Reviewer #3 (Public Review):

The regulation of transporters in many physiological systems is poorly known. Here, Forster and colleagues describe how activity of an inorganic carbon transporter, SbtA, in the bacterial carbon concentrating mechanism is regulated by the PII protein SbtB. Although there is now significant structural knowledge of the system and many potential SbtB-regulating small molecule effectors are known, Forster and colleagues clarify, how the adenylate charge in the cell, rather than any single metabolite, is the important regulatory effector. This is critical for the endogenous function, as the cyanobacterial host undergoes dramatic changes in adenylate charge over the course of a diurnal cycle and this result explains how the channel is regulated to efficiently function in CO2 assimilation. The manuscript is generally clear and the data generally supportive of the conclusions as written. However, there are several instances where additional clarification and/or experiments are needed to confirm the major findings of the paper.

Reviewer #1 (Public Review):

Here, Ensinck et al. investigated the composition of the yeast mRNA m6A methyltransferase complex required for meiosis. This complex was known to contain three proteins but is much more complex in mammals, insects, and plants. Through IP-MS analysis, they identified three more proteins Kar4, Ygl036w, and Dyn2. Of these, Kar4 and Ygl036w are homologous to Mettl14 and Virma, respectively, and, like the previously described factors, are essential for m6A deposition, mating, and binding of the reader Pho92 to mRNA during meiosis by evidence acquired with appropriate methodology. Dyn2 is a novel factor not described for any m6A complex and is not essential for m6A deposition, mating, and binding of the reader Pho92 to mRNA during meiosis.

In addition, detailed analysis of the Slz1 revealed homology to the mammalian factor m6A complex member ZC3H13 to comprise a conserved complex of five proteins, Mettl3, Mettl14, Mum2/WTAP, Virma, and Slz/ZC3H13. When co-expressed in insect cells, they co-purify stoichiometrically, and the presence of Mum2 as a dimer is also indicated, as shown for WTAP.

Complementary to these data, they show that the stability of the individual complex members is affected in mutants supporting that they are stabilized through complex formation.<br /> Furthermore, the authors then show that kar4 has additional roles in mating that are separable from its role through the m6A complex in meiosis.<br /> The authors employ appropriate methodology throughout to address their aims and present convincing evidence for their claims. The evidence presented here reinforces that the m6A complex is evolutionary and highly conserved, with a broad scope for its functional analysis in humans and model organisms.

Reviewer #2 (Public Review):

N6-methyladenosine (m6A), the most abundant mRNA modification, is deposited by the m6A methyltransferase complexes (MTC). While MTC in mammals/flies/plants consists of at least six subunits, yeast MTC was known to contain only three proteins. Ensinck, Maman, et al. revisited this question using a proteomic approach and uncovered three new yeast MTC components, Kar4/Ygl036w/Dyn2. By applying sequence and structure comparisons, they identified Kar4, Ygl036w, and Slz1 as homologs of the mammalian METTL14, VIRMA. ZC3H13, respectively. While these proteins are essential for m6A deposition, the dynein light chain protein, Dyn2, is not involved in mRNA methylation. Interestingly, while mammalian and fly MTCs are configured as MAC (METTL3 and METTL14), and MACOM (other subunits) complexes, yeast MTC subunits appear to have different configurations. Finally, Kar4 has a different role as a transcription regulator in mating, which is not mediated by other MTC members. These data establish an important framework for the yeast MTC and also provide novel insights for those studying m6A deposition.

Reviewer #1 (Public Review):

The current study was designed to test the hypothesis that neural circuit plasticity during adolescence can be targeted to restore cortical function under conditions of developmental disruptions that are relevant to psychiatric disorders. Specifically, the authors targeted the mesofrontal cortical dopamine system in 2 genetic mouse models of schizophrenia and performed optical recordings in combination with behavior and chemogenetic manipulations. Major findings and strengths include that stimulation of frontal dopaminergic projections in a limited adolescent time window can stably reverse defects in cortical neuronal activity and cognitive control in adulthood in 2 genetic mouse models of psychiatric disorders. While the precise postsynaptic mechanisms underlying the positive impact of adolescent mesofrontal dopamine stimulation were not address, another strength of this study is that the authors performed key manipulations using age and dose/intensity as dependent variables to show that the level of neural circuit activation during adolescence follows an inverted U-shape pattern. Collectively, this is a well-design study with many strengths and novel findings that are likely to positively impact a widespread of disciplines within the biological psychiatry and neuroscience field.

Reviewer #2 (Public Review):

The manuscript by Mastwal and colleagues explores how transient adolescent stimulation of ventral midbrain neurons that project to the frontal cortex may help to improve performance on certain memory tasks. The manuscript provides an interesting set of observations that DREADD-based activation over only 3 days during adolescence provides a fast-acting and long-lasting improvement in performance on Y-maze spontaneous alternation as well as aspects of neuronal function as assessed using in vivo imaging methods. While interesting, there are several weaknesses. First and foremost, it is not clear that the effects the authors are observing are mediated by dopamine. It has been clearly documented that the DAT-Cre line provides a better representation of midbrain dopamine cells in the mouse, particularly near the midline of the ventral midbrain (Lammel et al., Neuron 2015). This is precisely where the cells that project to the frontal cortex are located. Therefore, the selection of TH-Cre is problematic. It is very likely that the authors are labeling a substantial number of non-dopaminergic cells.

Reviewer #3 (Public Review):

In this manuscript, the authors use behavior, calcium imaging, and circuit modulation (DREADDs etc) to assess dopamine regulation of prefrontal cortical circuits in the mouse. The authors have previously established that activation of dopamine inputs to prefrontal cortex during adolescence can drive increases in mPFC DA bouton number and enhanced mPFC activity in WT mice. Here the authors use two mouse models - one with a reporter replacing the Arc gene, and another with knockout of the schizophrenia-associated gene Disc1, both of which are thought to have reduced prefrontal cortical activity. First they trained mice on a Y-maze and showed impaired performance in the Arc knockout. Then they demonstrated selective disruption of neuronal firing with calcium imaging at the time of the decision in the task. The Arc mice were found to have reduced dopamine bouton density, and adolescent activation of the DA neurons corrected this as well as the PFC firing and the behavior. Similar data were shown in the Disc1 KO. The data are well controlled and the authors use a number of leading edge methods.

Reviewer #1 (Public Review):

The authors aimed to contrast the effects of pharmacologically enhanced catecholamine and acetylcholine levels versus the effects of voluntary spatial attention on decision making in a standard spatial cueing paradigm. Meticulously reported, the authors show that atomoxetine, a norepinephrine reuptake inhibitor, and cue validity both enhance model-based evidence accumulation rate, but have several distinct effects on EEG signatures of pre-stimulus cortical excitability, evoked sensory EEG potentials and perceptual evidence accumulation. The results are based on a reasonable sample size (N=28) and state-of-the art modeling and EEG methods.

Although the authors draw a few partial conclusions that are not fully supported by the data (see below), I think that the authors' EEG findings provide sufficient support for the overall conclusion that "selective attention and neuromodulatory systems shape perception largely independently and in qualitatively different ways". This is an important conclusion because neuromodulatory systems and selective spatial attention are both known to regulate the neural gain of task-relevant single neurons and neural networks. Apparently, these effects on neural gain affect decision making in dissociable ways.

The effects of donepezil, a cholinesterase inhibitor, were generally less strong than those of atomoxetine, and in various analyses went in the opposite direction. The authors fairly conclude that more work is necessary to determine the effects of cholinergic neuromodulation on perceptual decision making.

1) I believe that the following partial conclusions are not fully supported by the data:

a) In the results section on page 6, the authors conclude that "Attention and ATX both enhanced the rate of evidence accumulation towards a decision threshold, whereas cholinergic effects were negligible." I believe "negligible" is wrong here: the corresponding effects of donepezil had p-values of .09 (effect of donepezil on drift rate), .07 (effect of donepezil on the cue validity effect on drift rate) and .09 (effect of donepezil on non-decision time), and were all in the same direction as the effects of atomoxetine, and would presumably have been significant with a somewhat larger sample size. I would say the effects of donepezil were "in the same direction but less robust" (or at the very least "less robust") instead of "negligible".

b) "In the results section on page 8, the authors conclude that "Summarizing, we show that drug condition and cue validity both affect the CPP, but they do so by affecting different features of this component (i.e. peak amplitude and slope, respectively)."<br /> This conclusion is a bit problematic for two reasons. First, drug condition had a significant effect not only on peak amplitude but also on slope. Second, cue validity had a significant effect not only on slope but also on peak amplitude. It may well be that some effects were more significant than others, but I think this does not warrant the authors' conclusion.

c) In the discussion section on page 11, the authors conclude that "First, although both attention and catecholaminergic enhancement affected centro-parietal decision signals in the EEG related to evidence accumulation (O'Connell et al., 2012; Twomey et al., 2015), attention mainly affected the build-up rate (slope) whereas ATX increased the amplitude of the CPP component (Figure 3D-F)."<br /> As I wrote above, I believe it is not correct that "attention mainly affected the build-up rate or slope", given that the effect of cue-validity on CPP slope was also significant. Also, while the authors' data do support the conclusion that ATX increased the amplitude and not the slope of the CPP component, a previous study in humans found the opposite: ATX increased the slope but did not affect the peak amplitude of the CPP (Loughnane et al 2019, JoCN, https://pubmed.ncbi.nlm.nih.gov/30883291/). Although the authors cite this study (as from 2018 instead of 2019), they do not draw attention to this important discrepancy between the two studies. I encourage the authors to dedicate some discussion to these conflicting findings.

2) On page 12 and page 14 the authors suggest a selective effect of ATX on *tonic* catecholamine activity, but to my knowledge the exact effects of ATX on phasic vs. tonic catecholamine activity are unknown. Although microdialysis studies have shown that a single dose of atomoxetine increases catecholamine concentrations in rodents, it is unknown whether this reflects an increase in tonic and/or phasic activity, due to the limited temporal resolution of microanalysis. Thus, atomoxetine may affect tonic and/or phasic catecholamine activity, and which of these two effects dominates is still unknown, I think.

Reviewer #2 (Public Review):

The authors attempt to show distinct contributions of selective attention and neuromodulators (both cholinergic and catecholaminergic) during a spatial attention task. To do this, they had participants perform a Posner cueing task using random dot motion stimuli, with a typical 80/20 split of valid to invalidly cued trials. In addition, they designed a within-subjects paradigm wherein participants took placebo (PLA), Donepezil (DNP), or Atomoxetine (ATX). Both behaviour and EEG measures were taken in order to investigate the interaction or lack thereof of Drug and Cue factors with respect to these measures, and relative timing of EEG differences to derive potential neuromechanistic similarities/differences. In this context, an interaction of Drug and Cue factors (e.g. faster valid vs invalid RTs in ATX vs PLA) might indicate a role of that neuromodulator in the mechanisms of spatial attention. This is in fact not what they found, rather most findings pointed towards a lack of interaction of Drug and Cue, hence the central thesis of the paper of distinct contributions of neuromodulator and selective attention.

Strengths:<br /> - The experimental design is well done, especially the blinding of the drug taken in each session. However, it is an important caveat to any results that participants were obviously aware they had taken an active drug in ATX condition (Supp Info).<br /> - The analyses are in general quite solidly performed, with most analysis choices relating to behaviour and EEG making sense, albeit with exceptions below.<br /> - The research question and how it relates to the experiment is very interesting, and the question worthy of consideration.

Weaknesses:<br /> - The main weakness of the paper lies in the strength of evidence provided, and how the results tally with each other. To begin with, there are a lot of significance tests performed here, increasing the chances of false positives. Multiple comparison testing is only performed across time in the EEG results, and not across post-hoc comparisons throughout the paper. In and of itself, it does not invalidate any result per se, but it does colour the interpretation of any results of weak significance, of which there are quite a few. For example, the effect of Drug on d' and subsequent post-hoc comparisons, also effect of ATX on CPP amplitude and others.<br /> - The lack of an overall RT effect of Drug leaves any DDM result a little underwhelming. How do these results tally? One potential avenue for lack of RT effect in ATX condition is increased drift rate but also increased non-decision time, working against each other. However, it may be difficult to validate these results theoretically.<br /> - There is an interaction between ATX and Cue in terms of drift rate, this goes against the main thesis of the paper of distinct and non-interacting contributions of neuromodulators and attention. This finding is then ignored. There is also a greater EDAN later for ATX compared to PLA later in the results, which would also indicate interaction of neuromodulators and attention but this is also somewhat ignored.<br /> - The CPP results are somewhat unclear. Although there is an effect of ATX on drift rate algorithmically, there is no effect of ATX on CPP slope. On the other hand, even though there is no effect of DNP on drift rate, there is an effect of DNP on CPP slope. Perhaps one may say that the effect of DNP on drift rate trended towards significance, but overall the combination of effects here is a little unconvincing. In addition, there is an effect of ATX on CPP amplitude, but how does this tally with behaviour? Would you expect greater CPP amplitude to lead to faster or slower RTs? The authors do recognise this discrepancy in the Discussion, but discount it by saying the relationship between algorithmic and CPP parameters in terms of DDM is unclear, which undermines the reasoning behind the CPP analyses (and especially the one correlating CPP slope with DDM drift rate).<br /> - The posterior component effects are problematic. The main issue is the lack of clarification of and justification for the choice of posterior component. The analysis is introduced in the context of the target selection signal the N2pc/N2c, but the component which follows is defined relative to Cue, albeit post-target. Thus this analysis tells us the effect of Cue on early posterior (possibly) visual ERP components, but it is not related to target selection as it is pooled across target/distractor. Even if we ignore this, the results themselves wrt Drug lack context. There is a trending lower amplitude for ATX at later latencies at temporo-parietal electrodes, and more positive for DNP, relative to PLA. Is this what one would expect given behaviour? This is where the issue of correct component identification becomes critical in order to inform any priors on expected ERP results given behaviour.

Given the issues above; mainly a) weak statistical evidence, b) contradictory behavioural and EEG evidence, and c) lack of theoretical background to inform priors on what to expect from the EEG results in order to develop a coherent narrative, I would say that what remains is moderate/incomplete evidence towards the thesis of the paper. This work is however a very fruitful effort at approaching the research question as to whether there is an interaction of neuromodulators and spatial attention. I commend the authors on a transparent and rigorous analysis of the current data.

Reviewer #2 (Public Review):

In this study, the authors explore the structure/function of the DCLK kinases, most specifically DCLK1 as it is the most studied to date. Recently, the C-terminal domain has garnered attention as it was found to regulate the kinase domain, however, the different isoforms retain additional amino acid sequences with as-yet-undefined functions. The authors provide an evolutionary and biochemical characterization of these regions and provide evidence for some functionality for these additional C-terminal sequences. While these experiments are informative they do require that the protein is soluble and not membrane-bound as has been suggested to be important for functionality in other studies. Still, this is a major contribution to understanding the structure/function of these proteins that will be important in future experimental designs.

Reviewer #1 (Public Review):

Ruby et al. have investigated patterns of age-specific mortality in the exceptionally long-lived naked mole-rat (NMR), under captive conditions. The authors first visited this topic five years previously with an unprecedently large data set and concluded that naked mole-rats are 'non-aging': because analyses of their survival did not detect an increasing mortality hazard with age. This result has obvious applied interest in humans because of its implications for maintaining health into later life. One criticism directed at this previous work was that a limited number 'old-aged' individuals in their data set (individuals in what might be expected to be the latter half of the life course) reduced the power with which to detect an age-related increase in mortality - or to convincingly demonstrate its absence. The current study revisits this topic with a larger sample across the life course. The authors also provide additional analyses that explore various predictors of mortality, including breeding status, body weight and colony size, and now also make direct comparisons to mortality patterns in other species of African mole-rat from the Fukomys clade (which share many convergent social and life history features). I found the analyses of Fukomys mortality particularly illuminating. However, while these additional analyses provide some useful context and can generate interesting discussion points about ageing patterns in an extremely unusual species, the principal issue at hand whether the absence of Gompertzian mortality in NMR is a robust pattern.

In this respect, a major limitation of the current study is that only 11% of the animals (n = 755) had died at the point of its conclusion- the remaining 89% being right-censored (n = 6138). This means that, as in the previous analysis, there are still relatively small numbers of individuals that have died in the older age classes (see Fig 1 for the high level of right-censoring between 15-20 years and the low numbers of deaths after this point, also Supp 1 for the raw data): the part of the life course where one would predict mortality rates to increase from an evolutionary perspective. Thus, while the authors claim very generally that the "demographic data has doubled", this in no way reflects whether the new data is informative to the question at hand, which relies on an ability to estimate death rates in older individuals accurately. If one looks more closely at the numbers which do matter, then one can see that the number of deaths in the data set has shifted from 447 in the former treatment (Ruby et al. 2018) to 755 currently, but that the number of later-stage deaths remains somewhat modest (and that this is probably reflected in the large confidence intervals for the mortality hazards at this time). I therefore remain unconvinced that the current study can rule out an exponential increase in hazard in older individuals.

The authors have also not provided any statistical evidence that the mortality hazard changes with age (or not), instead relying on visual comparisons of aggregated data. This is a fundamental problem and demands a more thorough treatment that compares survival models with different shape profiles. If anything, it seems that the hazard rate is declining with age - see Figures 1B & 2C, and while this may strengthen the authors argument if supported statistically, I would still wonder whether the higher mortality in early life - say 6 months to 3 years of age - is a consequence of the costs of early life development and that this is not a useful baseline against which to compare 'adult' mortality. It would also not overcome the data limitations identified above.

An additional concern is that the paper is selective in its presentation of previous work, with the authors focussing on results which support their main interpretations and glossing over those that don't. For example, the study refers to the fact that NMRs are resistant to various age-related diseases and do not show many age-related declines in physiology. Yet, while this argument of negligible senescence might hold generally, the literature contains various reports of later life declines in NMR physiology (Andziak et al. 2006; Edrey et al., 2011). Referring to work from your own group, Braude et al. (2021) write "several typical mammalian age-related lesions of muscles, bone, heart, liver, and eye, including sarcopenia, osteoarthritis, a decline in articular cartilage thickness of the condyles, lipofuscin accumulation in several organs, eye cataracts, and kidney fibrosis have been described in naked mole-rats older than 26 years (Edrey et al., 2011)". A more balanced treatment of physiology in extremely old individuals would prove constructive.

Another way in which the study fails to fully represent the literature is with respect to the divergence in ageing rates between breeders and non-breeders. This pattern has proved seductive for various mole-rat researchers because of its similarities to social insects and the suggestion that it is reproduction itself which delays ageing. While this is a clear possibility with some empirical support, it is important to also consider the question from the other way: which is to ask why non-breeders die at higher rates than breeders. For other cooperative breeders such as meerkats, the answer is clear: dominant, breeding individuals evict subordinates and once evicted from the group, the chances that these individuals will survive plummets (e.g. Cram et al. 2028). Is it possible that a similar form of dominance control might contribute the shorter life span of non-breeders in captivity? You reference Toor et al. (2020) elsewhere and this is relevant here again.<br /> Captivity also prevents non-breeders from dispersing when they would otherwise ordinarily do so (Braude 2000): is it possible that this also affects their mortality in captivity? Perhaps not being able to disperse induces chronic stress (see for example the discussion in Novikov et al. 2015). The idea that breeders show a lower intrinsic rate of aging is attractive, but many factors could contribute to this and alternatives should be considered unless they can be strongly refuted.

Lastly, it would be very beneficial to have more information on how individuals become breeders in the captive population/s. For the purposes of the analyses, individuals have been categorised as a breeder or a non-breeder based on whether they bred or not at some point in their life (i.e., they are a "breeder" for their whole life for the purposes of the Kaplan Meier curves and the estimation of mortality hazards). I think it is therefore important to rule out the possibility that only high-quality individuals become breeders and that this is what drives the contrast in breeder and non-breeder mortality. In short, is it the case that most breeders are created through the random pairing of a male and a female? Or do new breeders inherit the position once the old queen dies? The latter could lead to breeders being of generally higher quality, which might affect their mortality hazard independently of status.

Overall, I think that the authors can confidently conclude that any onset of actuarial senescence is heavily delayed in naked mole-rats, but the main conclusion that naked mole-rats "defy Gompertzian mortality" is based on inadequate evidence. It seems very possible that the inability to detect an increasing mortality hazard in such a long-lived species arises from data limitations. The central finding of the study should therefore be viewed very critically.

Refs:<br /> Anziak et al. (2006) Aging Cell 5:463-471.<br /> Braude et al. (2021) Biological Reviews 96: 376-293.<br /> Cram et al. (2018) Current Biology 28: 1-6.<br /> Edrey et al. (2011) ILAR Journal 52:41-53.<br /> Novikov et al. (2015) Biogerontology 16: 723-732.<br /> Toor et al. (2020) Animal Behaviour 168: 45-58.

Reviewer #2 (Public Review):

Ruby et al. investigated whether demographic aging was absent in the naked-mole rat (Heterocephalus glaber); an exceptionally long-lived small mammal that appears to challenge Gompertzian patterns of increased mortality hazard with age. In particular, this study replicates a previous one in which the authors show that the mortality hazard does not increase with age as it is expected for mammals, especially small ones. The main motivation of this replication is to address the current controversy surrounding the "perpetual neoteny" reported by the authors. The study also extends to the exploration of the role of social factors on the observed patterns in mortality hazard across age and to a meta-analysis comparing mortality hazards across species of mole-rats which highlights the unique pattern of demographic aging (or the absence of) in naked mole-rats. This study is of broad interest to readers in the field of demography, aging, and life history evolution. The key claims of the manuscript state that naked-mole rats avoid an increase in mortality hazard as they age. Although this work raises new evolutionary questions concerning the unexpected gradual (or fully absent) increase versus Gompertzian increase in hazard among mammals, I also identified weaknesses that I discuss below.

Strengths:<br /> Sample sizes - The sample sizes across analyses are vast and the data curation described demonstrates careful thought during the data analysis processes.

Social factors - The analysis testing associations between body mass (as proxy for dominance) and colony size (as proxy for social competition) are novel and provide insights into potential evolutionary drivers for the observed lack of increase in mortality hazard.

Across species comparison - The analysis using Fukomys mole-rats offered a novel phylogenetic comparison of the mortality hazard across age and raises new evolutionary questions concerning the unexpected gradual versus Gompertzian increase in hazard. This study encourages new ones exploring alternative life histories among mammals.

Weaknesses:<br /> Censored data - A significant number of individuals remained alive (~50%) at the end of the study, and thus I wonder how much can the authors say about increased hazard if the individuals have not reach old ages. Maybe the individuals do live long and show increased hazard are very old ages.

Independence between studies - The study provides the replication of a prior study using the same captive population, but I understand that many observations are not independent across studies given repeated measurements. Although this provides reliability, I wonder how independent the conclusions are. This represents a weakness to me because we still do not know whether this is a unique evolutionary trait of this particular captive population. If this is the case, I agree this makes the population a great model for aging studies but do the authors findings have further implications across populations or species? I wonder if populations raised under different conditions would present similar patterns of mortality hazard across age.

Analysis - Another weakness concerns the analysis used. Authors make the claims that social hierarchy may affect mortality hazards and decide to explore associations between body mass and hazard. I wonder if a Cox regression model is more appropriate for the available continuous data, relative to a Kaplan-Meir method. A Cox regression will allow the authors to control for several continuous variables simultaneously, without the limitation of categorical assumptions. A Cox model could also be extended to time-varying covariates allowing for the hazard to change over time (if that is the case). If the authors understand that their approach is equivalent, I suggest a discussion on it. This also applies to the analysis on colony size.

In summary, I see value in this study. There is vast evidence for the penalty of becoming old among mammals. Thus, studies like this one reporting novel patterns are of high impact. I agree that such findings must be replicated and validated. I also see a lot of potential for the use of the available data for more extensive meta-analyses comparing life histories across social mammals or across species with similar use of habitat (underground). Such analyses may allow the authors to move beyond descriptions and discuss why such life history traits may have evolved. Yet, I am not sure how much novelty this study brings, relative to prior studies. It seems the authors may need more than 5 years to allow their individuals to reach older ages.

Reviewer #3 (Public Review):

As a follow up from a manuscript previously published (Ruby et al. 2018), the authors use basic survival analysis methods to estimate hazard rates on an extended dataset of naked mole rats. They conclude that naked mole rats do not show the common exponential increase in mortality that has been typified in most mammals.

In fact, this species has attracted great interest due to their extreme longevity, and the physiological mechanisms that have been associated with slower aging. As the authors show, this species shows unprecedented longevity, particularly considering their body size and phylogenetic location.

However, the data available and the methods used cannot support the conclusion of an absence of increase in mortality for adults. As the authors show, the survivorship curves, calculated using Kaplan-Meier estimators, do not reach below values of 0.5. In short, nothing can be said about hazard rates after the age of median life expectancy. What the authors show is that, up to a certain age (when at least 50% of the individuals are still alive), the hazard rate is relatively constant. Beyond that age, the authors cannot draw any conclusions.

In addition, here is a summary of the methodological limitations I could find based on their limited description: 1) their survivorships do not go below 0.5 and thus cannot make any statements about actuarial senescence; 2) ignoring this last, to test whether the hazards follow a Gompertz mortality it would be more appropriate to use maximum likelihood and test alternative models (e.g., exponential, Siler), and not visually as they show in fig 1; 3) they seem to be confusing left-censoring with left-truncation; 4) given the left-truncation, they should be using product limit estimators and not Kaplan-Meier estimators (which they might, but it's not possible to know based on the limited description of the methods); 5) their treatment of the effects of colony size, breeding status, and body weight should be at least by means of a proportional hazards, not a simple visual inspection on arbitrary age intervals.

In light of these limitations, I would rank the significance of the study as not more than useful, and the strength of evidence inadequate. Still, and as I've stated above, this species is of great interest for ageing research, and the extensive work that the authors have done maintaining this captive colony is to be commended.

Reviewer #3 (Public Review):

Fission yeast is an important model organism and studies on fission yeast have provided many key insights into the understanding of genes and biological pathways. However, even in such a well-studied model organism, there are still many genes without known functions.

In this work, the authors took advantage of the availability of genome-wide fission yeast deletion mutants to systematically analyze the mutant phenotypes under 131 different conditions. This effort generated a genotype-phenotype dataset larger than the currently curated genotype-phenotype dataset, which is derived from studies over many decades by hundreds of fission yeast laboratories. The authors used the dataset to construct gene clusters that provide functional clues for many genes without previously known functions, including ones conserved in humans. This rich resource will surely be highly useful to the fission yeast community and beyond.

In addition, the authors also used machine learning to generate functional predictions of fission yeast genes and yield novel understandings, which are validated by experimental analysis of new ageing-related genes.

Overall, this study provides unprecedented and highly valuable resources for understanding fission yeast gene functions.

Reviewer #1 (Public Review):

In this manuscript, the authors aimed to provide information about the likely function of uncharacterised genes in fission yeast. The authors highlight the bias in the literature to well-studied genes/proteins and the fact that the functions of many proteins that are conserved from yeast to humans remain unknown. Initial functional characterisation could provide the impetus for researchers to dedicate time and resources to detailed investigations of protein function. The authors subject the fission yeast deletion set to a battery of perturbations (drug treatments etc) and measured the resultant colony size. In total, 131 conditions were analysed for nearly 3,500 mutants, representing a rich dataset. Clustering analysis was then used to identify common phenotype patterns and thereby infer protein functions using a "guilt by association approach. To assign potential GO terms to uncharacterised proteins, the authors developed a new computational approach (NET-FF) which combined two previous approaches, which they validated against curated annotations on the S. pombe database Pombase. Finally, the authors chose a group of genes which their analysis predicted to be involved in cellular ageing for experimental validation, cross-validating a priority unstudied novel gene (SPAC23C4.09c) to be involved in this process. Overall, the functional analysis performed in this manuscript is rigorous, thorough and incorporates some novel approaches leading to new insights and predicted protein functions. It will be an important resource for the fission yeast community.

Reviewer #2 (Public Review):

This manuscript describes colony-growth phenotypes to measure the fitness of deletion mutants for 3509 non-essential S. pombe genes in 131 conditions. 3492 mutants, including 124 mutants of 'priority unstudied' proteins conserved in humans, providing varied functional clues.

Phenotype-correlation networks provide evidence for the roles of poorly characterized proteins through guilt by association with known proteins. Gene Ontology (GO) terms were predicted using machine learning methods that take advantage of protein-network and protein-homology data.

Integrated analyses produced 1,675 novel GO predictions for 783 genes, including 47 predictions for 23 priority unstudied proteins. Experimental validation for genes involved in cellular ageing were obtained.

A method called NET-FF, which combines network embeddings and protein homology data to predict GO annotations, was developed. The authors demonstrate NET-FF predicts GO terms better than random and compare the information content of the predicted terms with the PomBase GO annotations. The phenotypic data was used to filter the GO annotation predictions made by NET-FF and then explore specific biological examples supported by both datasets

This is a very impressive and rich resource of phenotypic data and it will be particularly useful for the S. pombe research community and generally useful for the functional characterization of highly conserved eukaryotic genes. Overall, the analysis is powerful and sound.

Reviewer #2 (Public Review):

This paper is an attempt to extend or augment muscle synergy and motor primitive ideas with task measures. The authors idea is to use information metrics (mutual information, co-information) in 'synergy' creation including task information directly. My reading of the paper is that the framework proposed radically moves from attempts to be analytic in terms of physiology and compositionality with physiological bases, instead into more descriptive ML frameworks that may not support physiological work easily.

This approach is very different from the notions of physiological compositional elements as muscle synergies and motor primitives, and to me seems to really be striving to identify task relevant coordinative couplings. This is a meta problem for more classical analyses. Classical analyses seek compositional elements stable across tasks. These elements may then be explored in causal experiments and generative simulations of coupling and control strategies. The present work does not convince me that the joint 'meta' analysis proposed with task information added is not unmoored from physiology and causal modeling in some important ways. It also neglects publications and methods that might be inconvenient to the new framework.

Information based separation has been used in muscle synergy analyses using infomax ICA, which is information not variance based at core. Though linear mixing of sources is assumed, minimized mutual information is the basis.

Physiological causal testing of synergy ideas is neglected in the literature reviews in the paper. Although these are in animal work, the clear connection of muscle synergy choices and analyses to physiology is important, and needs to be managed in the new methods proposed. Is any correspondence assumed? Possible?

Questions and concerns with the framework as an overall tool:

First, muscle based motor information sources have influences on different time scales in the task mechanics. Analyses of synergies in the methods proposed will be very much dependent on the number and quality of task variables included and how these are managed. Standardizing and comparing among labs, tasks sets and instrumentation differences is not well enough considered as a problem in this new proposed method toolset, at least in my reading. Will replication, and testing across groups ever be truly feasible in this framework? Muscle based motor information sources have influences on different time scales in the task mechanics. Kinematic analyses, dynamic analyses and force plate analyses of the same task may provide task variables that alter the results in the proposed framework it seems.

Second, there is a sampling problem in all synergy analyses. We cannot record all muscles or all task parameters. Examining synergies across multiple tasks seeks 'stationary' compositionality. Including task specific elements may or may not reinforce or give increased coordinative precision to the stationary compositionality.<br /> To me the new methods proposed seem partly orthogonal to the ideas of stable compositionality. The 'synergies' obtained will likely differ, and are more likely to be coordinative control groupings of recurrent task and muscle motifs (based on instrumentation) which may or may not relate to core compositionality in physiology. Is there any expectation that the framework should relate to core compositionality and physiology. This is not clear in the paper as written.

It would be useful to explore the approach with a range of neuromechanical models and controllers and simulated data to explore the issues I am raising and convince readers that this analysis framework adds clarity rather than dissolving the generalizability and interpretability of analyses in terms of underlying causal mechanisms.

The authors need to better frame their work in relation to causal analyses if they are claiming links to muscle synergies analyses and claim extension/refinement. Alternatively, these may not be linked, and instead parallel approaches exploring different hypotheses and goals using different organizational data descriptors.<br /> To me this appears a data science tool that may not help any reductionist efforts and leads into less interpretable descriptions of motor control. Not invalid, but sufficiently different that common term use muddies the water.

Reviewer #1 (Public Review):

The proposed study provides an innovative framework for the identification of muscle synergies taking into account their task relevance. State-of-the-art techniques for extracting muscle interactions use unsupervised machine-learning algorithms applied to the envelopes of the electromyographic signals without taking into account the information related to the task being performed. In this work, the authors suggest including the task parameters in extracting muscle synergies using a network information framework previously proposed. This allows the identification of muscle interactions that are relevant, irrelevant, or redundant to the parameters of the task executed.

The proposed framework is a powerful tool to understand and identify muscle interactions for specific task parameters and it may be used to improve man-machine interfaces for the control of prostheses and robotic exoskeletons.

With respect to the network information framework recently published, this work added an important part to estimate the relevance of specific muscle interactions to the parameters of the task executed. However, the authors should better explain what is the added value of this contribution with respect to the previous one, also in terms of computational methods.

In general, the method proposed relies on several hyperparameters and cost functions that have been optimized for the specific datasets. A sensitivity analysis should be performed, varying these parameters and reporting the performance of the framework.

It is not clear how the well-known phenomenon of cross-talk during the recording of electromyographic muscle activity may affect the performance of the proposed technique and how it may bias the overall outcomes of the framework.

Reviewer #3 (Public Review):

In this study, the authors developed and tested a novel framework for extracting muscle synergies. The approach aims at removing some limitations and constrains typical of previous approaches used in the field. In particular, the authors propose a mathematical formulation that removes constrains of linearity and couple the synergies to their motor outcome, supporting the concept of functional synergies and distinguishing the task-related performance related to each synergy. While some concepts behind this work were already introduced in recent work in the field, the methodology provided here encapsulates all these features in an original formulation providing a step forward with respect to the currently available algorithms. The authors also successfully demonstrated the applicability of their method to previously available datasets of multi-joint movements.

Preliminary results positively support the scientific soundness of the presented approach and its potential. The added values of the method should be documented more in future work to understand how the presented formulation relates to previous approaches and what novel insights can be achieved in practical scenarios and confirm/exploit the potential of the theoretical findings.

Strengths:

This work proposes a novel framework that addresses physiologically non-verified hypothesis of standard muscle synergy methods: it removes restrictive model assumptions (e.g. linearity, same mixing coefficients) and the reliance on variance-accounted-for (VAF) metrics.

The method is solid and achieves the prescribed objectives at a computational level and in preliminary laboratory data.

A toolbox is available for testing the methods on a larger scale.

The paper is well written and shows a high level of innovation, original content and analysis

Weaknesses:

Task performance variables could be specified in more quantitative definition in future work (e.g.: articular angles rather than a generic starting point- end point).

The paper does not show a comparison with previous approaches (e.g.: NMF) or recently developed approaches (such as MMF).

A discussion of the likely impact of the work on the field, and the utility of the methods and data to the community.

In this work, the effort of the authors aimed at developing the field is clear. It is fundamental to develop novel frameworks for synergy extraction and use them to make them more interpretable and applicable to real scenarios, as well as more adherent to recent findings achieved in motor control and neuroscience that are not reflected in the standard models. At the same time, muscle synergies are being used more and more in research but their impact in practical scenarios is still limited, probably because synergies have rarely been analyzed in a functional context. This paper shows a very in-depth analysis and a novel framework to interpret data that links to the task space from a functional perspective. I also found that the results on the datasets are very well commented but could expand more to show why using this framework is advantageous.

There are some key points for discussion that follow from this paper which can be described more, maybe in future work, and that might contribute to major developments in the field, including:

The understanding of how the separation between relevant (redundant and synergistic) and irrelevant synergies impact on synergy analysis in practical works;

Interpreting how different synergistic organizations described in this work allows to better describe data from real scenarios (e.g.: motor recovery of patients after neurological diseases);

Discussing in detail how the presented findings compare with standard algorithms such as NMF to determine the added value provided with this approach;

Describe how redundant synergies reflect real neural organization and - if their "existence" is confirmed - how they contribute to redesign the concept of muscle synergies and of modular/synergistic control in general.

Reviewer #1 (Public Review):

This is a well-written manuscript, aiming to seek experimental evidence to establish anatomical and functional connectivity between the cerebellum and the nucleus accumbens (NAc). The authors combined anatomical, neural tracing, and electrophysiological approaches with electrical stimulation and optogenetics and provided a novel and solid set of data supporting the existence of disynaptic connections between the cerebellum and the NAc. The results are convincing and the main conclusion is supported by the data. Overall, this was a well-conceived project, and the experiments were conducted carefully, though some gaps remain to be filled. The knowledge generated from this manuscript will build a foundation for further research focusing on the interaction between cerebellum and limbic system as well as the role of such interaction in controlling motivated behavior.

Overall, this is a well-conceived project. The experiments were conducted carefully. The results support the conclusion of the existence of disynaptic circuits from the cerebellum to the NAc.

Reviewer #3 (Public Review):

The authors present a pipeline for generating strain-specific genome-scale metabolic models for bacteria using Klebsiella spp. as the demonstrative data. The proposed improvement of performance and accuracy in this process holds great value. However, the demonstrated evidence, justification, and validation methods require further discussion.

Apart from the claim to quickly and accurately produce strain-specific models, the manuscript highlights the need to create pan-metabolic models from manually curated models, which are relatively time-consuming and can only be done with well-established organisms. Therefore, claims to speed up the process are redundant.

The justification and evaluation of the generated models are inadequate and one-dimensional. The authors only focus on statistics such as the number of reactions and genes in the models, which does not accurately depict the completeness of the model.

Furthermore, the authors solely compare their results with the performance of the previously published CraveMe packages, and the results do not clearly demonstrate the superior performance of the Bactabolize tool that they developed.

The authors have not provided evidence or discussion on the accuracy of any metabolic fluxes, which are considered to be crucial for reconstructing metabolic models. Additionally, the authors have not mentioned the importance of non-growth associated maintenance and the criticality of biomass composition analysis, both of which significantly determine the fluxes in the system.

Overall, the work holds potential for direct application in certain specific aims and fields. However, the cryptic details and critical points of the justification regarding the completeness of the models require further discussion. A detailed discussion on the importance of manually curated models and the potential future direction of incorporating machine learning into the process would significantly enhance the quality of the manuscript.

Reviewer #2 (Public Review):

The authors present a computational tool for high-throughput generation of bacterial strain-specific metabolic models. The study seems interesting. However, I have the following concerns.

1. In the results section "description of Bactabolize", the authors present technical details on how to generate a metabolic model. For the input and output, please provide concrete examples to show the functionality of Bactabolize.

2. KpSC pan-metabolic reference model is provided. Are they required as input for Bactabolize? Are the gene, metabolite information open accessible by users?

3. To generate metabolic models, the authors present comparison results with other methods. However, the authors only present the numbers in genes, metabolites and substrates. Since the interactions between gene, metabolite, and substrate are also critical, if possible, please provide the coverage details about these interactions. Venn diagram is recommended to compare these coverage differences.

4. Are quality control and gap-filling needed to be processed when constructing a new metabolic model?

5. Are there any visualization results to check the status of the generated draft model?

Reviewer #1 (Public Review):

In this work, Vezina et al. present Bactabolize, a rapid reconstruction tool for the generation of strain-specific metabolic models. Similar to other reconstruction pipelines such as CarveMe, Bactabolize builds a strain-specific draft reconstruction and subsequently gap-fills it. The model can afterwards be used to predict growth in any defined medium the user specifies. The authors constructed a pan-model of the Klebsiella pneumoniae species complex (KpSC) and used it as input for Bactabolize to construct a genome-sale reconstruction of K. pneumoniae KPPR1. They compared the generated reconstruction with a reconstruction built through CarveMe as well as a manually curated reconstruction for the same strain. They then compared predictions of carbon, nitrogen, phosphor, and sulfur sources and found that the Bactabolize reconstruction had the overall highest accuracy. Finally, they built draft reconstructions for 10 clinical isolates of K. pneumoniae and evaluated their predictive performance. Overall, this is a useful tool, the data is well-presented, and the paper is well-written. However, the predictions are only compared with two existing reconstruction tools though more have been recently published.

Joint Public Review:

The manuscript by Lolicato and colleagues characterizes the role of FGF2 dimerization in the unconventional secretion of this signaling molecule using a combination of cell-based and in vitro assays. FGF2 is secreted from the cell via an unconventional mechanism because it lacks a signal sequence. Previous studies by the same group have established a compelling model in which FGF2 forms an oligomer in a PIP2-dependent manner at the plasma membrane, which drives its translocation to the cell exterior. The same group also identified two cysteine residues (C77 and C95) critical for FGF2 oligomerization and secretion.

In this study, the authors analyzed the impact of single cysteine to alanine substitution on the oligomerization and secretion of FGF2. They found that C95 but not C77 is required for PIP2-dependent membrane binding, FGF2 oligomerization, and secretion. On the other hand, C77 regulates the interaction of FGF2 with the plasma membrane Na, K-ATPase, which is thought to enhance the FGF2-PIP2 interaction. Using a set of bi-functional crosslinkers, the authors were able to capture an FGF2 homo-dimer whose formation is dependent on C95.

They propose that FGF2 forms a disulfide-bridged dimer via C95, the building block for FGF2 oligomerization in the plasma membrane.

While most experiments were carefully designed and the data are of high quality, a few issues need further clarification.

A significant concern is a need for more direct evidence for the proposed disulfide-bridged FGF2 dimer in the cytoplasm despite multiple assays highlighting the critical role of C95 in FGF2 oligomerization and secretion. The crosslinking experiments only suggest that C95 is close to another C95 in crosslinked FGF2 dimers. Given that the reducing cytosolic environment does not usually support disulfide bond formation and that no electron acceptor has been identified to support this unusual model, the reviewers feel that the authors should consider an alternative and more plausible explanation for their observations, which is that the C95A mutation disrupts the dimerization interface. This is actually the author's explanation for why the C77A FGF2 mutant fails to bind Na, K-ATPase. For these reasons, the reviewers feel it is an overstatement to claim that FGF2 forms a disulfide dimer in the cytoplasm.

Furthermore, the authors propose that FGF2 dimers can assemble into a transient higher-order FGF2 oligomer to form a toroidal pore for protein secretion. This is supported by a computational simulation study, which suggests that FGF2 dimers exhibit a higher affinity for PI(4,5)P2 than monomers. However, the model would be much stronger if the authors could provide additional experimental validation.

Additionally, the authors propose that C95-dependent FGF2 dimerization may generate a signaling module. They cited a few structure papers on page 9 (Plotnikov et al., 1999; Plotnikov et al., 2000; Schlessinger et al., 2000), suggesting that the FGF2 dimer reported here may be the primary signaling unit. However, this statement may mislead the reader, as it has been clearly stated in these papers that FGF2 does not form a dimer directly. Instead, heparin facilitates the dimerization of the FGF receptor, which results in the recruitment of two FGF2 molecules.

Reviewer #1 (Public Review):

This fascinating paper by A.L. Schneider et al. describes voyAGEr, a shiny-based interface for easy exploration of the GTEx dataset by non- or novice programmers. Importantly, voyAGEr is open source and available from github, which could greatly accelerate additional development and further uses of this interesting tool.

The authors developed a pipeline for modeling age-related changes in gene expression in the GTEx data called ShARP-LM, fitting a linear model for age, sex, and age&sex interaction terms. This pipeline underlies the later analyses that can be applied within voyAGEr. These analyses are labeled by tissue so that users can easily begin a query based on a tissue or a gene of possible interest.

voyAGEr implements many kinds of interesting R-based tools such as pathway overrepresentation analysis and gene co-expression module analysis, in a way that makes these approaches accessible to non-bioinformaticist aging researchers.

As the tidal wave of publicly available large, high-dimensional datasets such as transcriptomes continues to grow exponentially, the usefulness of tools such as voyAGEr will only increase. While test users may be able to imagine features or refinements they wish were already present, due to the open source approach they or anyone else including but not limited to the present authors can implement additional features in the future. I look forward to using this tool and to staying abreast of its future development.

Overall, this study describes a new tool of interest to the field. The manuscript is clearly written overall. The figures and supplementary information are all clear and all add to the manuscript.

Reviewer #2 (Public Review):

The purpose of this study is to develop a tool that serves as a starting point for investigating and uncovering genes and pathways associated with aging. The tool utilizes information from the GTEx public database, which contains post-mortem human data. It focuses on identifying age-related gene expression changes across different age range, biological sexes, and medical histories, with a focus on specific tissues.

Additionally, the authors envision the platform as continuously evolving, with ongoing development and expansion to include new data and features, ensuring it remains a cutting-edge resource for researchers studying aging.

# Strengths<br /> voyAGEr presents a tool for exploring gene expression changes across multiple tissues in the context of aging. One of the main strengths of the tool is its intuitive and user-friendly interface, which allows for easy navigation and exploration of gene expression patterns for biologists. Users can explore changes in gene expression of single genes across multiple tissues, enabling them to identify genes of interest that can be further investigated.

A particularly noteworthy strength of the tool is its ability to show tissue-specific gene expression patterns. This feature is essential for elucidating the paradigm of tissue-specific asynchronous aging and provides a unique and valuable resource for the aging community.

Overall, the tool offers an entry point for further investigation of genes involved in aging, and its ability to show tissue-specific gene expression patterns provides a unique and valuable resource for the scientific community.

Lastly, the tool is accompanied by a clear and thorough tutorial that explains each of its functionalities and provides examples. The authors also acknowledge the limitations of the statistical inference tests used in the tool, which adds to its overall transparency.

# Weaknesses

## Underlying data analysis<br /> In this tool/resource paper, it is crucial that the data used is up-to-date to provide the most comprehensive and relevant information to users. However, the authors utilized GTEx v7, which is an outdated (2016) version of the dataset. It is worth noting that GTEx v8 includes over 940 individuals, representing a 35% increase in individuals, and a 50% increase in the total number of samples. The authors should check the newer versions of GTEx and update the data.

The authors did not address any correction for batch effects or RNA integrity numbers, which are known to affect transcriptome profiles. For instance, our analysis of GTEx v8 Cortex tissue revealed that after filtering out lowly expressed genes, in the same way authors did, PC1 (which accounts for 24% of the variation) had a Spearman's correlation value of 0.48 (p<6.1e-16) with RNA integrity number.

The data analyzed in the GTEx dataset is not filtered or corrected for the cause of death, which can range from violent and sudden deaths to slow deaths or cases requiring a ventilator. As a result, the data may not accurately represent healthy aging profiles but rather reflect changes in the transcriptome specific to certain diseases due to the age-related increase in disease risk. While the authors do acknowledge this limitation in the discussion, stating that it is not a healthy cohort and disease-specific analysis is not feasible due to the limited number of samples, it would be useful for users to have the option to analyze only cases of fast death, excluding ventilator cases and deaths due to disease. This is typically how GTEx data is utilized in aging studies. Alternatively, the authors should consider including the "cause of death" variable in the model.

The age distribution varies across tissues which may impact the results of the study. The authors' claim that age distribution does not affect the outcomes is inconclusive. Since the study aims to provide cross-tissue analysis, it is important to note that differing age distributions across tissues can influence the overall results. To address this, the authors should conduct downsampling to different age distributions across tissues and evaluate the level of tissue-specific or common changes that remain after the distributions are made similar.

