, it would appear that it is the understanding of the mechanism that would be the finding of the greatest value as the guide to the clinical development of a pharmacologically tractable treatment. It seems that the authors believe that the combination acts through a mechanism that is unique to the combination, rather than through an enhanced version of the standalone mechanism of one of the agents with the other agent merely facilitating that mechanism, but a clearer analysis of that point would make the narrative easier to follow. It is unclear whether adding one of the agents allows the other to have the same effect at lower doses that would only be achievable at its higher doses if used alone.

All in all, the reported results are potentially very valuable, and the amount of work done is substantial. However, it is hard to determine what the main conclusion of the manuscript is. If, on the one hand, it were the efficacy of the vancomycin-unsaturated fatty acid combination against clinically relevant Gram-positive pathogens, then the evidence of Figs 2-6 could mean that a variety of antibacterial effects result from the combination treatment that does not result from either agent alone. This could explain its effectiveness, but that would then require demonstrating experimental evidence for its translational potential using clinically relevant infection model(s). If, on the other hand, the main conclusion is the mechanism of how the combination works, which also appears to be the authors' intent from the title and abstract, then a clear mechanistic model has to be proposed, as well as all the likely alternative models, and both the narrative and the figures should be structured to buttress the proposed model for the mechanism of the UFA-vancomycin combination and to rule out possible alternative mechanisms. However, from the perspective of a model/mechanism-centric narrative, it is hard to see why the figures after Fig. 1 appear in the order in which they do as opposed to appearing in a different order and how a given figure builds on the preceding figures and leads into the next one. More generally, the experiments do not attempt to modulate the strength of the antibacterial effect of the combined treatment on the basis of what the authors believe its mechanism to be, and if a mechanistic model for an effect does not lead to predictions of what one can do in order to alter that effect, which can be then experimentally tested, it is difficult to build on its basis, such as for example to try to develop this into a clinical treatment.

Thus while reporting promising observations, the paper does not achieve ultimate resolution of the questions raised by these observations, and difficulty in connecting the results of various reported experiments to each other makes it hard to evaluate the authors' claims about the mechanistic basis of their observations.

Reviewer #3 (Public Review):

They tried to examine the role of thyroid hormone on circadian coordination of physiological rhythmicity and hepatic gene expression, particularly under hyperthyroidism. They found that thyroid hormone status does not significantly alter the circadian rhythmicity in behavior, metabolic parameters, and gene expression pattern in the liver. For gene expression analysis, they used microarray analysis, which allowed them to analyze a large number of gene expressions simultaneously. They carefully analyzed a large number of data and the conclusion was made based on their findings.

However, for RNA quantification, they only used data obtained by microarray analysis. From our experience, microarray data may not always be consistent with RT-PCR data. Thus, I suggest applying the RT-PCR study to several representative genes shown in Figs. 2, 3, and 4 for accuracy. Along the same line, performing Western blot analysis of several genes may be also important to perform. Particularly, because they claimed that the time-dependent effect of T3 could be due to rhythmicity in TH transporters, Dol 1, and TH receptor expression and/or activity, protein analysis of activity analysis of such factors is strongly recommended.

Reviewer #3 (Public Review):

In the present study, the authors aim to assess network activity alterations in the prefrontal cortex of mice with a deletion variant in the schizophrenia susceptibility gene DISC1 ("DISC1 mutants"). Using silicon probe in vivo recordings from the prefrontal cortex, they find that mutant mice show reduced firing rates of fast-spiking interneurons, reduced spike transmission efficacy from pyramidal cells to interneurons, and enhanced synchronization and activation of cell assemblies. The authors conclude that "interneuron pathology is linked with the abnormal coordination of pyramidal cells, which might relate to impaired cognition in schizophrenia."

The cellular and circuit basis of psychiatric disorders has received strong interest in the recent past. In particular, alterations of the "excitation-inhibition balance" in cortical circuits has been the focus of extensive scrutiny (reviewed in pmid 22251963). Specifically, in both human samples as well as in mouse models, disruption of interneuron development and function have been implicated in the pathogenesis of schizophrenia. In the DISC1 mouse model, studies have reported disrupted interneuron development (e.g. pmid 23631734, 27244370), reduced numbers of GABAergic neurons (e.g. pmid 18945897), reduced inhibition from GABAergic neurons ex vivo (e.g. pmid 32029441), and reduced firing rates of fast-spiking neurons in vivo in the basal forebrain (pmid 34143365).

The present manuscript makes a potentially important contribution to this question by probing the microcircuitry of the prefrontal cortex in vivo in the DISC1 mouse model of schizophrenia. It goes beyond previous work in assessing circuit dynamics in vivo in more detail, albeit with indirect methods. The experiments and analysis have generally carefully been performed, though the statistical analysis raises some questions. The advances made by the present work compared to previous studies could be delineated more clearly.

Reviewer #3 (Public Review):

In this study, Garcia Fernandez et al. employ a variety of genetic constructs to define the mechanism underlying the global chromatin mobility elicited in response to a single DNA double-strand break (DSB). Such local and global chromatin mobility increases have been described a decade ago by the Gasser and Rothstein laboratories, and a number of determinants have been identified: one epistasis group results in H2A-S129 phosphorylation via Rad9 and Mec1 activation. The mechanism is thought to be due to chromatin rigidification (Herbert 2017; Miné-Hattab 2017) or general eviction of histones (Cheblal 2020). More enigmatic, global chromatin mobility increase also depends on Rad51, a central recombination protein downstream of checkpoint activation (Smith & Rothstein 2017), which is also required for local DSB mobility (Dion .. Gasser 2012). The authors set out to address this difficulty in the field.

A premise of their study is the convergence of two types of observations: First, the H2A phosphorylation ChIP profile matches that of Rad51, with both spreading in trans on other chromosomes at the level of centromeres when a DSB occurs in the vicinity of one of them (Renkawitz 2014). Second, global mobility depends on H2A phosphorylation and on Rad51 (their previous study Herbert 2017). They thus address whether the Rad51-ssDNA filament (and associated proteins) marks the chromatin engaged during the homology search. They found that the extent of the mobility depends on the residency time of the filament in a particular genomic and nuclear region, which can be induced at an initially distant trans site by providing a region of homology. Unfortunately, these findings are not clearly apparent from the title and the abstract, and in fact somewhat misrepresented in the manuscript, which would call for a rewrite (see points below).

To this end, they induce the formation of a site-specific DSB in either of two regions: a centromere-proximal region and a telomere-proximal region, and measure the mobility of an undamaged site near the centromere on another chromosome (with a LacO-LacI-GFP system). This system reveals that only the centromere-proximal DSB induces the mobility of the centromere-proximal undamaged site, in a Rad9- and Rad51-independent manner. Providing a homologous donor in the vicinity of the LacO array (albeit in trans) restores its mobility when the DSB is located in a subtelomeric region, in a Rad9- and Rad51-dependent fashion. These genetic requirements are the same as those described for local DSB mobility (Dion & Gasser 2012), drawing a link between the two types of mobility, which to my knowledge was not described. The authors should focus their message (too scattered in the current manuscript), on these key findings and the diffusive "painting" model, in which the canvas is H2A, the moving paintbrush Mec1, and the hand the Rad51-ssDNA filament whose movement depends on Rad9. In the absence of Rad51-Rad9 the hand stays still, only decorating H2A in its immediate environment. The amount of paint deposited depends on the residency time of the Rad51-ssDNA-Mec1 filament in a given nuclear region. This synthesis is in agreement with the data presented and contrasts with their proposal that "two types of global mobility" exist.

The rest of the manuscript attempts to define a role in DSB repair of this phosphor-H2A-dependent mobility, using a fluorescence recovery assay upon DSB repair. They correlate a defect in the centromere-proximal mobility (in the rad9 or h2a-s129a mutant) when a DSB is distantly induced in the subtelomere with a defect in repairing the DSB. Repair efficiency is not affected by these mutations when the donor is located initially close to the DSB site. This part is less convincing, as repair failure specifically at a distant donor in the rad9 and H2A-S129A mutants may result from other defects relating to chromatin than its mobility (i.e. affecting homology sampling, DNA strand invasion, D-loop extension, D-loop disruption, etc), which could be partially alleviated by repeated DSB-donor encounters when the two are spatially close. In fact, suggesting that undamaged site mobility is required for the early step of the homology search directly contradicts the fact that the centromere-proximal mobility induced by a subtelomeric DSB depends on the presence of a donor near the centromere: mobility is thus a product of homology identification and increased Rad51-ssDNA filament residency in the vicinity of the centromere, and so downstream of homology search. This is a major pitfall in their interpretation and model.

In conclusion, I think the data presented are of importance, as they identify a link between local and global chromatin mobility. The authors should rewrite their manuscript and reorganize the figures to focus on the painter model that their data support. I propose experiments that will help bolster the manuscript conclusions.

1. Attempt dual-color tracking of the DSB (i.e. Rad52-mCherry or Ddc1-mCherry) and the donor site, and track MSD as a function of proximity between the DSB and the Lac array (with DSB +/-dCen). The expectation is that only upon contact (or after getting in close range) should the MSD at the centromere-proximal LacO array increase with a DSB at a subtelomere. Furthermore, this approach will help distinguish MSDs in cells bearing a DSB (Rad52 foci) from undamaged ones (no Rad52 foci)(see Mine-Hattab & Rothstein 2012). This would help overcome the inefficient DSB induction of their system (less than 50% at 1 hr post-galactose addition, and reaching 80% at 6 hr). For the reader to have a better appreciation of the data distribution, replace the whisker plots of MSD at 10 seconds with either scatter dot plot or violin plots, whichever conveys most clearly the distribution of the data: indeed, a bimodal distribution is expected in the current data, with undamaged cells having lower, and damaged cells having higher MSDs.<br /> 2. Perform the phospho-H2A ChIP-qPCR in the C and S strains in the absence of Rad51 and Rad9, to strengthen the painter model.<br /> 3. Their data at least partly run against previously published results, or fail to account for them. For instance, it is hard to see how their model (or the painter model), could explain the constitutively activated global mobility increase observed by Smith .. Rothstein 2018 in a rad51 rad52 mutant. Furthermore, the gasser lab linked the increased chromatin mobility to a general loss of histones genome-wide, which would be inconsistent with the more localized mechanism proposed here. Do they represent an independent mechanism? These conflicting observations need to be discussed in detail.

Reviewer #3 (Public Review):

1. The insertion locations of new rex sites is clear in the top panels of Figures 1A and S1A, but not in the bottom panels of these two figures. My interpretation of these figures is that the lines with pink and grey boxes are shown to help the reader understand how many rex sites are inserted in each line but the location of these boxes does not coincide with the actual location of the insertion sites. In the top panel of Figure 1A, it appears that the two "pink" sites correspond to two very large peaks of Dp727, whereas in the bottom they appear to be present in a region devoid of Dpy27. Authors should fix this because it is very confusing, since the bottom panel suggest that condensin is recruited to rex sites and then spreads to other sites in the genome without any condensin remaining at the rex sites.

2. The idea that rex sites recruit condensin would require the existence of a sequence-specific DNA binding protein that binds to rex and then interacts with condensin. This protein would then release condensin, which would extrude away and stop at TSSs. Has this been actually shown previously or is this an interpretation of observations such as those shown in Figure 1A? If not, the results shown in Figure 1 would equally agree with a model suggesting that condensin loads randomly in both autosomes and the X chromosome, and extrusion is stopped by large protein complexes bound to rex sites, which explains the accumulation at these sites. TSSs contain large transcription complexes that are not sufficient to stop condensin on their own but are able to if the second anchor contains 1-2 rex sites. This would make more sense in the context of what is known about cohesin in mammals. If someone has unequivocally shown that this is not the case, authors should discuss this in the Introduction because most non-worm readers will be thinking in these terms.

3. Figure 1A. Authors should not ignore the large Dpy27 peak in the worms with one rex insertion. What is at this site, where one can also observe a Dpy27 peak in wt worms? Are there similar sites in other regions of the autosomes of wt worms? If so, if these sites do not contain rex motifs, they may indicate alternative regions of the genome that can either recruit or stop extrusion of condensin.

4. Please include a supplementary table describing all the Hi-C data used in the manuscript, including numbers of replicates, total number of sequenced read pairs, mapped reads, inter-and intra-chromosomal contacts, and number of contacts >20 kb and <20 kb.

5. Page 8, lines 24-38. Based on this discussion, it is difficult to visualize what is happening. First, the authors suggest that condensin is recruited to the ectopic rex sites and "spreads" bidirectionally away from these sites to stop at various sites in the genome. Now, in this discussion, the authors suggest that rex sites containing condensin make loops. Does this happen without extrusion, just by the rex sites coming together in the 3D space? Are the loops formed through interactions between two condensin rings? When the authors say that condensin "spreads", does this take place by extrusion or a different mechanism? As mentioned in #2 above, everything would make better sense if the accumulation of condensin at rex sites is not a consequence of initial recruitment but rather a consequence of random loading followed by extrusion and retention at rex sites.

6. Figure 2C. Were the interactions highlighted in this figure determined to be the only statistically significantly different between control and rex insertions or were they defined visually? The interaction between the center rex bait and the right rex pink site appears to be the same as in control. However, there seem to be some significantly visually different interactions between the center and right baits and other regions in the genome. Authors should test whether these interactions are statistically significant and, if so, what is located at these non-rex sites.

7. Figure 3A. The fact that rex sites can contain more than one motif, presumably a binding site for an unknown protein, complicates data interpretation. It would be helpful if the authors indicate at the top of Figure 3A the number of motifs and their orientation for each rex site currently shown. In the bottom panels of this figure, it appears that not all rex sites indicated at the top are able to "recruit" condensin. Authors should comment on this, and if there are differences in the number of motifs at these sites or the sequence of the motifs. Also, the newly inserted sites appear to "recruit" less condensin than some of the existing ones. Do the sites with the taller Dpy27 peaks have more motifs?

8. It is unclear from the experiments described in Figure 1 how the formation of new loops would affect transcription. In Figure 3A, it appears that some of the Hi-C heatmaps show signal that could correspond to compartmental interactions. I wonder if the authors have tested whether the formation of new loops disrupts these interactions, which may contribute to the stabilization of promoter contacts and affect transcription. It may be informative to look at subtraction heatmaps between the new insertion data and control, although the Hi-C data in the center panel appears to have lower quality.

9. Figure 4 and page 9 lines 16-36. It is not completely clear from the discussion of Figure 4 whether the Hi-C data from wildtype was obtained with fixed embryos whereas the data from X;V was obtained with unfixed embryos. If this is the case, it may not be appropriate to directly compare the two samples. When the authors say "the autosomal spreading region showed an increase in DNA contacts measured by Hi-C", is this within the region or between the region and other sites in the genome? Since the two datasets have been normalized to the same number of contacts, an increase in interactions within the chromosome V region adjacent to the X chromosome in the X;V sample could be explained if this region interacts less with the adjacent X chromosome. Authors should discuss in more detail how this analysis was performed and perhaps use subtraction heatmaps to illustrate the point.

10. Figure S4A. If there is an increase in condensin (Dpy27) in chromosome V and an increase in interactions in this region, would this imply that the "spreading" of condensin takes place by loop extrusion? Otherwise, the "spreading" of condensin as suggested in the model of Figure 6 would not create new interactions.

11. Figure 5 and page 10, lines 20-21. It is clear from Figure 5B that the presence of the block leads to an accumulation of condensin, although the bottom panels of Figure 5C suggest that this accumulation is lower than at the flanking rex-33 and rex-14 sites. However, contrary to the author's conclusion that this in vivo evidence for loop extrusion, the result may suggest the opposite. If condensin was extruding loops and stopped at the dCas9 site it should have formed a loop. Were the same cells used for the ChIP-seq and Hi-C experiments? If not, one trivial explanation is that dCas9 failed to work in the cells used for Hi-C. Authors should comment on the fact that the rex-23 and rex-34 sites do not seem to be located at TAD boundaries, whereas TAD boundaries in the left region of the figure seem to lack rex sites.

