Reviewer #3 (Public Review):

The authors aimed to use genome editing techniques to allow a thorough investigation of the interaction of peptide toxins with specific nicotinic acetylcholine receptor subunits. They also aimed to support the toxicity data by showing an interaction of the protein with the ligand and developed a technique to do this. The study has generated a set of mutants in the Drosophila nAChR subunit genes by disrupting an exon close to the N-terminal of the protein. This allowed the authors to examine the involvement of nine of these subunits in the response of D. melanogaster to two peptide toxins, alphaBtx and Hv1a, while as previously reported the Dbeta1 disruption was homozygous lethal and was not tested.

The final genetic background of the mutant flies should be made clearer. The statement on line 132 (p5) that the mutations were generated in virtually identical genetic backgrounds is accurate, however based on the crossing scheme described it appears that these were then crossed to a different background, w1118 (line 519, p22) and then balanced? The strain used to balance these could also be detailed and which chromosomes are in the different strains specified.

In particular, it is clear that the X-chromosome strains generated (alpha7 and alpha3) both have red eyes in figure 1A. There also appear to be phenotypic differences in body color for the Dalpha2 and Dalpha6 mutant strains. This is important with respect to at least two elements of the study.

1. The authors proposed that these mutant strains are virtually the same genetic background, and this was a benefit to those generated in a prior study. I would expect this to mean that the only difference is the insertion of the construct used to disrupt each gene.

2. There could be some confounding effects from using strains with different genetic backgrounds for behavioral assays, this could be particularly true for negative geotaxis assays. If the backgrounds are not precise, this potential confounding factor should be noted and discussed.

A real strength of the study is the support of toxicological data with their proteomic analysis. Nine of the mutant strains were tested for responses to alphaBtx and Hv1a using injection of larvae and results from their study support the claim that the Hv1a and α-Btx toxins do not share the same target. Not only from different effects on larvae when injected for each of the subunit mutants tested but also from the proteomics data where use of alphaBtx conjugated beads predominantly isolated Dalpha5/Dalpha6/Dalpha7 subunits.

In discussing the targets of Hv1a, further discussion of the potential for the Dbeta1 subunit to be involved, given it was not tested, would be valuable. This is particularly relevant given the results reported in Ihara, et al., 2020 of the importance of the Dbeta1 subunit in formation of functional receptors in their oocyte expression system.

Do homozygous Dbeta1 mutants generated in this study survive beyond eclosion? A recent study has shown that a mutant strain harboring a deletion of the Dbeta1 gene is not able to be maintained as a homozygous stock, however it does survive to adulthood, albeit with a significantly shortened life-span and also locomotory/mating defects. It would be useful to include further details on the Dbeta1 strain created in this study to determine if it has a more severe phenotype.

Another significant contribution of this study is the method reported for isolating and enriching nAChR subunits using SMALPs, which will help facilitate the study of native nAChR complexes. This technique was well validated using an analysis of the enrichment of transmembrane proteins in the presence and absence of styrene maleic acid copolymer. This method allowed Korona and colleagues to determine through proteomic analysis that there was significant binding of three nAChR subunits (Dalpha5/Dalpha6/Dalpha7) to alphaBtx. Further evidence was provided through use of different mutant backgrounds carrying disruptions in specific nAChR subunits. Hence they provide compelling evidence that Dalpha5/Dalpha6/Dalpha7 bind alphaBtx. They also identified glycosylation sites of Dalpha5/Dalpha6/Dalpha7 (and other subunits) adding to the evidence in the literature that glycosylation may be involved in the binding of alphaBtx to nAChR subunits.

This study makes a valuable contribution to the field of insecticide research, provides useful tools and methods for studying the biology of nicotinic acetylcholine receptors and has the potential to contribute to wider studies of membrane proteins.