The GTEx resource is extremely valuable, however, it comes with challenges. GTEx contains tissue samples from the same individuals across different tissues, resulting in varying degrees of overlap in sample origin across tissues as not all tissues are collected for all individuals. This could affect the similar/different patterns observed across tissues. As this tool is meant for broader use by the community, it is crucial for the authors to either rule out this possibility by conducting a cross-tissue comparison using a non-parametric model that accounts for the dependency between samples from the same individual, or to provide information on the degree of similarity between samples so that the users can keep this possibility in mind when using the tool for hypothesis generation.

## Visualisation and analysis platform<br /> The authors aimed to create an open-source and ever-evolving resource that could be adapted and improved with new functionality. However, this goal was only partially achieved. Although the code for the web app is open source, crucial components such as the statistical tests or the linear model are not included in the repository, limiting the tool's customizability and adaptability.

Furthermore, the authors' choice of visualization platform (R shiny) may not be the best fit for extensibility and open-source collaboration, as it lacks modularity. A more suitable alternative could be production-oriented platforms such as Flask or FastAPI.

To facilitate collaboration and improve the tool's adaptability, data resulting from the pre-processing pipeline should be made publicly available. This would make it easier for others to contribute and extend the tool's functionality, ultimately enhancing its value for the scientific community.

Reviewer #3 (Public Review):

In their manuscript, Schneider et al. aim to develop voyAGEr, a web-based tool that enables the exploration of gene expression changes over age in a tissue- and sex-specific manner. The authors achieved this goal by calculating the significance of gene expression alterations within a sliding window, using their unique algorithm, Shifting Age Range Pipeline for Linear Modelling (ShARP-LM), as well as tissue-level summaries that calculated the significance of the proportion of differentially expressed genes by the windows and calculated enrichments of pathways for showing biological relevance. Furthermore, the authors examined the enrichment of cell types, pathways, and diseases by defining the co-expressed gene modules in four selected tissues. The voyAGEr was developed as a discovery tool, providing researchers with easy access to the vast amount of transcriptome data from the GTEx project. Overall, the research design is unique and well-performed, with interesting results that provide useful resources for the field of human genetics of aging. I have a few questions and comments, which I hope the authors can address.

1. In the gene-centric analyses section of the result, to improve this manuscript and database, linear regression tests accounting for the entire range of age should be added. The authors' algorithm, ShARP-LM, tests locally within a 16-year window which makes it has lower power than the linear regression test with the whole ages. I suspect that the power reduction is strongly affected in the younger age range since a larger number of GTEx donors are enriched in old age. By adding the results from the lm tests, readers would gain more insight and evidence into how significantly their interest genes change with age.<br /> 2. In line with the ShARP-LM test results, it is not clear which criterion was used to define the significant genes and the following enrichment analyses. I assume that the criterion is P < 0.05, but it should be clearly noted. Additionally, the authors should apply adjusted p-values for multiple-test correction. The ideal criterion is an adjusted P < 0.05. However, if none or only a handful of genes were found to be significant, the authors could relax the criteria, such as using a regular P < 0.01 or 0.05.<br /> 3. In the gene-centric analyses section, authors should provide a full list of donor conditions and a summary table of conditions as supplementary.<br /> 4. The tissue-specific assessment section has poor sub-titles. Every title has to contain information.<br /> 5. I have an issue understanding the meaning of NES from GSEA in the tissue-specific assessment section. The authors performed GSEA for the DEGs against the background genes ordered by t-statistics (from positive to negative) calculated from the linear model. I understand the p-value was two-tailed, which means that both positive and negative NES are meaningful as they represent up-regulated expression direction (positive coefficient) and down-regulated expression direction (negative coefficient) with age, respectively, within a window. However, in the GSEA section of Methods, authors were not fully elaborate on this directionality but stated, "The NES for each pathway was used in subsequent analyses as a metric of its over- or down-representation in the Peak". The authors should clearly elaborate on how to interpret the NES from their results.<br /> 6. In the Modules of co-expressed genes section, the authors did not explain how or why they selected the four tissues: brain, skeletal muscle, heart (left ventricle), and whole blood. This should be elaborated on.<br /> 7. In the modules of the co-expressed genes section, the authors did not provide an explanation of the "diseases-manual" sub-tab of the "Pathway" tab of the voyAGEr tool. It would be helpful for readers to understand how the candidate disease list was prepared and what the results represent.

Reviewer #1 (Public Review):

This study by Sokač et al. entitled "GENIUS: GEnome traNsformatIon and spatial representation of mUltiomicS data" presents an integrative multi-omics approach which maps several genomic data sources onto an image structure on which established deep-learning methods are trained with the purpose of classifying samples by their metastatic disease progression signatures. Using published samples from the Cancer Genome Atlas the authors characterize the classification performance of their method which only seems to yield results when mapped onto one out of four tested image-layouts.

Major recommendations:

- In its current form, GENIUS analysis is neither computationally reproducible nor are the presented scripts on GitHub generic enough for varied applications with other data. The GENIUS GitHub repository provides a collection of analysis scripts and not a finished software solution (e.g. command line tool or other user interface) (the presented scripts do not even suffice for a software prototype). In detail, the README on their GitHub repository is largely incomplete and reads analogous to an incomplete and poorly documented analysis script and is far from serving as a manual for a generic software solution (this claim was made in the manuscript). The authors should invest substantially into adding more details on how data can be retrieved (with example code) from the cited databases and how such data should then be curated alongside the input genome to generically create the "genomic image". In addition, when looking at the source code, parameter configurations for training and running various modules of GENIUS were hard-coded into the source code and users would have to manually change them in the source code rather than as command line flags in the software call. Furthermore, file paths to the local machine of the author are hard-coded in the source code, suggesting that images are sourced from a local folder and won't work when other users wish to replicate the analysis with other data. I would strongly recommend building a comprehensive command line tool where parameter and threshold configurations can be generically altered by the user via command line flags. A comprehensive manual would need to be provided to ensure that users can easily run GENIUS with other types of input data (since this is the claim of the manuscript). Overall, due to the lack of documentation and hard-coded local-machine folder paths it was impossible to computationally reproduce this study or run GENIUS in general.

- In the Introduction the authors write: "To correct for such multiple hypothesis testing, drastic adjustments of p-values are often applied which ultimately leads to the rejection of all but the most significant results, likely eliminating a large number of weaker but true associations.". While this is surely true for any method attempting to separate noise from signal, their argument fails to substantiate how their data transformation will solve this issue. Data transformation and projection onto an image for deep-learning processing will only shift the noise-to-signal evaluation process to the postprocessing steps and won't "magically" solve it during training. In addition, multiple-testing correction is usually done based on one particular data source (e.g. expression data), while their approach claims to integrate five very different genomic data sources with different levels and structures of technical noise. How are these applications comparable and how is the training procedure able to account for these different structures of technical noise? Please provide sufficient evidence for making this claim (especially in the postprocessing steps after classification).

- I didn't find any computational benchmark of GENIUS. What are the computational run times, hardware requirements (e.g. memory usage) etc that a user will have to deal with when running an analogous experiment, but with different input data sources? What kind of hardware is required GPUs/CPUs/Cluster?

- A general comment about the Methods section: Models, training, and validation are very vaguely described and the source code on GitHub is very poorly documented so that parameter choices, model validation, test and validation frameworks and parameter choices are neither clear nor reproducible. Please provide a sufficient mathematical definition of the models, thresholds, training and testing frameworks.

- In chapter "Latent representation of genome" the authors write: "After successful model training, we extracted the latent representations of each genome and performed the Uniform Manifold Approximation and Projection (UMAP) of the data. The UMAP projected latent representations into two dimensions which could then be visualized. In order to avoid modeling noise, this step was used to address model accuracy and inspect if the model is distinguishing between variables of interest.". In the recent light of criticism when using the first two dimensions of UMAP projections with omics data, what is the evidence in support of the author's claim that model accuracy can be quantified with such a 2D UMAP projection? How is 'model accuracy' objectively quantified in this visual projection?

- In the same paragraph "Latent representation of genome" the authors write: "We observed that all training scenarios successfully utilized genome images to make predictions with the exception of Age and randomized cancer type (negative control), where the model performed poorly (Figure 2B).". Did I understand correctly that all negative controls performed poorly? How can the authors make any claims if the controls fail? In general, I was missing sufficient controls for any of their claims, but openly stating that even the most rudimentary controls fail to deliver sufficient signals raises substantial issues with their approach. A clarification would substantially improve this chapter combined with further controls.

Reviewer #2 (Public Review):

In this manuscript, Birkbak and colleagues use a novel approach to transform multi-omics datasets in images and apply Deep Learning methods for image analysis. Interestingly they find that the spatial representation of genes on chromosomes and the order of chromosomes based on 3D contacts leads to best performance. This supports that both 1D proximity and 3D proximity could be important for predicting different phenotypes. I appreciate that the code is made available as a github repository. The authors use their method to investigate different cancers and identify novel genes potentially involved in these cancers. Overall, I found this study important for the field.

The major points of this manuscript could be grouped in three parts:

1. While the authors have provided validation for their model, it is not always clear that best approaches have been used.<br /> a. In the methods there is no mention of a validation dataset. I would like to see the authors training on a cancer from one cohort and predict on the same cancer from a different cohort. This will convince the reader that their model can generalise. They do something along those lines for the bladder cancer, but no performance is reported. At the very least they should withhold a percentage of the data for validation. Maybe train on 100 and validate on the remaining 300 samples. They might have already done something along these lines, but it was not clear from the methods.<br /> b. It was not clear how they used "randomised cancer types as the negative control". Why not use normal tissue data or matched controls?<br /> c. If Figure 2B, the authors claim they have used cross validation. Maybe I missed it, but what sort of cross validation did they use?<br /> 2. Potential improvement to the method<br /> a. It is very encouraging the use of HiC data, but the authors used a very coarse approach to integrate it (by computing the chromosome order based on interaction score). We know that genes that are located far away on the same chromosome can interact more in 3D space than genes that are relatively close in 1D space. Did the authors consider this aspect? Why not group genes based on them being located in the same TAD?<br /> b. Authors claim that "given that methylation negatively correlates with gene expression, these were considered together". This is clearly not always the case. See for example https://genomebiology.biomedcentral.com/articles/10.1186/s13059-022-02728-5. What would happen if they were not considered together?<br /> 3. Interesting results that were not explained.<br /> a. In Figure 3A methylation seems to be the most important omics data, but in 3B, mutations and expression are dominating. The authors need to explain why this is the case.

Reviewer #1 (Public Review):

Gametocytes are erythrocytic sexual stages of the malaria-causing parasite Plasmodium, and are essential for parasite transmission and reproduction in the mosquito vector. In this study, Murata et al investigated the mechanisms of gene regulation in female gametocytes in the rodent malaria model parasite Plasmodium berghei. According to current views, gene regulation in Plasmodium parasites is dominated by the family of AP2 transcription factors (TFs), such as the AP2-G TF, which drives sexual commitment. The same authors previously identified one AP2 TF, called AP2-FG, as an essential TF mediating differentiation of female gametocytes. Here, they identified a novel protein, called PFG (for partner of AP2-FG), which cooperates with AP2-FG to regulate a subset of female gametocyte genes.

PFG was identified among AP2-G targets, but possesses no known DNA binding or other characterized domain. The authors show that PFG-knockout P. berghei parasites can form male and female gametocytes yet cannot transmit to mosquitoes, due to a defect in female gametocyte development. Using RNA-seq, they show that many female-specific genes are down-regulated in PFG(-)parasites. Chromatin immunoprecipitation combined with DNA sequencing (ChIP-seq) revealed that PFG colocalizes with AP2-FG on a ten-base motif that is enriched upstream of female-specific genes. Importantly, the ChIP-seq profile of PFG is unchanged in the absence of AP2-FG, suggesting that PFG binds to DNA independently of AP2-FG. Mutation of the ten-base motif in one target gene using CRISPR-Cas9 demonstrates that this motif is required for PFG localization at the gene locus. The data also show that binding of AP2-FG is affected in the absence of PFG, with disruption of AP2-FG interaction with the ten-base motif, but conservation of AP2-FG binding to distinct five-base motifs. Using a recombinant AP2 domain from AP2-FG, the authors demonstrate that the AP2 domain of AP2-FG binds to the five-base motifs. Using CRISPR they show that disruption of the five-base motifs in the genome abrogates AP2-FG binding, and using a reporter expression system they confirm that these motifs act as a cis-activating promoter element.

Through the analysis of target genes based on the presence of the ten- versus five-base motifs, the authors propose a model where AP2-FG can function in two forms, associated or not with PFG, and acting on the ten- or five-base motifs, respectively, to regulate distinct gene subsets during development of female gametocyte development.

The paper is very well written, with a clear narrative, and the work is very well performed, relying on robust molecular approaches. Generally the conclusions and the model proposed by the authors are well supported by the data. Nevertheless, the study as it stands raises a number of questions. First, it is unclear how the authors selected PFG as a candidate protein as the protein lacks any known DNA binding or regulatory domain. Detailing the reasoning that led to the identification of PFG would make the entire study more appealing. While the data convincingly show that PFG and AP2-FG cooperate to regulate the expression of a subset of female-specific genes, the paper does not show whether the two proteins actually interact with each other to form a complex. Finally, how PFG binds to DNA and whether the protein has transactivating activity remains elusive, as the protein apparently possesses no known DNA-binding or activating domain. These points could be discussed in more detail in the manuscript and/or be the subject of follow up studies.

In summary, this work reveals the essential role of a Plasmodium protein with no known DNA binding or regulatory domain, illustrating that unknown facets remain to be uncovered in this fascinating pathogen.

Reviewer #2 (Public Review):

Murata et al have characterized a new transcription activator termed PFG, which regulates gene expression in female gametocytes. The authors show solid evidence that PFG is a partner of the previously described transcription factor AP2-FG and describe three sets of genes: genes activated by PFG or AP2-FG alone and genes activated by the complex. The authors also show differential binding to the target DNA sequences by AP2-FG to either a 10bp, if in a complex with PFG or a 5bp motif if alone. In all, this is a useful study which further elucidates the underlying regulatory network that drives development of sexual stages and ultimately transmission to mosquitoes. The data presented are clear and solid and the conclusions drawn are mostly supported by the results shown. However, in the absence of evidence of physical interaction, it remains unclear if AP2-FG and PFG actually interact directly or as part of the same complex.

Reviewer #3 (Public Review):

This study is well designed and executed and provides new and important insights into the role of two TFs during the maturation of female gametocytes and fertilization in the mosquito midgut. However, it would benefit from a more thorough characterization of the phenotype to understand at which step of development these factors are required.

The gene at the center of this study (PBANKA_0902300) was identified in an earlier genetic screen by Russell et al. as being a female specific gene with essential role in transmission and named Fd2 (for female-defective 2). Since this name entered the literature first and is equally descriptive, the Fd2 name should be used instead of PFG to maintain clarity and avoid unnecessary confusion.

This study is well designed and executed and provides new and important insights into the role of two TFs during the maturation of female gametocytes and fertilization in the mosquito midgut. However, it would benefit from a more thorough characterization of the phenotype to understand at which step of development these factors are required.

The gene at the center of this study (PBANKA_0902300) was identified in an earlier genetic screen by Russell et al. as being a female specific gene with essential role in transmission and named Fd2 (for female-defective 2). Since this name entered the literature first and is equally descriptive, the Fd2 name should be used instead of PFG to maintain clarity and avoid unnecessary confusion.

Reviewer #1 (Public Review):

Thermogenic adipocyte activity associate with cardiometabolic health in humans, but decline with age. Identifying the underlying mechanisms of this decline is therefore highly important.

To address this task, Holman and co-authors investigated the effects of two major determinants of thermogenic activity: cold, which induce thermogenic de novo differentiation as well as conversion of dormant thermogenic inguinal adipocytes: and aging, which strongly reduce thermogenic activity. The authors study young and middle-aged mice at thermoneutrality and following cold exposure.

Using linage tracing, the authors conclude that the older group produce less thermogenic adipocytes from progenitor differentiation. However, they found no differences between thermogenic differentiation capacity between the age groups when progenitors are isolated and differentiated in vitro. This finding is consistent with previous findings in humans, demonstrating that progenitor cells derived from dormant perirenal brown fat of humans differentiate into thermogenic adipocytes in vitro. Taken together, this underscores that age-related changes in the microenvironment rather than autonomous alterations in the ASPCs explain the age related decline in thermogenic capacity, This is an important finding in terms of identifying new approaches to switch dormant adipocytes into an active thermogenic phenotype.

To gain insight into the age-related changes, the authors use single cell and single nuclei RNA sequencing mapping of their two age groups, comparing thermoneutral and cold conditions between the two groups. Interestingly, where the literature previously demonstrated that de novo lipogenesis (DNL) occurs in relation to thermogenic activation, the authors show that DNL in fact is activated in a white adipocyte cell type, whereas the beige thermogenic adipocytes form a separate cluster.

Considering recent findings, that adipose tissue contains several subtypes of ASPCs and adipocytes, mapping the changes at single cell resolution following cold intervention provides an important contribution to the field, in particular as an older group with limited thermogenic adaptation is analyzed in parallel with a younger, more responsive group. This model also allowed for detection of microenvironment as a determining factor of thermogenic response.

The use of only two time points (young and middle-aged) along the aging continuum limits the conclusions that can be made on aging as the only driver of the observed differences between the groups. It should for example be noted that the older mice had higher weights and larger fat depots, thus the phenotype is complex and this should be taken into consideration when interpreting the data.

In conclusion, this study provides an important resource for further studies on how to reactivate dormant thermogenic fat and potentially improve metabolic health.

Reviewer #2 (Public Review):

This manuscript focused on why aging leads to decreased beiging of white adipose tissue. The authors used an inducible lineage tracing system and provided in vivo evidence that de novo beige adipogenesis from Pdgfra+ adipocyte progenitor cells is blocked during early aging in subcutaneous fat. Single-cell RNA sequencing of adipocyte progenitor cells and in vitro assays showed that these cells have similar beige adipogenic capacities in vitro. Single-cell nucleus RNA sequencing of mature adipocytes indicated that aged mice have more Npr3 high-expressing adipocytes in the subcutaneous fat from aged mice. Meanwhile, adipocytes from aged mice have significantly lower expression of genes involved in de novo lipogenesis, which may contribute to the declined beige adipogenesis.

The mechanism that leads to age-related impairment of white adipose tissue beiging is not very clear. The finding that Pdgfra+ adipocyte progenitor cells contribute to beige adipogenesis is novel and interesting. It is more intriguing that the aging process represses Pdgfra+ adipocyte progenitor cells from differentiating into beige adipocytes during cold stimulation. Mature adipocytes that have high de novo lipogenesis activity may support beige adipogenesis is also novel and worth further pursuing. The study was carried out with a nice experimental design, and the authors provided sufficient data to support the major conclusions. I only have a few comments that could potentially improve the manuscript.

1. It is interesting that after three days of cold exposure, aged mice also have much fewer beige adipocytes. Is de novo adipogenesis involved at this early stage? Or does the previous beige adipocyte that acquired white morphology have a better "reactivation" in young mice? It would be nice if the author could discuss the possibilities.<br /> 2. Is the absolute number of Pdgfra+ cells decreased in aged mice? It would be nice to include quantifications of the percentage of tomato+ beige adipocytes in total tomato+ cells to reflect the adipogenic rate.

Reviewer #1 (Public Review):

Despite numerous studies on quinidine therapies for epilepsies associated with GOF mutant variants of Slack, there is no consensus on its utility due to contradictory results. In this study Yuan et al. investigated the role of different sodium selective ion channels on the sensitization of Slack to quinidine block. The study employed electrophysiological approaches, FRET studies, genetically modified proteins and biochemistry to demonstrate that Nav1.6 N- and C-tail interacts with Slack's C-terminus and significantly increases Slack sensitivity to quinidine blockade in vitro and in vivo. This finding inspired the authors to investigate whether they could rescue Slack GOF mutant variants by simply disrupting the interaction between Slack and Nav1.6. They find that the isolated C-terminus of Slack can reduce the current amplitude of Slack GOF mutant variants co-expressed with Nav1.6 in HEK cells and prevent Slack induced seizures in mouse models of epilepsy. This study adds to the growing list of channels that are modulated by protein-protein interactions, and is of great value for future therapeutic strategies.

I have a few comments with regard to how Nav1.6 sensitize Slack to block by quinidine.

It is not clear to me if the Slack induced current amplitude varies depending on the specific Nav subtype. To this end, it would be valuable to test if Slack open probability is affected by the presence of specific Nav subtypes. Nav induced differences in Slack current amplitude and open probability could explain why individual Nav subtypes show varied ability to sensitize Slack to quinidine blockade.

It has previously been shown that INaP (persistent sodium current) is important for inducing Slack currents. Here the authors show that INaT (transient sodium current) of Nav1.6 is necessary for the sensitization of Slack to quinidine block whereas INaP surprisingly has no effect. The authors then show that the N-tail together with C-tail of Nav1.6 can induce same effect on Slack as full-length Nav1.6 in presence of high intracellular concentrations of sodium. However, it is not clear to me how the isolated N- and C-tail of Nav1.6 can induce sensitization of Slack to quinidine by interacting with C-terminus of Slack, while sensitization also is dependent on INaT. The authors speculate on different slack open conformation, but one could speculate if there is a missing link, such as an un-identified additional interacting protein that causes the coupling.

Reviewer #2 (Public Review):

This is a very interesting paper about the coupling of Slack and Nav1.6 and the insight this brings to the effects of quinidine to treat some epilepsy syndromes.

Slack is a sodium-activated potassium channel that is important to hyperpolarization of neurons after an action potential. Slack is encoded by KNCT1 which has mutations in some epilepsy syndromes. These types of epilepsy are treated with quinidine but this is an atypical antiseizure drug, not used for other types of epilepsy. For sufficient sodium to activate Slack, Slack needs to be close to a channel that allows robust sodium entry, like Nav channels or AMPA receptors. but more mechanistic information is not available. Of particular interest to the authors is what allows quinidine to be effective in reducing Slack.

In the manuscript, the authors show that Nav, not AMPA receptors, are responsible for Slack's sensitization to quinidine blockade, at least in cultured neurons (HeK293, primary cortical neurons). Most of the paper focuses on the evidence that Nav1.6 promotes Slack sensitivity to quinidine.

The paper is very well written although there are reservations about the use of non-neuronal cells or cultured primary neurons rather than a more intact system. I also have questions about the figures. Finally, riluzole is not a selective drug, so the limitations of this drug should be discussed. On a minor point, the authors use the term in vivo but there are no in vivo experiments.

Reviewer #3 (Public Review):

Yuan et al., set out to examine the role of functional and structural interaction between Slac and NaVs on the Slack sensitivity to quinidine. Through pharmacological and genetic means they identify NaV1.6 as the privileged NaV isoform in sensitizing Slac to quinidine. Through biochemical assays, they then determine that the C-terminus of Slack physically interacts with the N- and C-termini of NaV1.6. Using the information gleaned from the in vitro experiments the authors then show that virally-mediated transduction of Slack's C-terminus lessens the extent of SlackG269S-induced seizures. These data uncover a previously unrecognized interaction between a sodium and a potassium channel, which contributes to the latter's sensitivity to quinidine.

The conclusions of this paper are mostly well supported by data, but some aspects of functional and structural studies in vivo as well as physically interaction need to be clarified and extended.

1) Immunolabeling of the hippocampus CA1 suggests sodium channels as well as Slac colocalization with AnkG (Fig 3A). Proximity ligation assay for NaV1.6 and Slac or a super-resolution microscopy approach would be needed to increase confidence in the presented colocalization results. Furthermore, coimmunoprecipitation studies on the membrane fraction would bolster the functional relevance of NaV1.6-Slac interaction on the cell surface.

2) Although hippocampal slices from Scn8a+/- were used for studies in Fig. S8, it is not clear whether Scn8a-/- or Scn8a+/- tissue was used in other studies (Fig 1J & 1K). It will be important to clarify whether genetic manipulation of NaV1.6 expression (Fig. 1K) has an impact on sodium-activated potassium current, level of surface Slac expression, or that of NaV1.6 near Slac.

3) Did the epilepsy-related Slac mutations have an impact on NaV1.6-mediated sodium current?

4) Showing the impact of quinidine on persistent sodium current in neurons and on NaV1.6-expressing cells would further increase confidence in the role of persistent sodium current on sensitivity of Slac to quinidine.

Reviewer #1 (Public Review):

With genephys, the author provides a generative model of brain responses to stimulation. This generative model allows mimicking of specific parameters of a brain response at the sensor level, to test the impact of those parameters on critical analytic methods utilized on real M/EEG data. Specifically, they compare the decoding output for differently set parameters to the decoding pattern observed in a classical passive viewing study in terms of the resulting temporal generalization matrix (TGM). They identify that the correspondence between the mimicked and the experimental TGM depends on an oscillatory component that spans multiple channels, frequencies, and latencies of response; and an additive, slower response with a specific (cross-frequency) relation to the phase of the oscillatory, faster component.

A strength of the article is that it considers the complexity of neural data that contributes to the findings obtained in stimulation experiments. An additional strength is the provision of a Python package that allows scientists to explore the potential contribution of different aspects of neural signals to obtained experimental data and thereby to potentially test their theoretical assumptions critical parameters that contribute to their experimental data.

A weakness of the paper is that the power of the model is illustrated for only one specific set of parameters, added in a stepwise manner and the comparison to on specific empirical TGM, assumed to be prototypical; And that this comparison remains descriptive. (That is could a different selection of parameters lead to similar results and is there TGM data which matches these settings less well.) It further remained unclear to me, which implications may be drawn from the generative model, following from the capacities to mimic this specific TGM (i) for more complex cases, such as the comparison between experimental conditions, and (ii) about the complex nature of neural processes involved.

Towards this end, I would appreciate (i) a more profound explanation of the conclusions that can be drawn from this specific showcase, including potential limitations, as well as wider considerations of how scientists may empower the generative model to (ii) understand their experimental data better and (iii) which added value the model may have in understanding the nature of underlying brain mechanism (rather than a mere technical characterization of sensor data).

Reviewer #2 (Public Review):

This paper introduces a new model that aims to explain the generators of temporal decoding matrices (TGMs) in terms of underlying signal properties. This is important because TGMs are regularly used to investigate neural mechanisms underlying cognitive processes, but their interpretation in terms of underlying signals often remains unclear. Furthermore, neural signals are often variant over different instances of stimulation despite behaviour being relatively stable. The author aims to tackle these concerns by developing a generative model of electrophysiological data and then showing how different parameterizations can explain different features of TGMs. The developed technique is able to capture empirical observations in terms of fundamental signal properties. Specifically, the model shows that complexity is necessary in terms of spatial configuration, frequencies and latencies to obtain a TGM that is comparable to empirical data.

The major strength of the paper is that the novel technique has the potential to further our understanding of the generators of electrophysiological signals which are an important way to understand brain function. Furthermore, the used techniques are state-of-the-art and the developed model is publicly shared in open source code.

On the other hand, the results of comparisons between simulations and real data are not always clear for an inexperienced reader. For example, the comparisons are qualitative rather than quantitative, making it hard to draw firm conclusions. Relatedly, it is unclear whether the chosen parameterizations are the only/best ones to generate the observed patterns or whether others are possible. In the case of the latter, it is unclear what we can actually conclude about underlying signal generators. It would have been different if the model was directly fitted to empirical data, maybe of different cognitive conditions. Finally, the neurobiological interpretation of different signal properties is not discussed. Therefore, taken together, in its currently presented form, it is unclear how this method could be used exactly to further our understanding of the brain.

Reviewer #1 (Public Review):

The paper by Dongsheng Xiao, Yuhao Yan and Timothy H Murphy presents a timely approach to record neuronal activity at multiple temporal and spatial scales. Such approaches are at the forefront of system neuroscience and a few examples include, among others, fMRI alongside electrophysiology (Logothetis et al, 2021. Nature) or widefield calcium imaging (Lake et al, 2020. Nat Meth) , or functional ultrasound imaging and multi unit recording (Claron et al, 2023 Cell Reports), The method presented here combines "low resolution" (i.e. cortical regions) widefield calcium imaging across most of the dorsal portions of the murine cortex combined with electrical recording of single neurons in specific cortical and subcortical locations (as a matter of fact, this later components can be used everywhere in the murine brain).

The method presented here is straightforward to implement and very well documented. Examples of novel insights that this approach can generate are well presented and demonstrate the strength of the presented approach, some aspects of the analysis require clarification.

For example, the author reveal Spike-Triggered average cortical activation Maps (STMs) linked to the activity of single neurons (Figs 4 and 5) This allows to directly asses the functional connectivity between cortical and sub-cortical areas. It nevertheless unclear what is the stability of the established relationships. The nature of the "recordings" in Fig 4. is unclear. It looks like these are imaging sessions on the same day, the length of these recordings as well as the interval between them is not stated. It will be fundamental to build a metric to compare STMs variability across sessions/recordings/days; a root-mean-square from an average map across all recordings could provide a starting point.

Also with respect to the STMs analysis, the data-driven choice of 10 clusters might need a bit more explorations. While the silhouette clustering accuracy peaks at 10 (Fig 5A), this metrics comes without a confidence intervals making it difficult to know if a difference of less than 10% (i.e. 11 or 13 clusters) should be deemed different. Maybe a bootstrapping approach could be used here to build such confidence intervals. Another approach to reach the number of cluster to use could be based on "consensus" between different partitioning algorithms (e.g. Strehl, A. & Ghosh, J. itions. J. Mach. Learn. Res. 3, 583-617 (2001). A much stronger argument should be provided to use the 0.3 correlation cutoff value which seems to be arbitrarily low. The main point here is that the authors should show that their conclusions hold within a range of parameter values (number of clusters and correlation threshold).

Reviewer #2 (Public Review):

The article presents 'Mesotrode,' a technique that integrates chronic widefield calcium imaging and electrophysiology recordings using tetrodes in head-fixed mice. This approach allows recording the activity of a few single neurons in multiple cortical/subcortical structures, in which the tetrodes are implanted, in combination with widefield imaging of dorsal cortex activity on the mesoscale level, albeit without cellular resolution. The authors claim that Mesotrode can be used to sample different combinations of cortico-subcortical networks over prolonged periods of time, up to 60 days post-implantation. The results demonstrate that the activity of neurons recorded from distinct cortical and subcortical structures are coupled to diverse but segregated cortical functional maps, suggesting that neurons of different origins participate in distinct cortico-subcortical pathways. The study also extends the capability of Mesotrode by conducting electrophysiological recordings from the facial motor nerve. It demonstrates that facial nerve spiking is functionally associated with several cortical areas( PTA, RSP, and M2), and optogenetic inhibition of the PTA area significantly reduced the facial movement of the mice.

Studying the relationship between widefield cortical activity patterns and the activity of individual neurons in cortical and subcortical areas is very important, and Murphy's lab has been a pioneer in the field. However, the choice of low-yield recording methods (tetrode) instead of more high-yield recording techniques, such as silicon probes, makes the approach presented in this study somewhat less appealing. Also, the authors claim that a tetrode-based approach can allow chronic recordings of single neural activity over days - a topic that is very controversial. In terms of results, I was under the impression that most of the conclusions presented in the bulk of the paper ( Figures 1-5) are very similar to what previous work from Murphy's lab and other labs has shown using acute preparation. In this respect, the paper can benefit from a more in-depth analysis of the heterogeneity of single-neuron functional coupling. The last part of the facial nerve recording is interesting (Figure 6), but I think it can be integrated better into the rest of the paper.

Reviewer #1 (Public Review):

In this study, Hoops et al. showed that Netrin-1 and UNC5c can guide dopaminergic innervation from nucleus accumbens to cortex during adolescence in rodent models. They found that these dopamine axons project to the prefrontal cortex in a Netrin-1 dependent manner and knocking down Netrin-1 disrupted motor and learning behaviors in mice. Furthermroe, the authors used hamsters, a seasonal model that is affected by the length of daylight, to demonstrate that the guidance of dopamine axons is mediated by the environmental factor such as daytime length and in sex dependent manner. While this study highlighted the important roles of two neurodevelopmental markers, netrin-1 and UNC5C, in the projection of dopaminergic azons in the adolescence/adult brain, the major weakness is that the data are quite superficial and do not establish any definitive evidence to support the causal relationship between the expression of netrin-1 and UNC5C in the projection of dopaminergic axons remain unclear.<br /> Below are several major concerns regarding the data presented in this manuscript:

1. Despite the well-established role of Netrin-1 and UNC5C axon guidance during embryonic commissural axons, it remains unclear which cell type(s) express Netrin-1 or UNC5C in the dopaminergic axons and their targets. For instance, the data in Figure 1F-G and Figure 2 are quite confusing. Does Netrin-1 or UNC5C express in all cell types or only dopamine-positive neurons in these two mouse models? It will also be important to provide quantitative assessments of UNC5C expression in dopaminergic axons at different ages.

2. Figure 1 used shRNA to knockdown Netrin-1 in the Septum and these mice were subjected to behavioral testing. These results, again, are not supported by any valid data that the knockdown approach actually worked in dopaminergic axons. It is also unclear whether knocking down Netrin-1 in the septum will re-route dopaminergic axons or lead to cell death in the dopaminergic neurons in the substantia nigra pars compacta?

3. Another issue with Figure1J. It is unclear whether the viruses were injected into a WT mouse model or into a Cre-mouse model driven by a promoter specifically expresses in dorsal peduncular cortex? The authors should provide evidence that Netrin-1 mRNA and proteins are indeed significantly reduced. The authors should address the anatomic results of the area of virus diffusion to confirm the virus specifically infected the cells in dorsal peduncular cortex.

4. The authors need to provide information regarding the efficiency and duration of knocking down. For instance, in Figure 1K, the mice were tested after 53 days post injection, can the virus activity in the brain last for such a long time?

5. In Figure 1N-Q, silencing Netrin-1 results in less DA axons targeting to infralimbic cortex, but why the Netrin-1 knocking down mice revealed the improved behavior?

6. What is the effect of knocking down UNC5C on dopamine axons guidance to the cortex?

7. In Figures 2-4, the authors only showed the amount of DA axons and UNC5C in NAcc. However, it remains unclear whether these experiments also impact the projections of dopaminergic axons to other brain regions, critical for the behavioral phenotypes. What about other brain regions such as prefrontal cortex? Do the projection of DA axons and UNC5c level in cortex have similar pattern to those in NAcc?

8. Can overexpression of UNC5c or Netrin-1 in male winter hamsters mimic the observations in summer hamsters? Or overexpression of UNC5c in female summer hamsters to mimic the winter hamster? This would be helpful to confirm the causal role of UNC5C in guiding DA axons during adolescence.

9. The entire study relied on using tyrosine hydroxylase (TH) as a marker for dopaminergic axons. However, the expression of TH (either by IHC or IF) can be influenced by other environmental factors, that could alter the expression of TH at the cellular level.

10. Are Netrin-1/UNC5C the only signal guiding dopamine axon during adolescence? Are there other neuronal circuits involved in this process?

11. Finally, despite the authors' claim that the dopaminergic axon project is sensitive to the duration of daylight in the hamster, they never provided definitive evidence to support this hypothesis.

Reviewer #2 (Public Review):

In this manuscript, Hoops et al., using two different model systems, identified key developmental changes in Netrin-1 and UNC5C signaling that correspond to behavioral changes and are sensitive to environmental factors that affect the timing of development. They found that Netrin-1 expression is highest in regions of the striatum and cortex where TH+ axons are travelling, and that knocking down Netrin-1 reduces TH+ varicosities in mPFC and reduces impulsive behaviors in a Go-No-Go test. Further, they show that the onset of Unc5 expression is sexually dimorphic in mice, and that in Siberian hamsters, environmental effects on development are also sexually dimorophic. This study addresses an important question using approaches that link molecular, circuit and behavioral changes. Understanding developmental trajectories of adolescence, and how they can be impacted by environmental factors, is an understudied area of neuroscience that is highly relevant to understanding the onset of mental health disorders. I appreciated the inclusion of replication cohorts within the study.

Reviewer #3 (Public Review):

This study from the Flores group aims at understanding neuronal circuit changes during adolescence which is an ill-defined, transitional period involving dramatic changes in behavior and anatomy. They focus on DA innervation of the prefrontal cortex, and their interaction with the guidance cue Netrin-1. They propose DA axons in the PFC increase in the postnatal period, and their density is reduced in a Netrin 1 knockdown, suggesting that Netrin abets the development of this mesocortical pathway. In such mice impulsivity gauged by a go-no go task is reduced. They then provide some evidence that Unc5c is developmentally regulated in DA axons. Finally they use an interesting hamster model, to study the effect of light hours on mesocortical innervation, and make some interesting observations about the timing of innervation and Unc5c expression, and the fact that females housed in winter day length conditions display an accelerated innervation of the prefrontal cortex. While this work is novel and on an interesting, understudied topic, several aspects need to be further consolidated, to make it more persuasive.

Main comments<br /> 1. Fig 1 A and B don't appear to be the same section level.<br /> 2. Fig 1C. It is not clear that these axons are crossing from the shell of the NAC.<br /> 3. Fig 1. Measuring width of the bundle is an odd way to measure DA axon numbers. First the width could be changing during adult for various reasons including change in brain size. Second, I wouldn't consider these axons in a traditional bundle. Third, could DA axon counts be provided, rather than these proxy measures.<br /> 4. TH in the cortex could also be of noradrenergic origin. This needs to be ruled out to score DA axons<br /> 5. Netrin staining should be provided with NeuN + DAPI; its not clear these are all cell bodies. An in situ of Netrin would help as well.<br /> 6. The Netrin knockdown needs validation. How strong was the knockdown etc?<br /> 7. If the conclusion that knocking down Netrin in cortex decreases DA innervation of the IL, how can that be reconciled with Netrin-Unc repulsion.<br /> 8. The behavioral phenotype in Fig 1 is interesting, but its not clear if its related to DA axons/signaling. IN general, no evidence in this paper is provided for the role of DA in the adolescent behaviors described.<br /> 9. Fig2 - boxes should be drawn on the NAc diagram to indicate sampled regions. Some quantification of Unc5c would be useful. Also, some validation of the Unc5c antibody would be nice.<br /> 10. "In adolescence, dopamine neurons begin to express the repulsive Netrin-1 receptor UNC5C, and reduction in UNC5C expression appears to cause growth of mesolimbic dopamine axons to the prefrontal cortex".....This is confusing. Figure 2 shows a developmental increase in UNc5c not a decrease. So when is the "reduction in Unc5c expression" occurring?<br /> 11. In Fig 3, a statistical comparison should be made between summer male and winter male, to justify the conclusions that the winter males have delayed DA innervation.<br /> 12. Should axon length also be measured here (Fig 3)? It is not clear why the authors have switched to varicosity density. Also, a box should be drawn in the NAC cartoon to indicate the region that was sampled.<br /> 13. In Fig 3, Unc5c should be quantified to bolster the interesting finding that Unc5c expression dynamics are different between summer and winter hamsters. Unc5c mRNA experiments would also be important to see if similar changes are observed at the transcript level.<br /> 14. Fig 4. The peak in exploratory behavior in winter females is counterintuitive and needs to be better discussed. IN general, the light dark behavior seems quite variable.

Reviewer #1 (Public Review):

This paper describes the development and initial validation of an approach-avoidance task and its relationship to anxiety. The task is a two-armed bandit where one choice is 'safer' - has no probability of punishment, delivered as an aversive sound, but also lower probability of reward - and the other choice involves a reward-punishment conflict. The authors fit a computational model of reinforcement learning to this task and found that self-reported state anxiety during the task was related to a greater likelihood of choosing the safe stimulus when the other (conflict) stimulus had a higher likelihood of punishment. Computationally, this was represented by a smaller value for the ratio of reward to punishment sensitivity in people with higher task-induced anxiety. They replicated this finding, but not another finding that this behavior was related to a measure of psychopathology (experiential avoidance), in a second sample. They also tested test-retest reliability in a sub-sample tested twice, one week apart and found that some aspects of task behavior had acceptable levels of reliability. The introduction makes a strong appeal to back-translation and computational validity, but many aspects of the rationale for this task need to be strengthened or better explained. The task design is clever and most methods are solid - it is encouraging to see attempts to validate tasks as they are developed. There are a few methodological questions and interpretation issues, but they do not affect the overall findings. The lack of replicated effects with psychopathology may mean that this task is better suited to assess state anxiety, or to serve as a foundation for additional task development.

Reviewer #2 (Public Review):

Summary:<br /> The authors develop a computational approach-avoidance-conflict (AAC) task, designed to overcome limitations of existing offer based AAC tasks. The task incorporated likelihoods of receiving rewards/ punishments that would be learned by the participants to ensure computational validity and estimated model parameters related to reward/punishment and task induced anxiety. Two independent samples of online participants were tested. In both samples participants who experienced greater task induced anxiety avoided choices associated with greater probability of punishment. Computational modelling revealed that this effect was explained by greater individual sensitivities to punishment relative to rewards.

Strengths:<br /> Large internet-based samples, with discovery sample (n = 369), pre-registered replication sample (n = 629) and test-retest sub group (n = 57). Extensive compliance measures (e.g. audio checks) seek to improve adherence.

There is a great need for RL tasks that model threatening outcomes rather than simply loss of reward. The main model parameters show strong effects and the additional indices with task based anxiety are a useful extension. Associations were broadly replicated across samples. Fair to excellent reliability of model parameters is encouraging and badly needed for behavioral tasks of threat sensitivity.

The task seems to have lower approach bias than some other AAC tasks in the literature. Although this was inferred by looking at Fig 2 (it doesn't seem to drop below 46%) and Fig 3d seems to show quite a strong approach bias when using a reward/punishment sensitivity index. It would be good to confirm some overall stats on % of trials approached/avoided overall.

Weaknesses:<br /> The negative reliability of punishment learning rate is concerning as this is an important outcome.

The Kendall's tau values underlying task induced anxiety and safety reference/ various indices are very weak (all < 0.1), as are the mediation effects (all beta < 0.01). This should be highlighted as a limitation, although the interaction with P(punishment|conflict) does explain some of this.

The inclusion of only one level of reward (and punishment) limits the ecological validity of the sensitivity indices.

Appraisal and impact:<br /> Overall this is a very strong paper, describing a novel task that could help move the field of RL forward to take account of threat processing more fully. The large sample size with discovery, replication and test-retest gives confidence in the findings. The task has good ecological validity and associations with task-based anxiety and clinical self-report demonstrate clinical relevance. The authors could give further context but test-retest of the punishment learning parameter is the only real concern. Overall this task provides an exciting new probe of reward/threat that could be used in mechanistic disease models.

Reviewer #3 (Public Review):

This study investigated cognitive mechanisms underlying approach-avoidance behavior using a novel reinforcement learning task and computational modelling. Participants could select a risky "conflict" option (latent, fluctuating probabilities of monetary reward and/or unpleasant sound [punishment]) or a safe option (separate, generally lower probability of reward). Overall, participant choices were skewed towards more rewarded options, but were also repelled by increasing probability of punishment. Individual patterns of behavior were well-captured by a reinforcement learning model that included parameters for reward and punishment sensitivity, and learning rates for reward and punishment. This is a nice replication of existing findings suggesting reward and punishment have opposing effects on behavior through dissociated sensitivity to reward versus punishment.

Interestingly, avoidance of the conflict option was predicted by self-reported task-induced anxiety. This effect of anxiety was mediated by the difference in modelled sensitivity to reward versus punishment (relative sensitivity). Importantly, when a subset of participants were retested over 1 week later, most behavioral tendencies and model parameters were recapitulated, suggesting the task may capture stable traits relevant to approach-avoidance decision-making.

However, interpretation of these findings are severely undermined by the fact that the aversiveness of the auditory punisher was largely determined by participants, with the far-reaching impacts of this not being accounted for in any of the analyses. The manipulation check to confirm participants did not mute their sound is highly commendable, but the thresholding of punisher volume to "loud but comfortable" at the outset of the task leaves substantial scope for variability in the punisher delivered to participants. Indeed, participants' ratings of the unpleasantness of the punishment was moderate and highly variable (M = 31.7 out of 50, SD = 12.8 [distribution unreported]). Despite having this rating, it is not incorporated into analyses. It is possible that the key finding of relationships between task-induced anxiety, reward-punishment sensitivity and avoidance are driven by differences in the punisher experienced; a louder punisher is more unpleasant, driving greater task-induced anxiety, model-derived punishment sensitivity, and avoidance (and vice versa). This issue can also explain the counterintuitive findings from re-tested participants; lower/negatively correlated task-induced anxiety and punishment-related cognitive parameters may have been due to participants adjusting their sound settings to make the task less aversive (retest punisher rating not reported). It can therefore be argued that the task may not actually capture meaningful cognitive/motivational traits and their effects on decision-making, but instead spurious differences in punisher intensity.

This undercuts the proposed significance of this task as a translational tool for understanding anxiety and avoidance. More information about ratings of punisher unpleasantness and its relationship to task behavior, anxiety and cognitive parameters would be valuable for interpreting findings. It would also be of interest whether the same results were observed if the aversiveness of the punisher was titrated prior to the task.

Although the procedure and findings reported here remain valuable to the field, claims of novelty including its translational potential are perhaps overstated. This study complements and sits within a much broader literature that investigates roles for aversion and cognitive traits in approach-avoidance decisions. This includes numerous studies that apply reinforcement learning models to behavior in two-choice tasks with latent probabilities of reward and punishment (e.g., see doi: 10.1001/jamapsychiatry.2022.0051), as well as other translationally-relevant paradigms (e.g., doi: 10.3389/fpsyg.2014.00203, 10.7554/eLife.69594, etc).

Reviewer #1 (Public Review):

Sensory neurons of the mechanosensory bristles on the head of the fly project to the sub esophageal ganglion (SEZ). In this manuscript, the authors have built on a large body of previous work to comprehensively classify and quantify the head bristles. They broadly identify the nerves that various bristles use to project to the SEZ and describe their region-specific innervation in the SEZ. They use dye-fills, clonal labelling, and electron microscopic reconstructions to describe in detail the phenomenon of somatotopy - conserved peripheral representations within the central brain - within the innervation of these neurons. In the process they develop novel tools to access subsets of these neurons. They use these to demostrate that groups of bristles in different parts of the head control different aspects of the grooming sequence.

Reviewer #2 (Public Review):

The authors combine genetic tools, dye fills and connectome analysis techniques to generate a "first-of-its-kind", near complete, synaptic resolution map of the head bristle neurons of Drosophila. While some of the BMN anatomy was already known based on previous work by the authors and other researchers, this is the first time a near complete map has been created for the head BMNs at electron microscopy resolution.

Strengths:<br /> 1. The authors cleverly use techniques that allow moving back and forth between periphery (head bristle location) and brain, as well as moving between light microscopy and electron microscopy data. This allows them to first characterize the pathways taken by different head BMNs to project to the brain and also characterize anatomical differences among individual neurons at the level of morphology and connectivity.<br /> 2. The work is very comprehensive and results in a near complete map of all head BMNs.<br /> 3. Authors also complement this anatomical characterization with a first-level functional analysis using optogenetic activation of BMNs that results in expected directed grooming behavior.