Reviewer #3 (Public Review):

The manuscript by Montgomery et al. describes an interesting phenomenon in the Marchantia plant, where the entire paternal genome is silenced by Polycomb-mediated repression in the diploid embryo. This species spends most of its life cycle as haploid, yet has a diploid stage during embryogenesis. By analyzing the transcriptome of embryos derived from genetically distinct strains of parents, the authors show that transcription is heavily maternally biased in such embryos (at least for genes, for which distinct SNPs could be used), suggesting that paternal genes are widely silenced. The authors further demonstrate via CUT&RUN that enrichment of the Polycomb histone mark H3K27Me3 is biased towards paternal alleles. Interestingly, the authors also find that nuclei of embryos of this early stage display DAPI-bright, condensed foci that are specifically enriched for the H3K27Me3 signal, suggesting that these represent paternal chromosomes. Further immunofluorescence characterization revealed a strong H3K27Me3 signal specifically in the male pronucleus at the 3 daf stage, before the genomes fuse, presenting a possible time point and mechanism for paternal-specific deposition of Polycomb marks.

Importantly, the authors show that when embryonic-specific E(z) homologues are knocked out, the large nuclear foci disperse. CUT&RUN and RNA-seq analysis on the mutant embryos further demonstrate the H3K27Me3 mark is widely reduced (although not abolished), especially on paternal alleles, and that expression becomes less maternally biased and more biallelic overall. The spores produced from such mutants are inviable. Together, these results support the authors' model that PRC2-mediated chromatin modification silences the paternal genome, revealing genomic imprinting on a global scale as a necessary part of the embryonic development of this species.

Overall, this is an interesting and newly described (at least to my knowledge) phenomenon of genome-wide and post-fertilization genomic imprinting, which expands our knowledge of gene dosage mechanisms. Although the involvement of Polycomb is not particularly surprising, such global paternal silencing itself seems to be. Therefore, this represents an additional system, where mechanisms of gene dosage, Polycomb repression, nuclear organization, and genomic imprinting can be investigated in a unique context. The data appears to be of high quality and generally supports the conclusions made by the authors.

Reviewer #3 (Public Review):

Dosil et al. identified NK-extracellular-vesicle (EV)-associated microRNAs and their post-transcriptional modifications signature by small RNA next-generation sequencing. Furthermore, they found that NK-EVs promote Th1 polarization and activation of monocyte and moDCs. They also suggested that the identified NK-EV-associated microRNAs partially recapitulate NK-EV effects in T cells in vivo.

The study contains some interesting findings made by next-generation sequencing. However, the impacts of NK-EVs and NK-EV-associated microRNAs on Th1 differentiation are not impressive. In addition, their proposal that NK-EV-associated microRNAs promote Th1-like responses via T-bet de-repression by down-regulation of GATA3 is not fully supported by their results.

Reviewer #3 (Public Review):

Strengths:<br /> • TGFB is a critical regulator of CD8 T cell fate and function; yet, the complex mechanisms of action and downstream regulatory factors remain unresolved. This works expands upon the role of TGFB in controlling expression of critical receptors and trafficking/retention molecules on CD8 T cells, such as CD127, KLRG1, CD103 and CD62L.<br /> • Downstream mediators of the TGFB signaling pathway are complex and at times operative counterintuitively. Therefore, carefully dissecting how TGFB and various SMAD factors impact T cell fate is an important and relevant research goal.<br /> • The authors should be commended for the wide range of tools and approaches they use to investigate the role of SMAD factors in CD8 T cell biology. This includes multiple strains of mice with perturbations in the TGFb/SMAD signaling pathway, diverse pathogens, RNAseq studies, in vitro and in vivo experiments, and manipulation of SMAD4 deficient cells through retroviral mediated genetic manipulations.<br /> • In vitro studies demonstrating that EOMES overexpression partially rescues the phenotype of SMAD4 deficiency in activated CD8 T cells are intriguing and generally well done.

Weaknesses:<br /> • Despite some nicely designed and executed experiments, the overall research question and goal of this manuscript are unclear. The title/abstract/background seem to indicate the goal is to clarify the role of SMAD4 and TGFB in controlling CD8 T cell differentiation into circulating or tissue-resident memory populations. This is an important research question. However, many of the key experiments are executed in vitro, complicating results and making it hard to discern how some of the experiments translate to in vivo differentiation. Further, there is not a consistent approach of carefully examining Trm cells as the experiments tend to switch back and forth across circulating CD8 T cell populations, non-lymphoid localized CD8 T cells, and in vitro cells.<br /> • Related to the point above, the manuscript is a bit disjointed and at times difficult to follow. It's hard to tell if the goal is to understand how TGFBRII/SMAD4 regulates CD8 T cell differentiation during influenza virus infection vs discerning molecular mechanisms of SMAD-mediated control of CD103. If it's the former, much of the key in vivo experiments are lacking or the results have already been published. For the latter question, more molecular and genetic studies are necessary to understand this process (and the overall background and focus do not seem to fit with this research question).<br /> • Many of the core findings in this manuscript have previously been reported either in the prior work from this group (J Immunol, 2015) or more recently by Wu et al. (Cell Moll Immunol 2020). Key factors reported to be modulated by SMAD4 in this manuscript include CD103, KLRG1, and CD62L. It has already been shown these factors are modulated by loss of SMAD4, and the experiments outlined in this study do not dramatically expand on these findings. Intriguing results with dual TGBRII/SMAD4 dual KO cells have also been reported in similar experiments by Wu et al.<br /> • Many of the conclusions are not properly supported by the data. Based on the discussion, it appears the main conclusions are 1) TGFB and SMAD4 exert reciprocal functions in controlling circulating and tissue-resident memory formation (last sentence of first paragraph in discussion). As mentioned above there is not a careful or consistent assessment of memory T cell populations in lymphoid or non-lymphoid compartments. 2) SMAD4 was required to maintain EOMES expression in activated CTLs. This data is fairly robust; however, could this be due to differences in cell states rather than a direct role for SMAD4 in sustaining EOMES expression? 3) SMAD4 has multiple roles in regulating expression of CD103, including complimentary or independent roles of Ski (last two sentences of paragraph describing Fig 3 in the results section). There was no assessment of Ski in the results of this study. Additionally, despite many conclusions about the roles of SMAD proteins in controlling gene expression, there are no experiments to assess binding of these factors (e.g. ChIP-qPCR etc) to key genes.

Reviewer #3 (Public Review):

In this paper Berryer et al. developed an efficient automated and quantitative high-content synaptic phenotyping platform to be used for human neurons and astrocytes derived from iPSCs. With this quantitative platform, the authors screened the effects of 376 small molecules on presynaptic density, neurite outgrowth, and cell viability. Interestingly six small molecules were identified that specifically enhanced human presynaptic density in vitro and the presence of astrocytes in culture was essential for mediating the effects of the six molecules. Among these molecules, the bromodomain and extraterminal (BET) inhibitors were the most effective in increasing the presynaptic clusters and in upregulating synaptic gene expression programs. Thus this paper provides strong evidence for the possibility to use a reproducible and automated screening platform for the identification of synaptic modulators in human neurons.

Reviewer #3 (Public Review):

Zanin and Friedman addressed a fundamental issue in cerebellar development: how cerebellar granule cell precursors avoid excessive migration from the external granule cell layer (EGL). Although many studies focused on the mechanisms that promote neuronal migration, little is known about the mechanisms that restrict neuronal migration. By combining in vivo and in vitro experimental approaches, the authors convincingly demonstrated that in the EGL, expression of p75NTR does not promote proliferation or cell cycle re-entry. Instead, p75NTR inhibits the radial migration of granule cells via activated RhoA. These experimental results are interesting, and the conclusions of this paper are well supported by data. The paper is mostly clearly written, but some areas of experimental findings and analysis need to be clarified or extended.

Reviewer #3 (Public Review):

In this work, the authors describe a novel method, based on deep learning, to analyze large numbers of yeast cells dividing in a controlled environment. The method builds on existing yeast cell trapping microfluidic devices that have been used for replicative lifespan assay. The authors demonstrate how an optimized microfluidic device can be coupled with deep learning methods to perform automatic cell division tracking and single cell trajectories quantification. The overall performance of the method is impressive: it allows to deal with large image datasets generated by timelapse microscopy several order of magnitudes faster than what manual annotation would require. The method has been carefully tested on several microscopy settings and datasets and compared with known results from the literature in a convincing manner. In addition, the authors show how the analysis pipeline can be enriched with semantic segmentation to quantify cellular physiology and gene expression during their lifespan, creating high quality, high throughput measurements of single cell trajectories. The software, its documentation and related datasets are available through public repository. Taken together, the author succeeded in setting up a method that can be a game changer for high throughput longitudinal analysis of yeast cells.

Overall, the method seems robust and powerful but some aspects need to be clarified and/or extended.<br /> - The authors chose MATLAB to develop DetecDiv. This is a valid choice but as Python is becoming the standard for deep learning developments it is important to 1/ better justify the use of MATLAB and 2/ discuss how this can be "translated into" and/or linked with Python. This would facilitate adoption by other research teams.<br /> - A critical aspect of deep learning methods is their potential ability to be used on a different datasets and/or experimental setup (transfer learning). The authors explained that a "generalist" model, trained using several datasets perform comparably (or even better) than "specialist" models that are independently trained on a specific dataset. Yet, they do not discuss how accurate would an already trained generalist model perform on a novel dataset made with a different imaging setup and/or a different yeast strain?

Reviewer #3 (Public Review):

McLachlan and colleagues investigated the molecular mechanisms that lead to an adaptive response of a single pair of chemosensory neurons. Taking advantage of the single cell resolution of the C. elegans nervous system as well as genetic and behavioural tools, they observe that after fasting, animals show an altered profile in the expression of chemosensory GPCRs. They focused on two GPCR genes, str-44 and srd-28, both highly upregulated in AWA neurons after fasting which correlated with altered chemosensory behaviour. They further showed that upregulation and behavioural changes depend on both, external (food cues and osmotic stress) as well as internal signals from food-sensing neurons and the intestine. Artificially increasing str-44 and srd-28 by overexpression in AWA mimics the fasted state. They provide evidence that STR-44 is a chemoreceptor of the attractants propyl acetate and butyl acetate by ectopically expressing str-44 and srd-28 in ASH. They provide evidence for a model in which a combination of pathways together modulates str-44 expression in AWA. These include other food-sensing neurons modulating the activity of AWA neurons, intestinally expressed factors that are involved in metabolism as well as pathways detecting environmental stress. A chemosensory role for the proposed phenotypes for STR-44 could be strengthened by providing AWA calcium imaging and behavioural evidence of chemosensory defects of mutants lacking str-44.

Reviewer #3 (Public Review):

The submitted study aims at uncovering novel genes that contribute to dental abnormalities. The authors use several techniques and demonstrate their expertise in genome analyses, and identification of potential genetic targets. However, the study has some serious flaws.

Strength: The authors have the expertise in obtaining and analysing complex transcriptome and genome data.

Weaknesses: It is evident that the authors are not experts in the field of tooth development. The authors do not discover any new genes that might be involved in the development of the dental abnormalities/anomalies, and the chosen methods of validation for some candidate genes are not suitable for the analysis of their expression or potential role in the tooth development.

The authors use a significant amount of data already published by others, and overall it is not clear what is the novelty this study brings to the tooth development field.

Reviewer #3 (Public Review):

This manuscript by Ansari and coworkers describes CriSNPr, a tool for designing gRNAs for CRISPR-based diagnostics for SNP detection. CriSNPr allows one to design assays to detect human and SARS-CoV-2 mutations, positioning the mismatches for optimal detection based on results from the literature. Designs can be generated for 6 different CRISPR effector proteins. The authors test their approach by designing assays to detect a single SNV using three different CRISPR effectors. A strength of the manuscript is that the method does appear to work, at least for the E484K mutation, for multiple CRISPR effector proteins.

The weaknesses of this manuscript are the lack of data demonstrating that the method works. There is only one very small experimental demonstration using a single mutation (Figure 4), and some very high-level analysis using two SNP/SNV databases (Figure 5). The authors do not provide any data to answer any basic questions about how well their designs work, how fast and easy it is to run their method, or which designs are predicted to work better than others. These weaknesses ultimately limit the impact of the work on the field, as it is not clear what the benefits of using the author's approach are versus simply applying the rules for the individual CRISPR effector proteins outlined in Figure 1 of the manuscript.

Reviewer #3 (Public Review):

The manuscript by Bonin et al describes the use of sensitive methyl-based nuclear magnetic resonance (NMR) spectroscopy methods to characterize the mechanism of substrate binding in human thymidylate synthase. Using NMR experiments that probe protein motions on the micro-to-millisecond timescale, they first show that the activation loop and substrate binding regions undergo conformational exchange between active and inactive states and in some cases a previously undescribed third state. Using a variety of NMR methods, the authors show that the active state is the dominant (>98%) conformer in the solution. With a small population (<2%), the inactive state cannot contribute significantly to cooperative substrate binding. Then using side-chain methyl-group relaxation methods, which are sensitive to protein motions on the nano-to-picosecond, they determine that thymidylate synthase becomes more rigid upon substrate binding; a result that is consistent with an entropic, dynamic version of cooperativity. Through the use of mutants and substrate-bound forms of the enzyme, the authors are able to trace the origin of this dynamic allostery to the unstructured N-terminus of the protein, which is lacking in bacterial versions of the enzyme and does not show the same cooperativity. In short, this work highlights the power of solution state NMR for the use of studying protein motions over 9 orders of magnitude in time and how these motions contribute to the thermodynamics and regulation of enzymatic activity.

Reviewer #3 (Public Review):

The manuscript by Klee and colleagues builds on work from this group and others mapping the molecular membrane trafficking machinery that ensures the delivery of apical-membrane proteins to the apical surface. This mapping has been essential as it has occurred in parallel to, and facilitated, the identification of how mutations in these machineries underpin enteropathies and congenital diarrhoea syndromes, such as Microvillus Inclusion Disease (MVID). This has revealed distinct profiles of dependency for differential apical membrane proteins on trafficking machineries: those that depend on Rab8/11-Myo5b-Stx3 (and others) and those that do not. An example cargo for this latter pathway is DPP4. The current work uses a genome-wide CRISPR screen in an intestinal epithelial cell line to identify novel machineries that regulate this 'Rab8/11-Myo5b-Stx3'-independent trafficking pathway, using DPP4 as a model cargo. The authors identify and characterise a number of new players in such an apical transport pathway.

Using an approach based on lentiviral transduction of a genome-wide CRISPR screen into intestinal epithelial cells, followed by multi-day differentiation into a monolayer, the authors then dissociate the previously polarised cells into single cell suspensions and use antibody staining and cell sorting of individual cells to enrich for those with a defect in cell surface labelling of DPP4. The aim of this approach is to find machineries that, upon knockout, present a defect in apical delivery of DPP4. The central assumption in this approach is that cell surface levels of DPP4 measured by cell sorting in these post-monolayer single cells will be directly related to, and maintained from, their previous state of apical labelling of DPP4 in polarised monolayers. Evidence for this is provided in Figure 1B wherein sorting for surface DPP4 in single cells derived from polarised monolayers, versus unpolarised cells, is increased in the former compared to the latter.

The authors identify a number of factors from the genome-wide screen for follow-up validation, focusing on 7 factors that have previously ascribed roles in membrane transport (though not apical membrane-specific transport pathways). The authors characterise that in addition to showing defects in DPP4 transport, a number of these factors do not disrupt the ability to form monolayers with some level of barrier function (as detected by transepithelial-resistance), but rather these present defects in collective cell polarisation related to the formation of an apical lumen. Such factors present defects in endo-lysosomal morphogenesis and defects in the formation and placement of microvillar structures.

A strengthening of the work could result from examining the molecular mechanisms of how any of these factors regulate membrane transport, as well as independent validation of whether the identified factors are bona fide regulators of apical transport versus potential off-target effects, which could be achieved through rescue experiments or multiple independent knockout approaches to the same target. Despite these avenues for expansion, the work provides a resource for the identification of new potential regulators of membrane transport and provides a methodology for the identification of other transport pathways related to the surface levels of other proteins from polarised cells. It may open avenues for screening of the identified potential machineries for mutations in congenital diarrhoea syndromes or enteropathies, as was successfully applied by the authors and others to machineries of the Rab8/11-Myo5b-Stx3 pathway.