Reviewer #1 (Public Review):

Lapinska et al., address the contribution of heterogeneity in antibiotic uptake towards bacterial survival against antibiotic treatment. To achieve specific inhibitory activity, the antibiotic compound needs to bind its target which could be on the outer cell wall, embedded in the membrane or intracellular within the cell. Therefore, the effective concentration of the compound able to reach the target is influenced by several variables such as size of the compound, permeability of the membrane, influx and efflux transport processes, stability of the compound etc. As each of these processes are heterogenous amongst individual members of an isogenic population, this generates extreme variability in uptake of compounds by individual cells.<br> Lapinska et al., utilize previously reported fluorescently-tagged antibiotics, targeting different subcellular locations, to measure accumulation dynamics at the single-cell level. As expected they observe heterogeneity in antibiotic accumulation both in gram-negative and gram-positive bacteria. Antibiotics targeting the outer membrane were observed to label their targets faster and earlier compared to antibiotics targeting intracellular components. They implement a mathematical model based on two differential equations which they use to infer kinetic parameters for the different compounds. These variability in antibiotic accumulation had functional consequence in that cells with no uptake were the ones that continued to grow in presence of the antibiotic and survived. Interestingly, at least for roxithromycin treatment, cells that were growing faster were observed to survive better which goes against the conventional belief that fast growing cells are more susceptible to antibiotics. The heterogeneity in compound accumulation was shown to be a function of ribosomal activity and levels of an efflux pump protein. Finally, deletion of the gene encoding the efflux pump or treatment with compounds to increase permeability caused faster accumulation of the antibiotic even though the heterogeneity was still maintained.

The authors have done a commendable job by choosing different classes of antibiotics and analysing quantitatively the drug accumulation in individual cells. However, they often tend to generalize their conclusions. From their observations, the take home message seems to be that each compound behaves uniquely in terms of accumulation and consequences. Still the use of fluorescent antibiotics to address this often-overlooked aspect in heterogenous response to antibiotics is definitely appreciated.

Reviewer #2 (Public Review):

The authors claim that phenotypic heterogeneity underlies heterogeneity in drug uptake, which in turn underlies heterogeneity in antibiotic-induced growth suppression. They characterize the uptake kinetics for a range of different antibiotics at the single cell level and find that membrane-targeting drugs accumulate significantly faster than other drugs. In contrast to the common belief that fast growth promotes drug sensitivity, they find the opposite trend for roxithromycin. They claim that growth-rate dependent drug efflux pump expression, but not drug influx porins, are largely responsible for this.

Quantifying the kinetics of drug uptake at the single cell level and correlating it with phenotypes is an important problem of great interest to a broad audience. The single cell approach to characterize drug uptake kinetics presented in this work has considerable potential and can provide new insights into cell-cell variability in the context of antibiotic sensitivity. The conclusion that roxithromycin sensitivity decreases with cellular growth is interesting but the interpretation and contextualization of this result should be improved. In principle, investigating which molecular players contribute to drug uptake dynamics could significantly strengthen the manuscript; this aspect will require more analysis and experiments. Moreover, some relevant control experiments seem to be missing and the enormous heterogeneity in single cell growth rate may indicate issues with the assay design.

Major suggestions

* The authors model drug uptake with a lag time (t0), after which there is a constant rate of drug uptake. But why is there such a lag time at all? This would suggest a positive feedback loop in drug binding. However, then one would not necessarily expect a constant drug uptake rate afterwards. The rationale for this model should be better explained. Correlating the fluorescence measurements (as in Fig. 1) with the single-cell elongation rates (as in Fig. 5) could help to identify if the lag in drug uptake coincides with the lag in cell growth.

* The fact that t0 and k1 are always strongly anti-correlated suggests that these two fit parameters are not independent and simply reflect the same underlying process. It would be critical to clarify this point for the entire analysis of correlations between fit parameters. To this end, the confidence intervals for the fit parameters and their correlations should be estimated using a suitable numerical optimization algorithm. It only makes sense to interpret correlations between fit parameters obtained from different cells if such an analysis shows that the fit parameters are independent (uncorrelated or only weakly correlated) for each individual cell.