Weaknesses:<br /> 1. The clustering analysis is compelling but cluster numbers seem to be arbitrarily chosen instead of by using some informed metrics.<br /> 2. It could help provide context if authors revealed some of the important downstream pathways that could explain optogenetics behavioral phenotypes and previously shown hierarchical organization of grooming sequences.<br /> 3. In contrast to the rigorous quantitative analysis of the anatomical data, the behavioral data is analyzed using much more subjective methods. While I do not think it is necessary to perform a rigorous analysis of behaviors in this anatomy focused manuscript, the conclusions based on behavioral analysis should be treated as speculative in the current form e.g. calling "nodding + backward walking" as an avoidance response is not justified as it currently stands. Strong optogenetic activation could lead to sudden postural changes that due to purely biomechanical constraints could lead to a couple of backward steps as seen in the example videos. Moreover since the quantification is manual, it is not clear what the analyst interprets as backward walking or nodding. Interpretation is also concerning because controls show backward walking (although in fewer instances based on subjective quantification).

Summary:<br /> The authors end up generating a near-complete map of head BMNs that will serve as a long-standing resource to the Drosophila research community. This will directly shape future experiments aimed at modeling or functionally analyzing the head grooming circuit to understand how somatotopy guides behaviors.

Reviewer #3 (Public Review):

Eichler et al. set out to map the locations of the mechanosensory bristles on the fly head, examine the axonal morphology of the bristle mechanosensory neurons (BMNs) that innervate them, and match these to electron microscopy reconstructions of the same BMNs in a previously published EM volume of the female adult fly brain. They used BMN synaptic connectivity information to create clusters of BMNs that they show occupy different regions of the subesophageal zone brain region and use optogenetic activation of subsets of BMNs to support the claim that the morphological projections and connectivity of defined groups of BMNs are consistent with the parallel model for behavioral sequence generation.

The authors have beautifully cataloged the mechanosensory bristles and the projection paths and patterns of the corresponding BMN axons in the brain using detailed and painstaking methods. The result is a neuroanatomy resource that will be an important community resource. To match BMNs reconstructed in an electron microscopy volume of the adult fly brain, the authors matched clustered reconstructed BMNs with light-level BMN classes using a variety of methods, but evidence for matching is only summarized and not demonstrated in a way that allows the reader to evaluate the strength of the evidence. The authors then switch from morphology-based categorization to non-BMN connectivity as a clustering method, which they claim demonstrates that BMNs form a somatotopic map in the brain. This map is not easily appreciated, and although contralateral projections in some populations are clear, the distinct projection zones that are mentioned by the authors are not readily apparent. Because of the extensive morphological overlap between connectivity-based clusters, it is not clear that small projection differences at the projection level are what determines the post-synaptic connectivity of a given BMN cluster or their functional role during behavior. The claim the somatotopic organization of BMN projections is preserved among their postsynaptic partners to form parallel sensory pathways is not supported by the result that different connectivity clusters still have high cosine similarity in a number of cases (i.e. Clusters 1 and 3, or Clusters 1 and 2). Finally, the authors use tools that were generated during the light-level characterization of BMN projections to show that specifically activating BMNs that innervate different areas of the head triggers different grooming behaviors. In one case, activation of a single population of sensory bristles (lnOm) triggers two different behaviors, both eye and dorsal head grooming. This result does not seem consistent with the parallel model, which suggests that these behaviors should be mutually exclusive and rely on parallel downstream circuitry.

This work will have a positive impact on the field by contributing a complete accounting of the mechanosensory bristles of the fruit fly head, describing the brain projection patterns of the BMNs that innervate them, and linking them to BMN sensory projections in an electron microscopy volume of the adult fly brain. It will also have a positive impact on the field by providing genetic tools to help functionally subdivide the contributions of different BMN populations to circuit computations and behavior. This contribution will pave the way for further mechanistic study of central circuits that subserve grooming circuits.

Reviewer #1 (Public Review):

This important study reveals the structure of human STEAP2 for the first time and suggests the electron transport pathway, but some questions remain regarding the interpretation of the in vitro electron transport experiments, the lack of available redox couples, and potential alternative hypotheses that would if addressed, strengthen the claims in the manuscript.

Strengths

One of the clear strengths of the manuscript that stands out is the determination of the structure of human STEAP2. The structures of some other homologs are known, but STEAP2's structure was not, and STEAP2 seems to have an unusually low activity towards certain metal chelates. The approach of producing the human STEAP2 in insect cells with the supplementation of cofactor biogenesis components nicely results in cofactor-replete protein. The structure of STEAP2 reveals a domain-swapped trimer, with the NADPH-binding domain of the neighboring protomer within electron-transport distance of the FAD-heme axis. The FAD has an interesting and somewhat unusual extended conformation and abuts a Leu residue that may regulate electron transport. Another strength of the manuscript is the demonstration that STEAP1, which does not have the internal NADPH binding domain, can interact modestly and shuttle electrons to the heme in STEAP1 through FAD. These experiments nicely expand information on the function of STEAP1 and provide a structural basis for electron transport in STEAP2.

Weaknesses

A major weakness in the manuscript lies with the kinetics data and how the data, as presented, are unclear to the reader regarding their impact and their connection to the purported electron transport scheme. While multiple sets of data are reported, the analysis in all cases is simply the reduction of a hexacoordinate heme and its related spectra and kinetic parameters. In most cases, it's unclear to the reader which part of the electron pathway is being tested in which experiment. Simple diagrams would be helpful in each case. However, it's also unclear if all of the potential order of addition experiments were actually performed; i.e., flavin but no NADPH; NADPH but no flavin; flavin before NADPH; flavin after NADPH, etc. As there are multiple permutations that should be tested to demonstrate the electron transport pathway, presenting the data in a way that makes it clear to the reader is challenging. Particularly missing are the determined redox potentials of the hemes in both STEAP1 and STEAP2. Could differences in these heme redox potentials be drivers of the difference in metal reduction rates? Also, the text indicates that STEAP2 does not show a reduction rate dependence on the [Fe3+-NTA], but Figure 1A shows a difference in rates dependent on [Fe3+-NTA], and the shape of the reduction curve also changes based on [Fe3+-NTA]. This discrepancy should be rectified.

A second major weakness is the lack of any verification of the relevance of the STEAP2 oligomerization to its in vivo function. Is the same domain-swapped trimer known to exist in vivo? If the protein were prepared in other detergents, is the oligomerization preserved? It is alluded to in the text that another STEAP protein is also a trimer. Was this oligomerization verified in vivo? Could this oligomerization be disrupted to impede or abrogate electron transport to underscore the oligomerization relevance? This point is germane, as it would further suggest that the domain-swapped trimer observed in the STEAP2 cryo-EM structure is physiologically relevant, especially given how far the distance between the NADPH and the FAD would otherwise be to support electron transport.

Beyond these two areas in which the manuscript could be improved there are a couple of minor considerations. First, the modest resolution of the STEAP2 structure prevents assigning the states of NADP+/NADPH and FAD/FADH2 with confidence. An orthogonal measure would be useful for modeling the accurate states in the structure. Finally, the BLI b5R/STEAP1 binding/unbinding fits seem somewhat poor, especially at high concentrations of b5R in the dissociation regime, which likely influences the derived value of Kd. A different fitting equilibrium might yield better agreement between the experimental and theoretical results. Moreover, whether this binding strength is influenced by the reduction state of the NADPH would be helpful in understanding and contextualizing the weak binding affinity.

Reviewer #2 (Public Review):

The manuscript provides new insight into a family of human enzymes. It demonstrates that STEAP2 can reduce iron and STEAP1 can be promiscuous regarding the source of electron donors that it can use. The quality of the kinetics experiment and the structural analysis is excellent. I am less enthusiastic about the interpretation of data and the take-home message that the manuscript intends to deliver. Above all, the work combines data on STEAP2 and STEAP1 and it remains unclear which questions are ultimately addressed. A second critical point is about the interpretation of the experiment demonstrating that STEAP1 can be reduced by cytochrome b5 reductase. The abstract states that "We show that STEAP1 can form an electron transfer chain with cytochrome b5 reductase" whereas the main text discusses the data by suggesting that "we speculate that FAD on b5R may partially dissociate to straddle between the two proteins". The two statements seem to be partly contradictory. Cytochrome b5 reductases do not easily release FAD but rather directly donate electrons to heme-protein acceptors (PMID: 36441026). According to the methods section, no FAD was added to the reaction mix used for the cytochrome b5 reductase experiment. Overall, the data seem to indicate that the reductase might reduce the heme of STEAP1 directly. Would it be possible to check whether FAD addition affects the kinetics of the process? And to perform a control experiment to check that NAD(P)H does not directly reduce the heme of STEAP1 (though unlikely)? A final point concerns the "slow Fe3+-NTA reduction by STEAP2". The reaction is not that slow as the initial phase is 2 per second. The reaction does not show dependence on the substrate concentration suggesting "poor substrate binding". I am not convinced by this interpretation. Poor substrate binding would give rise to substrate dependency as saturation would not be achieved. A possible interpretation could be that substrate binding is instead tight and the enzyme is saturated by the substrate. Can it be that the reaction is limited by non-productive substrate-binding and/or by interconversions between active and non-active conformations?

Reviewer #3 (Public Review):

The six-transmembrane epithelial antigen of the prostate (STEAP) family comprises four members in metazoans. STEAP1 was identified as integral membrane protein highly upregulated on the plasma membrane of prostate cancer cells (PMID: 10588738), and it later became evident that other STEAP proteins are also over expressed in cancers, making STEAPs potential therapeutic targets (PMID: 22804687). Functionally, STEAP2-4 are ferric and cupric reductases that are important for maintaining cellular metal uptake (PMIDs: 16227996, 16609065). The cellular function of STEAP1 remains unknown, as it cannot function as an independent metalloreductase. In the last years, structural and functional data have revealed that STEAPs form trimeric assemblies and that they transport electrons from intracellular NADPH, through membrane bound FAD and heme cofactors, to extracellular metal ions (PMIDs: 23733181, 26205815, 30337524). In addition, numerous studies (including a previous study from the senior authors) have provided strong implications for a potential metalloreductase function of STEAP1 (PMIDs: 27792302, 32409586).

This new study by Chen et al. aims to further characterize the previously established electron transport chain in STEAPs in high molecular detail through a variety of assays. This is a well-performed, highly specialized study that provides some useful extra insights into the established mechanism of electron transport in STEAP proteins. The authors first perform a detailed spectroscopic analysis of Fe3+-NTA reduction by STEAP2 and STEAP1, confirming that both purified proteins are capable of reducing metal ions. A cryo-EM structure of STEAP2 is also presented. It is then established that STEAP1 can receive electrons from cytochrome b5 reductase, and the authors provide experimental evidence that the flavin in STEAP proteins becomes diffusible.

The specific aims of the study are clear, but it is not always obvious why certain experiments are performed only on STEAP2, on STEAP1, or on both isoforms. A better justification of the performed experiments through connecting paragraphs and proper referencing of the literature would improve readability of the manuscript. Experimentally, the conclusions are appropriate and mostly consistent with the experimental data, although one important finding can benefit from further clarification. Namely, the observation that STEAP1 can form an electron transfer chain with cytochrome b5 reductase in vitro is an exciting finding, but its physiological relevance remains to be validated. The metalloreductase activity of STEAP1 in vitro has been described previously by the authors and by others (PMIDs: 27792302, 32409586). However, when over expressed in HEK cells, STEAP1 by itself does not display metal ion reductase activity (PMID: 16609065), and it was also found that STEAP1 over expression does not impact iron uptake and reduction in Ewing's sarcoma (cancer) cells (PMID: 22080479). Therefore, the physiological relevance of metal ion reduction by STEAP1 remains controversial. The current work establishes an electron transfer chain between STEAP1 and cytochrome b5 reductase in vitro with purified proteins. However, the conformation of this metalloreductase activity of the STEAP1-cytochrome b5 complex will be required in a cell line to prove that the two proteins indeed form a physiological relevant complex and that the results are not just an in vitro artefact from using high concentrations of purified proteins.

The work will be interesting for scientists working within the STEAP field. However, some of the presented data are redundant with previous findings, moderating the study's impact. For instance, the new structural insights into STEAP2 are limited because the structure is virtually identical to the published structures of STEAP4 and STEAP1 (PMIDs: 30337524, 32409586), which is not surprising because of the high sequence similarity between the STEAP isoforms. Moreover, the authors provide experimental evidence to prove the previous hypothesis (PMID: 30337524) that the flavin in STEAP proteins becomes diffusible, but the molecular arrangement of a STEAP protein, in which the flavin interacts with NADPH, remains unknown. Based on the manuscript title, I would also expect the in-depth characterization of STEAP1/STEAP2 hetero trimers (first identified by the authors), but this is only briefly mentioned. When taken together, this study by Chen et al. strengthens and supports previously published biochemical and structural data on STEAP proteins, without revealing many prominent conceptual advances.

Reviewer #1 (Public Review):

This study aims to identify the existence of hedonic feeding and to distinguish it from homeostatic feeding, in Drosophila. The authors use direct observation of feeding events, a novel automated feeding event detector, inventive behavioral assays, and genetics to separate out the ways that the Drosophila interacts with food. Using two choice assays, the authors find an increased duration of interactions with high-concentration sugars under conditions of expected satiety, which is considered to be hedonic feeding.

The technical advances in the measurement of animal interactions with food will help advance the understanding of feeding behavior and motivational states. The correlation of specific types of food interactions across satiation state, sex, and circadian time will help drive forward the understanding of the scope of an animal's goals with feeding, and likely their relation between species and eating disorders. The assessment of mushroom body circuitry in a type of food interaction is helpful for understanding the coding of feeding control in the brain.

The bulk of the feeding data presented in the manuscript are from the interactions of individual flies with a source of liquid food, where interaction is defined as 'physical contact of a specific duration.' Although the assay they use allows for measurements to be made at high temporal resolution, the authors include some data showing that solid food consumption follows the same trend.

Reviewer #2 (Public Review):

Weaver et al. used video analysis of flies that were feeding in their previously developed FLIC assay to begin to dissect the mechanisms of feeding. FLIC or Fly Liquid Interaction Counter records electrical signals that are generated when a fly touches a liquid food substrate with its legs or proboscis or both. Using video data of the liquid food interactions in the FLIC assay allowed the authors to precisely identify what a fly is doing in the feeding chamber and what the relationship is between the flies' behavior and the electrical signal recorded in the assay. This analysis produced the first detailed behavioral profile of feeding flies and allowed the authors to categorize different types of feeding in the FLIC assay, from tasting food (using their legs) to fast and long feeding bouts (using their proboscis).

After establishing what FLIC signals correspond to the different types of feeding, they used these signals to examine the food choices of starved and sated flies when presented with a sugar-rich (2% sucrose) or protein-rich (2% yeast + 1% sucrose) liquid food source. To represent hedonic feeding, they also presented flies with a choice between super sweet (20% sucrose) food or protein-rich (2% yeast + 1% sucrose) liquid food. Although fully fed flies show no difference in the number of times they visit either food choice, the flies spend more time feeding during their visits on 20% sucrose food than they do on regular sugar and on the yeast food source, suggesting that 20% sucrose is a more pleasurable food source. To make sure this was not due to the higher caloric content of 20% sucrose, they also offered flies food with the same sweetness as 20% sucrose (2% sucrose + 18% arabinose) but without caloric content and food with the same caloric content but the sweetness of 2% sucrose (2% sucrose + 18% sorbitol). This experiment showed that sweetness was the driver for the longer feeding bouts, confirming that sweeter food is apparently perceived as more pleasurable. They also looked at the effect of starving flies on the hedonic drive and found that starvation increases the time spent feeding on pleasurable food, consistent with findings in mammals that homeostatic feeding affects the hedonic drive.

To begin dissecting circuits underlying hedonic drive, the authors used CaMPARI expression in all neurons. CaMPARI is a green fluorescent reporter that turns red in the presence of Ca2+ (a measure of neuronal activity) and UV exposure. Fully fed flies in the super sweet food choice condition showed more red fluorescence in the mushroom bodies. Inhibiting a subset of these neurons acutely shows that horizontal lobes are required for the increased duration of feeding bouts on super sweet food. These lobes are innervated by a cluster of DA neurons and inhibiting them also blocks the increased super sweet feeding times.

The data in the paper largely support the conclusions. The application of this tool to distinguish between homeostatic and hedonic feeding is innovative and very compelling. As proof of the principle of the strength of their paradigm, the authors identify a distinct brain circuit involved in hedonic feeding. The methods established in the paper make a deeper understanding of feeding mechanisms possible at both a genetic and brain circuit level.

Reviewer #1 (Public Review):

The authors use a combination of crop modeling and field experiments to argue that drought during seedling establishment likely severely impacts the yield of pearl millet, an important but understudied cereal crop, and that rapid seedling root elongation could play a major role in mitigating this. They further argue that this trait has a strong genetic basis and that major polymorphisms in candidate genes can be identified using standard methods from modern genetics and genomics. Finally, they use homology with the model plant Arabidopsis thaliana to argue that the function of one putatively causal gene is to regulate root cell elongation.

The major strength of this paper is that it convincingly demonstrates how modern methods from plant breeding and model organisms can be combined to address questions of great practical importance in important but poorly understood crops. The notion that it is possible to connect single-locus polymorphism and cellular biology to drought tolerance and crop yield in pearl millet is not a trivial one.

The weakness is obvious: while the argument made is convincing, it must be recognized that the strength of the evidence is by no means of the level expected in a model organism. Conclusions could easily be wrong, and there is no direct evidence that regulatory variation in PgGRXC9 leads to higher crop yield via cell elongation and seedling drought tolerance. However, generating such evidence in a poorly studied crop would be a monumental undertaking, and should probably not be the priority of people working on pearl millet!

The utility of this work is that it suggests that it is practicable to gain valuable insight into crop adaptation by clever use of modern methods from a variety of sources.

Reviewer #2 (Public Review):

Carla de la Fuente et al., utilize a diversity of approaches to understand which plant traits contribute to the stress resilience of pearl millet in the Sahelian desert environment. By comparing data resulting from crop modeling of pearl millet growth and meteorological data from a span of 20 years, the authors clearly determined that early season drought resilience is contributed by accelerated growth of the seedling primary root, which confirms a hypothesis generated in a previous study, Passot et al., 2016. To determine the genetic basis for this trait, they performed a combination of GWAS, QTL analysis, and RNA sequencing and identified a previously unannotated coding sequence of a glutaredoxin C9-like protein, PgGRXC9, as the strongest candidate. Phenotypic analysis using a mutant of the closest Arabidopsis homolog AtROXY19 suggests the broad conservation of this pathway. Comparisons between the transcript of PgGRXC9 by in situ hybridization (this work) and AtROXY19 pattern expression (Belin et al., 2014) support the hypothesis that this pathway acts in the elongation zone of the root. Additional analysis of cell production and elongation rates in root apex in both pearl millet and A. thaliana suggests that PgGRXC9 specifically regulates primary root through the promotion of cell elongation. While several studies have established the connection between redox status of cells and root growth, the current study represents an important contribution to the field because of the agricultural importance of the plant studied, and the connection made between this developmental trait and stress resilience in a specific and stressful environmental context of the Sahelian desert.

While the study presents a compelling narrative that is based on a diverse range of approaches, some aspects require further refinement to be fully convincing.

First, while it is appreciated that working with pearl millet presents certain technical challenges regarding genetic characterization, and the authors have done outstanding work by combing the power of GWAS and QTL mapping to reproducibly identify genetic loci associated with root growth, the related work in Arabidopsis is not fully substantiated. In particular, only one mutant allele was utilized to test the function of this gene in root growth. The lack of a second characterized allele or evidence of genetic complementation makes it difficult to definitively contribute the root developmental defects to the characterized mutation in ROXY19.

The role of redox status in contributing to root growth differences between accessions was not directly tested here. The manuscript is not able to mechanistically link the molecular function of ROXY19 to the change in root growth rate, however, this limitation of the study was not clearly described in the text.

The authors state the use of cell elongation rate (Morris and Silk, 1992) as a parameter to estimate the difference in root growth between contrasted pearl millet lines and A. thaliana roxy19 mutant versus wild type; however, there are inconsistencies in what data are presented. First, in Figure 2E, regarding the comparison between different genotypes of pearl millet lines, they use the parameter of maximum cell length but when authors compare cell elongation between A. thaliana genotypes, in Figure 4D, they use the elongation rate parameter. Second, while the cell elongation rate is based exclusively on the cell length data of the "elongation only" zone (Morris and Silk, 1992), the authors profile the cell length in the whole root apex, from the quiescent center to the beginning of the differentiation zone and it is not clear how they discriminate between each developmental zone and what data was used to estimate elongation rate.

Reviewer #1 (Public Review):

Specifically controlling the level of proteins in bacteria is an important tool for many aspects of microbiology, from basic research to protein production. While there are several established methods for regulating transcription or translation of proteins with light, optogenetic protein degradation has so far not been established in bacteria. In this paper, the authors present a degradation sequence, which they name "LOVtag", based on iLID, a modified version of the blue-light-responsive LOV2 domain of Avena sativa phototropin I (AsLOV2). The authors reasoned that by removing the three C-terminal amino acids of iLID, the modified protein ends in "-E-A-A", similar to the "-L-A-A" C-terminus of the widely used SsrA degradation tag. The authors further speculated that, given the light-induced unfolding of the C-terminal domain of iLID and similar proteins, the "-E-A-A" C-terminus would become more accessible and, in turn, the protein would be more efficiently degraded in blue light than in the dark.

Indeed, several tested proteins tagged with the "LOVtag" show clearly lower cellular levels in blue light than in the dark. While the system works efficiently with mCherry (10-20x lower levels upon illumination), the effect is rather modest (2-3x lower levels) in most other cases. Accordingly, the authors propose to use their system in combination with other light-controlled expression systems and provide data validating this approach. Unfortunately, despite the claim that the "LOVtag" should work faster than optogenetic systems controlling transcription or translation of protein, the degradation kinetics are not consistently shown; in the one case where this is done, the response time and overall efficiency are similar or slightly worse than for EL222, an optogenetic expression system.

The manuscript and the figures are generally very well-composed and follow a clear structure. The schematics nicely explain the underlying principles. However, limitations of the method in its main proposed area of use, protein production, should be highlighted more clearly, e.g., (i) the need to attach a C-terminal tag of considerable size to the protein of interest, (ii) the limited efficiency (slightly less efficient and slower than EL222, a light-dependent transcriptional control mechanism), and (iii) the incompletely understood prerequisites for its application. In addition, several important controls and measurements of the characteristics of the systems, such as the degradation kinetics, would need to be shown to allow a comparison of the system with established approaches. The current version also contains several minor mistakes in the figures.

Reviewer #2 (Public Review):

In this manuscript the authors present and characterize LOVtag, a modified version of the blue-light sensitive AsLOV2 protein, which functions as a light-inducible degron in Escherichia coli. Light has been shown to be a powerful inducer in biological systems as it is often orthogonal and can be controlled in both space and time. Many optogenetic systems target regulation of transcription, however in this manuscript the authors target protein degradation to control protein levels in bacteria. This is an important advance in bacteria, as inducible protein degradation systems in bacteria have lagged behind eukaryotic systems due to protein targeting in bacteria being primarily dependent on primary amino acid sequence and thus more difficult to engineer. In this manuscript, the authors exploit the fact that the J-alpha helix of AsLOV2, which unwinds into a disordered domain in response to blue light, contains an E-A-A amino acid sequence which is very similar to the C-terminal L-A-A sequence in the SsrA tag which is targeted by the unfoldases ClpA and ClpX. They truncate AsLOV2 to create AsLOV2(543) and combine this truncation with a mutation that stabilizes the dark state to generate AsLOV2*(543) which, when fused to the C-terminus of mCherry, confers light-induced degradation. The authors do not verify the mechanism of degradation due to LOVtag, but evidence from deletion mutants contained in the supplemental material hints that there is a ClpA dominated mechanism. They demonstrate modularity of this LOVtag by using it to degrade the LacI repressor, CRISPRa activation through degradation of MCP-SoxS, and the AcrB protein which is part of the AcrAB-TolC multidrug efflux pump. In all cases, measurement of the effect of the LOVtag is indirect as the authors measure reduction in LacI repression, reduction in CRISPRa activation, and drug resistance rather than directly measuring protein levels. Nevertheless the evidence is convincing, although seemingly less effective than in the case of mCherry degradation, although it is hard to compare due to the different endpoints being measured. The authors further modify LOVtag to contain a known photocycle mutation that slows its reversion time in the dark, so that LOVtag is more sensitive to short pulses of light which could be useful in low light conditions or for very light sensitive organisms. They also demonstrate that combining LOVtag with a blue-light transcriptional repression system (EL222) can decrease protein levels an additional 269-fold (relative to 15-fold with LOVtag alone). Finally, the authors apply LOVtag to a metabolic engineering task, namely reducing expression of octanoic acid by regulating the enzyme CpFatB1, an acyl-ACP thioesterase. The authors show that tagging CpFatB1 with LOVtag allows light induced reduction in octanoic acid titer over a 24 hour fermentation. In particular, by comparing control of CpFatB1 with EL222 transcriptional repression alone, LOVtag, or both the authors show that light-induced protein degradation is more effective than light-induced transcriptional repression. The authors suggest that this is because transcriptional repression is not effective when cells are at stationary phase (and thus there is no protein dilution due to cell division), however it is not clear from the available data that the cells were in stationary phase during light exposure. Overall, the authors have generated a modular, light-activated degron tag for use in Escherichia coli that is likely to be a useful tool in the synthetic biology and metabolic engineering toolkit.

Reviewer #3 (Public Review):

The authors present the mechanism, validation, and modular application of LOVtag, a light-responsive protein degradation tag that is processed by the native degradosome of Escherichia coli. Upon exposure to blue light, the c-terminal alpha helix unfolds, essentially marking the protein for degradation. The authors demonstrate the engineered tag is modular across multiple complex regulatory systems, which shows its potential widespread use throughout the synthetic biology field. The step-by-step rational design of identifying the protein that was most dark-stabilized as well as most light-responsive for degradation, was useful in terms of understanding the key components of this system. The most compelling data shows that the engineered LOVTag can be fused to multiple proteins and achieve light-based degradation, without affecting the original function of the fused protein; however, results are not benchmarked against similar degradation tagging and optogenetic control constructs. Creating fusion proteins that do not alter either of the original functions, is often difficult to achieve, and the novelty of this should be expanded upon to drive further impact.

Joint Public Review:

The manuscript presented by Pabba et al. studied chromatin dynamics throughout the cell cycle. The authors used fluorescence signals and patterns of GFP-PCNA (GFP tagged proliferating cell nuclear antigen) and CY3-dUTP (which labels newly synthesized DNA but not the DNA template) to determine cell cycle stages in asynchronized HeLa (Kyoto) cells and track movements of chromatin domains. PCNA binds to replication forks and form replication foci during the S phase. The major conclusions are: (1) Labeled chromatin domains were more mobile in G1/G2 relative to the S-phase. (2) Restricted chromatin motion occurred at sites in proximity to DNA replication sites. (3) Chromatin motion was restricted by the loading of replisomes, independent of DNA synthesis. This work is based on previous work published in 2015, entitled "4D Visualization of replication foci in mammalian cells corresponding to individual replicons," in which the labeling method was demonstrated to be sound. Although interesting, reduced chromatin mobility in S relative to G1 phase is not new to the field. The genome in HeLa cells is greatly abnormal with heterogeneous aneuploidy, which makes quantification complicated and weakens the conclusions.

Reviewer #1 (Public Review):

She et al studied the evolution of gene expression reaction norms when individuals colonise a new environment that exposes them to physiologically challenging conditions. Their objective was to test the "plasticity first" hypothesis, which suggest that traits that are already plastic (their value changes when facing a new environment compared to the original environment) facilitates the colonisation of novel environments, which, if true, would be predicted to result in the evolution of gene expression values that are similar in the population that colonised the new environment and evolved under these particular selection pressures. To test this prediction, they studied gene expression in cardiac and muscle tissues in individuals originating from three conditions: lowland individuals in their natural environment (ancestral state), lowland individuals exposed to hypoxia (the plastic response state), and a highland population facing hypoxia for several generations (the coloniser state). They classified gene expression patterns as maladaptive or adaptive in lowland individuals responding to short term hypoxia by classifying gene expression patterns using genes that differed between the ancestral state (lowland) and colonised state (highland). Genes expressed in the same direction in lowland individuals facing hypoxia (the plastic state) as what is found in the colonised state are defined as adaptative, while genes with the opposite expression pattern were labelled as maladaptive, using the assumption that the colonised state must represent the result of natural selection. Furthermore, genes could be classified as representing reversion plasticity when the expression pattern differed between the plasticity and colonised states and as reinforcement when they were in the same direction (for example more expressed in the plastic state and the colonised state than in the ancestral state). They found that more genes had a plastic expression pattern that was labelled as maladaptive than adaptive. Therefore, some of the genes have an expression pattern in accordance with what would be predicted based on the plasticity-first hypothesis, while others do not.

As pointed out by the authors themselves, the fact that temperature was not included as a variable, which would make the experimental design much more complex, misses the opportunity to more accurately reflect the environmental conditions that the colonizer individuals face at high altitude. Also pointed out by the authors, the acclimation experiment in hypoxia lasted 4 weeks. It is possible that longer term effects would be identifiable in gene expression in the lowland individuals facing hypoxia on a longer time scale. Furthermore, a sample size of 3 or 4 individuals per group depending on the tissue for wild individuals may miss some of the natural variation present in these populations. Stating that they have a n=7 for the plastic stage and n= 14 for the ancestral and colonized stages refers to the total number of tissue samples and not the number of individuals, according to supplementary table 1. Finally, I could not find a statement indicating that the lowland individuals placed in hypoxia (plastic stage) were from the same population as the lowland individuals for which transcriptomic data was already available, used as the "ancestral state" group (which themselves seem to come from 3 populations Qinghuangdao, Beijing, and Tianjin, according to supplementary table 2) nor if they were sampled in the same time of year (pre reproduction, during breeding, after, or if they were juveniles, proportion of males or females, etc). These two aspects could affect both gene expression (through neutral or adaptive genetic variation among lowland populations that can affect gene expression, or environmental effects other than hypoxia that differ in these populations' environments or because of their sexes or age). This could potentially also affect the FST analysis done by the authors, which they use to claim that strong selective pressure acted on the expression level of some of the genes in the colonised group.

Impact of the work<br /> There has been work showing that populations adapted to high altitude environments show changes in their hypoxia response that differs from the short-term acclimation response of lowland population of the same species. For example, in humans, see Erzurum et al. 2007 and Peng et al. 2017, where they show that the hypoxia response cascade, which starts with the gene HIF (Hypoxia-Inducible Factor) and includes the EPO gene, which codes for erythropoietin, which in turns activates the production of red blood cell, is LESS activated in high altitude individuals compared to the activation level in lowland individuals (which gives it its name). The present work adds to this body of knowledge showing that the short-term response to hypoxia and the long term one can affect different pathways and that acclimation/plasticity does not always predict what physiological traits will evolve in populations that colonize these environments over many generations and additional selection pressure (UV exposure, temperature, nutrient availability).

Altogether, this work provides new information on the evolution of reaction norms of genes associated with the physiological response to one of the main environmental variables that affects almost all animals, oxygen availability. It also provides an interesting model system to study this type of question further in a natural population of homeotherms.

Erzurum, S. C., S. Ghosh, A. J. Janocha, W. Xu, S. Bauer, N. S. Bryan, J. Tejero et al. "Higher blood flow and circulating NO products offset high-altitude hypoxia among Tibetans." Proceedings of the National Academy of Sciences 104, no. 45 (2007): 17593-17598.

Peng, Y., C. Cui, Y. He, Ouzhuluobu, H. Zhang, D. Yang, Q. Zhang, Bianbazhuoma, L. Yang, Y. He, et al. 2017. Down-regulation of EPAS1 transcription and genetic adaptation of Tibetans to high-altitude hypoxia. Molecular biology and evolution 34:818-830.

Reviewer #2 (Public Review):

This is a well-written paper using gene expression in tree sparrow as model traits to distinguish between genetic effects that either reinforce or reverse initial plastic response to environmental changes. Tree sparrow tissues (cardiac and flight muscle) collected in lowland populations subject to hypoxia treatment were profiled for gene expression and compared with previously collected data in 1) highland birds; 2) lowland birds under normal condition to test for differences in directions of changes between initial plastic response and subsequent colonized response.

The question is an important and interesting one but I have several major concerns on experimental design and interpretations.

1) The datasets consist of two sources of data. The hypoxia treated birds collected from the current study and highland and lowland birds in their respective native environment from a previous study. This creates a complete confounding between the hypoxia treatment and experimental batches that it is impossible to draw any conclusions. The sample size is relatively small. Basically correlation among tens of thousands of genes was computed based on merely 12 or 9 samples.

2) Genes are classified into two classes (reversion and reinforcement) based on arbitrarily chosen thresholds. More "reversion" genes are found and this was taken as evidence reversal is more prominent. However, a trivial explanation is that genes must be expressed within a certain range and those plastic changes simply have more space to reverse direction rather than having any biological reason to do so.

3) The correlation between plastic change and evolved divergence is an artifact due to the definitions of adaptive versus maladaptive changes. For example, the definition of adaptive changes requires that plastic change and evolved divergence are in the same direction (Figure 3a), so the positive correlation was a result of this selection (Figure 3d).

Reviewer #1 (Public Review):

In this paper, authors used IL-33KO and ILC2KO mice to generate evidence for pregnancy specific enrichment of ILC2s and their unique molecular signatures. They documented that uterine ILC2s are distinct from the ILC2s residing in lung and lymph nodes. They have provided solid evidence that although litter size did not change, but fetuses from ILC2KO mice showed growth restriction. In the absence of ILC2s, LPS injections lead to increased abortion rates and fetal loss suggesting the critical role of ILC2s in protection against infection induced pathology. Most of the results presented in the paper support the conclusion, but in some instances the evidence is indirect. Some clarifications on experimental interpretations are required as below.

• The immunohistology experiments revealed that IL-33 predominately co-localizes with ILC2 in the myometrium, but the staining appears to be diffused throughout the myometrium. It is difficult to pinpoint ILC2 specific IL-33 colocalization. Is the decidual expression of IL-33 largely restricted to ILC2s? Also, IL-33 is produced by a variety of other cell types including endothelial cells, which is difficult to ascertain from the images in Fig 1.<br /> • Authors report that ILC2s are responsible for fetal growth restriction (FGR) noted in the ILC2 KO mice. They attribute FGR to utero-placental abnormalities but the experimental evidence, including spiral arterial remodelling and glucose transporter gene expression are indirect. Is there any compensation from other cell types such as macrophages in the absence of ILC2s as they reported increased dendritic cells, neutrophils, and macrophages in ILC2KO mice. Clarify whether IL-33 levels were consistent between WT and ILC2KO mice. How do these other increased numbers of macrophages, DCs and neutrophils fit in the FGR context besides gene expression changes they captured in DCs and macrophages.<br /> • They captured spiral arterial wall to lumen ratio alterations in ILC2KO mice suggesting sub-optimal vascular changes in ILC2KO mice. They did not find any changes in the uNK cells or IFN-production between WT and ILC2KO. What would be the mechanistic link between ILC2 and spiral arterial vascular changes. They indirectly link it to the IL1B gene expression.<br /> • In the LPS induced abortion in ILC2 KO mice experiments, how do they reconcile the predominant role of macrophages and LPS induced TNF-a in the pathology? They did not find any differences in the gene expression in the LPS induced signature cytokine, TNF-a despite increased numbers of macrophages in ILC2 KO mice. Clarification is required on whether these inflammatory alterations that they captured directly linked to utero-placental insufficiency between WT and ILC2KO. The type 2 cytokines were barely detectable in ILC2KO mice, which likely predispose them for utero-placental alterations.

Reviewer #2 (Public Review):

Balmas et al., continue the previous work from multiple groups that suggested the implication of uterine ILC2s and signals that activate them, i.e., IL-33/ST2 axis, in healthy and complicated pregnancies and move forward to understand further their role. The authors leverage available and appropriate tools to address more specifically the role of ILC2s during pregnancy and endotoxin-induced abortion, namely mouse models of selective ILC2 deficiency (Roraflox/floxIl7raCre/wt) and transcriptomic analysis of the immune response.

The authors demonstrate, and therewith confirm findings by Bartemes et al. (2018), that ILC2 reside in the mouse uterus, depend on IL-33 and expand during pregnancy. Moreover, they show the Il33 expression by CD45- cells of the uterine stroma. What remains unclear is the kinetics of Il33 expression and ILC2 expansion upon gestation and whether the local ILC2 population expands or arrives from the periphery.

Lack of maternal ILC2, in a mouse genetic model, resulted, as expected, in the absence of uILC2 but also in lighter fetuses at term, similar to the phenotype observed in the absence of maternal IL-33. It would be interesting to understand whether the effect of the IL-33 signaling is a direct ILC2 mediated effect, as for example by using the ST2flox/flox mice. Do the fetuses catch up in weight with their WT controls during weaning time? Do they have any long-term cognitive/behavioral impairment?<br /> The authors showcase the impairment in the remodeling of uterine wall vessels in dams lacking ILC2. It remains to be verified whether this is dependent on IL-33 and whether it is a direct effect of ILC2 or ILC2-dependent infiltration of eosinophils. Further, the absence of ILC2 is accompanied by an increase in Il1b in the uterine tissue suggesting that uILC2 contribute to the uterine microenvironment.

The authors perform RNA sequencing analysis on the bulk samples of uterine ILC2, where uILC2 cluster separately from corresponding lung and LN cells and are featured with higher expression of typical ILC2 markers. Somewhat odd, the authors report on the Foxp3/FoxP3 expression among uILC2, however the staining is not very bright and a Treg control as well as biological negative control should be provided. Moreover, FoxP3 is also not expressed among intestinal ILC2 with regulatory function (Wang et al. 2017). I suggest this data panel to be re-evaluated. A scRNA-Seq analysis would probably be more comprehensive in this case, but might be beyond the scope of this publication.

Absence of uILC2 results in the increased numbers of DCs, macrophages and neutrophils in the uterus, an impact which is not visible in the spleen, which is why the authors argue that this is a uterus-restricted phenomenon, although perturbances in the large intestine and lungs could be expected. Moreover, it remains to be investigated whether these effects are restricted to mid-term pregnancy or preserved until term.

Upon establishing the role of uILC2 in maintaining healthy pregnancy, the authors demonstrate a role for uILC2 in endotoxin-mimicked bacterial infection and abortion. An impressive set of data demonstrate that dams that lack uILC2 have a significantly higher fetus resorption rate than WT dams upon LPS challenge. It remains to be understood whether this phenotype is also dependent on IL-33. Finally, mechanistically, using a somewhat reductionist in vitro model, the authors suggest a protective feedback mechanism between type 2-secreting uILC2 and IL-1b-expressing DCs. This is an interesting concept that still needs a formal confirmation in vivo. Are uILC2 also subjected to plasticity upon IL-1b treatment (Ohne et al. 2016)?

In conclusion, the authors provide a well-conceived study that will be useful for reproductive and tissue immunologists. The data are collected using validated models and methodology and analyzed in a solid manner and can be used as a starting point for further mechanistic studies, assessing the protective potential of uILC2 in pregnancy during infections.

Reviewer #3 (Public Review):

The authors show that ILC2s seem to be important during pregnancy to achieve an optimal fetal growth. This is an important finding to the field and provides ILC2s with new roles distinct from parasite protection and allergy inducers. However, the fetal weight restriction phenotype does not seem very striking. Moreover, the mechanism by which ILC2s promote homeostasis in pregnancy are not well shown. The data shown in Figure 3 is overall a bit confusing and does not lead the reader to the conclusions stated in the text. Figure 4 conclusions are not very informative. The authors also show that ILC2s are protective to fetal loss during LPS infection. Again, the means by which ILC2s could be doing so are not well presented and the supporting data not fully convincing. Throughout the manuscript, the authors present quantitative data as fold change, expressing data in fold change is not as clear as showing actual numbers of cells for each group. Moreover, they should show the flow cytometry plots and gating strategy for all their FACS analysis.

Reviewer #1 (Public Review):

In this study Guss and colleagues identify a requirement of the ECM component Perlecan for the maintenance of neuronal structures. The authors convincingly demonstrate that the absence of Perlecan (in the entire organism) causes a severe perturbation of the ECM-based neural lamella, a support structure surrounding axon bundles and, to a lesser extent, the neuromuscular junction (NMJ). Likely because of these ECM perturbations, axons and even entire nerve bundles break at sites prior to the innervation of the peripheral muscles. Within hemisegments all affected motoneurons show signs of degeneration and synapses are retracted (degenerate). Through targeted genetic approaches in combination with immunohistochemical and electrophysiological approaches the authors aim to elucidate cell specific requirements of Perlecan. Interestingly, knock down of Perlecan in single tissues but also in combinations of tissues (neurons, glia and muscles) was not sufficient to recapitulate the phenotypes observed after ubiquitous knock down. Similarly, a rescue of these phenotypes via motoneuron expression in null mutants was not successful.

The authors very convincingly demonstrate that in the absence of Perlecan synaptic terminals degenerate and that axon and neural lamella morphology and structure is perturbed. All processes were analyzed using multiple and complementary approaches including live-imaging and electrophysiology. The precise correlation of these phenotypes and especially the careful classification into degenerated and non-affected NMJs revealed that the cause for all phenotypes is likely the disruption of the neural lamella that - through thus far unknown mechanisms - cause axonal breakage and subsequently synaptic retractions.

This study highlights the importance of the ECM to maintain neuronal structures, however, the precise source of Perlecan and the precise cause of axonal breakage remains still unresolved.

Further rescue experiments would be necessary to resolve the source of Perlecan. This requires a first demonstration that a rescue is possible with the available tools using a ubiquitous-expression analogous to the RNAi-experiments.<br /> In addition, a careful longitudinal analysis of the integrity of individual axons (e.g. MN1 or MN4) combined with an ECM analysis may provide insights into the place and cause of the axonal breakages that are likely causal for all other observed phenotypes. As pointed out in the discussion a disruption of the blood-brain barrier at specific (?) vulnerable sites seems currently the most reasonable explanation for the observed effects. Surprisingly, the authors did not observe any rescue effect after the inhibition of Wallarian degeneration mechanisms highlighting that the cellular mechanisms underlying these two forms of degeneration in which axons are disrupted may be different.

Reviewer #3 (Public Review):

The manuscript by Guss et al. characterizes an extracellular matrix protein, Perlecan (trol), in maintaining axon and synapse stability in motor neurons through its function in maintaining the neural lamella's integrity in Drosophila. Using a combination of immunostaining and protein labeling with fluorescent tags, the authors find that perlecan localizes to the neural lamella. When perlecan is deleted, the authors identify a synapse retraction phenotype as the subsequent result of axon damage. They further suggest that this axon instability is the result of loss of perlecan causing a disruption in the neural lamella, due to the mislocalization of neural lamella protein, Collagen IV (Vkg). Moreover, they find that perlecan acts independently of previously characterized interactions with the wnt signaling and Wallerian degradation pathways, however important controls for these negative results are lacking.

The manuscript offers an interesting and important role for perlecan in motor neuron axon maintenance. However, the experiments attempting to elucidate the mechanism of action of this protein require further validation and clarification.

Reviewer #1 (Public Review):

We conclude that this descriptive study has some strengths but additionally, we propose several ways in which to increase its potential impact and to strengthen some of the claims. This study describes the remodeling of Merkel cells and their innervating sensory axons in the skin. By using transgenic mouse lines in which these cells were genetically fluorescently labeled, the authors performed a series of analyses mostly focusing on the number and location of Merkel cells and the sensory axons that innervate them.

One of the major strengths of the study is the establishment of intravital imaging techniques to investigate the dynamic simultaneous behavior of Merkel cells and their innervation during homeostasis and hair regeneration. However, how the findings integrate into the existing knowledge of skin development is unfortunately only partially addressed.

To the best of our understanding, a few technical limitations of the study define its major weaknesses: First, Merkel cell loss is dramatic and it's unclear whether this reduction is part of a developmentally controlled reduction in cell number, or whether additional cells are expected to be integrated into the system. Longer windows of imaging might help here. Second, the depilation agent might be too aggressive and lead to cell death and thus better controls might be suggested. Similarly, ablating Merkal cells throughout development might cause developmental issues that might mask the proposed homeostasis analyses. A controlled adult specific ablation might be suggested. Finally, the TrkC based transgenic mouse is expected to be heterozygous - could that be an issue? Either better controls, or a textual addressing of this topic are advised.

All in all, we think this study has the potential to establish a high resolution description of Merkel cells - sensory axon dynamic interactions. We hope that the authors will be encouraged to improve the paper based on our comments, something that will likely improve its potential significance and impact.

Reviewer #2 (Public Review):

Clary et al. utilized 2-photon intravital imaging techniques to investigate the dynamic behavior of Merkel cells and their innervation during homeostasis and hair regeneration. The authors demonstrated that both Merkel cells (Atoh1-GFP) and the branched axons (TrkC) innervating them undergo significant plasticity and remodeling during homeostasis. Merkel cells were added, removed, and relocated, while axons showed growth and regression. By taking advantage of live imaging, the authors identified two different ways in which Merkel cells interact with axons: creating the stable kylikes and the previously undescribed dynamic Bouton structure. Using live imaging and extensive quantification tools, the authors thoroughly characterized Merkel cell and axon plasticity. They found that Merkel cell plasticity is associated with the hair cycle, while axon plasticity is not. Moreover, newly generated Merkel cells have a short lifespan. By comparing the survival of afferents associated with Merkel cells to empty ones and analyzing Atoh1 cKO, the authors concluded that Merkel cells have a stabilizing effect on axon terminals.

Strengths:

The authors developed an intravital imaging system that enables the simultaneous tracking of both Merkel cells and axon branches. Live imaging, combined with numerous quantification tools, enabled an in-depth characterization of the different behaviors and dynamic nature of Merkel cells, axon branches, and their interaction. The authors' approach has the particular strength of allowing for the comparison of the dynamic behavior of axons associated with Merkel cells to those not innervating Merkel cells within the same touch dome, as well as describing a Bouton structure as a novel morphology mediating Merkel cell and nerve interaction.

Weaknesses:

Although the authors provide an in-depth analysis of Merkel cell dynamics and its association with hair growth, these concepts have been previously reported by the authors and others. Therefore, the extent of novel concepts and scientific advances should be better explained.

The authors suggest that Merkel cell association is a stabilizing factor on innervated axon branches by comparing branch plasticity between branches connected to Merkel cells and empty ones and using Atoh cKO. While the first set of experiments are compelling and provide interesting observations, additional experimental models, such as Merkel cell ablation in adults, may better strengthen the authors' claims. The authors currently use K14-Cre;Atoh1 cKO to support their observations. However, the absence of Merkel cell development in this model, might also lead to developmental defects in nerve patterning (absence of target organ) leading to the phenotype observed by the authors.

Finally, the authors use intravital imaging to describe the Bouton structure and dynamic. Though very interesting there is not enough data to support authors claim for interaction between axon and Merkel cells through the Bouton structure. The paper can benefit from additional functional analysis of this structure.