Reviewer #3 (Public Review):

This study, conducted in 14 acute hospital trusts in the United Kingdom, compared SARS-CoV-2 hospital infection outcomes in a four week baseline period with outcomes in periods with 'rapid' (<48h) and 'longer-turnaround' (5-10 day) sequencing with results fed-back to infection prevention and control teams using a bespoke sequencing reporting tool. The question of whether rapid sequencing of hospital-onset SARS-CoV-2 infection can, by informing infection prevention and control (IPC) actions, reduce nosocomial transmission is interesting and potentially important. To our knowledge, this study represents the first large-scale formal evaluation of such technology. While the results are, on the face of it, disappointing in that hospitals were largely unable to meet target turnaround times for sequencing and results provide no evidence of benefit of the intervention in reducing hospital-acquired infection (and in some cases, such as for the "hospital outbreaks" outcome, the confidence intervals are so wide as to be unable to rule out substantial benefits or harms of the intervention) the are a number of important strengths of the study. These include the relatively strong quasi-experimental design (a type of non-randomised cluster crossover), the pre-defined analysis plan, and adequate power for the primary outcomes.

Limitations of the study include the practical difficulties that participating hospitals had in reporting sequencing results to the IPC teams in a timely manner that could be acted on and lack of sufficient consideration for the ways in which sequencing information could have directly informed IPC activities in ways that would have been likely to substantially reduce the spread of infection (for example, Table S2 reports changes to IPC as a result of sequencing reports which include generic activities such as "Assessment of alcogel stocks" or "IPC signage assessment", which seem like things which should be done anyway, and don't obviously depend on information from pathogen sequencing). There are also some aspects of transparency that need to be addressed: the analytical methods are not reported in sufficient detail to enable the work to be repeated, and the results are not reported with sufficient detail to an enable an assessment of the appropriateness or otherwise of the statistical models used in the analysis. Additionally, while the study protocol specified six secondary outcomes, not all of these are reported even where it appears that some (partial) information is available for unreported outcomes.

Reviewer #3 (Public Review):

While it has long been clear that animals in groups (e.g., fish schools) benefit in terms of safety in numbers, there has also been a keen interest in which animals in the group are at higher versus lower risk (e.g., those in front, or along the edges) and how that might depend on the predator's attack strategy. This study addresses these important predator-prey details using a common predatory fish (northern Pike) attacking schools of prey fish (golden shiners). A strength of the study is that it uses cutting-edge video tracking and computational/statistical methods that allow it to quantify and follow each fish's (1 predator and 40 prey in a group) spatial position, relative spacing, orientation and even each individual's visual field and movement throughout each of 125 attacks. Most (70%) of these attacks were successful, but many were not. The variation in attack success allowed the investigators to do statistical analyses to identify key predator and prey behaviors that are associated with successful vs. unsuccessful attacks.

The study yielded numerous interesting insights. While conventional wisdom pictures predators initiating an attack from outside of the group thus putting individuals at the group's edge at greatest risk, this study found that pike typically approached the school of prey head-on both in terms of the group's orientation and direction of movement, and often stealthily moved within the group before initiating an attack. To understand which prey individual was targeted by the predator, the highly quantitative video analyses examined 11 measures of each individual prey's position and orientation at the time that the pike initiated its attack. Of course, pike showed a strong tendency to target one of the 3 closest prey, particularly prey that were more or less directly in front of the pike. However, contrary to conventional wisdom, the analysis showed that targeted prey were closer to the center than the edge, and that an individual's position and orientation relative to other nearby prey also played an important role in whether it might be targeted by the predator. Not surprisingly, analyses showed that targeted prey were more likely to escape if they were further from the predator's head and if they exhibited higher maximum acceleration. Interestingly, during the actual strike, on average, the predator accelerated to a speed about 50% faster than the velocity of the targeted prey.

A limitation of the study (that the authors describe and discuss) is that it was conducted in a tank with no spatial refuges whereas in nature, pike are often found in areas with vegetation, and schools of prey can often potentially respond to the presence of a predator by moving towards refuge (e.g., vegetation). Also, the study was done in very shallow water (6 cm) -- likely shallower than many, if not most, natural predator-prey interactions for these species. In deeper water, the predator-prey interaction might be better analyzed in three dimensions (i.e., also accounting for variation in vertical height in the water), though the authors argue that this conventional idea is not necessarily true.

Overall, this study provides an impressive example of the use of modern technology and statistical analyses allows us to better describe and understand the fine-scale behaviors that affect an interaction of high importance for ecology and evolution.

Reviewer #3 (Public Review):

Overall, the authors sought to explain the epidemiological, behavioral, and immunological underpinnings across multiple COVID-19 waves in South Africa using an infectious disease model and statistical framework. In doing so, they hoped to learn about the different emerging variant properties and provide a modeling framework for understanding risk upon future variant emergence.

Strengths:<br /> The manuscript uses an epidemiological and statistical modeling framework that has been validated across a number of different diseases, time periods, and regions.<br /> The researchers have validated their modeling results using multiple separate lines of evidence and data including laboratory results, seroprevalence, forecasting, and other epidemiological studies.<br /> While not independent from one another, agreement across multiple regions within South Africa enhances the confidence in modeling results

Weaknesses:<br /> The model complexity adds some opaqueness to the results due to the presence of many hidden parameters and potential correlations and interactions between them, so I suggest that the authors further validate the convergence of their model fitting and visualize the results of their hidden parameters.

Conclusions justified:<br /> Overall I believe the conclusions the authors have provided are justified by their analysis. It appears their analysis is statistically rigorous, and there are multiple independent lines of evidence that agree with and validate their conclusions.

Reviewer #3 (Public Review):

Strengths: In the vascular field, previous implementation of optogenetics to constrict and dilate blood vessels, has used either single photon full field and fiber illumination, or alternatively confocal and 2-photon scanning of individual vascular segments with raster scanning. The former is limited in spatial precision, activating multiple vessels over a large area, whereas raster scanning is not ideal for accumulating currents and often results in slow temporal precision. Spatial light modulator (SLM) generated diffraction patterns to achieve patterned illumination have become increasingly used in neuroscience to achieve reliable 2-photon activation of targeted neuron populations. Here the authors use this technology to depolarize and constrict smooth muscle cells in vivo. By imaging and stimulating with 2 laser lines and different optical paths they are able to stimulate opsin expressing cells and image simultaneously, which is advantageous. By using the Red-shifted opsin ReaChR for their experiments, it is possible to combine this approach (cautiously) with imaging many of the classically used 2-photon fluorophores and genetic indicators, with excitation spectrums <1040nm. Future work using variations of the technique is likely to gain valuable insight into neurovascular biology.

Weaknesses: A major limitation of the current study is that although the authors achieve high spatial precision of ReaChR activation in the xy plane, the axial precision appears extremely poor compared to what would have been expected. For example, in Fig. 5-1 (using a 0.8NA, 16x objective), the authors achieve equivalent levels of surface arteriole constriction even when the SLM is focused 200um above the brain, and even larger constrictions as they initially move the focus away from the imaging plane. Although the axial spatial resolution appears better with the 1.1NA - 25X objective, such a large point spread function largely limits the utility of the technique, as there will always be a concern as whether the effects are spatially specific and not due to activation of vascular cells above and/or below the site of interest. This experiment that the authors have presented on axial precision is extremely important as it outlines a very important limitation of the technique (which is likely power dependent), but it remains to be completely characterized and understood. One possibility is that the power levels used by the authors are already above saturation, a problem raised by Rickgauer and Tank (2009)- PMID: 19706471, and therefore they may be able to refine the axial precision by using lower power. Further controls would be valuable to understand the precise cause of this large axial spread as it doesn't quite add up with the diameter of the bleach spot shown in figure 5-1D (some suggestions outlined in recommendations to the authors).

The current version of the paper also lacks adequate quantification of the results as it is composed primarily of representative examples, which limits a proper assessment of reproducibility and variability of the effects.

Reviewer #3 (Public Review):

Ströh et al use time-lapse X-ray imaging to monitor the diffusion of heavy metal stains into large brain samples. Uniform staining of large (thicker than 1 mm) tissue samples is a prerequisite for future whole-brain 3D EM reconstructions of synaptic connectivity. Until now, staining optimization has essentially been achieved through trial and error. The reported approach allows the rapid measurement of staining gradients and the determination of diffusion rates within tissue specimens. This offers the possibility to modify staining parameters with a more rapid turn-around. The authors develop a diffusion/binding model to describe the occupancy of free and masked osmium binding sites and fit the model parameters to the diffusion of osmium solution. The authors also demonstrate that an approach that separates the osmium staining and reduction steps seems to counterintuitively 'washout' the osmium in the tissue.

While the approach seems promising as a diagnostic tool and offers a principled approach to gaining a better understanding of staining processes, a weakness is the lack of a demonstration that the x-ray imaged staining gradients correlate with what is actually observed under the electron microscope. For example, the figures show that reduced osmium stains tissue with a maximum intensity of ~1.1 (a.u.) compared to osmium alone at ~0.9 (a.u.). Because these intensities are not calibrated against the appearance of the staining in EM sections, their interpretation is limited.

Reviewer #3 (Public Review):

In this manuscript, Dikstein and colleagues perform CAGE-Seq from total and polysome-associated RNA to detect alternative promoter usage in B-cells from Eµ-Tcl1 mice. They find considerable alternative transcription start site (TSS) usage compared with B-cells from wt mice, including intragenic events ultimately potentially resulting in N-terminally truncated proteins. The authors propose that there is a feed-forward mechanism by which Tcl1-promoted alternative TSS usage in genes encoding chromatin regulators contributes to the 'openness' of chromatin and to transcriptional alterations (including overexpression of Myc) observed in Eµ-Tcl1 cells. These results are interesting and provide a solid analysis of TSS usage in B-cells. However, the extensive bioinformatics analysis provided in this manuscript is often not followed by validation. The manuscript would benefit from validation to support the major conclusions, including assessment of stable expression of N-terminally truncated products, assessment of chromatin accessibility, and a deeper understanding of the alternative TSS events that are directly due to Tcl1 over-expression.

Reviewer #3 (Public Review):

The manuscript is well written and nicely presented but the Materials and Methods section is very weak. A detailed explanation on the experimental design and setup is currently missing. The authors need to be very clear on the number of specimens they measured in each analysis. Moreover, while the results of this study are relevant, they should not be generalized, as analyses were conducted, for all the investigated species, on fragments derived from the same mother colony kept in aquaria for 10 years, thus excluding the natural variability in a natural population.

References in the Introduction are a bit outdated, the reference list should be double checked (e.g., some references are incomplete) and page numbers should be included.

Reviewer #3 (Public Review):

The authors have successfully characterized a specific mechanisms that occurs during NET-formation: a NET-specififc histone H3 cleavage event. The monoclonal antibody 3D9 detects evidence of the proteolytic events that occur in NETosis -the proteolytic signature, histone cleavage at H3R49. Based on this finding they have developed a new method to detect and quantify NETs and differentiate NET-formation from apoptosis or necroptosis. The method can be used to stain mixed cell populations or also human tissue material.

The major strength of this manuscript is that it gives mechanistical insight into NET-formation and presents at the same time a novel techique that shows several advantages compared to existing techniques.

The methods and results are presented in detail and well controlled and presented.

Reviewer #3 (Public Review):

Rate-distortion theory is a mathematical framework that describes the optimal solution to the lossy data compression problem (optimizing a performance metric, subject to a cost or constraint on information rate). This framework has previously been utilized to understand human visual working memory at the abstract computational level. This paper seeks to extend prior work by implementing a detailed and biologically plausible neural population coding model of visual memory, that achieves the normative performance bounds predicted by rate-distortion theory. The model is shown to be able to reproduce previously described empirical phenomena, including set size effects, and serial order effects, among others, and is also applied to previously-collected neural data.

Strengths

• The model proposed by the authors is closely connected to a principled and well-understood theoretical framework (rate-distortion theory). Hence, the model can be seen as successfully bridging Marr's levels of analysis (computational, algorithm, and implementation-level).<br /> • The resulting model is also fairly parsimonious (e.g., it has no ad hoc components or mechanisms whose only seeming role is to account for specific empirical phenomena).

Weaknesses

• There are numerous existing computational models of visual working memory, including models not based on information-theoretic principles. While the authors show their model successfully reproduces a range of known behavioral phenomena, there are no formal model comparisons to alternative models.<br /> • How the model might scale to more complex visual information is largely unknown. For example, the model is designed to optimize a fixed cost function (cosine error for circular stimuli such as colors or oriented lines). It is not clear whether this is an appropriate cost function for visual memory for complex stimuli. Although the authors reference models that utilize variational autoencoders as a possible solution to this dilemma, it is not clear exactly how such models relate to the present work.

Appraisal

• The claims of the paper are relatively straightforward: The authors show that their model can be derived in a principled fashion from rate-distortion theory, and show that the resulting model successfully reproduces a range of documented empirical phenomena. Each of these claims is well-supported by the data.

Potential impact

• Visual working memory is an important field of study in neuroscience and psychology, as it bridges perception, learning, and memory. Many, many models have been proposed in this space. The current work is notable in that it offers a detailed neural implementation that retains a close connection to well-understood computational principles. In addition, the work expands upon recent and growing interest in "computational rationality", or the idea of systems that optimize performance subject to their resource or information processing constraints. Hence, the work is likely of interest to a wide audience in computational cognitive science.

Reviewer #3 (Public Review):

The authors were investigating the influence of Ia afferent fibers on the excitability of motor neurons innervating soleus using two different methodologies. One method involves conditioning the common peroneal nerve in advance of the tibial nerve stimulation which is shown to suppress the H-reflex via presynaptic inhibition. The other method involves conditioning the femoral nerve following stimulation of the tibial nerve that acts to facilitate the H-reflex. Two groups were compared; individuals with spinal cord injury and uninjured controls. Two conditions were compared; muscle at rest and muscle contracting at 30% MVC. Both groups showed reduced H-reflex during common peroneal conditioning during a contraction but the SCI group showed less of this reduction. For the femoral conditioning, only the control group showed facilitation during a contraction while both groups showed facilitation at rest. The data indicate that individuals with SCI have reduced facilitation of motor output during voluntary contraction.

Reviewer #3 (Public Review):

This manuscript presents two main contributions. First, the authors modified a CRISPR base editing system for use in an important model organism: budding yeast. Second, they demonstrate the utility of this system by using it to conduct an extremely high throughput study the effects of mutation on protein abundance. This study confirms known protein regulatory relationships and detects several important new ones. It also reveals trends in the type of mutations that influence protein abundances. Overall, the findings are of high significance and the method appears to be extremely useful. I found the conclusions to be justified by the data.

One potential weakness is that some of the methods are not described in main body of the paper, so the reader has to really dive into the methods section to understand particular aspects of the study, for example, how the fitness competition was conducted. Another potential weakness is the comparison of this study (of protein abundances) to previous studies (of transcript abundances) was a little cursory, and left some open questions. For example, is it remarkable that the mutations affecting protein abundance are predominantly in genes involved in translation rather than transcription, or is this an expected result of a study focusing on protein levels?

Overall, the strengths of this study far outweigh these weaknesses. This manuscript represents a very large amount of work and demonstrates important new insights into protein regulatory networks.

Reviewer #3 (Public Review):

In this study, the authors investigated the impact of HIV infection on T cell immune responses to SARS-CoV-2 infection. To do this, in vitro stimulation with control and strain-specific peptides was used to activate T cells, and the secretion of IL2, IFNg, and TGFa was used as a proxy for T cell-mediated function. The authors also attempted to define peptide specificity to establish the breadth of the T cell responses and which mutations were responsible for any loss of cross-recognition. The results show that individuals with unsuppressed HIV infection defined by a viraemia above 1000 viral copies per ml, had poorer T cell polyfunctionality compared to those who were HIV negative or avireamic. Unsuppressed HIV-infected individuals also had lower cross-reactive responses to SARS-CoV-2 variants dominating the first and second COVID-19 waves in South Africa. Contrary, aviremic HIV-infected individuals had similar responses to those observed in healthy individuals. The conclusions of this paper are well supported by the data. Using a flow cytometric approach and bulking of T cells enabled phenotypic and peptide-specific analysis. It is however worth noting that HIV infection may have a direct impact on the survival of cells in long-term cultures and outcomes from those assays may be more reflective of invitro survival than the true in vivo situation. In addition, previous studies have shown notable levels of cross-reactive responses of SARS-CoV-2 and other human coronaviruses present prior to the pandemic, it is surprising that very low levels of cross-reactivity were observed across SARs-CoV-2 variants even in the healthy individuals.