* The results in Figure 4 are confusing: It seems unlikely that cells are a large enough 'antibiotic sink' to protect neighbouring cells, especially given that cells do not seem to be affected by the nutrient uptake of their neighbours. Furthermore, the opposite correlation (where drug accumulation increases with more 'screening' cells) is very hard to rationalize. A plausible explanation for this effect would be needed. Here, it would be helpful to estimate the molecule numbers (concentrations), uptake rates, and diffusion coefficients of the different antibiotics, compare them to those of nutrient molecules in the growth medium, and explain based on this why the 'screening' cells can have different effects for these molecules on the relevant time and length scales. Without more support there is a concern that these observations may be due to technical artefacts.

Reviewer #1 (Public Review): 

Antal et al. analyzed human datasets from UK Biobank and compared the results with published data in the literature to prove the association between type-2- diabetes mellitus (T2DM), brain aging, and cognitive deficits. Using highly effective bioinformatics platforms and applying strict correlation parameters combined with statistical analysis, the authors probed the human datasets for cognition, brain degeneration, and brain function. The research focused on five cognition domains and measured structural alterations by quantifying brain atrophy associated with aging and T2DM. Results showed a significant correlation between age, T2DM, and decline in cognition. The executive function domain required for controlling behavior, working memory, and cognitive flexibility decreased during aging, and T2DM further enhanced this decline. Meta-analysis data of published literature supported the authors' findings. The authors analyzed the death of brain cells that occurs during normal aging and T2DM by measuring the loss of grey matter in different brain regions. Increased atrophy was found in brain regions, mainly in the ventral striatum and putamen, in T2DM patients compared to the control group. Interestingly authors did not see any changes in the hippocampus, which plays a significant role in memory and learning. Considering the role of the ventral striatum in understanding and responding to external stimuli and insulin-dependent secretion of neurotransmitters results from the current study bridge the gap between T2DM, insulin resistance, brain atrophy, and executive functions. The authors observed similar alterations in brain activity that support reduced energy utilization in both the healthy and T2DM groups. 

Based on the observed results, the authors concluded that T2DM affects pathways like aging, but it accelerates brain aging and leads to early cognitive decline. The study suggests neuroimaging biomarkers over the traditional biochemical screen for identifying and following the progression of diabetes because the brain irreversible brain damage occurs much earlier than biochemical alterations in blood. This manuscript improves our understanding of how diabetes influences cognitive performances and why most diabetic patients are susceptible to neurodegeneration. 

Strengths:

The major strength of this paper is the dataset used for the study and the rigorous analysis. The UK biobank is the most extensive collection of human datasets available for researchers to perform medical research. The study is well validated by comparing the observed results with a meta-analysis that supports the conclusion of this study. 

Weaknesses:

Although this study is well designed and executed, specific details that would help the readers to understand this work better are missing. Since T2DM onset time and patient age are closely associated with cognitive decline, the manuscript must provide this information. Though mentioned by the authors, BMI is not the best measure for determining the severity of T2DM. While BMI can be a better measurement for determining risk for diabetes, it does not provide clear information regarding the severity of the disease. Biochemical measures like fasting glucose level in at least two or three consecutive visits would have served as a better marker for disease severity. Further, this study is missing details on the co-morbidities associated with the T2DM group. Diseases like hypertension and cerebrovascular complications common in the aged and T2DM patients can significantly influence cognition and brain atrophy. Probing the analyzed dataset for this information would be helpful to understand how other diseases can exacerbate the effect of T2DM. As the current study is cross-sectional, the data presented here should be considered a preliminary suggestion regarding T2DM and its impact on cognition as individual variations can significantly affect the data and disease outcome.