Reviewer #3 (Public Review):

This study documents the dynamics of Merkel cells and their axonal afferents during the hair growth cycle. Methodologically, the study is impressive-using two transgenic lines and repeated 2-photon imaging allowed the researchers to monitor Merkel cells and afferent axons over the course of weeks. These exciting tools and methods will enable future studies of these cell interactions. The manuscript is well written, the figures are clear and appealing, the statistical analyses are rigorous and appropriate, and potentially confounding issues (e.g., damage caused by 2-photon imaging or hair removal) were thoughtfully considered and controlled for. The clear and rigorously analyzed findings make the conclusions well justified. The impact of this study could be enhanced with further experiments that provide more functional characterization of boutons and kylikes, and that characterize axonal dynamics in Atoh mutants lacking Merkel cells.

Reviewer #4 (Public Review):

In this manuscript, Clary and colleagues use two-photon imaging to visualize the dynamics of Merkel cells and their innervating sensory axons using a combination of transgenic lines, where these parts of the mechanosensory organs of the skin are labelled with distinct fluorescent proteins. It is noteworthy that this study does not stand alone, but should be compared to prior published work cited by the authors, such as Wright et al., Developmental Biology 422 (2017) 4-13.

The study demonstrates a comparably high degree of remodelling, with a large fraction of Merkel cells (50% in three weeks) and a similar fraction of elaborated (cup-like) axons endings disappearing. It appears by timing and correlation that changes in Merkel cells can clearly drive axonal remodelling, while axons can still remodel even if the Merkel cells remain stable by the parameters measured here. Moreover, changes in Merkel cells partially relate to the hair growth cycle.

The imaging approach chosen is straightforward and clearly suited in principle to reveal the dynamism of the studied cellular structures. To co-visualize two synaptic partners in a vertebrate sensory organ in vivo - while not unprecedented - certainly remains quite challenging, and represents a strength of the paper. Similarly, understanding how stable structures in the nervous system are under homeostatic (rather than developmental) conditions, remains an understudied topic. I also found some of the correlative analysis in the later parts of the study quite interesting, albeit not always straightforward to interpret.

My central concern is the very high disappearance rate of Merkel cells. This, in my view is not compatible with a steady state situation in an adult animal - and not with the prior literature (especially the similar study by Wright et al. cited above). Obviously, if this rate were to continue, Merkel cells would all be lost in early adulthood in mice. Whether this is the case in the specific anatomical location was not examined in the study - but it would also imply that the study really addresses a dynamic developmental remodelling situation and should be written up accordingly. I am more suspicious of the depilation agent (plus the shaving). As Wright et al. already show that shaving causes some changes in Merkel cell dynamics (but, as far as I can tell, did not chemically depilate), I would not be surprised that we see an artificially high remodelling rate. Such skin treatment-related biology is probably less relevant in the context of neurobiology (albeit probably quite interesting to other audiences). So, my recommendation to the authors would be to invest some energy to find out, what causes the swift Merkel cell loss.

Another technical point that warrants discussion is the axonal labelling - first, I do not find the innervation patterns always easy to discern in the images provided, so I am not always sure how reliable this part is. Any artefact here creates the impression of dynamics, as during in vivo imaging stability is more reassuring than change. There are many ways not to see or recognize something, while there are few options to explain by an artefact why something did not change. Additionally, it might be good to explicitly mention that the TrkC mice are knock-in/knock-out (this is how I understood the JAX entry) - so the observations were made under reduced TrkC expression. It would help to explain, why this cannot affect axonal dynamics or Merkel cell-axon interactions.

Overall, while I feel that the authors performed an interesting in vivo imaging study, I think technical aspects make it difficult to conclude with confidence, whether we are watching a normal and physiological process here or dynamics that are induced by specific interventions. While these interventions might represent conditions that can occur also outside the laboratory, it would be important to clarify how the reader should contextualize this study.

Reviewer #1 (Public Review):

In the manuscript, the authors tried to explore the molecular alterations of adipose tissue and skeletal muscle in PCOS by global proteomic and phosphorylation site analysis. In the study, the samples are valuable, while there are no repeats for MS and there are no functional studies for the indicted proteins, phosphorylation sites. The authors achieved their aims to some extent, but not enough.

Reviewer #2 (Public Review):

This study provides the proteomic and phosphoproteomics data for our understanding of the molecular alterations in adipose tissue and skeletal muscle from women with PCOS. This work is useful for understanding of the characteristics of PCOS, as it may provide potential targets and strategies for the future treatment of PCOS. While the manuscript presents interesting findings on omics and phenotypic research, the lack of in-depth mechanistic exploration limits its potential impact.

The study primarily presents findings from omics and phenotypic research, but fails to provide a thorough investigation into the underlying mechanisms driving the observed results. Without a thorough elucidation of the mechanistic underpinnings, the significance and novelty of the study are compromised.

Reviewer #2 (Public Review):

In this study, Isoe and team produced an atlas of the telencephalon of the adult medaka fish with which they better defined pallial and subpallial regions, characterized the expression of neurotransmitters, and performed clonal analysis to address their organization and maintenance during the continuous neurogenesis. They show that pallial anatomical regions are formed by independent clonal units. Furthermore, the authors demonstrate that pallial compartments exhibit region-specific chromatin landscapes, suggesting that gene expression is differentially regulated. Specifically, synaptic genes have a distinct chromatin landscape and expression in one of the regions of the dorsal pallium, the Dd2. Using the region-specific RNA expression and chromatin accessibility data they have generated; the authors propose several transcription factors as candidate regulators of Dd2 specification. Lastly, the authors use the enrichment of transcription factor binding motifs to establish homology between medaka and human telencephalon, aiming to describe an evolutionary origin for the Dd2 region.

Overall, the study carefully describes diverse aspects of neurogenesis in the telencephalon of the adult medaka fish. As such, the manuscript has the potential to contribute insights to the understanding of circuits and neurogenesis in teleosts and the medaka fish, as well as the evolution of cellular heterogeneity and organization of the telencephalon. Furthermore, the atlas, if easily accessible to the broader community, could be a substantial resource to researchers interested in medaka and teleosts neuroscience. However, there are some conceptual and technical concerns that should be addressed to strengthen this work.

Improving the atlas: The different interpretations of the imaging data generated remain isolated or fragmented and could be better integrated to describe anatomical, connectivity, and ontogeny differences through pallial and subpallial regions.<br /> Molecular differences across regions and species: Differential gene expression and chromatin accessibility throughout regions should be better and more deeply characterized and presented, exhibiting more region-specific features, and leading to a better description of candidate transcription factors that could differentially regulate regional gene expression. The comparison between medaka fish and human telencephalon regions would benefit from a more extensive molecular analysis. Comparison of gene expression and accessible regions could expand the analysis together with TF-binding motif enrichment.<br /> Lineage tracing: The authors claim that the functional compartmentalization of the pallium relies on different cell lineages, which also mostly share connectivity patterns and, at least to some extent, expression patterns. It would be interesting to see how homogenous these lineages are at the molecular level and whether their compartmentalization is retained when neurons reach maturity.

Reviewer #1 (Public Review):

In the present study, Yasuko Isoe, Ryohei Nakamura & colleagues follow a lineage analysis study aiming at identifying the clonal organization of the dorsal telencephalon. The authors use the teleost fish medaka to conduct their experiments since it displays a clearly delineated dorsal pallium. After identifying the clonal units that constitute the dorsal telencephalon, they analyze the epigenetic landscape in each unit. The authors identify then differential open chromatin patterns that they relate to functional aspects of each unit, and additionally, use the epigenetic landscape to infer the identity of transcription factors operating as putative regulators. Overall, the study consists of an impressive amount of data that shed light on the organization of a central brain region in vertebrates.

The findings in the manuscript are organized into two main sections: lineage analysis and epigenetic organization. The authors combine genetic tools with laser dissections of specific clones and ATAC-seq and RNA-seq analysis in multiple samples, an approach that is very elegant and follows high technical standards. For lineage analysis, the authors used a basic, but appropriate, tool to induce and follow clones generated in early embryos, with the side note that lineages are followed using a non-ubiquitous promoter so that the authors restrict their analysis to neural progenitors. My overall impression is that the authors have collected a massive amount of high-quality data, which unfortunately is not properly integrated or discussed in the manuscript. There is only a superficial effort in incorporating the two main findings, which contrasts with the depth of acquired data.

The observation of clonal sectors in the pallium is a great finding that deserves a more detailed analysis in terms of their developmental and evolutionary origin: How may progenitors are used to set up the entire pallium? What is the smallest clone that contributes to it? Is there any laterality bias in the clonal architecture? Is the clonal architecture exclusive for progenitors or does it extend to neurons as well? How has the clonal architecture impacted the morphological diversity of the pallium among teleosts? What are possible evolutionary paths to explain this phenomenon? The authors' discussion on this point circles around one concept, illustrated in the following sentence: " (The clonal architecture) ... possibly explains how the difference in diversity between the pallium and subpallium has emerged: the subpallium is conserved because cells belong to various clonal units intertwined with each other, which has constrained their modification during evolution; whereas the pallium is diverse because of the modular nature of the clonal units which allows for the emergence of diversity". This is the concept that I have the most problems with. The authors' reason that a more defined clonal structure (pallium) makes a system more prone to evolutionary novelties, while a region where clones intermingle (subpallium) is more rigid and therefore more conserved between species.  Is there experimental data that backs up this statement in any other systems? If there is, I urge the authors to share these here. If this is just a speculation, then the argument would benefit from an explanation of how this clonal organization allows for evolutionary novelty. Would it happen by the appearance of more clones at the early stages of development? The authors leave this central point untouched even when discussing the evolutionary origin of the pallium in teleosts.

Having shown the clonal architecture of the pallium and conducted a detailed epigenetic analysis in clones, the authors could also speculate on what is special about this type of organisation. Particularly, how they envision that cells belonging to the same clone inherit a common epigenetic landscape that will define their function later on. There is little analysis of the cellular organization of each clone, mainly because the authors labeled only a subset of the real, genetic clone. The authors present images of entire brains and optical horizontal and transverse sections, which largely sustain their claims for a clonal organization. The difference in the clonal arrangements between the Dld and the Vd is clear, but the authors could provide a higher-resolution image of some clones in the telencephalon to get an idea of the cellular composition of the regions they use for their analysis. What is the extent of non-GFP cells in the regions they use for RNAseq and ATACseq? From the images shown it is very difficult to realize whether all cells in the clonal sector do indeed belong to the clone.

Reviewer #3 (Public Review):

In this manuscript, the authors characterized the clonal composition in the medaka pallium and found the dorsal pallium region to be a compartment constituting repeatedly identifiable clonal units. By performing ATAC-seq on the clonal units as well as RNA-seq on different subregions of the pallium, the dorsal pallium was further identified as a unique region with open chromatin regions with regulatory elements enriched for synapse-related genes. Further experimental and bioinformatic evidence supports the region's putative function of synapse generation, with similar TF-binding motifs to homologous brain regions in human. Although the "uniqueness" of the dorsal pallium might be a coincidence of the timing of clonal tracing, the conclusions in the manuscript are largely supported by experimental evidence. The study showcases an elegant model of how anatomical, molecular, and functional diversity arises in a previously under-characterized brain region. The enriched genes in Dd2 are an interesting candidate for future investigation, and the function of the dorsal pallium of teleost fish is of general interest for studies of brain evolution.

Reviewer #1 (Public Review):

Holzinger et al. investigated potential antimicrobial compounds for cystic fibrosis (CF) infection with similarity to a host-derived antimicrobial, bactericidal permeability-increasing protein (BPI). Human BPI (huBPI) is neutralised by anti-BPI antibodies, rendering it ineffective at eradicating Pseudomonas aeruginosa infection in a large proportion of people with cystic fibrosis. BPI produced by mice (muBPI), scorpionfish (scoBPI), and oysters (oyBPI) was evaluated on their anti-inflammatory, bactericidal, and immunogenic potency. The authors showed that each BPI orthologue evaded recognition by anti-BPI. The cationic BPI orthologues also reduced bacterial burden in vitro, reducing the expression of proinflammatory cytokines (IL-6 and TNF) and significantly decreasing cell culture density in the laboratory and multidrug-resistant P. aeruginosa strains. ScoBPI was the most potent, with greater anti-inflammatory and bactericidal activity than huBPI and all other orthologues.

This study investigates the action of BPI orthologues as potential CF antimicrobials. While scoBPI appears significantly more effective as an anti-inflammatory and bactericidal agent compared to huBPI, the orthologue has not been tested in environments that model the CF lung environment. The authors describe the cationic BPI as binding to the LPS via electrostatic interaction. This interaction could be limited in vivo, as anionic extracellular DNA, cationic metal ions and polyamines, and other charged substances may impede interaction. Further, delivery of scoBPI to the infection site may be detrimentally impacted, due to the viscous mucous in the lungs and the biofilm mode of growth of P. aeruginosa. The discussion of the study could be improved by describing important considerations for future development. These could include in vitro testing against P. aeruginosa biofilms in relevant sputum-mimicking media, and in vivo validation in Galleria mellonella, and CF and non-CF mouse models.

Overall, the authors present an interesting study that provides a compelling basis for a potential novel antimicrobial for CF chronic airway infection. The authors' claims are well supported by their data, which they present in a clear, logical manner. To build on these findings, the authors could test scoBPI in models that recapitulate core factors of the CF environment.

Reviewer #2 (Public Review):

Human bactericidal/permeability-increasing protein (huBPI) is known to have in vitro antibacterial activity against Pa, but in vivo, its antibacterial activity is significantly lowered due to binding by autoantibodies called BPI-ANCA. The authors of this study hypothesized that non-human BPIs would escape neutralization by intrinsic BPI-ANCA and retain full antibacterial activity against Pa. Through bioinformatic analysis, the authors anticipated that scorpion BPI (scoBPI) has enough similarity with huBPI to retain antimicrobial activity while escaping recognition by BPI-ANCA. This hypothesis is supported by the following observations: 1) scoBPI fails to capture any BPI-ANCA, 2) scoBPI prevents E. coli- and Pa-LPS dependent inflammatory responses like huBPI and 3) scoBPI exhibits remarkable antimicrobial activity against MDR-Pa in the nanomolar range. Antimicrobial activity of scoBPI was also demonstrated against E. coli suggesting a conserved mechanism of activity against Gram-negative bacteria. The authors use immobilization methods to demonstrate that scoBPI does not bind BPI-ANCA, but a drawback of this method is that some molecular interactions may be disrupted due to immobilization. Moreover, any inhibitory effects of BPI-ANCA on scoBPI activity in the bactericidal assays were not explored. Regardless, the results of this study clearly support their original hypothesis. These findings have broad implications in identifying novel chemotherapies to treat drug-resistant Gram-negative bacterial infections.

Reviewer #1 (Public Review):

In this paper, the authors use patch-clamp recordings of immature (4w and 5w) and mature granule cells (GC) in hippocampal slices to study stimulus-response properties at different cell ages. First analyzing spike trains generated by a fluctuating stimulus, they show that the reliability of spiking responses increases with cell age. They then fit a Spike Response Model (SRM), a type of GLM that translates inputs to membrane potential and then membrane potential to spikes. Using this model they compare the model parameters from different cells. Time constants for the input-voltage filter are faster for the mDGCs than the 4w, with 5w intermediate, and time constants across all cells appear to be faster when reliability is higher. They analyze stimulus reconstruction and stimulus-response information using the recordings and then extend this to pseudo-populations to test how heterogeneous properties contribute to coding efficacy. They find that mixed pools of neurons, including cells of multiple ages, decode stimuli more accurately.

Overall, this is a cleverly designed study with sound methodology. A major contribution of the paper is demonstrating with precise, quantitative methods how a degree of heterogeneity that naturally arises in neural populations may be beneficial to decoding the stimulus, despite the fact that some of the heterogeneity arises from variability in single cells. This is an intriguing result showing how neural coding and decoding may actually benefit from heterogeneous response properties rather than only be hindered by variation.

The paper has a couple of weaknesses. First, it is difficult to assess how meaningful the effects that the authors measure are. For example, is a 3% improvement in decoding (Fig. 4H) with mixed populations of GCs substantial? A second issue not currently addressed in the paper is the relative roles of age-dependent variability and within-group variability: how much of the improvement in stimulus decoding/information encoding is achieved by heterogeneity across model parameters that appears in each age group? Further analyses and clarifications in this vein are suggested.

Reviewer #2 (Public Review):

The paper by Arribas et al. examines the coding properties of adult-born granule cells in the hippocampus at both single cell and network level. To address this question, the authors combine electrophysiology and modeling. The main findings are:<br /> - Noisy stimulus patterns produce unreliable spiking in adult-born granule cells, but more reliable responses in mature granule cells.<br /> - Analysis of spike patterns with a spike response model (SRM) demonstrates that adult-born and mature GCs show different coding properties.<br /> - Whereas mature GCs are better decoders on the single cell level, heterogeneous networks comprised of both mature and adult-born cells are better encoders at the network level.

Based on these results, the authors conclude that granule cell heterogeneity confers enhanced encoding capabilities to the dentate gyrus network.

Although the manuscript contains interesting ideas and initial data, several major points need to be addressed.

Major points:<br /> 1. The authors use and noisy stimulation paradigm to activate granule cells at a relatively high frequency. However, in the intact network in vivo, granule cells fire much more sparsely. Furthermore, granule cells often fire in bursts. How these properties affect the coding properties of granule cells proposed in the present paper remains unclear. At the very least, this point needs to be better discussed.

2. The authors induce spiking in granule cells by injection of current waveforms. However, in the intact network, neurons are activated by synaptic conductances. As current and conductance have been shown to affect spike output differently, controls with conductance stimuli need to be provided. Dynamic clamp is not a miracle anymore these days.

3. The greedy procedure is a good idea, but there are several issues with its implementation. First, it is unclear how the results depend on the starting value. What we end up with the same mixed network if we would start with adult-born cells? Second, the size of the greedy network is very small. It is unclear whether the main conclusion holds in larger networks, up to the level of biological network size (1 million). Finally, the fraction of adult-born granule cells in the optimal network comes out very large. This is different from the biological network, where clearly four or five-week-old granule cells cannot represent the majority. Much more work is needed to address these issues.

4. Likewise, the idea of dynamic pattern separation seems quite nice. However, the authors focus on the differences between mixed and pure networks, which are extremely small. Furthermore, the correlation coefficients of "low", "medium", and "high" correlation groups are chosen completely arbitrarily. A correlation coefficient of 0.99, considered low here, would seem extremely high in other contexts. Whether dynamic pattern separation is possible over a wider range of input correlation coefficients is unclear (see O'Reilly and McClelland, 1995, Hippocampus, for a possible relationship). Finally, aren't code expansion and lateral inhibition the key mechanisms underlying pattern separation? None of these potential mechanisms are incorporated here.

5. A main conclusion of the paper is that while mature GCs are better decoders on the single cell level, heterogeneity in mixtures improves coding in neuronal networks. However, this seems to be true only for r^2 as a readout criterion (Fig. 4F). For information, the result is less clear (Fig. 4G). The results must be discussed in a more objective way. Furthermore, intuitive explanations for this paradoxical observation are not provided. Saying that "this is an interesting open question for future work" is not enough.

6. The authors ignore possible differences in the output of mature and adult-born granule cells in their thinking. If mature and adult-born granule cells had different outputs, this could affect their contributions to the code (either positively or negatively). At the very least, this possibility should be discussed.

Reviewer #3 (Public Review):

This is a highly interesting paper that comprehensively investigates the electrophysiological properties of granule cells in the dentate gyrus at different developmental stages. Using state-of-the-art in vitro electrophysiological techniques, the authors record granule cell responses to fluctuating current injections to study how they encode stimuli. The authors find that while immature granule cells produce less reliable stimulus responses and worse stimulus representations than mature cells (8wks and older), cell populations containing neurons of mixed ages improve overall stimulus reconstruction. These data suggest that the cellular diversity contributed by immature granule cells could be beneficial for transmitting distinct properties of stimuli with rich temporal structure, potentially improving the cellular process of pattern separation.<br /> Major strengths of the paper lie in the precise age determination of immature neurons in Ascl1-CreERT2-Tom mice, recordings of immature neurons, which are rare in in vivo and in vitro studies, precise control over cell-intrinsic properties by blocking excitatory and inhibitory inputs in vitro, and characterization of encoding properties using a spike response model (SRM).

The conclusions drawn are supported by the data, and the results are likely of great interest to a specialist community of hippocampal electrophysiologists.

Reviewer #1 (Public Review):

This manuscript by Toshima et al. describes a study of the organization of traffic in the endomembrane system of the budding yeast Saccharomyces cerevisiae. The authors address the relation between endocytosis and the Golgi (TGN: a collection of maturing membrane elements derived from the trans-Golgi). The study builds on a previous article by the group of Benjamin Glick. In that study (Day et al., 2018), it was postulated that the TGN is the first destination for yeast endocytic traffic after internalization from the plasma membrane. Additionally, Day et al. had shown that endocytic recycling traffic towards the plasma membrane departs from the TGN as well. Therefore, early endosome and recycling endosome compartments would be identical to the TGN or contained within it. Here, Toshima et al. use super-resolution confocal live imaging microscopy (SCLIM) to refine a model of endocytic pathway organization. This powerful imaging technology allows them to show that out of two partially overlapping TGN markers, namely Tlg2 and Sec7, the syntaxin Tlg2 correlates better with the arrival of fluorescently labeled endocytic cargo than alternative TGN marker Sec7. Building on this main finding, the authors conclude that a specific part of the TGN (an "independent sub-compartment") functions as the early endosome. Further experiments in mutants for GGA clathrin adaptors, required for departure of endocytic cargo from the TGN to the Rab5-positive prevacuolar endosome, show again that endocytosed cargo accumulation correlates better with Tlg2 than with Sec7. Furthermore, in GGA mutants the overlap between Tlg2 and Sec7 is decreased, suggesting that GGA is required for maturation of this Tlg2 sub-compartment.

The study is well conducted and its main conclusion that a Tlg2 subregion within the TGN functions as the early endosome seems well supported by the superb live imaging and the analysis of GGA mutants.

Although a technical feat in live superresolution imaging, this single kind of data (moving, shape-shifting blobs of fluorescently-labeled proteins) does not totally fill with meaning the terms "compartment", "sub-compartment", or "independent sub-compartment". This, I think, is the main limitation of the study. Are these compartments or sub-compartments individuated membrane elements, collections of vesicles, regions of the same cisterna or saccule? For this, electron microscopy would be needed.

Reviewer #2 (Public Review):

In this manuscript Toshima et al document the use of sophisticated microscopy - with powerful spatial and time resolution - to image markers of the yeast endosomal system.

The initial work documented in this paper does a good job of defining the compartment endocytic cargoes internalise to. This is convincingly shown to be a compartment that is not marked by Sec7 but is instead a distinct (sub)compartment marked by the SNARE protein Tlg2. This agrees with many previous studies, (including biochemical experiments and microscopy of cargoes in a series of membrane trafficking mutants) but has different conclusions to another study (Day et al 2018 - Developmental Cell). Although the microscopy techniques used in the two studies are different, the yeast system and many of the reporters (FP tagged Tlg1, Sec7, Vps21 and fluorescently labelled mating factor) are the same. The Day et al study is suitably referenced throughout the manuscript but as to why the authors have come to fundamentally different answers about endocytic cargoes internalising to a Sec7+ compartment, is not discussed.

The work goes on to show endocytic carriers (marked by Abp1) and endocytic cargoes like fluorescently labelled mating factor internalise to the Tlg2+ compartment. The forward trafficking of these molecules is then observed to transit to a later endosome compartment labelled by Vps21. The super-resolution and time lapse imaging, sometimes even using 3 colours - is of very high quality and fully support the model presented at the end of the paper for this trafficking itinerary. Trafficking mutants are also used (such as a defective allele of arp3 and deletion of VPS21 / YPT52 GTPases) to interrupt trafficking routes and define the pathways followed by endocytosed mating factor.

The endocytic trafficking from Tlg2+ to Vps21 compartments is shown to be defective in mutants lacking GGA adaptors (gga1∆ gga2∆), with cargoes accumulating in the Tlg2+ compartment and other clathrin adaptor mutants not causing this defect. This research avenue also reveals that the GGA proteins are required to maintain the distinct Tlg2 sub compartment.

The final section of the paper uses the same tools to analyse the localisation of the recycling v-SNARE protein Snc1. This is arguably the most important set of experiments in the paper, not only is Snc1 a putative v-SNARE that functionally interacts with Tlg2, but this cargo, unlike pheromone, allows the investigation of recycling back to the PM from TGN/endosomes. However, the authors do not comment on the fact that Snc1 does not localise to the plasma membrane in either experiments using different microscopy techniques (Figure 5A + 5B), calling into question whether the recycling pathway is operating properly or that the FP-tagged machinery has disrupted processing? The steady state localisation of Snc1 in WT cells only looks normal in Supplemental figure, this discrepancy should be discussed or addressed.

Reviewer #3 (Public Review):

The manuscript by JY Toshima et al. is an excellent and important study that demonstrates very clearly the existence of an endosomal compartment in yeast, distinct from the trans-Golgi network, to which endocytic vesicles fuse upon internalization. They show that this compartment is enriched in the SNARE protein Tlg2, a yeast homologue of syntaxin, and is segregated from the Golgi-localized Sec7-containing compartment, indicating that the organization of the endocytic system in yeast is similar to that of animal cells. Furthermore, they demonstrate the trafficking machinery required for maturation of this compartment, and that it is also a station on the pathway back to the plasma membrane. Because there have been conflicting reports in the literature as to the existence of an endosomal compartment in yeast distinct from the trans-Golgi network, this paper is of great importance for the cell biology community.

Major strengths of this study are the cutting-edge imaging technology used, and the careful, quantitative analyses carried out. The authors use a super-resolution live cell imaging approach that allows them to discriminate to a high resolution different compartments and membrane domains of highly dynamic yeast organelles, and to follow an internalizing cargo over time. With their manuscript, they have provided a full set of movies, along with quantifications, to support their conclusions.

The authors use fluorescent-protein-labelled endocytic cargo (alpha-factor) and florescent-protein-labelled compartment markers, assaying them in high resolution and super-resolution live cell imaging microscopy systems. In this way, they are able to follow trafficking of cargo through compartments in real time. The authors first demonstrate that the alpha-factor cargo substantially colocalized with the SNARE protein Tlg2, a marker of early endosomes, but very little with Sec7. They also show that Tlg2 marks a sub-compartment distinct from the Sec7 compartment, but adjacent to it. Furthermore, they demonstrate using super-resolution microscopy and triple color 4D imaging that endocytosed alpha-factor cargo structures make contact with the Tlg2 compartment, adjacent to the Sec7 compartment, then disappear, supporting the conclusion that endocytic vesicles first fuse with the Tlg2 compartment. Next the authors show that alpha factor is transported from the Tlg2 compartment to the Vps21 compartment, a process that requires the GGA adaptors Gga1 and Gga2. Finally, the authors show that recycling of the endocytic R-SNARE Snc1 also occurs by passage through the Tlg2 compartment.

The use of mutants that affect different stages of endosomal trafficking is a strength of the manuscript, as it allows elucidation of the mechanism of transport through successive compartments. Importantly, using a gga1-delta gga2-delta mutant, the authors demonstrate convincingly that the GGA adaptors Gga1 and Gga2 are required for alpha factor transport from the Tlg2 compartment to the Vps21 compartment.

Throughout this study, the authors use fluorescent protein-labelled cargo and compartment markers (EGFP, mCherry, iRFP), but don't explicitly state to what extent these fusion proteins are functional compared to the endogenous proteins. They could cite previous publications or their results describing the functionality of the fusion proteins used.

Reviewer #1 (Public Review):

Luu et al. have developed a genome-edited African elite rice variety, Komboka. The work was initiated in response to the outbreak in Eastern Africa by Xanthomonas oryzae strains that are phylogenetically related to Asian strains and carry TALes, similar to strains from China, possessing an expanded repertoire of TALes compared to those in endemic strains. As these emerging strains contain TALe targeting SWEET11a, as well as that suppressing Xa1, pthXo1, and iTALes, the authors have generated edited lines targeting promoter regions of SWEET11a, 13 and 14 in African elite rice variety, Komboka. The same team has previously generated genome-edited lines targeting the promoter regions of SWEET11a, 13, and 14 in varieties Kitaake, IR64, and Ciherang-Sub1. Bacterial blight outbreaks and emerging pathogen lineages remain to be a threat to rice production. Thus, efforts in targeting pathogen weaknesses to generate genome-edited varieties possessing broad-spectrum resistance are required. The survey, collection of isolates, and strain characterization studies on >800 strains are commendable. This study has taken advantage of this ongoing collection to stay ahead in the arms race to deploy broad-spectrum resistance in an elite rice variety using TALe targets.

Overall conclusions presented here are supported to some extent; however, I have listed some of my comments and concerns below.

1. Data in supplementary table 2 suggests that Komboka is still a moderately resistant variety under field conditions in Africa, with a disease severity scale of 2 i.e. 4-6% disease severity, compared to other varieties having a disease severity scale of 5. Thus, I am not convinced that emerging strains are of concern on the Komboka variety under field conditions, thus, question the justification of Komboka being a choice for editing to tackle emerging strains.

2. Is Xa4 from Komboka related to Xa4_Teqing? The breakdown of Xa4T was due to the mutant allele of avrXa4 in virulent Xoo CR6. But this breakdown was accompanied by a fitness penalty and residual QTL had a significant residual effect on virulent strains. Would this be why Komboka carrying Xa1 (Xa45(t) and Xa4 under field conditions still showed moderate resistance? But Xoo strains showed susceptibility in leaf clipping assays.

3. I felt a bit of a disconnect in sections on phenotypic assays confirming the virulence profile of strains on Komboka and then understanding mechanisms underlying virulence since the same strains used in path data were not the ones mentioned in WGS and TALe analysis, leaving the readers with the only one strain to support the hypothesis of the basis for higher disease severity on Komboka due to new TALes, pthXo1, and iTALe. Do authors have pathogenicity data for African strains T19, Dak16, and Xoo3-1 that grouped with endemic African strains on Komboka? Authors present data on CIX4457, 4458, and 4462 being virulent on Komboka, and show that they cluster with Asian strains. However, in the tree, 4462 is the only one shown to be closely related to Chinese strains. Where are 4457 and 4458 placed? Do 4457 and 4458 also contain pthXo1 and iTALe? Authors could also provide path data for 4506/4509 that they included in TALe figure and in the phylogenetic tree.

4. The authors present pathogenicity data on EBE-edited T0, T1, and T2 lines of Komboka which are promising against the Tanzanian strains carrying new TALes. The cas9/cpf1 system developed here to target multiple EBEs will be a valuable contribution to the scientific community. What are the agronomic characteristics of these edited lines? As the edited lines have not been tested against a diversity panel of Asian and African strains, I would be skeptical of the choice of the term "broad-spectrum" yet.<br /> Regardless of my comment earlier on Komboka being moderately resistant under field conditions and thus a questionable choice for EBE-editing here, the genome-edited lines in any variety imparting resistance to bacterial blight remain to be a valuable contribution to managing disease outbreaks.

5. As this manuscript utilizes the diversity of African strains to generate edited lines, it would be good to make diagnostics and path data for 833 strains available to the scientific community (instead of select strains as indicated in the supplementary table), especially for the fact stated here in the manuscript about scarce data on Xoo in Africa and the goal of systematic comparison of the pathogen population.

Reviewer #2 (Public Review):

This study describes the emergence of virulent strains of the rice bacterial blight pathogen Xanthomonas oryze pv. oryzae in the Morogoro rice-growing region in Tanzania. The aims of the study were to describe the virulence features of the emerging population, as compared to previous bacterial blight outbreaks in Africa, and generate an elite rice variety that is resistant to both pathogen populations. To achieve these aims, the authors characterized the genetic basis of the virulence of these new strains by sequencing the genomes of three representative strains and phenotyping using excellent genetic resources for identifying the susceptibility gene targets of this pathogen in rice. They then used two rounds of hybrid CRISPR-Cas9/Cpf1 to successfully edit six targets of the pathogen in an East African rice variety, which conferred resistance to all strains tested.

The strengths of this paper are the systematic analysis of the virulence of emerging pathogen strains relative to strains from previous outbreaks and the successful creation of edited lines that will form the basis for continued efforts to gain regulatory approval for the introduction of resistant rice in East Africa. The creation of the edited line is a substantial and important contribution, indeed, the authors include strains collected in 2021 and include disease severity data from 2022 in the supplementary data, illustrating the urgent need for solutions.

The weaknesses of the study are largely related to the quick turnaround between data collection and manuscript submission.<br /> (1) Different strains are used for different experimental work and sequence analysis, making relationships between different parts of the work unclear and also more challenging for the reader to follow because of changing strain designations. CIX4457, CIX4458, and CIX4462 were virulent on rice near-isogenic-lines, CIX4457 and CIX4505 were used for identifying SWEET targets and phenotyping edited lines, while whole genome sequencing was conducted with CIX4462, CIX4506, CIX4509.<br /> (2) Disease survey results from 2022 are listed in Supplementary Table 2, but it is challenging for the reader to summarize across many lines of data, which appear to represent individual samples.<br /> (3) The focus of the editing is Komboka but bacterial blight in 2022 was mostly on other varieties. It would be helpful to have more context on this variety and what has prevented adoption by the growers in the Morogoro region to date.

Reviewer #3 (Public Review):

One key finding of this work is the identification of Xanthomonas oryzae pv. oryzae (Xoo) strains in Africa, based on their genomes sequence and their TALE repertoires, have high similarity with Asian strains. Asian Xoo strains typically overcome NLR-mediated recognition of TALEs in rice by so-called iTALEs. Moreover, some Asian strains contain a TALE resembling PthXo1, a TALE protein that was shown to overcome xa5 resistance.

The authors now found that some of the newly identified African strains have iTALEs and PthXo1-like TALEs. Such newly evolved African strains were found to be fully virulent on the African rice elite variety Komboka, which is resistant to a broad panel of African Xoo strains.

Previous studies have shown that TALEs bind to effector binding elements (EBEs) present in promoters of rice SWEET genes to promote disease. Work from the lab of the authors and other labs has shown that TALEs can no longer promote the disease if matching EBEs are changed or deleted by CRISPR or TALEN-mediated mutagenesis. In fact, pioneering work by Bing Yang, one of the authors of this article published about ten years ago a Nature Biotechnology article where he showed that rice plants with mutated EBEs are resistant to Xoo. Recently, a combined effort of the Yang and Frommer labs resulted in two further Nature Biotechnology publications (2019), in which they described along with other useful tools rice lines where multiple EBEs were mutagenized in parallel and that provide broad spectrum resistance.

The work under review describes now CRISPR mutagenesis of an African elite cultivar resulting in a line that mediates resistance to Asian and newly evolved African strains.

Overall, the work is technically sound. Yet, the approach that has been described - mutagenesis of multiple EBEs - has been used before and is a routine procedure for labs that are focused on such undertakings. While such approaches do not provide new insights for fundamental research, they nevertheless are certainly important and useful in translational research, as demonstrated here.

Reviewer #1 (Public Review):

This study used intersectional genetic approaches to stimulate a specific brainstem region while recording swallow/laryngeal motor responses. These results, coupled with histology, demonstrate that the PiCo region of the IRt mediates swallow/laryngeal behaviors, and their coordination with breathing. The data were gathered using solid methods and difficult electrophysiological techniques. This study and its findings are interesting and relevant. The analysis (and/or the presentation of the analysis) is incomplete, as there are analyses that need to be added to the manuscript. The interpretation of the data is mostly valid, but there are claims that are too speculative and are not well-supported by the results. The introduction and discussion would benefit from more citations and a deeper exploration of how this study relates to other work - especially a thorough accounting of and comparison to other studies concerning putative swallow gates.

General/major concerns:

• The field of respiratory control is far from unified regarding the role of PiCo in breathing or any other laryngeal behaviors. If anything, the current consensus does not support the triple-oscillator hypothesis (in which PiCo is one of 3 essential respiratory oscillators). The name "PiCo", short for "post-inspiratory complex", suggests a function that has not been well-supported by data - it is a putative post-inspiratory complex, at best. I suggest putting this area in context with other discussions i.e. IRt (such as in Toor et al., 2019) or Dhingra et al. 2020 showed broad activation of many brainstem sites at the post-I period (including pons, BotC, NTS)

• Did you perform control experiments in which the opto stimulations were done on animals without the genetic channels (for example, WT or uncrossed ChAT-ires-cre, etc.), or in mice with the genetic channels that weren't crossed (uncrossed Ai32 mice)? If so, please include. If not, why?

• How do you know that your opto activations simulate physiological activation? First, the intensive optical activation at the stim site does not occur in those neurons naturally. Doing a natural (water) stim for comparison is good, but it cannot necessarily be directly compared to the opto stim. The water stim would activate many other brainstem regions in addition to PiCo. A caveat is that opto PiCo stim =/= water stim (in terms of underlying mechanisms) should be included. Second, in looking at the differences between water vs opto swallows in Table S2: it appears that the ChAT animals (S2A) have something weaker than a swallow with opto stim. For the Vglut2 and ChAT/Vglut2 (S2B&C), the opto swallows also aren't as "strong" as the water swallows (the X and EMG amplitudes are smaller). The interpretation/discussion attributes this to the lack of sensory input during opto stim, but does not mention the strong possibility that there is a difference in central mechanisms occurring. It also seems to be dismissed with the characterization of the swallow as "all-or-none" (see note on Fig 3 results).

• The writing needs extensive copy editing to improve clarity and precision, and to fix errors.

• Results/Fig 1: What proportion had no/other motor response (non-swallow, non-laryngeal) to the opto stim? I can extrapolate by subtraction, but it would be nice to see the "no/other response" on the plot.

• The explanation of genetics is too spread out and confusing. There needs to be more detail about all the genetic tools used, using the standard language for such tools, in one spot. Please also provide a clear explanation of what those tools accomplish. Include a figure if necessary. Pick a conventional designator/abbreviation for the different strains, define them in the methods and in the first paragraph of the results section, and use those abbreviations throughout. I think that using ChAT as an abbreviation for your ChAT-ires-cre x Ai32 mice is confusing because it makes it sound like you're talking about the enzyme rather than the specific strain/neurons. Saying "ChAT stimulated swallows... swallows evoked by water or ChAT" makes it sound like the enzyme choline acetyltransferase itself is stimulating swallow. As is convention, pick a more precise abbreviation like ChAT-cre/Ai32 or ChAT:Ai32 or ChAT-ChR2 or ChAT/EYFP. This goes for the other strains as well.

• For Fig S2C&D, why does it say mCherry? Isn't it tdTomato? Is it just an anti-ChAT antibody and then the tdTomato Ai65 is only labeling Vglut2? I don't see this in the methods section.

• I also don't see methods for all the staining in Fig S3. The photomicrograph says Vglut2-cre Ai6, but there's no mention of Ai6 anywhere else. Which mice are these? Did you cross Vglut2-cre with an Ai6 reporter mouse? How can you image an Ai6 mouse (which I assume expresses ZsGreen? and that you excited at 488?) and a 488 anti-goat in the same section (that's the only secondary listed in the methods that would work with your goat anti-ChAT)? Is there an error in listing the fluorophores in the methods? Please give more details on the microscopy including which filters were used for the triple staining.

• Regarding the staining: I would expect the staining/maps in for the 2 different ChAT/Vglut2 intersectional strains to be similar (Fig 5A/B and S2C/D). The photomicrographs look very different to me, while the heat maps (this goes for all the heat maps in the paper) have barely distinguishable differences. In Fig 5, the staining looks much stronger than in Fig S2C. Why does it look like there are so many more transfected neurons in Fig 5A2 than there are red neurons in the corresponding panel Fig S2C2? And for Fig 5A4 and Fig S2C44? The plot and results text for Fig 5 says the avg number of neurons was 123+¬11. The plot for Fig S2D says 112+¬15, but the results text says 242+¬12 (not sure which is the correct number).

• The results text for Fig S2C also says the staining is "similar to the previous ChAT staining...", which I assume refers to S2A/B. The plot and results text for Fig S2B reports 403+¬39 neurons, while S2D is either 112 or 242 (not sure?). The plots have different Y scales, which should be changed to be the same. But why do the photomicrographs and the heat maps look so similar? I would expect far fewer neurons to be stained in the intersectional mice (Fig 5 and Fig S2C/D) than in the ChAT staining (Fig S2A/B). I am having trouble reconciling the different presentations/quantifications and making sense of the data in these histology figures.

• How can you distinguish PiCo from non-PiCo in the histology, especially in the ChAT-only staining? It seems that you have arbitrarily defined the PiCo region, and only counted neurons within that very constrained area. I can see stained neurons in the area immediately outside of PiCo, and I'd like to see lower-magnification images that show the staining distribution in a broader region surrounding PiCo as well, especially in the rest of the reticular formation.

• Similarly, how can you be sure you're stereotaxically targeting PiCo precisely (600um in diameter?) with your opto fiber (200um in diameter). Wouldn't small variations in anatomy put the fiber outside the tiny PiCo area?

• Please put N's and stats results in Table S1 for both swallow and laryngeal activity. I took what I assume to be the Ns (10, 11, and 4) and did some stats like the ones you presented for the laryngeal duration. The differences between vagus duration for 40 and 200 ms pulse durations are all significant for each strain, by my calculations. Also, I think there must be an error in the orange swallow plot in Fig 3A. The orange dots don't correspond to the table values. I plotted all the Table S1 values for each strain. Each line looks similar to the blue laryngeal activation plot in Fig 3A. The slopes of the Vglut2 were less than the other strains, and the slopes for the swallow behavior were less than the laryngeal behavior for all strains. Otherwise, they all look similar. Please double-check your values/stats to address these discrepancies. If it is indeed true that the stim pulse duration affects swallow duration, revise the interpretations and manuscript accordingly.

• Please add more details on stats in general, including the specific tests that were performed, F values and degrees of freedom, etc.

• How do you know that you're not just activating motoneurons in the NA when you stimulate your ChAT animals, especially given the results in Fig 1B? In this case, the phase-specific results could be explained by inhibitory inputs (during inspiration) to motoneurons in the region of the opto stim.

• While the study from Toor et al is cited, there needs to be a much more thorough discussion of how their findings relate to the current study. They demonstrated that PiCo isn't necessary for the apneic portion of swallow. Inhibiting this region also didn't affect TI. PiCo cannot be the sole source of post-I timing, and the evidence overwhelmingly favors the major involvement of other regions such as the pons. They also showed that inhibition of all neurons (not just ChAT/Vglut) in the PiCo region suppresses post-I activity in eupnea. This suppression was overcome by the increased respiratory drive during hypoxia.

• This study has not demonstrated some of the things that are depicted in Fig 7 and included in the discussion. While swallow can inhibit inspiration, there are many mechanisms by which this can happen other than a direct inhibitory connection from the DGS to PreBotC. You cite Sun et al., 2011 findings of "a group of neurons that inhibits inspiration" during SLN stim, but don't mention that it is the BotC and that the paper shows that swallow apnea is dependent on BotC. That is also supported by the Toor study. I don't understand how post-I (aka E) can be discussed without discussion of the BotC - this is a glaring omission.

• Why is it necessary for PiCo to innervate the cNTS? That is true if the conjecture that PiCo gates swallowing is true, as the cNTS is the only known region for central swallow gating. However, PiCo could influence afferent input to the NTS less directly, and therefore not function as a gating hub per se. The experimental evidence does not warrant the claim that PiCo gates swallowing. The definition of a swallow gate(s) is a topic of much debate and no conclusive experimental evidence has emerged for swallow gating regions to exist anywhere except in the NTS. The current study's evidence also does not meet the criteria necessary to conclusively call PiCo a swallow gate. The authors should soften this claim and language throughout the manuscript.<br /> It is also unclear that PiCo acts directly on the swallow pattern generator to gate swallowing. It is not just "conceivable that the gating mechanism involves" the pons, but nearly certain. Swallow gating by respiratory activity may not be able to be ascribed to one particular location. At a minimum, it likely involves the NTS/DSG, pons, and possibly IRt (inclusive of PiCo). The authors are correct that "further studies are necessary to understand the interaction between PiCo and the pontine respiratory group on the gating swallow and other airway protective behaviors." This is why it shouldn't be stated that "this small brainstem microcircuits acts as a central gating mechanism for airway protective behaviors."<br /> PiCo is likely part of the VSG (and thus the swallow pattern generator). PiCo, as part of the IRt/VSG could indeed be surveilling afferent information and providing output that affects swallow or other laryngeal activation and the coordination of these behaviors with breathing. However, this is not the responsibility of PiCo alone. This role is likely shared by other parts of the SPG, and may require distributed SPG network participation to be functional. If one were to stim other regions of the distributed SPG, similar results might be expected. When Toor et al silenced the PiCo area (and locations that lie at least lightly beyond the borders of what the present study defines as PiCo), stim-evoked fictive swallows were greatly suppressed. However, swallow-related apnea was unaffected. This supports the role of PiCo as a premotor relay for swallow motor activation, but not as the site that terminates inspiration. Therefore, it cannot be called a gate.

• Similarly, Fig 7 does not accurately depict things that are already well-supported by evidence. PiCo should be included as part of the swallow pattern generator (VSG), not as a separate entity acting on it. The BotC and pons are glaring omissions. This study has not demonstrated the labeled inhibitory connection from DSG to PreBotC. The legend states speculations as fact and needs to be dialed way back to either include statements with solid experimental evidence or to clearly mark things as putative/speculative.

• The discussion of expiratory laryngeal motoneurons needs to be expanded and integrated better into the discussion of swallow, post-I, and other laryngeal motor activation. Why can't PiCo just be premotor to ELMs?

• Concerning the discussion of "PiCo's influence as a gate for airway protective behaviors is blurred...": The incomplete swallow motor sequence didn't seem super different in timing or duration compared to the fully transfected animals (comparing plots from Fig 6 to Fig S1, and Table S2 to Table S3. The values for swallow durations (XII and X) for each group for water and opto seem within similar ranges, as do the differences between water & opto-evoked swallows between strains. While the motor pattern is distinctive from the normal swallow, with laryngeal activity rather than submental activity leading, one might not even be able to call that a swallow. Is it evidence against a classic all-or-nothing swallow behavior any more than the graded swallow results from (fully transfected) Table S1?

• Please expand on this point and put it into context with others' results: "This brings into question whether this is the first evidence against the classic dogma of swallow as an "all or nothing" behavior, and/or whether this is an indication that activating the cholinergic/glutamatergic neurons in PiCo is not only gating the SPG, but is actually involved in assembling the swallow motor pattern itself."

Reviewer #2 (Public Review):

The study "Postinspiratory complex acts as a gating mechanism regulating swallow-breathing coordination and other laryngeal behaviors" by Huff et al., provides additional insight into the role of the recently discovered postinspiratory complex during swallow-breathing coordination. The authors used optogenetics in mice to show that activation of the PiCo during inspiration or at the start of post-inspiration can evoke swallowing. At later stages of expiration, PiCo activation activates undefined laryngeal activities. The analysis of respiratory phase reset leads to the conclusion that the PiCo is important for central gating of swallow. In conclusion, the authors claim that swallow-breathing coordination depends on a defined microcircuit compromising the PiCo and the pre-Botzinger complex.

