199 Matching Annotations
  1. Jul 2022
    1. 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.

    1. 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.

    1. 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.

    1. 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.

    1. 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.

    1. 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.

    1. 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.

    1. 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.

    1. 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.

    1. 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).

    1. 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).

    1. 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.

    1. 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.

    1. 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.

    1. 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.

    1. 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.

    1. 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.

    1. 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.

    1. 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.

    1. 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.

    1. 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.

    1. 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.

    1. 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.

    1. 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.

    1. 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.

    1. 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.

    1. 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.

    1. 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.

    1. 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.

    1. 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.

    1. 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.

    1. 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.

    1. 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.

    1. 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.

    1. 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.

    1. 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.)

    1. 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.

    1. 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.

    1. 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.

    1. 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.

    1. 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.

    1. 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.

    1. 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.

    1. 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.

    1. 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?

    1. 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.

    1. 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.

    1. 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.

    1. 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.

    1. 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.

    1. 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.

    1. 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.

    1. 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.

    1. 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.

    1. 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.

    1. 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.

    1. 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.

    1. 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.

    1. 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.

    1. 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.

    1. 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.

    1. 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.

    1. 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?

    1. 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.

    1. 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.

    1. 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.

    1. 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.

  2. Jun 2022
    1. 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.

    1. 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.

    1. 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.

    1. 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.

    1. 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.

    1. 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.

    1. 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.

    1. 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.

    1. 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.

    1. 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.

    1. 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.

    1. 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.

    1. 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.

    1. 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.

    1. 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.

    1. 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.

    1. 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.

    1. 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.

    1. 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.

    1. 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.

    1. 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).

    1. 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.

    1. 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.

    1. 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.

    1. 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.

    1. 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?

    1. 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.

    1. 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.

    1. 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.

    1. 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.

    1. 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."

    1. 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?

    1. 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.

    1. 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.

    1. 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.

    1. 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.

    1. 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.").

    1. 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.

    1. 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?

    1. 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.

    1. 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?

    1. 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.

    1. 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.

    1. 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.

    1. 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?

    1. 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.

    1. Reviewer #3 (Public Review):

      This paper represents an important contribution to the field of pore-forming toxins, in particular peptide toxins. In nature, several families of pore-forming proteins are known, each of them showing the unique structure of monomeric units as well as a unique mechanism of pore formation that usually involves significant conformational changes in molecules upon membrane insertion. The mechanism proposed in this study is completely different from the ones that we know to date. The authors use an array of complementary biophysical approaches as well as simulations to show this. It also nicely shows the capability of methodological approaches such as AFM or CD, beyond the modules used on average in other studies. This adds strength to the topic of the paper as well as gives the reader ideas on how these approaches could be used in their studies as well (educational aspect).

      In terms of weakness, there are some details that should be better addressed. For example, the model of the CL peptide is only poorly presented, the orientation of N and C termini is not proposed on figures describing polymers of 8-mer, which is important. A hypothesis on how flipping from unrimmed to rimmed pore happens would be welcome. The lipid specificity is not addressed, and in fact, experiments with different approaches are in some cases done with different lipids, with no explanation why. The discussion is rather weak. The authors compare this novel mechanism of pore formation to other families of pore-forming proteins, but there seems to be some lack of insight in other mechanisms of action (and structures of soluble proteins and their respective pores). However, I believe that this study importantly paves the path to further more detailed studies of CL pore-forming mechanism.

    1. Reviewer #3 (Public Review):

      The authors attempted to identify an optimal combination of broadly neutralizing antibodies (bNAbs) that can suppress escape of HIV-1 from the therapy. To do so, the authors fit a birth-death model of viral dynamics using published longitudinal HIV sequence data from 9 untreated patients. Using inferred quantities to parametrize the model subject to bNAb infusion, they predict the distribution of rebound times of HIV in therapy trials with two mono-therapy and their combination. Finally, using deep mutational scanning (DMS) data to identify escape-mediating variants against 9 bnAbs for HIV, they propose a triplet combination that may best suppress early viral rebound.

      While the goal is clear, there are a number of major weaknesses that curtail the quality of the work:

      First, the approach is not novel. It at best represents a synthesis of known methods and published data sets.

      Second, the analyses and computational data cannot justify the major claims, in particular the prediction on optimal bnAb combinations - the central goal of this work. Specifically, match of rebound time distribution is only achieved for early rebound due to ineffective bNAbs. This limited validity under restrictive assumptions (within a limited time window) thus cannot validate the optimality of identified combinations that count on effective bNAbs for delayed rebound. More importantly, the proposed optimal combinations are highly sensitive to data quality and depth. In particular, DMS data cannot faithfully probe low-frequency variants that are chiefly responsible for rebound, which undermines the predictive power of the approach.

      Third, the main results are already known from earlier work. It has been long known that a combination of more than two bnAbs is more effective in suppressing early rebound than fewer. Moreover, it has been shown recently that bnAb (VRC01) infusion acts to amplify pre-existing bnAb-resistant viral strains, leading to fast HIV rebound. Hence, it is unclear what new insight this work confers.

      Lastly, suppression of early rebound alone is not a sufficient measure of therapy efficacy. Late rebound is not necessarily a sign of viral control, but might instead indicate selection for cross-resistant viral mutants - an even more detrimental outcome. In addition, this work has neglected bnAb dynamics or influence of infused bnAbs on the response of endogenous B cells, which will be essential for understanding viral dynamics, especially when infused bnAbs are relatively effective at suppressing early rebound.

    1. Reviewer #3 (Public Review):

      The authors of the present manuscript had previously shown that a complex of mouse meiosis-expressed gene 1 (MEIG1) and Parkin co-regulated gene (PACRG) are required to deliver essential cargoes to the developing sperm tail through the poorly understood microtubule-based process of intramanchette transport (IMT). How this MEIG1/PACRG complex associates with microtubule-based motors in IMT, however, was unclear. In this manuscript, the authors identified a microtubule motor protein, axonemal dynein light intermediate polypeptide 1 (DNALI1), as a PARG binding partner via a yeast two-hybrid screen. Further, they revealed that DNALI1 stabilises PACRG abundance in vitro, is expressed in the spermatid manchette, and that PACRG localisation to the manchette is dependent on DNALI1 but not vice versa. Utilising a Dnali1 germ cell-specific knockout mouse they reveal DNALI1 is essential for male fertility, with Dnali1 cKO sperm having a number of abnormalities that could be consistent with abnormal intramanchette transport, in addition to other abnormalities that are consistent with DNALI1 also having roles prior to the manchette appearing and after its dissolution (e.g. double flagella, spermiation failure). This paper is a key conceptual advance in understanding the mechanisms of intramanchette transport, a process that is still very poorly defined and direct evidence for has been largely lacking. Indeed, the strength of this manuscript is the data defining the interaction and the nature of the interaction, between DNALI1 and MEIG1/PACRG. However, in its current form, the characterisation of the role of DNALI1 in spermatogenesis needs to be improved to properly validate some of the data and to clarify the functions of DNALI1 in spermatogenesis. Once this is done the manuscript will be of interest and of use to both the germ cell biology and broader cell and cytoskeletal biology fields.

    1. Reviewer #3 (Public Review):

      The authors designed a behavioral experiment based on a Go/ No-Go paradigm, to train guinea pigs on call categorization. They used two different pairs of call categories: chuts vs. purrs and wheeks vs. whines. During the training of the animals, it turned out that they change their behavioral strategies. Initially, they do not associate the auditory stimuli with rewards, and hence they overweight the No-Go behavior (low hit and false alarm rate). Subsequently, they learned the association between auditory stimuli and reward, leading to overweighting the Go behavior (high hit and false alarm rates). Finally, they learn to discriminate between the two call categories and show the corresponding behaviors, i.e. suppress the Go behavior for No-go stimuli (improved discrimination performance due to stable hit rates but lower false alarm rates).<br /> In order to derive a mechanistic explanation of the observed behaviors, the authors implemented a computational feature-based model, with which they mirrored all animal experiments, and subsequently compared the resulting performances.

      Strengths:<br /> In order to construct their model, the authors identified several different sets of so-called MIFs (most informative features) for each call category, that were best suited to accomplish the categorization task. Overall, model performance was in general agreement with behavioral performance for both the chuts vs. purrs and wheeks vs. whines tasks, in a wide range of different scenarios.

      Different instances of their model, i.e. models using different of those sets of MIFs, performed equally well. In addition, the authors could show that guinea pigs and models can generalize to categorize new call exemplars very rapidly.<br /> The authors also tested the categorization performance of guinea pigs and models in a more realistic scenario, i.e. communication in noisy environments. They find that both, guinea pigs and the model exhibit similar categorization-in-noise thresholds.

      Additionally, the authors also investigated the effect of temporal stretching/compression of calls on categorization performance. Remarkably, this had virtually no negative effect on both, models and animals. And both performed equally well, even for time reversal.<br /> Finally, the authors tested the effect of pitch change on categorization performance, and found very similar effects in guinea pigs and models: discrimination performance crucially depends on pitch change, i.e. systematically decreases with the percentage of change.

      Weaknesses:<br /> While their computational model can explain certain aspects of call categorization after training, it cannot explain the time course of different behavioral strategies shown by the guinea pigs during learning/training.<br /> Furthermore, the model cannot account for the fact that short-duration segments of calls (50ms) already carry sufficient information for call categorization in the guinea pig experiment. Model performance, however, only plateaued after a 200 ms duration, which might be due to the fact that the MIFs were on average about 110 ms long.

      In the temporal stretching/compressing experiment, it remains unclear, if the corresponding MIF kernels used by the models were just stretched/compressed in a temporal direction to compensate for the changed auditory input. If so, the modelling results are trivial. Furthermore, in this case, the model provides no mechanistic explanation of the underlying neural processes. Similarly, in the pitch change experiment, if MIF kernels have been stretched/compressed in the pitch direction, the same drawback applies.

      Discussion:<br /> The authors claim that intermediate-level features of auditory stimuli, like the identified MIFs, are most useful to accomplish call categorization. This is supported by several findings, e.g. that both, guinea pigs and the model exhibit similar categorization-in-noise thresholds. Furthermore, animals and models are astonishingly robust against temporal stretching/compression, and even time reversal. Finally, both show a strong effect of pitch change on discrimination performance.

    1. Reviewer #3 (Public Review):

      This paper titled "Epigenetic remodeling by Vit C potentiates the differentiation of mouse and human plasma cells" is a very interesting paper with novel findings and mechanisms of Vit C in plasma cell differentiation. They elegantly show that Vit C enhances plasma cell differentiation and that the mechanisms involve TET activity and DNA demethylation. Their current model supports plasma cell differentiation of IgM, IgE and IgG1 in mouse which is a type 2 immune response model. If VIt C can be shown in a type 1 immune model, it would important. Then, these data may help to support Linus Pauling's claims that Vit C prevents and alleviates the common cold if the model can be applied broadly.

    1. Reviewer #3 (Public Review):

      In this work, the authors design experiments to determine whether Otop channels are gated by changes in extracellular pH. Understanding pH-dependent conformational changes in proteins (including ion channels) is fundamentally important. For Otops, which are voltage-independent, pH changes may represent the main physiological mechanism for controlling channel activity in vivo, so the authors' line of inquiry is of high potential value to researchers who seek to understand how Otops contribute to normal physiological mechanisms of extracellular pH sensing, intracellular pH control, and membrane potential homeostasis.

      The techniques used here are generally appropriate, but electrophysiology is clearly the most incisive and the resulting data represent the major strength of the work. The authors convincingly demonstrate that proton currents mediated by Otops 1-3 are differentially sensitive to changes in extracellular pH, and are therefore most likely proton-gated channels. A potential weakness of the work is that individual residues which mediate pH-dependent gating are not identified, and it is therefore difficult to ascertain any details about the structural basis of gating.

    1. Reviewer #3 (Public Review):

      Satou et al. report a viral toolbox by:

      1) Inventing a novel way through temperature-dependence of HSV1-mediated gene expression for adult and larval zebrafish;

      2) Employing Gal4/UAS system to achieve cell types specific expression in this model;

      3) Combining the modified rabies viruses and HSV1 for transneuronal tracing of neural circuits in zebrafish that is kept in a higher temperature environment.

      This toolbox in the manuscript will be of great interest to the neuroscience field when they are using zebrafish as a model.

      The strength is these novel methods will offer more experimental opportunities and will facilitate more exciting basic scientific discoveries. However, some concerns still exist as below:

      1) What's the mechanism of temperature-dependence expressions with these HSV1 and rabies virus in this study? At least the authors should discuss it. Have the authors done experiments like this: after getting enough gene expression from these viruses when maintaining these fishes in 35-37 degree, bring them back to normal temperature as they usually live to see what happen? Does this higher temperature help the fish brain cells get infected with more viral particles or just help increase the expression level? Or does just the higher temperature help produce more proteins?

      2) The authors should address or discuss more whether the higher temperature affects these fishes' brain activity? The reason is if someone will use this method for a most important experiment like GCaMP7s calcium imaging, in order to get good expression with these viruses that authors described in the manuscript they should raise the temperature but they have no idea about whether these higher temperatures affect the behavior or brain activity in some special brain regions they are interested in.

    1. Reviewer #3 (Public Review):

      The manuscript from Hososhima et al. entitled "Proton-transporting heliorhodopsins from marine giant viruses" reports for the first time proton-translocation activity for heliorhodopsins. Heliorhodopsin (HeR) is a newly discovered family of opsin proteins that are distinct from either type-1 or type-2 rhodopsins and are found in Archaea, Bacteria and Eukarya as well as giant viruses (Pushkareve et al. 2018). A unique feature of HeR is their inverted topology compared to the microbial and type-2 opsins. Despite the availability of detailed structural information on members of the HeR family (Kovalev et al. 2020, Lue et al. 2020 and Shihoya et al. 2019), their function and mode of action remain unknown. In this manuscript, the authors use the heterologous expression of synthesized HeR genes from giant viruses (V1HeR1-2,V2HeR1-3) to investigate the ion transporting properties of these viral HeRs (VHeR). Authors demonstrate that one of the viral HeR genes (V2HeR3) exhibits a unique photon-induced current that translocates protons across the membrane. Interestingly all other tested viral HeR do not show any proton-translocating activity (similarly to previously tested HeR such as Ehux-HeR, TaHeR or HeR 48C12) potentially pointing to enzymatic/signalling function of these members. Furthermore, the authors characterized the basic electrophysiological parameters of these photocurrent components in terms of their light sensitivity, kinetics, ion selectivity and more. A mutational study identifies key residues that are likely controlling the direction of ion transport. Protein purification and UV-VIS spectroscopy further reveal a prototypical slow photocycle that is similar to other HeR with maximum absorption of around 500 nm. The authors identify the M-state as a putative conducting state.<br /> Overall, the work demonstrates nicely the mode of operation for a member of the HeR family that will pave the way to understanding the biological role and evolution of these rhodopsins. Also, the absence of any ion-translocating activity for the other HeR genes potentially underlines the diverse functions that lie within this new opsins family. The authors hand-wavingly discuss the functional role of a proton-transport activity for V2HeR3 as either depolarizing the host cell and thereby facilitating entry into the cell, or preventing superinfection.

      The authors carefully chose their wording in the title as " ... proton-transporting", but then focused very much on channel activity on V2HeR3. Yet, the contribution of the passive conductance (I1 and I2) is rather small compared to the pump current I0. Could the author add some information on the initial pumping current in terms of kinetic (on- and off-kinetic for I0 are also important parameters to evaluate the potential application for HeR), ion selectivity, or spectral properties? Authors should also show wavelength dependence for all components (I0 - I2). Does it follow the spectroscopic absorption? I was a bit puzzled by the light intensity curve for the I0 component - why is the pump current not saturating at such high light powers (off-kinetic/photocycle does not look so fast to account for that!).

      Authors should reconsider their terminology of the photocurrent components. I find peak photocurrent for I1 misleading, especially since I0 is called transient photocurrent. Maybe authors should stick to I0, I1, and I2? Also, is I1 an independent component? Ion selectivity looks very similar to I2, and maybe the observed overshoot at positive potentials (figure 1 C red arrow I1) is an effect of a slightly higher [H+] in the vicinity of HeR after the pumping current?

      In figure 1D authors should check if changes in Erev or photocurrent for NaCl_e and KCl_e are significantly different when compared to NMG at pH_e 7.4.<br /> As the authors claim that there is an extracellular binding site for Cl- based on their results in figure S4. The larger photocurrent for Na2SO4 is a bit puzzling. So there is also a binding site for SO42- or did the authors not correct for double the amount of sodium? Table S7 is potentially useful in this respect but would need to be filled out completely.

      I believe that the use of HeR as an optogenetic tool is limited; the authors should not try to build such an artificial link to such an application. I believe their finding is of high value independent of optogenetic use. Yet, if the authors believe that it is hard to foresee how the community will embrace HeR, I suggest a more vigorous analysis. First, the expression in ND7/23 looks very cytoplasmatic (Fig 1B). Could the authors provide images from cortical neurons they use for the measurements in figure 1E (the image quality of all figures is very poor in the manuscript - it needs to be improved)? Secondly, I do not agree that the experimental design the authors chose to test neuronal fitness after overexpression of HeR is appropriate. The electrical induction of APs (300pA, pulse width?) is not a good read-out for neuronal excitability levels (or alteration of those). Therefore the authors should measure rheobase (current steps or ramps). Additionally, parameters such as Ri, Cm, Vm, or Rm should be used to evaluate the fitness of cells.

    1. Reviewer #3 (Public Review):

      In this manuscript, the authors have investigated how weight loss by bariatric surgery or weight-matched dietary intervention impairs breast cancer growth. They have shown that post-bariatric surgery, the tumors show augmented inflammation and an immune checkpoint; PD-L1 expression, which suppresses the anti-tumor immune responses. In addition, anti-PD-L1 therapy in these mice has shown to be more effective at slowing tumor growth. The authors report interesting observations, and the findings are well supported by the data, however, the use of only one syngeneic model tampers the reviewer's enthusiasm. Overall, the study is clinically important and helps in stratifying obese breast cancer patients that may respond to anti-PD-L1 immune checkpoint inhibition.

    1. Reviewer #3 (Public Review):

      In this manuscript, the authors investigate the regulation of the 'timer' genes, cad, D, and opa, and their roles in the formation of the most posterior segments of Drosophila using analysis of gene expression genetics and modelling.

      The authors re-examined posterior expression of en and wg and corroborated previous findings of additional stripes corresponding to the most posterior parasegments. This expression is pre-figured by slp and eve expression. This evidences sequential addition of the terminal segments and segment polarity expression of eve that is not prefigured by a pair-rule expression.

      Analysis of the three timer genes' expression in the posterior region shows that they are expressed in the same temporal sequence as in the trunk.

      The authors then showed that there is cross-regulation of timer genes by examining the effect of expression of these genes in mutants of the others. The boundaries of these genes in the posterior of the embryo also appear to be regulated by tll and hkb, and the posterior patterning of these genes is lost in tor mutants.

      Taken together the gene expression and genetic analyses allow the authors to 'wire' the timer genes into the GRN for posterior patterning significantly adding to our understanding of these interactions as summarised in fig 8.

      Finally, the authors modelled these interactions. They found that simulating the inferred genetic interactions was broadly able to explain their observations and help to better understand the patterning of the posterior of the embryo.

      This is very solid work, illustrated by excellent figures, that provides new insights into the regulation of posterior development in Drosophila and has important implications for the regulation of segmentation in other insects.

    1. Reviewer #3 (Public Review):

      This paper reports the results of an interesting fMRI study examining the neural correlates of time estimation with an elegant design and a sensorimotor timing task. Results show that hippocampal activity and connectivity are modulated by performance on the task as well as the valence of the feedback provided. This study addresses a very important question in the field which relates to the function of the hippocampus in sensorimotor timing. However, a lack of clarity in the description of the MRI results (and associated methods) currently prevents the evaluation of the results and the interpretations made by the authors. Specifically, the model testing for timing-specific/timing-independent effects is questionable and needs to be clarified. In the current form, several conclusions appear to not be fully supported by the data.

      Major points

      Some methodological points lack clarity which makes it difficult to evaluate the results and the interpretation of the data.

      1) It is unclear how the 3 levels of accuracy and feedback (high, medium, and low performance) were computed. Please provide the performance range used for this classification. Was this adjusted to the participants' performance?

      2) The description of the MRI results lacks details. It is not always clear in the results section which models were used and whether parametric modulators were included or not in the model. This makes the results section difficult to follow. For example,

      a. Figure 2: According to the description in the text, it appears that panels A and B report the results of a model with 3 regressors, ie one for each accuracy/feedback level (high, medium, low) without parametric modulators included. However, the figure legend for panel B mentions a parametric modulator suggesting that feedback was modelled for each trial as a parametric modulator. The distinction between these 2 models must be clarified in the result section. Additionally, it is unclear how Figure 2A supports the following statement: "Moreover, the voxel-wise analysis revealed similar feedback-related activity in the thalamus and the striatum (Fig. 2A), and in the hippocampus when the feedback of the current trial was modeled (Fig. S3)." This is confusing as Figure 2A reports an opposite pattern of results between the striatum/thalamus and the hippocampus. It appears that the statement highlighted above is supported by results from a model including current trial feedback as a parametric modulator (reported in Figure S3). Also, note that it is unclear from Figure 2A what is the direction of the contrast highlighting the hippocampal cluster (high vs. low according to the text but the figure shows negative values in the hippocampus and positive values in the thalamus). These discrepancies need to be addressed and the models used to support the statements made in the results sections need to be explicitly described.

      b. Connectivity analyses: It is also unclear here which model was used in the PPI analyses presented in Figure 2. As it appears that the seed region was extracted from a high vs. low contrast (without modulators), the PPI should be built using the same model. I assume this was the case as the authors mentioned "These co-fluctuations were stronger when participants performed poorly in the previous trial and therefore when they received low-accuracy feedback." if this refers to low vs. high contrast. Please clarify.

      c. It is unclear why the model testing TTC-specific / TTC-independent effects (results presented in Figure 3) used 2 parametric modulators (as opposed to building two separate models with a different modulator each). I wonder how the authors dealt with the orthogonalization between parametric modulators with such a model. In SPM, the orthogonalization of parametric modulators is based on the order of the modulators in the design matrix. In this case, parametric modulator #2 would be orthogonalized to the preceding modulator so that a contrast focusing on the parametric modulator #2 would highlight any modulation that is above and beyond that explained by modulator #1. In this case, modulation of brain activity that is TTC-specific would have to be above and beyond a modulation that is TTC-independent to be highlighted. I am unsure that this is what the authors wanted to test here (or whether this is how the MRI design was built). Importantly, this might bias the interpretation of their results as - by design - it is less likely to observe TTC-specific modulations in the hippocampus as there is significant TTC-independent modulation. In other words, switching the order of the modulators in the model (or building two separate models) might yield different results. This is an important point to address as this might challenge the TTC-specific/TTC-independent results described in the manuscript.

      d. It is also unclear how the behavioral improvement was coded/classified "we contrasted trials in which participants had improved versus the ones in which they had not improved or got worse"- It appears that improvement computation was based on the change of feedback valence (between high, medium and low). It is unclear why performance wasn't used instead? This would provide a finer-grained modulation? If the feedback valence was used to classify trials as improved or not, how was this modelled (one regressor for improved, one for no improvement? As opposed to a parametric modulator with performance improvement?). Last, it is also unclear how ITI was modelled as a regressor. Did the authors mean a parametric modulator here? Some clarification on the events modelled would also be helpful. What was the onset of a trial in the MRI design? The start of the trial? Then end? The onset of the prediction time?

      Perhaps as a result of a lack of clarity in the result section and the MRI methods, it appears that some conclusions presented in the result section are not supported by the data.<br /> E.g. "Instead, these results are consistent with the notion that hippocampal activity signals the updating of task-relevant sensorimotor representations in real-time." The data show that hippocampal activity is higher during and after an accurate trial. This pattern of results could be attributed to various processes such as e.g. reward or learning etc. I would recommend not providing such interpretations in the result section and addressing these points in the discussion.

      Similar to above, statements like "These results suggest that the hippocampus updates information that is independent of the target TTC". The data show that higher hippocampal activity is linked to greater improvement across trials independent of the timing of the trial. The point about updating is rather speculative and should be presented in the discussion instead of the result section.

    1. Reviewer #3 (Public Review):

      Results presented in this manuscript demonstrate a strong correspondence between interface size and cotranslational subunit assembly mechanism. Additionally, in this manuscript, the authors have compared the sizes of first and last translated interfaces of heteromers in bacterial, yeast, and human complexes and detected a clear preference in N-terminal interfaces, which will be translated first and thus more likely to form cotranslationally, to be larger than C-terminal interfaces. The authors concluded that large interfaces have evolved as a means to maximize the chance of successful co-translational subunit assembly thus minimizing misfolding and unwanted interactions with other proteins in the cell.

      This is a very interesting study further shedding light on the mechanism and the pathway of cotranslational protein folding, which, however, prompts many additional questions as they relate to the exact nature of the cotranslationally assembling interfaces.