This paper addresses a critical issue within the African context where HIV is prevalent and may have a direct impact on the continent's success in controlling SARS-CoV-2 infections. The low cross-reactive responses more so in individuals with unsuppressed HIV reduce the benefits of the first-generation SARS-CoV-2 vaccine warranting additional considerations of emerging variants for future vaccine development. Unsuppressed HIV infection also places this population at increased risk of infection and more severe form of SARS-CoV-2 disease from future emerging variants. It is therefore important that uninterrupted ART is available to maintain viral suppression in HIV-infected individuals. Generation of second line SARS-Cov-2 vaccine designs will have to consider emerging variants and what are the true longer-term benefits of vaccination.

Reviewer #3 (Public Review):

S Luo and co-workers asked whether NKT17 subset had lineage-specific requirement/s for thymic development beyond what is currently known. Further, they determined what role such a requirement played in activating NKT cells in vivo and in vitro. The strength of the report is the finding that DR3 functions as a selective co-stimulator of NKT17 subset. Experiments appear well-thought out and executed, and the emergent data reasonably carefully interpreted. Some points to consider:<br /> 1. The statement in the abstract and elsewhere that "However, the molecular mechanisms that drive the thymic development and subset-specific activation of NKT17 cells remain mostly unknown" is incorrect. It is better to say, "Much is known yet how this subset develops in the thymus and is activated in the periphery is incompletely understood."<br /> 2. In this regard, if subsets are already formed, why should there be a subset-specific mechanism/s of activation beyond affinity thresholds? The literature suggests that different routes of bacterial inoculation results in the activation of all subsets within a tissue where infection has occurred.<br /> 3. If there is subset specific activation, does this mean that downstream responses from DR3 activation of NKT17 cells prevents the activation of NKT1 & NKT2 subsets? Otherwise, how does one reconcile with the inability of alphaGalCer to activate NKT1 & NKT2 subsets?<br /> 4. The statement "However, the role of CD138 in NKT17 cell biology remains mostly unclear" is incorrect as it was recently reported that CD138 serves are a NKT17 subset-specific marker but the development and function of NKT17 cells do not depend on CD138! So also, results presented herein also supports this view of CD138 about NKT17 cell development and function-nothing new here.<br /> 5. The MS requires proper editing: e.g., "[Please add: Luo S., 2021, JCI Insight];" this incompleteness was found in the introduction.<br /> 6. Please provide original references to "Because NKT17 cells are the major producers of IL-17 in the thymus and in barrier tissues, such as the lung and skin ..."<br /> 7. Whilst "unveiling a new layer of control in NKT17 cell biology" is quite interesting, it is not as surprising as relayed! That NKT cells use second signals to elaborate type I immune responses has been known for at least a couple of decades now.<br /> 8. "... we identified the TNF receptor superfamily member 25 (TNFRS25), also known as DR3 (Meylan et al., 2011), being highly expressed on thymic NKT17 cells (Figure 1A and 1B)" while true of BALB/c thymuses, seems less true of C56BL/6 thymuses based on their figure s1. This should be clearly stated in the results. Strain differences in NKT cell content and relative ratios of the subsets are known; hence, it is important to indicate of which strains a particular property/ies is true.<br /> 9. It is indeed surprising that the cytokine profile post alphaGalCer+anti-CD3 stimulation was not assessed.<br /> 10. And lastly in a similar vein, a mechanism and the in vivo relevance of this curious co-stimulatory finding remain wanting.

Reviewer #3 (Public Review):

The authors have studied the transcriptome of the smooth chorion. The paper is of potential interest but would be improved by analysing from earlier in gestation as it does not indicate how the chorion laeve is formed from the regressing villi.

Reviewer #3 (Public Review):

This manuscript characterizes inter-individual variants in rDNA at the 45S rDNA unit; these variants are associated with variable DNA methylation and histone modification and sensitivity to environmental stimuli (thus, epialleles). Moreover, rDNA copy number was associated with variant frequency and DNA methylation.

Abundant data in the primary and supplementary figures. provide support for the conclusions in the paper. The data consistently demonstrate an association between the ATA variant and increased rDNA methylation, increased H3K9me3, and relative depletion of H3K27me3, and reduced binding of UBTF (which binds unmethylated sites). Moreover, the ATA variant appears to be more sensitive than other variants to prenatal and postnatal dietary factors. Moreover, the authors demonstrate that allele frequencies can be detected whether or not ribosomal depletion steps are performed prior to RNA seq.

Several questions remain in order to more fully integrate these data into concepts of regulation of rDNA and rRNA and responsiveness to environmental stimuli:

1. What is the relationship between allelic variants and physiology? Are allelic variants associated with differential expression of rRNA or ribosome subunits /composition at a protein level? The current manuscript provides only preliminary data from a small number of samples (Figure S16) to support a relationship between variant frequency in RNA and in polysome-associated RNA. Expansion of these data to assess ribosomal function will be required in future studies to provide more functional relevance for the findings.

2. rDNA promoter methylation varies across mouse strains, in proportion to rDNA copy number. It remains uncertain whether differences in allelic variant distribution are driving differences in copy number across strains, or between animals of the same strain, or vice versa. Expansion of sample number within a strain would help to address this question and allow examination of additional determinants of rDNA copy number within individual animals.

Reviewer #3 (Public Review):

In the present study, the authors address the underlying mechanisms of TTR neuritogenic role in DRG neurons. They showed that TTR increased microtubule dynamics in the distal end of growing axons but had no impact on microtubule dynamics in the axon shaft. TTR KO mice had a reduced level of α-tubulin acetylation and an intrinsic increase of dynamic microtubules in uninjured nerves, and failed to modulate dynamic microtubules in injured nerves in response to sciatic nerve injury. The use of an HDAC6 inhibitor to increase acetylated tubulin level restored dynamic microtubules and neurite outgrowth of TTR KO neurons. These findings aid our understanding in the roles of TTR during axon growth and regeneration. However, systematic detection is required for changes in α-tubulin acetylation level and microtubule dynamics after TTR treatment and TTR KO. Furthermore, lacking specific manipulation of α-tubulin acetylation level and microtubule dynamics to rescue the phenotype is also a shortcoming of current study.

Reviewer #3 (Public Review):

The 1-cell stage embryo of the nematode, C. elegans, is a good model system for studying the mechanism of asymmetric division. The cell division at the 1-cell stage is asymmetric, in which fertilized P0 cells divide into large AB cells and small P1 cells. The different sizes of the daughter cells are caused by the asymmetric (off-center) localization of the mitotic spindle, and the asymmetry is caused by the asymmetry in the pulling forces from the anterior and posterior cell cortex. Therefore, characterization of the forces pulling the spindle from the cortex is important to understand the mechanism of asymmetric cell division.

In some nematode species including C. elegans, an oscillatory behavior is observed upon the asymmetric localization of the spindle. In other species, the oscillation does not accompany the off-center motion of the spindle. The difference in the oscillation should be caused by the difference in either the magnitude of the pulling forces, or the magnitude of viscous drag (or both). It was not clear which is the case.

This study solved this question by a nice collaboration between the Delattre group, who is an expert in comparative studies among the nematode species, and the Athale group, who developed image processing tools to characterize physical properties of the cytoplasm for non-labelled cells. Quantitative data on granule tracking and spindle laser ablation results are provided for 6 nematode species. The results will be a variable resource for future studies on diversity among species.

The answer to the question on the difference behind the existence of the oscillation is provided in two ways, which are not mutually exclusive. One explanation is that, in non-oscillation species, the force is low (P. pacificus), or the viscosity of the cytoplasm is high (C. monodelphis), or both (D. sp. 1, and O. tipulae), compared to the oscillation species (C. elegans and C. remanei) (Fig. 5G). The second explanation is that the viscosity of the cytoplasm alone can explain the difference. It is known that, in theory, even a slight difference in viscosity can cause a large difference in oscillation. In the present study, the authors observed certain, but not always statistically significant, differences in viscosity that might account for the difference in oscillation (Fig. 5F).

The data provided in this study is valuable, and the conclusions are overall reasonable. I find some aspect of force measurements and quantification of the parameters need to be clarified.

1. I wonder the force measurement, which is a critical part of this paper, is conducted appropriately. Previous studies on the C. elegans embryo observed the difference in cortical pulling forces between the anterior and posterior sides. This aspect was not reproduced in this study. The authors should explain the consistency of their measurements with the previous study, and evaluate whether the accuracy of their measurement of the forces is sufficient to draw the conclusion of this study.

2. Absolute values related to force, viscosity, and elasticity are quantified in this study. While the information is valuable, the values are obtained by incorporating several assumptions which is not so solid (for me). There would need to be careful discussion about the uncertainties associated with the assumptions. Such discussion is important for future research based on this study.

Reviewer #3 (Public Review):

In this study, Hsu et al. studied a protease system in Pseudomonas aeruginosa composed of CtpA (protease) and LbcA (am outer membrane lipoprotein) primarily using crystallization and EM strategies. They find that the protease CtpA alone forms an inactive trimer of dimers (hexamer) that limits substrate access to prevent nonspecific protein degradation, and that the N- and C- termini of CtpA function in both dimerization and CtpA protease activity. The CtpA alone is inactive unless its partner LbcA is present. They then solved the crystal structure of LbcA. In the presence of both CtpA and LbcA, they observed by cryo-EM that three LbcA molecules, each with a spiral structure, bind a CtpA hexamer and potentially induce conformational changes in CtpA, switching on CtpA. Moreover, additional data indicated that the N-terminal helices of LbcA, especially H1, are involved in CtpA binding and protease system activation. Overall, this paper describes the potential mechanism of CtpA-LbcA complex and reports the most essential regions in both proteins that play indispensable roles. In general, conclusions and predictions in this paper were supported by the data obtained from properly designed biochemical and biophysical experiments. However, a few points are still not clear enough and could be improved.

1. In the section that describes the 3:6 active protease complex, it would be better to explain why the protease dead mutant CtpA (S302A) is used.

2. Even though the overall data analysis and predications of CtpA-LbcA complex make sense, an 8 Å resolution of the complex and the high flexibility of the PDZ domain of CtpA still bring uncertainty. Moreover, for the movements reported in the PDZ region of CtpA upon LbcA binding, please make it clear if it is observed in both conformer I and II, or only in conformer II, the active mode. The current descriptions in results, discussions and figures are ambiguous. Also, other than the twist in PDZ, there is no other changes in CtpA from model fitting? As shown in Fig 5 c, between the two conformers, the difference of the LbcA N-terminal densities at the binding interface is obvious. Changes in CtpA NDR region, which is involved in the binding interface; or in the CtpA cap region, which is associated with the catalytic features and is pushed downward by LbcA in conformer II, should be interesting.

3. The consistency of the texts and figure legends on which LbcA construct is used in the protease complex should be double checked. Same for the descriptions of the LbcA structure. A four-helix bundle is delineated, but the helices in text (H1H2, H3H4) and figures (H1H4, H2H3) are different. In addition, the four-helix bundle is shown in Figure 4 d, but the design of Figure 4 d does not explain it well. Actually, supplementary figure 3 C and D make this point more understandable. Also, for Figure 4 c, it would be better to mark all the helices, which could directly show TPR-A and B line the outer and inner surfaces and explain the hydrophobic and hydrophilic idea. Figure 5 d could be improved since the current version is not adequate to elucidate how PDZ gets shifted and rotated in a big complex. Besides, the color scheme in figures may also cause some confusion, such as in Figure 1 and 2, the same colors are used to represent different concepts. Since this paper is rich in content, it is necessary to keep consistency and avoid potentially misleading readers.

Reviewer #3 (Public Review):

This manuscript wades into a research area that has risen to prominence during the COVID-19 pandemic, namely the estimation of time-varying quantities to describe transmission dynamics, based on case data collected in a given location. The authors focus on the interesting and challenging setting of low-incidence periods that arise after epidemic waves, when local spread of the virus has been contained, but new cases continue to be seeded by travelers and local spread potential can change as control measures are relaxed. There are important questions that arise in this context, such as when it is safe to declare the pathogen locally eliminated, and how to detect a flare-up quickly enough to stamp it out.

The authors propose a new framework, made up of a smoothed estimate of the local reproductive number, R, and another quantity they call Z, which is a measure of confidence that the local epidemic has been eliminated. They apply this framework to three public data sets of COVID-19 case reports (in New Zealand, Hong Kong and Victoria, Australia), each spanning multiple waves of infections interspersed with quieter periods when most cases arise from importation. They show how the smoothed R estimates align with the reported case data, and accurately capture periods of supercritical (R>1, so epidemics can take off) and subcritical (R<1, so epidemics wane) local transmission. They also show how the Z metric fluctuates through time, rising to near 100% at a few points which correspond closely to official declarations of elimination in the respective settings. The authors draw some parallels between their inferred R and Z metrics and the changes in control policies on the ground. They also highlight a number of points where the R and Z metrics seem to anticipate changes in the epidemiology on the ground, which are interpreted as advance 'signals' or 'early-warning' of ensuing waves of cases. This interpretation seems to underlie the manuscript's overall framing in terms of 'early-warning signals' that can be used 'in real time'.

Taken at face value, these are exciting claims that could form the basis of a useful public health tool. However I was not convinced that the framework was actually making these predictions in real time, i.e. strictly prospectively using available data. The approach would still have value if applied retrospectively, particularly with regard to understanding the impact of interventions applied in each setting. To this end, a more formal analysis of the relation between control measures and the R and Z metrics would benefit the paper.

Strengths

The paper is exemplary in clearly delineating the roles of importation versus local transmission in shaping case incidence during these low-incidence periods. This is a crucial distinction in this context, which is too often blurred.

The authors also innovate by bringing a suite of Bayesian filtering and smoothing techniques to bear on inferring R from these data, with the goal of extracting the cleanest signal possible from the noisy data. These approaches are well contextualized relative to more standard techniques in epidemiology, and appear to bear fruit in terms of smooth and stable estimates. However, it is important to note that this manuscript is not the primary report on these methods; the authors have written up this work elsewhere (ref. 16) and it is not described with sufficient detail for this manuscript to stand alone.

It is an interesting and valuable idea to derive a metric (Z) that explicitly estimates the degree of confidence that the pathogen has been eliminated locally. Again, the present manuscript builds closely on prior work by the authors (ref. 15), with the innovation of blending the earlier theory with the new Bayesian smoothed estimates of R.

The selected data sets are perfectly suited to the problem at hand, and analyzing three parallel case studies allows for the behavior of the R-Z framework to be observed across contexts, which is valuable.

Weaknesses

As presented, the manuscript does not seem to show real-time early-warning signals, as I understand those terms. The forward-backward smoothing algorithms that form the backbone of the study estimate R_s (i.e. the value of R at time s) using case data from both before and after time s. That is, the algorithm relies on knowledge of future events and so it cannot be said to provide early warning in any practical sense. Similarly, the estimates of Z draw upon the same 'smoothing posterior' q_s, so they also rely on future knowledge. (I doubted my understanding of this point, given the strong framing of the manuscript and limited methodological details, but the full explication of the method in ref. 16 is quite clear that the 'filtering posterior' p_s is suitable for real-time estimation, but the smoothing approach is retrospective and requires knowledge of the full dataset.)

Viewed in this light, the 'early-warning signals' in the Results are actually just smoothing of the yet-to-occur case data, and thus sadly are much less exciting. It did seem too good to be true. If I have understood correctly, then the current framing of the work seems inappropriate - unless the authors can show that R and Z metrics estimated strictly from past data can provide reliable signals of coming events.

An alternative approach would be to use the framework as a retrospective tool, and use it to build quantitative understanding of the impact of control measures and to revisit the timing of declarations of elimination. Table 1 goes some distance toward describing the relationships between R and Z values and these policy shifts and announcements, but I struggled to pull much value from it. The table and associated text mostly come across as a series of anecdotes where R fell after NPIs were imposed, or rose again when local transmission occurred, but there is no analysis that takes advantage of the more refined estimates of R the authors have obtained with their smoothing approach. One issue is that the time windows included in the table are not contiguous, so all the vignettes feel disjointed.