Reviewer #3 (Public Review):

Huff et.al further characterise the anatomy and function of a population of excitatory medullary neurons, the Post-inspiratory Complex (PiCo), which they first described in 2016 as the origin of the laryngeal adduction that occurs in the post-inspiratory phase of quiet breathing. They propose an additional role for the glutamatergic and cholinergic PiCo interneurons in coordinating swallowing and protective airway reflexes with breathing, a critical function of the central respiratory apparatus, the neural mechanics of which have remained enigmatic. Using single allelic and intersectional allelic recombinase transgenic approaches, Huff et al. selectively excited choline acetyltransferase (ChAT) and vesicular glutamate transporter-2 (VGluT2) expressing neurons in the intermediate reticular nucleus of anesthetised mice using an optogenetic approach, evoking a stereotyped swallowing motor pattern (indistinguishable from a water-induced swallow) during the early phase of the breathing cycle (within the first 10% of the cycle) or tonic laryngeal adduction (which tracked tetanically with stimulus length) during the later phase of the breathing cycle (after 70% of the cycle).

They further refine the anatomical demarcation of the PiCo using a combination of ChAT immunohistochemistry and an intersectional transgenic strategy by which fluorescent reporter expression (tdTomato) is regulated by a combinatorial flippase and cre recombinase-dependent cassette in triple allelic mice (Vglut2-ires2-FLPO; ChAT-ires-cre; Ai65).

Lastly, they demonstrate that the PiCo is anatomically positioned to influence the induction of swallowing through a series of neuroanatomical experiments in which the retrograde tracer Cholera Toxin B (CTB) was transported from the proposed location of the putative swallowing pattern generator within the caudal nucleus of the solitary tract (NTS) to glutamatergic ChAT neurons located within the PiCo.

Methods and Results<br /> The experimental approach is appropriate and at the cutting edge for the field: advanced neuroscience techniques for neuronal stimulation (virally driven opsin expression within a genetically intersecting subset of neurons) applied within a sophisticated in vivo preparation in the anaesthetized mouse with electrophysiological recordings from functionally discrete respiratory and swallowing muscles. This approach permits selective stimulation of target cell types and simultaneous assessment of gain-of-function on multiple respiratory and swallowing outputs. This intersectional method ensures PiCo activation occurs in isolation from surrounding glutamatergic IRt interneurons, which serve a diverse range of homeostatic and locomotor functions, and immediately adjacent cholinergic laryngeal motor neurons within the nucleus ambiguous (seen by some as a limitation of the original study that first described the PiCo and its roll in post-I rhythm generation Anderson et al., 2016 Nature 536, 76-80). These experiments are technically demanding and have been expertly performed.

The supplemental tracing experiments are of a lower standard. CTB is a robust retrograde tracer with some inherent limitations, paramount of which is the inadvertent labelling of neurons whose axons pass through the site of tracer deposition, commonly leading to false positives. In the context of labelling promiscuity by CTB, the small number of PiCo neurons labelled from the NTS (maybe 5 or 6 at most in an optical plane that features 20 or more PiCo neurons) is a concern. Even assuming that only a small subset of PiCo neurons makes this connection with the presumed swallowing CPG within the cNTS, interpretation is not helped by the low contrast of the tracer labelling (relative to the background) and the poor quality of the image itself. The connection the authors are trying to demonstrate between PiCo and the cNTS could be solidified using anterograde tracing data the authors should already have at hand (i.e. EYFP labelling driven by the con-fon AAV vectors from PiCo neurons (shown in Fig5), which should robustly label any projections to the cNTS).

The retrograde labelling from laryngeal muscles seems unnecessary: the laryngeal motor pool is well established (within the nAmb and ventral medulla), and it would be unprecedented for a population of glutamatergic neurons to form direct connections with muscles (beyond the sensory pool).

The authors support their claim that PiCo neurons gate laryngeal activity with breathing through the demonstration that selective activation of glutamatergic and cholinergic PiCo neurons is sufficient to drive oral/pharyngeal/laryngeal motor responses under anaesthesia and that such responses are qualitatively shaped by the phase of the respiratory cycle within which stimulation occurs. Optical stimulation within the first 10% of the respiratory cycle was sufficient to evoke a complete, stereotyped swallow that reset the breathing cycle, while stimuli within the later 70% of the cycle, evoked discharge of the laryngeal muscles in a stimulus length-dependent manner. Induced swallows were qualitatively indistinguishable from naturalistic swallow induced by the introduction of water into the oral cavity. The authors note that a detailed interpretation of induced laryngeal activity is probably beyond the technical limits of their recordings, but they speculate that this activity may represent the laryngeal adductor reflex. This seems like a reasonable conclusion.

The authors propose a model whereby the PiCo impinges upon the swallowing CPG (itself a poorly resolved structure) to explain their physiological data. The anatomical data presented in this study (retrograde transport of CTB from cNTS to PiCo) are insufficient to support this claim. As suggested above, complementary, high-quality, anterograde tracing data demonstrating connectivity between these structures as well as other brain regions would help to support this claim and broaden the impact of the study.

This study proposes that the PiCo in addition to serving as the site of generation of the post-I rhythm also gates swallowing and respiration. The scope of the study is small, and limited to the subfields of swallowing and respiratory neuroscience, however, this is an important basic biological question within these fields. The basic biological mechanisms that link these two behaviors, breathing and swallowing, are elusive and are critical in understanding how the brain achieves robust regulation of motor patterning of the aerodigestive tract, a mechanism that prevents aspiration of food and drink during ingestion. This study pushes the PiCo as a key candidate and supports this claim with solid functional data. A more comprehensive study demonstrating the necessity of the PiCo for swallow/breathing coordination through loss of function experiments (inhibitory optogenetics applied in the same transgenic context) along with robust connectivity data would solidify this claim.

Reviewer #1 (Public Review):

This paper explores the potential regulatory role of a previously unstudied phosphorylation site in the Src kinase SH3 domain. The data presented conclusively demonstrate that a phosphomimetic mutation of this site, src90E, causes an elevation in Src kinase activity, changes the structure of the Src catalytic domain as determined with a FRET sensor, disrupts certain SH3 domain interactions, causes changes in kinase intracellular dynamicity, and promotes cell invasiveness. Based on the behavior of the phosphomimetic mutant, the idea that constitutive phosphorylation of Y90 could have all of these effects is well-supported by the data. However, in wild-type cells or cells transformed by activated forms of Src, there is no constitutive phosphorylation of this site. Therefore, the question remains whether Y90 phosphorylation occurs to any significant extent in cells, and the data suggesting that it could do so is limited. It also remains to be conclusively established whether Y90 phosphorylation occurs via autophosphorylation.

Major comments:

1. Y90 was identified as a site of phosphorylation in Luo et al. It would be helpful if more information were provided about its significance relative to other sites identified in that study. Was it detected in non-transformed cells? Was it a major site? How does it relate to Y416 in abundance? The reference to the identification of the site in a different study from the White lab is made in the discussion but not in the introduction (this should be corrected). How abundant was it that study? A fuller description of its detection would strengthen the rationale for this study. Any additional phosphoproteomics studies that identified it should also be included.

2. Related to point 1, is there evidence from the literature indicating a significant site of phosphorylation in Src has been overlooked? Or, was this site only identified because of the recent advances in MS technology and increased sensitivity of this methodology? An introduction to these points would also enhance the rationale for the study.

3. The explanation of the MS experiment designed to show that Y90 phosphorylation happens in cells is insufficient in the text. It is not clear why the SYF cells were not used and not clear why the FRET sensor constructs were used. It is also not clear whether or how the proteins were purified before MS analysis. Also, rather than showing the MS data as a relative level, it would be preferable to provide the number of spectra obtained for each peptide/phosphopeptide and compare this also to Y416. A fuller comparison between the phosphorylation of Y90 to that of Y416 is necessary in order to place the potential Y90-mediated phosphoregulation in context.

4. I would like to see conclusive evidence that Y90 phosphorylation is due to autophosphorylation. This would involve relatively simple experiments. As one possibility, an IP kinase assay followed by immunoblotting with a site-specific antibody or MS or other types of phosphopeptide visualization/identification.

5. A few other mutations would be interesting to examine in both kinase and transformation assays for comparison to the mutants that were: Y527F Y416F; Y527F Y416F Y90E. The first is a low activity control and the second is for understanding whether Y90E could overcome the lack of Y416 phosphorylation.

5. I recommend that the results are discussed in a more circumspect manner. The results presented in Figure 7 on the double mutant, Y527F Y90F, suggest that phosphorylation of Y90 is not a very significant component of Src kinase regulation, at least in these biological contexts. That Y90 phosphorylation isn't a major regulatory factor does not diminish the value of the work describing Y90 phosphorylation. However, it does alter the interpretations. I encourage a more conservative discussion of its importance and a broader discussion of why it isn't a major site of Src phosphorylation, particularly if it is due to autophosphorylation.

Reviewer #2 (Public Review):

The manuscript "Phosphorylation of tyrosine 90 in SH3 domain is a new regulatory switch controlling Src kinase" describes efforts to understand how phosphorylation of tyrosine (Y90) in the SH3 domain of Src affects the activity and function of this multi-domain kinase. The authors find that an Src variant containing a phospho-mimetic mutation (Glu) at position 90 demonstrates elevated activation levels in lysates and cells (Figure 1) and adopts a less compact autoinhibited conformation within the context of a SrcFRET biosensor in lysates (Figures 3A, 3B). A series of pulldown experiments with an isolated SH3 domain (Figure 2A, 2B) or full-length Src (Figure 2C, 2D) that contain the phospho-mimetic Y90E mutation demonstrates that phosphorylation of Tyr90 would likely disrupt the interaction of Src's SH3 domain with intermolecular binding partners and the linker that couples SH2 domain/C-tail binding to autoinhibition, which provides a mechanistic basis for the observed elevated kinase activity of Src Y90E. By performing a series of imaging experiments with a SrcFRET biosensor, the authors show that the Y90E mutation does not show enhanced localization at focal adhesions like a hyperactivated Src mutant (Y527F) that contains a non-phosphorylatable C-tail (Figure 4A). However, using ImFCS combined with TIRF microscopy (Figure 4B), the authors demonstrate that Src Y90E shows similarly reduced mobility (relative to the WT SrcFRET biosensor) at the plasma membrane (especially at focal adhesions) as Src Y527F. Consistent with the elevated kinase activity of Src Y90E, the authors go on to demonstrate that the Src Y90E variant shows an ability to transform fibroblasts-at levels that are intermediate between wild-type Src and the hyperactive Src mutant Y527F (Figure 5). Similarly, Src Y90E confers an intermediate level (between wild-type Src and Src Y527F) of invasiveness and ability to form spheroids. Together, these comprehensive experiments with a Y90 phospho-mimetic strongly support a model where phosphorylation of Src's SH3 domain at Tyr90 would lead to a more intramolecularly disengaged SH3 regulatory domain and enhanced kinase activity in cells.

Most of the conclusions in this paper are well supported by solid data, but confidence in several assays would be higher if additional technical detail or controls were provided and the biological significance of these findings would be higher if the role that Y90 phosphorylation plays in Src regulation and function were better delineated.

1) The kinase activity assays in Figures 1C,1D, and 7A need to be scaled to the Src variant levels present in the lysate (quantification of relative Src levels is not provided).

2) More details are required for the experiments quantifying Y90 phosphorylation levels in Figure 3C. The experimental states that equal amounts of IP'd proteins were used for these analyses but there are no details on how this was confirmed. In addition, the experimental states that normalized intensities were used for your quantifying the Y90 phospho-peptide but no details are provided on how normalization was performed (the legend states that a base peptide was used but it is unclear what this means).

3) A key question is whether Y90 phosphorylation serves a regulatory role in Src's cellular activity and, if so, what is the regulatory network that mediates this phospho-event. Using a mass spectrometry readout with three Src variants (wild type vs. Y527F vs. E381G) that possess differing kinase activities, the authors demonstrate that Y90 phosphorylation levels correlate to Src's kinase activity (Figure 3C), which they suggest is an indication that this residue is an autophosphorylation site (or phosphorylated by another Src family kinase). However, as Src's kinase activity correlates with SH3 domain disengagement (which leads to a more accessible Y90), it is also entirely possible that another tyrosine kinase is responsible for this phosphorylation event. More importantly, it is unclear under which signaling regime Y90 phosphorylation would play a significant regulatory role. This phospho-event was observed in a previous phospho-proteomic study but it is unclear whether the phosphorylation levels of this site occur high enough stoichiometry to modulate the intracellular function of Src and whether there is a regulatory signaling network that influences Y90 phosphorylation levels.

Reviewer #3 (Public Review):

The relevance of Y90 phosphorylation as a regulatory mechanism is shown by the comparison of Src kinase activity, transforming potential, cell invasiveness, and lateral diffusion in membranes. Mechanistically, Y90E mutation affects the opening of the structure, estimated from FRET experiments, and the phosphorylation status of the three main downstream signaling pathways.

The effect of the Y90E mutation is very clear, although its description as "phosphomimicking" is, in my opinion, not accurate. Glutamic acid has a negative charge but is significantly different from phosphotyrosine. Maybe other polar mutants (lysine, glutamine...) would have a similar destabilizing effect on hydrophobic interactions. Erpel 1995 showed some effects of the Y90A mutants.

The effect of tyrosine phosphorylation on the SH3 domain of proteins having the conserved ALYDY motif supports the proposed role, although the evidence for in vivo Y90 phosphorylation in c-Src is scarce. The possible autophosphorylation of Y90 is suggested but the evidence is not very strong and does not rule out other kinases, especially some downstream of Src itself -as already suggested by the authors.

The authors suggest that the perturbation of Y90 reduces the interaction with the connector domain. This is a reasonable explanation, supported by the opening of the structure, but additional effects may exist: The SH3 hydrophobic region including Y90 is also the binding site for the myristoyl group (Le Roux et al. iScience, 12, 194-203) and mutations in the SH3 RT loop significantly affected lipid binding. This could contribute to the observed reduced diffusion in the lipid bilayer.

Reviewer #1 (Public Review):

This manuscript reports the results of a clever chemogenetic manipulation study designed to probe how stimulation (excitation and inhibition) of D1-expressing spiny projection neurons in the striatum (direct pathway) influences hemodynamic characteristics of local and global regions in the brain in mice. Stimulating in the dorsal caudate, the authors found alteration of the local hemodynamic BOLD responses in adjacent areas of the striatum, regions of the thalamus known to project back to the striatum, and more unimodal cortical regions. The authors also observed a decrease in functional connectivity between several cortical regions and the striatum with direct pathway excitation, and the opposite effect with direct pathway inhibition. Put together the results begin to paint a picture of the macroscopic signatures of direct pathway stimulation (and inhibition) that could be used to help infer global BOLD patterns in task-related experiments.

Overall, this appears to be a timely study. The rise of papers using chemo/optogenetic methods to probe the underlying mechanisms of the BOLD response is crucial for the neuroimaging field to continue moving forward. The methods are, for the most part, rigorous and clear. There are, however, a few open issues that require addressing in order for readers to reach the same conclusion as the authors.

MAJOR CONCERNS

1. Classifier as an evaluation method.

The authors largely rely on a support vector machine (SVM) classifier to predict whether BOLD dynamics within atlas-defined regions reflect stimulation-on or stimulation-off windows. While in one way this is a conservative method for evaluating stimulation effects in the resting BOLD fluctuations, the authors largely report their findings as accuracies of the classifier. Figures 3-5 largely only report model accuracy effects, but we get no sense as to what exactly is happening to the BOLD dynamics in each region. The autocorrelation analysis (Fig. 6) somewhat tries to get at this, but only for a subset of regions and the results are largely unclear (see comment below). As a result, a key goal of the study is left largely unaddressed for the reader: i.e. how do intra-region BOLD dynamics change with direct pathway stimulation? The study needs more effort put into this descriptive level of analysis to complement the rigorous classifier analysis.

Also, the classifier method itself seems highly parameterized. The hctsa method returns 7702 features for each time series. It is unclear exactly how many were in the final set used to run the classification, but even if half of the features were removed, it would still make the classification problem highly overparameterized (e.g., 23 and 25 observations against thousands of features for the excitation and inhibition classifiers respectively). Assuming the authors used cross-validation correctly (which we need more information to support), the risk of inflated classification performance is mitigated. However, we need the details to be able to vet that the bias-variance tradeoff was resolved effectively in this model. In addition, it would be nice to know the features that loaded highly on the final model to resolve the questions about what changes in the local BOLD dynamics from excitation and inhibition of the direct pathway.

2. Local stimulation effects in the striatum

Figure 3 is quite confusing. The classifier is supposed to predict stimulation (excitation or inhibition) on vs. stimulation off (control) periods. This would predict a single number (balanced prediction accuracy) per striatal nuclei. Yet the heat maps shown in this figure show classification accuracies for both stimulation and control conditions. Where do the two numbers come from? Also, given the extremely limited short-range lateral connectivity in the striatum, why are the only stimulation effects observed not in the subnucleus being stimulated (Cpl,dm,cd), but for adjacent subnuclei (CPre, CPivmv) and *only* for excitation conditions? This lack of direct change in BOLD dynamics at the stimulated site seems important and largely ignored.

3. Autocorrelation findings.

The one attempt to characterize what happens in the intra-region BOLD dynamics is the autocorrelation analysis reported on page 11 and in Figure 6. However this analysis a) only focuses on the thalamic nuclei (not also the cortical and the single striatal site shown to exhibit stimulation effects) and b) only focuses on a few time series measures. Why this limited focus of both target (thalamic nuclei) and measure (first lag of the autocorrelation)? There are many measures for characterizing the temporal characteristics of autocorrelated series from the hctsa analysis. This selective focus seems both narrow and incomplete.

4. Connectivity results

The changes in functional connectivity as a result of direct pathway stimulation (excitation and inhibition) are both fascinating and limited. There is a clear excitation/inhibition difference in effects, as shown in Figure 7 B-C. However, Figure 7B suggests something different than the change results shown in Figure 7C. It appears that the application of clozapine increases functional connectivity in the control mice (black line Fig. 7B). This effect is exaggerated in the inhibition condition, but (most importantly) direct pathway excitation does not really reveal a significant change in the BOLD connectivity patterns. Now this does not change the authors' overall conclusions (connectivity is suppressed with direct pathway excitation relative to control mice), but the nuance of what is happening in the control mice is important for interpretation purposes: direct pathway excitation does not necessarily decrease functional connectivity but does not express the increase in connectivity observed from the application of clozapine. This needs to be elaborated more.

Along the same lines, there is an interesting disconnect between the intra-region results and the inter-region (connectivity) results. It is clear that resting BOLD dynamics in thalamic nuclei that project back to the striatum, as well as more unimodal cortical areas, change from direct pathway stimulation in the dorsal caudate. Yet, only one cortical region (MOp) with significant functional connectivity changes overlaps with the set of nuclei that exhibit intra-region BOLD changes. This suggests that local BOLD dynamics and global connectivity are largely disconnected effects. Yet this seems to be largely ignored in the current work. It would be nice to see more analysis, and discussion, of the intra-region and inter-region stimulation effects.

Reviewer #2 (Public Review):

In their manuscript, Markicevic et al. report that manipulation of D1 spiny neurons in the right dorsomedial striatum results in a behavioral effect observed in motor movement. This behavioral effect is accompanied by changes in BOLD fMRI changes as estimated by a classification approach and pairwise regional correlation. These brain-wide analyses reveal a number of important outcomes. First, alterations in signal dynamics are observed in the striatum most dominantly in the injection site when contrasting excitation to inhibition. Second, thalamic regions that have reciprocal anatomical connections with the injection site show greater classification accuracy. Third, evaluation of cortical regions demonstrates increased classification accuracy for unimodal regions including primary motor, visual, primary somatosensory, and posterior parietal association regions. Lastly, using pairwise correlations, a decrease is observed when comparing excitation to either inhibition or no modulation of activity in the primary motor cortex, anterior cingulate, and retrosplenial cortices.

This report effectively demonstrates that excitation or inhibition of a large population of D1 spiny neurons results in disruption of basic motor behavior. The greatest strength of the work is derived from identifying that features in the time-series of regions in the thalamus that project and receive projections to the injected site are impacted as well unimodal cortical regions. Moreover, a differential effect is observed for excitatory drive relative to both no drive and inhibition. The use of the approach by Fulcher and Jones (2017) provides an important addition to the more commonly used pairwise correlation approach as it relies on the dynamics of the fMRI signal.

While the methods adopted by the authors to acquire the data and evaluate the experimental manipulations are robust and the obtained results are compelling, the current analysis comes short of relating whether variation that can be estimated across the animals has an impact on these results. Specifically, the authors do not leverage the individual animal viral expression or impact on behavior to constrain and estimate the observed responses reported subsequently. Several reports in humans have used individual variability to estimate the relation between behavior and changes in the BOLD fMRI responses at rest, and a basic demonstration of this type of result has been achieved in mice. Applying a similar approach here would further strengthen the result reported here by identifying which regions are linked to the behavioral deficit (e.g., whether the primary motor cortex is linked to contraversive/ipsiversive rotations at the individual level).

Complementing linking the behavior of individual animals to changes in the fMRI signal, an estimation of structure-function that is driven by each individual animal's expression map may enhance the current analysis approach by leveraging potential subtle expression variations to reveal whether the observed changes can be explained by the extent to which expression is different across animals. In addition, a quantification of the difference between the excitatory and inhibitory cohorts will rule out that differences in the impact on the fMRI signal were a result of unintentional group differences in expression extent.

A significant weakness in the current version of the manuscript is the lack of quantification of the viral expression. Currently, the authors do not provide enough information on the extent of coverage of viral expression on average or at the individual level. In particular, while the authors are careful to use the Allen Mouse Brain Connectivity atlas to constrain the fMRI results, they do not relate the specific expression extent, to clearly communicate to the readers, which regions within the striatum are likely to have better representation given the actual expression levels. Moreover, the authors do not use their own nor the Allen Institute data to carry out a formal structure-function analysis (following Stafford et al., 2014 PNAS, for example). This is critical since the authors wish to infer on the impact of their manipulation on both cortical and thalamic regions while the precise region in the striatum that they affect is never quantified.

Reviewer #3 (Public Review):

By using chemogenetic manipulations of direct pathway neurons in the dorsomedial part of the striatum (DMS) of anesthetized mice combined with fMRI, Markicevic et al explore changes in BOLD dynamics at local (striatum) and macro-scale (brain-wide) levels. The article is appropriately written, and the main findings are well organized and presented in 7 figures. Figures 1 and 2 schematize the techniques and document the motor effects of chemogenetic manipulations. Figures 3-7 describe neural changes induced by these manipulations. The main strength of this work is the level of specificity of the chemogenetic manipulations, which combined with brain-wide functional exploration, provide a very useful map of the consequences of activating a specific striatal subpopulation. In my opinion, the main weakness of this work is that the results are under-discussed and not appropriately contextualized in the current views of the functions of the basal ganglia. My main concerns are exposed in the following lines:

1. In the first finding the authors show that D1 activation/inactivation produces reliable changes in the infected region (DMS), but most importantly, also produced changes in adjacent areas, suggesting intra-striatal communication. The way the data is presented and discussed appears to be confirmatory of what has been previously described with electrophysiological recordings. In my opinion, the most important part of this section would be to fully describe the differences between activation and inactivation groups. Is interesting that opposite manipulations of D1 receptors produced very similar maps of discrimination (Fig. 3). Therefore, it would be necessary to discuss the meaning of obtaining similar classification accuracy indices with opposite manipulations. Perhaps, the use of SVM classifiers can be complemented with other analytical techniques to further disentangle the consequences of manipulating intrastriatal D1 receptors.

2. The second finding (Fig. 4) indicates that thalamic regions forming "closed loops" with the striatum were more affected by chemogenetic manipulations. We knew from anatomical studies that the BG are part of anatomically segregated cortico-BG-thalamic loops. Therefore, it would be expected that these anatomical boundaries would somehow limit functional connectivity maps. Here again, I consider that the manuscript would be improved with further analysis or discussion. For example, it would be interesting to perform further analysis relating the previous section (local striatal connectivity) with this one. In this section, several thalamic nuclei presented higher levels of classification accuracy, but in the previous section, the authors showed that DMS manipulation also produced the same effects in different intrastriatal regions. Therefore, it is not possible to know if the thalamic effects are related to the manipulation of D1 in the DMS or its adjacent regions.

3. In the third finding (Fig. 5) the authors show that the most "sensitive" cortical regions to the manipulations were classified as "unimodal". This is an interesting result; however, it would be necessary to at least provide further discussion on its potential meaning. It is important to consider that the cortical regions with significant changes, for example, primary sensorimotor cortices, mainly target the dorsolateral, not the dorsomedial striatum. In this context, would it be possible to establish a new analysis to characterize potential correlations between cortical regions and striatal subregions?

4. The fourth finding (Figure 6) is that thalamic but not cortical regions presented low-frequency fluctuations. What is the meaning of an increase in slow fluctuations? Why did D1 activation (and not inactivation) induced this effect? Are striatal sub-regions also presenting these slow fluctuations?

5. In the last finding (Figure 7), the authors explored potential changes in functional connectivity (FC) between the striatum and cortical and subcortical regions. Contrary to the results obtained with the SVM-based analytical tool, FC analysis revealed that D1 activation and inactivation produced opposite results, while D1 activation decreased FC in several cortical and subcortical regions, D1 inactivation increased it. While this set of data is clearly described, the implications of these relationships could be further discussed. For example, how do the authors explain that FC with SSp was not significantly changed with this analytical method, but was one of the most affected regions with the Balanced Classification Accuracy method?

6. Finally, there is no section in the discussion where the behavioral effects observed in figure 2 are contextualized in the massive set of BOLD results presented in the following sections.

Reviewer #1 (Public Review):

The authors carried out a clever and systematic analysis. Most SNARE complex assembly reactions are thought to involve multi-subunit tethers, and it now seems possible that Uso1/p115 plays this role in the case of the ER-to-Golgi SNARE complex. In addition to documenting the main conclusions of the paper, they characterized Uso1 in various ways, including an analysis of how the different domains of Uso1 contribute to the higher-order structure of the protein and to interactions with other components.

I have only one significant comment about the presentation. The concept that the mutant Uso1 rescues the loss of Rab1 by binding more tightly to SNAREs is reasonable and likely but is not formally proven by the data. Instead, the data show that the mutant binds better to Golgi membranes in the absence of Rab1 and also binds better to Bos1. It should be acknowledged that this correlation is merely suggestive.

Reviewer #2 (Public Review):

This is a very dense and thorough analysis of the role of Uso1 in Aspergillus using genetics, pulldown assays, and modelling.<br /> Uso1 has been established as an essential tethering factor that acts in conjunction with Rab1 to deliver ER-derived vesicles to the Golgi. The current picture is that Uso1 is a Rab1 effector, but the authors challenge this interpretation using a combination of genetics experiments, biochemical analysis of protein-protein interactions, and alphafold2 prediction.

While Rab1 is essential, they identify strains of Aspergillus that bypass the need for Rab1, which carry two mutations in Uso1. They go on to show that Uso1 binds directly to the Bos1 and Bet1 components of the SNARE complex and that the rescue mutations cause tighter binding of the Uso1 globular head domain to Bos1 and (hypothetically) to the membrane. They support their genetics and biochemical analysis by doing structure predictions with alphafold2 and suggesting how these mutants might act. They also show that an overexpressed mutated monomeric globular domain of Uso1 (without the coiled-coil 'tether' that causes dimerisation) rescues growth defects of delta Uso1, suggesting the essential activity of Uso1 is not the tethering but its being part of the SNARE complex.

The data is solid, and the interpretation is convincing, showing Uso1 is not 'merely' a tethering factor. It has multiple roles, and this study opens up new questions regarding what exactly is Uso1's function as part of the SNARE bundle, and also in which way the Rab1-mediated tethering and the SNARE complex aspects of Uso1 are linked and/or regulated.

However, there are some aspects of this work that need to be strengthened/clarified including some of the modelling and the interpretation of the role of Uso1 dimerisation. Also, given the availability of models for all homologues, it would be interesting to test whether analogous Uso1 mutant in S.cerevisiae can also rescue rab1- lethality. This would suggest the new proposed role of Uso1 is a general feature, at least for fungi, rather than a particularity of Aspergillus.

Reviewer #3 (Public Review):

The manuscript by Bravo-Plaza et al. identifies and characterizes new mutations (E6K and G540S) in the Uso1 globular head domain that suppress the loss of function mutations in Rab1. Further experiments show that the combined E6K/G540S mutant restores apparent Golgi-localization of Uso1 in Rab1 deficient cells, that this mutant preferentially co-purifies with ER/Golgi SNARE proteins, that monomeric E6K/G540S globular head-domain binds more avidly to purified Bos1 SNARE protein than wild type head-domain, and that overexpression of E6K/G540S or wild type head-domain alone is sufficient for viability. Based on these findings the authors propose that long-distance tethering by Uso1 is dispensable and that the head domain provides an essential function to directly regulate ER/Golgi SNARE-dependent membrane fusion.

Strengths of the study are that an unbiased screen was used to identify new Rab1 suppresser mutations that land in the Uso1 globular head domain. Characterization of these suppressor mutants reveals that SNARE binding activity of Uso1 resides in the head domain and that elevated expression of the Uso1 head domain is sufficient for viability. Imaging experiments document the localization and dynamics of Uso1 on Golgi compartments and biochemical studies show the properties and binding activity of Uso1 domain mutants. These are new findings and the conclusion that monomeric globular head-domain interacts with specific SNAREs to maintain viability is justified.

Weaknesses are that it is well documented that both Rab1 and Uso1 activity can be bypassed by activation of ER/Golgi SNARE machinery either by overexpression of SNARE proteins or by the single copy SLY1-20 allele. Therefore, it was not surprising that tethering by the Uso1 coiled-coil domain is dispensable. The proposal that the E6K mutation in the head domain of Uso1 promotes membrane targeting was not well supported by experimental evidence. And while the AlphaFold modeling of Uso1 with the ER/Golgi fusion machinery was intriguing, the proposed molecular models remain speculative until further tested.

Reviewer #1 (Public Review):

This work by Stauber et al. is focused on understanding the signaling mechanisms that are associated with tendinopathy development, and by screening a panel of human tendinopathy samples, identified IL-6/JAK/STAT as a potential mediator of this pathology. Using an innovative explant model they delineated the requirement for IL-6 in the main body of the tendon to alter the dynamics of cells in the peritendinous synovial sheath space.

The use of a publicly available existing dataset is considered a strength since this dataset includes expression data from several different human tendons experiencing tendinopathy. This facilitates the identification of potentially conserved regulators of the tendinopathy phenotype.

The clear transcriptional shifts between WT and IL6-/- cores demonstrates the utility of the assembloid model, and supports the importance of IL6 in potentiating the cell response to this stimuli.

There are two main concerns with the manuscript in its current form:<br /> First, the experimental approach does not directly assess proliferation, as such the conclusions regarding proliferation are not well supported. In the ex-vivo model, the use of cell counting approaches is somewhat acceptable since the system is constrained by the absence of potential influx of new cells. However, given the nearly unlimited supply of extrinsically derived cells in vivo (vs. the explant model), assessment of actual proliferation (e.g. Edu, BrdU, Ki67) is critical to support this conclusion.

Second, the justification for the use of Scx-GFP+ cells as a progenitor population is not well supported. Indeed, in the discussion, Scx+ cells are treated as though they are uniformly a progenitor population, when the diversity of this population has been established by the cited studies, which do not suggest that these are progenitor populations. Additional definition/ delineation of these cells to identify the subset of these cells that may actually display other putative progenitor markers would support the conclusions. As it stands, the study currently provides important information on the impact of IL6 on Scx+ cells, but not tendon progenitors.

Reviewer #2 (Public Review):

The authors of this study describe a goal of elucidating the signaling pathways that are upregulated in tendinopathy in order to target these pathways for effective treatments. Their goal is honorable, as tendinopathy is a common debilitating condition with limited treatments. The authors find that IL-6 signaling is upregulated in human tendinopathy samples with transcriptomic and GSEA analyses. The evidence of their initial findings are strong, providing a clinically-relevant phenotype that can be further studied using animal models.

Along these lines, the authors continue with an advanced in vitro system using the mouse tail tendon as the core with progenitors isolated from the Achilles tendon as the external sheath embedded in a hydrogel matrix. One question that comes to mind is whether the fibroblast progenitors in the extrinsic sheath of Achilles tendon is similar to those surrounding the tail tendon. The similarity of progenitors between different tendons is assumed with this model. I would consider this to be a minor issue, and would consider the in vitro system to be an additional strength of this study.

In order to address the IL-6 signaling pathway, the authors use core tendons from IL-6 knockout mice and progenitors from wild-type mice. The reasoning behind this approach was a little confusing... is IL-6 expressed solely in the tendon core compared to the extrinsic sheath? Furthermore, is a co-culture system for 7 days appropriate to model tendinopathy without the supplementation of exogenous inflammatory compounds? The transcriptomic differences in Figure 3 seem to be subtle, and may perhaps suggest that it could be a model that more closely resembles steady state compared to tendinopathy. If so, is IL-6 still relevant during steady state?

Nevertheless, the results presented in Figures 4 and 5 are impressive, demonstrating a link between IL-6 and fibroblast progenitor numbers and migration. Their experimental design in these figures show strong evidence, using Tocilizumab and recombinant IL-6 to rescue shown phenotypes. I would reduce the claims on proliferation, however, unless a proliferation-specific marker (e.g., Ki67, BrdU, EdU) is included in confocal analyses of Scx+ progenitors. The Achilles tendon injury model provides a nice in vivo confirmation of Scx-progenitor migration to the neotendon.

Given their goal to elucidate signaling pathways that could be targeted in the clinic, I think it would significantly strengthen the study if they could measure tendon healing in IL-6 knockouts or in wild-type mice treated with IL-6 inhibitors, since conventional ablation of IL-6 may lead to the elevation of compensatory IL-6 superfamily ligands that could activate STAT signaling. The authors claim that reducing IL-6 signaling decreases transcriptomic signatures of tendinopathy, but IL-6 may be necessary to promote normal healing of the tendon following injury. It is supposed that a lack of Scx+ progenitor migration would delay tendon healing.

Overall, the authors of this study elucidated IL-6 signaling in tendinopathy and provided a strong level of evidence to support their conclusions at the transcriptomic level. However, functional studies are needed to confirm these phenotypes and fully support their aims and conclusions. With these additional studies, this work has the potential to significantly influence treatments for those suffering from tendinopathy.

Reviewer #3 (Public Review):

The strongest aspects of this study are the structural analysis of the 90 residue KER domain. This is an important advance, discovering a founding member of a novel class of DNA binding motifs, termed a SAH-DBD (single alpha helix-DNA binding domain). Interestingly, they define a subregion of KER (termed "middle-A", residues 155-204 of Cac1) that has nearly the same DNA binding affinity and confers similar in vivo phenotypes as the full KER domain.

This study also shows that the biological role of KER partially overlaps compensatory factors in vivo, both within the same Cac1 protein subunit (e.g. the WHD domain) and also with other proteins acting in parallel (e.g. Rtt106). That is, the presence of either WHD or Rtt106 renders the drug-resistance and silencing assays employed here insensitive to loss of the KER domain.

However, the drug resistance and gene silencing phenotypes are inherently indirect measures of the most important claim of this work, that KER is a molecular ruler for DNA for the purpose of ensuring sufficiently large templates deposition of histone H3/H4 cargoes. Therefore, this study would be of greater impact if the authors more directly tested this measurement idea in assays that directly assess histone deposition. There are multiple options. Since the authors have in hand recombinant wild-type and mutant CAF-1 complexes, one could examine the number and/or spacing of nucleosomes formed during in vitro deposition reactions. Complementary in vivo experiments using the authors' existing mutant strains could be based on the finding that CAF-1 is particularly important for histone deposition onto nascent Okazaki fragments during DNA replication (Smith and Whitehouse, 2012; pmid: 22419157), and that the spacing pattern of nucleosomes on this DNA is greatly perturbed in cac1-delete cells.

Reviewer #1 (Public Review):

In this study, the authors examined the putative functions of hypothalamic groups identifiable through Foxb1 expression, namely the parvofox Foxb1 of the LHA and the PMd Foxb1, with emphasis on innate defensive responses. First, they reported that chemogenetic activation of Foxb1hypothalamic cell groups led to tachypnea. The authors tend to attribute this effect to the activation of hM3Dq expressed in the parvofox Foxb1 but did not rule out the participation of the PMd Foxb1 cell group which may as well have expressed hM3Dq, particularly considering the large volume (200 nl) of the viral construct injected. It is also noteworthy that the activation of the Foxb1hypothalamic cell groups in this experiment did not alter the gross locomotor activity, such as time spent immobile state. Thus, contrasts with the authors finding on the optogenetic activation of the Foxb1hypothalamic fibers projecting to the dorsolateral PAG. In the second experiment, the authors applied optogenetic ChR2-mediated excitation of the Foxb1+ cell bodies' axonal endings in the dlPAG leading to freezing and, in a few cases, bradycardia as well. The effective site to evoke freezing was the rostral PAGdl, and fibers positioned either ventral or caudal to this target had no response. Considering the pattern of Foxb1hypothalamic cell groups projection to the PAG, the fibers projecting to the rostral PAGdl are likely to arise from the PMd Foxb1 cell group, and not from the parvofox Foxb1 of the LHA. Here it is important to consider that optogenetic ChR2-mediated excitation of the axonal endings is likely to have activated the cell bodies originating these fibers, and one cannot ascertain whether the behavioral effects are related to the activation of the terminals in the PAGdl or the cell bodies originating the projection. Moreover, activation of PMd CCK cell group, which consists of around 90% of the PMd cells, evokes escape, and not freezing. According to the present findings, a specific population of PMd Foxb1 cells may be involved in producing freezing. In addition, only a small number of the animals with correct fiber placement presented sudden onset of bradycardia in response to the photostimulation. Considering the authors' findings, the Foxb1+ hypothalamic groups are likely to mediate behavioral responses related to innate defensive responses, where the parvofox Foxb1 of the LHA would be involved in promoting tachypnea and the PMd Foxb1group in mediating freezing and bradycardia. These findings are very interesting, and, at this point, they need to be tested in a scenario of real exposure to a natural predator.

Reviewer #2 (Public Review):

The authors aimed to examine the role of a group of neurons expressing Foxb1 in behaviors through projections to the dlPAG. Standard chemogenetic activation or inhibition and optogentic terminal activation or inhibition at local PAG were used and results suggested that, while activation led to reduced locomotion and breathing, inhibition led to a small degree of increased locomotion.

The observed effects on breathing are evident and dramatic. However, this study needs significant improvements in terms of data analysis and presentation and some of studies seem incomplete; and therefore the data may not yet support the conclusion.

1) Fig.1 has no experimental data and needs to be replaced with detailed pictures from the viral injected mice showing the projections diagrammed.

2) Fig. 3 needs control pictures and statistical comparison with different conditions in c-Fos. Also expression in other nearby regions needs to be presented to demonstrate the specificity of the expression.

3) Fig. 5, a great effort has been made to illustrate the point that CCK and Foxb1 are differentially expressed. Why not just perform a double in situ experiment to directly illustrate the point?

4) Fig. 7 data on optogenetic stimulation on immobility and breathing, since not all mice showed the same phenotype, what is the criterion for allocating these mice to hit or no hit groups? Given the dramatically reduced breathing and locomotion, what is the temperature response? More data needs to be gathered to support that this is a defense behavior.

5) The authors claim to target dlPAG. However, in the picture shown in Fig. 8C, almost all PAG contains ChR2 fibers and it is likely all the fibers will be activated by light. Thus, as presented, the data does not support the claim of the specificity on dlPAG. Also c-Fos data needs to be presented on the degree of activation of downstream PAG neurons after light exposure.

6) Fig. 9 only showed one case. A statistical comparison needs to be presented.

7) Optogentic terminal activation in the PAG will likely elicit back-propagation and subsequent activation of additional downstream brain sites of Foxb1 neurons. More experiments need to be done to assess this and as presented, the data does not support the role of PAG necessarily.

8) The authors claim negative data from PVH-Cre mice. More data need to be presented to make this case.

The conclusion, even as presented, adds to the known evidence of the PAG in the defense behavior.

Reviewer #1 (Public Review):

Somasundaram and colleagues explore the role of transcription factors in retinal ganglion cell (RGC) death and axonal regeneration after a disease relevant insult (mechanical axonal injury). The work significantly extends our knowledge of the role of MAPK and integrated stress response (ISR) in controlling RGC fate after injury. Specifically, the manuscript shows that after axonal injury PERK-activated ISR acts through Atf4 to drive a prodeath transcriptional response in RGCs, in part by crosstalk with the prodeath JUN transcriptional program. Also, and perhaps most interesting, the work shows that PERK-ATF4 pathway activation is pro-regenerative for RGC axons. A major plus of the manuscript is that many new RNA-seq datasets are generated that describe the major prodegenerative and proregenerative gene networks altered after axonal injury.

A limitation of the study is that it does not directly compare the effect of inhibiting the PERK-ATF4 pathway with inhibiting JUN and/or JUN-CHOP double deficient animals. It would also be useful, for the cell survival experiments shown in Figure 1, to examine a longer time point than 14 days to understand the long-term consequence of manipulating the PERK-ATF4 pathway.

Reviewer #2 (Public Review):

This manuscript investigates the role of Perk (Protein kinase RNA-like endoplasmic reticulum kinase) and Atf4 (Activating Transcription Factor-4) in neurodegenerative and regenerative responses following optic nerve injury. The authors employed conditional knockout mice to examine the impact of the Perk/Atf4 pathway on transcriptional responses, with a particular focus on canonical Atf4 target genes and the involvement of C/ebp homologous protein (Chop).

The study demonstrates that Perk primarily operates through Atf4 to stimulate both pro-apoptotic and pro-regenerative responses after optic nerve injury. This Perk/Atf4-dependent response encompasses canonical Atf4 target genes and limited contributions from Chop, exhibiting overlap with c-Jun-dependent transcription. Consequently, the Perk/Atf4 pathway appears crucial for coordinating neurodegenerative and regenerative responses to central nervous system (CNS) axon injury. Additionally, the authors observed that neuronal knockout of Atf4 mimics the neuroprotection resulting from Perk deficiency. Moreover, Perk or Atf4 knockout hinders optic axon regeneration facilitated by the deletion of the tumor suppressor Pten.

These findings contrast with the transcriptional and functional outcomes reported for CRISPR targeting of Atf4 or Chop, revealing a vital role for the Perk/Atf4 pathway in orchestrating neurodegenerative and regenerative responses to CNS axon injury.

However, the main concern is the overall data quality, which appears to be suboptimal. The transfection efficiency of AAV2-hSyn1-mTagBFP2-ires-Cre used in this study does not seem highly effective, as evidenced by the data presented in Supplementary Figure 1. The manuscript also contains several inconsistencies and a mix of methods in data collection, analysis, and interpretation, such as the labeling and quantification of RGCs and the combination of bulk and single-cell sequencing results.

Despite these limitations, the study offers valuable insights into the role of the Perk/Atf4 pathway in determining neuronal fate after axon injury, emphasizing the significance of understanding the molecular mechanisms that govern neuronal survival and regeneration. This knowledge could potentially inform the development of targeted therapies to promote neuroprotection and CNS repair following injury.

Reviewer #1 (Public Review):

The study by Oikawa and colleagues demonstrates for the first time that a descending inhibitory pathway for nociception exists in non-mammalian organisms, such as Drosophila. This descending inhibitory pathway is mediated by a Drosophila neuropeptide called Drosulfakinin (DSK), which is homologous to mammalian cholecystokinin (CCK). The study creates and uses several Drosophila mutants to convincingly show that DSK negatively regulates nociception. They then use several sophisticated transgenic manipulations to demonstrate that a descending inhibitory pathway for nociception exists in Drosophila.

Strengths:

This study creates the possibility of using Drosophila to study descending nociceptive systems.

CRISPR/Cas9 is used to generate mutants of dsk, CCKLR-17D1, and CCKLR-17D3. The authors then use these mutants to clearly show that DSK negatively regulates nociception.

Several GAL4s are used to clearly show that these effects are likely mediated by two sets of neurons in the brain, MP1 and Sv.

RNAi and rescue experiments further show that CCKLR-17D1, a DSK receptor, functions in Goro neurons to negatively regulate nociception.

Thermogenetic experiments nicely show that activation of DSK neurons attenuates the nociceptive response.

Weaknesses:

Future studies should address how DSK negatively regulates nociception. An earlier study at the Drosophila nmj shows that loss of DSK signaling impairs neurotransmission and synaptic growth. In the current study, loss of CCKLR-17D1 in Goro neurons seems to increase intracellular calcium levels in the presence of noxious heat. An interesting future study would be the examination of the underlying mechanisms for this increase in intracellular calcium.

Reviewer #2 (Public Review):

This is an exceptional study that provides conclusive evidence for the existence of a descending pathway from the brain that inhibits nociceptive behavioral outputs in larvae of Drosophila melanogaster. The authors identify both molecular and neuronal/cellular components of this pathway. Converging lines of evidence and conclusive genetic experiments indicate that the neuropeptide, drosulfakinin (DSK), and its receptors (CCK1 and CCK2) function to inhibit nociception behaviors. Interestingly, the authors show that the relevant DSK neurons have cell bodies that are in the larval brain and that these neurons send projections into the thoracic ganglion and ventral nerve cord. Several lines of evidence support the hypothesis that fourth-order nociceptive neurons called Goro, are one relevant target for these outputs. RNAi knockdown of the CCK1 receptor in these cells sensitizes behavioral and physiological responses to noxious heat. Second, the axons of DSK neurons form physical contact with processes of Goro neurons as revealed by GRASP analysis. However, the authors' careful experiments indicate that the contacts between axons and Goro neurites might not be indicative of direct synapses and instead might operate through the bulk transmission of the peptidergic signals. The study raises many interesting questions for future study such as what behavioral contexts might depend on this pathway. Using the CAMPARI approach, the authors do not find that the DSK neurons are activated in response to nociceptive input but instead suggest that these cells may be tonically active in gating nociception. Future studies may find contexts in which the output of the DSK neurons is inhibited to facilitate nociception or contexts in which the cells are more active to inhibit nociception.

Reviewer #3 (Public Review):

This study describes a descending circuit that can modulate pain perception in the drosophila larvae. While descending inhibition is a major component of mammalian pain perception, it is not known if a similar circuit design exists in fruit flies. Overall the authors use clean logic to establish a role for DSK and its receptor in regulating nociception. The following concerns still stand:

1) It's not completely clear why the authors are staining animals with an FLRFa antibody. Can the authors stain WT and DSK KO animals with a DSK antibody? Also, can the authors show in supplemental what antigen the FLRFa antibody was raised against, and what part of that peptide sequence is retained in the DSK sequence? This overall seems like a weakness in the study that could be improved on in some way by using DSK-specific tools.

2) What is the phenotype of DSK-Gal4 x UAS-TET animals? They should be hyper-reactive. If it's lethal maybe try an inducible approach.

3) Figure 9. This was not totally clear, but I think the authors were evaluating spontaneous (i.e. TRPA1-driven) rolling at 35C. The critical question is "Does activating DSK-expressing neurons suppress acute heat nociception?" and this hasn't really been addressed. The inclusion of PPK Gal4 + DSK Gal4 in the same animal clouds the overall conclusions the reader can draw. The essential experiment is to express UAS-dTRPA1 in DSK-Gal4 or GORO-Gal4 cells, heat the animals to ~29C, and then test latency to a thermal heat probe (over a range of sub and noxious temperatures). Basically, prove the model in Figure 10 showing ectopic activation or inhibition for each major step, then test heat probe responses.