    1. Reviewer #3 (Public Review):

      Yamada et al utilizes the full strength of Drosophila neural circuit approaches to investigate second-order conditioning. The new insights into the mechanisms of how a learned cue can act as reinforcement are relevant beyond the fly field and have the potential to spark broad interest. The main conclusions of the authors are justified and the experiments, to my understanding, are well done.

      Some minor aspects must be addressed. To avoid misunderstandings a clear distinction should be made between those experiments using real world sugar and those using artificial activation of dopamine neurons as reward. For example, the proposed teacher - student model is mostly based on the work established with artificial activation. To emphasize the generality of the model, it might help to provide some further evidence using real world sugar approaches, especially since the only known sugar-reward driven plasticity is reported in the student (g5b`2a) but not the teacher compartments. In this line, it would be useful to extend the functional interference used during the sugar experiments beyond the a1 compartment. Further, general statements about the compartments, for example for g5 and a1, might need adjustment since the tools used, the respective driver lines, often don't label all dopamine neurons in one specific compartment. In fact, functional heterogeneity among dopamine neurons innervating the g5 compartment have been recently established (sugar-reward, extinction) and might apply here. Lastly, I would like to recommend that the authors discuss alternative feedback pathways that might serve similar or parallel functions.

      Despite these minor points, the study is impressive.

    1. Reviewer #3 (Public Review):

      The manuscript presents a carefully designed and well-controlled study on active tactile perception and its relationship to internal bodily rhythms - the cardiac cycle. This work builds on previous studies which also showed that active perception/voluntary actions occur in certain phases of the cardiac cycle, but the previous research failed to show/was not designed to show the significance of these synchronizations for perception or behaviour. To my knowledge, this is the first report that seems to experimentally show that active perception in the cardiac diastole leads to behavioural advantages - better tactile discrimination.

      The manuscript itself is very clearly written, the introduction is concise but sufficient, while the results section is very well organised and I especially like how the authors guide the reader through the analysis and additional steps taken to understand the findings even better.

      Yet, despite careful study design, effective visualisations, and elegantly constructed story, there are some analytical choices that, in my opinion, are not sufficiently justified or explained (e.g., selecting a diastolic window equal in length to the duration of systole, instead of using the whole duration of diastole). Such analytical decisions could have (at least some) effects on the obtained results and thus conclusions drawn.

    1. Reviewer #3 (Public Review):

      The authors are attempting to provide an extensive anatomical map of the circuits that promote feeding initiation, identify genetic tools to manipulate individual circuit elements, and then use these tools to begin to directly link the (mostly) newly identified circuit elements to behavior.

      The paper has many strengths: In the first section of the paper, the authors provide a connectome of the neural circuit that drives proboscis extension upon activation of sweet-response gustatory receptor neurons (GRNs) in the Drosophila melanogaster labellum. They also identify a companion set of genetic tools that permits cell-specific physiological analysis and activity manipulation of many of the individual neurons within the circuit. Together these advances mark a quantum leap, greatly expanding our knowledge of the circuitry that drives the initiation of feeding in the fly. Identifying so many of the neurons involved and providing a wiring diagram describing their contacts will be of great interest to the field and an important reference work. Furthermore, the cell-specific genetic tools identified in this paper will also be of tremendous utility to the field. One anticipates that this manuscript will be foundational for many subsequent studies of taste processing in the fly.

      Having laid this initial groundwork, the authors then begin to interrogate the circuit functionally and examine multiple aspects of taste processing. The work presented in these sections is clear and the data are convincing, but the large number of neurons under consideration and the variety of phenomena examined makes it a bit scattered and unwieldy in parts. To take the topics in order: The authors first examine the necessity of individual identified neurons in this circuit for the proboscis extension response (PER) upon tastant detection (sucrose) as well as the sufficiency of activation of individual circuit elements to drive proboscis extension. They then assess how gustatory neuron activation modulates the activity of these brain neurons and how hunger modulates their ability to drive proboscis extension. Finally, the authors identify a pre-motor neuron as a site of intersection between the sweet and bitter detection pathways that can mediate the inhibitory effects of bitter compounds on PER centrally. Together these experiments demonstrate convincingly that the neuronal elements under investigation are indeed important for controlling feeding initiation (PER) behavior and that (most) of these neurons respond to the gustatory neuron activation as expected. They also begin to define specific neurons in the brain that are targeted by modulatory interactions in the form of bitter perception and physiological state.

    1. Reviewer #3 (Public Review):

      Bone marrow adipocytes (BMAds) are a major component of the bone marrow and accumulate further in diverse clinical conditions, suggesting that these cells play important roles in both normal physiology and in disease states. BMAds also accumulate during caloric restriction, demonstrating that they are functionally distinct from adipocytes in white or brown adipose tissues. Despite their pathophysiological potential and unique characteristics, the functions of BMAds have remained poorly understood, especially in comparison to adipocytes elsewhere in the body.

      In this manuscript, Li et al address this important gap in knowledge by establishing a new mouse model that allows specific transgenic targeting of BMAds. To do so they use an ingenious approach involving the combination of two novel transgenic mouse lines. In one line, named Osterix-FLPo mice, a flippase (FLPo) is expressed under the control of the Osterix promoter, which is active in osteoblasts and BMAds but not in white adipocytes. In the second line, named FAC (FLPo-dependent Adipoq-Cre) mice, an inverted Cre sequence, flanked by flippase recombination sites, is inserted into the 3`-untranslated region of the adiponectin gene (Adipoq); importantly, Adipoq is expressed in adipocytes (including BMAds) but not in osteoblasts. Because the Cre sequence is inverted in the FAC mice, in the absence of flippase no functional Cre is expressed. Thus, the authors bred the Osterix-FLPo mice with the FAC mice to generate BMAd-Cre mice, in which flippase is expressed in both osteoblasts and BMAds only. This causes the FAC Cre sequence to be flipped into the correct orientation in BMAds and osteoblasts, but not in other cell types. Because the Adipoq promoter is not active in osteoblasts, Cre expression is therefore specific to BMAds.

      After convincingly validating this BMAd-Cre transgenic model, the authors use it to investigate how BMAds influence metabolism, bone remodelling and hematopoiesis. To do so they combine the BMAd-Cre mice with floxed-Pnpla2 mice, thereby deleting Pnpla2 in BMAds but not in other cell types. Pnpla2 encodes adipose triglyceride lipase (ATGL), the first and rate-limiting enzyme in lipolysis, in which triacylglycerols are catabolised to release non-esterified fatty acids and glycerol. The resulting Pnpla2 KO mice, therefore, have BMAds that are unable to undergo stimulated lipolysis. The authors convincingly demonstrate this lipolytic defect and how it results in increased bone marrow adiposity in the KO mice. They then assess the metabolic and skeletal consequences of this lipolytic defect.

      On a normal, ad libitum (AL) diet, the BMAd-Pnpla2-KO mice display no overt metabolic or skeletal phenotype compared to BMAd-Pnpla2-WT mice. Therefore, the authors challenged the mice with caloric restriction (CR), reasoning that BMAd lipolysis may be more important under conditions of limited energy availability. During CR the KO mice show a similar metabolic phenotype to the WT mice; however, differences in bone and hematopoietic parameters become apparent. The CR studies are done in males and females and, after observing some sex differences, a further cohort of females is studied following ovariectomy. The molecular basis for these skeletal and hematological phenotypes is then investigated by bulk RNA sequencing of the distal tibial bone marrow samples from the KO and WT males under AL or CR conditions. Finally, the authors move beyond CR to investigate if BMAd lipolysis influences skeletal remodelling following bone injury or during cold exposure.

      The authors conclude that BMAd lipolysis is required to support bone homeostasis and myelopoiesis during caloric restriction, at least in male mice. They also show convincingly that deficient BMAd lipolysis compromises post-injury bone regeneration and might exacerbate bone loss during cold exposure. Through comprehensive skeletal phenotyping they conclude that, during CR, BMAd-Pnpla2-KO mice have decreased trabecular bone owing to an impaired ability of osteoblasts to secrete osteoid; this conclusion is further supported by RNA sequencing analyses of distal tibial bone marrow. Most of the authors' other conclusions are also supported by the data presented. Despite some weaknesses, overall this study represents an important advance in our understanding of BMAd function and stands out for establishing a new transgenic model that is likely to be extremely useful for future BMAd research.

      Strengths:

      1. The authors have taken an ingenious approach for specific transgenic targeting of BMAds, establishing the first BMAd-specific model. This should be an extremely useful tool for the burgeoning field of BMAd research. The authors convincingly demonstrate the specificity of this BMAd-Cre model, including the use of lineage tracing and PCR to show that Cre is expressed in BMAds but not in other cell types. One exception is Cre expression in a small subset of stromal/dendritic cells within the bone marrow, but this is only a minor limitation that should not compromise the utility of the model for further exploring BMAd function. However, there are two more-substantial limitations to the model: unexpected effects on circulating adiponectin, and relatively low Cre expression in younger mice (see below under 'Weaknesses').

      2. The effects of CR are studied in males and females, highlighting sex differences in the phenotypes.

      3. For the CR studies, the skeletal phenotyping and RNAseq analyses have generally been done to a very high standard. The authors conclude that, under CR, BMAds provide energy to maintain osteoblasts' secretion of collagen matrix for osteoid synthesis. The RNAseq data identify molecular changes consistent with these conclusions. However, there is one minor limitation to how the RNAseq data are analysed (see 'Weaknesses').

      Weaknesses:

      1. One of the biggest limitations concerns the BMAd-Cre model: compared to non-Cre controls, the BMAd-Cre mice have decreased adiponectin protein expression in white adipose tissue and bone marrow adipose tissue and a ~70% decrease in circulating adiponectin concentrations. The authors speculate that this may result from the insertion of the inverted Cre sequence within the 3`-UTR impairing translation of adiponectin protein. One previous mouse CR study found that adiponectin knockout limits BMAd accumulation and bone loss during CR; thus, decreased circulating adiponectin in the BMAd-Cre mice might confound the interpretation of how the BMAd-Pnpla2-KO is impacting bone homeostasis during CR. Despite this possibility, the authors do not measure circulating adiponectin in the BMAd-Pnpla2-WT or BMAd-Pnpla2-KO mice on AL or CR diets. This should be measured to determine if CR is still capable of increasing circulating adiponectin in the BMAd-Pnpla2-WT and BMAd-Pnpla2-KO mice. It would also be informative to compare their circulating adiponectin concentrations to those of non-Cre Pnpla2-fl/fl mice, both on AL and CR diets, as an additional control.

      Another limitation, highlighted by the authors, is that, because of the architecture of the FAC transgene, the BMAd-Cre mice have relatively low expression of Cre, resulting in lower rates of recombination in younger mice. This may limit the use of the model to mice older than 16 weeks of age.

      2. The second general limitation of the study is that the effects of the KO (i.e. BMAd-Pnpla2-WT vs BMAd-Pnpla2-KO) are generally assessed within each diet (AL or CR), thereby missing the possibility of detecting genotype-diet interactions. Moreover, for the male CR studies data for metabolic and bone parameters are presented completely separately for the AL mice and for the CR mice, which prevents analysis of CR effects. For some studies the four groups of mice (i.e. with diet and genotype as the two independent variables) are shown on the same graphs, yet the data are analysed only using 1-way ANOVA. As a result, it is not possible to detect genotype-diet interactions. It would be hugely informative for all the data to be presented with the four groups alongside each other, and to analyse these using 2-way ANOVA. This would confirm if the CR intervention has had expected metabolic and skeletal effects and may reveal new effects of the BMAd-Pnpla2-KO that currently are going undetected.

      3. The bone regeneration studies are done in male mice only, while the cold-exposure studies are in females only. Given the sex differences in how BMAd-Pnpla2-KO impacts CR, it would be informative to know if similar sex differences occur in these other contexts.

      4. The cold exposure studies present only a limited analysis of the bone phenotype that reveals only minor effects. Therefore, the conclusion from these studies (that BMAd lipolysis is needed to maintain bone mass during cold exposure) is less convincing than the conclusions from the CR or bone regeneration studies.

      5. In the RNAseq analysis, the authors first compare the effects of CR in the BMAd-Pnpla2-WT mice. To determine how BMAd-Pnpla2-KO influences the CR response, they then compare transcript expression between the CR BMAd-Pnpla2-KO mice and the AL BMAd-Pnpla2-WT mice. This comparison is invalid because it is being made across two independent variables (diet and genotype) and therefore it may miss some of the true effects of the KO on the CR response. However, it appears that transcript expression is similar between the KO and WT mice on an AL diet, and therefore correcting this analysis (so that CR KO mice are compared to AL KO mice) may not substantially alter the authors' conclusions.

    1. Reviewer #3 (Public Review):

      The main finding in this study is that during repeated exposure to a visual sequence (A-B-C-D), merely presenting single sequence items (e.g., - B - -) leads to V1 reinstatement of subsequent items in the sequence. Importantly, the successor stimuli (e.g., C-D) are reinstated, but not the predecessor stimuli (e.g., A). The authors propose that this predictive activity adheres to the postulated properties of the successor representation (SR). The SR, which has previously been used to describe activity in the hippocampus (Stachenfeld et al., Nature Neuroscience 2017), can be defined as a predictive representation where each state is represented in terms of its successor states, in a temporally discounted fashion. The idea that V1 might also employ the computationally efficient and flexible properties of the SR is highly interesting.

      Overall, the data presented in this article provide evidence for an SR-like representation in V1 during a visual sequence task but not during a post-scan localiser scan. I have several queries for the authors which they may wish to address. For example, using their data are the authors able to distinguish between an SR and other predictive sequence models? Why is the predictive activity only observed during the task and not during the post-task localiser? How does the interpretation of the data differ from the authors' previous reports of preplay in V1? Why do the authors fit certain models to data acquired during the task, and other models to data acquired during a localiser scan?

    1. Reviewer #3 (Public Review):

      Haggerty et al. assess how the projection from the agranular insular cortex to the dorsolateral striatum contributes to binge drinking in mice. The authors use whole-cell patch-clamp electrophysiology to examine synaptic adaptations following binge drinking (Drinking-in-the-Dark) in male mice, finding a constellation of changes that include increased AMPA and NMDA receptor function at insula synapses onto striatal projection neurons. They go on to assess a causal role for this projection in regulating binge drinking using optogenetics, finding that stimulating insula->striatal transmission in vivo reduces total ethanol consumed during DID, along with several specific behavioral measurements of drinking microstructure. One of the most interesting of these findings is a decrease in "front-loading", or drinking during the very beginning of the session, a phenotype that has been associated with problematic drinking and alcohol use disorder in humans. Finally, the authors use machine learning to build a predictive model that can reliably discern stimulated mice from controls. These studies improve our understanding of the neurocircuitry that mediates binge drinking and synaptic and circuit adaptations that occur following binge drinking. Experiments are blinded and performed in a rigorous manner, including physiological validation experiments in support of the in vivo optogenetic manipulation. Despite many strengths, there are significant limitations and gaps in the electrophysiology studies included in this version of the manuscript. As acknowledged by the authors, there are curious findings that are seemingly at odds with each other, and further studies addressing cell type specificity and/or feedforward inhibition would significantly improve the interpretation of this work. Furthermore, the manuscript would be significantly improved by an expanded Introduction containing more specific background information along with a standalone Discussion to place these findings within the broader literature. Lastly, a major limitation of these studies is the low number of mice used for the in vivo optogenetic control experiments and the exclusion of female mice throughout.

      Major concerns:

      1) Expanded Introduction and Discussion. The Introduction does not discuss and/or downplays historical literature investigating neuroadaptations following binge drinking. Studies examining changes in glutamate receptor function within striatal circuits should be discussed in greater detail, rather than the broad pass and review citation included. Behavioral studies examining how the function of the insula and DLS regulate ethanol exposure should also be discussed, especially including work examining the insula to accumbens pathway. It would also be worthwhile to reference human studies implicating the insula and DLS in AUDs.

      2) It is difficult to form a comprehensive picture of the electrophysiological changes reported in Figure 1. The data seems to indicate increased AMPAR function, even more increased NMDAR function, decreased glutamate release probability, and decreased population spikes. These conflicting findings are acknowledged and there are two possible factors mentioned in the manuscript - differential engagement of MSN populations and changes in feedforward inhibition through local interneurons. I disagree with the authors' dismissal of potential MSN subtype-specific effects contributing to these discrepancies. Although AIC inputs innervate D1 and D2 MSNs comparably under control conditions, it is quite possible that the pathways are differentially altered following DID, as has been observed in many reports of alcohol or drug exposure (e.g. Cheng et al. Biological Psychiatry 2017). On the other hand, I wholeheartedly agree with the authors that AIC-driven feedforward inhibition through local interneurons (or even MSNs) could explain the curious divergence between the synaptic and population-level changes depicted in Figure 1. I think additional experiments addressing to help connect the dots are critical in interpreting the changes described in this manuscript. The authors could consider targeted recordings from specific cell types (e.g. D1, D2, and/or interneurons), measurements of AMPA/NMDA receptor subunit stoichiometry, and/or additional experiments in conditions where feedforward transmission is blocked (e.g. PTX or TTX/4AP).

      3) N=2 mice in the ICSS experiment in Figure 4J is not sufficient to interpret, and including error bars on this data set is misleading. There also appears to be a difference in distance traveled between GFP and ChR2 mice in Figure 4C, but statistics are not reported. It is also hard to understand what that might mean given the way these data are normalized.

    1. Reviewer #3 (Public Review):

      Matsuo et al. have authored a manuscript describing the effects of depletion of the forkhead box gene, Foxa2, on embryogenesis and gestation in the mouse. The effects of this treatment are the induction of the diapause arrest in the development of the embryo and consequent dormancy. The manuscript is well-prepared, and the figures, for the most part, are didactic and interpretable. Although the conclusions are interesting, the principal weaknesses of the manuscript are the lack of novelty and the perceived absence of some controls and follow-up experiments.

      Controls and Follow-ups:<br /> 1. The Cre/lox system depletes rather than deletes genes. Although in situ data are presented, these are not judged to be quantitative. The usual qPCR analysis of tissues could have established the quantity of depletion. This is important because the frequency of implantation sites in both Cre/lox models (lines 111-113) may be attributable to the residual expression of Foxa2.<br /> 2. The most novel and salient finding of the present study is that the depletion of Foxa2 results in embryos that are in a state that "morphologically resembled dormant blastocysts". A useful experiment would have been to transplant these embryos to normal recipients or to culture them in vitro to determine whether they were capable of reactivation from the dormant state.<br /> 3. Figure 3C indicates that embryos recovered on Day 8 had an extensive proliferation of ICM cells, but not trophoblast. Previous studies have explored the progression of entry and exit from diapause in the mouse (DOI: 10.1093/biolre/ioz017) showing that reactivation of the embryo from diapause commences in the ICM and then proceeds to the trophoblast. It therefore may be possible that proliferation in the trophoblast is not suspended, rather than the recovered blastocyst has resumed development and that mitotic activity has not yet reached the trophoblast.<br /> 4. In Figure 4B, neither the Ltf nor the Pgr Cre treated uteri appear normal on Day 8. This is not consistent with the conclusion in lines 170 et seq. of the manuscript. It is difficult to discern normality from Figure 4C, but it is clear that the PgrCre-lox uterus does not conform to the controls. It is later noted that there is edema in the uteri at this time in the Day 8-treated PgrCre/lox mice (lines 217-218).<br /> 5. In Figure 6B, the implantation sites appear substantially smaller in mice of both mutant genotypes. Supplemental Figure 4 suggests that this is not the case. It is unclear whether the samples chosen for figures are representative of the uteri and whether variation in the size of implantation sites was observed.

    1. Reviewer #3 (Public Review):

      In this manuscript, the authors address whether the mechanisms mediating intergenerational effects are conserved in evolution. This question is important not only to frame this phenomenon in an evolutionary context, but to address several interlinked questions: is there a mechanism in common between adaptive versus deleterious effects? What makes some effects last one instead of several generations? What is the ecological relevance for those mechanisms? Using Caenorhabditis elegans as a model of reference, they compare four types of intergenerational effects on additional three Caenorhabditis species.

      The authors used previously characterized models of intergenerational inheritance, focusing on those that are likely to have adaptive significance. This is relevant, because the adaptive relevance of other published examples of inter- and transgenerational inheritance is not clear. They used functional studies to probe for conservation of mechanisms for bacterial infection and resistance to osmolarity stress, which is a major strength of this study. The data supports the claim of conservation in some types of intergenerational inheritance and divergence in others. One major question addressed in this manuscript is whether there is a potential overarching mechanism that confers stress-resistance across generations. Their experiments convincingly show that this is not the case, but that instead, there are stress-specific mechanisms responsible for intergenerational inheritance.

    1. Reviewer #3 (Public Review):

      In this paper, the authors ask whether selective sweeps (as measured by the iHS and nSL statistics) are more or less likely to occur in or near genes associated with Mendelian diseases ("disease genes") than those that are not ("non-disease genes"). The main result put forward by the authors is that genes associated with Mendelian diseases are depleted for sweep signatures, as measured by the iHS and nSL statistics, relative to those which are not.

      The evidence for this comes from an empirical randomization scheme to assess whether genes with signatures of a selective sweep are more likely to be Mendelian disease genes that not. The analysis relies on a somewhat complicated sliding threshold scheme that effectively acts to incorporate evidence from both genes with very large iHS/nSL values, as well as those with weaker signals, while upweighting the signal from those genes with the strongest iHS/nSL values. Although I think the anlaysis could be presented more clearly, it does seem like a better analysis than a simple outlier test, if for no other reason than that the sliding threshold scheme can be seen as a way of averaging over uncertainty in where one should set the threshold in an outlier test (along with some further averaging across the two different sweeps statistics, and the size of the window around disease associated genes that the sweep statistics are averaged over). That said, the particular approach to doing so is somewhat arbitrary, but it's not clear that there's a good way to avoid that.

      In addition to reporting that extreme values of iHS/nSL are generally less likely at Mendelian disease genes, the authors also report that this depletion is strongest in genes from low recombination regions, or which have >5 specific variants associated with disease.

      Drawing on this result, the authors read this evidence to imply that sweeps are generally impeded or slowed in the vicinity of genes associated with Mendelian diseases due to linkage to recessive deleterious variants, which hitchhike to high enough frequencies that the selection against homozygotes becomes an important form of interference. This phenomenon was theoretically characterized by Assaf et al 2015, who the authors point to for support. That such a phenomenon may be acting systematically to shape the process of adaptation is an interesting suggestions. It's a bit unclear to me why the authors specifically invoke recessive deleterious mutations as an explanation though. Presumably any form of interference could create the patterns they observe? This part of the paper is, as the authors acknowledge, speculative at this point.

      I'm also a bit concerned by the fact that the signal is only present in the African samples studied. The authors suggest that this is simply due to stronger drift in the history of European and Asian samples. This could be, but as a reader it's a bit frustrating to have to take this on faith.

      There are other analyses that I don't find terribly convincing. For example, one of the anlayses shows that iHS signals are no less depleted at genes associated with >5 diseases than with 1 does little to convince me of anything. It's not particularly clear that # of associated disease for a given gene should predict the degree of pleiotropy experienced by a variant emerging in that gene with some kind of adaptive function. Failure to find any association here might just mean that this is not a particularly good measure of the relevant pleiotropy.

      A last parting thought is that it's not clear to me that the authors have excluded the hypothesis that adaptive variants simply arise less often near genes associated with disease. The fact that the signal is strongest in regions of low recombination is meant to be evidence in favor of selective interference as the explanation, but it is also the regime in which sweeps should be easiest to detect, so it may be just that the analysis is best powered to detect a difference in sweep initiation, independent of possible interference dynamics, in that regime.

    1. Reviewer #3 (Public Review):

      In this manuscript, Pohlkamp, Xian, Wang et al. investigated the role of the sodium-hydrogen exchanger NHE6 in synaptic plasticity and Aβ plaque load in a mouse model of Alzheimer's disease (AD) in the presence or absence of Apolipoprotein E4 (APOE4), a major genetic risk factor for sporadic AD. They initially report that NHE6 deletion causes cerebellar neurodegeneration. They find that genetic deletion of NHE6 alleviates impairments in reelin-induced synaptic plasticity in mice expressing human APOE4. The main novelty of this study is that NHE6 suppression significantly reduced amyloid plaque load in a mouse model of AD expressing humanized Aβ, either in the presence or absence of ApoE4. This is interesting, as it potentially opens new roads to understand and control amyloid pathology in the AD brain. Although the data are intriguing and relevant for the community, some issues need to be addressed so that conclusions are justified by data:

      1) The leading hypothesis of this work is that APOE4 impairs synapse function through prolonged association with endosomes, thereby making brain cells vulnerable to AD-related pathological changes. However, the positive effects of NHE6 in a mouse model of Aβ accumulation occurs regardless of APOE4. This suggests that NHE6 may contribute to pathology by mechanisms other than APOE4-mediated retention of endosomal trafficking.

      2) With the current data, it is not possible to exclude possible nonspecific effects resulting from NHE6 genetic deletion. Additional experiments to measure the endosomal pH would add support to the hypothesis.