As presented, while the concept of the Z metric is attractive, it was hard to discern any conclusions about how to make use of its value. In two of the datasets it rose to near 100%, which is a clear signal of elimination, but as noted these were periods when the WHO rule of thumb (28 days without new cases) was sufficient. At some other points, the authors emphasize the implications of Z dropping close to 0% (e.g. at the top of page 7: on July 5 in Hong Kong, Z  0% despite 21 days without local cases, and the authors highlight the contrast with the WHO rule). However these findings clearly arise from the smoothing of future data mentioned above (i.e. on July 5 in Hong Kong, R is rising to supercritical levels based on advance knowledge of the rapid rise in cases in the next few weeks). Thus these findings are not relevant to real-time decision support. Finally, there are several periods where Z fluctuates around 20-50% for reasons that are hard to discern (e.g. July in New Zealand, or April-May in Hong Kong). The authors write in that the Z score may exhibit a peak due to extinction of a particular viral lineage in Hong Kong, while other lineages continued to circulate. It is hard to grasp how this interpretation could apply, given the aggregated nature of the data; more evidence, or more refined arguments, are needed for this to be convincing.

In the big picture, the proposed framework is based on two quantities, R and Z, but there is no systematic analysis of how to interpret these two quantities jointly. It would be valuable, for instance, to see how these metrics perform on a two-dimensional R-Z phase space.

The authors acknowledge a number of assumptions and data requirements needed for this approach, as presented. These include perfect case observation, no asymptomatic transmission, perfect identification of imported versus locally infected cases, and no delays in reporting. The authors state that the excellent surveillance systems in their case-study locales minimize the impact of these assumptions, but the same cannot be said of most other places around the world. Digging deeper into the epidemiology, the distribution of serial intervals (a crucial input to the algorithm) is assumed to be invariant, even though it's been demonstrated to change when interventions are imposed (ref. 26), i.e. exactly the conditions of interest. Finally, superspreading is a prominent feature of the COVID-19 epidemiology (as nicely documented for Hong Kong, by one of the authors), but is not addressed by this model beyond allowing subtle fluctuations in R from day to day. Taken together, these strong assumptions and omissions raise questions about the real-world reliability of this framework. Given that the point of the manuscript is to develop more refined quantitative metrics, and that most of these assumptions will be violated in most settings, it would be valuable to demonstrate that the framework is robust to these violations.

Reviewer #3 (Public Review):

T lymphocytes are essential players of adaptive immunity and the extent of their functional, transcriptomic, and immunophenotypic heterogeneity is still unknown. Wang et al. reanalyzed previously published single-cell RNAseq data of sorted T cell populations from human peripheral blood (Zheng et al. Nature Communications, 8:14049, 2017). They described their composition and overlaps, and highlight a subpopulation of T cells with high interferon-responsive gene expression. They identified BTS2 as a candidate surface marker to enrich them, only to show low sensitivity and specificity.

The manuscript shows significant weaknesses:

1) Analyzing the heterogeneity within sorted populations of T cells is not a novel approach. The authors should cite previous attempts by single-cell RNA sequencing to characterize T cell heterogeneity (PMIDS: 30664737, 31371561, 30958799, 31227543, 29858286, 29352091, 30737144, 29434354, etc.).

2) Regarding the dataset, the authors used a previously published dataset from 2017 of a single donor (Zheng et al. Nature Communications, 8:14049, 2017). Several donors would be needed in order to test the statistical significance and robustness of the results.

3) The technology has largely improved since the publication of this dataset (in terms of sequencing depth, multimodal analysis). Sequencing depth, in particular, is an important parameter in cluster definition (Heimberg Cell Syst 2016, PMID 27135536). The authors cluster single-cell data without justifying the robustness of the clustering and discussing the role of sequencing depth. Newer datasets have now been published with better resolution and discrimination between T cell populations. Considering the "blob" appearance of the data, one could also argue that discrete clustering is somewhat arbitrary.

4) Single-cell clusters overlap between sorted populations. The authors fail to discuss the various intricated technical explanations 1) cell sorting impurity 2) imperfect protein-RNA expression correlation 3) arbitrary cluster borders in the single-cell data in a tSNE "blob" 4) role of the depth of the sequencing.

5) Pseudotime analysis: the authors misinterpret transcriptome overlap as temporal dynamics. Considering the highly polyclonal repertoire of peripheral T cells, the studied T cells do not represent temporal evolutions of single clones. As an example, it is well known that circulating Tregs do not differentiate from other circulating T cells and cannot be put in a trajectory.

6) Their authors discuss the discovery of cytotoxic CD4 and CD8 T cells. These cells have however been previously described (Patil et al. Science Immunol 2018 PMID 29352091). Moreover, the interpretation that these might represent "convergent differentiation" is inappropriate: CD4 and CD8 T cells are restricted by MHC I and II, respectively, and therefore these cytotoxic cells are not a single population. It merely represents a shared expression of cytotoxic genes.

7) scIFNhi cells are been previously described in various datasets [REF]. the authors should address and/or discuss the various intricated technical explanations: do they represent technical artifacts (sample processing/cellular stress)? Are they reproduced in different donors? Different conditions? The authors identified BTS2 as a candidate surface marker to enrich them, only to show low sensitivity and specificity, so its significance is unclear. The authors claim a higher rate of T cell activation in BST2+ cells but the in vitro experiment does not have replicates and therefore cannot be statistically interpreted.

8) The manuscript is sometimes unclear. It is not clearly stated that the dataset represents human cells (it is only mentioned in the method section). Importantly, an effort should be made to recognize the difference between the functional classification of T cells, the classification based on surface markers, and the classification based on clustering by single-cell transcriptomics. Cytotoxic T cells, helper, T cells memory T cells are functional concepts that can only be evaluated by experimentation (cell transfer, chimeras, infection models, etc). However, the authors infer functions to T cell clusters (memory, naïve, effector, etc.) based on a few pre-selected markers in the RNAseq data without further investigations or whole transcriptome comparison (enrichment in gene expression signatures for examples).

Reviewer #3 (Public Review):

In this study, Otubo and colleagues describe the detection of synaptic vesicle-associated peptide antigens, glutamate and VGLUT2 at the electron microscope (EM) level in hypothalamus and pituitary from macaque brains that were perfused with paraformaldehyde, cryostat-sectioned and stored in cryoprotectant at -25oC for up to 5 years. Formaldehyde-fixed tissue is generally considered unsuitable for any type of EM study so Otubo et al's success with EM immunostaining is surprising. Congratulations to them for ignoring conventional wisdom and attempting EM immunolabelling on sections of formaldehyde-fixed tissue after long term storage. Unfortunately, issues with the experimental approaches used by the authors mean that a number of their conclusions are not soundly based. The authors demonstrate that their EM immunogold staining protocol is good for detecting high-abundance antigens, like peptides and proteins that are stored in dense cored vesicles (DCV); but there is no data on antigens that are present at low concentrations. This possible shortcoming could render Obuto and colleague's protocol of limited use for future studies on fixed and archived human and primate brain samples.

STRENGTHS

The immunofluorescent staining for vasopressin-associated neurophysin (NPII), copeptin and corticotropin releasing factor (CRF) shown in Figures 2, 8 and 9 in this manuscript is beautiful and confirms the quality of results that can be obtained from sections stored long term in cryoprotectant at -25oC. Nevertheless, this finding is not surprising or novel. Banking sections of formaldehyde-fixed tissue in anti-freeze solution in the freezer for future immunohistochemical processing is common practice in many laboratories around the world. For example, in my laboratory, we have immunohistochemically processed brain sections stored in this way for up to 20 years and obtained staining that is as good as in freshly-cut sections from the same brains.

Otubo et al's successful localization of NPII, copeptin and CRF at the EM level is obvious from their micrographs showing post-embedding immunogold staining. The choice of these antigens was a good one. Peptides/proteins, like vasopressin (AVP), NPII, copeptin and CRF, are highly concentrated in the DCV of peptidergic neurons. As this study shows, even if considerable antigen is lost due to suboptimal primary fixation, enough persists in DCV to be localized with the method described in this manuscript. It would be interesting to know whether less abundant antigens, such as enzymes or receptors, can be localized at the EM level in sections of formaldehyde-fixed tissue after long term storage at -25oC. If low abundance antigens can be detected, the authors' protocol would be much more useful.

WEAKNESSES

The Results section does not highlight several technical details (documented in the following Methods section) that are vitally important for assessing and interpreting Otubo et al's findings. In Methods, we learn that cryostat sections rather than intact brain blocks were stored at -25oC in a standard cryoprotectant solution for up to 5 years. After removal from cryoprotectant, the sections were post-fixed with glutaraldehyde to improve retention of antigens and then dehydrated and embedded into resin without prior treatment with osmium tetroxide (OsO4).

Based on these technical details, I question Otubo's et al's assertions that immunoreactivity for any of the antigens investigated is associated with membranes or occurs within membrane-bound structures. Aldehyde fixatives preserve peptides, proteins and nucleic acids. OsO4 treatment is required for the preservation of lipids in membranes. Without this membrane preservation step, lipids are dissolve away by the solvents used for dehydrating sections (i.e., alcohols, acetone and propylene oxide) before the sections are embedded in resin. Because Otubo et al omitted OsO4 and dehydrated their sections through methanol, there cannot be any membranes in the samples that they examined ultrastructurally. They do comment that "the membrane of single microvesicles could not be clearly distinguished" and may have been deceived into thinking that membranes were present. In their ultrathin sections, membrane-bound proteins appear as linear arrays despite extraction of the lipid bilayer with which the proteins were associated. Interpreting ultrastructure without the presence of membranes is a serious problem, which is illustrated by what the authors call MV, i.e., clusters of "microvesicles". In my opinion, without membranes to show the boundaries of cellular and subcellular structures, it is not possible to unequivocally identify synaptic vesicles (i.e., "microvesicles") in axon terminals. For example, the indistinct EM appearance of the lower left MV in Figure 4C could just as likely be Golgi apparatus as clustered synaptic vesicles.

The authors' demonstration of neurotransmitter glutamate in macaque AVP neurons is suspect. All cells use glutamate to make protein; in addition, some neurons use glutamate as a neurotransmitter. Before claiming that a neuron is glutamatergic based on immunogold labelling, it is mandatory to establish that the density of gold particles over axon terminals is significantly different from the background density (see, for example, Llewellyn-Smith et al, 1992, 1997, 1998). Unless the authors perform this type of quantitative analysis of gold particle densities, their claim that their EM immunostaining has revealed glutamate in macaque vasopressin (AVP) neurons is not justified.

The immunogold labelling pattern that the authors achieved with anti-VGLUT2 is perplexing. VGLUT2 is involved in packaging glutamate into synaptic vesicles and these glutamate-containing vesicles are generally small and clear. Thus, I would expect that small gold particles detecting VGLUT2-immunoreactivity would be concentrated over MV, i.e., clumps of "microvesicles". In Figure 13, however, small gold particles are sparse over MV, where labelling would be expected, and appear in similar density on/near DCV, where the concentration of neurotransmitter glutamate is expected to be low. The observed distribution of gold particles suggests that the antibody may be detecting something other than VGLUT2. Although the anti-VGLUT2 used in this study shows a single band on western blotting, fixation and tissue processing can alter epitopes on antigens. It would be advisable for the authors to confirm the antibody's specificity on their tissue by doing immunogold staining with anti-VGLUT2 absorbed with the antigen against which it was raised.

Otubo et al make a number of conclusions from their measurements of vesicle sizes. Figure 5 contains enough data to confirm statistically that DCV in the external median eminence (ME) are smaller than DCV in either the internal ME or the posterior pituitary - as long as a reasonable numbers of DCV were measured in sections from each of at least 3 of the 4 monkey used for anatomical studies. An unknown number of monkeys also provided the data presented in Table 2.

CONCLUSIONS

As indicated by its title, the most interesting aspect of this paper is the use of years-old sections of formaldehyde-fixed tissue for EM localization of neuropeptides in brain tissue. The applicability of this method for studying banked sections of human and primate brains will depend upon whether or not low abundance antigens can be detected using the methods described here.

The paper provides only an incremental advance in our understanding of AVP circuitry in the monkey hypothalamus and pituitary. Because the results on glutamate and VGLUT2 immunoreactivity in the present study are questionable, the only reliable new information is on the co-existence (as expected) of copeptin, one of the vasopressin gene products, with another vasopressin gene product, NPII, in AVP neurons. Otubo and colleagues have previously published the co-localization of CRF in AVP neurons in their 2020 paper in J Neuroendocrinol.

Reviewer #3 (Public Review):

In this manuscript the authors unify a public datasets of capture HiC data within a common framework and use these data to examine the relationships between topological chromatin organization, enhancer function, gene expression, and the evolutionary conservation thereof. The introduction correctly states several pending and exciting questions in the field and the authors performed a large body of work in multiple directions to address some of them. The results are well presented in clear and pleasant figures, even though the text of the manuscript sometimes lacks similar clarity (see some examples below). Overall, I feel that the manuscript can be substantially improved in several key areas.

1. First, it critically lacks focus, accumulating analyses in many directions, most of which lead to either unsurprising, or sometimes unconvincing conclusions (see examples below). This huge amount of results (6 Figures and 36 supplementary figures!) hampers the reader's interest and dilute the few novel and exciting results in a crowd of less significant observations. In their current form, the results remain too descriptive, with lots of scattered observations.

2. Most experiments presented in the manuscript use a 'control' dataset, constructed by a sort of 'shuffling' of the actual data. While this sounds like a good idea in principle, I remained unable to grasp exactly how this procedure was performed, which unfortunately prevented me from fully appreciating the significance of the results.

Broadly, I understand that the simulated dataset is made by attributing to each promoter the same number of enhancers as in the real data, picked among all enhancers in its vicinity with a probability depending only on their distance to the promoter of interest but irrespective of their 'real' HiC target. If this is correct, some results seem to raise unaddressed questions about its relevance and possible biases.<br /> - l 222-225: the authors note that restriction fragments are more conserved than control data in gene-rich, but not in in gene-poor regions. Couldn't this happen simply because in gene-poor regions, the simulated data are in fact closer to the real data : if there are no other genes in the vicinity of the promoter of interest there will be no fragments targeting other promoters, hence no shuffling of the enhancer-promoter links can occur.<br /> - In the simulated data, one expects that some fragments will contact 0 baits. Why are they not shown in figures 1, 1S1c,f, 1S2b, 1S3b ?<br /> - Fig 1S1c,f show that in the simulated dataset, each fragment contacts less baits than in the actual dataset. Why can we see the opposite in Fig 1S2b and 1S3b ?<br /> - While Fig1S1b,e show that each bait contacts the same number of fragments in the simulated and actual dataset, which is expected by construction, why can we see a marked difference in Fig1S2a and 1S3a? Even if there is a small difference due to a posteriori filtering of simulated data, it should go in the opposite direction of what is seen (it should lower the number of fragments per bait, not increase it).

3. I appreciate that the authors do not attempt to overestimate the importance of their results, but my impression is that almost none of the conclusions are really novel with respect to the existing literature. Roughly, figures 1 to 4 do not say much more beyond the fact that the dataset is enriched in enhancer-promoter interactions. This is not uninteresting, but not really a surprise in itself either, given that it represents topological contacts of promoters.<br /> Being enriched in enhancer-promoter interactions, it ensues that the dataset also tends to be more conserved, both sequence-wise (Figure 3) and synteny-wise (Figure 4).<br /> Not only is this expected, but the observed size effect seems very small, both for the enrichment itself (measured overlap with known enhancers in Fig.2) and for the consequences on conservation. This is exemplified in lines 195-197 of the manuscript results section: "For the comparison between human and mouse, the median aligned length fraction of contacted fragments is 27% in PCHi-C data, which is significantly higher than the 23% observed in the simulated dataset". It seems to me that even a small enrichment could generate such small effects, with clear statistical significance but limited biological significance.