4) It would also then be interesting to see how strong the descending inhibition circuit is in the context of UV burn. If this is a real descending circuit, it should presumably be able to override sensitization after injury.

Reviewer #1 (Public Review):

stdpopsim is an existing, community-driven resource to support population genetics simulations across multiple species. This paper describes improvements and extensions to this resource and discusses various considerations of relevance to chromosome-scale evolutionary simulations. As such, the paper does not analyse data or present new results but rather serves as a general and useful guide for anyone interested in using the stdpopsim resource or in population genetics simulations in general.

Two new features in stdpopsim are described, which expand the types of evolutionary processes that can be simulated. First, the authors describe the addition of the ability to simulate non-crossover recombination events, i.e. gene conversion, in addition to standard crossover recombination. This will allow for simulations that come closer to the actual recombination processes occurring in many species. Second, the authors mention how genome annotations can now be incorporated into the simulations, to allow different processes to apply to different parts of the genome - however, the authors note that this addition will be further detailed in a separate, future publication. These additions to stdpopsim will certainly be useful to many users and represent a step forward in the degree of ambition for realistic population genetics simulations.

The paper also describes the expansion of the community-curated catalog of pre-defined, ready-to-use simulation set-ups for various species, from the previous 6 to 21 species (though not all new species have demographic models implemented, some have just population genetic parameters such as mutation rates and generation times). For each species, an attempt was made to implement parameters and simulations that are as realistic as possible with respect to what's known about the evolutionary history of that species, using only information that can be traced to the published literature. This process by which this was done appears quite rigorous and includes a quality-control process involving two people. Two examples are given, for Anopheles gambiae and Bos taurus. The detailed discussion of how various population genetic and demographic parameters were extracted from the literature for these two species usefully highlights the numerous non-trivial steps involved and showcases the great deal of care that underlies the stdpopsim resource.

The paper is clearly written and well-referenced, and I have no technical or conceptual concerns. The paper will be useful to anyone interested in population genetics simulations, and will hopefully serve as an inspiration for the broader effort of making simulations increasingly more realistic and flexible, while at the same time trying to make them accessible not just to a small number of experts.

Reviewer #2 (Public Review):

Lauterbur et al. present a description of recent additions to the stdpopsim simulation software for generating whole-genome sequences under population genetic models, as well as detailed general guidelines and best practices for implementing realistic simulations within stdpopsim and other simulation software. Such realistic simulations are critical for understanding patterns in genetic variation expected under diverse processes for study organisms, training simulation-intensive models (e.g., machine learning and approximate Bayesian computation) to make predictions about factors shaping observed genetic variation, and for generating null distributions for testing hypotheses about evolutionary phenomena. However, realistic population genomic simulations can be challenging for those who have never implemented such models, particularly when different evolutionary parameters are taken from a variety of literature sources. Importantly, the goal of the authors is to expand the inclusivity of the field of population genomic simulation, by empowering investigators, regardless of model or non-model study system, to ultimately be able to effectively test hypotheses, make predictions, and learn about processes from simulated genomic variation. Continued expansion of the stdpopsim software is likely to have a significant impact on the evolutionary genomics community.

Strengths:

This work details an expansion from 6 to 21 species to gain a greater breadth of simulation capacity across the tree of life. Due to the nature of some of the species added, the authors implemented finite-site substitution models allowing for more than two allelic states at loci, permitting proper simulations of organisms with fast mutation rates, small genomes, or large effect sizes. Moreover, related to some of the newly added species, the authors incorporated a mechanism for simulating non-crossover recombination, such as gene conversion and horizontal gene transfer between individuals. The authors also added the ability to annotate and model coding genomic regions.

In addition to these added software features, the authors detail guidelines and best practices for implementing realistic population genetic simulations at the genome-scale, including encouraging and discussing the importance of code review, as well as highlighting the sufficient parameters for simulation: chromosome level assembly, mean mutation rate, mean recombination rate or recombination map if available, effective size or more realistic demographic model if available, and mean generation time. Much of these best practices are commonly followed by population genetic modelers, but new researchers in the field seeking to simulate data under population genetic models may be unfamiliar with these practices, making their clear enumeration (as done in this work) highly valuable for a broad audience. Moreover, the mechanisms for dealing with issues of missing parameters discussed in this work are particularly useful, as more often than not, estimates of certain model parameters may not be readily available from the literature for a given study system.

Weaknesses:

An important update to the stdpopsim software is the capacity for researchers to annotate coding regions of the genome, permitting distributions of fitness effects and linked selection to be modeled. However, though this novel feature expands the breadth of processes that can be evaluated as well as is applicable to all species within the stdpopsim framework, the authors do not provide significant detail regarding this feature, stating that they will provide more details about it in a forthcoming publication. Compared to this feature, the additions of extra species, finite-site substitution models, and non-crossover recombination are more specialized updates to the software.

Reviewer #3 (Public Review):

Lauterbur et al. present an expansion of the whole-genome evolution simulation software "stdpopsim", which includes new features of the simulator itself, and 15 new species in their catalog of demographic models and genetic parameters (which previously had 6 species). The list of new species includes mostly animals (12), but also one species of plant, one of algae, and one of bacteria. While only five of the new animal species (and none of the other organisms) have a demographic model described in the catalog, those species showcase a variety of demographic models (e.g. extreme inbreeding of cattle). The authors describe in detail how to go about gathering genetic and demographic parameters from the literature, which is helpful for others aiming to add new species and demographic models to the stdpopsim catalog. This part of the paper is the most widely relevant not only for stdpopsim users but for any researcher performing population genomics simulations. This work is a concrete contribution towards increasing the number of users of population genomic simulations and improving reproducibility in research that uses this type of simulations.

Reviewer #1 (Public Review):

In this study, Dominici et. al. show that small molecule inhibition of Type I PRMTs in muscle stem cells (MSCs) can result in the expansion of this cell type in vitro, solving a major limitation in the field. Importantly, once the inhibitor is removed these stem cells differentiate "normally". This advance will likely facilitate CRISPR-based screening approaches and stem cell engraftment therapy. Furthermore, they show that when a mouse model of Duchenne muscular dystrophy is treated with these same inhibitors these mice rather rapidly gain grip strength, demonstrating the therapeutic value of these findings.

Strengths:

- Previous studies from the same group have shown that the conditional ablation of PRMT1 in MSCs results in the expansion of this cell type, but this expanded PRMT1-null MSC pool cannot terminate the myogenic differentiation program. This raises the question of whether PRMT1 small molecule inhibition of MSCs will also facilitate the expansion of these cells, and if the removal of the inhibitor after expansion will result in a large functional pool of MSCs, which could then be used for both in vitro and in vivo studies.

- Using a combination of muscle fiber culture, myoblast culture, and single-cell RNA-seq, this is indeed what they show.

- They also perform two types of in vivo experiments to validate their cell culture findings; 1) MSCs expanded under the treatment of MS023 were washed clean of the inhibitor and engrafted into the tibialis anterior muscle. These cells were marked with GFP to allow efficient tracking. Mice receiving the MS023-treated MSCs produced more than double the mature GFP+ muscle fibers than cells treated with DMSO. 2) A mouse model of Duchenne muscular dystrophy displayed grip strength improvement after just one treatment of MS023.

- MS023 is a Type I PRMT inhibitor and thus can also target CARM1. CARM1 has been implicated in MSC function by the Rudnicki group. Importantly, they exclude a role for CARM1 in the expansion of MSC cell numbers by treatment with a very specific CARM1 inhibitor, TP064. Thus, indicating that PRMT1 inhibition is likely the main driver of this expansion phenotype.

Reviewer #2 (Public Review):

In this manuscript, Dominici et al. aim to determine whether the reversible inhibition of the type I protein arginine methyltransferases (PRMT) would maintain the stemness of muscle stem cells in culture and enable subsequent regenerative capacities. They demonstrate that the type I PRMT inhibitor MS023 enhances self-renewal and in vitro expansion of muscle stem cells isolated from mice. Using a very rigorous single-cell RNA-sequencing approach, they further demonstrate that distinct sub-populations of cells emerge under type I PRMT inhibition and that these cells entered the differentiation program more efficiently. Moreover, they revealed a shift in metabolism in these cells, which they confirmed in vitro. Finally, they demonstrate that MS023 enhances muscle stem cell engraftment in vivo and that the direct injection of MS023 increases muscle strength in a mice model of Duchenne muscular dystrophy.

This study will have a great impact on the field of stem cells and offer potential therapeutic avenues for diseases such as Duchenne muscular dystrophy.

Reviewer #3 (Public Review):

Dominici et al studied the effects of the type I PRMT inhibitor MS023 on skeletal muscle stem cells (MuSCs) and on muscle strength in dystrophin-deficient mdx mice. The authors observed an enhanced proliferative capacity of cultured MuSCs with an increase of Pax7+/MyoD- cells. The observations are more or less in line with previous studies of the same group, describing reduced differentiation but enhanced proliferation of MuSCs after genetic inactivation of Prmt1. scRNA-seq identified different subpopulations of MuSCs, showing a shift to increased Pax7 expression and elevated oxidative phosphorylation and glycolysis after treatment with MS023. Treatment of MuSC with MS023 during expansion in vitro enhanced engraftment of MuSCs and treatment of dystrophic mdx mice increased muscle strength.

Overall, the manuscript provides new insights into the beneficial effects of the type I PRMT inhibitor MS023 for skeletal muscle regeneration. The description of the MS023-induced transcriptional and metabolic changes in MuSC is interesting and the effects on MuSC transplantation and muscle strength are stunning. However, I have the following comments and concerns:

* Control experiments with the TP-064 inhibitor (previously shown to be specific for CARM1/PRMT4) were not done for the transplantation and muscle strength experiments, which is a clear shortcoming in my view. Since MS023 is a non-selective inhibitor of type I PRMTs with comparable IC50 values for PRMT1 and PRMT4 (CARM1), and lower IC50 values for PRMT6 and PRMT8, it is still not clear whether the enhanced transplantation efficiency and the increased muscle strength is indeed only caused by inhibition of PRMT1. The authors justify their statements by pointing out that gene expression of Prmt1 is highest among the type I PRMTs in MuSCs, which is a rather poor argument, as seen by the strong effects caused by the inactivation of PRMT4.

* Clustering of the M1-M5 subpopulations. I expressed my concern about the separation of the subclusters, which appear more or less in the same cloud. The authors answered that each cluster has some genes, which are only expressed in the respective cluster. I do not doubt this observation but apparently, the transcriptional differences are minor, otherwise one would have seen a much better separation of the subpopulations.

* The authors have not done additional experiments but simply toned-down the statements about the relevance of the proposed "metabolic reprogramming" of MuSC by the type I PRMT inhibitor MS023, which was a major conclusion in the original submission. Again, the changes in the expression of metabolically relevant genes upon MS023 treatment are interesting and should be analyzed in respect to causality. It is not a solution to more or less disabandon the original hypothesis by changing the wording.

* I specifically asked the authors to check whether the dramatic six-fold increase of MuSC engraftment after MS023 treatment really goes along with the incorporation of transplanted MuSC into the MuSC niche, raising concerns that a huge share of the transplanted cells may linger around in the interstitium. It should be very easy to identify and quantify transplanted MuSC outside and inside the basal lamina. Instead of doing the requested experiment, the authors argue about suppression of endogenous MuSC competition by irradiation, at the same time admitting that several GFP-negative fibers have formed.

* I expressed my doubts that a 3-day treatment with MS023 is sufficient to dramatically enhance muscle function in mdx mice via "improvement" of the MuSC population, as reported by the authors, even 30 days after administration of MS023. It seems much more likely that MS023 exerts additional effects that are responsible for the dramatic improvement of muscle function in mdx mice. I maintain my view that this needs to be interrogated more carefully since the improvement of muscle function of dystrophic mice is a central point of the study. It has to be made clear whether this is really due to "improved" functions of MuSC. Many other processes might be involved or responsible for the effect (e.g. impact on inflammation?).

Public Review:

Barreat and Katzourakis analyze the evolutionary history of eukaryotic viruses (and related mobile elements) in the Bamfordvirae kingdom, and discuss potential scenarios regarding the origin of different viral taxa in this group. This version of their manuscript now includes a larger number of sequences to better represent diversity in these viral groups, and explored new evolutionary scenarios, including a "virophage-first" hypothesis now presented as the one best supported by phylogenetic analyses. The authors also present compelling analyses suggesting that the "nuclear escape" hypothesis in which these different viral groups separately "escaped" from nuclear (integrated) elements is not consistent with the current genomic and phylogenetic information available.

This work is thus an important step in our collective understanding of the ancient evolutionary history of eukaryotic viruses, and more generally of the constraints and main drivers of virus evolution.

Joint Public Review:

In this manuscript, Xie et al report the development of SCA-seq, a multiOME mapping method that can obtain chromatin accessibility, methylation, and 3D genome information at the same time. This method is highly relevant to a few previously reported long read sequencing technologies. Specifically, NanoNome, SMAC-seq, and Fiber-seq have been reported to use m6A or GpC methyltransferase accessibility to map open chromatin, or open chromatin together with CpG methylation; Pore-C and MC-3C have been reported to use long read sequencing to map multiplex chromatin interactions, or together with CpG methylation. Therefore, as a combination of NanoNome/SMAC-seq/Fiber-seq and Pore-C/MC-3C, SCA-seq is one step forward. The authors tested SCA-seq in 293T cells and performed benchmark analyses testing the performance of SCA-seq in generating each data module (open chromatin and 3D genome). The QC metrics appear to be good and the methods, data and analyses broadly support the claims. However, there are some concerns regarding data analysis and conclusions, and some important information seems to be missing.

1. The chromatin accessibility tracks from SCA-seq seem to be noisy, with higher background than DNase-seq and ATAC-seq (Fig. 2f, Fig. 4a and Fig. S5). Also, SCA-seq is much less sensitive than both DNase-seq and ATAC-seq (Figs. 2a and 2b). This and other limitations of SCA-seq (high background, high sequencing cost, requirement of specific equipment, etc) need to be carefully discussed.

2. In Fig. 2f, many smaller peaks are present besides the major peaks. Are they caused by baseline DNA methylation? How many of the small methylation signals are called peaks? In Fig. 4a, it seems that the authors define many more enhancers from SCA-seq data than what will be defined from ATAC-seq or DHS. Are those additional enhancers false positives? Also, it is difficult to distinguish the gray "inaccessible segments" from the light purple "accessible segments.

3. For 3D genome analysis, it is important to provide information about data yield from SCA-seq. With 30X sequencing depth, how many contacts are obtained (with long-read sequencing, this should be the number of ligation junctions)? How is the number compared to Hi-C.

4. Fig 3j. Because SCA-seq only do GpC methylation, the capability to detect the footprint at individual CTCF peaks depends on the density of GpC nearby. Have the authors taken GpC density into account when defining CTCF sites with or without footprint?<br /> 5. This study only performs higher resolution chromatin interaction analysis based on individual read concatenates. It is unclear to me if the data have enough depth to perform loop analysis with Hi-C pipelines.

6. It appears that SCA-seq is of low efficiency in detecting chromatin interactions. As shown in Fig. S7a, 65.4% of sequenced reads contained only one restriction enzyme (RE) fragment/segment (with no genomic contact), which is much higher than that reported in published PORE-C methods. In addition, Fig. S7g is very confusing and in conflict with Fig. S7a. For example, in Fig. S7g, 21.4% and 22.2% of CSA-seq concatemers contain one and two segments, whereas the numbers are 65.4% and 14.7% in Fig. S7a, respectively. Please explain.

7. I disagree with the rationale of the entire Fig. S9. Biologically there is no evidence that chromatin accessibility will change due to genome interactions (the opposite is more likely), therefore the definition of "expected chromatin accessibility" is hard to believe. If the authors truly believe this is possible, they will need to test their hypothesis by deleting cohesin and check if the chromatin accessibility driven by "power center" are truly abolished. The math in Fig. S9 is also confusing. Firstly, the dimension of the contact matrix in Fig. S9 appears to be wrong, it should have 8 rows. Secondly, I don't understand why the interaction matrix is not symmetric. Third, if I understand correctly the diagonal of the matrix should be all 1, it is also hard to understand why the matrix only has 1, 0 or -1. It appears that the authors assume that the observed accessibility is a simple sum of the expected accessibility of all its interacting regions; this is wrong. In my opinion, the whole Fig. S9 should be deleted unless the authors can make sense of it and ideally also provide more evidence.

Reviewer #1 (Public Review):

The paper is based around one very nice new marine reptile fossil from South China, but the authors make an excellent case in their Introduction that this can shed light on a wide range of fundamental phylogenetic problems around a whole array of Early and Middle Triassic marine reptiles. The description of the fossil is detailed and thorough and makes constant reference to comparative material of other taxa of saurosphargids. The phylogenetic analysis smartly adds some Triassic turtles and some other Early Triassic marine reptiles to a published cladistic data matrix and then can provide some really significant phylogenetic conclusions around Sauropterygia origins and Archelosauria.

Reviewer #2 (Public Review):

The study describes and names a new marine reptile taxon on the basis of an incomplete postcranial skeleton from the early Triassic of China. The morphologial description and comparison is well concucted/informative and very detailed. The paper and results (phylo. analyseis and hypothesis on ancestral body shape) of Wang et al. 2022 should be discussed in more detail.

Reviewer #1 (Public Review):

This manuscript focuses on a set of neurons from the border between the central and medial amygdala (AMGc/m-PAG ) that project to neurons in the periaqueductal gray (PAG) that gate ultrasonic vocalizations (USVs). These neurons suppress vocal production and are active in contexts where vocalizations would be inappropriate (e.g. in the presence of predator cues, or aggressive encounters with conspecifics). They then further characterized these neurons, demonstrating that like in males, these neurons are GABAergic in females and in both sexes, half of these neurons express estrogen receptor alpha (Esr1). To examine the inputs into these neurons, the authors performed monosynaptically-restricted transsynaptic rabies tracing and identified numerous cortical and subcortical projections. Of particular interest, neurons from the preoptic area of the hypothalamus (POA) in addition to terminating on PAG-USV neurons also project to AMGc/m-PAG neurons. Imaging the terminals of these neurons revealed elevated activity during vocalization-promoting contexts and optogenetically stimulating them resulted in evoking USVs. Together, these experiments further identify and quantify a circuit incorporating external factors (e.g. predatory factors, social interactions) in the drive to produce vocalizations.

The authors are commended for use of male and female mice, demonstrating that even though they produce USVs in different social contexts, AMGc/m-PAG neurons share a function in suppressing USV production in both sexes. They do this convincingly with a variety of methodologies while incorporating appropriate controls (e.g. light-only and GFP-control in optogenetic experiments). The experiments are performed in a logical order and the data generated is elaborate.

Reviewer #2 (Public Review):

The existence of PAG-USV-producing neurons has been recently established, alongside two independent pathways, POA->PAG, and AMG->PAG, that promote and inhibit the production of ultrasound vocalizations in female and male mice, respectively. Because vocalizations can be modulated in a variety of contexts, such as in the presence of a predator, the authors first show that the AMG->PAG pathway is activated in situations where mice stop vocalizing, such as in the presence of a predator or aggressive conspecifics, and can inhibit natural vocalizations in contexts where females vocalize (extending to their previous findings in male mice). Interestingly, AMG->PAG neurons also receive input from POA neurons that are known to promote vocalizations via their connection to PAG interneurons that inhibit PAG-USV-producing neurons. This POA->AMG and PAG pathway is inhibitory and therefore its capacity to promote vocalizations via these two parallel pathways might be achieved by its inhibition of AMG and PAG neurons that inhibit the PAG-USV producing neurons. While these results hint at possible mechanisms that could underlie the hierarchical control of vocalization, and how different external signals impinge on existing pathways to produce behavior flexibility, the study is missing important elements to draw such conclusions. Overall, the study is also missing important information on how experiments were performed.

Reviewer #1 (Public Review):

This study presents a valuable finding for the incidence of bone avascular necrosis (AVN) in patients with Gaucher's Disease (GD) for twenty years. Furthermore, the evidence supporting the claims of the authors is solid.

The study's significant limitations relate to small numbers of patients, with only 155 GD patients analyzed. While the study period is excellent for incidence detection at 20 years, the overall number limits the strength of the analysis for cofactors. For example, there is an analysis for linkage to the type of therapy, the GBA1 genotype, spleen status, biomarkers, and other disease indicators. However, substantial numbers that would dictate changes to a preferential enzyme are not convincing. Moreover, the authors described 16 episodes of AVN in 14 patients, again making generalization difficult. Finally, there was a focus on Serum GlcSph levels, and the authors attempted to correlate levels according to probabilities for AVN occurrence while on treatment.

Overall, however, this is one of the best longitudinal studies for the incidence of AVN in GD patients, and the work will be of interest to medical biologists and professionals treating GD patients.

Reviewer #2 (Public Review):

Gaucher disease is a rare genetic disorder that is commonly treated by either administration of a functional enzyme or reduction of the substrate. Some patients receiving enzyme replacement therapy develop avascular osteonecrosis (AVN), but the risk factors were not known. In this study, a cohort of 155 patients was followed longitudinally for two decades, and their risk of developing AVN was analyzed. The data convincingly shows that patients with heterozygous N409S mutation, a past history of AVN, receiving velaglucerase therapy, or with higher serum glucosylsphingosine levels have a higher risk of AVN. These findings will provide a means to identify Gaucher disease patients at higher risk of AVN and to provide them with an optimal treatment. In addition, the study establishes that it is prudent to achieve a low glucoylspingosine level as a therapeutic goal in Gaucher patients with risk of AVN.

Reviewer #1 (Public Review):

The authors worked towards a better understanding of the functional diversification of flavodoxins among diatoms, and this represents a quantum contribution building on the initial findings of Whitney, Lins, Hughes, Wells, Chappelle, and Jenkins (2011), with the inclusion of metatranscriptomic and other data from field collections and on-deck incubation experiments, relatively new genomic and transcriptomic datasets, and the adoption of reverse genetics tools that are not yet widely used in T. pseudonana. They hypothesize that clade I flavodoxins play a role in mitigating oxidative stress, while additional clade II flavodoxins would respond according to canon, in response to low iron availability.

The authors embarked on several field campaigns across environmental gradients where iron-responsive and oxidative stress-responsive flavodoxins were expected to show differential expression. The use of metatranscriptomics allowed taxa-specific assignment of relative transcript expression levels, and the results of both measurements across the environmental gradient and manipulative incubation experiments show the widespread taxonomic distribution of iron-responsive clade II flavodoxin. The fieldwork was well thought out, and biogeochemical trends comported to expectations. It's worth noting that the concomitant inclusion of geochemical data such as dissolved iron further strengthened the work. The authors also found clade I flavodoxins were not iron-responsive (as expected), but rather exhibited diel patterns in transcript abundance that suggest responses to photo-oxidative stress. Taken together, these field data are stunning.

Lab experiments with five diatom species grown under varied iron and induced oxidative (H2O2) stress and transcript abundances for flavodoxin genes are reported. One reservation concerns the untoward and unknown effects of inducing outright iron starvation with the strong chelator, DFB (as opposed to achieving steady-state growth rate limitation from low iron by use of weak chelators such as EDTA). With DFB it is also difficult to predict sample timing (when cells have hit that "correct" and reproducible iron-limited space) when independent replicates are collected on different dates. Similarly, the use of DFB also makes it difficult to sample low and high iron cells at the same density or to maintain densities among replicate samples collected on different dates. pH and CO2 availability change with density unless special measures are taken.

A second set of lab experiments involved the (non-trivial) establishment and use of "knock out" clones of the clade I flavodoxin gene in the model diatom T. pseudonana to test the oxidative stress hypothesis. This is an exciting idea and the data suggest this flavodoxin may confer resistance to oxidative stress. The conclusion would be greatly strengthened if different phenotypes could be observed between WT and KO clones in response to environmentally relevant oxidative stress (such as supra-optimal irradiance), rather than exogenous H2O2 addition. The relationship between the experimental conditions and results in Figure 3C and Supplemental Figure 3H was not clear.

In the introduction, the authors suggest that Fe-S-containing proteins are particularly sensitive to damage via oxygen and ROS and that reliance on ferredoxin (Fd) for electron shuttling carries an enhanced sensitivity to the ROS generated during photosynthesis. References would be helpful here. Fe-S cluster-containing proteins are not monolithic regarding their behavior or susceptibility towards ROS. My limited understanding is that (i) several 4Fe-4S cluster proteins (such as aconitase, isopropylmalate isomerase) are particularly sensitive but that (ii) this is less so for canonical 2Fe-2S cluster ferredoxins; (iii) in some phototrophs Fd catalyzes the reduction of molecular oxygen to superoxide, as part of a mechanism that keeps the electron transport chain less reduced under extremely high light. Thus, ferredoxins may not necessarily be susceptible to in vivo ROS-mediated damage.

Reviewer #2 (Public Review):

In their manuscript, Van Creveld et al. set out to demonstrate divergent functions for two clades of flavodoxin in diatoms. To achieve their goals, the authors combined metatranscriptomic results originating from three separate research cruises in the North Pacific Ocean with laboratory experiments with a clade I flavodoxin knock-out mutant in the diatom Thalassiosira pseudonana. Overall, their field study confirmed that Clade II flavodoxin is mostly up-regulated under iron limitation in most diatoms that were represented in their metatranscriptomic data (Figure 5 A-F). Their field study also demonstrated that clade I flavodoxin is expressed at levels that are several orders of magnitude lower than clade II flavodoxin (figure 5H). The lower expression of clade I flavodoxin was also observed in laboratory culture experiments (Figure 2). The laboratory experiments also demonstrated that the clade I flavodoxins were responsive to iron limitation in some of the species studied (Their Figure 2C), such that the assignment of function based solely on the clade I and clade II flavodoxin classification may not always be straight forward, and that exceptions will likely be found as more diatom species are studied.

In their quest to determine whether Clade I flavodoxin plays a role in adaptation to oxidative stress, the authors created several knock-out mutants where the clade I flavodoxin is not functional. These mutant strains responded to iron limitation in the same way as the WT strains. However, the mutant strains defective in the clade I flavodoxin were more slightly more sensitive to oxidative stress (created by exposure to lethal doses of hydrogen peroxide) than the wild-type strains. The results of the oxidative stress challenges would have been stronger if a broader concentration range of hydrogen peroxide had been used in the experiments leading to a dose-response curve for both the mutant and wild-type strains.

The supplemental information provided in the main manuscript holds a lot of important information. Take for example Figure S4 showing the placement of reads for Clade I and Clade II in a Maximum-likelihood tree for flavodoxin in the North Pacific Ocean. The results show that clade II flavodoxin is much more commonly found in the transcripts than clade I flavodoxin. Perhaps different results would have been obtained by conducting a similar sampling of metatranscriptome in the Atlantic Ocean that is less subject to iron limitation.

Overall, the authors have provided results that support a role for Clade I flavodoxin in alleviating oxidative stress in Thalassiosira pseudonana, however, whether or not this role is universal for clade I flavodoxin in other diatom species will require further studies.

Reviewer #1 (Public Review):

In their study Mas Sandoval and colleagues estimate, from human genomic data, two important parameters that measure how intermarriages have been affected by social stratification in the Americas: sex-biased admixture (SB), which refers to sex differences in the chances to intermarry with another ethnic group, and ancestry-based assortative mating (AM), which refers to the higher probability of partners to intermarry when they carry similar genetic ancestries. To do so, the authors train a deep neural network (DNN) with simulations of admixture with non-random mating and use ancestry tract length distributions to infer the two parameters. They show that their approach estimates SB and AM parameters with a relatively good accuracy in a number of scenarios. When applying the DNN to empirical data, they find solid evidence that social stratification has constrained the admixture processes in the Americas for the last centuries.

In contrast with the vast majority of population genetic studies, which assume random mating, this study assesses if mating has been random or not in American populations. Furthermore, the study is very valuable because it leverages, for the first time, a deep learning approach and local ancestry inference to co-estimate the extent of SB and AM from genomic data. One limitation of the study, however, is that it assumes that (i) the admixture date in the simulations is known and equals 19 generations and (ii) admixture started at the same time in all admixed American populations. The authors also implicitly assume that the variance of the difference between male and female ancestry proportions only depends on AM, and not admixture timing. This may be problematic, as it has been shown that linkage disequilibrium between local ancestry tracts depends both on AM and admixture timing (Zaitlen et al., Genetics 2017). This is also suggested by the authors' results, showing that AM estimates are much lower in admixed Americans under the two-pulse model, relative to the one-pulse model, i.e., when admixture extends over time. Estimates of AM in admixed Americans may thus be biased, if admixture actually started less (or more) than 19 generations ago. Another potential limitation concerns local ancestry inference. The authors assume that RFMix makes no errors when inferring ancestry tracts. This can be a concern, as recent studies have shown that RFMix has reduced accuracy compared to other methods (Hilmarsson et al., bioRxiv 2022). In addition, the authors do not report a measure of uncertainty for the estimation of SB and AM, which is another important weakness. Interpretation of parameter estimates is limited if no measures of uncertainty are provided. Finally, the authors compare the likelihood of two competing models, assuming a single or two admixture pulses, but do not determine the accuracy of their model choice procedure. Overall, besides these methodological limitations, I expect that the study by Mas Sandoval and colleagues could be of great and broad interest for the scientific community studying population genetics, anthropology, sociology and history.

Reviewer #2 (Public Review):

This paper introduces a method to quantify how genetic ancestry drives non-random mating in admixed populations. Admixed American populations are structured by racial, gender, and class hierarchies. This has the potential to cause both ancestry-related assortative mating, in which the ancestry of mates tends to be correlated, and ancestry-related sex bias, in which individuals have a preference for mates with a particular ancestry composition. By applying their method to several African American and Latin American populations, Sandoval et al. further our understanding of ancestry-based population structure in this region more broadly.

Strengths<br /> As many others have recently done, Sandoval et al. leverage the ability of a neural network to predict demographic parameters from high-dimensional population genomic data. Sandoval et al. first develop a clever probabilistic model of mating by defining the probability of a male and female mating as a function of the difference in ancestry between the individuals. They use this model to simulate population genomic data under various demographic scenarios, and then train a neural network on these simulated data. Finally, they apply the neural network to empirical data and learn the parameters of the underlying probability distribution, which can be related back to assortative mating and sex bias.

One clear strength of this paper is their ability to jointly assess assortative mating and sex bias, as well as their ability to apply their model to multiple contemporary admixed populations.

Importantly, the authors couch their results in an intersectional understanding of populations and consistently refer to research from historians and other social scientists throughout their paper, which reflects a very thoughtful awareness of the interdisciplinary nature of this research.

Weaknesses<br /> The definition of assortative mating is conceptually confusing - in the text, assortative mating is introduced as genetic similarity between mates, i.e. positive assortative mating. However, based on the definition of assortative mating in their model, a population can have high assortative mating for a particular ancestry component even when there is non-zero sex bias for that component (e.g. males with low Native American ancestry are more likely to mate with females with high Native American ancestry). Fundamentally, this scenario cannot reflect positive assortative mating; rather, it reflects negative assortative mating (i.e. there is structured genetic dissimilarity between mates). However, the authors do not discuss the fact that the interpretation of the assortative mating parameter changes with the value of the sex bias parameter.

In addition, the results of the inference in ASW are difficult to interpret. They find that males of high African ancestry are more likely to mate with females of low African ancestry. This result seems counterintuitive given the body of literature that suggests sex-biased admixture in African Americans has greater male European and female African contributions. The authors do not suggest potential explanations for this observation.

Lastly, the authors have not done any simulations to assess how accurate parameter estimates are if the demographic model is misspecified, which weakens the interpretability of the results.

Reviewer #1 (Public Review):

This is a generally well-written manuscript that elegantly begins to explore the molecular basis of exosome release under conditions of sheer stress or calcium influx. The authors use a sensitive luciferase assay that enables them to monitor the release of exosomes from CD63-tag-expressing cells. Upon SLO pore formation or sheer stress, cells release exosomes in a calcium-dependent manner; MVBs are (indirectly) shown to undergo calcium-dependent plasma membrane fusion in a process that depends on a set of 4 proteins that were identified by an unbiased analysis of proteins that associate with MVBs. One of these is Annexin A6, a protein shown by several other groups to participate in membrane repair. Thus, calcium triggers the binding of 4 proteins to the surface of MVBs, and likely also to the plasma membrane, driving MVB fusion at the cell surface. The authors also present a semi-intact cell system that will permit functional analysis of the MVB fusion process.

Reviewer #2 (Public Review):

The authors improved significantly a previously published luminescence-based assay for the detection of MVB-derived exosome secretion, by using a membrane-impermeable Nluc inhibitor to make sure only intact vesicles and not cellular debris are quantified. Using this improved assay they confirmed prior reports that exposure to the Ca2+ ionophore ionomycin triggers exosome release. They then build on this by showing that exosomes are also released when Ca2+ influx is caused by plasma membrane (PM) wounding, using pore-forming toxins or mechanical stress. Investigating possible molecular mechanisms involved in Ca2+-regulated MVB exocytosis/exosome release, the authors use proteomics to identify proteins recruited to purified MVBs in an ionomycin-dependent fashion. One of these proteins is ANX6, which interestingly was previously implicated in the repair of PM wounds in other cell types. The paper then explores the possible role of ANX6, showing that ionophore-dependent exosome secretion is inhibited in ANX6-depleted intact cells, or in permeabilized cells reconstituted with cytosol in the presence of anti-ANX6 antibodies. These results are convincing and very consistent with prior findings from other groups. The interesting advance is the demonstration that Ca2+ influx through PM lesions also triggers exocytosis of MVBs, and not only mature lysosomes as previously described. This reveals that PM injury, a frequent event in vivo, could play a role in the extensively documented detection of extracellular exosomes in biological fluids.

They also present some imaging data suggesting that ANX6 inhibition stalls MVBs at the cell surface and that ANX6 may promote MVB exocytosis and exosome release by tethering different intracellular membranes. These results are consistent with the author's interpretation but less compelling since they are based on limited confocal imaging without markers for specific compartments such as the PM and without quantification.

Another limitation of the study is that most experiments were performed using 30 min of cell exposure to micromolar concentrations of ionomycin, and the kinetics of exosome secretion after shorter times of ionophore exposure is not shown. The improved luminescence assay is described as sensitive and linear, but a linear time course over 24 h is only shown for constitutive exosome release, not for cells treated with ionomycin. Nocodazole experiments led to the conclusion that microtubules are required for 'sustained' exosome release, but this is somewhat misleading since ionophores markedly enhance exocytosis, raising questions as to whether the process is still linear after 30 min in the presence of ionomycin. The permeabilized-cell reconstitution assay apparently detected a requirement for ANX6 after just 2 min, which is reassuring but also raises the possibility that exosome release may not be sustained up to 30 min. PM resealing is a rapid process, completed in 1-2 min, so if one of the goals was to explore a connection between MVB exocytosis and PM repair, shorter time points would make more sense. This is particularly important since prolonged exposure to micromolar concentrations of ionomycin is known to cause extensive cytotoxicity, including actin cytoskeleton alterations, changes in ATP levels, and apoptosis (the authors perform only one limited control for apoptosis, a western that did not detect PARP cleavage).

Overall, this is an interesting study that brings together earlier observations but places them in a new context - that Ca2+-dependent exosome release from MVBs may occur in the context of PM wounding, and thus might play a role in PM resealing. Strong evidence was presented for the ANX6 requirement in ionophore-induced exosome release. However, since most previous studies implicating ANX6 in PM repair in other cell types involved a non-physiological form of laser wounding, it is still unclear if ANX6 is required for PM resealing after mechanical wounding, in the cells used in this study.

Reviewer #3 (Public Review):

The authors report that the secretion of endosome-derived exosomes is enhanced by a calcium-dependent response to damage to the plasma membrane of cells. The authors present convincing evidence that in response to the influx of calcium that follows damage to the plasma membrane annexin A6 is recruited to multivesicular bodies (MVBs) and likely serves to tether the MVBs to the plasma membrane causing a concomitant release of exosomes. Although it is not directly addressed in the Discussion, I am left with the impression that the authors are hinting that exosome secretion is more a byproduct of plasma membrane repair rather than a means of intercellular communication. In other words, the cell needs the membrane material from the MVB to patch and repair holes in the plasma membrane and exosome ejection from the cell is a secondary (perhaps even irrelevant) consequence. Obviously, these two possibilities are not mutually exclusive. The authors are encouraged to speculate about which possibility they favor and how their findings might change our understanding of the cell biology of exosome secretion.

Reviewer #1 (Public Review):

In this manuscript Radaelli et al investigate the effects of knocking out Parl, encoding a mitochondrial rhomboid protease, on spermatogenesis. Parl knockout has been used as a genetic model for Leigh syndrome, which in humans can be caused by mutations in several different components of the mitochondrial respiratory chain. This study describes the nature of the spermatogenesis defect found in Parl mutant mice, evaluates double mutants for Parl and other factors known to act with Parl in the context of neurodegeneration, and investigates the changes to mitochondrial function that occur in mutant testes. The authors conclude that Parl-/- males have a severe spermatogenesis defects with arrest at the spermatocyte stage, and that Parl function in spermatogenesis depends on different factors compared to neurons. Detailed characterization of mitochondrial function in mutant testis shows a variety of defects, including lower overall levels of coenzyme Q (coQ) and a higher ratio of reduced to oxidized coQ. They also conclude that ferroptosis is responsible for spermatocyte cell death in Parl mutants based on the presence of increased transferrin receptor, reduced GPX4 and increases in the ferroptosis end-product 4-hydroxynonenal (HNE).

The conclusions of this manuscript are well supported by (a) strong genetics including phenotype analysis in multiple double knockout mouse strains to show that Parl acts through different pathways in spermatogenic cells compared to neurons; (b) a clear spermatogenesis phenotype as shown by histology and immunostaining; (c) demonstration of mitochondrial defects during spermatogenesis using electron microscopy and respirometry of testis mitochondria; and (d) evidence for a mechanism of spermatocyte death by ferroptosis based on changes in transferrin receptor protein 1, coQ, GPX4, and HNE. Overall, this study advances understanding of the effects of mitochondrial dysfunction on spermatogenesis and may shed light on patient phenotypes in Leigh syndrome. The study will be useful in the fields of fertility and mitochondrial biology. There are a few places where the conclusions are not robustly supported by the data, especially inadequate quantification of some of the phenotype data and some cases where the data presented is not consistent with the model proposed:

1) In Figure 2, electron microscopy images represent n=1 cell, making it hard to know how generalizable the mitochondrial phenotypes are. It would be useful to see a quantitative summary of a larger dataset indicating how frequently the mitochondrial defects are seen.<br /> 2) In Figure 3, representative images are shown for a single field from n=1 animal. It is hard to decisively conclude that the phenotype of Pink1-/-;Pgam5-/- and Ttc19-/- testes is completely normal based on this limited data. There may be other tubules outside the field of view that are abnormal, or more subtle changes in cell ratios. This conclusion would be significantly strengthened by cell counting (e.g. # round spermatids per Sertoli cell per tubule and # spermatocytes per Sertoli cell per tubule) or other quantitation. Likewise, the similarities in phenotype between Parl-/-, Parl-/-;Pink2-/-, and Parl-/-;Pgam5-/- should be more thoroughly documented. At least some additional images should be shown.<br /> 3) In Figure 4, it looks like there is a significant decrease in CIV-driven respiration in Parl knockouts, but the text describes this as "did not significantly enhance" - that is, the absence of an increase. This result is difficult to interpret without further explanation.<br /> 4) In Figure 5B, there is some variation in band intensity between replicates. Quantifying the band intensity relative to the loading control would help to increase confidence in the conclusion that coQ levels are reduced.<br /> 5) GPX4 is not a Parl substrate, and no explanation is provided for why it might be reduced in Parl-/- testes. This makes the result and model difficult to interpret.<br /> 6) Since Parl knockout induces necrosis in the brain, necrosis could be a contributing factor to cell death in spermatocytes alongside ferroptosis. No data is presented that can exclude this possibility.<br /> 7) The severe spermatogenesis phenotype implies that Parl knockout males should be infertile, but the fertility status is not described in the manuscript. It may be difficult to test fertility in these animals due to the neurodegeneration phenotype; if so, this can be clarified. If it is feasible to test fertility, demonstration of a fertility phenotype would significantly strengthen the conclusion that loss of Parl leads to spermatogenic arrest.

Reviewer #2 (Public Review):

This study characterized the mice deficient for PARL and concluded that mitochondrial defects lead to ferroptosis and spermatogenic cell death. In mammalian germ cells, the existence of ferroptosis is not known so far. Interestingly, a study using C. elegans recently established the occurrence of germ cell ferroptosis (Perez et al., Dev Cell 2020: PMID: 32652074). Thus, if the conclusion of this study is valid, this study can be a timely demonstration of germ cell ferroptosis in mammals. I understand the potential value of this study. However, in this study, although several indirect data were provided, I do not think the results firmly established the occurrence of germ cell ferroptosis. Further, some major technical barriers prevent the interpretation of these results. In general, perturbations in mitochondria dynamics could be expected to disrupt spermatogenesis. It would be necessary to establish germ-cell ferroptosis to explain the specific phenotype of the PARL mutants. Overall, I appreciate the potential impact; but I am not fully convinced by the main conclusion reported in this study.

Reviewer #1 (Public Review):

The authors tried to measure the accuracy of the decision-making of honey bees by carrying out behavioural experiments in which they trained the bees to forage on artificial flowers of 5 different colours that offered different levels of reward. Subsequently, the bees' decision-making behaviour was tested with flowers of the same or different colours, with no reward present. The authors found that bees tend to approach a flower only when they are highly certain of a reward, and these decisions are made quickly. The majority of flowers were rejected by the bees. Based on the results of the tests, the authors created a model to identify what circuit elements or connections would be necessary to mimic the bees' decisions. This model could be potentially used for robotics.

The study is well supported by the signal detection theory and the experiments are well designed which is a major strength. However, the methods are not completely clear, so would be better to make a clearer description. Another weakness is the lack of clear explanations of the importance and relevance of the model.

Given the experimental design was optimal, the authors could potentially achieve the aims of this study.

Reviewer #2 (Public Review):

By elegantly designing experiments, MaBouDi et al. elucidated honeybee's behavioral strategy to quantitatively associate sensory cues with valences. The description is simple and concise enough to understand the logic. Particularly, the authors clearly demonstrated how sensory evidence and reward likelihood quantitatively affect the decision-making process and animals' response time. Their behavioral characterization approach and proposed model could also be helpful for studies using higher animal species. I have a few doubts regarding the definition of rejection behavior and the structure of the model that is critical to lead their main conclusions.

Reviewer #1 (Public Review):

Using in vitro assays that take advantage of thymic slices, with or without the ability to present pMHC antigens, the authors define an early period in which CCR4 expression is induced, which induces their migration to the medulla and likely encounter with cDC2 and other APCs. Notably, the timing for CCR4 expression precedes that of CCR7 and illustrates the potential role for this early expression to initiate the movement of post-positive selection thymocytes to the medulla. The evidence for supporting a role for CCR4, as well as CCR7, in sequential tolerance induction is provided using multiple approaches, and although the observed changes amount to small percent changes, the significance is clear and likely biologically relevant over the lifespan of a developing T cell repertoire. Overall, the model provides a holistic view of how tolerance to self-antigens is likely induced during T cell development, which makes this work highly topical and influential to the field.

Reviewer #2 (Public Review):

This manuscript describes that CCR4 and CCR7 differentially regulate thymocyte localization with distinct outcomes for central tolerance. Overall, the data are presented clearly. The distinct roles of CCR4 and CCR7 at different phases of thymocyte deletion (shown in Figure 6C) are novel and important. However, the conclusion that expression profiles of CCR4 and CCR7 are different during DP to SP thymocyte development was documented previously. More importantly, the data presented in this manuscript do not support the conclusion that CCR7 is uncoupled from medullary entry. Moreover, it is unclear how the short-term thymus slice culture experiments reflect thymocyte migration from the cortex to the medulla.

1. Differential profiles in the expression of chemokine receptors, including CCR4, CCR7, and CXCR4, during DP to SP thymocyte development were well documented. Previous papers reported an early and transient expression of CCR4, a subsequent and persistent expression of CCR7, and an inverse reduction of CXCR4 (Campbell, et al., 1999, Cowan, et al., 2014, and Kadakia, et al. 2019). The data shown in Figures 1, 2, and 3 are repetitive to previously published data.

2. The manuscript describes the lack of CCR7 at early stages during DP to SP thymocyte development (Figure 1-3). However, CCR7 expression is detected insensitively in this study. Unlike CCR4 detection with a wide fluorescence range between 0 and 2x10*4 on the horizontal axis, CCR7 detection has a narrow range between 0 and 2x10*3 on the vertical axis (Figure 1C, 1D, 4B, 4C, 6B, S2, S3), so that flow cytometric CCR7 detection in this study is 10-times less sensitive than CCR4 detection. It is therefore likely that the "CCR7-negative" cells described in this manuscript actually include "CCR7-low/intermediate" thymocytes described previously (for example, Figure S5A in Van Laethem, et al. Cell 2013 and Figure 6 in Kadakia, et al. J Exp Med 2019).

3. Low levels of CCR7 expression could be functionally evaluated by the chemotactic assay as shown in Figure 2. However, the data in Figure 2 are unequally interpreted for CCR4 and CCR7; CCR4 assays are sensitive where a migration index at less than 1.5 is described as positive (Figure 2A and 2B), whereas CCR7 assays are dismissal to such a small migration index and are only judged positive when the migration index exceeds 10 or 20 (Figure 2C and 2D). CCR7 chemotaxis assays should be carried out more sensitively, to equivalently evaluate the chemotactic function of CCR4 and CCR7 during thymocyte development.

4. Together, this manuscript suffers from the poor sensitivity for CCR7 detection both in flow cytometric analysis and chemotactic functional analysis. Conclusions that CCR7 is absent at early stages of DP to SP thymocyte development and that CCR7 is uncoupled from medullary entry are the overinterpretation of those results with the poor sensitivity for CCR7. The oversimplified scheme in Figure 3D is misleading.

5. The short-term thymus slice culture experiments should be described more carefully in terms of selection events during DP to SP thymocyte development, which takes at least 2 days for CD4 lineage T cells and approximately 4 days for CD8 lineage T cells (Saini, et al. Sci Signal 2010 and Kimura, et al. Nat Immunol 2016). The slice culture experiments in this manuscript examined cellular localization within 12 hours and chemokine receptor expression within 24 hours (Figures 4, 5) even for the development of CD8 lineage T cells (Figure S2), which are too short to examine entire events during DP to SP thymocyte development and are designed to only detect early phase events of thymocyte selection.