      3) The authors attribute reduced amyloid plaque load in NHE6-deficient APP KI mice to increased glial responses, which would promote plaque clearance. This is a very interesting hypothesis, but it is not supported by the experimental data reported in Supplemental Figure 6. Additional experimentation is needed to more thoroughly characterize astrocytic and microglial phenotypes caused by NHE6 genetic depletion in APP KI mice. Functional assays, including cytokine release, nitric oxide production (Griess reaction), and Aβ uptake experiments would be desired to strengthen these conclusions.

      4) The authors demonstrate that global or conditional NHE6 deletion causes severe Purkinje cell loss in the mouse cerebellum (Figure 2). Although the authors included representative images of H&E staining indicating no gross histological abnormalities (Supplemental Figure 3), a more detailed investigation is required to assess neuronal survival in the hippocampus and cortex upon NHE6 suppression, given the relevance of these regions to AD pathology. Indeed, previous evidence (Xu et al., eNeuro, 2017) showed that NHE6 depletion leads to significant cortical and hippocampal atrophy, in addition to the cerebellum. Could the reductions in plaque load in NHE6 depleted mice (Figure 5, 6; Supplemental Figure 5) be somehow a reflection of neuronal loss? It is important that the authors discuss this issue in the manuscript.

    1. Reviewer #3 (Public Review):

      Gorur-Shandilya et al. apply an unsupervised dimensionality reduction (t-SNE) to characterize neural spiking dynamics in the pyloric circuit in the stomatogastric ganglion of the crab. The application of unsupervised methods to characterize qualitatively distinct regimes of spiking neural circuits is very interesting and novel, and the manuscript provides a comprehensive demonstration of its utility by analyzing dynamical variability in function and dysfunction in an important rhythm-generating circuit. The system is highly tractable with small numbers of neurons, and the study here provides an important new characterization of the system that can be used to further understand the mapping between gene expression, circuit activity, and functional regimes. The explicit note about the importance of visualization and manual labeling was also nice, since this is often brushed under the rug in other studies.

      Major concern:

      While the specific analysis pipeline clearly identifies qualitatively distinct regimes of spike patterns in the LP/PD neurons, it is not clear how much of this is due to t-SNE itself vs the initial pre-processing and feature definition (ISI and spike phase percentiles). Analyses that would help clarify this would be to check whether the same clusters emerge after (1) applying ordinary PCA to the feature vectors and plotting the projections of the data along the first two PCs, or (2) defining input features as the concatenated binned spike rates over time of the LP & PD neurons (which would also yield a fixed-length vector per 20 s trial), and then passing these inputs to PCA or t-SNE. As the significance of this work is largely motivated by using unsupervised vs ad hoc descriptors of circuit dynamics, it will be important to clarify how much of the results derive from the use of ISI and phase representation percentiles, etc. as input features, vs how much emerge from the dimensionality reduction.

    1. Reviewer #3 (Public Review):

      The paper represents an innovative and comprehensive body of work aimed to assess the function of cortical offset responses in the anterior auditory field (SSF) in sound perception. Whereas the majority of work to date in auditory neuroscience has focused on the sound onset responses (largely due to the quick adaptation of cortical responses), the offset responses, which are present in the cortex, and especially, as the authors show, in AAF, have received less attention. The offset responses can play an important role in sound segregation and auditory scene analysis. The authors used a combination of behavioral, electrophysiological and optogenetic techniques to study the properties of cortical offset responses. The authors first test whether and how offset responses correlate and affect behavioral detection of sound offsets. They find that suppressing offset responses in AAF reduces the responses of mice to sound offsets and that there is a significant correlation between cortical responses and behavioral report. The authors next use elegant electrophysiological and manipulation methods to find that cortical offset responses have a component that is generated in the AAF and therefore not only inherited from the periphery. The authors also find that offset responses increase with sounds that have longer duration and therefore do not simply encode for silence. Such an extensive description of these types of responses provides for an advance in our understanding of cortical function in audition.

    1. Reviewer #3 (Public Review):

      In "Zika virus causes placental pyroptosis and associated adverse fetal outcomes by activating GSDME," Zhao et al. investigated the mechanism of fetal growth restriction caused by maternal Zika virus infection.

      Strengths:<br /> The in vitro studies (knockouts) are clear in showing a role for GSDME in cell death. They show that GSDME may be functioning similarly in several cell types in addition to placental cells. They also show that RIG-I recognition of the viral 5' UTR is critical for the cellular pyroptotic response. Using a pregnant mouse model, they show that GSDME knockout prevents disease in fetuses.

      Weaknesses:<br /> Given that the authors describe pyroptosis in other cell types, it seems possible that the effects of GSDME knockout on the fetus could be indirect and due to decreased pyroptosis in elsewhere in the dams. How did GSDME knockout alter the clinical signs of disease (weight loss, histopathology) in the dams?

      Figure 5D/E/F and Figure 6C/D- how are the authors distinguishing between apoptosis and pyroptosis as the cause of cell death in the placental tissue?

    1. Reviewer #3 (Public Review):

      The study design was excellent, and I really enjoyed seeing how the physiological (urinary hormones) and behavioral (infant feeding and proximity to the mother) measures related to one another. The set-up of the article was good and outlined why this study was necessary and important. The authors did a great job using statistical analyses to disentangle the various effects of different variables, particularly that of TTS (birth of a new sibling) from age and sex on the main behavioral and physiological measures. The conclusions of the article are well supported by the data and the analyses, and the discussion addresses several possibilities and lines of evidence, which I agreed with, and thought was quite thorough. I am not an expert on the urinary hormones tested and used in the study (cortisol, T3, neopterin), so am unfortunately not able to thoroughly evaluate the credibility of these measures and methods. My main requested changes concern clarity of various concepts and ideas, and an elaboration of two major concepts that are assumed but that are not properly discussed or fleshed out in the paper. Namely, the idea of a nutritional weaning that is separate from social/behavioral weaning, and the discussion about the evolutionary origins of the mechanism uncovered in the paper.

      First, throughout the manuscript, the authors assume a distinction between nutritional versus social or behavioral weaning. I am very happy the authors are making this important distinction; however, this concept in itself is fairly innovative. The idea that there are two components to weaning, the milk-transfer component versus the psycho-social relationship between the mother and the infant (sometimes including continued nipple contacts without milk transfer, so regardless of whether or not milk transfer occurs), is an idea that needs to be supported with literature in your paper, and that needs to be explained a little bit somewhere in the text. For example, research has shown that comfort nursing, without milk transfer, can occur for years after lactation has ended in some primates (e.g., chimpanzees). This sets up a situation where you have two separate weaning periods: weaning from milk and weaning from nipple contact, or weaning from milk and weaning from behaving as an infant (i.e., the nutritional versus social/behavioral weaning that you talk about). Thus, the mother-infant behavioral relationship can develop separately from the mother-infant nutritional relationship, despite considerable overlap between the two. This concept is innovative because it is a little bit different from the classic mother-offspring conflict and maternal investment theories (Trivers). I would like the authors to explain this distinction between behavioral and nutritional weaning, and explain its importance for the infant and mother, and give credit to others who have worked on this idea.

      Second, the concluding statements in the discussion that the results highlight the evolutionary history of a stress response and TTS for immature individuals is vague and almost seems like a throw away idea because it is not specific enough. I would like the authors to go a step further and talk about the possible timing of an evolutionary link between stress and TTS. Presumably, this important interaction would have appeared with the great ape transition, or possibly with the transition between genus Pan and Homo. I would like the authors to dig a little deeper in this idea and use the comparisons in life history traits and infant development of humans and the other great apes that would be set up in the introduction to infer when this stress mechanism they found could have become more prominent in the evolutionary history of primates.

      Lastly, the introduction (and a bit in the discussion) at times lacked clarity because the reader did not receive all the information needed, or the connection between two ideas was not made explicit enough. Sometimes things were implied (and would be understood by a specialist audience) but not made explicit so that a non-specialist reader might have trouble completely making the link between the ideas. Thus, there were several sections I will point out in my line-by-line review that could use either a reformulation of the statement or an elaboration of the ideas to make them clearer for the reader.

    1. Reviewer #3 (Public Review): 

      By modelling trypanosoma cruzi infection in mice, the authors highlighted the presence of a subsets of CD4 T cells expressing canonical markers and transcription factors of CTLs and capable of exerting antigen specific and MHC class II restricted cytotoxic activity. Mechanistically, using KO mice, the authors have shown that myd88 expression is required for strengthening the CD4 CTLs phenotype during the infection. 

      Moreover, by investigating the presence of a previously published CD4 CTLs gene signature in a mixed bone marrow chimera settings they highlighted a cell intrinsic role for Myd88 in imprinting the signature. The study also identifies Il18R as a myd88 upstream receptor potentially responsible for CD4 CTLs development by showing that lack of IL18R phenocopied myd88 deficiency in failing to promote a CD4 CTLs phenotype. 

      Finally, by showing the direct correlation between perforin expressing CD4 T cells in Chagas infected individuals and parameters of heart disfunction the authors hinted at a possible involvement of CD4CTLs in a clinical setting. 

      -The core finding of the paper, providing the first evidence of CD4 CTLs development in a mouse model of intracellular parasite is well supported by the data. The expression of markers correlated to CD4 cytotoxicity in other settings and gene signatures fits well the phenotype described and suggests possible common features for CD4 CTLs development across infection with different pathogens. 

      This manuscript will boost the knowledge over the involvement of non canonical CD4 types in the immune responses to parasites. Moreover the finding that CD4 CTLs are the predominant phenotype in organs importants for viral replication imply an involvement of these cells in the development of the pathology that will have to be taken into accounts in future studies. 

      - The understanding of the parental relationship beteween CD4CTLs and Th1 remains unclear and it's complicated by the low numbers of IFNg (regarded as an hallmark of functional Th1) producing CD4 T cells detected in the model. IFN-g production by CD4 is lower than 10% even when achieved by PMA/Iono stimulation and half of Gzb+ CD4 stain positive for the cytokine. On the other hand the putative transcription factor of Th1 development, Tbet, is expressed by all Gzb positive CD4s. This discrepancy and the low number of IFNG+ should be better discussed by the authors. 

      On the same note, while the confirmation of a CD4 CTLs gene signature in the model is very convincing, it must be noted that the one used as a reference was obtained by performing single cell RNA seq , taking into account only IFNg+ CD4 cells and then comparing Gzb+ and Gzb- negative in the setting. The authors are instead using bulk RNA seq and comparing populations of cells that would have none VS low levels of Th1. In this view, while the confirmation of the CD4 CTLs signature is striking, addressing the relative relationship with Th1 cells is complicated. Using Gzb YFP reporters in the setting could help improving the resolution between the 2 subsets. 

      - The dependancy on the Myd88/IL18r axis to promote CD4 CTLs is well characterized and the prolonged survival rate of IL18r-/- after the adoptive transfer of Gmb YFP+ CD4 is very convincing. However instead of using PBS as control the authors could have used YFP- or total CD4 cells for the task. While in previous publication it was already showed that protection was achieved by transferring the total CD4 population; comparing GzB + VS GzB- would have added useful insights over the amount of protection conferred by the subtypes and relative roles of CD4 CTLs and Th1 in the model. Parasitemia could also be reassessed in this view.

    1. Reviewer #3 (Public Review):

      In this study, the authors were exploring the structural biology and mechanism of Bacillus cereus ribonucleotide reductase R2b-NrdI complex, an essential component of the system for formation of deoxyribonucleotides for DNA synthesis. The R2 subunit relies on two divalent cations (including iron or manganese) for its activity and associates with the Nrdl subunit. Nrdl is a flavoprotein that utilizes the redox activity of FMN to reduce molecular oxygen (O2) to a superoxide radical (O2•-). The superoxide then transits through a channel formed at the interface between Nrdl and R2b.

      Previous structure-function studies of this system of enzymes (of which there are many) utilized traditional x-ray diffraction methodologies (i.e. synchrotron radiation) which are damaging to the FMN cofactor and/or divalent cations by reducing their oxidation states, preventing a structural picture of the oxidized state of the enzyme complex.

      Therefore, the authors utilized serial femtosecond crystallography with an x-ray free electron laser, which allows for room temperature x-ray diffraction data collection. The authors were then able to visualize any structural changes to the flavin ring of FMN between the redox states, any changes in the interaction surface between Nrdl and R2b and the channel between them, and the di-divalent ion binding site of R2b (in this case, manganese). In these regards, the authors were successful in the experiment and solved the FMN oxidized and/or reduced (hydroquinone, FMNH2) states of the Nrdl-R2b complex and could make numerous important observations. Most notably, they observed that the flavin ring of FMN does not undergo major bending between its oxidized and reduced states, which contrasted with previous observations of cryo-cooled synchrotron radiation-solved structure of Nrdl, and with quantum mechanics/molecular mechanics calculations. The evidence presented in terms of quality of the electron density of the FMN moieties in each structure were convincing to me. They also observed that the R2b subunit contributes amino acids that hold the FMN ring in the planar state, suggesting that the Nrdl-R2b complex places strain on the FMN ring allowing favouring of reduction of molecular oxygen to superoxide. The evidence for this observation was also convincing to me and in honesty, it was quite amazing to see the conformational differences in amino acids between the redox states, as nicely presented in electron density difference maps.

      Overall, this represents a strong study and excellent example of the utility of serial femtosecond crystallography with an XFEL for providing important mechanistic details into redox-active enzymes. I believe this paper will provide inspiration to the structural biology community to use this approach and provide important appropriate technical details.

    1. Reviewer #3 (Public Review):

      The authors used state-of-the-art techniques to investigate the role of centrally located (GABAergic APT neurons) CaV3.2 isoform of T-channels in an animal model of neuropathic pain using speared nerve injury model. This is generally an excellent and very rigorous study. The data is very compelling and it is likely going to have a major impact in the field of ion channels and pain transmission. The data presentation is superb and major conclusions are highly justified. Major strengths include the use of powerful complementary techniques such as molecular (single-cell PCR), mouse genetics, and pain testing in vivo, as well as sophisticated ex vivo (slice physiology) and in vivo recordings (burst analysis using tetrodes). This study may explain recent clinical studies that failed to show the efficacy of peripherally acting Cav3.2 channel blockers in patients with neuropathic pain. Hence, this study has the potential to change the focus from peripheral to supraspinal Cav3.2 channels in various pain pathologies.

      Some moderate weaknesses are identified and should be addressed:<br /> 1) The data showing the effect of T-channel deletion on the excitability of GABAergic neurons of APT is very convincing. However, what is missing is a discussion of how changes in the excitability of inhibitory APT neurons impact the circuitry that is involved. Without knowing the circuitry involved, one could speculate that blocking inhibitory drive may do just the opposite effect of what is proposed and increase hyperalgesia.<br /> 2) Methods should clearly state if any experiments were done in a blinded fashion.<br /> 3) There is no mention anywhere of how was selective Cav3.2 knock-out achieved, nor how was this assessed. It would be very helpful if authors could perform recordings of T-channel amplitudes in sham animals, animals after SNI and after selective knock-out in the SNI group.<br /> 4) It should be discussed that global Cav3.2 animals had only minimal neuropathic pain phenotype (Choi et al., 2007).

    1. Reviewer #3 (Public Review):

      In this manuscript, Siller et al describe the role of beta subunit variants in facilitating CaV2.3 current at voltages positive to -50 mV. They propose that this could be one of the mechanisms that allow CaV2.3 to contribute to Ca2+ influx during regular pacemaking and burst activities of dopaminergic substantia nigra neurons and thus potentially contribute to the pathophysiology of Parkinson's disease.

      Strengths:<br /> 1. The novelty of this manuscript includes the authors' identification of an important and previously unrecognized role for CaV2.3 in dopaminergic substantia nigra neurons. This discovery could shed light on the pathophysiology of Parkinson's disease.<br /> 2. The authors nicely correlated the expression pattern of beta subunit variants in mouse brain and their functional effects on CaV2.3 currents. In order to precisely measure the effect of beta subunit variants on CaV2.3 currents, they perform voltage clamp experiments in a heterologous system (tsA-201 cells) using physiologic solutions. Based on these results, they demonstrate that β2a and β2e shift CaV2.3 inactivation to more positive voltages, thus allowing channels to remain available to open in the voltage range of physiologic burst activities. To support this claim, they measured the relative levels of transcription of beta subunit variants in mouse substantia niagra and ventral tegmental area using RT-qPCR and demonstrated significant expression of β2a and β2e variants compared to the rest of the beta subunits.<br /> 3. Authors have thoughtfully designed experimental methods and provided rigorous controls which make the results compelling. Moreover, the dosage of channel blockers (SNX-482, isradipine, and Cd2+) used was appropriately chosen to avoid off-target effects and was consistent with what is currently described in the literature.

      Weaknesses:<br /> 1. Although the data is compelling and experimental designs were rigorous, the inference about the potential contribution of the role of beta subunits in Parkinson's disease is still limited due to use of primarily heterologous systems and wild-type neurons. This consideration is appropriately discussed by the authors.<br /> 2. The description of how this study fits into what is already known about beta subunit function is somewhat limited. For example, a considerable amount of literature describes the structure and function of the β2a/β2e interactions with CaV and their impact on various channel subtypes. The paper could be strengthened by a discussion of how the results relate to these previously published studies.

    1. Reviewer #3 (Public Review):

      First, a note on nomenclature. The authors use the term 'auto-immune' uveitis to encapsulate three different conditions -- HLA-B27 anterior uveitis, idiopathic intermediate uveitis, and birdshot choroidopathy. While I would agree with this terminology for the third set, there is substantial controversy as to whether HLA-B27 is truly autoimmune or autoinflammatory. Indeed, one major hypothesis is that this condition is driven by changes in gut microbiome. Intermediate uveitis is even more problematic; a substantial number of cases of this condition will turn out to be associated with demyelinating disease, which has recently been linked to Epstein Barr virus disease. To my knowledge in none of these diseases has a definitive autoantigen been identified nor passive transfer via transfusion shown; I would suggest the authors abandon this terminology and simply refer to the conditions as they are called.

      Further, it would have been very desirable to compare the DC transcriptome for the other class of uveitic disease -- infectious -- for acute retinal necrosis or similar. As well it would have been very useful to compare profiles to other, related immune-mediated diseases such as ankylosing spondylitis.

      Finally, it must be noted that looking for systemic signals in dendritic gene expression may be a bit of a needle in the haystack approach. Presumably, the function of the dendritic cells in uveitis is largely centered on those cells in the eye. It would have been highly desirable to examine the expression profile of intraocular DCs in at least a subset of patients who may have come to surgery (for instance, steroid implantation or vitrectomy).

      It is also problematic that no effort has been made to assess the severity of uveitis. Flares of disease can range from extremely mild to debilitating. Similarly, intermediate uveitis and BSCR can range greatly in severity. Without normalizing for disease severity it is difficult to fully understand the range of transcriptional changes between cases.

      The results are intriguing, but several additional analyses would further support these findings.

      1. The use of principal component analysis for clustering may be underpowered; I would suggest the authors apply UMAP to determine if higher dimensional component analyses correlate with disease type.

      2. The false-discovery rate in large transcriptomic projects is challenging. While the authors are to be commended for employing a validation set, it would be useful to employ a Monte Carlo simulation in which groups are arbitrarily relabeled to determine the number of expected false discoveries within this data set (i.e. akin to Significance Analysis of Microarray techniques).

      3. I do not fully understand the significance of the mouse CD11c-Runx3delta mice. It appears these data were derived from previous datasets or from bone marrow stromal line cultures. Did the authors attempt to generate autoimmune uveitis (i.e. EAU) in these animals? Without this the relevance for uveitis is unclear.

    1. Reviewer #3 (Public Review):

      Understanding how transcriptional activity is regulated is a very important topic. The authors provide a detailed structural and biophysical characterization of several complexes of the p52 homodimer of NF kB and different DNA binding sites. The focus is on the central base pair as well as the flanking base pairs on both sides. By x-ray crystallography the authors show that the minor grove of the central base pairs is widened with the G:C base pairs compared to the A:T base pairs. Using MD simulations of the DNA the authors show that DNA molecules can adopt different conformations and the binding of the p52 homodimer induces the least conformational changes in the naturally occurring sequence. The authors go further and correlate these conformational changes with binding affinity and with kinetic kon and koff measurements. Overall, there is little correlation between affinity and transcriptional activity. The only correlation they observe is between fast on and off kinetics and higher transcriptional activity. This result is surprising and does not immediately provide a mechanistic interpretation of how high transcriptional activity is achieved. The authors try to provide an explanation via binding of co-repressors but the fundamental biophysical investigations for such a model are missing at the moment.

    1. Reviewer #3 (Public Review):

      A new mouse model was developed for studying a cancer-associated factor named HOTAIR. Increased levels of HOTAIR promoted the spread ("metastasis") of breast cancer to the lung. The high levels of HOTAIR consequently activated many specific genes that are known to play important roles in the ability of cancer cells to spread. As breast cancer cells depended on HOTAIR in order to spread, targeting HOTAIR may be a therapeutic target for treating breast cancer in patients in general and preventing cancer cells from metastasizing to the other parts of the body.

      This is an exciting and important study. The iHOT-PyMT mouse is novel and for the first time provides a valuable model for studying the in vivo function and mechanism of action of one of the most well-known and important lncRNAs HOTAIR. The study represents an important contribution to basic, clinical and translational research. The experiments are rigorous and appropriate controls were used throughout the study, providing confidence in the large amount of data presented in the manuscript. The results support the conclusions made by the authors.

    1. Reviewer #3 (Public Review):

      This paper discusses the effect of the low-pass filtering between outer hair cell transducer current and receptor voltage. The filter's cut-off frequency (where the response is down by a factor of 0.71 of its maximum) can be quantified by the resistance and capacitance of the cell hair cell's basolateral membrane. The capacitance value is determined mainly by the lipid membrane and is augmented by the charge movement of the piezoelectric prestin molecule, which endows the OHC with its electromotile properties. The OHC's capacitance (C) value is pretty well known. The resistance (R) is determined mainly by K+ channels in the basolateral membrane, a value that is also known reasonably well. The low-pass cut-off frequency is equal to (2pi*RC)^-1 and has a value of a ~1 to a few kHz - a value that has both experimental and theoretical support. The low-pass filtering of membrane voltage is important because the cell responds to membrane voltage by shortening and lengthening - this electromotility is thought to be key to the cochlea's operation and in particular to cochlear amplification, the process that enhances the magnitude and tuning of the cochlea's passive response to sound. However, the auditory system works to 80 kHz and even higher in some animals. Thus, it has been posed (let's say by team A) that the RC cut-off frequency value of a few kHz makes electromotility too slow to operate "cycle-by-cycle" up to several 10s of kHz. The article under review, representing team B, supports "cycle-by-cycle" action, arguing that the several kHz cut off frequency is not a problem and is even an advantage.

      The arguments put forward in favor of cycle-by-cycle action are:<br /> 1. The size of the motions, even with the low-pass-filtered attenuation are as large or larger as those measured in the cochlea at high frequencies.<br /> 2. Noise is often increasing as frequency decreases, thus low-pass-filtering is actually good, to reduce the predominantly low frequency noise.<br /> 3. Harmonic distortion is at supra-CF frequencies, so it's good if the hair cell is low-pass-filtering to reduce harmonics.

      These three points are reasonable, and the quantification relating to statement 1 is convincing. However, the quantification associated with point 2 is muddled. The hair cell voltage signal is expressed in volts, but the noise value is given in terms of the current mediated by 1-5 channels. A quantitative comparison should be made, with signal and noise expressed in the same units, preferably volts and volts/root(Hz), with a bandwidth estimated. The appendix attempts to be more quantitative and something like that short appendix should be incorporated into the paper. If a quantitative comparison in standard units is not possible with current data, that can be stated and underscores that we really don't know whether the noise is a problem for cycle-by-cycle amplification. Point 3 is reasonable and nicely illustrated in Fig. 3B. I did not get anything from Fig. 3A and the corresponding discussion on page 8 lines 320-335. Panels C and D were under-explained and could be removed, and the caption's reference to "short wave hydrodynamics" was also under-explained.

      The arguments put forward to challenge gain control mechanics, which employ DC shifts to set effective operating conditions:<br /> 4. Operation based on DC and quasi-DC operating points is sensitive to noise, which as noted above is often increasing as frequency decreases.<br /> 5. Operation that employs a DC shift for operating point is likely to work in such a way to reduce stiffness, which has been shown to be inconsistent with active cochlear responses. For example, stiffness reduction would reduce traveling wave wavelength and thus alter the response phase and timing to a degree that has not been observed experimentally. This has long been known and relevant papers are cited.

      Point 4 was not convincing to me because the motions related to setting operating conditions could be larger than the nanoscale cycle-by-cycle response motions - thus these operating point motions could be above the noise values that seem limiting to cycle-by-cycle amplification.<br /> Point 5 is a nice reminder of the conclusion that, based on experimental findings and physics-based basic cochlear models, the cochlear amplifier must work by means of energy injection. This point was made clearly by Kolston (well cited in this paper) and later supported by other work.

      The present paper is informative in many ways and offers useful insights for further exploration. It is nicely written and illustrated. Because the signal and noise values are not quantified, the basic claim, that the cochlea amplifier can amplify a noisy signal effectively, is not convincing and that basic question is still unsettled. Overall, the paper would be improved if the claims and arguments were presented more tightly, with fewer digressions, and more modestly.