4. More exciting observations come only with Figures 5 and 6. They however still need more solid support.

a) For example, data in Figures 5c and Fig5-Supp 4 and Fig5-Supp 5 would be a lot more interesting if restricted to interactions within synteny blocks, thus measuring solely interactions that are lost/kept between human and mouse independent on synteny conservation. This would be very interesting, as it has not been measured before. Would the conservation be dependent on the distance? This cannot be seen in the present data.

b) The question of the link between the evolution of gene expression and that of enhancer landscapes, asked in Figure 6 is of major interest and has not been much explored so far. The result is however disappointing in that it only confirms the findings of a previous study (Berthelot et al., 2018), with a weaker signal than in the original study. The correlation between conservation of expression and number of chromatin contacts (Fig6c), which is supposed to be the key result, seems extremely modest, to say the least. The correlation with expression specificity or with expression levels is more convincing, but also of lesser interest.

5. GO enrichment analyses of the conserved contacts are only briefly mentioned and relegated to supplementary data. The only conclusion of the manuscript is that it is "consistent with the presence of strong functional constraints on the cis-regulatory landscapes of developmental genes". This is already very well known. I am sure more can be drawn from these analyses, even though they should be carefully controlled for important confounding factors (eg gene density). For example, if the conservation of contacts were studied independent on the synteny, would contacts of specific GO categories be more or less conserved than others? In other terms, do rules of chromatin contact vary depending on gene function? This would be new.

Reviewer #3 (Public Review):

Milighetti and colleagues describe a structure-based approach to predicting TCR antigen specificity. The area is of high priority in areas ranging from viral immunity to cancer. Overall performance is not greater than sequence based approaches but the authors correctly indicate that accumulation of new structures will lead to improvements. The work is intriguing as ultimately antigen recognition is a structural and biophysical problem, and while sequence-based approaches are enticing in their comparative simplicity, they can only work if the structural and biophysical data is reflected in how sequences are analyzed. That has not been possible yet, and structure based approaches are the way to get there. The manuscript is refreshing in its open take on performance.

There are some obvious areas for immediate improvement: as the authors discuss, TCRs recognize composite peptide/MHC complexes, yet the analysis is focused on peptides alone. Much has been shown over the years about how peptides influence MHC and vice versa. Thus an accurate assessment needs to take into account the MHC. For example, in the A6-Tax/A2 system the authors use an example, even going back to 1996 we knew that CDR3 loops make strong contacts to the MHC. We have since learned that these strong contacts are a consequence of peptide conformation, which is a consequence of MHC binding... There are similar examples that have been studied at that level of detail. So considering the peptide in the absence of MHC *will* reduce performance. The paper is fine in asking the question "what happens if we do make that separation" - but a point for discussion and a lesson learned is the composite surface needs to be considered.

There are some other areas that need clarification/comment: the models, for example - a 2A RMSD for a model that is be used for predicting specificity is not really that good. At a high level the footprint might look similar, but the physics of binding is highly dependent on interatomic distances, geometries, etc. The accuracy of structural modeling is a major area that needs improvement, and something the authors can and should comment on.

The use of databases such as ATLAS is another strength and weakness. The strength lies in the ability to mine the data, but a limitation is found in the very simple brush which is used to classify such data. The data in these repositories relies on a wide range of measurements from different groups with different levels of accuracy and precision as well as thresholds about what is measurable and not.

Lastly, the authors miss an opportunity to get back to the biophysics encoded by structure: we are still miles away from accurately and repeatedly predicting affinities from structure in *any* system, much less TCR-peptide/MHC structures. The section in the Discussion that addresses this is an opportunity to talk about where we are generally in relating structure to binding - still a long ways to go.

So overall, I find this an exciting step in the right direction, with the paper identifying some holes and showing what is currently possible. A closer attention to the structural biophysics of TCR recognition of pMHC and protein-protein interactions in general would improve it.

Reviewer #3 (Public Review):

All optical electrophysiology, which combines an optogenetic actuator with an optical voltage sensor, offers the potential for versatile and higher throughput neurophysiology studies. One possible challenge with using a genetically encoded voltage sensor is that the resulting large delivery cassette is challenging to package in a viral vector at high titer, and in general these constructs can compromise cell health in some cases. This manuscript combines viral vector delivery of the optogenetic actuator with a previously developed organic voltage sensitive dye for all optical electrophysiology. The work does represent an advance, particularly the combination of optogenetics with two organic dyes (for voltage and calcium), though with some caveats.

One concern is that the toxicity of vectors with large payloads or use of multiple vectors is somewhat overstated. Toxicity due to lentiviral transduction of primary cells or differentiated neurons can be challenging. However, when studying human pluripotent stem cell (hPSC) derived neurons (the approach of this manuscript), it's very straightforward to use lenti generate a stable hPSC line carrying multiple expression cassettes, then do the differentiation into neurons.

In addition, voltage dyes have been used extensively in hPSC-derived neurons, in work for example out of E. Miller's lab (which generated the voltage dye used in this work). That prior work didn't use optogenetics, but it does reduce the novelty of this study somewhat.

Reviewer #3 (Public Review):

Slimani and colleagues provide different groups of participants with offers to accept different levels of pain - in the form of electric shocks with different levels of intensity - for different amounts of money. They use their responses to map out the rate at which participants trade money for pain, explore the form of this function, examine how this form varies with the range and distribution of rewards available and examine how differences relate to questionnaire measures. They find evidence that the value of pain is curvilinear, exhibits range normalisation (i.e., adapts to context) and interacts with a factor relating to harm avoidance and persistence.

The manuscript does a good job of articulating why understanding how individuals value pain could be important. But I felt it lacked a set of clearly defined hypotheses for the research questions they are interested in. As one example, why would the distribution of rewards potentially influence the value of pain and what prediction could we make about how it might do this? As another example, why use factor analysis to try to relate individual differences to pain evaluation? Without this, it currently reads as a mixture of different research questions without a clear understanding of how they are connected exactly or what predictions could be made.

How humans put a price on pain is interesting and as the authors rightly point out, there is a lack of knowledge in the field about valuation in aversive domains. The design used to investigate this is simple and ideal to start to address questions that relate to this. However, the manuscript needs to be much tighter in terms of reporting the methods and statistics. I felt that there were some key gaps in the statistical reporting such as not making clear what the statistical tests used are, what the exact value of N is in each group and confidence intervals. I try to put some specific examples of this below.

• To create the pain value function, a logistic regression predicting choices (accept/reject) using different levels of shock and reward is used. But key details about how this model is specified are missing (e.g., if this is a multilevel analysis, what were taken as random effects? In the quadratic model, did they include the linear effect as well?). When analysing questionnaire responses, did the authors include all 5 PCA components in their model to predict acceptance rates? Or did they run 5 separate models with a different component in each? And are the results for the questionnaire analysis (e.g., Figure 3a) pooled over all 4 groups of participants (if so did they control for group in the analysis, do the effects interact with group, etc.)?

• Sample Size: The authors do not report whether an appropriate sample size was computed when the study was being designed - how did they determine the sample sizes that were used (e.g., power analysis)? The authors state in the transparent reporting form that this information is reported in the Statistical Analysis section but this is not the case from what I could see. They do report how they calculate the effect size (and they report the effect sizes throughout the paper); but this doesn't say anything about how the sample size was arrived at. The authors also don't state how many participants are assigned to each group, it should be stated clearly within the manuscript, figure legends etc.

• Model Comparison: The authors compare models using AIC scores and report that the AIC was lowest for the quadratic model (Group 1 = 58, Group 2 = 60, Group 3 = 57), compared to the linear (Group 1 = 60, Group 2 = 60, Group 3 = 59) and the cubic (Group 1 = 60, Group 2 = 62, Group 3 = 58) models. But there is no metric provided whether the improvement of one model over and above another is meaningful or not - can anything really conclusive be taken from a difference in AIC scores of 2 for instance? Maybe it can - but the authors really need to make the case. It also seems that the scores are actually the same for Group 2 between quadratic and linear?

• The authors report a t statistic showing the significance of the curvilinear effect in Figure 1a (t = 5.04, p < 0.001). But what is this testing exactly and why are there no degrees of freedom or confidence intervals reported?

• Similarly, when comparing differences in the profitability index metric between groups, the authors report significant differences between group 3 vs all other groups (in the text) and between group 1 vs all other groups (in table 1). But there is no indication as to what the tests are used to make these comparisons and if any correction has been made for multiple comparisons.

• There are some decision steps taken in various analysis that are not justified. For example, they use a smoothing kernel in the design matrix - is that necessary, what's the motivation? Why control for trial number with not just 1 but 3 different regressors? How did the authors decide on a criteria for deciding how many factors the optimal solution has?

• Figure 3A and 3B: Is it the case that pain ratings are correlated with the factor ratings for harm avoidance / persistence. To put it another way, do the results in 3A hold if they control for pain ratings?

• What software and packages were used to run the models?

Reviewer #3 (Public Review):

In this study, Williams and colleagues use single cell transcriptomics to describe the cell fates and lineages in the dorsal ectoderm of chick embryos taken from gastrula to early neurula stage. Specifically, they provide an atlas of whole embryo epiblast at stages 4,HH, 5HH, 6HH and 7H. They further subcluster the ectoderm cells and use cVelo algorithm (based on velocity of RNA splicing) to infer trajectories and lineages in this dataset.

Description of each stage and each cluster of cells is carefully done and retrieves the canonical markers for each territory. The neural plate border is found expressing pax7 early on, as previously described in chick embryos (Basch et al., 2006) but a specific clustering of these cells is not found, suggesting that the identity of the neural plate border is rather the overlap of gene signatures from adjacent tissues and that the neural crest and the placode signatures arise at the early neurula stage, in the elevating neural folds of the embryo.

From previous lineage tracing studies done at the gastrula stage, it was shown that the border between the prospective neural plate and the future non-neural ectoderm gives rise to four main cell fates: dorsal neural tube cells, neural crest cells, posterior placodal cells and non-neural ectoderm cells (e.g. Steventon et al., 2009). Genetically, this territory has previously been defined by the overlap between gene expressions defining the non-neural ectoderm and the neural ectoderm (e.g. De Crozé et al., 2011, Grooves and Labonne 2014)

Previous transcriptomic studies have pointed out the high similarity of the neural plate border territory compared to adjacent ectoderm regions, the failure of "classical" biostatistics tools to evidence a specific signature and the need for tailored strategies to do so (Plouhinec et al., 2017). Using those, the neural plate border could be defined from gastrula stage by its expression of pax3/pax7 ortholog and a couple of other genes, then by a more extended signature at neurulation stage.

In conclusion, although this study explores the same question with the latest tools of single cell transcriptomics, it is mostly descriptive and brings little novel insight into the biology of neural and neural plate border induction. However, it highlights a series of additional genes that could be of interest for further functional study.

Reviewer #3 (Public Review):

In this paper, the authors use a mathematical model of plant and water dynamics in drylands to show that drylands adaptive capacity to respond to changes, via spatial self-organization in space has also beneficial effects in preserving its biodiversity and ecosystem functions.

The model is an extension of a large body of previous, well-established works on plant self-organisation in drylands. The model is well described and motivated (with one main exception, see below), the analyses are robust and the results are very convincingly supporting the conclusions. I however have an issue with one of the assumptions in the model equations. The authors included a term for "mutations" in traits that 1) is not introduced or motivated 2) its effects/importance are not highlighted by specific analyses 3) the possible implications or limitations connected to it are not discussed. To my knowledge, this term is also not based on earlier work. All these elements need to be included, as at the moment is for example unclear what the authors intended to represent by including the mutation term (evolutionary time scales? Or adaptation?). Also, it would be especially good to include an analysis of how influential this term is for the final results.

Assuming the authors can address this one concern, the results are surely important as they connect for the first time plant spatial self-organization to its biodiversity preservation, in the face of future expected climatic changes and probable land degradation. These findings, although theoretical, have the potential to be useful also for guiding adaptive and dynamic land management, as they underline the importance of taking into account spatial vegetation distribution in drylands management.

Reviewer #3 (Public Review):

The authors set out to identify factors involved in bacterial predation and have identified a set of genes that are homologous to secretion systems. The study focuses on M. xanthus as the predator with E. coli as the prey source. The authors demonstrate that M. xanthus A-Motility (focal adhesion mediated motility) is required for efficient penetration of E. coli under the conditions of their assays. The authors identified two genes that encode proteins thought to assemble into a type IV filament-like machine, designated "Kil". This system inhibits motility of prey cells and stimulates lysis of those cells. They show that the Kil apparatus assembles near contact sites between predator and prey cells using protein-fusion constructs. However, there are limitations based on experimental design that diminish support for the stated conclusions. The assay used to identify genes of interest was conducted in aqueous media where the motility system of interest is not required. Futhermore, the microscopic techniques used here do not allow for the visualization of pores or precise localization of machinery thought to be involved in the mechanism for delivery of toxins from predator to prey cells.

Reviewer #3 (Public Review):

In their manuscript "Inhibition of mutant RAS-RAF interaction by mimicking structural and dynamic properties of phosphorylated RAS", Ilter, Kasmer, et al. search for druggable sites in the RAS mutant G12D in computer calculations, and verify their results by experiments. RAS is a major oncogene for various types of cancer and is notoriously hard to target with drugs. Any significant insight into how to find drugs targeting RAS mutants is therefore of high interest. The present manuscript tries to provide such insight, and the connection between simulation and theory appears sound, as the identified compound cerubidine apparently indeed blocks mutant RAS activity.

As I am an expert in simulations, but not in experiments, I will only focus on the presented computational part. In this function, however, I see some significant problems with the results: The data basis that the authors base their analysis on is quite small (only two simulations of 2.5 µs total simulation time), and from the presented data set I do not see any information on if the results on Y32 dynamics are anecdotal or reproducible. All presented distance distribution plots miss error bars/error ranges, as well as some time course plots that the simulations have indeed converged. So I cannot confirm whether the presented results are valid or if the authors were just lucky in their small data set.<br /> Furthermore, it might be that I have overlooked this information, but this work is not the first finding of druggable sites in RAS (see e.g. review of Moor et al., Nat. Rev. Drug Discov. 2020). The authors should include such a comparison in their manuscript. Especially the PMF presented in Figure 9 is erroneous, and all arguments based on this plot need to be discarded from the manuscript. From the Methods and Eq. (9), I assume the authors indeed use only the first two cumulants to calculate the PMF. The artificially low PMF with a difference of up to ~800 kcal/mol is a well-understood artefact (see Jäger et al., J. Chem. Mol. Model. 2022) that indicates the breakdown of the second-order approximation in Eq. (9) due to the presence of different pathways in the steered MD data set. This artefact overlays the PMF and obfuscates any information on the true free energy profile.

Reviewer #3 (Public Review):

The molecular basis for distinct mechanisms of mRNA quality control is poorly understood and an important problem in gene expression. In particular, the molecular mechanisms that act in response to a strong translation stall appear to differ from the mechanisms that act in response to continued slow translation. In this manuscript, a genetic screen revealed a quantitatively significant role for Syh1 in decay of an mRNA with a strong translation stall; similar effects of Syh1 on the same reporter had been reported in a 2020 publication from this laboratory. Here, they find that Syh1 function in mRNA decay does not require function of the major NGD regulator Hel2, of the NGD endonuclease Cue2 or of the ribosome disassociation factor Slh1. By contrast, none of these factors affect mRNA stability of a reporter in which translation elongation is likely uniformly slowed by suboptimal codons, but this reporter is a target of COMD (codon optimality mediated decay) and is stabilized by deletion of NOT5. The surprising result is that the strong stalling reporter is also regulated by Not5, in a manner quantitatively similar that of Syh1. Thus, mRNA stability of the strong stalling reporter is regulated by both NGD and COMD. To understand the molecular basis for recruitment of distinct decay factors, the authors investigate the ribosome states on these reporters using ribosome profiling. In the reporters without a strong stall site, single ribosomes and collided ribosomes are uniformly positioned across the coding sequence, while in the reporter with strong stall (due to CGA codons), ribosomes are absent from the region downstream of the CGA repeats and collided ribosome are substantially increased and stacked at the CGA repeat (compared to the OPT reporter). In addition, ribosomes lacking tRNA from the A site are enriched on the slowly translated reporter. The authors infer that the distinct ribosome signals of collided ribosomes or ribosomes with empty A sites are strong determinants of the factors that lead to mRNA decay.

A major strength of the manuscript is the direct comparison between one mRNA with a single strong translational stall and another similar mRNA with many slow translation sites (caused by changes in the genetic code). The analysis of both the factors that cause decay of these mRNAs as well as the ribosome states on the different mRNAs has the potential to reveal the molecular basis for the different mechanisms of mRNA quality control.