6. It is unclear what the medullary density alteration measured in the thymus slice culture experiments represents. Although the manuscript describes that the increase in the medullary density reflects the entry of cortical thymocytes to the medulla (Figure 4E and S2E), this medullary density can be affected by other mechanisms, including different survival of the cells seeded on the top of different thymus microenvironments. Thymocytes seeded on the medulla may be more resistant to cell death than thymocytes seeded in the cortex, for example, because of the rich supply of cytokines by the medullary cells. So, the detected alterations in the medullary density may be affected by the differential survival of thymocytes seeded in the cortex and the medulla. Also, the medullary density is measured only within a short period of up to 12 hours. The use of MHC-II-negative slices and CCR4- or CCR7-deficient thymocytes in the thymus slice cultures may verify whether the detected alteration in the medullary density is dependent on TCR-initiated and chemokine-dependent cortex-to-medulla migration.

Reviewer #3 (Public Review):

In this manuscript, Li et al. examine how the expression of the chemokine receptor CCR4 impacts the movement of thymocytes within the thymus. It is currently known that the chemokine receptor CCR7 is important for developing thymocytes to migrate from the cortical region into the medullary region and CCR7 expression is therefore often used to define medullary localization. This is important because key developmental outcomes, like enforcing tolerance to self-antigens amongst others, occur in the medullary environment. The authors demonstrate that the chemokine receptor CCR4 is induced on thymocytes prior to expression of CCR7 and thymocytes exhibit responsiveness to CCR4 ligands earlier in development. Using elegant live confocal microscopy experiments, the authors demonstrate that CCR4 expression is important for the entry and accumulation of specific thymocyte subsets while CCR7 expression is needed for the accumulation of more mature thymocyte subsets. The use of cells deficient in both CCR4 and CCR7 and competitive migration/accumulation experiments provide strong support for this conclusion. The elimination of CCR4 expression results in decreases in apoptosis of thymocyte subsets that have been signalled through their antigen receptor and are responsive to CCR4 ligands. As expected, more mature thymocyte subsets show decreased apoptosis when CCR7 is absent. Distinct antigen-presenting cells in the thymus express CCR4 ligands supporting a model where CCR4 expressing thymocytes can interact with thymic antigen-presenting cells for induction of apoptosis. The absence of CCR4 results in an increase in peripheral T cells that can respond to self-antigens presented by LPS-activated antigen-presenting cells providing further support for the model. Collectively, the manuscript convincingly demonstrates a previously unappreciated role for CCR4 in directing a subset of thymocytes to the medulla.

Strengths:

Relevant model systems and elegant experimental techniques are used throughout the manuscript. The experiments are extensively replicated resulting in robust and convincing data sets. These findings represent an important conceptual advance in our understanding of the processes and cellular regulation of T cell development in the thymus.

Weaknesses:

Evidence demonstrating a direct interaction between CCR4 expressing thymocytes and CCR4-ligand expressing antigen-presenting cells is lacking. Furthermore, increased self-reactivity in the absence of CCR4 is measured using mature peripheral CD4 T cells, but altered self-reactivity of thymocytes is not evaluated similarly.

Reviewer #1 (Public Review):

The sustainability of vaccination programs is subject to multiple threats, from a pandemic like COVID-19 to political changes. The present study assesses different strategies, including gender-neutral vaccination, to better respond to threats in HPV national immunization programs. The authors showed that vaccinating boys against HPV (compared to vaccinating girls alone), would not only prevent more cases of cervical cancer but also limit the impact of disruptions in the program. Moreover, it would help attain the goal set by the World Health Organization of eliminating cervical cancer as a public health problem sooner, even in the case of disruptions.

Strengths and weaknesses: I found the manuscript well-written and easy to read. Decision-makers may find the results helpful in policy development and other researchers may use the study as an example to investigate similar scenarios in their local contexts. Nevertheless, there are some limitations. First, it should be considered that the present study is only applicable to India and other countries with a similar HPV context. Second, because it is a study based on a mathematical model, errors might arise from the assumptions considered for its construction. It also relies on the quality of the data used to construct and calibrate the model.

Models are important tools for decision-making, they allow us to assess different scenarios when obtaining real-world data is not feasible. They also allow to carried-out multiple sensitivity analyses to test the strengths of the results. The study carries out a necessary assessment of different vaccination strategies to minimize the impact on cervical cancer prevention due to disruptions in the HPV immunization program. By using a mathematical model, the authors are able to assess different scenarios regarding vaccination coverage rates, disruption time, and cervical cancer incidence. Therefore, decision-makers can consider the scenario which best represents their current situation.

The present study is not only valuable for decision-making, but also from a methodological point of view as future research can be conducted exploring more in deep the impact of vaccination disruptions and prevention measures.

The conclusions of this paper are mostly well supported by data, but some aspects of the methodology need clarification; furthermore, some aspects of the calculations can be improved. It would be more informative, and better for comparisons between the four scenarios, to have relative measures instead of the absolute numbers of cases prevented.

Reviewer #2 (Public Review):

This study evaluated the effect of population-based HPV vaccination programs in India which is suffering from the disease burden of cervical cancer. The authors used model simulations for estimating the outcomes by adopting the latest available data in the literature. The findings provide evidence-based support for policymakers to devise efficient strategies to reduce the impacts of cervical cancer in the country.

Strengths.<br /> The study investigated the potential impact of cervical cancer elimination when HPV vaccination was disrupted (e.g., during the COVID-19 pandemic) and for meeting the WHO's initiatives. The authors considered several settings from the low to high effects of vaccination disruption when concluding the findings. The natural history was calibrated to local-specific epidemiological data which helps highlight the validity of the estimation.

Weaknesses.<br /> Despite the importance and strengths, the current study may likely be improved in several directions. First, the study considered the scenario of using a recently developed domestic HPV vaccine but assuming vaccine efficacy based on another foreign HPV vaccine that has been developed and used (overseas) for more than 10 years. More information should be provided to support this important setting.

Second, the authors are advised to discuss the vaccine acceptability and particularly the feasibility to achieve high coverage scenarios in relatively conservative countries where HPV vaccines aim to prevent sexually transmitted infection. Third, as the authors highlighted, the health economics of gender-neutral strategies, which is currently missing in the manuscript, would be a substantial consideration for policymakers to implement a national, population-based vaccination program.

Reviewer #3 (Public Review):

The authors put together a rigorous study to model the impact of HPV vaccine programme disruptions on cervical cancer incidence and meeting WHO elimination goals in a low-income country - using India as an example. The study explores possible scenarios by varying HPV vaccination strategies for 10-year-old children between a) increasing vaccine coverage in a girls-only vaccination programme and b) vaccinating boys in addition to girls (i.e a gender-neutral vaccination programme).

The main strength of this study is the strength of the modelling methodology in helping to make predictions and in contingency planning. The study methodology is rigorous and uses models that have been validated in other settings. The study employs a high level of detail in calibrating and adapting the model to the Indian context despite poor data availability. The detailed methodology allows future studies to employ the model and techniques with locally-contextualised parameters to study the potential impact of HPV vaccine programme disruptions in other countries.

The work in this field can begin to help lower-income countries explore varying HPV vaccination strategies to reduce cervical cancer incidence, keeping in mind the potential for future supply chains or other related disruptions. However, the scenarios could be better sculpted to model potentially realistic scenarios to guide policymakers to make decisions in situations with limited vaccine supplies - in other words comparing scenario alternatives based on a fixed number of vaccines being available. Using comparative alternatives will help policymakers grapple with the decisions that need to be made regarding planning national HPV vaccination programmes. The results could afford to provide readers with a clearer measure of vaccine strategy 'resilience'.

In all, the authors are able to successfully explore the potential impact of varying HPV vaccination strategies on cervical cancer cases prevented in the context of vaccine disruptions, and make valid conclusions. The results produced are rich in information and are worthy of deeper discussion.

Reviewer #1 (Public Review):

In this manuscript authors examined the effect of rif1 knockout on replication timing and transcription in early embryos of zebrafish. Contrary to the expectation, genome-wide replication timing domains did not significantly change upon Rif1 knockout, although the replication timing became less dynamic in the mutant, meaning the entire genomes are replicated toward the mid S. In contrast, transcriptional profiles change by rif1 mutation throughout the embryo stage. These effects were more predominantly observed after gastrulation at the early stages of zebrafish development.

The results presented in this manuscript provide new information on the effects of rif1 mutation on early zebrafish development, although the underlying mechanism has not been explored. The information is useful for researchers in the field of early development, with specific focus on replication and transcription regulation.

The genome wide analyses of replication timing has been conducted and analyzed properly. The transcriptional analyses are conducted by RNA-seq and SLAM-seq (determining the nascent mRNA), and the results convincingly show the overall transcriptional patterns at different developmental stages.

This work shows that Rif1 regulates replication timing and transcription in zebrafish embryos, while the extents of the effects vary during the developmental process. Although the data convincingly illustrate the whole picture of Rif1 KO on replication and transcription during zebrafish development, the mechanistic insight is missing. Especially, how Rif1 may or may not coordinately regulate replication and transcription during the zebrafish development has not been addressed.

Reviewer #2 (Public Review):

This study by Masser et al. analyzes global replication timing and gene expression in rif-1 null zebrafish. This work is an extension of their previous report on the normal replication timing pattern during wild-type zebrafish development. The major valuable finding here is that Rif1 is not essential for viability in zebrafish, and - counter to expectation from studies in cultured cells and other species - late replication does not strongly depend on Rif1. Instead, the data suggest that Rif1 subtly sharpens replication timing pattern during normal development rather than function generally to delay replication timing. In the absence of Rif1, the normal pattern establishment is somewhat delayed. The authors also document some changes in expression during development with more genes being repressed by Rif1 than activated at some early stages.

The study and analysis are generally rigorous, and the conclusions are supported by convincing data. The manuscript is well written, though there are aspects of the presentation that could be improved for a broader scientific audience. Given the strong link between replication timing and cell type/development, studying timing in a whole developing organism is important. The experimental approach is technically challenging, particularly the bioinformatic analysis. The scientific advance here is largely confined to documenting the timing of Rif1-affected transcription, the unanticipated effect of the rif1 deletion on replication timing and on sex determination, though the latter is not explored. The work is descriptive and feels like two relatively unconnected studies, transcription and replication plus a small bit of development, and the difference in timing of the transcription phenotypes and replication phenotypes suggests they may be very distinct Rif1 roles. There isn't a lot of new insight into the mechanism of how Rif1 affects either replication timing or gene expression. As such, the overall study is an useful set of findings and detailed data for future work, but it doesn't make a big step forward in understanding the role of Rif1 or the biological processes it affects.

Weaknesses worth addressing include the following:

1. Loss of Rif1 did not affect viability, but it did strongly influence sex determination, resulting in a lower population of females. This effect is the strongest organismal phenotype, but the study provides no explanation for the loss of females from the data gathered here.<br /> 2. The approach to distinguish nascent zygotically expressed mRNAs from maternal mRNAs is a strength. Are the differentially expressed genes related at all to regions of the genome whose replication timing is most affected? Are any of them related to the sex determination or developmental phenotypes?

Reviewer #3 (Public Review):

Using the zebrafish model system, this manuscript assessed the roles of Rif1 protein in replication timing control and transcription during early development, and successfully demonstrated the differential impact of Rif1 protein in replication timing control and transcription. Moreover, the comprehensive assessments of the impacts of mutating Rif1 on animal development (including animal survival and sexual development) were assessed. Although there are works that examined Rif1's implications in replication timing and transcription separately, this work is unique in assessing all these points at once.

The strength of this manuscript is the genomic analyses of replication timing and transcription being combined in a single model system. Consequently, this manuscript clearly demonstrates the differential impact of Rif1 in these processes during zebrafish development.

The weakness of this manuscript is, as the authors comment in the Discussion, analyses of replication timing and transcription were performed using bulk embryos. There is a possibility that tissue-specific changes could have been masked. Tissue-specific or single-cell analysis in the future will fill the gap in the knowledge.

Some of the findings presented in this manuscript are consistent with previous findings using different models such as Drosophila and mice, whereas other findings do not necessarily agree. I hope further studies will reveal more clearly what is common in these systems, and what is different.

Also, the suggestion that the Rif1 protein may be implicated in a function similar to Fanconi-Anemia genes/proteins is very intriguing.

Overall, the data presented in this manuscript sufficiently justify the authors' claims. Moreover, this manuscript provides interesting insights into Rif1's function, as well as how development could be controlled.

Reviewer #1 (Public Review):

The manuscript by Mullen et al. investigated the gene expression changes in cancer cells treated with the DHODH inhibitor brequinar (BQ), to explore the therapeutic vulnerabilities induced by DHODH inhibition. The study found that BQ treatment causes upregulation of antigen presentation pathway (APP) genes and cell surface MHC class I expression, mechanistically which is mediated by the CDK9/PTEFb pathway triggered by pyrimidine nucleotide depletion. The combination of BQ and immune checkpoint therapy demonstrated a synergistic (or additive) anti-cancer effect against xenografted melanoma, suggesting the potential use of BQ and immune checkpoint blockade as a combination therapy in clinical therapeutics.

The interesting findings in the present study include demonstrating a novel cellular response in cancer cells induced by DHODH inhibition. However, whether the increased antigen presentation by DHODH inhibition actually contributed to the potentiation of the efficacy of immune-check blockade (ICB) is not directly examined is the limitation of the study. Moreover, the mechanism of the increased antigen presentation pathway by pyrimidine depletion mediated by CDK9/PTEFb was not validated by genetic KD or KO targeting by CDK9/PTEFb pathways. Finally, high concentrations of BQ have been reported to show off-target effects, sensitizing cancer cells to ferroptosis, and the authors should discuss whether the dose used in the in vivo study reached the ferroptotic sensitizing dose or not.

Reviewer #2 (Public Review):

In their manuscript entitled "DHODH inhibition enhances the efficacy of immune checkpoint blockade by increasing cancer cell antigen presentation", Mullen et al. describe an interesting mechanism of inducing antigen presentation. The manuscript includes a series of experiments that demonstrate that blockade of pyrimidine synthesis with DHODH inhibitors (i.e. brequinar (BQ)) stimulates the expression of genes involved in antigen presentation. The authors provide evidence that BQ mediated induction of MHC is independent of interferon signaling. A subsequent targeted chemical screen yielded evidence that CDK9 is the critical downstream mediator that induces RNA Pol II pause release on antigen presentation genes to increase expression. Finally, the authors demonstrate that BQ elicits strong anti-tumor activity in vivo in syngeneic models, and that combination of BQ with immune checkpoint blockade (ICB) results in significant lifespan extension in the B16-F10 melanoma model. Overall, the manuscript uncovers an interesting and unexpected mechanism that influences antigen presentation and provides an avenue for pharmacological manipulation of MHC genes, which is therapeutically relevant in many cancers. However, a few key experiments are needed to ensure that the proposed mechanism is indeed functional in vivo.

The combination of DHODH inhibition with ICB reflects more of an additive response instead of a synergistic combination. Moreover, the temporal separation of BQ and ICB raises the question of whether the induction of antigen presentation with BQ is persistent during the course of delayed ICB treatment. To confidently conclude that induction of antigen presentation is a fundamental component of the in vivo response to DHODH inhibition, the authors should examine whether depletion of immune cells can reduce the therapeutic efficacy of BQ in vivo. Moreover, they should examine whether BQ treatment induces antigen presentation in non-malignant cells and APCs to determine the cancer specificity. Finally, although the authors show that DHODH inhibition induces expression of both MHC-I and MHC-II genes at the RNA level, only MHC-I is validated by flow cytometry given the importance of MHC-II expression on epithelial cancers, including melanoma, MHC-II should be validated as well.

Overall, the paper is clearly written and presented. With the additional experiments described above, especially in vivo, this manuscript would provide a strong contribution to the field of antigen presentation in cancer. The distinct mechanisms by which DHODH inhibition induces antigen presentation will also set the stage for future exploration into alternative methods of antigen induction.

Reviewer #3 (Public Review):

Mullen et al present an important study describing how DHODH inhibition enhances efficacy of immune checkpoint blockade by increasing cell surface expression of MHC I in cancer cells. DHODH inhibitors have been used in the clinic for many years to treat patients with rheumatoid arthritis and there has been a growing interest in repurposing these inhibitors as anti-cancer drugs. In this manuscript, the Singh group build on their previous work defining combinatorial strategies with DHODH inhibitors to improve efficacy. The authors identify an increase in expression of genes involved in the antigen presentation pathway and MHC I after BQ treatment and they narrow the mechanism to be strictly pyrimidine and CDK9/P-TEFb dependent. The authors rationalize that increased MHC I expression induced by DHODH inhibition might favor efficacy of dual immune checkpoint blockade. This combinatorial treatment prolonged survival in an immunocompetent B16F10 melanoma model.

Previous studies have shown that DHODH inhibitors can increase expression of innate immunity-related genes but the role of DHODH and pyrimidine nucleotides in antigen presentation has not been previously reported. A strength of the manuscript is the use of multiple controls across a panel of cell lines to exclude off-target effects and to confirm that effects are exclusively dependent on pyrimidine depletion. Overall, the authors do a thorough characterization of the mechanism that mediates MHC I upregulation using multiple strategies. Furthermore, the in vivo studies provide solid evidence for combining DHODH inhibitors with immune checkpoint blockade.

However, despite the use of multiple cell lines, most experiments are only performed in one cell line, and it is hard to understand why particular gene sets, cell lines or time points are selected for each experiment. It would be beneficial to standardize experimental conditions and confirm the most relevant findings in multiple cell lines. The differential in vivo survival depending on dosing schedule is interesting. However, this section could be strengthened with a more thorough evaluation of the tumors at endpoint.

Overall, this is an interesting manuscript proposing a mechanistic link between pyrimidine depletion and MHC I expression and a novel therapeutic strategy combining DHODH inhibitors with dual checkpoint blockade. These results might be relevant for the clinical development of DHODH inhibitors in the treatment of solid tumors, a setting where these inhibitors have not shown optimal efficacy yet.

Reviewer #1 (Public Review):

Gap junctions, formed from connexins, are important in cell communication, allowing ions and small molecules to move directly between cells. While structures of connexins have previously reported, the structure of Connexin 43, which is the most widely expressed connexin and is important in many physiological processes was not known. Qi et al used cryo-EM to solve the structure of Connexin 43. They then compared this structure to structures of other connexins. Connexin gap junctions are built from two "hemichannels" consisting of hexamers of connexins. Hemichannels from two opposing cells dock together to form a complete channel that allows the movement of molecules between cells. N-terminal helices from each of the 6 subunits of each hemichannel allow control of whether the channels are open or closed. Previously solved structures of Cx26 and Cx46/50 have the N-termini pointing down into the pore of the protein leaving a central pore and so these channels have been considered to be open. The structure that Qi et al observed has the N-termini in a more raised position with a narrower pore through the centre. This led them to speculate whether this was the "closed" form of the protein. They also noted that, if only the protein was considered, there were gaps between the N-terminal helices, but these gaps were filled with lipid-like molecules. They therefore speculated that lipids were important in the closure mechanism. To address whether their structure was open or closed with respect to ions they carried out molecular dynamics studies, and demonstrated that under the conditions of the molecular dynamics ions did not traverse the channel when the lipids were present.

Strengths<br /> The high resolution cryo-EM density maps clearly show the structure of the protein with the N-termini in a lateral position and lipid density blocking the gaps between the neighbouring helices. The conformation that they observe when they have solved the structure from protein in detergent is also seen when they reconstitute the protein into nanodiscs, which is ostensibly a more membrane-like environment. They, therefore, would appear to have trapped the protein in a stable conformational state.<br /> The molecular dynamics simulations are consistent with the channel being closed when the lipid is present and raises the possibility of lipids being involved in regulation.<br /> A comparison of this structure with other structures of connexin channels and hemichannels gives another representation of how the N-terminal helix of connexins can variously be involved in the regulation of channel opening.

Weaknesses<br /> While the authors have trapped a relatively stable state of the protein and shown that, under the conditions of their molecular dynamics simulations, ions do not pass through, it is harder to understand whether this is physiologically relevant. Determining this would be beyond the scope of the article. To my knowledge there is no direct evidence that lipids are involved in regulation of connexins in this way, but this is also an interesting area for future exploration. It is also possible that lipids were trapped in the pore during the solubilisation process making it non-physiological. The authors acknowledge this and they describe the structure as a "putative" closed state.<br /> The positions of the mutations in disease shown in Figure 4 is interesting. However, the authors don't discuss/speculate how any of these mutations could affect the binding of the lipids or the conformational state of the protein.

It should also be noted that a structure of the same protein has recently been published. This shows a very similar conformation of the N-termini with lipids bound in the same way, despite solubilising in a different detergent.

Reviewer #2 (Public Review):

The manuscript from Qi et. al. provides novel structures for connexin 43 (Cx43) gap junction channels (GJCs) and hemichannels, which they claim correspond to the closed conformations of these channels. This leads the authors to propose a mechanism of gating that implicates the existence of lipids in the pore, which could stabilize the N-terminal domain as the gate region within the pore. The authors performed a lipidomic assay in their structures and identified a dehydroepiandrosterone (DHEA), a sterol compound specifically enriched in their Cx43 purified samples. However, at the current structural resolution, they cannot conclude whether DHEA is the small lipid-like density found at the pore of closed channels. Further studies, including functional studies, are needed to determine whether DHEA is a gating intermediary. Interestingly, other recently published structures of large-pore channels support the notion that lipids are found inside the pore. However, this evidence is only supported by Cryo-EM structures and is an issue generating major controversy in the field, particularly when these molecules are implicated in the gating mechanisms. The finding of putative lipids-pore interactions is a very intriguing observation, but it should be interpreted carefully. A major concern is that channel reconstitution is performed in an excess of lipids and detergents that could lead to artifacts. Thus, these lipid-like densities observed in Cx43 (and other structures) after single particle analysis could not represent native lipid-protein interactions. Subsequently, all conclusions for the role of lipids in gating could rely on a potential protein purification-induced artifact. Also, it is hard to visualize how the lipids can move in/out of the pore during gating, particularly from this putative lipids-pore conformation to an open conformation.

Another important aspect of this work is that provided structures for both Cx43 GJCs and hemichannels. As expected, there are differences in extracellular loops rearrangements between these two structures. One issue, however, is that the resolution for Cx43 hemichannels is still low (3.98 Å), thus interpretations need to be taken with caution. In addition, the intracellular domains that are important for the gating and regulation of Cx43, including the intracellular loop and the carboxyl-terminal domain were not resolved in these structures. Nevertheless, this is a common issue for other connexin Cryo-EM structures reported in the literature.

Reviewer #1 (Public Review):

The authors examine signaling factors that differentiate parallel routes to activating phosphoinositide 3-kinase gamma (PI3Kγ). Dissecting the convergent pathways that control PI3Kγ activity is critical because PI3Kγ is a therapeutic target for treating inflammatory disease and cancer. Here, the authors employ a multipronged approach to reveal new aspects for how p84 and p101 pair with p110γ to activate the PI3Kγ heterodimer. The key instigator to this study is a previously reported inhibitory Nanobody, NB7. The hypothesized mechanism for NB7 allosteric inhibition of p84- p110γ was previously proposed to involve blockage of the Ras-binding domain. The authors revise the allosteric inhibition model based on meticulous profiling of various PI3Kγ complex interactions with NB7. In parallel, a cryo-EM-derived model of NB7 bound to the p110γ subunit convincingly reveals a Nanobody interaction pocket involving the helical domain and regulatory motifs of the kinase domain. This revelation shifts the focus to the helical domain, a known target of PKC phosphorylation. While the connections between NB7 interactions and the effects of PKC phosphorylation are sometimes tenuous, it could be argued that the Nanobody served as a tool to reveal the importance of the helical domain to p110γ regulation.

The sites of PKC-mediated p110γ helical domain phosphorylation were unexpectedly inaccessible in the available structural models. Nevertheless, mass spectrometry (MS)-based phosphorylation profiling indicates that PKC can phosphorylate the helical domain of p110γ and p84/p110γ (but not p101/p110γ) in vitro. The authors hypothesize that helical domain dynamics dictate susceptibility to PKC phosphorylation. To explore this notion, carefully executed, rigorous H/D exchange MS (HDX-MS) experiments were performed comparing phosphorylated vs. unphosphorylated p110γ. Notably, this design reveals more about the consequences of p110γ phosphorylation, rather than the mechanisms of p84/p101 promoting/resisting phosphorylation. Nevertheless, HDX-MS is very well suited to exploring secondary structure dynamics, and helical domain phosphorylation strikingly increases dynamics consistent with increased regional accessibility. The increased dynamics also nicely map to the pocket enveloped by the inhibitory NB7 Nanobody.

Ultimately, this study reveals an unexpected p110γ pocket that allows an engineered Nanobody to allosterically inhibit PI3Kγ complexes. The cryo-EM characterization of the interaction inspired an HDX-MS investigation of known sites of phosphorylation in the region. These insights could be linked to differences/convergences of p84 and p101 complex formation and activation of PI3Kγ, and future work may clarify these mechanisms further. The data presented herein will also be useful for broadening the target surface for future therapeutic developments. New allosteric connections between effector binding sites and post-translational modifications are always welcome.

Reviewer #2 (Public Review):

Harris et al. have described the cryo-EM structure of PI3K p110gamma in a complex with a nanobody that inhibits the enzyme. This provided the first structure of full-length of PI3Kgamma in the absence of a regulatory subunit. This nanobody is a potent allosteric inhibitor of the enzyme, and might provide a starting point for developing allosteric, isotype-specific inhibitors of the enzyme. One distinct effect of the nanobody is to greatly decrease the dynamics of the enzyme as shown by HDX-MS, which is consistent with a growing body of observations suggesting that for the whole PI3K superfamily, enzyme activators increase enzyme dynamics.

The most remarkable outcome of the study is that upon observing the site of nanobody binding, the authors searched the literature and found that there was a previous report of a PKCbeta phosphorylation of PI3Kgamma in the helical domain that is near the nanobody binding site. This led the authors to re-examine the consequence of the phosphorylation armed with better structural models and the tools to study the effects of this phosphorylation on enzyme dynamics. They found that the site of phosphorylation is buried in the helical domain, suggesting that a large conformational change would have to take place to enable the phosphorylation. HDX-MS showed that phosphorylation at three sites clustered in the helical domain generate a distinctly different conformation with rapid deuterium exchange. This suggests that the phosphorylation locks the enzyme in a more dynamic state. Their enzyme kinetics show that the phosphorylated, dynamic enzyme is activated.

While this phosphorylation was reported before, the authors have provided a mechanism for why this activates the enzyme, and they have shown why binders that stabilise the helical domain (such as binding to the p101 regulatory subunit and the nanobody) prevent the phosphorylation. It is this insight into the dynamics of the PI3Kgamma that will likely be the long-lasting influence of the work.

The paper is well written and the methods are clear.

Reviewer #1 (Public Review):

This paper consists in a comprehensive analysis of the malaria parasite Plasmodium falciparum during its development in erythrocytes, using expansion microscopy. The authors used general dyes to stain membranes or proteins and a set of specific markers to label diverse cellular structures of the parasite, with a particular focus on the microtubule organizing center (MTOC).

This is by nature a purely descriptive study, providing remarkable images with great details on subcellular structures such as the MTOC, the basal complex, the cytostome and rhoptries. The work is extremely well performed and the images are beautiful. It confirms a number of previous observations, but does not bring much novel biological insights. However, the study illustrates the strength of expansion microscopy, an affordable and adaptable sample preparation method that will undoubtedly become standard in the field.

While the narrative could be improved, this study provides a valuable resource that can serve as a reference dataset for analysis of P. falciparum and other apicomplexan parasites.

Reviewer #2 (Public Review):

In this work the authors describe the shape and interconnectedness of intracellular structures of malaria blood stage parasites by taking advantage of expansion microscopy. Compared to previous microscopy work with these parasites, the strength of this paper lies in the increased resolution and the fact that the NHE ester highlights protein densities. Together with the BodipyC membrane staining, this results in data that is somewhere in between EM and standard fluorescence microscopy: it has higher resolution than standard fluorescence microscopy and provides some points of reference of different cellular structures due to the NHE ester/BodipyC.

This study makes many interesting and useful observations and although it is somewhat "old school descriptory" in its presentation, researchers working in many different areas will find something of interest here. This ranges from mitosis, to organisation and distribution of major cellular structures, endocytosis and invasion, overall providing a rich and interesting resource. The results section is long but by taking the space to explain everything in detail, it has the advantage that it clearly transpires how things were done and on how many cells a conclusion is based on. Further the authors often also included a brief interpretation of their findings with a very open assessment what it does and what it does not show, highlighting interesting questions left by the data.

Overall this is a very nice and useful paper that will be of interest to many, particularly those working on nuclear division, cytokinesis, endocytosis or invasion in malaria parasites. The spatiotemporal arrangement and interconnection of subcellular structures will also give a framework for specific functional studies.

Reviewer #3 (Public Review):

Summary:

In their study the authors analyze the localization of multiple organelles and subcellular structure of blood stage malaria parasites with unprecedented detail. They use a 3D super-resolution imaging technique that has gained popularity in the protozoan field, ultrastructure expansion microscopy. Building on markers and labels established in the field they generate an appealing collection of images for all stages of the intraerythrocytic developmental stages of asexual blood stage parasites with some focus on nuclear division and cell segmentation stages.

Strengths:

The authors generated an impressive amount of imaging data that presents the most comprehensive analysis of ultrastructural organization of the parasite cell so far. This atlas can serve as a reference for researchers studying the cell biology of the intraerythrocytic development cycle. The authors achieve a nice catalogue of the reorganization of well-established markers, which together with the improved resolution allows them to highlight some novel observations and consolidate previous findings. They e.g. improve our understanding of organization, duplication and constitutive tethering of the malaria parasite centrosome to the plasma membrane. Further they provide some interesting observations on rhoptry biogenesis, cytostome morphology, and organelle fission during segmentation.

Weaknesses:

While the comprehensiveness of the study is its strength the authors do not present any novel markers, stainings, or imaging protocols. There is no fundamentally new mechanistic insight derived from this study although some earlier findings are consolidated by the higher spatial resolution.

In the following I want to comment on some major points.

Most importantly, in order to justify the authors claim to provide an "Atlas", I want to strongly suggest they share their raw 3D-imaging data (at least of the main figures) in a data repository. This would allow the readers to browse their structure of interest in 3D and significantly improve the impact of their study in the malaria cell biology field.

The organization of the manuscript can be improved. Aside some obvious modifications as citing the figures in the correct order (see also further comments and recommendations), I would maybe suggest one subsection and one figure per analyzed cellular structure/organelle (i.e. 13 sections). This would in my opinion improve readability and facilitate "browsing the atlas".

Considering the importance of reliability of the U-ExM protocol for this study the authors should provide some validation for the isotropic expansion of the sample e.g. by measuring one well defined cellular structure.

In the absence of time-resolved data and more in-depth mechanistic analysis the authors must down tone some of their conclusions specifically around mitochondrial membrane potential, supellicular microtubule depolymerization, and kinetics of the basal complex. More detailed suggestions for improvement are provided as further comments.

In conclusion the authors provide an exciting cell biological reference framework and new working hypotheses about the function of some subcellular structures, which are still largely enigmatic in the malaria parasite, and can be investigated in the future.

Joint Public Review:

Tilk and colleagues present a computational investigation of tumor transcriptomes to investigate the hypothesis that the large number of somatic mutations in some tumors is detrimental and that these detrimental effects are mitigated by an up-regulation by pathways and mechanisms that prevent protein misfolding.

The authors address this question by fitting a model that explains the log expression of a gene as a linear function of the log number of mutations in the tumor and additional effects for tumor homogeneity and type. This analysis identified a large number of genes (5000) that are more highly expressed at high mutational load at a FDR of 0.05. These genes are enriched in many core categories, most prominently in the proteasome, translation, and mitochondral translation. The authors then proceed to investigate specific categories of upregulated genes further.

The individual reviews, and the discussion among the reviewers, raised several issues that could potentially undermine or weaken some of the findings presented in this paper.

1) Systematic differences in expression of some genes from one tumor class to another might generate spurious associations with mutational load (ML), which would affect the results presented in Figs 1 and 3. The case of a causal link between ML and over-expression of genes that mitigate deleterious effects of misfolding would be stronger if these results were replicated within single cancer types with many samples with different ML (similar to how Fig S6 relates to Fig 3). A related concern might be an association between increased variance of expression and ML. The compositional nature of expression data could generate trends like the ones shown in Fig. 2 with changing variance.

2) Fig 4, Fig S5 and Fig S8 show results for the regression coefficient of expression on ML after leaving out one cancer at a time. All of us initially read this as results for 'one cancer at a time', rather than 'leave-one-out'. These figures are used to argue that the results are not driven by specific cancer types. However, this analysis would not reveal if the signal was driven by a (small) subset of cancer types. To justify claims like "significant negative relationship between mutational load and cell viability across almost all cancer types", one needs to analyze individual cancer types. Results for specific genes, rather than broad groups would also help interpret these results.

3) You use different model architecture for the TCGA and CCLE analysis because you suspect that the sample size imbalance in the latter might mean that a GLMM can not capture the different variance components accurately. Did you test this? Could you downsample to avoid this? Cancer type is likely a strong confounder of ML.

4) In the splicing analysis (Fig 2 and Fig S4), you report a 10% variation in splicing for a 100-fold variation in ML. This weak trend is replicated in very similar ways for many different types of alternative splicing events. It is not clear why different events (exon skipping, intron retention, etc) should respond in the same way to ML. A weak but homogeneous effect like the one shown here might result from some common confounder (see point 1). Similarly, it is not clear why with increasing intron retention PSI threshold the fraction of under-expressed transcripts would decrease and not increase.

Reviewer #1 (Public Review):

Wang and all present an interesting body of work focused on the effects of high altitude and hypoxia on erythropoiesis, resulting in erythrocytosis. This work is specifically focused on the spleen, identifying splenic macrophages as central cells in this effect. This is logical since these cells are involved in erythrophagocytosis and iron recycling. The results suggest that hypoxia induces splenomegaly with decreased number of splenic macrophages. There is also evidence that ferroptosis is induced in these macrophages, leading to cell destruction. Finally, the data suggest that ferroptosis in splenic red pulp macrophages causes the decrease in RBC clearance, resulting in erythrocytosis aka lengthening the RBC lifespan. However, there are many issues with the presented results, with somewhat superficial data, meaning the conclusions are overstated and there is decreased confidence that the hypotheses and observed results are directly causally related to hypoxia.

Major points:

1) The spleen is a relatively poorly understood organ but what is known about its role in erythropoiesis especially in mice is that it functions both to clear as well as to generate RBCs. The later process is termed extramedullary hematopoiesis and can occur in other bones beyond the pelvis, liver, and spleen. In mice, the spleen is the main organ of extramedullary erythropoiesis. The finding of transiently decreased spleen size prior to splenomegaly under hypoxic conditions is interesting but not well developed in the manuscript. This is a shortcoming as this is an opportunity to evaluate the immediate effect of hypoxia separately from its more chronic effect. Based just on spleen size, no conclusions can be drawn about what happens in the spleen in response to hypoxia.

2) Monocyte repopulation of tissue resident macrophages is a minor component of the process being described and it is surprising that monocytes in the bone marrow and spleen are also decreased. Can the authors conjecture why this is happening? Typically, the expectation would be that a decrease in tissue resident macrophages would be accompanied by an increase in monocyte migration into the organ in a compensatory manner.

3) Figure 3 does not definitively provide evidence that cell death is specifically occurring in splenic macrophages and the fraction of Cd11b+ cells is not changed in NN vs HH. Furthermore, the IHC of F4/80 in Fig 3U is not definitive as cells can express F4/80 more or less brightly and no negative/positive controls are shown for this panel.

4) The phagocytic function of splenic red pulp macrophages relative to infection cannot be used directly to understand erythrophagocytosis. The standard approach is to use opsonized RBCs in vitro. Furthermore, RBC survival is a standard method to assess erythrophagocytosis function. In this method, biotin is injected via tail vein directly and small blood samples are collected to measure the clearance of biotinilation by flow; kits are available to accomplish this. Because the method is standard, Fig 4D is not necessary and Fig 4E needs to be performed only in blood by sampling mice repeatedly and comparing the rate of biotin decline in HH with NN (not comparing 7 d with 14 d).

5) It is unclear whether Tuftsin has a specific effect on phagocytosis of RBCs without other potential confounding effects. Furthermore, quantifying iron in red pulp splenic macrophages requires alternative readily available more quantitative methods (e.g. sorted red pulp macrophages non-heme iron concentration).

6) In Fig 5, PBMCs are not thought to represent splenic macrophages and although of some interest, does not contribute significantly to the conclusions regarding splenic macrophages at the heart of the current work. The data is also in the wrong direction, namely providing evidence that PBMCs are relatively iron poor which is not consistent with ferroptosis which would increase cellular iron.

7) Tfr1 increase is typically correlated with cellular iron deficiency while ferroptosis consistent with iron loading. The direction of the changes in multiple elements relevant to iron trafficking is somewhat confusing and without additional evidence, there is little confidence that the authors have reached the correct conclusion. Furthermore, the results here are analyses of total spleen samples rather than specific cells in the spleen.

Reviewer #2 (Public Review):

The authors aimed at elucidating the development of high altitude polycythemia which affects mice and men staying in the hypoxic atmosphere at high altitude (hypobaric hypoxia; HH). HH causes increased erythropoietin production which stimulates the production of red blood cells. The authors hypothesize that increased production is only partially responsible for exaggerated red blood cell production, i.e. polycythemia, but that decreased erythrophagocytosis in the spleen contributes to high red blood cells counts.

The main strength of the study is the use of a mouse model exposed to HH in a hypobaric chamber. However, not all of the reported results are convincing due to some smaller effects which one may doubt to result in the overall increase in red blood cells as claimed by the authors. Moreover, direct proof for reduced erythrophagocytosis is compromised due to a strong spontaneous loss of labelled red blood cells, although effects of labelled E. coli phagocytosis are shown.

Their discussion addresses some of the unexpected results, such as the reduced expression of HO-1 under hypoxia but due to the above mentioned limitations much of the discussion remains hypothetical.

Reviewer #3 (Public Review):

The manuscript by Yang et al. investigated in mice how hypobaric hypoxia can modify the RBC clearance function of the spleen, a concept that is of interest. Via interpretation of their data, the authors proposed a model that hypoxia causes an increase in cellular iron levels, possibly in RPMs, leading to ferroptosis, and downregulates their erythrophagocytic capacity. However, most of the data is generated on total splenocytes/total spleen, and the conclusions are not always supported by the presented data. The model of the authors could be questioned by the paper by Youssef et al. (which the authors cite, but in an unclear context) that the ferroptosis in RPMs could be mediated by augmented erythrophagocytosis. As such, the loss of RPMs in vivo which is indeed clear in the histological section shown (and is a strong and interesting finding) can be not directly caused by hypoxia, but by enhanced RBC clearance. Such a possibility should be taken into account.

Major points:

1) The authors present data from total splenocytes and then relate the obtained data to RPMs, which are quantitatively a minor population in the spleen. Eg, labile iron is increased in the splenocytes upon HH, but the manuscript does not show that this occurs in the red pulp or RPMs. They also measure gene/protein expression changes in the total spleen and connect them to changes in macrophages, as indicated in the model Figure (Fig. 7). HO-1 and levels of Ferritin (L and H) can be attributed to the drop in RPMs in the spleen. Are any of these changes preserved cell-intrinsically in cultured macrophages? This should be shown to support the model (relates also to lines 487-88, where the authors again speculate that hypoxia decreases HO-1 which was not demonstrated). In the current stage, for example, we do not know if the labile iron increase in cultured cells and in the spleen in vivo upon hypoxia is the same phenomenon, and why labile iron is increased. To improve the manuscript, the authors should study specifically RPMs.

2) The paper uses flow cytometry, but how this method was applied is suboptimal: there are no gating strategies, no indication if single events were determined, and how cell viability was assessed, which are the parent populations when % of cells is shown on the graphs. How RBCs in the spleen could be analyzed without dedicated cell surface markers? A drop in splenic RPMs is presented as the key finding of the manuscript but Fig. 3M shows gating (suboptimal) for monocytes, not RPMs. RPMs are typically F4/80-high, CD11-low (again no gating strategy is shown for RPMs). Also, the authors used single-cell RNAseq to detect a drop in splenic macrophages upon HH, but they do not indicate in Fig. A-C which cluster of cells relates to macrophages. Cell clusters are not identified in these panels, hence the data is not interpretable).

3) The authors draw conclusions that are not supported by the data, some examples:

a) they cannot exclude eg the compensatory involvement of the liver in the RBCs clearance (the differences between HH sham and HH splenectomy is mild in Fig. 2 E, F and G)

b) splenomegaly is typically caused by increased extramedullary erythropoiesis, not RBC retention. Why do the authors support the second possibility? Related to this, why do the authors conclude that data in Fig. 4 G,H support the model of RBC retention? A significant drop in splenic RBCs (poorly gated) was observed at 7 days, between NN and HH groups, which could actually indicate increased RBC clearance capacity = less retention.

c) lines 452-54: there is no data for decreased phagocytosis in vivo, especially in the context of erythrophagocytosis. This should be done with stressed RBCs transfusion assays, very good examples, like from Youssef et al. or Threul et al. are available in the literature.

d) Line 475 - ferritinophagy was not shown in response to hypoxia by the manuscript, especially that NCOA4 is decreased, at least in the total spleen.

4) In a few cases, the authors show only representative dot plots or histograms, without quantification for n>1. In Fig. 4B the authors write about a significant decrease (although with n=1 no statistics could be applied here; of note, it is not clear what kind of samples were analyzed here). Another example is Fig. 6I. In this case, it is even more important as the data are conflicting the cited article and the new one: PMCID: PMC9908853 which shows that hypoxia stimulates efferocytosis. Sometimes the manuscript claim that some changes are observed, although they are not visible in representative figures (eg for M1 and M2 macrophages in Fig. 3M)

5) There are several unclear issues in methodology:

- what is the purity of primary RPMs in the culture? RPMs are quantitatively poorly represented in splenocyte single-cell suspensions. This reviewer is quite skeptical that the processing of splenocytes from approx 1 mm3 of tissue was sufficient to establish primary RPM cultures. The authors should prove that the cultured cells were indeed RPMs, not monocyte-derived macrophages or other splenic macrophage subtypes.<br /> - (around line 183) In the description of flow cytometry, there are several missing issues. In 1) it is unclear which type of samples were analyzed. In 2) it is not clear how splenocyte cell suspension was prepared.<br /> - In line 192: what does it mean: 'This step can be omitted from cell samples'?<br /> - 'TO method' is not commonly used anymore and hence it was unclear to this Reviewer. Reticulocytes should be analyzed with proper gating, using cell surface markers.<br /> - The description of 'phagocytosis of E. coli and RBCs' in the Methods section is unclear and incomplete. The Results section suggests that for the biotinylated RBCs, phagocytosis? or retention? Of RBCs was quantified in vivo, upon transfusion. However, the Methods section suggests either in vitro/ex vivo approach. It is vague what was indeed performed and how in detail. If RBC transfusion was done, this should be properly described. Of note, biotinylation of RBCs is typically done in vivo only, being a first step in RBC lifespan assay. The such assay is missing in the manuscript. Also, it is not clear if the detection of biotinylated RBCs was performed in permeablized cells (this would be required).

Joint Public Review:

The authors report the first use of the bacterial Tus-Ter replication block system in human cells. A single plasmid containing two divergently oriented five-fold TerB repeats was integrated on chromosome 12 of MCF7 cells. ChIP and PLA experiments convincingly demonstrate the occupancy of Tus at the Ter sites in cells. Using an elegant Single Molecule Analysis of Replicated DNA (SMARD) assay, convincing data demonstrate the replication block at Ter sites dependent on the presence of the protein. As an orthogonal method to demonstrate fork stalling, ChIP data show the accumulation of the replicative helicase component MCM3 and the repair protein FANCM around the Ter sites. It is unclear whether the Ter sites integrated by a single copy plasmid have any effect on the replication of this region but the data show that the observed effects are dependent on expression of the Tus protein. The SMARD data do not reveal what proportion of forks are arrested at Tus/Ter, or how long the fork delay is imposed. Fork stalling led to a highly localized gammaH2AX response, as monitored by ChIP using primer pairs spread along the integrated plasmid carrying the Ter sites. This response was shown to be dependent on ATR using the ATR inhibitor VE-822. This contrasts with a single Cas9-induced DSB between the two Ter sites, which causes a more spread gammaH2AX response. While this was monitored only at a single distal site, the difference between the DSB and the Tus-induced stall is very significant. Interestingly, despite evidence for ATR activation through the gammaH2AX response, no evidence for phosphorylation of ATR-T1989, CHK1-S345, or RPA2-S33 could be found under fork stalling conditions. The global replication inhibitor hydroxyurea (HU) elicited phosphorylation of ATR-T1989, CHK1-S345, or RPA2-S33. In this context, it would have been of interest to examine if a single DSB in the Ter region leads to phosphorylation of ATR-T1989, CHK1-S345, or RPA2-S33 and cell cycle arrest. It is not shown whether the replication inhibitor HU leads to the same widely spread gamma H2AX response. Overall, this is a well written manuscript, and the data provide convincing evidence that the Tus-Ter system poses a site-specific replication fork block in MCF7 cells leading to a localized ATR-dependent DNA damage checkpoint response that is distinct from the more global response to HU or DSBs.

Reviewer #1 (Public Review):

The authors sought to address the longstanding question of which cell types are infected during congenital or perinatal rubella virus infection. They used brain slice and organoid-microglia experimental models to demonstrate that the main cell types targeted by rubella virus are microglia. It does not appear that microglia support rubella virus production in this experimental system, though future studies would be needed to address this more thoroughly. The authors further show that infection results in augmented interferon responses in neighboring neuronal cells but not in the microglia themselves. The data support the conclusions, with major strengths being the sophisticated primary cell models and single-cell RNA-Seq used to pinpoint microglia as the main cellular targets of rubella virus, and neurons as the bystander targets of immune signaling. This study reveals a new cellular target that will have important implications for basic studies on rubella virus-host interactions and for the potential development of therapies or improved vaccines targeting this virus. As rubella virus is a pathogen of high concern during human pregnancy, this study also has important implications in the field of neonatal infectious diseases.

Reviewer #2 (Public Review):

In this manuscript by Popova et al., the authors report the pathological impact of Rubella virus (RV) infection on human brain development. In particular, they uncovered a selective tropism of Rubella virus for microglial cells in cultured slices of human developing brain and 2D mixed fetal brain cell culture. Their results suggest that RV infection of microglia relies on the presence of diffusible factors from other cell populations. Moreover, the authors showed that RV infection of human brain organoids supplemented or not with microglia leads to interferon response and dysregulation of gene involved in brain development. This set of data will help understanding the cellular specificity and pathological mechanisms occurring in the developing brain upon RV infection. The data provided are overall of high quality and shed new light on the cellular tropism and the pathomechanisms of RV infection.

Reviewer #1 (Public Review):

This manuscript uses 3 large neuroimaging datasets - which together span childhood to late adulthood - to model the relationship between birthweight (BW) and cortical anatomy over time. The authors separately consider BW associations with the "height" of cortical anatomy trajectories (intercept effects) vs. BW associations with trajectory shape. The authors also distinguish between BW associations with cortical surface area (SA) and cortical thickness (CT), which together determine cortical volume (CV). Prior studies have firmly established robust positive associations between BW and cortical SA, but this study adds evidence for the protracted lifespan persistence of these associations, and the degree to which BW associations with cortical change over time are much weaker.