    1. Reviewer #3 (Public Review):

      The manuscript by Barzilai-Tutsch and colleagues describes a new transgenic reporter model in quail to monitor active beta-catenin transcriptional activity during embryonic development. The transgene presented here goes beyond previous mouse TCF/LEF transgenic reporter lines transgenic models to increase sensitivity by further concatemerising TCF/LEF binding sites, adding viral translational enhancers and using an unstable GFP to limit expression to cells with active b-cat signalling. The major weakness is that the analysis of cis regulatory elements is incomplete and only a single transgenic line produced for each construct.

      The most successful aspects are the beautiful expression of the transgene reporter in the migratory neural crest, the dermomyotome, and the limb buds. The use of the syn21 and p10 viral translational elements in the transgene is of interest. The quail produced containing this construct had increased expression of nuclear Venus FP (although not well described or shown to be nuclear) in the embryos. The observation of reduced expression of the reporter in the roofplate of the neural tube and increasing expression as the neural crest migrated from the neural tube is fascinating. The question to be answered is does this reflect a physiological difference or is it a characteristic of the transgene.

      The major weakness of this analysis is that there is no substantial transgenic analysis of the elements in animals. Two different constructs were tested and a single transgenic quail produced for each line. Expression levels may not depend on transposon copy number; however, the expression level due to integration site is a potential problem. Drawing a conclusion is difficult when comparing a single transgenic line with a second transgenic line.<br /> The neural tube electroporation analysis is not complete. A transcriptional analysis of the constructs in cells, or using their embryo transfection system in the neural tube to analyse the constructs with or without the addition of v10 and syn21. At that point, the authors can claim that the elements are actually useful for increasing reporter gene expression/stability/translation.

    1. Reviewer #3 (Public Review):

      The authors goal is to create an in vitro analogue of the in vivo activation-inhibition cycle of flagellar dynein, in order to study its mechanism. In their new model system, two sets of outer arm dyneins link a pair of parallel, taxol-stabilised microtubules and (upon photolysis of caged ATP) slide the microtubules back and forth, switching direction every 40 milliseconds, during which time the microtubules slide a few tens of nanometers. This oscillation depends on the sliding of the two microtubules being restricted by DNA origami cross linkers. At any one time, only one set of dyneins, attached to only one microtubule, appears active. The switch point appears defined by the stretching out of the DNA origami crosslinks.

      The authors have studied the structure of their system using electron microscopy and studied its mechanics by optical trapping. By harnessing the intrinsic tendency of outer arm dynein to assemble in patches on microtubules and to link microtubules in parallel, they have recapitulated the in vivo arrangement of outer arm dynein, thereby allowing it to be studied in isolation but in an in vivo-like geometry. By adding in a designed DNA origami as a further crosslinker, they convert the system to an oscillator. To my mind this is a very revealing result. The (relative) structural simplicity allows electron microscopy to be used to inspect dynein conformation. The optical trapping has to be done in a relatively weak/compliant trap, which makes for noisy data, but the data are nonetheless revealing, with single molecular steps discernible at some points, and forces and timings accurately determined.

      I think the authors have achieved their aims and that their results, subject to minor clarifications of interpretation, support their conclusions.

      This is an exciting advance that will stimulate more use of simplified systems to dissect the beating mechanism of flagella. It seems plausible that there are several regulatory mechanisms for beating at the molecular level that overlie one another. Establishing the mechanical properties of dynein in a more lifelike yet less complex structural context is evidently a good way to go - this will hopefully encourage more work along the same lines.

    1. Reviewer #3 (Public Review):

      Although the experiments were well performed, the manuscript could be improved by addressing the points below.<br /> 1. CD73 could be induced on NK cells upon engagement with tumor cells, thereby impairing the antitumor effect of these engineered anti-CD73 CAR-NK cells in the TME. Thus, the authors should examine this possibility in their work.<br /> 2. In Fig. 4C, the authors should add the control group "untransfected NK cells only".<br /> 3. The tumor weight data (Fig. 5E) about the antitumor effect of anti-CD73 CAR-NK cells vs untransduced NK cells seem inconsistent with the survival data (Fig. 5B) as well as the bioluminescence data (Fig. 5C). Are there any significant differences of tumor weights among different groups?

    1. Reviewer #3 (Public Review):

      How do cortical neurons represent multiple concurrent stimuli? Does the representation depend on the stimuli being segregated versus fused into a single object? This manuscript addresses those questions, focusing on the response statistics of single neurons and pairs, mostly in macaque primary visual cortex V1, to paired visual stimuli (gratings) that are either spatially separate (segregated) or superimposed (fused).

      Although V1 responses to combinations of gratings have been studied extensively, the authors offer an innovative perspective focusing on aspects of response statistics that have remained underexplored in past work. In my opinion, their findings are of broad interest because they challenge traditional understanding based on responses to 1-dimensional stimuli, and shed new light on the longstanding "binding" problem. In particular, leveraging methods and findings previously published by some of the authors in the auditory cortex, they ask whether the responses of neurons to simultaneous stimuli switch, from trial to trial, between the responses to either of the individual stimuli. This would correspond to a coding scheme in which individual neurons encode, at each moment in time, either one of the two stimuli. The authors point out that such a coding scheme is reminiscent of early ideas on neural coding of perceptually ambiguous stimuli, and is conceptually distinct (although not in contradiction) from divisive normalization, the more prominent existing framework to understand neural responses to composite stimuli. Having provided empirical evidence that this kind of coding appears only when simultaneous stimuli are segregated, and not when they are superimposed, the larger part of the manuscript then addresses the follow-up question: for segregated stimuli, do populations of V1 neurons all encode the same stimulus component at each point in time? Or are there subgroups of neurons that encode distinct stimulus components, thus allowing for both stimuli to be represented simultaneously? To address this question, the authors study how noise correlations (i.e. correlations in the response variability of pairs of neurons, to repeated presentations of the same visual input) depend on stimulus conditions and on the tuning preference of the neurons. Their main finding is that neurons with similar tuning (i.e. representing preferentially the same stimulus) are often positively correlated, i.e. when they switch from one stimulus to the other stimulus, they do so at the same time and therefore both neurons always tend to encode the same stimulus. Conversely, neurons with opposite tuning are often negatively correlated, also consistent with both neurons always encoding the same stimulus. However, across their datasets, the distribution of correlation values are broad enough to suggest a strategy where, at each moment in time, the majority of the neural population encodes preferentially one stimulus, but a minority of the population encodes the other stimulus thus preserving the ability to represent multiple stimuli simultaneously. Importantly, these patterns of single-neuron and pairwise response statistics (i.e. shifting between component stimuli in coordinated fashion) are absent when stimuli are superimposed or presented in isolation. Therefore, the results implicate the structure of cortical responses (beyond the much-studied average tuning and variability) in the process of object grouping and segregation.

      These results are potentially of very broad interest for the field, and the manuscript clearly places them in context. In addition, the analysis is sound (with a couple of minor details about the assumption of Poisson variability), the effect sizes are large and convincing, and the data will be useful to the community. However, in my view, an important methodological consideration deserves more scrutiny, namely to what extent the results can be a consequence of eye movements. And if so, what does that imply for the proposed coding scheme? Specifically, uncontrolled eye movements can effectively change the visual input from trial to trial, and so affect the structure of response variability. The authors state that they excluded trials in which microsaccades were detected, but more details on the detection of microsaccades and threshold values for inclusion (relative to stimulus and RF sizes) should be provided, given how central a role they might play. In particular, the authors state in Discussion that small residual eye movements would inflate response variability in all stimulus conditions. This is correct, but because of the stimulus design, it is possible (likely?) that the effects are quite different for segregated stimuli versus superimposed and single-stimulus conditions. Furthermore, the difference might be precisely in the direction of the effects reported. That is because segregated stimuli are spatially separate, and each stimulus only covers some receptive fields in the recording, it is possible that eye movements would bring inside the RF a different stimulus in each trial. In addition to producing bimodal response distributions for individual neurons, this would also induce positive correlations for pairs with the same preference and negative correlations for pairs with opposite preferences. On the other hand, in the superimposed condition where the stimulus is large enough to cover all RFs, at most eye movements would bring (part of) the stimulus inside versus outside the RF across trials, therefore contributing to positive noise correlations for all pairs (ie. to shifts from stimulus driven to spontaneous activity, in the extreme case).

    1. Reviewer #3 (Public Review):

      The authors characterize the ion channel function of ancestral beta-subunits of the muscle-type acetylcholine receptor (AchR). They present the striking discovery that these ancestral beta-subunits form spontaneously-active homopentameric channels. Analysis of the single channel currents shows some functional properties similar to the AchR. These include gating kinetics with multiple closed time components suggestive of non-conducting "primed" or "flipped" states previously described in the heteropentameric muscle-type nAchR. The properties of open channel pore block by acetylcholine and QX-222 are also similar to muscle nAchR, indicating a conserved structure in the pore of the ancestral channels. The most notable finding is that these ancestral channels assemble into homopentameric channels that gate in the absence of ligand. This is a unique finding for a nAchR-like channel suggesting the possibility that ligand-gating evolved subsequently to other features of these ion channels.

      The authors achieve their aim in characterizing beta-Anc and beta-AncS by providing high-quality single channel analyses, which definitively confirm the presence of these channels and the fact that they form homopentamers. Analysis of the single channel kinetics indicates multiple closed states. The authors appropriately acknowledge that the single channel kinetics cannot distinguish between multiple gating models, but are consistent with and reminiscent of the presence of "primed" or "flipped" states previously described in other pentameric ligand-gated ion channels. The manuscript is well-written. No significant weaknesses are identified with regards to the methodology and conclusions of the study. However, a broader goal of this study is to gain insight into the structure-function relationships of pentameric ligand-gated ion channels by analyzing reconstructed ancestral beta subunits. To this end, the authors provide minimal insight into the structural determinants underlying distinct aspects of the beta-Anc channel function such as the high-P(open) unliganded gating.

    1. Reviewer #3 (Public Review):

      This review is carried out with the caveat that I am not a structural biologist and therefore, cannot judge the correctness of the structures presented in this paper. Several cryoEM structures are presented of yeast RFC with PCNA and ATPgammaS. Many clamp-clamp loader structures are already available, including several from these authors in a 2022 eLife paper. However, the focus of this paper is on the BRCT domain that resides in the N-terminal region of the Rfc1 subunit. The authors used gapped DNA substrates with 5 or 6 nucleotides of single-stranded DNA, in which PCNA is found in a post-loading closed conformation with the 3'-junction buried inside the clamp loader. The 5'-junction and the double-stranded DNA beyond the junction are bound by the BRCT domain. Interestingly, when the gap is zero, i.e. a nick, the DNA at both the 3'- and 5'-junction is melted out such that the intervening ssDNA is 5 nucleotides.

      These are fascinating structures that are suggestive of a potential role of the RFC NTD in DNA repair, such as base excision repair. Indeed, a study in yeast cells shows that a BRCT deletion of Rfc1 results in sensitivity to an alkylating agent. This paper would have had a higher impact if more directed genetic experiments were carried out, which identified the pathway benefitting from the presence of the BRCT motif.

      My major concern with this study is in the choice of DNA substrate. Previous biochemical studies from several labs have shown that binding of 5'-junction DNA to an isolated BRCT domain strongly depends on the presence of the 5'-phosphate. Other single BRCT domains that bind DNA, e.g. from Rev1, also show a strong dependence on the 5'-phosphate. DNA repair intermediates, such as base excision repair products after incision by Apn1/2, carry 5'-phosphates. Very surprisingly, the DNA substrates used in this study lack the physiologically relevant 5'-phosphate. The only experiment in the paper that indirectly addresses the issue is in Fig. 4A; it shows that the melting of the 5'-nucleotide occurs independently of the presence of the phosphate. There is no discussion of why the authors chose the unphosphorylated DNA substrate. If the phosphate indeed is an important feature, it would benefit the authors to determine cryoEM studies with the proper DNA.

    1. Reviewer #3 (Public Review):

      In the presented paper, Crawford et al. used polysome profiling and mass spectrometry to identify and quantify proteins associated with translating ribosomes in yeast cells under unstressed vs. ISR-inducing stressed conditions (oxidative stress and amino acid starvation). Numerous proteins, including expected ribosomal proteins and translation factors, as well as proteins with less known translation-related roles such as metabolic enzymes were identified and clustered based on their polysome enrichment (PE) during tested conditions. One such a polysome-enriched protein, the cytosolic aspartate aminotransferase, Aat2, was characterized further. It was shown that its deletion conferred growth sensitivity to oxidative stress, causing aberrantly high activation of the ISR via enhanced eIF2a phosphorylation and GCN4 activation. Since non-catalytic AAT2 mutants retained polysome association and did not show heightened stress sensitivity, the authors proposed that Aat2 has a separate ribosome-associated translational regulatory / moonlighting function that modulates the ISR. Despite some reservations listed below, overall I conclude that the authors achieved their aims and stress that for the most part the results perfectly support the outlined conclusions.

    1. Reviewer #3 (Public Review):

      Considerable progress has been made in moving to more open and reproducible fMRI research. However, an accessible end-to-end solution that meets these standards has remained elusive, in part because it requires the combination of many tools. Neuroscout aims to try to provide this platform. Key elements of Neuroscout include:

      - An easy-to-use web application for designing the GLM analysis of naturalistic experiments;<br /> - Data ingestion server with a growing repository of naturalistic fMRI studies curated and preprocessed for these analyses;<br /> - Feature extraction server for the generation of different regressors for analyses;<br /> - Tooling for implementing these analyses;<br /> - Automated generation of citations for these analyses.

      This platform has no clear precedents, is reasonably mature, is easy to use, and has an impressive number of curated datasets. With a focus on large naturalistic datasets, there should be a wide range of legitimately novel analyses that are made easily accessible with this tool, and this will increase as Neuroscout evolves to offer a wider range of datasets and functionality. A key benefit of easy-to-use platforms of this nature is that researchers gain the ability to quickly implement analyses of phenomena and hypotheses generated from their own work, accelerating science. Documentation and data and code accessibility are excellent. The existing analysis examples are interesting, accessible to users, and generally provide good insight into the use and value of the platform for general users.

      A weakness of many automated systems of this nature is that users rapidly find limitations in the types of analyses that can be set up. In the worst cases, this leaves the platform providing largely a demonstration. However, here, the well-developed open-science components make this unlikely. The authors have strong records in developing widely used open software for fMRI, and the considerable number of datasets and feature-generation algorithms that have been integrated into the platform already bodes well for uptake. Nevertheless, while described as end-to-end, the current scope for analysis design is somewhat limited, restricted largely to the specification of the GLM design. Furthermore, it was not clear if or how the platform might scale and develop an active community of data, algorithm, and code contributors. Similarly, choices of preprocessing algorithms are not extensively motivated, and how these might evolve with input from a wider community is unclear.

      Overall this is a promising tool that develops upon a burgeoning set of open-science tools for functional neuroimaging and presents new strategies for how fMRI analysis can be made more accessible and reproducible. While a software tool's success is ultimately measured by its uptake, Neuroscout presents a successful implementation of a concept that may provide researchers with or without extensive experience of fMRI the ability to efficiently implement novel analyses to a high standard. If Neuroscout is to be a success, it would be expected to evolve considerably from its current state. Determining how to balance the flexibility of the tool with ease of use will be an ongoing challenge.

    1. Reviewer #3 (Public Review):

      The authors demonstrate a fascinating phenotype wherein combined disruption of both complex I and II activity led to a restoration of proliferative capacity compared to when only Complex II is disrupted. While this phenomenon is interesting, we were unable to decipher a coherent mechanistic explanation for these observations. In addition, the authors would need to address a few points to strengthen the claims they make within this work.

      • In Fig. 1D, the authors state that a 1.9x increase is not noteworthy, while in Fig. 1B, a 2.3x increase is noteworthy. Can the authors more clearly describe how they are determining their thresholds?<br /> o On a related note, more evidence is needed to conclude that the small changes/restoration in aspartate abundance is sufficient to completely, or almost completely, restore proliferation. This seems surprising and the data must be strengthened.<br /> • The data presented between Fig. 1F and 3A appear to be inconsistent. It is our understanding that the conditions are identical (same media, cell type, etc.), yet in 1F, addition of AA5 without PYR leads to minimal change in the NAD+/NADH ratio, while in 3A, sole addition of AA5 roughly doubles the NAD+/NADH ratio.<br /> • While we appreciate the different approaches of utilizing both pharmacological and genetic inhibition of SDH, we are not sufficiently convinced that AA5 acts fully on-target and is not causing additional off-target effects. Perhaps this has been sufficiently described in previous literature but based on the data contained within this work and the citations provided, this wasn't evident. It would also be helpful to include the source of AA5 used - we were unable to find such details in the main text nor in the methods section. In many cases, there seem to be discordant results between the AA5 treatment and SDH inhibition or knockout. For example:<br /> o Fig. 2F,G: Based on the proposed model, if the authors were to add AA5 in the SDHB KO, this would presumably not effect proliferation, aspartate levels, or NAD+/NADH balance and would implicate that AA5 is not effecting anything other than SDH. This experiment should be done to test for off-target effects of AA5.<br /> • There are several sub-figures where we feel there are missing controls. Examples include:<br /> o Fig 3A: Why is a rotenone-only control not provided in this sub-panel?<br /> o Fig 3D,E: What does the no-cyto & no-mitoLbNOX look like under these different treatment conditions?<br /> o Fig 4A,B,F: Why is the add-back not included in these panels, but it is in several others within this figure? We would be concerned if they did not show the expected CI activity, etc. that is being assumed by the authors.<br /> o Fig S3D: Why is a AZD7545-only control not provided?<br /> • We feel like there are several instances where the connections to NAD+/NADH balance need to be improved to support the claims being made by the authors:<br /> o Fig 3: It is clear from the data that the NAD+/NADH pools are compartmentalized, but their connection to proliferation and aspartate synthesis is not clear from the presented data.<br /> • Presumably the authors have the NAD+ and NADH ion counts for the LbNOX experiments since they have the aspartate levels. Are cyto- and mito-LbNox causing the same level of reversal of the NAD+/NADH balance? Is the LbNOX construct just less functional when in the mitochondrial versus the cytosolic form?<br /> • Fig 3G,H: The change in proliferation seems to be relatively minor compared to other proliferation changes presented in this work. Can the authors speak to the correlation between the NAD+/NADH ratio and proliferation?<br /> o Why would NADH levels, or the NAD+/NADH ratio influence proliferation rates? Similarly, why would AA5 increase NAD+/NADH ratio (Fig. 3A)? This is not particularly clear in the text.<br /> o There seem to be many claims that NAD+/NADH ratio is tied to reductive carboxylation, yet this connection is not clear from the data and seems to just be pure conjecture. If this is essentially the only mechanistic hypothesis of the manuscript, it is important to show it.

      We feel the current manuscript lacks a coherent explanation for the restoration of proliferation. We encourage the authors to take one step further to demonstrate some of the possible mechanisms. Mito NAD+/NADH ratio seems to be important, but how does this explain the proliferation phenotype? Aspartate levels indeed seem to be correlated to some extent with the restoration of proliferation, yet this alone does not appear sufficient to explain the phenomenon. This is perhaps beyond the scope of this work, but we feel some sort of CRISPR screen using proliferation as a read-out would be beneficial to this work. The authors may choose to use some other methods to address this problem. For example, at the end of the first paragraph in the discussion section, the authors suggested two modalities that can potentially be tested to make sense of the current observations.

    1. Reviewer #3 (Public Review):

      Sender & Bar-On et al. perform robust analyses of early SARS-CoV-2 line list data from China to estimate the intrinsic generation interval in the absence of interventions. This is an important topic, as most SARS-CoV-2 data are from periods when transmission-reducing interventions are in place, which will lead to underestimation of the potential infectious period.

      The authors highlight two shortcomings in previous approaches. First, the distribution of 'observed' serial intervals (the time between symptom onset in the infector and symptom onset in the infectee) depends not only on the timeline of each infector's infection, but also the epidemic growth rate, which weights the proportion of observed short vs. long serial intervals. The authors argue that by accounting for this weighting, more accurate estimates of the intrinsic generation interval - the metric on which isolation policies are based - can be obtained. Second, the authors find that the original SARS-CoV-2 generation interval distribution has both a higher mean and longer tail than previous estimates when using only data prior to the introduction of interventions. Finally, the authors use publicly available data on viral load trajectories to extrapolate their estimates to other SARS-CoV-2 variants, finding that alpha, delta, and omicron may have shorter generation intervals than original SARS-CoV-2. These findings are important, as case isolation policies are based on assumptions for how long individuals remain infectious. More broadly, these methods will be important for future work to correctly estimate generation intervals in other outbreaks.

      The conclusions are well supported by the data, and a suite of sensitivity analyses give confidence that the findings are robust to deviations from many of the key assumptions. The code is well documented and publicly available, and thus the findings are easily reproducible. Key strengths of the paper include the clarity and rigor of the modeling methods, and the exhaustive consideration of potential biases and corresponding sensitivity analyses - it is very difficult to think of potential biases that the authors have not already considered! I think this is a well-written and well-executed study. The work is likely to be impactful for reconsidering SARS-CoV-2 isolation policies and revisiting generation interval estimates from other data sources. I also expect this to be a key reference and method for future studies estimating the generation interval.

      I have some minor comments on potential weaknesses and interpretation:

      1. Uncertainty in early generation interval estimates<br /> One of the conclusions is that the estimated mean generation interval is longer than the observed mean serial interval. However, this conclusion does not seem justified given that the observed mean serial interval (9.1 days) is well within the 95% CI of 8.3-11.2 days for the mean generation interval. The confidence intervals for the serial interval in figure 2 are also wide for pre-Jan 17th (though presumably these would be reduced if all pre-Jan 17th serial intervals were combined). Further, only 77 of the ~1000 transmission pairs are actually from pre-January 17th. The actual sample size used for these estimates is much smaller than suggested by Figure S1 and thus this should be made clear. Therefore, although the intuition for why observed serial intervals may differ from the generation interval is correct, I do not think that the data alone demonstrate this.

      A related issue is on ascertainment bias - could the early serial interval data be biased longer because ascertainment is initially poor and thus more intermediate infectors are missed? The authors consider removing particularly long serial intervals to try and account for this, but that does not deal with e.g. chains of multiple short serial intervals being incorrectly recorded as a single long serial interval (but still within 16 days).

      2. Frailty of using viral loads to extrapolate generation intervals<br /> The authors take the observation that variants of concern demonstrate faster viral clearance on average to estimate shorter generation intervals for alpha, delta, and omicron. The authors rightly point out in the discussion that using viral load as a proxy for infectiousness has many limitations. I would emphasize even further that it is very difficult to extrapolate from viral load data in this way, as infectiousness appears to vary far more between variants than can be explained by duration positive or peak viral load. Other factors are potentially at play, such as compartmentalization in the respiratory tract, aerosolization, receptor binding, immunity, etc. Further, there is considerable individual-level variation in viral trajectories and thus the use of a population-mean model overlooks a key component of SARS-CoV-2 infection dynamics. An important reference, which came out recently and thus makes sense to have been missed from the initial submission, is Puhach et al. Nature Medicine 2022 https://doi.org/10.1038/s41591-022-01816-0.

      3. Lack of validation with other datasets<br /> This study hinges on data from a single setting in a short window of time. Although the data are from multiple publications, the fact that so many reported the same transmission pair data demonstrates that these are overlapping datasets. As the authors note, there are potential biases e.g., ascertainment rates and behavioral changes which will impact the generation interval estimates. Thus, generalizability to other settings is limited.

      4. The impact of epidemic dynamics on infector vs. infectee serial intervals<br /> It took me a long time to get my head around the assertion that the forward serial interval distribution will be longer during epidemic growth due to the overrepresentation of short incubation periods among infectors relative to infectees. A supplementary figure, similar to the way Figure 1 is laid out, to illustrate this concept may go a long way to aid the reader's understanding.

      5. Simulations to illustrate concepts and power<br /> Given the assertion that observed serial intervals will depend on epidemic growth rates, reporting, and timing of interventions, I think a simple simulation to illustrate some of these ideas would be very helpful. For example, a simple agent-based model with simulated infectivity profiles and incubation periods using the estimated bivariate distribution would be extremely helpful in illustrating how serial intervals and estimates of the generation interval can differ from the true intrinsic generation interval (I coded such a simulation to help me understand this paper in a couple of hours with <100 lines of R code, so I do not think this would be much work). This would also be very helpful for illustrating statistical power re. comment 1.

    1. Reviewer #3 (Public Review):

      In this paper, the authors investigate the evolutionary origin of small proteins. These proteins could arise from the de novo evolution of new genes through mutations that create a new short open reading frame, or through the truncation of once larger protein-encoding open reading frame. The authors seek to demonstrate that small proteins that are accidentally translated from a randomly-occurring sequence could confer evolutionary advantage, which would then fix the new gene in the species' genome if that evolutionary pressure persists. The authors seek to replicate these conditions by generating a random library of short protein-encoding sequences and find sequences that could rescue the ability of E. coli to grow in an otherwise unfavorable environment. In this case, the authors use an E. coli serB auxotrophic mutant grown in minimal media since the serB mutant strain cannot grow in minimal media because it is incapable of synthesizing serine. They identified three small protein sequences that allowed survival on minimal media. The authors use classic genetics experiments (deletions and reporter fusions) and modern proteomics approaches to uncover that rescue likely occurs via the increased expression of the HisB protein, which is able to compensate for the serB mutation and rescue serine biosynthesis. Biochemical experiments demonstrate that this small protein can bind to the operator of the his operon, causing structural changes in the terminator that may affect the expression of operon-encoded proteins.