Reviewer #2 (Public Review): 

This is an important study that explores the impact of age and T2MD status on brain structural indices and cognitive functional status. The study takes advantage of the extensive UK Biobank data set and validates findings using a meta-analysis of the relevant clinical literature with data analysis conducted using the NeuroQuery tool. The major finding is that T2DM is associated with brain structural and cognitive functional changes that are similar to those identified in aging but occurring earlier in chronological age and being accentuated in individuals with longer duration T2DM. 

Strengths of the study include: 1) identification of overlap between the age-sensitive brain structural and functional changes with those in the T2DM group; 2) demonstration that age-related declines occur earlier with T2DM and that the duration of T2DM impacts the extent of change; 3) interesting outcome of the ALFF pointing to reorganization rather than simple decline in "activity" by brain region, 4) the validation showing congruence for structural outcomes and for cognitive outcomes as a function of T2DM status between the UK Biobank and Meta-analysis approaches. 

Some concerns include: 1) It is not clear from the narrative what the healthy control (HC) reference group was, the number of T2D subjects that were included for each of the increments from 50 to 75 years of age, and whether the data were evenly dispersed across that timeline; 2) Although sex, age, and education were pairwise matched for the T2DM analysis, and were used as covariates for the aging analysis, it seems a missed opportunity not to have parsed the data by sex and by BMI; 3) In the abstract the authors conclude that features of brain atrophy may be useful in spotting T2DM and confirming treatment efficacy but this is not supported by the data and analysis shown.

Reviewer #1 (Public Review): 

Strengths of the manuscript include the important research question addressed, the robust functional genomics methodology used, the relatively large sample size, and the translational implications of the study findings that pinpointed new potential drug targets in autoimmune diabetes. Weaknesses include the analysis of immune responses at a certain time point that may not represent the dynamic immune phenotype of the disease over time, the testing of immune responses in peripheral blood mononuclear cells (PBMC's) that may not represent the islet infiltrating immune cells that cause autoimmune diabetes, using generic stimulants to activate PBMC's instead of beta-cell autoantigens, and that the QTL analysis may not be relevant to the etiology of autoimmune diabetes as it identified QTLs associated with immune cell proportion and cytokine production, but these do not necessarily influence the development of autoimmune diabetes.

Reviewer #2 (Public Review): 

This manuscript presents data collected from two cohorts of individuals, one including patients with type 1 diabetes, the other encompassing non-diabetic persons. Of note, the cohorts are not contemporary and samples from the two groups were collected several years apart (2013/14 for controls, 2016/17 for the diabetic group). This is not an issue for any genetic comparisons. However, comparing immune phenotypes in non-contemporary cohorts, particularly with respect to seasonal variations as the authors attempt in some of their analyses, is not useful as it lacks the rigor of collecting samples under identical conditions. This caveat aside, the overall aim of the study was to compare the function of immune cells, with a focus on the distribution of various cell populations and their cytokine secretion, between individuals with and without type 1 diabetes. Many of the analyses are difficult to interpret because the authors use measures and correlations for which the rationale is not well explained and whose presentation in the rather busy figures lacks detailed descriptions. There is no doubt that the authors amassed a substantial amount of data in what appears to be an ambitious study of hundreds of blood samples. However, the authors do not do their data justice by failing to present it in a easily comprehensible and interpretable data. Much of the description of the results makes the assumption that readers are familiar with the very particular way the authors analyzed the data (e.g. refering to parental and grandparental percentages, where it is entirely unclear what the authors are refering to). 

Many of the observations presented are trivial and could have been omitted from the manuscript, for example showing that the immune system acquires more memory lymphocytes as people age, with no apparent difference between the groups studied. The fact that our immune system gets more experienced as we age is both unsurprising and a well known phenomenon. Similarly, the correlations between immune cells and cytokine secretion compared between groups yield no discernable differences and this could have been summarized much more succinctly in the interest of clarity. The more interesting data relating to gene variations that appear to impact immune phenotypes could have been given more weight in the overall manuscript to better describe them and discuss possible implications. 