The study has several strengths including: clear motivation of this work in the Introduction and contextualization of the results in Discussion; use of three large neuroimaging datasets; inclusion of sensible sensitivity analyses; disambiguation of SA and CT findings; and use of formal spatial analysis to quantify the reproducibility of effects across cohorts.

The primary way in which this work seeks to extend beyond established findings is to determine if BW is associated with differences in cortical change over time. The results presented clearly establish that such BW-change associations are much more localized and less consistent across cohorts that BW-intercept associations. However, the evidential basis for this statement is partly limited by the nature of the neuroimaging cohorts used and the specific approaches taken to statistical modeling. Interpretation of findings for both BW-change and BW-intercept associations would also be assisted by greater clarity regarding the specification of statistical models, and the provision of effect-size maps.

Moreover, several factors complicate interpretation of the BW effects on cortical change - which are arguably the main way in which this work could extend on established knowledge of BW associations with brain anatomy. Under the study design presented, inferences regarding age-varying BW effects come from two main sources ... age effects which are quantitatively modeled within each sample, and qualitative differences in age effects between samples. Any inferences from the latter source of evidence are weakened by the fact that (i) no direct statistical comparisons are conducted between samples (beyond the spin tests), and (ii) the composition of samples with regard to age span covered (e..g 2 in ABCD vs longer in UKB and longest in LCBC) and density of longitudinal data makes it hard to know if between-samples differences in age*BW effects are about biology or methodology. Inferences about age*BW effects from models within each sample are also limited by the fact that (i) some samples (ABCD) have very narrow age ranges precluding detection of age-related effects, and (ii) the modeling strategy used does not allow for non-linear interactions between age and BW or linear interactions that occur in the context of e.g. non-linear BW effects. For this last concern, it would be helpful to know that there is no evidence in the data for such non-linear effects

The tests for spatial consistency between BW effects are a valuable aspect of the manuscript and provide a solid quantitative test for the main effects of BW. For the reasons detailed above however, I think that the more variable (and sometimes negative) correlations in age*BW maps are harder to interpret. One could argue for example that bivariate spline models of age*BW interactions on a lifespan dataset assembled from different COMBAT-aligned cohorts would provide a more solid basis for inference regarding the degree to which BW effects on cortical anatomy vary with age

Overall, this work provides a valuable new data point in our understanding of the profound and protracted influences that prenatal developmental features can have on postnatal outcomes.

Reviewer #2 (Public Review):

This study focuses on the association between weight at birth and area, volume and thickness of the cerebral cortex measured at timepoints throughout the lifespan. Overall, the study is well designed, and supported by evidence from a large sample drawn from three geographically distinct cohorts with robust analytical and statistical methods.

The authors test three hypotheses: (1) that higher birth weight is associated with greater cortical area in later life; (2) that associations are robust across samples and age; and (3) that associations are stable across the lifespan. Analyses are performed separately in three cohorts: ABCD, UKBB and LCBC and the pattern of associations compared by means of spatial correlations. They find that BW is positively associated with cortical area (and, as a consequence, cortical volume) across most of the cortex, with effect sizes being greatest in frontal and temporal regions. These associations remain largely unchanged when accounting for age, sex, length of gestation and (in one cohort) ethnicity. Variations due to MRI scanner and site are accounted for statistically. Measures are taken to determine within sample replicability through split-half analyses.

The authors conclude that BW, as a marker of early development, is consistently associated with brain characteristics throughout the lifespan, acting as an 'intercept' and promoting brain reserve, i.e.: the capacity of the brain to withstand aging effects. Indeed, the authors calculate that 600g lower BW results in reductions in cortical volume akin to 6-7 years of aging in middle to later life. This is perhaps a startling statistic but one that is not entirely supported by the data presented.

A key piece of information lacking from this study is the functional importance of the reported associations. That lower BW is associated with lower cortical volume and that cortical volume decreases with age is perhaps not surprising - the same could be said for height - one cannot conclude that the same processes underpin the two factors without examining the functional consequences of BW-related volume reductions in older age. The notion of 'brain reserve' indicates a protective effect. If this is the case, one might expect to see a mediating effect of BW on age-related cognitive effects. Without this data, it is difficult to reach the authors conclusions that decreased birthweight has the same effect as 7 years of aging in later life.

In addition, it is not clear to what degree the association between BW and cortical area/volume is simply reflecting overall somatic growth: brain mass scales with body height, and birth weight and length are associated with adult height. While the specificity of the associations between cortical area/volume and BW are not fully tested, the effects are significantly diminished when controlling for a related measure of somatic growth: intracranial volume (Fig S5). In this context, additional commentary on the specificity of the reported BW-brain associations (or lack thereof) would be helpful.

Finally, the authors use linear models to model brain area, thickness and volume as a function of age. The authors' previous studies have demonstrated nonlinear trajectories of cortical thickness in the LCBC cohort across most of the cortex. A stronger rationale (e.g.: theoretical or model selection) supporting the use of GLM in this study would be more compelling.

Reviewer #1 (Public Review):

The study by Sianga-Mete et al revisits the effects of substitution model selection on phylogenetics by comparing reversible and non-reversible DNA substitution models. This topic is not new, previous works already showed that non-reversible, and also covarion, substitution models can fit the real data better than the reversible substitution models commonly used in phylogenetics. In this regard, the results of the present study are not surprising. Specific comments are shown below.

Major comments

It is well known that non-reversible models can fit the real data better than the commonly used reversible substitution models, see for example,<br /> https://academic.oup.com/sysbio/article/71/5/1110/6525257<br /> https://onlinelibrary.wiley.com/doi/10.1111/jeb.14147?af=R<br /> The manuscript indicates that the results (better fitting of non-reversible models compared to reversible models) are surprising but I do not think so, I think the results would be surprising if the reversible models provide a better fitting.<br /> I think the introduction of the manuscript should be increased with more information about non-reversible models and the diverse previous studies that already evaluated them. Also I think the manuscript should indicate that the results are not surprising, or more clearly justify why they are surprising.

In the introduction and/or discussion I missed a discussion about the recent works on the influence of substitution model selection on phylogenetic tree reconstruction. Some works indicated that substitution model selection is not necessary for phylogenetic tree reconstruction,<br /> https://academic.oup.com/mbe/article/37/7/2110/5810088<br /> https://www.nature.com/articles/s41467-019-08822-w<br /> https://academic.oup.com/mbe/article/35/9/2307/5040133<br /> While others indicated that substitution model selection is recommended for phylogenetic tree reconstruction,<br /> https://www.sciencedirect.com/science/article/pii/S0378111923001774<br /> https://academic.oup.com/sysbio/article/53/2/278/1690801<br /> https://academic.oup.com/mbe/article/33/1/255/2579471<br /> The results of the present study seem to support this second view. I think this study could be improved by providing a discussion about this aspect, including the specific contribution of this study to that.

The real data was downloaded from Los Alamos HIV database. I am wondering if there were any criterion for selecting the sequences or if just all the sequences of the database for every studied virus category were analysed. Also, was any quality filter applied? How gaps and ambiguous nucleotides were considered? Notice that these aspects could affect the fitting of the models with the data.

How the non-reversible model and the data are compared considering the non-reversible substitution process? In particular, given an input MSA, how to know if the nucleotide substitution goes from state x to state y or from state y to state x in the real data if there is not a reference (i.e., wild type) sequence? All the sequences are mutants and one may not have a reference to identify the direction of the mutation, which is required for the non-reversible model. Maybe one could consider that the most abundant state is the wild type state but that may not be the case in reality. I think this is a main problem for the practical application of non-reversible substitution models in phylogenetics.

Reviewer #2 (Public Review):

The authors evaluate whether non time reversible models fit better data presenting strand-specific substitution biases than time reversible models. Specifically, the authors consider what they call NREV6 and NREV12 as candidate non time-reversible models. On the one hand, they show that AIC tends to select NREV12 more often than GTR on real virus data sets. On the other hand, they show using simulated data that NREV12 leads to inferred trees that are closer to the true generating tree when the data incorporates a certain degree of non time-reversibility. Based on these two experimental results, the authors conclude that "We show that non-reversible models such as NREV12 should be evaluated during the model selection phase of phylogenetic analyses involving viral genomic sequences". This is a valuable finding, and I agree that this is potentially good practice. However, I miss an experiment that links the two findings to support the conclusion: in particular, an experiment that solves the following question: does the best-fit model also lead to better tree topologies?

On simulated data, the significance of the difference between GTR and NREV12 inferences is evaluated using a paired t test. I miss a rationale or a reference to support that a paired t test is suitable to measure the significance of the differences of the wRF distance. Also, the results show that on average NREV12 performs better than GTR, but a pairwise comparison would be more informative: for how many sequence alignments does NREV12 perform better than GTR?

Reviewer #1 (Public Review):

Tippett et al present whole cell and proteoliposome transport data showing unequivocally that purified recombinant SLC26A6 reconstituted in proteoliposomes mediates electroneutral chloride/bicarbonate exchange, as well as coupled chloride/oxalate exchange unassociated with detectable current. Both functions contrast with the uncoupled chloride conductance mediated by SLC26A9. The authors also present a novel cryo-EM structure of full-length human SLC26A6 chloride/anion exchanger. As part of the structure, they offer the first partial view of the STAS domain previously predicted to be unstructured. They further define a single Arg residue of the SLC26A6 transmembrane domain required for coupled exchange, mutation of which yields apparently uncoupled electrogenic chloride transport mechanistically resembling that of SLC26A9, although of lower magnitude. The authors further apply to proteoliposomes for the first time a still novel approach to the measurement of bicarbonate transport using a bicarbonate-selective Europium fluorophor. The evidence strongly supports the authors' claims and conclusions, with one exception.

The manuscript has numerous strengths:

As a structural biology contribution, the authors extend the range of SLC26 structures to SLC26A6, comparing it in considerable detail to the published SLC26A9 structure, and presenting for the first time the structure of a portion of the STAS IVS domain of SLC26A6 long considered unstructured.

The authors also apply a remarkably extensive range of creative technical approaches to assess the functional mechanisms of anion transport by SLC26A6, among them the first application of the novel, specific bicarbonate sensor Eu-L1+ to directly assess bicarbonate transport in reconstituted proteoliposomes. The authors also present the first (to this reviewer's knowledge) functional proteoliposome reconstitution of chloride-bicarbonate exchange mediated by an SLC26 protein. They define a residue in surrounding the anion binding pocket which explains part of the difference in anion exchange coupling between SLC26A6 and SLC26A9. In the setting of past conflicting results, the current work also contributes to the weight of previous evidence demonstrating that SLC26A6 mediates electroneutral rather than electrogenic Cl-/HCO3- exchange.

Each of these achievements constitutes a significant advance in our understanding.

The paper has only a few weaknesses:

One is an incomplete explanation of the mechanistic determinants of anion exchange coupling in SLC26A6 vs. uncoupled anion transport by SLC26A9.

A second weakness is the inconsistent, technique-dependent detection of SLC26A6- mediated electrogenic chloride/oxalate exchange. In particular whole cell currents attributable to SLC26A6 in SLC26A6-expressing HEK293 cells in an oxalate bath could not be detected, whereas robust, saturable Cl- efflux into oxalate solution from proteoliposomes reconstituted with recombinant SLC626A6 was detectable by AMCA fluorescence decay. This discrepancy was attributed to the relative sensitivities and/or signal-to-noise ratios of the assays.

Overall, the manuscript represents an important advance in our understanding of the SLC26 protein family and of coupled vs uncoupled carrier-mediated anion transport.

Reviewer #2 (Public Review):

The eleven paralogs of SLC26 proteins in humans exhibit a remarkable range of functional diversity, spanning from slow anion exchangers and fast anion transporters with channel-like properties, to motor proteins found in the cochlear outer hair cells. In this study, the authors investigate human SLC26A6, which functions as a bicarbonate (HCO3-)/chloride (Cl-) and oxalate (C2O42-)/Cl- exchanger, combining cryo-electron microscopy, electrophysiology, and in vitro transport assays. The authors provide compelling evidence to support the idea that SLC26A6's exchange anions at equimolar stoichiometry, leading to the electroneutral and electrogenic transport of HCO3-/ Cl- and C2O42-/Cl-, respectively. Furthermore, the structure of SLC26A6 reveals a close resemblance to the fast, uncoupled Cl- transporter SLC26A9, with the major structural differences observed within the anion binding site. By characterizing an amino acid substitution within the SLC26A6 anion binding site (R404V), the authors also show that the size and charge variance of the binding pocket between the two paralogs could, in part, contribute to the differences in their transport mechanisms.

This is a well-executed study, and the strength of this work lies in the reductionist, in vitro approach that the authors took to characterize the transport process of SLC26A6. The authors used and developed an array of functional experiments, including two electrogenic transport assays - a fast kinetic (electrophysiology) and a slow-kinetic (fluorescent-based ACMA) - and two electroneutral transport assays, probing for Cl- (lucigenin) and HCO3- (europium), which are well executed and characterized. The structural data is also of high quality and is the first structure of an SLC26 coupled anion exchanger, providing essential information for clarifying our understanding of the functional diversity between the SLC26 family of proteins.

Reviewer #3 (Public Review):

The mechanistically diverse SLC26 transporters play a variety of physiological roles. The current manuscript establishes the SLC26A6 subtype as electroneutral chloride/bicarbonate exchanges and reports its high-resolution structure with chloride bound.

The claims in this manuscript are all well-supported by the data. Strengths include the comprehensive functional analysis of SLC26A6 in reconstituted liposome vesicles. The authors employ an array of assays, including chloride sensors, a newly developed fluorescent probe for bicarbonate, and assays to detect the electrogenicity of anion exchange. With this assortment of assays, the authors are able to establish the anion selectivity and stoichiometry of SLC26A6. Another strength of the manuscript is the functional comparison with SLC26A9, which permits fast, passive chloride transport, in order to benchmark the SLC26A6 activity. The structural analysis, including the assignment of the chloride binding site, is also convincing. The structural details and the chloride binding site are well-conserved among SLC26s. Finally, the authors present an interesting discussion comparing the structures of SLC26A5, SLC26A6, and SLC26A9, and how the details of the chloride binding site might influence the mechanistic distinctions between these similar transporters.

Reviewer #1 (Public Review):

Watanuki et al used metabolomic tracing strategies of U-13C6-labeled glucose and 13C-MFA to quantitatively identify the metabolic programs of HSCs during steady-state, cell-cycling, and OXPHOS inhibition. They found that 5-FU administration in mice increased anaerobic glycolytic flux and decreased ATP concentration in HSCs, suggesting that HSC differentiation and cell cycle progression are closely related to intracellular metabolism and can be monitored by measuring ATP concentration. Using the GO-ATeam2 system to analyze ATP levels in single hematopoietic cells, they found that PFKFB3 can accelerate glycolytic ATP production during HSC cell cycling by activating the rate-limiting enzyme PFK of glycolysis. Additionally, by using Pfkfb3 knockout or overexpressing strategies and conducting experiments with cytokine stimulation or transplantation stress, they found that PFKFB3 governs cell cycle progression and promotes the production of differentiated cells from HSCs in proliferative environments by activating glycolysis. Overall, in their study, Watanuki et al combined metabolomic tracing to quantitatively identify metabolic programs of HSCs and found that PFKFB3 confers glycolytic dependence onto HSCs to help coordinate their response to stress. Even so, several important questions need to be addressed as below:

1. Based on previous reports, the authors expanded the LSK gate to include as many HSCs as possible (Supplemental Figure 1B). However, while they showed the gating strategy on Day 6 after 5-FU treatment, results from other time-points should also be displayed to ensure the strict selection of time-points.

2. In Figure 1, the authors examined the metabolite changes on Day 6 after 5-FU treatment. However, it is important to consider whether there are any dynamic adjustments to metabolism during the early and late stages of 5-FU treatment in HSCs compared to PBS treatment, in order to coordinate cell homeostasis despite no significant changes in cell cycle progression at other time-points.

3. As is well known, ATP can be produced through various pathways, including glycolysis, the TCA cycle, the PPP, NAS, lipid metabolism, amino acid metabolism and so on. Therefore, it is important to investigate whether treatment with 5-FU or oligomycin affects these other metabolic pathways in HSCs.

4. In part 2, they showed that oligomycin treatment of HSCs exhibited activation of the glycolytic system, but what about the changes in ATP concentration under oligomycin treatment? Are other metabolic systems affected by oligomycin treatment?

5. In Figure 5M, it would be helpful to include a control group that was not treated with 2-DG. Additionally, if Figure 5L is used as the control, it is unclear why the level of ATP does not show significant downregulation after 2-DG treatment. Similarly, in Figure 5O, a control group with no glucose addition should be included.

6. In this study, their findings suggest that PFKFB3 is required for glycolysis of HSCs under stress, including transplantation. In Figure 7B, the results showed that donor-derived chimerism in PB cells decreased relative to that in the WT control group during the early phase (1 month post-transplant) but recovered thereafter. Although the transplantation cell number is equal in two groups of donor cells, it is unclear why the donor-derived cell count decreased in the 2-week post-transplantation period and recovered thereafter in the Pfkgb3 KO group. Therefore, they should provide an explanation for this. Additionally, they only detected the percentage of donor-derived cells in PB but not from BM, which makes it difficult to support the argument for increasing the HSPC pool.

7. In Figure 7E, they collected the BM reconstructed with Pfkfb3- or Rosa-KO HSPCs two months after transplantation, and then tested their resistance to 5-FU. However, the short duration of the reconstruction period makes it difficult to draw conclusions about the effects on steady-state blood cell production.

8. PFK is allosterically activated by PFKFB, and other members of the PFKFB family could also participate in the glycolytic program. Therefore, they should investigate their function in contributing to glycolytic plasticity in HSCs during proliferation. Additionally, they should also analyze the protein expression and modification levels of other members. Although PFKFB3 is the most favorable for PFK activation, the role of other members should also be explored in HSC cell cycling to provide sufficient reasoning for choosing PFKFB3.

9. In this study, the authors identified PRMT1 as the upstream regulator of PFKFB3 that is involved in the glycolysis activation of HSCs. However, PRMT1 is also known to participate in various transcriptional activations. Thus, it is important to determine whether PRMT1 affects glycolysis through transcriptional regulation or through its direct regulation of PFKFB3? Additionally, the authors should investigate whether PRMT1i inhibits ATP production in normal HSCs. Moreover, could we combine Figure 6I and 6J for analysis. Finally, the authors could conduct additional rescue experiments to demonstrate that the effect of PRMT1 inhibitors on ATP production can be rescued by overexpression of PFKFB3.

Reviewer #2 (Public Review):

In the manuscript Watanuki et al. want to define the metabolic profile of HSCs in stress/proliferative (myelosuppression with 5-FU), and mitochondrial inhibition and homeostatic conditions. Their conclusions are that during proliferation HSCs rely more on glycolysis (as other cell types) while HSCs in homeostatic conditions are mostly dependent on mitochondrial metabolism. Mitochondrial inhibition is used to demonstrate that blocking mitochondrial metabolism results in similar features of proliferative conditions.

The authors used state-of-the-art technologies that allow metabolic readout in a limited number of cells like rare HSCs. These applications could be of help in the field since one of the major issues in studying HSCs metabolism is the limited sensitivity of the "standard" assays, which make them not suitable for HSC studies.

However, the observations do not fully support the claims. There are no direct evidence/experiments tackling cell cycle state and metabolism in HSCs. Often the observations for their claims are indirect, while key points on cell cycle state-metabolism, OCR analysis should be addressed directly.

Specifically, there are several major points that rise concerns about the claims:

1. The gating strategy to select HSCs with enlarged Sca1 gating is not convincing. I understand the rationale to have a sufficient number of cells to analyze, however this gating strategy should be applied also in the control group. From the FACS plot seems that there are more HSCs upon 5FU treatment (Figure S1b). How that is possible? Is it because of the 20% more of cycling cells at day 6? To prove that this gating strategy still represents a pure HSC population, authors should compare the blood reconstitution capability of this population with a "standard" gated population. If the starting population is highly heterogeneous then the metabolic readout could simply reflect cell heterogeneity.

2. S2 does not show major differences before and after sorting. However, a key metabolite like Lactate is decreased, which is also one of the most present. Wouldn't that mean that HSCs once they move out from the hypoxic niche, they decrease lactate production? Do they decrease anaerobic glycolysis? How can quiescent HSC mostly rely on OXPHOS being located in hypoxic niche?

3. The authors performed challenging experiments to track radiolabeled glucose, which are quite remarkable. However, the data do not fully support the conclusions. Mitochondrial metabolism in HSCs can be supported by fatty acid and glutamate, thus authors should track the fate of other energy sources to fully discriminate the glycolysis vs mito-metabolism dependency. From the data on S2 and Fig1 1C-F, the authors can conclude that upon 5FU treatment HSCs increase glycolytic rate.

4. In Figure S1, 5-FU leads to the induction of cycling HSCs and in figure 1, 5-FU results in higher activation of glycolysis. Would it be possible to correlate these two phenotypes together? For example, by sorting NBDG+ cells and checking the cell cycle status of these cells?

5. FIG.2B-C: Increase of Glycolysis upon oligomycin treatment is common in many different cell types. As explained before, other radiolabeled substrates should be used to understand the real effect on mitochondria metabolism.

6. Why are only ECAR measurements (and not OCR measurements) shown? In Fig.2G, why are HSCs compared with cKit+ myeloid progenitors, and not with MPP1? The ECAR increased observed in HSC upon oligomycin treatment is shared with many other types of cells. However, cKit+ cells have a weird behavior. Upon oligo treatment cKit+ cells decrease ECAR, which is quite unusual. The data of both HSCs and cKit+ cells could be clarified by adding OCR curves. Moreover, it is recommended to run glycolysis stress test profile to assess the dependency to glycolysis (Glucose, Oligomycin, 2DG).

7. Since HSCs in the niche are located in hypoxic regions of the bone marrow, would that not mimic OxPhos inhibition (oligomycin)? Would that not mean that HSCs in the niche are more glycolytic (anaerobic glycolysis)?

FIG.3 A-C. As mentioned previously, the flux analyses should be integrated with data using other energy sources. If cycling HSCs are less dependent to OXPHOS, what happen if you inhibit OXHPHOS in 5-FU condition? Since the authors are linking OXPHOS inhibition and upregulation of Glycolysis to increase proliferation, do HSCs proliferate more when treated with oligomycin?

8. FIG.4 shows that in vivo administration of radiolabeled glucose especially marks metabolites of TCA cycle and Glycolysis. The authors interpret enhanced anaerobic glycolysis, but I am not sure this is correct; if more glycolysis products go in the TCA cycle, it might mean that HSC start engaging mitochondrial metabolism. What do the authors think about that?

9. FIG.4: the experimental design is not clear. Are BMNNCs stained and then put in culture? Is it 6-day culture or BMNNCs are purified at day 6 post 5FU? FIG-4B-C The difference between PBS vs 5FU conditions are the most significant; however, the effect of oligomycin in both conditions is the most dramatic one. From this readout, it seems that HSCs are more dependent on mitochondria for energy production both upon 5FU treatment and in PBS conditions.

10. In Figure 5B, the orange line (Glucose+OXPHOS inhibition) remains stable, which means HSCs prefer to use glycolysis when OXPHOS is inhibited. Which metabolic pathway would HSCs use under hypoxic conditions? As HSCs resides in hypoxic niche, does it mean that these steady-state HSCs prefer to use glycolysis for ATP production? As mentioned before, mitochondrial inhibition can be comparable at the in vivo condition of the niche, where low pO2 will "inhibit" mitochondria metabolism.

11. FIG.6H should be extended with cell cycle analyses. There are no differences between 5FU and ctrl groups. If 5FU induces HSCs cycling and increases glycolysis I would expect higher 2-NBDG uptake in the 5FU group. How do the authors explain this?

12. In S7 the experimental design is not clear. What are quiescent vs proliferative conditions? What does it mean "cell number of HSC-derived colony"? Is it a CFU assay? Then you should show colony numbers. When HSCs proliferate, they need more energy thus inhibition of metabolism will impact proliferation. What happens if you inhibit mitochondrial metabolism with oligomycin?

13. In FIG 7 since homing of HSCs is influenced by the cell cycle state, should be important to show if in the genetic model for PFKFB3 in HSCs there's a difference in homing efficiency.

Reviewer #1 (Public Review):

The article "A randomized multiplex CRISPRi-Seq approach for the identification of critical combinations of genes" describes the development of a multiplex randomized CRISPRi screening method that they named MurCiS and applied it to study redundancy of L. pneumophila virulence factors. The authors used a L. pneumophila strain carrying dCas9 on the chromosome that they had constructed for a CRISPRi screen they had published recently and here combined it with self-assembly randomized multiplex CRISPR arrays that they developed. The strains carrying the dCas9 and the different CRISPRi arrays were used to infect U937 or Acanthamoeba castellanii cells and the intracellular growth phenotypes were recorded as readout. This allowed the authors to identify certain gene combinations that when knocked down induced a growth defect in either or both cells tested but not when they were knocked down alone. A particular gene combination caught their attention, as the genes lpg2888 and lpg3000 were inducing a growth defect only when both were knocked down in U937 cells but in A. castellanii cells lpg3000 alone was sufficient to cause a growth defect.

The concept of using CRISPRi to look at functional redundancy in effectors is a very useful one to the Legionella field and where biological redundancy limits studies. It has the potential to uncover virulence effectors of importance that have not been described before. However, my enthusiasm for the work was dampened when reading the article. The work presented here does not really flow and it seems to be more a method description than a research article but does not meet the requirements to be either.

The strength of the study is undermined by how it is set up. The set-up of the CRISPRi technology deployed by the authors may explain why the authors found only very few examples of redundant genes in this study.

Reviewer #2 (Public Review):

The study by Ellis et al. documents the development of a CRISPR interference (CRISPRi) screen aiming at identifying virulence-critical genes of Legionella pneumophila, the facultative intracellular bacterium causing Legionnaires' disease. L. pneumophila employs the Dot/Icm type IV secretion system to translocate more than 300 different "effector proteins" into host cells. Many effector proteins appear to have redundant functions, and therefore, depleting several of them is required to observe a strong intracellular replication phenotype. In the current study, Ellis et al. develop a "multiplex, randomized CRISPRi sequencing" (MuRCiS) approach to silence several effector genes simultaneously and randomly, thereby possibly causing synthetic lethality for L. pneumophila upon infection of host cells.

The MuRCiS approach comprises the ligation of different CRISPR spacers flanked by repeats in presence of "dead end" oligonucleotide pairs capping a random array of building blocks to be inserted into a library vector. Thus, spacer arrays with an average of 3.3 spacers per array were obtained. As a proof-of-concept, spacer arrays targeting 44 transmembrane effector-encoding L. pneumophila genes were employed to screen for intracellular growth defects in macrophages and amoeba. Consequently, novel pairs of synergistically functioning effector genes were identified by comparative next-generation sequencing of the input and output pools of spacer arrays.

A major strength of this well-written and straightforward study is the construction and use of random and multiplexed CRISPRi arrays, allowing an unbiased and comprehensive screen for multiple genes affecting the intracellular growth of L. pneumophila. The ingenious approach established by Ellis et al. will be useful for further genetic analysis of L. pneumophila infection and might also be adopted for other pathogens employing a large set of (functionally redundant) virulence factors.

Reviewer #1 (Public Review):

In this manuscript, the authors identified and characterized the five C-terminus repeats and a 14aa acidic tail of the mouse Dux protein. They found that repeat 3&5, but not other repeats, contribute to transcriptional activation when combined with the 14aa tail. Importantly, they were able to narrow done to a 6 aa region that can distinguish "active" repeats from "inactive" repeats. Using proximal labeling proteomics, the authors identified candidate proteins that are implicated in Dux-mediated gene activation. They were able to showcase that the C-terminal repeat 3 binds to some proteins, including Smarcc1, a component of SWI/SNF (BAF) complex. In addition, by overexpressing different Dux variants, the authors characterized how repeats in different combinations, with or without the 14aa tail, contribute to Dux binding, H3K9ac, chromatin accessibility, and transcription. In general, the data is of high quality and convincing. The identification of the functionally important two C-terminal repeats and the 6 aa tail is enlightening. The work shined light on the mechanism of Dux function.

A few major comments that the authors may want to address to further improve the work:

1) The summary table for the Dux domain construct characteristics in Fig. 6a could be more accurate. For example, C3+14 clearly showed moderate weaker Dux binding and H3K9ac enrichment in Fig 3c and 3e. However, this is not illustrated in Fig. 6a. The authors may consider applying statistical tests to more precisely determine how the different Dux constructs contribute to DNA binding (Fig. 3c), H3K9ac enrichment (Fig. 3e), Smarcc1 binding (Fig. 5e), and ATAC-seq signal (Fig. 5f).

2) Another concern is that exogenous overexpressed Dux was used throughout the experiments. The authors may consider validating some of the protein-protein interactions using spontaneous or induced 2CLCs (where Dux is expressed).

3) It could be technically challenging, but the authors may consider to validate Dux and Smarcc1 interaction in a biologically more relevant context such as mouse 2-cell embryos where both proteins are expressed. Whether Smarcc1 binding will be dramatically reduced at 4-cell embryos due to loss of Dux expression?

Reviewer #2 (Public Review):

In this manuscript, Smith et al. delineated novel mechanistic insights into the structure-function relationships of the C-terminal repeat domains within the mouse DUX protein. Specifically, they identified and characterised the transcriptionally active repeat domains, and narrowed down to a critical 6aa region that is required for interacting with key transcription and chromatin regulators. The authors further showed how the DUX active repeats collaborate with the C-terminal acidic tail to facilitate chromatin opening and transcriptional activation at DUX genomic targets.

Although this study attempts to provide mechanistic insights into how DUX4 works, the authors will need to perform a number of additional experiments and controls to bolster their claims, as well as provide detailed analyses and clarifications.

Reviewer #3 (Public Review):

Dux (or DUX4 in human) is a master transcription factor regulating early embryonic gene activation and has garnered much attention also for its involvement in reprogramming pluripotent embryonic stem cells to totipotent "2C-like" cells. The presented work starts with the recognition that DUX contains five conserved c. 100-amino acid carboxy-terminal repeats (called C1-C5) in the murine protein but not in that of other mammals (e.g. human DUX4). Using state-of-the-art techniques and cell models (BioID, Cut&Tag; rescue experiments and functional reporter assays in ESCs), the authors dissect the activity of each repeat, concluding that repeats C3 and C5 possess the strongest transactivation potential in synergy with a short C-terminal 14 AA acidic motif. In agreement with these findings, the authors find that full-length and active (C3) repeat containing Dux leads to increased chromatin accessibility and active histone mark (H3K9Ac) signals at genomic Dux binding sites. A further significant conclusion of this mutational analysis is the proposal that the weakly activating repeats C2 and C4 may function as attenuators of C3+C5-driven activity.

By next pulling down and identifying proteins bound to Dux (or its repeat-deleted derivatives) using BioID-LC/MS/MS, the authors find a significant number of interactors, notably chromatin remodellers (SMARCC1), a histone chaperone (CHAF1A/p150) and transcription factors previously (ZSCAN4D) implicated in embryonic gene activation.

The experiments are of high quality, with appropriate controls, thus providing a rich compendium of Dux interactors for future study. Indeed, a number of these (SMARCC1, SMCHD1, ZSCAN4) make biological sense, both for embryonic genome activation and for FSHD (SMCHD1).

A critical question raised by this study, however, concerns the function of the Dux repeats, apparently unique to mice. While it is possible, as the authors propose, that the weak activating C1, C2 C4 repeats may exert an attenuating function on activation (and thus may have been selected for under an "adaptationist" paradigm), it is also possible that they are simply the result of Jacobian evolutionary bricolage (tinkering) that happens to work in mice. The finding that Dux itself is not essential, in fact appears to be redundant (or cooperates with) the OBOX4 factor, in addition to the absence of these repeats in the DUX protein of all other mammals (as pointed out by the authors), might indeed argue for the second, perhaps less attractive possibility.

In summary, while the present work provides a valuable resource for future study of Dux and its interactors, it fails, however, to tell a compelling story that could link the obtained data together.

Reviewer #1 (Public Review):

Recent studies in plants and human cell lines argued for a central role of 1,5-InsP8 as the central nutrient messenger in eukaryotic cells, but previous studies concluded that this function is performed by 1-InsP7 in baker's yeast. Chabert et al now performed an elegant set of capillary electrophoresis coupled to mass spectrometry time course experiments to define the cellular concentrations of different inositol pyrophosphosphates (PP-InsPs) in wild-type yeast cells under normal and phosphate (Pi) starvation growth conditions. These experiments, in my opinion, form the center of the present study and clearly highlight that the levels of all major PP-InsPs drop under Pi starvation, with the 1,5-InsP8 isomer showing the most rapid changes.

The analysis of known mutants in the PP-InsP biosynthetic pathways furthermore demonstrate that loss-of-function of the PPIP5K enzymes Kcs1 and Vip1 result in a loss of 1,5-InsP8 and a hyperaccumulation of 5-InsP7, respectively. In line with this, loss-of-function of known PP-InsP phosphatases Ddp1 and Swi14 result in hyperaccumulation of either 1- or 5-InsP7, as anticipated from their in vitro substrate specificities. These experiments are of high technical quality and add to our understanding of the kinetics of PP-InsP metabolism/catabolism in yeast.

Next, the authors use changes in subcellular localisation of the central transcription factor Pho4 to assay at which time point after onset of Pi starvation the PHO pathway becomes activated. The early onset of the response, the behavior of the kcs1D mutant and of the ksc1D/vip1D all strongly argue for 1,5-InsP8 as the central nutrient messenger. I find this part of the manuscript well argued, nicely correlating PP-InsP levels, dynamics and the different mutant phenotypes.

The third part of the manuscript is a structure-function study of the CDK inhibitor Pho81, basically using a reverse genetics approach. This analysis demonstrates at the genetic level that the Pho81 SPX domain is required for activation of the PHO pathway. Next, the authors design point mutations that should block either interaction of Pho81-SPX with 1,5-InsP8 or interaction of Pho81 with the Pho80/Pho85 complex. In my opinion, these data can only provide limited insight into the molecular mechanism, as no complementary in vitro binding assays / in vivo co-IP experiments with the wild-type and mutant forms of Pho81 are presented.

The discussion section of the manuscript contains additional data such as PP-InsP levels for C. neoformans and complex structure predictions of Pho80 - Pho81. This, in my opinion, renders the discussion section of the work overly speculative. Perhaps, these results should be presented in the results section, and ideally (in the case of the complex structure predictions), be complemented by quantitative in vitro and/or qualitative in vivo binding assays.

Taken together, the work by Chabert et al, reinvestigates and clarifies the activation of the yeast PHO pathway by PP-InsP nutrient messengers and their cellular SPX receptors. From this work, a more unified eukaryotic mechanism emerges, in which 1,5-InsP8 represents the central signaling molecule in different species, with conserved SPX receptors sensing this signaling molecule.

Reviewer #2 (Public Review):

The manuscript by Chambert et al. describes a thorough and careful characterization of inositol pyrophosphate isomers and the PHO pathways in different genetic backgrounds in S. cerevisiae. The paper ultimately arrives at a proposed model in which the inositol pyrophosphate 1,5-IP8 signals phosphate abundance to SPX-domain containing proteins. To arrive at their conclusion, the authors rely heavily on CE-MS analysis of inositol pyrophosphates in different yeast strains, and monitoring inositol pyrophosphate depletion over time in response to phosphate starvation. This analysis is complemented by different reporter systems of PHO pathway activation, such as Pho4 translocation and Pho81 expression.

The experiments are well-designed and the results interpreted with care. With their findings, the authors demonstrate convincingly, that a previous study by O'Shea and co-workers (reference 15 and 16) had been misleading. Lee et al. claimed that the PHO pathway in S. cerevisiae is triggered by an increase in 1-IP7. This claim has been debated heavily in the community, and several groups were not able to reproduce this putative increase of inositol pyrophosphates (references 6, 11, 18). The confusion regarding these discrepancies has been resolved by the current study and is of significant importance to the community.

Reviewer #3 (Public Review):

Summary. This study sought to clarify the connection between inositol pyrophosphates (PP-IPs) and their regulation of phosphate homeostasis in the yeast Saccharomyces cerevisiae to answer the question of whether any of the PP-IPs (1-IP7, 5-IP7, and IP8) or only particular PP-IPs are involved in regulation. PP-IPs bind to SPX domains in proteins to affect their activity, and there are several key proteins in the PHO pathway that have an SPX domain, including Pho81. The authors use the latest methodology, capillary electrophoresis and mass spectrometry (CE-MS), to examine the cytosolic concentrations of PP-IPs in wild-type and strains carrying mutations in the enzymes that metabolize these compounds in rich medium and during a phosphate starvation time-course for the wild-type.

Major strengths and weaknesses. The authors have strong premises for performing these experiments: clarifying the regulatory molecule(s) in yeast and providing a unifying mechanism across eukaryotes. They use the latest methodologies and a variety of approaches including genetics, biochemistry, cell biology and protein structure to examine phosphate regulation. Their experiments are rigorous and well controlled, and the story is clearly told. The consideration of physiological levels of PP-IPs throughout the study was critical to the interpretation of the data and the strength of the manuscript.

There were a few places in which a deeper discussion of the data was warranted: not discussed was an explanation for the decrease in the levels of all of the PP-IPs upon phosphate starvation, nor of the phosphate regulation of two target genes of Pho4 when Pho4 is constitutively nuclear.

Appraisal. The authors achieved their goal of determining the mechanistic details for phosphate regulation, revising the prior model with new insights. Additionally, they provided strong support for the idea that IP8 regulates phosphate metabolism across eukaryotes - including animals and plants in addition to fungi.

Impact. This study is likely to have a broad impact because it addresses prior findings that are inconsistent with current understanding, and they provide good reasoning as to how older methods were inadequate.

Reviewer #1 (Public Review):

In this study, the authors investigate the biological function of the FK506-binding protein FKBP35 in the malaria-causing parasite Plasmodium falciparum. Like its homologs in other organisms, PfFKBP35 harbors peptidyl-prolyl isomerase (PPIase) and chaperoning activities, and has been considered a promising drug target due to its high affinity to the macrolide compound FK506. However, PfFKBP35 has not been validated as a drug target using reverse genetics, and the link between PfFKBP35-interacting drugs and their antimalarial activity remains elusive. The manuscript is structured in two parts addressing the biological function of PfFKBP35 and the antimalarial activity of FK506, respectively.

The first part combines conditional genome editing, proteomics and transcriptomics analysis to investigate the effects of FKBP35 depletion in P. falciparum. The work is very well performed and clearly described. The data provide definitive evidence that FKBP35 is essential for P. falciparum blood stage growth. Conditional knockout of PfFKBP35 leads to a delayed death phenotype, associated with defects in ribosome maturation as detected by quantitative proteomics and stalling of protein synthesis in the parasite. The authors propose that FKBP35 regulates ribosome homeostasis but an alternative explanation could be that changes in the ribosome proteome are downstream consequences of the abrogation of FKBP35 essential activities as chaperone and/or PPIase. It is unclear whether FKBP35 has a specific function in P. falciparum as compared to other organisms. The knockdown of PfFKBP35 has no phenotypic consequence, showing that very low amounts of FKBP35 are sufficient for parasite survival and growth. In the absence of quantification of the protein during the course of the experiments, it remains unclear whether the delayed death phenotype in the knockout is due to the delayed depletion of the protein or to a delayed consequence of early protein depletion. This limitation also impacts the interpretation of the drug assays.

In the second part, the authors investigate the activity of FK506 on P. falciparum, and conclude that FK506 exerts its antimalarial effects independently of FKBP35. This conclusion is based on the observation that FK506 has the same activity on FKBP35 wild type and knock-out parasites, suggesting that FK506 activity is independent of FKBP35 levels, and on the fact that FK506 kills the parasite rapidly whereas inducible gene knockout results in delayed death phenotype. However, there are alternative explanations for these observations. As mentioned above, the delayed death phenotype could be due to delayed depletion of the protein upon induction of gene knockout. FK506 could have a similar activity on WT and mutant parasites when added before sufficient depletion of FKBP35 protein. In some experiments, the authors exposed KO parasites to FK506 later, presumably when the KO is effective, and obtained similar results. However, in these conditions, the death induced by the knockout could be a confounding factor when measuring the effects of the drug. Furthermore, the authors show that FK506 binds to FKBP35, and propose that the FK506-FKBP35 complex interferes with ribosome maturation, which would point towards a role of FKBP35 in FK506 action. In summary, the study does not provide sufficient evidence to rule out that FK506 exerts its effects via FKBP35.

Reviewer #2 (Public Review):

The manuscript by Thomen et al. FKBP secures ribosome homeostasis in Plasmodium falciparum and focuses on the importance of PfKBP35 protein, its interaction with the FK506 compound, and the role of PfKBP35 in ribosome biogenesis. The authors showed the interaction of the PfKBP54 with FK506, but the part of the FK506 and PfKBP54 in ribosome biogenesis based on the data is unclear.

The introduction is plotted with two parallel stories about PfKBP35 and FK506, with ribosome biogenesis as the central question at the end. In its current form, the manuscript suffers from two stories that are not entirely interconnected, unfinished, and somewhat confusing. Both stories need additional experiments to make the manuscript(s) more complete. The results from PfFBP35 need more evidence for the proposed ribosome biogenesis pathway control. On the other hand, the results from the drug FK506 point to different targets with lower EC50, and other follow-up experiments are needed to substantiate the authors' claims.

The strengths of the manuscript are the figures and experimental design. The combination of omics methods is informative and gives an opportunity for follow-up experiments.

Reviewer #3 (Public Review):

The study by Thommen et al. sought to identify the native role of the Plasmodium falciparum FKBP35 protein, which has been identified as a potential drug target due to the antiplasmodial activity of the immunosuppressant FK506. This compound has multiple binding proteins in many organisms; however, only one FKBP exists in P. falciparum (FKBP35). Using genetically-modified parasites and mass spectrometry-based cellular thermal shift assays (CETSA), the authors suggest that this protein is in involved in ribosome homeostasis and that the antiplasmodial activity of FK506 is separate from its activity on the FKBP35 protein. The authors first created a conditional knockdown using the destruction domain/shield system, which demonstrated no change in asexual blood stage parasites. A conditional knockout was then generated using the DiCre system. FKBP35KO parasites survived the first generation but died in the second generation. The authors called this "a delayed death phenotype", although it was not secondary to drug treatment, so this may be a misnomer. This slow death was unrelated to apicoplast dysfunction, as demonstrated by lack of alterations in sensitivity to apicoplast inhibitors. Quantitative proteomics on the FKBP35KO vs FKBP35WT parasites demonstrated enrichment of proteins involved in pre-ribosome development and the nucleolus. Interestingly, the KO parasites were not more susceptible to cycloheximide, a translation inhibitor, in the first generation (G1), suggesting that mature ribosomes still exist at this point. The SunSET technique, which incorporates puromycin into nascent peptide chains, also showed that in G1 the FKBP35KO parasites were still able to synthesize proteins. But in the second generation (G2), there was a significant decrease in protein synthesis. Transcriptomics were also performed at multiple time points. The effects of knockout of FKBP35 were transcriptionally silent in G1, and the parasites then slowed their cell cycles as compared to the FKBP35WT parasites.

The authors next sought to evaluate whether killing by FK506 was dependent upon the inhibition of PfKBP35. Interestingly, both FKBP35KO and FKBP35WT parasites were equally susceptible to FK506. This suggested that the antiplasmodial activity of FK506 was related to activity targeting essential functions in the parasite separate from binding to FKBP35. To identify these potential targets, the authors used MS-CETSA on lysates to test for thermal stabilization of proteins after exposure to drug, which suggests drug-protein interactions. As expected, FK506 bound FKBP35 at low nM concentrations. However, given that the parasite IC50 of this compound is in the uM range, the authors searched for proteins stabilized at these concentrations as putative secondary targets. Using live cell MS-CETSA, FK506 bound FKBP35 at low nM concentrations; however, in these experiments over 50 ribosomal proteins were stabilized by the drug at higher concentrations. Of note, there was also an increase in soluble ribosomal factors in the absence of denaturing conditions. The authors suggested that the drug itself led to these smaller factors disengaging from a larger ribosomal complex, leading to an increase in soluble factors. Ultimately, the authors conclude that the native function of FKBP35 is involved in ribosome homeostasis and that the antiplasmodial activity of FK506 is not related to the binding of FKBP35, but instead results from inhibition of essential functions of secondary targets.

Strengths:

This study has many strengths. It addresses an important gap in parasite biology and drug development, by addressing the native role of the potential antiplasmodial drug target FKBP35 and whether the compound FK506 works through inhibition of that putative target. The knockout data provide compelling evidence that the KBP35 protein is essential for asexual parasite growth after one growth cycle. Analysis of the FKBP35KO line also provides evidence that the effects of FK506 are likely not solely due to inhibition of that protein, but instead must have secondary targets whose function is essential. These data are important in the field of drug development as they may guide development away from structure-based FK506 analogs that bind more specifically to the FKBP35 protein.

Weaknesses:

There are also a few notable weaknesses in the evidence that call into question the conclusion in the article title that FKBP35 is definitely involved in ribosomal homeostasis. While the proteomics supports alterations in ribosome biogenesis factors, it is unclear whether this is a direct role of the loss of the FKBP35 protein or is more related to non-specific downstream effects of knocking down the protein. The CETSA data clearly demonstrate that FK506 binds PfKB35 at low nM concentrations, which is different than the IC50 noted in the parasite; however, the evidence that the proteins stabilized by uM concentrations of drug are actual targets is not completely convincing. Especially, given the high uM amounts of drug required to stabilize these proteins. This section of the manuscript would benefit from validation of a least one or two of the putative candidates noted in the text. In the live cell CETSA, it is noted that >50 ribosomal components are stabilized in drug treated but not lysate controls. Similarly, the authors suggest that the -soluble fraction of ribosomal components increases in drug-exposed parasites even at 37{degree sign}C and suggests that this is likely from smaller ribosomal proteins disengaging from larger ribosomal complexes. While the evidence is convincing that this protein may play a role in ribosome homeostasis in some capacity, it is not sure that the title of the paper "FKBP secures ribosome homeostasis" holds true given the lack of mechanistic data. A minor weakness, but one that should nonetheless be addressed, is the use of the term "delayed death phenotype" with regards to the knockout parasite killing. This term is most frequently used in a very specific setting of apicoplast drugs that inhibit apicoplast ribosomes, so the term is misleading. It is also possible that the parasites are able to go through a normal cycle because of the kinetics of the knockout and that the time needed for protein clearance in the parasite to a level that is lethal.

Overall, the authors set out to identify the native role of FKB35 in the P. falciparum parasites and to identify whether this is, in fact, the target of FK506. The data clearly demonstrate that FKBP35 is essential for parasite growth and provide evidence that alterations in its levels have proteomic but not transcriptional changes. However, the conclusion that FKBP35 actually stabilizes ribosomal complexes remains intermediate. The data are also very compelling that FK506 has secondary targets in the parasite aside from FKBP35; however, the high uM concentrations of the drug needed to attain results and the lack of biological validation of the CETSA hits makes it difficult to know whether any of these are actually the target of the compound or instead are nonspecific downstream consequences of treatment.