      The general approach described in this paper was previously successfully used by this research group in two other publications to identify small protein sequences that affect other E. coli phenotypes. Overall, the experiments were well-designed and thorough and support the authors' claims. However, the inclusion of some data that were discussed in the text but not shown and the inclusion of some additional control experiments would strengthen the author's conclusions.

      These findings not only support the idea that randomly expressed short protein sequences could provide evolutionary advantages to an organism but also suggest the existence of a class of small proteins that regulate gene expression by directly binding to mRNA. To my knowledge, this function has not been reported for any of the naturally encoded small proteins and it would be very exciting to observe this mechanism occurring in nature. Given the wealth of sORFs that have been newly identified and the dearth of characterized sORF products, it is likely that at least one such small protein exists.

    1. Reviewer #3 (Public Review):

      This is an interesting study that identifies why or how the ISR pathway regulates cell recovery upon proteotoxic stress, which is especially interesting in cancer cells resistant to proteasome inhibitors. The study concludes that only by favouring canonical translation initiation of mRNAs encoding microtubule cytoskeleton, centrosome and ATF5 proteins are necessary to recover from proteotoxic stress. The study is robust and uses advanced pre-clinical models and sequencing techniques to explore the translatome of stressed cancer cells.

      The authors claim that they find a proteotoxic mechanism exclusive to SCC stem cells. However the authors do not use stem cells, they work with primary SCC cells. They would need to actually show in stem cells that this is the case and that normal keratinocytes or epidermal stem cells do not use this exclusive mechanism. In addition, it would be very interesting to translate these findings into the clinic. It would be interesting to know how relevant this mechanism is for human tumour cells.

    1. Reviewer #3 (Public Review):

      The authors present a scalable workflow for the stitching of petabyte-sized image data. This is a relevant achievement necessary for the processing of large electron microscopy data sets occurring for example in the field of neuronal connectomics, where sufficiently large brain regions must be imaged for biologically meaningful results. The key concept in the approach is to modularize the individual processing steps and manage the transformation metadata for each tile separately in a database structure, enabling massive parallelization. The authors demonstrate that the workflow runs fast, automated and robust with inbuilt quality control algorithms and can be deployed on scalable (cloud-based) hardware infrastructure.

      While the presented software tools are of very high quality and great utility, the challenge of, in practice, setting up the same soft- and hardware infrastructure in other labs is not to be underestimated and will likely require technically skilled and dedicated personnel.

      For non-experts, the article in its current form is partially challenging to read as it requires detailed technical knowledge of previous publications or documents on GitHub repositories. A recommendation would be to rewrite the article to differentiate between the expected readerships. The manuscript could start with a section discussing the challenges and conceptual solutions along with an example data set. This first section could inform the interested reader whether the presented solutions are of general interest, e.g. for the reader's host institution or microscopy facility without going into any technicalities such as abbreviated software tools or discussing specific client-server architectures. Then, a second section should follow where the actual implementations and the respective software tools that solve bespoke challenges are introduced. A third section may then outline what it takes to actually implement the software stack on a specific hardware infrastructure. Such a structure will make the great achievements of this work more accessible to different readerships with more or less software development background and/or interest.

    1. Reviewer #3 (Public Review):

      Wang. et al explore the relationship between birth order and connectivity of neurons that wire together in a specific circuit. Using a refined single-cell clonal technique, the authors generate embryonic clones to map the birth order of neurons that derive from distinct stem cell lineages yet contribute to the same circuit in the Drosophila ventral nerve cord. Wang et. al map neurons of this circuit to discrete developmental windows, or "temporal cohorts," and show that neurons belonging to early vs. late temporal cohorts have stereotyped morphologies, wiring patterns, and birth orders. They convincingly show that distinct stem cell lineages contribute to the output vs input neurons of the circuit and that the output neurons are born before the input neurons. As a result, the authors provide novel insights into the relationship between neurogenesis and circuit assembly.

      Strengths<br /> The relationship between birth order and connectivity at a single-cell resolution is a valuable step forward in understanding how cells are wired together during development. By dissecting the circuit into its individual subtypes and working backwards to birthdate the neurons, Wang et al take an unbiased and effective approach to understand how cells involved in sensing vibrational stimuli assemble within the ventral nerve cord.

      The temporal mapping of neurons within and between lineages is challenging and laborious work and the data presented is clear and convincing. The authors modified the existing ts-MARCM system with the addition of another recombinase to facilitate the visualization of clones at earlier stages of development; this technique will be of use to members of the fly community.

      The authors effectively demonstrate how analysis of the connectome data can be used to infer multiple aspects of neuronal development, including whether neurons share a common neuroblast parent (clustering of cell bodies) and their relative birth order (comparative cortex-neurite length).

      Weaknesses<br /> A major conclusion of the paper is that the output neurons in a circuit are made before the input neurons. However, the strength of this conclusion is weakened by the fact that ~34% of the interneuron inputs to the EL-early neurons remain unmapped. This includes six neurons that synapse onto the EL-early neurons over 10 times each. It is therefore likely that other lineages contribute neurons that synapse onto the EL-early neurons. Without knowing the relative birthdates of these neurons to the early-EL neurons, the output first-input second conclusion should be tempered.

      More consideration/discussion should be given to the tTF windows that these cohorts are derived from. For example, it would be intriguing if the early-EL, Ladder and Basin neuronal cohorts are all derived from the same tTF window. This would suggest that wiring specificity within a circuit is driven by the tTFs.

    1. Reviewer #3 (Public Review):

      This study addresses the gene regulatory network that controls lung branching and proximal/distal patterning. Focusing on E11.5 versus E16.5 epithelium, Monocle analysis of transcriptome signatures revealed 26 gene expression modules. This was followed by RNA-seq and ATAC-seq analysis of Sox9+ cells from these same two stages. Integration of the two datasets, and comparison to ENCODE histone data revealed links from differential accessible chromatin peaks to gene expression differences, to likely transcription factors that may bind to CREs. These analysis revealed a role of PI3K in Sox9+ cell differentiation. Pharmacological inhibition of PI3k led to increase in branching. In vivo inactivation of Pi3kca led to persistence of Sox9+ cells, alveolar cysts and reduction of airway cell differentiation.

      This is a stellar study that integrates transcriptomic and epigenomic signatures of the same lung cell population. The authors used innovative algorithms and the ENCODE dataset to tease out key drivers in GRN. This is effectively coupled with in vitro and in vivo functional tests. The findings advance current understanding of lung development. Deeper interrogation of the predicted link between transcription factors and PI3K pathway in the mutant would strengthen the overall cohesiveness of the message from this study.

    1. Reviewer #3 (Public Review):

      This manuscript deals with the anatomical diversity of Paleozoic lungfishes through a descriptive and quantitative lens that makes possible to draw comparisons between taxa and make tentative correlations between form and function on lungfish endocasts. The work redescribes the morphology of six lungifish endocasts based on virtual reconstructions obtained through CT-scanning. Then, the authors put these virtual models into a quantitative context through principal component analysis which highlights interesting morphological features and the morphological diversity across Devonian and extant forms.

      The anatomical description of the endocasts is well-detailed and add to the knowledge of lungfish neuroanatomy. Some of these endocasts have been previously described in the literature but the authors re-description adds considerably to the original descriptions. Additionally, by having these descriptions together in a context they provide a good starting point for future anatomical comparison of the studied taxa.

      The quantitative analysis of the endocast models was conducted through principal component analysis (PCA) and other iterations of this methodology (BPCA and InDaPCA) of selected meristics of these endocasts. This analysis demonstrates that there is clustering of some Devonian forms while others show more extreme morphologies. The meristics used might not represent the complex morphology of these endocasts on its totality, but a reductive approach as this is sound with an exploratory analysis as the one presented here. However, the most interesting results are those related to the relative contribution of different structures for the observed variation and proposed interactions between different regions (e.g. relation between sacculus - utriculus and semicircular canals). The authors clearly demonstrate that the forebrain and olfactory capsules are highly plastic while the rhombencephalon seems to be more conservative, to the exception of the inner ear.

    1. Reviewer #3 (Public Review):

      Since it was presented to the scientific community as a viral entity, mimivirus has the unlimited capacity to cause surprise and admiration. In this manuscript, Villalta, Schmitt, Estrozi, et al. and Abergel present how the mimivirus gigantic genome is organized into the virion. The authors succeeded in developing a protocol to trigger virus genome uncoating followed by genome-associated proteins purification. The presented data indicates that a helical shield composed of two GMC-type oxidoreductases is associated with the mimivirus genome, named genomic fiber. By cryo-EM, and cryo-tomography different forms and stages of the genomic fiber were detailed described, indicating the dynamics of fibers conformational changes, likely related to genome packing and uncoating during the virus replication cycle. In-depth analysis of a substantial number of individual virus fibers revealed that the mimivirus genome is folded and organized inside the aforementioned helical shield, which seems to be novel among giant icosahedral viruses. Proteomics in association with image analysis indicates that mimivirus packed genome forms a channel, which accommodates key enzymes related to early phases of the replication cycle, especially RNA polymerase subunits.

      I must disclose that I am not an expert on structural virology and proteomic analysis. Therefore, I don't feel I can contribute to the improvement of this kind of analysis. That said, I congratulate the authors for their efforts to make the manuscript story understandable to non-experts.

      I have a few suggestions and comments:

      1. Please consider the "nucleocapsid" concept during genomic fiber presentation. I believe it fits in;

      2. The "ball of yarn" analogy is nice, but fig 1C shows several fibers unconnected (free) in one of their ends. I am wondering if it means that the genomic fiber is not a long-single structure covering the whole genome, but a bunch of several independent helical structures covering the whole genome and attached in such "ball of yarn". Like several threads connected. Could the authors clarify that please?

      3. Considering previously published data on proteomics of viral factories and transcriptomics of mimivirus: is there any temporal association between GMC-type oxidoreductases' peak of expression and genome replication during the viral cycle? what about RNA pol subunits? Are all those proteins highly expressed during the late cycle? or do they reach the peak concomitantly with genome replication? This information can support the discussion on the genome-fibers assembly during the cycle.

      4- Taken together, data seem convincing to demonstrate that the virus genome is located inside the helical shield. However, I believe that the authors could better explain why we only see 20 kb fragments in the gel, including in the control (in Fig S2).

    1. Reviewer #3 (Public Review):

      In this study the authors use GCaMP6s fiber photometry to determine neural activity in the subthalamic nucleus (STN), substantia nigra pars reticulata (SNr) and STN projecting neurons in motor cortex (M1-STN) while therapeutic electric deep brain stimulation (DBS) is delivered via electric stimulation pulses to the STN of mice rendered parkinsonian with unilateral 6-OHDA lesions. This technique avoids electrical stimulation artifacts obscuring the effects of DBS on neural activity that makes electrical recordings during DBS problematic. The authors find that activity levels in STN and SNr are increased during therapeutic DBS. They also find that equally therapeutic L-DOPA treatment as measured by locomotor speed improvement leads to a reduction in STN and SNr activity. Overall, these findings contradict a rate coding model as underlying therapeutic DBS or indeed parkinsonian motor deficits. Another candidate mechanism for DBS given by antidromic activation of STN projecting neurons in motor cortex is partially refuted by finding inconsistent activity changes in M1-STN neurons during DBS and importantly the maintenance of therapeutic DBS effects on locomotor speed after ablating motor cortex unilaterally. In contrast a consistent mechanism correlated with therapeutic effects correlated across all conditions and treatments was the suppression of locomotor related activity increases in STN. The authors then causally assessed this mechanism by optically stimulating glutamatergic STN neurons with 50 Hz pulse trains which also improved locomotor speed and suppressed locomotor activity increases in STN as assessed by electrical recordings in this case.

      Strengths

      This study directly addresses an important discussion on the mechanism(s) by which DBS acts. It assesses activity levels during DBS in 3 key areas (STN, SNr and M1-STN) and provides some of the clearest evidence yet that the traditional rate model of parkinsonism is not suitable to explain the effect of STN DBS.

      By directly comparing levodopa treatment and STN DBS under matching conditions, the opposite rate changes seen with these treatments while giving a similar therapeutic effect provide convincing key evidence.

      A key insight is also given by the maintenance of locomotor speedup with DBS after ipsilateral M1 ablation. While it is known that spontaneous mouse locomotion does not depend on cortex, this finding provides clear evidence for direct STN DBS effects on descending pathways.

      The mechanism of DBS action most consistent in the recordings across conditions that provide therapeutic effect is a suppression of locomotor related activity increases in STN. This mechanism is directly tested by optical stimulation of STN neurons which indeed is sufficient to exert a therapeutic effect.

      Weaknesses

      The assessment of therapeutic efficacy remains limited to locomotor speed changes in mice. It is well known that Parkinson's Disease (PD) patients have multiple symptoms that are differentially responsive to different DBS locations or in some cases not responsive to DBS. The current findings strictly only apply to effects of unilateral 6-OHDA lesions on spontaneous locomotor speed. As this therapeutic effect is not dependent on cortex, it may or may not transfer to cortically dependent parkinsonian deficits in humans.

      The assessment of antidromic activation is limited to M1, while STN projections also emanate from more frontal premotor and more posterior sensory areas of cortex.

      As acknowledged by the authors, therapeutic effects were only validated for 1 min periods. Long term effects could differ.

      While fiber photometry overcomes the problem of electrical stimulation artifacts, it loses cellular resolution and has low temporal resolution. Important aspects of previously proposed pathological dynamics of STN activity such as bursting and beta oscillations could not be assessed. Therefore a potential primary mechanism of DBS given suppressing such dynamics could be missed. Endoscopic imaging with single cell resolution would provide a stronger approach.

    1. Reviewer #3 (Public Review):

      Song learning in songbirds largely depends on the auditory feedback provided by the perception of the bird's own song. Changes in the auditory feedback can drive song plasticity. Indeed, the online processing of the auditory feedback allows the birds to rapidly adjust their singing behavior when facing artificial or natural disturbance in the acoustic domain. Here, McGregor et coll. Investigated whether non-auditory feedback can drive vocal learning in adult male Bengalese finches. They modified a classical reinforcement learning protocol used to study adult birdsong plasticity. In this paradigm, an auditory feedback is contingent on a song syllable feature. Here, the authors used a somatosensory, instead of an auditory, feedback consisting in mild electrical stimulations on the skin made contingent to the song syllable pitch. The results show that this somatosensory feedback drives vocal plasticity in adult birds as efficiently as a contingent auditory feedback (white noise). Using brain lesions and pharmacological approaches, they demonstrate that the basal ganglia-cortical network involved in auditory reinforcement vocal learning is also required for non-auditory reinforcement vocal learning.

      Overall, the experiments are well-designed. I particularly appreciated the fact that, in most of the experiments, the subjects were their own controls which is a clear strength for such surveys (to control for interindividual variability). The data provided support the main conclusions of the paper but some more analyses would strengthen the message. Finally, the paper is overall easy to follow, even for naïve non-birdsong readers.

      My main concern however is that it is quite unfortunate that the authors forget to refer to and discuss the study of Zai, A.T., Cavé-Lopez, S., Rolland, M., Giret, N., Hahnloser, R.H.R., 2020. Sensory substitution reveals a manipulation bias. Nature Communications 11, 5940. https://doi.org/10.1038/s41467-020-19686-w in which non-auditory (visual) reinforcement vocal learning and the contribution of the Area X are shown in adult male zebra finches. I think it is particularly interesting that diverse non-auditory signals can drive vocal learning in adults and that the mechanisms involved seem to be shared. I encourage the authors to introduce and discuss it in order to provide a bigger picture on non-auditory vocal learning but also on reinforcement learning paradigms.

      I understand that the birds can always stop singing in order to get less electrical shocks but the absence of transient effect of the electrical stimulation on the ongoing song (not only song stopping but also FM, pitch, entropy etc.) is not demonstrated. As the authors did quantify some important features (as stated in the methods, l. 567-568), at least one example for some feature (in suppl. Fig) should be shown.

      I wonder why the analysis was restricted to the song syllables that were produced between 10am and 12pm. What is the rationale forsuch a restriction? Are the results different when considering all the song syllables per day? Also, the reader only finds that information in the method section although it seems to me as an important one that needs to be provided in the main text.

      I was a bit surprised by the distribution of the adaptive pitch changes between cutaneous and white noise feedback (fig 2f). The sham and unoperated birds are actually quite different: the unoperated seem to have more change their pitch when exposed to the white noise, while it is the opposite for the sham who seem to change more with the cutaneous stimulation. Could the authors provide some more statistics to justify the pooling of the two groups of birds?

      Surprisingly, five days after the depletion of the DA inputs to the basal ganglia (Area X), there is a change of the pitch in the anti-adaptative direction that reaches statistical significance on day 5 (fig 4c). This effect on the 5th day only might be related to the fact that the depletion of the DA spares about 50% of the inputs to Area X. But what could be the explanation for the change in the anti-adaptative direction?

      The claim in the discussion that the experiments show that LMAN is required for the expression of the non-auditory vocal learning is to my point of view not clearly supported by the data (l.377-379). To me, the data shows that LMAN is required for the non-auditory vocal learning but it is not clearly demonstrated here that only the expression of the learning is ensured by LMAN. In order to show the role of LMAN in the expression of the behavior, the authors should adopt a similar strategy than in Charlesworth et al, Nature 2012 (10.1038/nature11078). So, I would suggest that the authors refer to data from the literature to reach that conclusion.

      The discussion paragraph on the pathway that may convey the somatosensory signal to the song system is interesting but I would encourage the authors to speculate a bit more on the pathway, considering the paper of Chen R, Puzerey PA, Roeser AC, Riccelli TE, Podury A, Maher K, Farhang AR & Goldberg JH (2019). Songbird Ventral Pallidum Sends Diverse Performance Error Signals to Dopaminergic Midbrain. Neuron; DOI: 10.1016/j.neuron.2019.04.038 (already cited), the related review from Chen R & Goldberg JH (2020). Actor-critic reinforcement learning in the songbird. Current Opinion in Neurobiology 65, 1-9 (http://www.sciencedirect.com/science/article/pii/S0959438820301173) and the study of Wild JM (1994). Visual and somatosensory inputs to the avian song system via nucleus uvaeformis (Uva) and a comparison with the projections of a similar thalamic nucleus in a nonsongbird, Columbia livia. J Comp Neurol 349, 512-535 (http://onlinelibrary.wiley.com/doi/10.1002/cne.903490403/abstract).

    1. Reviewer #3 (Public Review):

      In this manuscript, the authors define 966 different media combinations on which they run over 12,000 growth curves for E. coli. After fitting the growth curves to estimate classical growth parameters (e.g. lag, growth rate and carrying capacity) the authors evaluate different machine learning methods in their ability to predict growth parameters from media composition. They use the results of the modeling to determine what media components are more important in affecting a certain parameter. The authors use the findings to try to explain why distinct "decision-making" components are found to associate with each of the growth parameters under an ecology and evolutionary biology light.

      The experiment appears executed well. However, apart from making sure the 966 media combinations are well defined, this is running growth curves with E. coli. This has been established for many years. The machine learning modeling is not innovative. Better posed, the authors use off-the-shelf machine learning methods available from different python packages to perform regression. Overall, the paper lacks motivation for why is this work done and what implications this work has. Based on the regression analysis the authors find that different growth medium components are more important (or associate specifically with) in predicting classical growth curve parameters including growth rate, carrying capacity and lag time. Knowing that the amount of glucose in the media determines the carrying capacity value has been known for several decades and does not need machine learning to tell us.

      Given that the authors use the most studied and genetically manipulatable model system in biology, and they use growth curves as the experimental system I would have expected some creative validation experiment to confirm the biological interpretation that they give to the data. After reading and evaluating the paper I cannot say I have learned anything new.

    1. Reviewer #3 (Public Review): 

      I think this is an interesting study on an important topic. I agree that there is not enough research to understand how the dopaminergic system interfaces with goal-directed planning, and I like the focus on specific types of dopamine receptors. It is interesting that they seem to find a specific effect on just the dopamine antagonist. I also appreciate the clarity with which the authors describe this field of research and their results. However, I also feel that there are several concerns with this paper, both in terms of framing and in terms of the experimental design and analysis. For completeness, I must note that I am not a dopamine expert. 

      I felt that the introduction of the paper did not sufficiently motivate the focus on the comparison between neurotransmitters systems, and (for the dopaminergic system) the distinction between D1/D2 receptors. Why is the mapping between stability/flexibility and D1/D2 receptors important? How does this relate to model-based control? Why do the authors predict that model-based control would increase when D2 receptors are blocked? If the hypothesis is about contrasting the contribution of D1 and D2 receptors to goal-directed control, why did the authors not use antagonists directly targeting these two systems? 

      In addition, the predictions that are more explicit, for example, that blocking D2 receptors increases MB control by stabilizing goal-relevant information, are fairly specific. However, the current version of the two-step task is not amenable to testing such a specific hypothesis, because it doesn't allow us to measure the specific components of planning (e.g., maintaining goals, the representation of the structure, prospective reasoning). Moreover, MB control in this version of the two-step task is marked by flexibility, because it requires the agent to be sensitive to switching starting states. 

      The predictions for the opioid system are also lacking. Why are the authors targeting this system? Why are they comparing the effects of the D2 antagonist with the opioid agonist? Why do the authors predict that amisulpride should have a stronger effect than naltrexone? In my opinion, these predictions were not sufficiently laid out, which made it difficult to appreciate the authors' motivation to run the study. 

      Related to this, I felt that the introduction was a bit too quiet on the genetic markers. Their discussion in the results was a bit surprising, and it wasn't quite clear why the authors decided to investigate these interaction effects. 

      I found some of the core results confusing. Most importantly, why does amisulpride make people less like to stay after a reward when the first-stage state is the same? When first-stage states repeat, both an MB agent and an MF agent will be more likely to stay after a reward. To me, this kind of behavior doesn't seem particularly model-based. Why does this behavior occur under amisulpride? I was surprised that the authors did not really address it. 

      With regards to the design, it is unfortunate that the order of drug administration is not counterbalanced. As far as I understand, model-based control is always measured without a drug in the first session, and then with the drug (or placebo) in the second. The change between sessions is then tested for all three conditions. Of course, it is possible that the increase in model-based control in the amisulpride condition is only driven by the drug. However, given the lack of counterbalancing, it's also possible that amisulpride increases model-based control only after the experience with the task. That is, if the authors had counterbalanced the drug effect, they may have found that amisulpride had a different effect if it was administered in the first session. That would have changed their interpretation quite a bit! As it stands, they are unable to verify their (admittedly simpler) hypothesis that there is only a main effect.

    1. Reviewer #3 (Public Review):

      The authors evaluated the connection between regional blood flow changes and local cortical maturation in 48 human subjects aged 0-24 months, focusing on the default mode network, visual cortex, and somatomotor cortex. The major strength involves the use of sophisticated MR methods to non-invasively measure blood flow and cortical network maturation. The results are consistent with their conclusion that local cerebral flow increases to meet the demands of cortical maturation in regions undergoing rapid development, though other interpretations of the data are also possible. Their approach has the potential to improve our understanding of neurovascular coupling in the context of maturation.

    1. Reviewer #3 (Public Review):

      Martin and coworkers have investigated the consequences of nr2f1a gene disruption for heart development, focusing on the period between 48 and 96 hours after fertilization. Nr2f1a is the functional homologue of mouse Nr2f2 (Coup-TFII) which was previously found to be required for atrial development and maintenance and suppression of ventricular gene expression and phenotype in the atrial compartments in mice. Using a marker (amhc-gfp), the transcriptomes of 48 hpf atrial cardiomyocytes wt and nr2f1a null mutants were defined. Among the differentially expressed genes, several markers of atrial identity were downregulated, of ventricular identity were upregulated (as expected) and some genes associated with pacemaker cardiomyocytes (sinoatrial node cardiomyocytes in mouse) were up (or de-)regulated.

      Using markers vmhc for ventricular cardiomyocytes, amhc for atrial cardiomyocytes, and fgf13-gfp (enhancer trap) for pacemaker cardiomyocytes, the number of cardiomyocytes of each type was counted at subsequent stages of development in wt and nr2f1a mutants. The authors found a strong reduction of atrial cells in mutants at 48, 72, and 96 hpf (all stages investigated). Notably, the amhc+ population did not change during these developmental stages in wt and mutant hearts (suppl fig 1A). Vmhc+ cell populations were not different between genotypes. In mutants, but not in wt, the number of fgf13a-gfp+ cells increased during development. The number of amhc+/fgf13a-gfp- cells declined only in mutants during development.