The genetic analysis is complicated and not completely consistent with the claim in the abstract that "Syh1 acts as the primary link to mRNA decay in NGD". While deletion of SYH1 does stabilize mRNA with a strong stalling site, the deletion of both SYH1 and genes encoding other factors known to be in the NGD pathway results in much greater stabilization of the mRNA with the strong stalling site. In the discussion, the authors correctly interpret this result as evidence of two independent mechanisms by which NGD is triggered. This is one of the most novel results presented in this manuscript, but is not followed up and leaves some questions about the primary role of Syh1 in NGD. Subsequent findings that neither Syh1 nor Hel2 are involved in decay of an mRNA encoded with non-optimal codons, that Not5 is involved in decay of mRNAs with non-optimal codons or strong stalls are convincing. The analysis of ribosome states on the same reporters in wild type and mutant strains provides clear and convincing differences in the ribosome states and distribution across the different reporters, which are likely to provide mechanistic insights into the distinct pathways. However, in the absence of any evidence of a causal relationship between these differences in ribosomal state and the difference in mRNA decay, the paper lacks sufficient support for the title "distinct ribosome states trigger diverse mRNA quality control pathways."

Reviewer #3 (Public Review):

The authors used a combination of site-specific labelling at distinct sites within the mGluR2- the VFT domain in the ligand binding site, ECL2 (newly developed here), and the cysteine-rich domain (CRD) the latter of which is located between the VFT and ECL2. Using live cell FRET based on SNAP-tagged or unnatural amino acids, site-labeled with Cy3 or Cy5 tags, they validate that orthosteric ligands generate FRET changes consistent with their know efficacies and potencies, validating them for use in studying the effects of allosteric modulators. They next use single-molecule FRET to study the effects of the allosteric modulators on the receptor in the presence or absence of the orthosteric ligand, glutamate.

Major strengths include the careful design, conduct, and analysis of the experiments and the validation of the effects of orthosteric ligands alone before proceeding to measurements of allosteric effects. They produce some very interesting results with the allosteric modulators in both experimental formats - the whole cell FRET consistent with known allosteric effects and the single molecule FRET identifying some independent effects of the allosteric modulators - this was quite striking. The approach is scalable to other GPCRs and to other membrane proteins in general.

The main concerns I had were with respect to labelling stoichiometry of the mixed Cy3/Cy5 compounds or SNAP-tag labels. How was this controlled? Clearly, both label cells, as shown in supplemental data and the single molecule FRET data support that both sites are labelled. Are there any concerns about larger molecular complexes such as oligomers that may confound the simple interpretation of interactions between the dimers?

Some additional context might be a discussion of approaches used and results obtained for other types of conformational biosensors for GPCRs in other classes? Can we learn anything by comparison?

Reviewer #3 (Public Review):

In this manuscript, Viola and co-authors address the question of how far-red-light-adapted (FRL) Photosystem II (PSII) is able to bypass the "red limit", or the minimum photon energy/frequency for charge separation to proceed effectively. They attempt to do so primarily by measuring the consequence of failure to overcome the red limit: charge recombination. From this work they have concluded that FRL PSIIs are able to achieve similar efficiency of flash-induced water-oxidizing complex turnover to those adapted to standard visible light. However, they conclude that FRL PSII which uses chlorophyll-d is significantly more susceptible to charge recombination and singlet oxygen formation, leading to increased sensitivity to high-light conditions. FRL PSII which uses chlorophyll-f, however, is adapted to be more resistant to photodamage. These strategies are differentiated by the number and type of far-red chlorophyll used and tuning of redox potentials of cofactors in PSII.

The methods employed are well-chosen to present complementary evidence to address the questions posed. The authors have supported themselves using polarography, fluorescence decay, absorption, luminescence and thermoluminescence, and spectrometry, all of which are employed in a manner well-established in the quantification of processes in standard PSII preparations. The results, however, have some loss of data such as total yields which would be useful in interpretation as the authors have chosen to extensively normalize data for ease of visual comparison of certain features.

Overall, the authors have adequately achieved their aims and their conclusions are well-supported. The authors also clearly state their own expectations of the impact of their work at the end of the Discussion; thanks to these results, we can better understand the ecological niche of each type of FRL-PSII and how these significantly disparate systems may be used in future agricultural research and development.

Reviewer #3 (Public Review):

In this study, Menicucci et al. induced plastic changes in ocular dominance by applying an eye-patch to the dominant eye (monocular deprivation, MD). This manipulation resulted in a shift toward even more dominance of the deprived eye, as assessed though a binocular rivalry protocol. This effect was stabilized during sleep whereas it quickly decreases in waking (in the dark). The authors interpret the MD effect as the resultant of cortical plasticity over primary visual areas and its maintenance during sleep as the consolidation of these changes. The authors thus connect their work to the literature on sleep consolidation. They further show that the magnitude of the MD effect is positively correlated with sleep markers that are involved in memory consolidation (slow oscillations and sleep spindles).

However, I have first conceptual issues with this study. Indeed, previous findings on the replay of memories during sleep and their consolidation were mostly obtained in hippocampus-dependent forms of learning. Here, I do not really see what is it that would be replayed. Thus, I struggle understanding how rhythms, such as sleep spindles, that have been linked to the transfer of hippocampal memories to the neocortex, would be mechanistically associated with low-level plastic changes restricted to primary visual areas. In addition, the effects were observed over occipital electrodes, where sleep spindles are far fewer and lower in amplitude than other cortical regions. Furthermore, the association between MD-related plasticity and slow oscillations is interesting but, since these slow oscillations organize sleep slow waves, the lack of correlation with slow wave is surprising.

Connected to these conceptual issues, I think the present work has some important methodological limitations. First of all, the analyses included a rather small number of participants, which could make some analyses, in particular correlational analyses, severely underpowered. Secondly, the approach used to explore the correlation between plasticity and sleep features focused on subset of electrodes (ROI) defined a priori. It is therefore difficult to conclude on the specificity of the results. Given the topographical maps provided by the authors, I am wondering if a more exhaustive analysis of the effect at the electrode level could not yield more robust findings. Finally, given the number of features tested, I think it is important to clarify the strategy used to correct for multiple comparisons.

Reviewer #3 (Public Review):

Previous research has sought to understand the correlation between neuronal activity and decision-making in different regions of the cortex, and a plethora of cortical regions have been tested for necessity in a variety of decision-making tasks. For example, the posterior parietal cortex (PPC) has been shown to be necessary for visual decision-making tasks, whereas the retrosplenial cortex (RSC) is important in navigational planning. Although the necessity of different cortical regions in completion of certain discrimination tasks has yielded insights into these brain regions' roles, the previous experiences of each individual animal has not been explored. This raises the possibility that the previous experience and learned associations may affect how cortical areas process subsequent decision-making tasks.

To test this hypothesis, the authors used in vivo optogenetic activation of GABAergic interneurons to silence excitatory activity in the PPC, RSC, and S1 (a control, as S1 has not been shown to be involved in visual decision-making). They also employed 2-photon in vivo calcium imaging in head-fixed mice. A virtual Y-maze enabled the mice to decide to turn left or right to receive a reward. In the "simple task," mice had one rule - horizontal or vertical bars indicating whether the mouse should turn left or right to receive the reward. In the "complex task," there were two rules, A and B. Rule A was the exact same parameters as the simple task, whereas Rule B switched the left/right reward association with the horizontal/vertical bars. In some cases, there was an additional "complex task" where there was a delay between cue onset and decision making.

Inhibition of the PPC or RSC during the simple task resulted in small decreases in performance. Interestingly, inhibition of the PPC or RSC during more complex tasks, such as the delay task or switching task, resulted in much greater decrease in correct decisions. S1 inhibition decreased performance in the complex tasks, but not the simple task. Interestingly, when the animal was trained on a complex task (either the delay task or switching task) prior to testing in the simple task, there was a greater decrease in performance upon inhibition of the PPC or RSC compared to mice that had only undergone the simple task, implying that the prior experience of the complex task altered the cortical requirements for performing the simple task.

The next question was whether the neural activity was different in these cortical regions between tasks and between mice with and without previous experience. Neural activity in both the PPC and RSC was greater in mice that had solely undergone the switching task compared to mice that had only undergone the simple task. The trial-type selectivity of neurons was also higher in the switching task in both the PPC and RSC, and it took fewer cells to decode a trial accurately in mice from the switching task. Interestingly, compared to mice who had only experienced the simple task, animals that had prior experience with the switching task showed greater neuronal activity and neuronal selectivity in the PPC and RSC while performing the simple task.

Reviewer #3 (Public Review): 

This work concerns the mechanisms underlying homeostatic synaptic plasticity, a type of negative feedback that helps normalize neuronal activity levels. In the mammalian system, this occurs primarily by post-synaptic changes in neurotransmitter receptor content but under certain conditions pre-synaptic changes in transmitter release also occur. 

Following earlier work from this lab using dissociated hippocampal cultures, here they find that older (21+ day old) hippocampal slices culture have both pre- and post-synaptic changes in response to prolonged (36h) blockade of neuronal activity, as measured by an increase in the amplitude and frequency of spontaneous transmitter release from excitatory synapses. This is the standard approach for this type of work. They also verify (as per the earlier work) that both types of changes depend on retinoic acid (RA) signaling on post-synaptic retinoic acid receptor alpha (RAR). They make nice use of conditional gene deletion to show this. 

They also confirm earlier work from the Sutton and Tsien labs that the pre-synaptic homeostatic synaptic plasticity is due to BDNF signaling, with BDNF being produced post-synaptically and signaling retrogradely to TrkB receptors on the pre-synapse. Again, they make good use of conditional gene deletions to demonstrate this clearly. 

They then provide new data to link these signals, showing the RAR (a known transcriptional regulator) can directly bind BDNF RNA in synaptoneurosomes and seems to basally suppress translation, as treatment with RA increases BDNF protein production. These data are also convincing. They have done a nice job of linking the previous findings into a complete signaling pathway.

Reviewer #3 (Public Review):

Symanski et al. describe a set of interesting results derived from analyzing electrophysiological recordings performed in rats well trained on an associative memory task on a spatial maze (a T maze), in which animals learned to associate a given odor delivered in an initial maze region (upon a nose poke) with a subsequent spatial choice (a left or a right turn) to receive a reward. The authors have obtained LFPs from the OB, PFC, and CA1 from 8 animals subjected to this task, along with single-unit activity from the PFC and CA1. The authors describe that, during odor sampling, there is prominent LFP activity in the beta range (20-30 Hz) as well as prominent activity of the respiration-entrained LFP rhythm (RR, 7-8 Hz). The authors convincingly show that beta activity - but not RR - is specific to odor sampling (RR also shows up during other immobility periods within the task and when animals breathed clean air). They further show that not only beta power but also inter-regional beta coherence significantly enhances during the odor sampling period. In addition, the authors find a higher beta phase modulation of spiking in a subset of neurons associated with subsequent correct decisions. Since the authors also prove - based on behavioral analysis - that the odor-sampling period corresponds to the decision-making period in this task, they propose a role for beta coordination of hippocampal-prefrontal networks in sensory-cued decision making. The paper also brings along a set of complementary findings looking at the single unit and ensemble activity in both regions (CA1 and PFC), which are capable of predicting future spatial choices.

I consider the investigated topic relevant to modern neuroscience and likely to interest a broad audience. Nevertheless, while there is much to like about this paper (e.g., carefully done experiments, advanced computational data analyses, well-written text, and well-crafted figures), I caught some issues that called my attention upon a careful reading, which I list below:

A) The paper is written in a way clearly centered on rhymical brain activity (c.f. title, abstract, introduction, and discussion). Yet, out of 7 main figures, only 2 of them show data related to oscillations (while 1 figure shows behavioral data and 4 figures show spiking analysis not related to brain rhythms). Therefore, the presentation of the results seems unbalanced and disconnected from the main story.

B) Somewhat related to the point above, in a strict sense, the title is not well justified ("Rhythmic coordination of hippocampal-prefrontal ensembles (...)") since there is no analysis relating assembly activity with either beta or RR (their results show beta or RR modulating a subset of single units), nor there is a combined ensemble analysis of PFC and hippocampal units (i.e., interregional cell assemblies). Why not try to relate ensemble activity to the observed oscillations?

C) The main result of increased interregional beta coherence specifically during odor sampling is very interesting and seems quite solid. Though I hate being the one raising questions about the level of advancement, I cannot avoid noticing that similar increases in beta coherence in odor-sampling-based tasks have been observed before (e.g., increased OB-HPC beta coherence during odor sampling has been shown in Martin et al 2007 and between LEC and HPC in Igarashi et al 2014), which is to say that there is overlap between this core finding and previous research. But that said, in times where the reproducibility of our scientific endeavor has been put into question, this particular reviewer favors the publication of similar findings by independent labs, especially given this neatly collected dataset. It is recommended to highlight which results constitute novel insights here and which results provide support for previously published results.

D) It called to my attention that many of the spiking results were obtained for a small percentage of neurons. For instance, how can the authors be confident that the choice-selective neurons are actually coding for the choice as opposed to being randomly detected by statistical chance? As a case in point, the authors mention that 1309 units were recorded in CA1, and from these 42 cells were choice selective. If the authors have employed a typical alpha of 5% for detecting such neurons, chance alone would predict ~60 neurons being false positives. I apologize if I am missing something, but could the authors clarify? On a related note, even though most findings hold true for a small percentage of neurons, the writing also tends to generalize the findings to the whole population (e.g., "Beta phase modulation of CA1 and PFC neuronal activity during this period was linked to accurate decisions, suggesting that this temporal modulation influences sensory-cued decision making.").

Reviewer #3 (Public Review):

In this manuscript Houy and coworkers report new experiments regarding the role of phorbolester-activated Munc13 paralogs, Munc13-1 and ubMunc13-2, on the secretion response of mouse chromaffin cells. They report that expression of either paralog enhanced secretion. Using single knock outs (Figs. 1, 2) or with the expression of either paralog (Figs 3, 4) they found that treatment with the phorbolester PMA was stimulatory when ubMunc13-2 was the predominating paralog, but inhibitory when Munc13-1 dominated. The opposing PMA effects in the presence of either Munc13-1 or ubMunc13-2 were interpreted in the context of a potential competition of both proteins in essential priming reactions (Fig. 5). In simultaneous fluorescence recordings of EGFP tagged Munc13 variants they studied the Ca2+- and PMA-dependent translocation of Munc13 to the plasma membrane (PM). They found that only Munc13-2 (Fig. 3) but not Munc13-1 (Figs. 4, 5) is translocated to the PM in response an intracellular Ca2+-elevation. In this context, they also report that Ca2+ -dependent recruitment of ubMunc13-2 is independent of Synaptotagmin-7 (Fig. 6) and that in the absence of Synaptotagmin-7, ubMunc13-2-dependent secretion is inhibited by PMA (Fig. 7). Based on these results the authors argue that ubMunc13-2, Synaptotagmin-7 and DAG/phorbolester form a stimulatory entity to facilitate dense core vesicle fusion.<br /> Although the manuscript presents interesting observations, some conclusions appear to be compromised by methodological and conceptual concerns.