      The authors measured several functions of wt and mutant hearts and found the heart rate of mutants did not increase during development, whereas it does in wt, prolonged repolarization, prolonged AP20 (slower depolarization), atrial conduct howed differential accessibility (loss of accessibility in mutants) that also harboured a motif for Nr2f factors. When testing the function of the enhancer in an embryo reporter assay, they found that the enhancer was active in atrial cells adjacent to pacemaker cells and that the activity domain of the enhancer decreased in nr2f1a mutants. The authors conclude that nr2f1a maintains atrial identity and limits the differentiation of venous atrial cardiomyocytes into pacemaker cells by maintaining nkx2-5 expression.

      The study is well done, and the results interesting. While several observations in this study validate previous results (Nr2f1a/Nr2f2 function in determining atrial phenotype, suppressing ventricular gene expression, Nkx2-5 function to repress pacemaker program), the main novelties are the expansion of the pacemaker marker expression in the atrial domain in nr2f1a mutants, the identification of nkx2-5 as a target of Nr2f1a involved in the pacemaker phenotype expansion, and the identification of a putative Nr2f1a target enhancer. The transdifferentiation of atrial cells into pacemaker cells is less convincingly demonstrated, as is the phenotype of the pacemaker cells in mutants. An in-depth analysis of the ATAC-seq (and RNA-seq) analysis of wt and mutant atrial cells and its implications and comparison with epigenetic state data from other models is lacking, which is a missed opportunity, as this would provide novel insights complementing this study.

    1. Reviewer #3 (Public Review):

      Zadbood and colleagues investigated the way key information used to update interpretations of events alter patterns of activity in the brain. This was cleverly done by the use of "The Sixth Sense," a film featuring a famous "twist ending," which fundamentally alters the way the events in the film are understood. Participants were assigned to three groups: (1) a Spoiled group, in which the twist was revealed at the outset, (2) a Twist group, who experienced the film as normal, and (3) a No-Twist group, in which the twist was removed. Participants were scanned while watching the movie and while performing cued recall of specific scenes. Verbal recall was scored based on recall success, and evidence for descriptive bias toward two ways of understanding the events (specifically, whether a particular character was or was not a ghost). Importantly, this allowed the authors to show that the Twist group updated their interpretation. The authors focused on regions of the Default Mode Network (DMN) based on prior studies showing responsiveness to naturalistic memory paradigms in these areas and analyzed the fMRI data using intersubject pattern similarity analysis. Regions of the DMN carried patterns indicative of story interpretation. That is, encoding similarity was greater between the Twist and No-Twist groups than in the Spoiled group, and retrieval similarity was greater between the Twist and Spoiled groups than in the No-Twist group. The Spoiled group also showed greater pattern similarity with the Twist group's recall than the No-Twist group's recall. The authors also report a weaker effect of greater pattern similarity between the Spoiled group's encoding and the Twist group's recall than between the Twist group's own encoding and recall. Together, the data all converge on the point that one's interpretation of an event is an important determinant of the way it is represented in the brain.

      This is a really nice experiment, with straightforward predictions and analyses that support the claims being made. The results build directly on a prior study by this research group showing how interpretational differences in a narrative drive distinct neural representations (Yeshurun et al., 2017), but extend an understanding of how these interpretational differences might work retrospectively. I do not have any serious concerns or problems with the manuscript, the data, or the analyses. However I have a few points to raise that, if addressed, would make for a stronger paper in my opinion.

      1) My most substantive comment is that I did not find the interpretive framework to be very clear with respect to the brain regions involved. The basic effects the authors report strongly support their claims, but the particular contributions to the field might be stronger if the interpretations could be made more strongly or more specifically. In other words: the DMN is involved in updating interpretations, but how should we now think about the role of the DMN and its constituent regions as a result of this study? There are a number of ideas briefly presented about what the DMN might be doing, but it just did not feel very coherent at times. I will break this down into a few more specific points:

      While many of us would agree that the DMN is likely to be involved in the phenomena at hand, I did not find that the paper communicated the logic for singularly focusing on this subset of regions very compellingly. The authors note a few studies whose main results are found in DMN regions, but I think that this could stand to be unpacked in a more theoretically interesting way in the Introduction.

      Relatedly, I found the summary/description of regional effects in the Discussion to be a bit unsatisfying. The various pattern similarity comparisons yielded results that were actually quite nonoverlapping among DMN regions, which was not really unpacked. To be clear, it is not a 'problem' that the regional effects varied from comparison to comparison, but I do think that a more theoretical exploration of what this could mean would strengthen the paper. To the authors' credit, they describe mPFC effects through the lens of schemas, but this stands in contrast to many other regions which do not receive much consideration.

      Finally, although there is evidence that regions of the DMN act in a coordinated way under some circumstances, there is also ample evidence for distinct regional contributions to cognitive processes, memory being just one of them (e.g., Cooper & Ritchey, 2020; Robin & Moscovitch, 2017; Ranganath & Ritchey, 2012). The authors themselves introduce the idea of temporal receptive windows in a cortical hierarchy, and while DMN regions do appear to show slower temporal drift than sensory areas, those studies show regional differences in pattern stability across time even within DMN regions. Simply put, it is worth considering whether it is ideal to treat the DMN as a singular unit.

      2) I think that some direct comparison to regions outside the DMN would speak to whether the DMN is truly unique in carrying the key representations being discussed here. I was reluctant to suggest this because I think that the authors are justified in expecting that DMN regions would show the effects in question. However, there really is no "null" comparison here wherein a set of regions not expected to show these effects (e.g., a somatosensory network, or the frontoparietal network) in fact do not show them. There are not really controls or key differences being hypothesized across different conditions or regions. Rather, we have a set of regions that may or may not show pattern similarity differences to varying degrees, which feels very exploratory. The inclusion of some principled control comparisons, etc. would bolster these findings. The authors do include a whole-brain analysis in Supplementary Figure 1, which indeed produced many DMN regions. However, notably, regions outside the DMN such as the primary visual cortex and mid-cingulate cortex appear to show significant effects (which, based on the color bar, might actually be stronger than effects seen in the DMN). Given the specificity of the language in the paper in terms of the DMN, I think that some direct regional or network-level comparison is needed.

      3) If I understand correctly, the main analyses of the fMRI data were limited to across-group comparisons of "critical scenes" that were maximally affected by the twist at the end of the movie. In other words, the analyses focused on the scenes whose interpretation hinged on the "doctor" versus "ghost" interpretation. I would be interested in seeing a comparison of "critical" scenes directly against scenes where the interpretation did not change with the twist. This "critical" versus "non-critical" contrast would be a strong confirmatory analysis that could further bolster the authors' claims, but on the other hand, it would be interesting to know whether the overall story interpretation led to any differences in neural patterns assigned to scenes that would not be expected to depend on differences in interpretation. (As a final note, such a comparison might provide additional analytical leverage for exploring the effect described in Figure 3B, which did not survive correction for multiple comparisons.)

      4) I appreciate the code being made available and that the neuroimaging data will be made available soon. I would also appreciate it if the authors made the movie stimulus and behavioral data available. The movie stimulus itself is of interest because it was edited down, and it would be nice for readers to be able to see which scenes were included.

      To sum up, I think that this is a great experiment with a lot of strengths. The design is fairly clean (especially for a movie stimulus), the analyses are well reasoned, and the data are clear. The only weaknesses I would suggest addressing are with regards to how the DMN is being described and evaluated, and the communication of how this work informs the field on a theoretical level.

    1. Reviewer #3 (Public Review):

      This manuscript presents a series of experiments that together carry a significant message: inhibition is functionally present in the newborn frontal areas. The work challenges the simplistic view on the switch in GABAergic excitation to inhibition. Showing the phenomenological comparison between experimental work to human infant EEG brings a nice translational bridge that may turn out to have significant impact on clinical studies as well.

    1. Reviewer #3 (Public Review):

      Jangir et al. used an 'evolutionary ramp' experiment to evolve E. coli strains under the selection pressure of increasing colistin concentrations wherein the surviving fractions were collected for genomic analysis. They report that the mcr-1 carrying strain evolved higher colistin resistance much faster only in presence of lpxC mutations in the genome. They identify the mcr-1 and lpxC interactions to be positively epistatic and mutations only in lpxC do not lead to resistance to colistin. Taking a cue from their evolution experiments, they looked for the variations in lpxC sequences in the genomic datasets of clinical E. coli strains. They found many such variations in the genomes of clinical isolates. Importantly, they found those variations to be present even in non-resistant strains which might predispose those strains to gain untreatable levels of colistin resistance.

      Strengths:

      The study focuses on two key aspects of antibiotic resistance in clinical settings. First, is the antibiotic colistin itself which is part of the last line of defense. Second, is the importance of genomic variations in clinical isolates that have not been linked to any antibiotic resistance mechanisms. The data were presented in a logical sequence and maintained brevity. The link of lpxC to mcr-1 resistance is convincing.

      Weaknesses:

      The basic premise of the paper is solid but the following should be addressed.<br /> 1. In Figure 1, the authors applied the 'evolutionary ramp' method to isolate evolved strains with higher MIC to colistin; but, the conditions for the evolution of WT and strain carrying mcr-1 are different. Maintaining mcr-1 requires antibiotic selection which WT cannot withstand. Hence, if I am not mistaken, WT was not grown in the presence of any antibiotic. Not only that, maintaining a ~32 Kb plasmid itself can have different selective landscapes. The authors may replicate the experiment with their low-copy clone of mcr-1 which would make it easier for the authors to have an empty vector in WT as a proper control. Since now they know the expected mutations to be in lpxC, they might sequence a PCR amplicon of that region for validation of their hypothesis.<br /> 2. In Figure 2, what are the effects of these mutations in lpxC? The authors state that many mutations map on to the metal binding domain; but are those significant changes? LpxC is relatively well characterized and authors may want to comment on these mutations a little more.<br /> Also, lpxC mutations showed enrichment but lpxA did not. Is this suggestive of the type of Lipid A that is more preferred for the epistatic interactions? The authors may want to comment on that.<br /> 3. In Figure 3, the lpxC mutant shows a reduction in fitness in a competition assay. What is the growth pattern of individual strains? There is a possibility that slow growth of lpxC mutant provides benefit under antibiotic stress.<br /> Minor comment: the three individual replicates shown in Figure 3a are all identical within a sample and do not add to the figure where n=3. The authors can simply show SD or report correct values of replicates.<br /> 4. In Figure 4, as the authors themselves have stated, the difference in heterogeneity could be simply due to variation within phylogroups and subsequent compositional differences within the populations. The authors must check if mutations were found in the same location of lpxA as found in their own evolved strains. Without this information, the heterogeneity data would be speculative. Adding the lpxC variants reported in figure 2 to the trees of figure 4 (right) will make it clear if their conclusion is justified.<br /> 5. The authors can perform a confirmatory experiment for the pre-existing part of their hypothesis. If they perform the evolutionary ramp experiment with a strain carrying lpxC mutant strain, will they see faster evolution of high MIC mutants?<br /> 6. The rationale of how the presence of lpxC mutations can cause a strain without any colistin resistance to acquire mcr-1 is not addressed. The authors may want to comment on that.

    1. Reviewer #3 (Public Review):

      The authors attempt to track the effect vaccination can have upon viral evolution within the swine host. This is important because novel viruses emerging from animal hosts have the ability to spark the next influenza pandemic.

      The paper presents data showing pigs receiving two doses of vaccine had fewer reassortant viruses than non-vaccinated pigs. This is an interesting finding in the context of controlling the genetic diversity of influenza A virus in swine. The study is using samples generated in a previous study (doi.org/10.1186/s13567-020-00810-z). It is unclear how the samples that were used in the present study were selected from the previous study, which introduces significant concerns about selection bias.

      The present study does not discuss the homology between vaccine and challenge strains. Readers have to refer to Table 2 in the parent paper to find the data. Upon closer examination, at least one of the components of every prime-boost treatment had a vaccine with >95% amino acid homology to a challenge virus. Furthermore, it appears that 25% of the sequenced BALF samples were collected from animals that received at least one dose of an autogenous vaccine that was 99.1% homologous to the H3 challenge virus. With the estimation of antigenic distance, it is hard to predict exactly how protective each vaccine would have been. It is very hard to know if the prime-boost strategy or similarity between the vaccine strains and challenge virus is truly responsible for the observed results.

    1. Reviewer #3 (Public Review):

      This paper will be welcome for clinicians and researchers related to the field. The authors, applying a well-structured meta-analysis, showed that calcium supplementation or calcium intake during 20-35 years is better than the <20 years. The clinical impact is directly associated with improving the bone mass of the femoral neck, and thus proposes a window of intervention for osteoporosis treatment. The manuscript is very well prepared and represents a thorough analysis of available randomized controlled clinical trials, but a few issues require additional consideration.

      After a careful read of the literature, it is important to highlight that the paper is a statistically robust study with a well-delineated meta-analysis of youth-adult subjects.<br /> But, I would like better to understand why the authors didn't use other datasets such as WHO Global Index Medicus (Index Medicus for Africa, the Eastern Mediterranean Region, South-East Asia, and Western Pacific, and Latin America and the Caribbean Literature on Health Sciences, Index Medicus), ClinicalTrials.gov, and the WHO ICTRP.<br /> The manuscript compares two sources of participants (in line 233) evaluating the effect of improvements on the femoral neck being "obviously stronger in Western countries than in Asian countries". But, I didn't identify if the searches were conducted applying language restrictions. This is important because we can be considering the entire world or specific countries.

      The manuscript does not describe which version was used with the RoB tool.

      Figures and Supplementary: No critique.

    1. Reviewer #3 (Public Review):

      The authors present a systematic assessment of low complexity sequences (LCRs) apply the dotplot matrix method for sequence comparison to identify low-complexity regions based on per-residue similarity. By taking the resulting self-comparison matrices and leveraging tools from image processing, the authors define LCRs based on similarity or non-similarity to one another. Taking the composition of these LCRs, the authors then compare how distinct regions of LCR sequence space compare across different proteomes.

      The paper is well-written and easy to follow, and the results are consistent with prior work. The figures and data are presented in an extremely accessible way and the conclusions seem logical and sound.

      My big picture concern stems from one that is perhaps challenging to evaluate, but it is not really clear to me exactly what we learn here. The authors do a fine job of cataloging LCRs, offer a number of anecdotal inferences and observations are made - perhaps this is sufficient in terms of novelty and interest, but if anyone takes a proteome and identifies sequences based on some set of features that sit in the tails of the feature distribution, they can similarly construct intriguing but somewhat speculative hypotheses regarding the possible origins or meaning of those features.

      The authors use the lysine-repeats as specific examples where they test a hypothesis, which is good, but the importance of lysine repeats in driving nucleolar localization is well established at this point - i.e. to me at least the bioinformatics analysis that precedes those results is unnecessary to have made the resulting prediction. Similarly, the authors find compositional biases in LCR proteins that are found in certain organelles, but those biases are also already established. These are not strictly criticisms, in that it's good that established patterns are found with this method, but I suppose my concern is that this is a lot of work that perhaps does not really push the needle particularly far.

      As an important caveat to this somewhat muted reception, I recognize that having worked on problems in this area for 10+ years I may also be displaying my own biases, and perhaps things that are "already established" warrant repeating with a new approach and a new light. As such, this particular criticism may well be one that can and should be ignored.

      That overall concern notwithstanding, I had several other questions that sprung to mind.

      Dotplot matrix approach<br /> The authors do a fantastic job of explaining this, but I'm left wondering, if one used an algorithm like (say) SEG, defined LCRs, and then compared between LCRs based on composition, would we expect the results to be so different? i.e. the authors make a big deal about the dotplot matrix approach enabling comparison of LCR type, but, it's not clear to me that this is just because it combines a two-step operation into a one-step operation. It would be useful I think to perform a similar analysis as is done later on using SEG and ask if the same UMAP structure appears (and discuss if yes/no).

      LCRs from repeat expansions<br /> I did not see any discussion on the role that repeat expansions can play in defining LCRs. This seems like an important area that should be considered, especially if we expect certain LCRs to appear more frequently due to a combination of slippy codons and minimal impact due to the biochemical properties of the resulting LCR. The authors pursue a (very reasonable) model in which LCRs are functional and important, but it seems the alternative (that LCRs are simply an unavoidable product of large proteomes and emerge through genetic events that are insufficiently deleterious to be selected against). Some discussion on this would be helpful. it also makes me wonder if the authors' null proteome model is the "right" model, although I would also say developing an accurate and reasonable null model that accounts for repeat expansions is beyond what I would consider the scope of this paper.

      Minor points<br /> Early on the authors discuss the roles of LCRs in higher-order assemblies. They then make reference to the lysine tracts as having a valence of 2 or 3. It is possibly useful to mention that valence reflects the number of simultaneous partners that a protein can interact with - while it is certainly possible that a single lysine tracts interacts with a single partner simultaneously (meaning the tract contributes a valence of 1) I don't think the authors can know that, so it may be wise to avoid specifying the specific valence.

      The authors make reference to Q/H LCRs. Recent work from Gutiérrez et al. eLife (2022) has argued that histidine-richness in some glutamine-rich LCRs is above the number expected based on codon bias, and may reflect a mode of pH sensing. This may be worth discussing.

      Eric Ross has a number of very nice papers on this topic, but sadly I don't think any of them are cited here. On the question of LCR composition and condensate recruitment, I would recommend Boncella et al. PNAS (2020). On the question of proteome-wide LCR analysis, see Cascarina et al PLoS CompBio (2018) and Cascarina et al PLoS CompBio 2020.

    1. Reviewer #3 (Public Review):

      This paper is describing a machine learning method applied to videos of animals. The method requires very little pre-processing (end-to-end) such as image segmentation or background subtraction. The input images have three channels, mapping temporal information (live-frames). The architecture is based on tween deep neural networks (Siamese network) and does not require human annotated labels (unsupervised learning). However, labels can still be used if they are produced, as in this case, by the algorithm itself - self-supervised learning. This flavor of machine learning is reflected in the name of the method: "Selfee." The authors are convincingly applying the Selfee to several challenging animal behavior tasks which results in biologically relevant discoveries.

      A significant advantage of unsupervised and self-supervised learning is twofold: 1) it allows for discovering new behaviors, and 2) it doesn't require human-produced labels.

      In this case of self-supervised learning the features (meta-representations) are learned from two views of the same original image (live-frame), where one of the views is augmented in several different ways, with a hope to let the deep neural network (ResNet-50 architecture in this case) learn to ignore such augmentations, i.e. learn the meta-representations invariant to natural changes in the data similar to the augmentations. This is accomplished by utilizing a Siamese Convolutional Neural Network (CNN) with the ResNet-50 version as a backbone. Siamese networks are composed of tween deep nets, where each member of the pair is trying to predict the output of another. In applications such as face recognition they normally work in the supervised learning setting, by utilizing "triplets" containing "negative samples." These are the labels.

      However, in the self-supervised setting, which "Selfee" is implementing, the negative samples are not required. Instead the same image (a positive sample) is viewed twice, as described above. Here the authors use the SimSiam core architecture described by Chen, X. & He, K (reference 29 in the paper). They add Cross-Level Discrimination (CLD) to the SimSiam core. Together these two components provide two Loss functions (Loss 1 and Loss 2). Both are critical for the extraction of useful features. In fact, removing the CLD causes major deterioration of the classification performance (Figure 2-figure supplement 5).

      The authors demonstrate the utility of the Selfee by using the learned features (meta-representations) for classification (supervised learning; with human annotation), discovering short-lasting new behaviors in flies by anomaly detection, long time-scale dynamics by AR-HMM, and Dynamic Time Warping (DTW).

      For the classification the authors use k-NN (flies) and LightGBM (mice) classifiers and they infer the labels from the Selfee embedding (for each frame), and the temporal context, using the time-windows of 21 frames and 81 frames, for k-NN classification and LightGBM classification, respectively. Accounting for the temporal context is especially important in mice (LightGBM classification) so the authors add additional windowed features, including frequency information. This is a neat approach. They quantify the classification performance by confusion matrices and compute the F1 for each.

      Overall, I find these classification results compelling, but one general concern is the criticality of the CLD component for achieving any meaningful classification. I would suggest that the authors discuss in more depth why this component is so critical for the extraction of features (used in supervised classification) and compare their SimSiam architecture to other methods where the CLD component is implemented. In other words, to what degree is the SimSiam implementation an overkill? Could a simpler (and thus faster) method be used - with the CLD component - instead to achieve similar end-to-end classification? The answer would help illuminate the importance of the SimSiam architecture in Selfee.

      One potential issue with unsupervised/self-supervised learning is that it "discovers" new classes based, not on behavioral features but rather on some other, irrelevant, properties of the video, e.g. proximity to the edges, a particular camera angle, or a distortion. In supervised learning the algorithm learns the features that are invariant to such properties, because human-made labels are used and humans are great at finding these invariant features. The authors do mention a potential limitation, related to this issue, in the Discussion ("mode splitting"). One way of getting around this issue, other than providing negative samples, is to use a very homogeneous environment (so that only invariance to orientation, translation, etc, needs to be accomplished). This has worked nicely, for example, with posture embedding (Berman, G. J., et al; reference 19 in the manuscript). Looking at the t-SNE plots in Figure 2 one must wonder how many of the "clusters" present there are the result of such learning of irrelevant (for behavior) features, i.e. how good is the generalization of the meta-representations. The authors should explore the behaviors found in different parts of the t-SNE maps and evaluate the effect of the irrelevant features on their distributions. For example, they may ask: to what extent does the distance of an animal from the nearest wall affect the position in the t-SNE map? It would be nice to see how various simple pre-processing steps might affect the t-SNE maps, as well as the classification performance. Some form of segmentation, even very crude, or simply background subtraction, could go a very long way towards improving the features learned by Selfee.

      The anomaly detection is used to find unusual short-lasting events during male-male interaction behavior (Figure 3). The method is explained clearly. The results show how Selfee discovered a mutant line with a particularly high anomaly score. The authors managed to identify this behavior as "brief tussle behavior mixed with copulation attempts." The anomaly detection analyses were also applied to discover another unusual phenotype (close body contact) in another mutant line. Both results are significant when compared to the control groups.

      The authors then apply AR-HMM and DTW to study the time dynamics of courtship behavior. Here too, they discover two phenotypes with unusual courtship dynamics, one in an olfactory mutant, and another in flies where the mutation affects visual transduction. Both results are compelling.

      The authors explain their usage of DTW clearly, but they should expand the description of the AR-HMM so that the reader doesn't have to study the original sources.

      Overall this paper introduces a potentially useful tool as well as several interesting biological results obtained by applying it to videos with very little pre-processing. Both, the method and the results are convincing.

    1. Reviewer #3 (Public Review):

      This manuscript by Lemière and colleagues presents a view on how nuclear size is set by simple physical principles. The first part of the work describes a theoretical framework with the nucleus and the cell as two nested osmometers. Using fission yeast as a model, the authors then show that protoplasts and nuclei behave as ideal osmometers, i.e. show linear changes in volume upon change in external osmotic pressure. Consequently, the nuclear to cell volume ratio remains constant upon osmotic changes, but increases upon block of nuclear export, which leads to higher nuclear protein contents. Measurements of diffusion in the cytoplasm and nucleoplasm back these data. Finally, in the last part of the manuscript, the authors show that nuclear growth through a passive osmotic model can explain the previously described homeostasis of nuclear volume.

      The manuscript is clearly written, and the data are clean and overall solid. I very much liked the simple view on the phenomenon of constant nuclear to cytosol ratio and the mix of modelling and experiments supporting the model that nuclear size is set passively by osmotic principles.

      There are however a few points that are slightly at odds with the model and/or require further explanation to make the model compelling and discuss it in view of previous findings.

      1. Isn't the finding that diffusion rates are faster in the nucleus (line 298, Fig S4C), indicating lower crowding in the nucleus, at odds with the finding that the non-osmotic volumes are similar in the two compartments? If the nucleus is less crowded, does this not suggest a lower pressure than the cytosol? I would also like to see this finding appear in Figure 4, which only reports on the normalized diffusion rates in both nuclei and cytosol.

      2. Similarly, I don't understand the observed change in diffusion rates of GEMs upon LMB treatment (Fig 5F). If the nucleus behaves as an ideal osmometer, then any change in protein density between the nucleus and the cytosol, leading to change in osmotic pressure, will lead to a change in nuclear size that should re-equilibrate the osmotic pressures between the two compartments. The prediction would thus be that, if LMB treatment does not change overall protein concentration, at equilibrium there is no change in either osmotic pressure or density as measured by GEM diffusion rates. This is indeed illustrated by the constant normalized non-osmotic volume of the nucleus after LMB treatment. Is the change in diffusion rates perhaps only transient until a new steady state is reached? Or is there a change upon total protein content in the cell after LMB treatment?

      3. In the experiments labelling proteins with FITC, are the reported values really those of protein concentrations or rather protein amounts? Isn't the enlargement of the nucleus upon LMB treatment compensating for this increase in amounts, returning the nucleus to a similar concentration as before treatment? A change in concentration is not in agreement with the reported constant non-osmotic volume of the nucleus.

      4. The authors state that "a previous paper proposed a model for N/C ratio homeostasis based upon an active feedback mechanism (Cantwell and Nurse, 2019)" (lines 471-472). My understanding of this previous study is that nuclear size was proposed to be set by a limiting component, itself proportional to cell volume. No feedback was postulated. This previous model is in fact not too different from what the authors propose here, with the previously proposed limiting component now corresponding to the nuclear macromolecules that produce colloid osmotic pressure and thus set nuclear size. Though the present study goes significantly further in presenting the passive role of osmosis in setting nuclear size, it is a misrepresentation to portray this previous model as fundamentally different. Furthermore, it is not clear whether the new osmotic pressure-based model produces a better fit than the previous 'limiting component model'. Figure 7E here is very similar to Fig 4I in Cantwell and Nurse 2019, but it is difficult to judge the similarity of the fits.

      5. If nuclear size is set purely by osmotic regulation, how do you explain that mutants in membrane regulation (such as nem1 and spo7, see Kume et al 2017; or lem2, see Kume et al 2019) previously shown to have an enlarged nucleus, display increased nuclear size?

    1. Reviewer #3 (Public Review):

      In this paper, Kosillo et al. investigated the structural and functional alterations of dopamine neurons in dopamine neuron-specific Raptor and Rictor KO mice. Physiological functions and cellular structures were broadly and markedly affected in Raptor cKO mice, while Rictor cKO mice exhibited marginal changes, indicating that each adaptor protein of mTOR in either mTORC1 or mTORC2 may play both similar or distinct roles in the maintenance of dopaminergic structures and functions. Non-specific activation or inhibition of mTOR pathways in the previous literatures have hampered the understanding of molecular mechanisms behind the functions of mTOR pathways in dopamine neurons and related brain diseases. By utilizing dopamine neuron-specific Raptor and Rictor cKO mice, this paper elucidated which of these mTOR complexes are responsible for the regulation of dopamine neuronal functions, revealing the importance of mTORC1/2 signaling for the structure and function of dopamine neurons. Providing comprehensive data including structural, physiological, and biochemical alterations by genetic deletion of Raptor/Rictor in dopamine neurons is another strong point of this paper. However, lack of mechanistic evidence directly (or indirectly) linking the deletion of Raptor (or Rictor) to the alterations in TH/DAT/p-DAT/neuronal structures is a weak point of this manuscript. Overall, the conclusion of this paper is unbiased, just reflecting the data presented.

    1. Reviewer #3 (Public Review):

      Condensin I plays a dominant role in chromosome condensation (at least globally). To uncover and investigate the specific functions of condensin II, the authors compare the phenotypes caused by condensin I-depletion with those of combined depletion of condensin I and condensin II. They show that condensin II (alone) promotes the formation of 'chenille' chromatids (lacking lateral compaction).

      The work underscores the different functions of condensin I and II in shaping chromatids and reveals distinct roles of their HEAT subunits in regulating chromosome condensation in mitosis. They uncover a putative self-inhibitory mechanism via one of the HEAT subunits (CAP-G2) which requires the C-terminal tail of the other HEAT subunit (CAP-D3) and potentially involves phosphorylation by mitotic CDK.

      The work also highlights differences in the role of conserved motifs (III and IV) in CAP-H and CAP-H2 (condensin I and II) with mutations hindering chromosome association in the latter but not the former.

      Of note, many conclusions rely on the efficient depletion of endogenous condensin I and II subunits from the extracts.

    1. Reviewer #3 (Public Review):

      Caillet et al. performed a regression analysis to estimate mathematical functions relating electrophysiological and morphological properties of spinal motor neurons, as well as motor neuron and muscle unit properties. Power functions relating to pairs of properties were parameterized using data from cat motor neurons with a subsequent validation using rodent data. The study's main conclusion is that the estimated relations extend Henneman's size principle, providing "unknown" associations between motor unit properties.

      Despite the great effort of the authors in reconciling a large amount of data from several studies, my primary concern is that the proposed method neglect data variability and their ensuing distribution. It is well known that several electrophysiological properties present a multimodal distribution with overlapping values for motor neurons of different sizes (see Figure 7 in Zengel et al. 1985, for instance). Additionally, the authors disregard the well-defined biophysical relations, for example, the relation between membrane time constant and the product of membrane resistance and capacitance (tau = R . C). We do not have any experimental evidence to hypothesize the motor neuron will behave differently from a resistive-capacitive system (at least in subthreshold potentials); on the contrary, several experimental results are showing the RC behavior of the motoneuronal membrane, along with different computational models (conceptualized as an RC network) that closely resemble the electrophysiological behaviors of individual neurons.

      The enthusiastic tone adopted in the paper regarding the novelty and potential relevance for future studies seems unsupported. For instance, in p. 4, l. 66-67, the authors state that the estimated relationships "can accelerate future research in the behavioural of individual MNs". However, the relations obtained between electrophysiological and morphological properties of motor neurons did not consider important aspects of motor neuron physiology, namely the active properties yielded by ionic channels and a distributed synaptic integration along the dendritic tree of the motor neuron. Properties such as f-I gain, hysteresis, EPSP and IPSP amplitude, electrotonic length, and others are lacking. If the authors want to make such assertions, they should provide how the empirical static relations among (mostly) passive properties would be translated to a functional context.

      Another problematic aspect of the authors' arguments is that they did not consider previous evidence from the literature as valid quantitative relations among variables. Eccles et al. (J. Physiol. 142: 275-91, 1958) have shown a clear linear relation (strong correlation indeed) between AHP and axon conduction velocity. The provided graph (with a regression) in the referred paper is not "speculative", despite the fact the authors did not give the slope and intercept of the curve (can be easily estimated). Also, Powers and Binder (2001) present clear evidence that electrophysiological data from cat motor neurons can be adjusted by the theoretical functions from Rall's cable theory (see Figure 4 in the referred review paper). Considering Rall's approach, it is not surprising that all parameters used in the present study are related. Nonetheless, the unexpected point here is the unjustified choice of power functions to fit the relations. How "flexibility and simplicity" would affect the interpretations and results? Why not use functions more adherent to the theoretical relations expected from biophysical studies?

      Another aspect not fully explained in the manuscript (and related to my first point) is how data scarcity in some dataset areas would influence the proposed method. There are regions with a limited number of data points, while other areas are very dense. R-squared values (I am unsure if they are valid for non-linear regressions) are not high enough (< 0.70), and the validation results show normalized mean errors as high as 400%. Moreover, the authors did not discuss the possible bias of using several datasets from the same research group (or lab). Methodological bias can also be a confounding factor in the analysis. Finally, in the inter-species analysis, the authors did use data from studies with mutated animals (SOD-1 rodents, for example). It is not clear if the data included in the analysis were from wild-type animals or all animals in the study dataset. Particularly for the Huh et al. (2021) dataset, data from animals of different ages can also influence the analysis (see Highlander et al. 2020).

    1. Reviewer #3 (Public Review):

      In the manuscript by Zhu, Haoran et al., titled "Cystathionine-β-synthase is essential for AKT-induced senescence and suppresses the development of gastric cancers with PI3K/AKT activation", the authors investigated the contributions of cystathionine-β-synthase (CBS) to AKT-induced senescence (AIS) and the potential mechanisms which drove these phenotypes. The authors showed that AKT hyperactivation (using myristoylated AKT) promoted H2S production and treatment with a compound (AOAA) that blocked H2S production, reduced proliferation, and promoted senescence in cells with hyperactivated AKT, compared to normally proliferating cells or cells that have expressed other oncogenes (i.e., HRAS). Next, they used genetic approaches (both knockdown of CBS and rescue experiments with re-expression of CBS in CBS-knockdown cells) to clearly demonstrate that CBS was required for AIS and loss of CBS promoted AIS-escape. The authors then extended these findings to patient tumors and in vivo systems. They found reduced CBS expression in gastric cancer samples compared to matched normal samples and that the reduced expression was due to hypermethylation of DNA encoding CBS. Finally, they found that CBS functions as a tumor suppressor in gastric cancer cells by showing that depletion of CBS promoted colony formation, and overexpression of CBS blocked tumor growth in vivo. This is a very strong study with relevance to numerous research fields. However, a major weakness of the study is the proposed mechanism by which CBS functions in AIS-escape, as the data are largely not supported by the mechanistic conclusions.

      1. In Figure 1, the authors show that AIS cells are unaffected by cysteine depletion and conclude, "Furthermore, cysteine deprivation potently increased the expression levels of CBS and CTH in AIS cells (Figure 1B) and did not affect the survival of AIS cells, consistent with increased cysteine synthesis due to elevated CBS expression being critical for cell viability (Figure 1E)". Although the authors show in Fig. 3F that cysteine levels are elevated in AIS cells compared to control cells in cystine-replete media, they do not measure cysteine synthesis via the transsulfuration pathway in AIS and control cells in cystine-replete and cystine-depleted media.

      2. The metabolic changes presented in Figure 3 are unclear. The authors state, "Depletion of CBS in AIS cells increases GSH metabolism in cysteine-replete condition", but it is not clear what "GSH metabolism" means, especially for the AIS-related phenotypes. Further, the authors appear to use "GSH metabolism" interchangeable with GSH synthesis; in the Discussion, they state, "In this study we uncovered another mechanism of AKT-mediated ROS detoxification by upregulation of transsulfuration pathway activity and enhancing glutathione and H2S synthesis (Fig.4H)." These conclusions are not supported by the findings presented in Figure 3 that show GSH levels are unchanged between control, AIS, and CBS-depleted AIS cells. While the authors show an increased abundance of the GSH precursor gamma-glutamylcysteine and the GSH catabolic product cysteinylglycine, how CBS would alter these metabolites are unclear. Additionally, they show that H2S levels are unaffected by CBS depletion, which further confounds the conclusions.

    1. Reviewer #3 (Public Review):

      In this manuscript, Wang et al describe a series of experiments aimed at optimizing the experimental and computational approach to the detection of projection-specific neurons across the entire mouse brain. This work builds on a large body of work that has developed nuclear-fused viral labelling, next-generation fluorophores, tissue clearing, image registration, and automated cell segmentation. They apply their techniques to understand projection-specific patterns of supraspinal neurons to the cervical and lumbar spinal cord, and to reveal brain and brainstem connections that are preferentially spared or lost after spinal cord injury.

      Strengths:

      Although this work does not put forward any fundamentally new methodologies, their careful optimization of the experimental and quantification process will be appreciated by other laboratories attempting to use these types of methods. Moreover, the observations of topological arrangement of various supraspinal centres are important and I believe will be interesting to others in the field.

      The web app provided by the authors provides a nice interface for users to explore these data. I think this will be appreciated by people in the field interested in what happens to their brain or brainstem region of interest.

      Weaknesses:

      Overall the work is well done; however, some of the novelty claims should be better aligned with the experimental findings. Moreover, the statistical approaches put forward to understand the relationship between spinal cord injury severity and cell counts across the mouse brain needs to be more carefully considered.

      The authors state that they provide an experimental platform for these types of analysis to be done. My apologies if I missed it but I could not find anywhere the information on viral construct availability or code availability to reproduce the results. Certainly both of these aspects would be required for people to replicate the pipeline. Moreover, the described methodology for imaging and processing is quite sparse. While I appreciate that this information is widely provided in papers that have developed these methods, I do not think it is appropriate to claim to have provided a platform for people to enable these types of analyses without a more in-depth description of the methods. Alternatively, the authors could instead focus on how they optimized current methodologies and avoid the overstatement that this work provides a tool for users. The exception to this is of course the viral constructs, the plasmids of which should be deposited.

      It was not completely to me clear why or when the authors switch back and forth between different resolutions throughout the manuscript. In the abstract it states that 60 regions were examined, but elsewhere the number is as many as 500. My understanding is that current versions of the Allen Brain Annotation include more than 2000 regions. I think it would make things clear for the readers if a single resolution was used throughout, or at least justified narratively throughout the text to avoid confusion.

      The others provide an interesting analysis of the difference between cervical and lumbar projections. I think this might be one of the more interesting aspects of the paper - yet I found myself a bit confused by the analysis, and whether any of the differences observed were robust. Just prior to this experiment the authors provide a comparison of the mScarlet vs. the mGL, and demonstrate that mGL may label more cells. Yet, in the cervical vs. lumbar analysis it appears they are being treated 1 to 1. Moreover, I could not find any actual statistical analysis of this data? My impression would be that given the potential difference in labelling efficiency between the mScarlet and mGL this should be done using some kind of count analysis that takes into account the overall number of neurons labelled, such as a Chi-sq test or perhaps something more sophisticated. Then, with this kind of statistical analysis in place, do any of the discussed differences hold up? If not, I do not think this would detract from the interesting topological observations - but would call on the authors to be a bit more conservative about their statements and discussion regarding differences in the proportions of neurons projecting to certain supraspinal centres.

      Finally, I do have some concerns about the author's use of linear regression in their analysis of brain regions after varying severities of SCI. First of all, the BMS score is notoriously non-linear. Despite wide use of linear regressions in the field to attempt to associate various outcomes to these kinds of ordinal measures, this is not appropriate. Some have suggested a rank conversion of the BMS prior to linear analyses, but even this comes with its own problems. Ultimately, the authors have here 2-3 clear cohorts of behavioural scores and drawing a linear regression between these is unlikely to be robustly informative. Moreover, it is unclear whether the authors properly adjusted their p-values from running these regressions on 60 (600?) regions. Finally, the statement in the abstract and discussion that the authors "explain more variability" compared to typical lesion severity analysis is also unsupported. My suggestion would be the following:

      Remove the linear regression analyses associated with BMS. I do not think these add value to the paper, and if anything provide a large window of false interpretation due to a violation of the assumptions of this test.

      Consider adding a more appropriate statistical analysis of the brain regions, such as a non-parametric group analysis. Knowing which brain regions are severity dependent, and which ones are not, would already be an interesting finding. This finding would not be confounded by any attempt to link it to crude measures of behaviour.

      If the authors would like to state anything about 'explaining more variability' then the proper statistical analysis should be used, which in this case would be to compare the models using a LRT or equivalent. However, as I mentioned it does not seem to be appropriate to be doing this with linear models so the authors should consider a non-linear equivalent if they choose to proceed with this.

    1. Reviewer #3 (Public Review):

      Neuron visualization in a developing animal embryo gives important insights into the earliest stages of neurite outgrowth and nervous system assembly, but existing imaging methods are confounded in one way or another by a multitude of challenges, including temporal and spatial resolution, cell-specific labelling, cell identity and annotation. In this manuscript, Santella et al. cleverly integrate two contrasting methods of visualization, electron microscopy (EM) and fluorescence microscopy (FM) to cross-compensate for some of these shortfalls. Through this method, the authors achieve spatial and temporal resolution in visualizing developing neurons in the C. elegans embryo across a time series. This cross-modality analysis revealed important insights into early formation of the nerve ring as well as the amphid sensory organ.

    1. Reviewer #3 (Public Review):

      In their manuscript, Narayanan and colleagues use ultrasound imaging to investigate the development of prenatal orofacial movements as a precursor to neonatal vocalizations in common marmosets. Using their experimental approach, the authors identify prenatal sensorimotor precursors to vocalization by 1) distinguishing rhythmic orofacial movements associated with vocalizations from general movement patterns and 2) identifying neonatal vocalization-specific features prenatally. Studying the prenatal development of neonatal vocalizations is of great interest, and common marmosets are a good model for investigating this topic. Simultaneous spatiotemporal tracking of fetal mouth and head movements in four pregnancies makes the methodological approach comprehensive. Overall, this is important work.

    1. Reviewer #3 (Public Review):

      In this manuscript, Surrett and coworkers aimed to identify the mechanism that regulates the transcription of Qrr1 sRNA in the squid symbiont Vibrio fischeri. In many Vibrio species, Qrr1 transcription is regulated by quorum sensing (QS) and activated only at low cell density. Qrr1 is important for V. fischeri to colonize the squid host. In the QS systems that have been studied so far, LuxO is the only known response regulator that activates Qrr sRNA transcription. However, the authors argued that since V. fischeri forms aggregates before entering into the light organ of the squid, Qrr1 would not be made as high cell density QS state is likely induced within the aggregates. Therefore, they hypothesized that additional regulatory systems must exist to allow Qrr1 expression in V. fischeri to initiate colonization of the light organ. In turn, the authors identified that disruption of the function of the sensor kinase BinK allowed Qrr1 expression even at high cell density. Through a series of cell-based reporter assays and an in vivo squid colonization assay, they concluded that BinK is also involved in Qrr1 regulation within the squid light organ. They went on to show that another sigma54-dependent response regulator SypG is also involved in controlling Qrr1 expression. The authors propose dual regulation of LuxO and SypG on Qrr could be a common regulatory mechanism on Qrr expression in a subset of Vibiro species.

      Overall, the experiments were carefully performed and the findings that BinK and SypG are involved in Qrr1 regulation are interesting. This paper is of potential interest to an audience in the field of QS and Vibrio-host interaction. However, experimental deficiencies and alternative explanations of the results have been identified in the manuscript that prevents a thorough mechanistic understanding of the interplay between QS and these new regulators.

      1. The premise that Qrr1 expression in the light organ has to be regulated by systems other than QS is unclear. In lines 108-109, it was stated that "...prior to entering the light organ, bacterial cells are collected from the environment and form aggregates that are densely packed", however, in lines 184-185, it was stated that "The majority of crypt spaces each contained only one strain type (Fig. 3B), which is consistent with most populations arising from only 1-2 cells that enter the corresponding crypt spaces". So, if the latter case is true (i.e., 1-2 cells/crypt), why Qrr1 could not be made at that time point as predicted by a QS regulation model?

      2. The involvement of the rscS* allele for the ∆binK mutant to show an altered bioluminescence phenotype is confusing. It is unclear why a WT genetic background was sufficient to show the high Qrr1 phenotype in the original genetic screen that identified BinK (Fig. 2A-B), while the rcsS* allele is now required for the rest of the experiments to show the involvement of BinK in bioluminescence regulation (Fig 2C). Is the decreased bioluminescence phenotype observed in rcsS* ∆binK mutant (fig. 2C) dependent on LuxU/LuxO/Qrr1/LitR? Could it be through another indirect mechanism (e.g., SypK as discussed in line 403)? A better explanation of the connection between RcsS/Syp and BinK and perhaps additional mutant characterization are necessary to interpret the observed phenotypes.

      3. In squid colonization competition assays (Fig. 3), it was concluded that the ∆qrr1 allele is epistatic to the ∆binK allele (line 204), and the enhanced colonization of the ∆binK mutant is dependent on Qrr1 (section title, line 162). This conclusion is hard to interpret. The results can be interpreted as ∆qrr1 mutation lowers the colonization efficiency of the ∆binK mutant which could imply BinK regulates Qrr1 in vivo. Alternatively, it could be interpreted that the ∆binK mutation increases the colonization efficiency of the ∆qrr1 mutant. Direct competition between single and double mutants in the same animals may resolve the complexity. And direct comparison of Qrr1 expression of WT and ∆binK mutants inside the animals, if possible, will also help interpret these results.

      4. Similar concern to above (#2), in Fig. 4, the link between BinK and Qrr1 regulation is not fully explored. What connects BinK and Qrr1 expression? Does BinK function via LuxU (or other HPT) to control SypG like the other QS kinases? And what is the role of other known kinases (e.g., SypF) in the signaling pathway? And did the authors test other bEBPs found in V. fischeri for their role in Qrr1 regulation?

      5. In addition to the genetic analysis, additional characterization of SypG is required to demonstrate the proposed regulatory mechanism: What is the expression level (and phosphorylation state) of SypG and LuxO at different cell densities? Does purified SypG directly bind to the qrr1 promoter region?<br /> c. How do these two bEBPs compete with each other if they are both made and active?

      6. The molecular OR logic gate is used to describe the relationship between LuxO and SypG, but this logic relationship is not always true in all conditions (if at all). In WT, deletion of luxO completely abolished Qrr1 expression (Fig. 4C). Even in the binK mutant, LuxO still seems to be the more prominent regulator (Fig. 4D) as deletion of luxO already caused a smaller but significant drop in Qrr1 expression. The authors may need to use this term more precisely.

    1. Reviewer #3 (Public Review):

      This paper addresses the mechanism underlying a well-documented finding whereby the numbers of resident macrophages increase in dorsal root ganglia following peripheral nerve injury. It delineates the relative contribution of monocyte recruitment via circulation and local proliferation. The paper is clearly structured and written, and the data overall support the main conclusion that the increase in nerve-associated macrophages is primarily driven by proliferation, not monocyte recruitment. Its main weakness is that the question that is being asked is rather restricted, so the additional insight gained for the field will be incremental. It would be particularly interesting in the future to address whether the existence of a protective barrier indeed is the reason peripheral cells are not recruited to the nerve injury lesion and to assess e.g. whether forced breaching of this barrier results in monocyte influx and altered injury response.

    1. Reviewer #3 (Public Review):

      Ogasawara and Ueda investigated the role of ATP-binding cassette A1 (ABCA1) in regulating the recruitment of endoplasmic reticulum (ER)-anchored cholesterol transporter, Aster-A/GRAMD1a, to ER-plasma membrane (PM) contacts in HeLa cells using several techniques, including fluorescence-activated cell sorting (FACS) and live-cell imaging. Firstly, they show that Aster-A is recruited to ER-PM contacts upon cholesterol loading (by treating cells with cholesterol/MCD complex) or sphingomyelin hydrolysis (by treating cells with sphingomyelinase to increase cholesterol accessibility in the PM) as previously reported by the Tontonoz lab and the Saheki lab. Secondly, by assessing the binding of Alexa647-conjugated D4 domain of PFO, a probe that detects the accessible (i.e., chemically active) pool of cholesterol, to the outer leaflet of the PM via FACS and by monitoring the binding of EGFP-tagged D4H (a variant of the D4, which detects accessible cholesterol with higher sensitivity) to the inner leaflet of the PM via total internal reflection fluorescence (TIRF) microscopy, they confirm their lab's previous findings showing that ABCA1 flops cholesterol from the inner to the outer leaflet of the PM [in addition to its classical role in exporting cholesterol for the generation of high-density lipoprotein (HDL)]. They then find that overexpression of ABCA1, but not ABCA1 carrying ATP hydrolysis-deficient mutation, inhibits the recruitment of Aster-A to ER-PM contacts upon treatment of cells with either cholesterol/MCD complex or sphingomyelinase. They also show that overexpression of ABCG1, which flops cholesterol similar to ABCA1, results in the similar inhibition of Aster-A recruitment to ER-PM contacts.

      Overall, the combined approaches using FACS and TIRF-based imaging provide good evidence to support that ABCA1, when overexpressed, inhibits the recruitment of Aster-A to ER-PM contacts by its function to flop cholesterol from the inner to the outer leaflet of the PM. However, the major weakness of this study is the lack of the analysis of cells with normal expression of ABCA1 (without overexpression). This study also does not explain the specificity of their findings to Aster-A, while the findings could potentially be more generalized to other Aster proteins.

    1. Reviewer #3 (Public Review):

      This manuscript addresses the question of how neuronal numbers are determined and whether invariant cell patterns reflect deterministic developmental programmes or the reliability of cell interactions and non-autonomous processes. This question has long been addressed and there is previous evidence that non-autonomous, trophic mechanisms - including by EGF ligands - maintain cell survival in insects, just as they do in mammals, but further evidence is necessary to reaffirm the shift in the wider understanding of fundamental principles of development. This work is a beautiful contribution in support of the above notion. This work shows that non-autonomous control of neuronal survival and differentiation involves a relay mechanism through two distinct glial cell types, enabling the specification of distinct neuronal classes. This is a very nice demonstration of the importance of interactions and coupling between interacting cell populations. The work builds on the previous publication by this author, Fernandes et al in Science 2017 "Glia relay differentiation cues to coordinate neuronal development in Drosophila", where they showed that photoreceptors produce the EGFR ligand Spi, which is received by wrapping glia, which then secretes insulin to promote differentiation of L1-L4 neurons. There, they showed that some of the lamina neurons die and EGFR is required to maintain their survival. With the current manuscript, the authors show that a second class of glia, the outer chiasm giant glia (xg{degree sign}), receive the EGF ligand Spi from photoreceptors, in turn, produce Spi and Col4a1 to activate proximal L5 neuron differentiation from lamina precursor cells, L5 neurons, in turn, produce argos, an antagonist of EGFR signalling, which by preventing survival signalling in distal L5 neurons cause their apoptosis.

      The strength of this manuscript is its beautiful cell biology and microscopy data, where cellular events can be seen with single-cell resolution. The images and data are of excellent quality. The clear narrative supports the concept that stereotypy arises from reliable non-autonomous trophic interactions. The weakness is that novelty is limited as the overall idea was already presented in the Science paper, the current work completes the details of the original model and there is data overlap. There are also technical issues, which if solved, would strengthen the evidence in support of the claims, and the quantitative analysis would benefit from improvement.

      1. There is considerable overlap with Fernandes et al 2017 Science paper: (1) That EGFR signalling is required for L5 neuron survival had been shown in their Fernandes et al 2017 Science paper, as over-expression of p35 rescued apoptosis caused by EGFRDN. Now, using Dronc mutants in the current manuscript is an equivalent experiment. (2) In Fernandes et al 2017 Science, they over-express activated MAPK in lamina neurons (Fig.1G), and in the current, they over-express its target Pnt-P1 (Fig.1I) - equivalent experiment. (3) Figure S1 reports Lamina>MAPKACT rescues Bsh and Spl2 positive neurons. These data are similar to those reported in Fernandes et al 2017 Science, where they showed the rescue of lamina neurons with this same genotype. (4) rho3 mutants cannot secrete Spi and L1-4 cannot differentiate and only a few L5 do (Fernandes et al 2017 Science), they then rescued this phenotype including L5s by over-expressing EGFRACT or Ras in wrapping glia (Figure 2F-I). With the submitted manuscript, they rescue with rho3 over-expression in photoreceptors - genetically different, but rather similar, as together they demonstrate that rescue of L5 requires rho or spi. These close similarities reduce the appeal and novelty of the current manuscript.