Major criticism<br /> 1. In order to track Munc13 translocation the authors have chosen EGFP-tagged variants which overlap in the emission with the standard FuraII/Furaptra emission. Consequently, the authors omitted Ca2+-imaging in these experiments and thereby lost crucial information regarding the development of [Ca]I before and after the uncaging flash. These parameters are of central importance for the Ca2+-dependent priming and exocytosis timing, respectively. This is particularly worrisome, because in several experiments with Munc13 expression hardly any RRP component is apparent in the displayed capacitance traces, which may indicate insufficient Ca2+-dependent vesicle priming (Fig. 4). Under proper calcium control, both Ashery et al 2000 (Fig. 2) and Betz et al 2001 (Fig. 6) reported that Munc13-1 overexpression in wt chromaffin cells causes at least a 300% increase in the size of the EB compared to wt cells. Performing the same experiment, but without calcium imaging, the authors in Fig4-Sup1 show hardly any increase in the size of the EB (violet trace Fig4-Sup1) but a rather strong increase in the sustained phase of exocytosis, a phenotype that could be a result of low intracellular pre-flash calcium levels leading to insufficient vesicle priming. I do not understand why the authors have not chosen any other red-shifted protein tag to prevent such uncertainties. Furthermore, the display of the capacitance traces in several figures does not allow the appreciation of changes in the EB size or its components (e.g. RRP).<br /> 2. The authors speculate about the possibility, that PMA treatment PMA-treatment of Unc13b KO cells may lead to spontaneous release, depleting the cells of secretory vesicles. To test this, they determined the integrated CgA-fluorescence over the entire cell (Fig. 1M, N) rather than analyzing submembrane CgA-fluorescence. With the latter strategy, they will be able to focus on a potential subcellular depletion of release-ready vesicles.<br /> 3. After showing a detailed analysis of the exocytotic burst components and their kinetics in Fig. 1 and 2 the authors argue on page 9 Line 275 'Since the measurements above indicated that the main effect of PMA is on secretion amplitude, not kinetics (see also (Nagy et al., 2006)), we only distinguished between burst secretion (first 1s secretion after Ca2+ uncaging, corresponding approximately to RRP and SRP fusion) and sustained secretion (last 4 s of secretion), as well as total secretion (the sum of burst and sustained release). '<br /> I have some concerns with this argumentation because the expression of Munc13 paralogs apparently leads to changes in the burst components and/or it kinetics (e.g. Fig. 4B compare to Fig. 1 or 2). In fact, these differences cannot be directly appreciated, because experiments like in Fig. 3 and 4 lack the littermate wt control without and with PMA.<br /> Moreover, Munc13 expression leads to a disproportionate increase in the sustained phase of release, which is not present with PMA.<br /> I would recommend at least to include detailed analyses of the exocytotic burst components and their kinetics to address these uncertainties.

4. As central hypothesis, the authors propose that they have identified a unique stimulatory triad of ubMunc13-2, Syt7 and DAG/phorbolesters, which is needed for dense core vesicle priming and fusion. For example, in contrast to the behavior of wt cells (e.g. Fig 1A) phorbolester treatment becomes inhibitory in cells lacking Syt7 and expressing ubMunc13-2 (Fig. 7). Nonetheless, previously published data by Sorensen's group, obtained under similar preflash [Ca]I conditions (Tawfik et al., 2021; Fig 6-figure supplement 2 E-H), clearly show that PMA strongly potentiates exocytosis even in the absence of Syt7. Therefore, these previous findings by Tawfik et al. clearly counter the central hypothesis of the manuscript. The authors should clarify these disparate results.

Reviewer #3 (Public Review):

This manuscript describes results from a set of experiments to explore the effects of cervical spinal cord stimulation on motor control of the arm. The long-term clinical goal is to use spinal cord stimulation to improve motor function after neural injuries by facilitating volitional control of the limb. Towards that goal, they performed a set of experiments in two awake, behaving monkeys in which stimulation was delivered to the spinal cord via a set of electrodes implanted subdurally, and the monkeys were trained to perform a target tracking task using wrist flexion. Stimulation was delivered during the task and wrist torque and arm muscle EMG were recorded to quantify the effects of stimulation. The authors found that cervical stimulation can produce either facilitation or suppression of volitional muscle activity and that the direction of that facilitation or suppression was typically aligned with the direction of volitional movement. Further, they report that the amplitude of stimulation and the amplitude of background muscle activity affected the degree of facilitation or suppression observed in the muscle activity, and that high amplitude stimulation was likely causing direct activation of the ventral motor pathway, rather than indirect muscle activation via the dorsal sensory pathway. These results suggest that tuning of stimulation amplitude will be important for achieving facilitatory responses in a motor neuroprosthesis.

The conclusions of this paper are well supported by data, although they could be made stronger with additional analysis and clarification.

1. To characterize the effects of stimulation, stimulation was first delivered during an anesthetized experiment to map the evoked responses from each electrode. A major result of the paper is that the level of background activity affects the response to stimulation. It would be interesting to see these baseline responses to stimulation in awake monkeys while they were sitting quietly and not attempting a task to see if these align well with the anesthetized responses.

2. To understand the coordinated effects of stimulation across muscles, the authors present wrist torque data in Figure 7. These data are certainly important from a functional perspective and provide some information about coordination, but additional detail about coordination across muscles would be helpful throughout the paper. Currently, most of the results are presented on a per-muscle basis but don't describe whether there were (un)coordinated responses across muscles. For example, was there co-contraction of agonists or antagonists during stimulation? Increased activity of multiple antagonists could potentially lead to increased joint stiffness or fatigue without resulting in an increase in joint torque at the wrist.

3. Authors infer from the consistent ulnar wrist torque during high amplitude stimulation that these responses are likely to direct activation of the ventral motor pathway rather than activation through the dorsal sensory pathway and spinal circuitry. Is there any evidence in the EMG data (e.g. decreased latency, more consistent pulse-to-pulse amplitude of evoked EMG responses) to further support this finding?

Reviewer #3 (Public Review): 

Vide et al. present new insights into the interactions between LRRK2 and Rab GTPases. They identified two distinct Rab-binding sites in the N-terminal Armadillo (ARM) domain of LRRK2, which they named Site #1 and Site #2. One of the main findings is the striking effect of Rab GTPase phosphorylation on LRRK2's recruitment to and activation on membranes; both unmodified and phosphorylated Rabs (pRab) bind to the N-terminus of LRRK2, but to different regions. Site #1, located closer to the C-terminus of the ARM domain, binds unmodified Rab8A, Rab10, and Rab29, with Rab29 showing the highest affinity. Site #2, located at the extreme N-terminus of LRRK2, binds to the modified pRab8A and pRab10. Combining structure prediction and conservation analysis they identified the potential interaction interfaces of Site #1 and Site #2, including two conserved lysine residues (K17 and K18) in Site #2 that are critical for pRab binding. The authors propose a model where initial membrane association is mediated by binding unphosphorylated Rab8A, 10, or 29 to the lower-affinity Site #1. Membrane-associated LRRK2 then phosphorylates one of its substrates, which can now engage the higher-affinity Site #2, starting a cascade of phosphorylation events (the feed-forward mechanism). 

Overall, the authors present clear and convincing data showing the interaction between LRRK2's N-terminal ARM domain and Rab/pRab, and supporting their feed-forward mechanism. The main shortcoming in the manuscript is the absence of data directly addressing two important features of their feed-forward model: (1) The proposal that the increased activity of LRRK2 upon recruitment to membranes is only the result of its increased local concentration (without any contributions from a potential Rab-dependent activation); and (2) The ability of LRRK2 to simultaneously bind Rab and pRab. Despite this shortcoming, this manuscript presents an important contribution to our understanding of LRRK2 function, providing an elegant model for LRRK2's recruitment to and activation on membranes. This paper will be of much interest to a broad readership.

Reviewer #3 (Public Review): 

Cell division pattern is a crucial factor to organize the tissue morphogenesis including the early embryo. However, it is still unclear how the robustness of cellular arrangement is ensured under the noise of cell division patterns. In this manuscript, the authors reconstructed the lineage of cell shape in Arabidopsis embryos by categorizing cellular topology in each stage. Through this reconstruction, the authors demonstrated that the diversity of cellular topology is reduced along embryo development, where most cells finally showed cuboid shape. Also, using graph theory, they showed that this transition of topology didn't follow a random division pattern. Next, using the modeling approach, the authors found that some domain in the embryo follows a cellular geometrical rule, in which the division plane passes through the cell centroid with minimized area, while the other domain does not. In addition, they also showed the asymmetry of mother cell geometry is highly correlated with division patterns. Finally, the authors reported that the variability of division patterns is buffered along embryo development to enable robust cellular organization. These findings suggested that the variability of division patterns is restricted due to reduced diversity of cell topology and this limitation is highly important for the robustness of tissue organization. I agree this manuscript is attractive for people who are seeking fundamental mechanisms underlying proper cellular patterning in plant tissue morphogenesis. However, this manuscript lacks explanations to understand the novelty and superiority of their methods or results, especially for people of non-theoretical or -mathematical backgrounds. For example, what "graph theory" is and what do Fig 4A and B indicate? How can we interpret Figure 5-7 (Which point corresponds to each division pattern in the right panels?) How can we conclude that variability is buffered from Figure 7C?

Reviewer #3 (Public Review):

In this manuscript, the authors used a publicly available dataset to extract information about connectivity in the circadian network. I found this paper hard to evaluate. It does not rely on newly obtained data, only on the analysis of existing information. I am not an expert in EM data analysis and do not know if the analysis employed here is particularly sophisticated. Even relatively simple analysis could in principle be OK if it leads to significant biological insight. In this case, there are some ideas that are proposed but are not tested, so it is difficult to know what the value of the paper will be in the future. I could see it being relevant for people in the circadian field, but the authors should convince us by experimentally testing at least one of the predictions generated by their analysis.

Reviewer #3 (Public Review):

This is a well-replicated study: the authors sampled over a thousand field voles (Microtus agrestis), over three years at seven different sites, with a combination of cross-sectional and longitudinal sampling. The authors compared individuals carrying the GC haplotype (<10% of the population) of the high-affinity immunoglobulin receptor gene (Fcer1). They recorded parasite infections (Babesia, Bartonella, ticks, fleas, gastrointestinal helminths), expression levels of inflammatory and immune genes using transcriptomes and quantitative PCR, and genotype and pedigree.

A comparison of overall gene expression between GC-carrying and all other voles indicated two sex-dependent differences, the expression in males of Il33, which is associated with antihelminthic responses, and in females of Socs3, which is implicated in regulating immune responses. One substantial issue with the authors' interpretation of these data is to attribute Il33 to the inflammatory response - this taints the rest of their interpretation (e.g., Fig 1A, see below); instead, this is a key cytokine of the antihelminthic Th2 response and its detection suggests there might be a difference in helminth infection between the haplotypes - which is consistent with the role of IgE. Therefore, the authors would need to explore further how the GC haplotype, IgE, and parasite burdens might be driving the expression of IL-33. Specifically, the authors did not control for potential confounding effects of infection, which might be expected to differ based on the rest of their data.

Among a narrow panel of immune genes measured in ex vivo settings, the authors reported elevated expression of Il17a, which is associated with inflammatory, antibacterial responses. Of note, the panel of genes they measured did not contain antihelminth effectors beyond the transcription factor GATA3, and therefore could not confirm the expression of IL-33 observed in the transcriptomes. However, the expression of IL-17a appears consistent with the elevated activity of antioxidant SOD1.

Somewhat unexpectedly given the authors' claim that in males the GC haplotype is prone to a more inflammatory immune phenotype, it had no effect on infection in that sex. However, the identity of the genes and pathways matter and the authors do not provide sufficient detail to evaluate their interpretation (GSEA analysis and Figure 1A).

An intriguing and potentially important finding is that males carrying the GC haplotype appeared to have fewer offspring (little to no effect detected in the females). To confirm whether the effect of the haplotype is direct or mediated by other factors, it would be useful to test how other covariates, like infection, might contribute to this.

Reviewer #3 (Public Review):

This study provides a great example of dissecting the function of complex microbiota. Huang et al. developed an approach to reduce the complexity of a natural microbial community, the resulting minimal core community is amenable to mechanistic studies while still retaining key structural and biochemical features of the original community. The authors use kombucha tea (KT) microbial community to i) characterize several KTs with respect to their microbial composition and physicochemical profile, ii) define the most prominent community functions (pellicle formation, fermentation of sucrose with formation of ethanol, acetate and other metabolites), and iii) select and study bacteria-yeast subcommunities that best capture functions of original KT.

The study is thorough, systematic and well-executed. Conclusions are generally supported by the data. The presented approach can serve as a blueprint and step-by-step manual for extracting mechanistic insights from a complex system. This work is of high quality, is clearly presented and will benefit the scientific community with practical ideas. The value of this work is not only in its approach but also in the insights it provides into the connection between metabolic function and growth dynamics of individual species of KT culture. That said, there are also a few minor limitations.

First, this approach is particularly well suited for 'in vitro' communities, such as fermented food, where the metabolic input and output relatively clearly define the function of the community. However, the functions of most communities are defined by their interaction within their natural hosts and ecosystems and are not easily defined or quantified, even with regard to major nutrient sources and metabolic products. Therefore, applying this methodology inherently imposes a narrow choice of representative community function, which will bias the core selection and conclusions. While not at all preventing important scientific discoveries, it may be prudent to take this bias into account.

Second (and I understand that this may be a feasibility issue), the authors infer contributions of 1 and 2 species communities to 10 species community behavior. However, the power of these interpretations is limited by the lack of quantified species-level compositional dynamics of the 10 species community (amounts and growth of individual yeast and bacteria species are not clear). In addition, emergent community properties (which cannot be inferred from analyzing subcommunities in isolation), could have been missed because of qualitative rather than quantitative approach of comparisons between communities of different complexities.

I want to stress that these limitations are not taking away from the utility of the study, but are mentioned here to be considered when extrapolating the results obtained with simple communities to the original consortia.

Reviewer #3 (Public Review):

Clement et al. aimed at addressing the functional relevance, along with ontogeny of IL-10-producing CD4+ T cells, in the context of chronic MCMV infection, using bioinformatics approach and in vivo studies with multiple mouse mutants.

The same group of authors, along with others, previously observed the accumulation of IL-10+ CD4+ T cells in the salivary gland in the context of acute/chronic MCMV infection which were dependent on IL-27 and ICOS in a temporal- and site-manner. Accumulation of IL-10+ CD4+ T cells favored virus persistence. In line with that, T cells from IL-10−/− mice were oligoclonal, exhibited a highly activated phenotype, expressed antiviral cytokines, and degranulated in response to cognate Ag encounter ex vivo. Data presented here provide some extended insight into the biology of IL-10 producing CD4+ T cells.

Strengths: experiments are properly designed and executed, and address a relevant and interesting research question. Moreover, they provide valuable sequencing data (bulk RNA Seq, TCR Seq, ATAC Seq) for better phenotypical analysis of IL-10-producing CD4+ T cells. The authors describe highly differentiated Th1 cells that express IL-10 and arginase-1, where arginase-1 has an inhibitory effect on the T cell control of the viral load.

Weaknesses: Somehow the study is confusing as some parts relate to IL-10 and some to arginase-1 without clearly referring to the same cells. The study is in part deprived of novelty as the inhibitory effect of arginase-1 as well as the role of IL-10 in CMV control has been studied. Some of the conclusions are not supported by data. Few critical points remain unclear in this manuscript which would provide novelty in relation to previous studies:<br /> 1. does the arginase-1 effect take place in the periphery or in the salivary gland, i.e. could arginase-1 play a role at other mucosal sites?<br /> 2. what controls the expression of Arginase-1? Can the role for chronic antigen exposure be addressed?<br /> 3. how does Arg-1 promote viral persistence and how T cell specific is the observed effect?

Reviewer #3 (Public Review):

Yildirim et al describe a novel three-photon (3P) imaging approach which concomitantly addresses several notable roadblocks in the current state of the art when it comes to functional imaging using organoid cultures. The authors use a 3P system modified with custom laser and optics which enables label-free, deep, high-resolution, non-phototoxic, long-term imaging of intact brain organoids achieving close to 1mm penetration and imaging periods up to 96 hours. Leveraging the capacity of third harmonic generation (THG) signal to differentiate regions with distinct cellular densities, the authors demonstrate effective, label-free demarcation of ventricular zone-like regions vs regions resembling the cortical plate. Moreover, through a set of well-designed and well-powered experiments, the authors apply their system to uncover structural and functional phenotypes in organoids derived from Rett's Syndrome patient lines and corrected isogenic lines. All without the need for a fluorescent label, they describe structural changes to VZ-like regions and migration deficits in cells that emanate from these regions. The imaging of migrating cells in relation to their VZ of origin reveals an especially novel look at the radial migration of cortical neurons in organoids, something which has not been possible to assay since the VZ structures remain deep within organoids and inaccessible to co-image with migrating cells using standard approaches.

The study is highly innovative, and looking ahead, a standardized 3P/THG imaging platform that enables deep and label-free imaging of organoids at scale, holds a lot of promise in illuminating a lot of biology which currently remains beyond reach, as well as in designing large scale, non-invasive, multi-parameter phenotyping screens using patient samples. The manuscript is well-written and the results clearly demonstrated.