      2. Establishing the cells expressing spi, argos, Col41a and Ddr is key to supporting the hypothesis. The authors claim that they confirmed the best screen candidates by testing their expression using enhancer trap lines. What is the evidence that these enhancer trap reporters reproduce the endogenous expression patterns of these genes? A description of their location in the loci and potential drawbacks should be provided and discussed.

      Fig.4A and Fig.S3K do not demonstrate that aos-lacZ and Ddr-lacZ are in L5 neurons, and showing this with Bsh and Spl2 as they do for other data would support the claim that L5 neurons receive Col4a1 and distal L5 neurons can receive aos.

      Fig.S3M uses HCR in situ to show that spi mRNA is found in xg{degree sign} glia. However, the given images are not convincing. Since in situs detect mRNA, wouldn't the nuclear signal correspond to two sites of transcription, whereas a more abundant signal would be expected in the cytoplasm? Instead, the nucleus contains as many spots as the surrounding background and there is no clear signal in the cytoplasm. The authors must provide separate channels and convincing evidence that spi mRNA is present in xg{degree sign} glia or remove/weaken the claim (ie use only the GAL4 evidence).

      3. Involvement of Spi does not seem to have been entirely unresolved. They show that over-expression of rho3 in photoreceptors in rho 3 mutants rescued L5 neurons, suggesting that Spi from photoreceptors can rescue L5 neurons. As this is slightly different from what they saw before, what is the penetrance of these phenotypes? These phenotypes have not been quantified (other than providing sample size) and the incomplete penetrance of phenotypes could explain both observations.

      They claim that whereas L5 neurons are lost in xg{degree sign}>EGFRDN over-expressing glia, concomitant over-expression of Spi rescues L5 neurons. Also, over-expression of spi with xg{degree sign}>spi clearly results in ectopic L5 neurons. However, in Fig.3P they show rescue with membrane-tethered m.spi and not secreted s.spi. Why was secreted s.spi not used instead? How does membrane-tethered spi from glia reach to rescue distal L5 neurons?

      To support the involvement of spi in promoting survival of proximal L5 in wild-type, a loss of function experiment would be required e.g. xg{degree sign}>spi-RNAi, and visualise apoptosis with Dcp1 and remaining L5 neurons.

      4. Quantifications are incomplete in places and statistical analysis is incorrect in places. For genotypes that are not quantified in graphs (ie cell number), sample sizes have been provided, but phenotypic penetrance has not (Fig.1F dronc-/-; Fig.2K, L rho3 and rescue) and this is required to report variability.

      Fig.2I, J: A quantification is provided within the text for apoptosis caused by xg{degree sign}>EGFRDN, with 5.93{plus minus}0.18 Dcp1 cells per column (N=19). However, this number alone does not mean much unless it is compared to Dcp1 in wild-type. Apoptosis in wild-type is shown but not quantified in Fig.2I. A comparison of Dcp1 counts in control and xg{degree sign}>EGFRDN is required and validated with statistical analysis.<br /> Fig.S3L, P: authors claim that over-expression of spi in xg{degree sign}>EGFRDN does not rescue nuclear dpMAPK in xg{degree sign}, but it does in L5 neurons. However, the quantification of these data in Fig.S3L shows that nuclear:cytopl dpMAPK levels are not statistically significantly different from xg{degree sign}>EGFRDN. No evidence has been provided of how this single piece of data supports both contradictory claims. The authors must either quantify accurately and separately dpMAPK in xg{degree sign} glia and L5 neurons - it is unclear how this could be done from the data provided - or remove or modify the claim to adjust accurately to the data.

      Statistical analysis needs revising. It is unclear why they use non-parametric tests throughout, are data always not normally distributed? The use of bar charts, means, and s.e.m. combined with non-parametric tests does not faithfully represent the data, and box plots or other displays (eg volcano or dot plots, etc) that show the distribution would be more appropriate. And multiple comparison corrections are required. For example, if Fig.S3F is a Kurskal Wallis ANOVA (should be, but it is not stated explicitly), then this requires multiple comparison tests to a fixed control (post hoc Dunn test), and the figure legend should provide the p-value for the ANOVA. Fig.3K, P use Mann Whitney test, whereas these graphs have both more than 2 sample types and therefore should be Kruskal Wallis ANOVA (if distributions are not normal, if they are normal they should be One Way ANOVA), and Dunn post hoc comparison to fixed control, box plots, and no s.e.m as above.

    1. Reviewer #3 (Public Review):

      In this elegant genetic study, Bailon-Zambrano et al. draw on classical genetic concepts to address the clinically pertinent question of how genetic variants in the same gene can yield wildly different phenotypes in different individuals. They focus on the Mef2c gene, which is required for craniofacial and cardiac development in humans and model organisms yet shows highly variable phenotypes across and within individuals. Previous work by this lab had established that zebrafish mef2ca craniofacial phenotypes are highly variable and, importantly, that this variability is heritable and can be selectively bred for low vs. high penetrance. The authors hypothesize that vestigial expression of paralogous genes variably compensates for loss of mef2ca, such that individuals with higher levels of paralogous genes will show lessened severity and vice versa. To test their hypothesis, they methodically quantify the penetrance, expressivity, and variability of all known mef2ca-associated craniofacial phenotypes in fish carrying 1) different mef2ca mutations, 2) the same mutation but after selecting for high vs. low penetrance for many generations, and 3) mef2ca mutations combined with mutations in paralogous genes. They find that not only does allele severity directly correlate with variation, but also that different paralogs buffer the severity and variability of different craniofacial phenotypes. Another particularly interesting finding is that some of the craniofacial phenotypes are apparent even in mef2ca wild-types from the high penetrance strain, which they explain by the very low expression of paralogs on this background. A weakness of the study is that the authors do not directly show whether paralog expression is increased in the low-penetrance strain relative to the initial, unselected genetic background. It is therefore not clear whether the selection for low penetrance worked in this manner, as the authors imply. Overall, the authors have achieved an important step forward in understanding the genetic basis for the high variability of human faces among both healthy individuals and those with craniofacial anomalies.

  3. May 2022
    1. Reviewer #3 (Public Review):

      Rhodes et al. explored novel signalling peptides by searching genes encoding small proteins having signal peptide, which are transcriptionally induced upon biotic elicitor treatments in Arabidopsis thaliana. They found that small potentially secreted proteins, designate as CTNIPs based on the conserved sequence motif, are transcriptionally induced upon 7 different elicitors. In A. thaliana, 5 CTINPs are encoded in the genome, and CTNIP4 is strongly induced upon the elicitor treatments. Chemically synthesized signal peptide-deleted CTNIP proteins except for CTNIP5 show the activities to induce Ca2+ influx and MAP kinases phosphorylation in A. thaliana, which are the hallmarks of elicitor-induced immune signalling. The authors found that CTNIP4 can induce ROS burst in a BAK1-dependent manner in A. thaliana, suggesting that CTNIP4 receptor uses BAK1 as a co-receptor for CTNIP4-induced signalling. Moreover, they show that the C-terminal 23 amino acids of CTINP4 is sufficient to induce the responses, and the conserved 2 Cys residues in this C-terminal region is required for the activity. Based on these findings, the authors further explored a CTNIP receptor by identifying proteins that interact with BAK1 upon CTNIP4 treatment using an IP-MS approach. This approach identified HSL3, which is a leucine-rich repeat receptor-like kinase (LRR-RLK), as a receptor candidate. The authors elegantly demonstrate that HSL3 is a receptor of CTNIPs in A. thaliana by taking complementary biochemical and genetic approaches. They provide some evidences that CTNIP4-HSL3 pathway regulates root growth of A. thaliana. Lastly, the authors proposed that the HSL3-CTNIP signalling module is evolutionarily ancient, which appeared before the divergence of angiosperm species.

      The conclusions of this paper regarding the CTNIP-HSL3 pair in A. thaliana are well supported by data. The identification of the CTNIP-HSL3 pair is very significant in the area of plant research.

      Weaknesses of this paper would be<br /> 1) There is no evidence provided that CTNIPs are actually secreted from plant cells. And, mature forms of CTNIPs are not examined. And, thus, there is space for discussion whether CTNIPs function as secreted peptide hormones. However, generally speaking, addressing these are rather challenging.<br /> 2) The CTNIPs in A. thaliana are initially screened and identified based on the inducibility by biotic elicitors. However, contributions of the CTNIP-HSL3 module to disease resistance are not examined.<br /> 3) The authors have performed a phylogenetic analysis using full-length sequences of the receptor kinases. However, in order to discuss co-evolution of ligand-receptor pairs, it would be more appropriate to use ectodomains of the receptor kinases for the purpose. Actually, the phylogenetic tree in this paper is different form the trees in the published study (Furumizu et al. doi:10.1093/PLCELL/KOAB173), which used ectodomains for the analysis. Conclusions in this paper can be drawn differently. Based on Furumizu et al., HSL3-related LRR-RLKs in monocots are diversified and less related to HSL3 homologs in dicots. This raise a question whether HSL3 homologs in monocots are HSL3 orthologs to draw the conclusion that the HSL3-CTNIP module is conserved and diversified among angiosperms. It is favourable to test and show that CTNIP-HSL3 combinations from monocots also function as the functional module, for instance, using the Nicotiana benthamiana system. Related, testing one pair each for A. thaliana and M. truncatula is not sufficient to deliver the conclusion related to a co-evolution of ligand-receptor specificity because A. thaliana has 5 CTNIPs and Medicago truncatula has 7 HSL3 homologs and 5 CTNIPs, and thus different combinations may still function.

    1. Reviewer #3 (Public Review):

      The major strength of the report lies in its study of informative KO mice and targeted inhibitors to validate the involvement of Piezzo1 in host dendritic cells on T cell response and tumor growth outcomes. This allows the authors to confirm the importance of specific downstream mediators and signaling pathways in study results and to develop an operating paradigm for mechanisms of action mediated by Piezzo1 in DCs. The conclusions reached by the authors appear appropriate and represent an advance in the field.

    1. Reviewer #3 (Public Review):

      In this study, Kamal et al. use canonical amyloid and chitin-bindings dyes and available gene expression datasets to develop a spatiotemporal blueprint for the C. elegans pharyngeal cuticle. They observe three known and three novel families of low complexity protein families expressed in successive waves to detach and reconstruct pharyngeal cuticles during molts. They predict that these IDR-rich proteins promote phase separation and malleability of the pharyngeal cuticle.

    1. Reviewer #3 (Public Review):

      Kuey et al describe an interesting new biochemical system that allows the chemically induced recruitment of clathrin to various organelles of eukaryotic cells. Their major focus is on recruitment to outer mitochondrial membranes. Upon recruiting clathrin to these surfaces, bright puncta of clathrin are formed, which subsequently form buds and vesicles that separate from the parent membrane, as confirmed by fluorescence and electron microscopy. The authors then ask what is responsible for the fission of these vesicles. They knockdown dynamin, and the mitochondrial dynamin, drp1, with little effect on the efficiency of fission. From these results, they conclude that fission is spontaneous, likely owing to the clathrin coat itself. The authors also examine the recruitment of clathrin adaptor and accessory proteins to these mitochondrial buds, finding that only epsin and fcho are recruited in measurable quantities. Further, when these proteins are knocked down, the number of fission events is unchanged, suggesting that they did not play a major role in membrane vesiculation in this specific setting.

      Overall the experimental work is innovative, clear, and well controlled from my perspective as a biophysicist. That being said, cell biologists will have a clearer understanding of the necessary controls and should be consulted and deferred to on this point.

      Unfortunately, the authors spoil the considerable appeal of their paper with a discussion section that is highly speculative and at times illogical. Overall it promotes an over-extension of the authors' findings to questions that are not addressed by their results. The most significant issue is the authors' assertion that their data prove that the many adaptors of the clathrin pathway, many of which have been directly shown to bend membranes to very high curvatures, are only "modulators" rather than true drivers of membrane bending in cells. The authors should consult the recent preprint of Cail and Drubin, which shows the exact opposite: https://www.biorxiv.org/content/10.1101/2021.07.15.452420v3. Note: I am not an author of this paper. Here the authors are also working in mammalian cells and have pre-defined the curvature by culturing cells on top of ridged substrates. They make a nearly complete knockdown of the clathrin heavy chain with the result that clathrin is no longer detected at the plasma membrane. Nevertheless, they observe that puncta form and vesiculate from the membrane surface, which is enriched in the many adaptor proteins of the clathrin pathway. These results show that when curvature is pre-imposed, as is the case (and to a very similar degree) for budding from mitochondria, clathrin is NOT required. Taking the two papers together, my interpretation is that BOTH clathrin AND adaptors make substantial contributions to membrane curvature. When curvature is pre-imposed, EITHER clathrin or adaptors can drive vesiculation. However, on the relatively flat plasma membrane, BOTH clathrin and adaptors are required, as has been shown in many studies.

      A second major issue is the authors' attempt to put forth a biophysical model of bending. Here they simply compare clathrin's estimated contribution to curvature energy with the energetic cost of membrane bending. Notably, this model overlooks the sizable energy barrier to initiating the initial curvature. The authors should look at the work of Derenyi and Prost, among others to understand this better. But putting that significant issue aside, the authors argue that they can use a very low membrane bending rigidity of 15 kbT to account for the cost of bending the mitochondrial membrane. 15kbT would be a relevant value for membrane consisting of pure, unsaturated lipids like DOPC or at most POPC. This is certainly a vast underestimate, given that the mitochondrial membrane, like all biological membranes, is filled with proteins that substantially raise its rigidity. 50kbT or greater would be a more relevant value, which would reverse the authors' conclusions.

      A third major issue is the authors' dismissal of molecular crowding as a possible explanation for their results. They acknowledge the molecular bulk of the engineered adaptor that they use for the recruitment of clathrin. However, they conclude that because the adaptor alone does not cause membrane budding, crowding cannot contribute. In making this statement, the authors reveal a fundamental misunderstanding of the crowding hypothesis. In order for crowding to occur, the adaptor proteins need to be clustered together within some sort of boundary. Clathrin can potentially serve in this role. If clathrin is not present, the adaptors diffuse apart and the steric pressure returns to zero. So, the authors have not presented data to evaluate the contribution of crowding to the curvature of the structures that they observe. Instead, they should measure the stoichiometric ratio of linkers to triskelia and determine what fraction of the membrane below the coat is covered by adaptors. When this fraction exceeds 20-30%, steric pressure will rise dramatically and crowding will make a significant contribution.

    1. Reviewer #3 (Public Review):

      This work aims to investigate the role of self-assembly in the regulation of enzyme activity of E. coli PRPS (EcPRPS). EcPRPS is an important enzyme in the biosynthesis of nucleic acids and some amino acids, forms micron-sized self-assemblies in cells (cytoophidia), is allosterically inhibited by ADP, and is activated by inorganic phosphate, Pi. The authors set out to investigate the structure and function of the filamentous form of the enzyme responsible for cytoophidia formation.

      The authors present two new, high-resolution cryo-EM structures of filamentous forms of EcPRPS, each assembling via unique stacking of EcPRPS hexamers. One form, type A, was formed in the presence of ATP and Mg2+, and the cryo-EM map was interpreted as containing one ADP and one R5P (ribose 5-phosphate) in the active site (as well as two Mg2+). The substrates of EcPRPS are ATP and R5P and the ADP occupies the expected ATP binding site, though neither ADP nor R5P was supplied in the experimental solution. Surprisingly, a second ADP molecule is also identified, bound near the active site at a location named site 2. In addition, one Pi is bound in the canonical allosteric site, site 1. A second type of filament (type B) was formed in the presence of Pi and contains two Pi, one in site 1 and one in the R5P binding site of the active site.

      Analysis of these two structures revealed a significant change in the positioning of a segment of the enzyme, named the Regulatory Flexible (RF) loop. In the type A filament structure, with the active site occupied by ADP and R5P, the RF loop interacts with the ADP in site 2. In the type B structure, with an empty site 2, the RF loop sits in a different position and occupies the ATP binding site of the active site. The suggestion made by these observations is that the binding of ADP in site 2 stabilizes the RF loop such that ATP may bind the active site, and without ADP in site 2, the loop will block ATP from binding the active site. This suggests that ADP is not merely an allosteric inhibitor, but could also act as an activator.

      As for allosteric site 1, which is occupied only by Pi in both structures, the authors suggest it binds ADP as seen in structures of homologous PRPS enzymes, and that this binding is the cause of allosteric inhibition by ADP. Structural comparisons suggest that the binding of ADP to site 1 is controlled by the position of the RF loop, which in turn can be controlled by interactions with ADP at site 2. The authors suggest that the binding of ADP to site 1 and to site 2 is mutually exclusive via this mechanism of RF positioning.

      In addition to the structural analysis, a strength of the paper is the use of point mutations to investigate the effects of eliminating one or both types of filaments on enzyme activity, cell growth, and cytoophodia formation. When either one or the other type of filament form is knocked out, cytoophidia still forms, but when both types are knocked out using a double-mutant, no cytoophidia form. This suggests that both filament types form in cells and are responsible for cytoophidia formation. Effects on cellular growth were nominal, and largest only with the double-mutant, which grew much more slowly than the wild-type enzyme. At longer time points only, each single point-mutant showed faster growth than the wild-type enzyme. These results are interpreted by the authors to mean that both types of filaments form in cells and have functional consequences.

      In activity assays, the double-mutant, which does not form either type of filament, showed a very high level of sensitivity to allosteric inhibition by ADP, suggesting that filament formation mitigates this to some degree (and that filament formation is not necessary for allosteric inhibition by ADP). The absence of type B and presence of type A filaments leads to lower sensitivity to allosteric inhibition by ADP: lower than wild type EcPRPS and lower than when neither filament forms. Hence the presence of the type A and absence of type B filament leads to greater enzymatic activity and less allosteric inhibition by ADP than no filamentation or when both types are present. Absence of the type A filament appears similar to the double-mutant (which does not have filament) in that it is very sensitive to ADP inhibition. The conclusion is that the type A filament mitigates allosteric inhibition by ADP, while the type B filament is allosterically inhibited by ADP (similar to the non-filamenting enzyme).

      The work has a few weaknesses. First, R5P was not included in the solution used to prepare the type A filaments, yet is built into the cryoEM map. The map around the modeled R5P is not shown, making it difficult to assess this interpretation. Second, no filament structure with ADP in site 1 has been determined. Instead, the structure of a related PRPS with ADP in site 1 is shown, but the position of the RF loop in that structure does not occupy the ATP binding site as implied by the authors to be the function of this conformation (i.e. when ADP is bound in site 1).

      The authors also claim that the type B filament enhances inhibition, but in fact, shows similar inhibition to the enzyme which cannot form filaments. However, when type A filaments are present, it appears that type B filaments are necessary to allow for some allosteric inhibition by ADP. Though not discussed, it may be that levels of the two types of filaments are altered to control overall enzyme activity. In addition, much of the discussion deals with interpretations about binding affinities of ligands to various sites, but all evidence used is indirect, as no binding affinities are measured directly.

      Another weakness is in the investigation of site 2. The authors claim that ADP binding to site 2 enhances ATP binding in the active site, however, the mutation designed to disrupt ADP binding to site 2 results in reduced ATP in the absence of ADP, not in its presence.

      Finally, a discussion of the role of Pi, and how the choice of the two filamentous forms is chosen is not addressed in the study. The authors show compelling evidence to support their coexistence in vitro and in cells, and differing activity, however, how and why one is formed over the other has yet to be uncovered.

    1. Reviewer #3 (Public Review):

      The work proposed by DeKraker and colleagues is the extension of a long-standing research program by the senior author's laboratory to using histologically defined methods to inform surface-based measures that better conform to the unique "rolling" anatomy of the human hippocampus and its subfields. The group has previously the hippocampal "unfolding" technique as a means to capture different metrics and their variation along a two-dimensional surface manifold. The current work improves the implementation of this software using a 'U-Net' deep convolutional neural network as means to successfully identify variation in the subfields that cannot be seen using standard techniques (like atlas or multi-atlas based segmentation techniques. In terms of novelty, and given the previous work from this group its unclear what the novelty of this particular work is. Is it simply the integration of the U-Net into this application? Is it demonstrating generalizability or superior performance to previous pipelines? There are no putative demonstrations of the applicability of the pipeline as well.

      In general, the paper is well written, but there are multiple areas that I have some issues with following the logical flow of what is being proposed. For example, the paper begins by demonstrating multiple metrics that are projected onto the hippocampal flatmap that includes thickness, myelin, curvature, gyrification, etc. It is unclear as to what information the authors want to convey here. This is the first mention of many of these multiple metrics as well and therefore their relevance is ultimately not extremely clear. As a result, it is hard to support their claim that "differences in morphological and quantitative features can be seen across the hippocampus, particularly across the subfields" as the goals of this particular figure are not at all clear.

      Line 147: It is not totally accurate to state that ASHS makes use of multi-atlas registration as it also uses AdaBoost to correct for segmentation inaccuracies.

      For the FreeSufer and ASHS comparisons - is it possible to provide some quantification of errors or anything like that? I think it would be helpful to quantify the differences in a more accurate manner. If this is in a previous publication and I missed it, it could be useful to reiterate here. The qualitative difference is nice - but there is room to compare them more quantitatively to one another.

      For the validation of the U-NET, details on the manual segmentation protocol, who did it, and its reliability are crucial. Training/testing paradigms would be helpful here. So would Bland-Altmann plots. I think in general the validation of these segmentations is quite poor - so more metrics that demonstrate the segmentation beyond dice overlaps would be helpful.

      It is unclear how generalizable the method is outside of HCP acquisitions.

    1. Reviewer #3 (Public Review):

      The authors present a study on the collective behaviour of E.coli during migration in a self-generated gradient. Taking into account phenotypic variation within a biological population, they performed experiments and complemented the study with a predictive model used for simulation to understand how bacteria can move as a group and how the individual bacterium defines its own position within the group.

      They observed experimentally that phenotype variation within the bacterial population causes a spatial distribution within the chemotactic band that is not continuous but formed by subpopulations with specific properties such as run length, run duration, angular distribution of trajectories, drift velocity. They attribute this behaviour to the chemotaxis ability, which varies between phenotypes and defines a potential well that anchors each bacterium in its own group. This was proven by the subdiffusive dynamics of the bacteria in each subgroup. Many cases were studied in the experiments and the authors present many controls to clearly demonstrate their hypothesis.

      These are interesting results that prove how a discretised distribution can produce continuous collective behaviour. It presents also an interesting example in the field of active matter about collective behaviour on a large scale that is generated by a different behaviour of individuals on a much smaller scale. However, it is not clear how the subpopulations can be held together in the group. Moreover, a link between bacterial dynamics and the biological necessary mechanism is not clear.

      They formulate a theoretical description based on the classical Keller-Segel model. Langevin dynamics was used to describe bacterial activity in terms of drift velocity for simulation, which agrees very well with experimental observations.

      One can appreciate the interesting results of the study describing Ecoli chemotaxis as a mean-reversion process with an associated potential, but it is not clear to what extent the results can be generalised to all bacteria or rather relate to the strain the authors investigated.

    1. Reviewer #3 (Public Review):

      The authors convincingly show by RNAseq, that microbiota ablation by antibiotics treatment deprives WT mice of a tonic and interferon lambda induced interferon stimulated gene (ISG) response. Alongside they test interferon lambda receptor ko mice, which do not show a tonic induction of ISGs or a response to antibiotics dependent removal of microbiota. Induction of ISGs in intestinal tissue depends on TLR signaling, which would support a microbiota dependent induction. Intriguingly this ISG response is highly localised in very focussed areas of the intestine. Fecal transfer or LPS treatment restore the antibiotics induced phenotype of basal ISG expression loss and partially restore antiviral protection in intestinal epithelial cells.

      These data complement recent publications [PMID: 32380006][PMID: 31269444] of tonic type I interferon signaling induced by microbiota could explain a series of publications showing the importance of microbiota for antiviral defence in the gut.

      The authors build a line of arguments based on the correlation of data from IFNLR -/- mice and ABX treated mice. They omit however an important alternative explanation, which might be qualitative differences in the microbiota composition between IFNLR -/- and WT mice.

      An explanation for the focussed ISG response in the intestine and how this explains the reduced resistance to enteric viruses is not provided.

    1. Reviewer #3 (Public Review):

      This paper describes an analysis of fluorescence lifetime imaging (FLIM) of NADH in mitochondria in intact mouse oocytes, using a mathematical model to interpret the fluorescence data to infer mitochondrial NADH redox fluxes. The authors measure FLIM data for varying oxygen concentrations and using several other perturbations to mitochondrial respiration, in order to infer consequential changes to key mitochondrial metabolic fluxes. One striking observation is of subcellular spatial gradients in the inferred metabolic flux across the oocytes.