In sum, this is a manuscript with a very large data set whose presentation lacks focus on the key points that would emphasize the novelty of the findings put forward by the authors. As such, it is not very accessible to a general readership.

Reviewer #1 (Public Review): 

The paper by Sun et al uses a previously published computational model of the insect central complex and expands the applicability of this model. While the original model was developed to generate a biologically plausible neural circuit for producing visually guided navigation behavior (integrating three distinct navigation strategies), the new paper shows that the same model can be used to produce navigation behavior in response to multimodal sensory information. In particular, the authors show that olfactory navigation as well as wind-guided navigation can be seamlessly integrated with visual behaviors. 

The authors link the computational model to postulate neural mechanisms that are inspired by known features of the insect central complex. Using the model, behavioral observations, in particular from ants, can be readily reproduced, including tasks in which the animals had to switch between guidance cues, e.g. from visually driven path integration to odor based location of a nest entrance, or were blown off course by wind. 

The manuscript clearly requires that the first paper by the same group is read first, as many core concepts of the computational model are introduced in that paper. When viewed as such an extension (as intended by the 'Research Advances' article type), the paper adds valuable insights and stimulates thought and hypothesis development regarding concepts of multimodal integration. In light of the increasing amount of data on insect brain connectomics, hypotheses based on biologically inspired computational models are highly useful. 

While the authors refer to their computational model as 'biologically realistic', several features (e.g. a ring attractor circuit in the fan shaped body) are speculative and, to date, unconfirmed in any insect, despite the existence of the fruit fly connectome. This does not mean that the model is conceptually wrong, especially as it allows to faithfully reproduce complex behavioral data and shifting of activity 'bumps' across the width of the central complex (as is key for the model) is likely one of the principal functions of the fan-shaped body circuit. Yet the exact nature of the neural implementation might have to be adjusted once relevant data on ants becomes available. The model, as it stands, should hence be seen as 'biologically plausible" rather than 'realistic'. 

That said, the addition of the new aspects of the model shows how flexible the proposed circuit is for coordinated navigational control in insects, and, interestingly, highlights analogous concepts found in the basal ganglia of mammals - a thought-provoking parallel that is in line with ideas of deep homology between these distant brain regions.

Reviewer #2 (Public Review): 

In their previous manuscript, Sun et al. combined existing and hypothesized circuit motifs within the insect central complex (CX) to propose an integrated model for how the region might enable visual route following and homing. In their original framework, circuit motifs within the fan-shaped body allowed for appropriate context-switching. They now show how roughly the same motifs could also allow the model to (optimally) incorporate other sensory inputs, such as odor concentration gradients and wind direction cues, thereby enabling an insect to use the CX for additional behaviors in a context-dependent manner. The model's performance is evaluated in comparison to the behavior of larval and adult insect behavior (flies and ants, for example). This study represents a useful extension of the model's scope, but it would benefit from some additional computational exploration and explanation. As it stands, the figures and figure legends are not self-contained enough to be clearly understandable to the average reader. This new piece would also benefit from a greater focus on alternative models and alternative neural pathways that also subserve at least some of the additional navigational behaviors. The existence of direct olfactory-motor pathways is mentioned in Discussion for example, but deserves to be explored in Results as well. Otherwise, the significance of the authors' model reproducing Drosophila larval chemotaxis is not clear: note that larvae do not have CX circuits of the sort that the model proposes.

Reviewer #3 (Public Review): 

Sun et al. propose an excellent study on multi-sensory fusion in ant when the animal is confronted to both wind and odour source or when conflict exists between chemotaxis and path integration. The paper is very well written and the figures are very clearly designed. The list of references is complete. On my opinion, this paper might be considered as a companion paper of a previous paper published in eLife (Sun et al 2020) featuring a strong impact on the plausible strategy that can be used by ant to integrate various cues (odour, wind, proprioception) but a weaker impact (because already published in the first paper) on the neuronal model of the various structures involved in the central complex of the ant's brain: protocerebrum bridge (PB), fan-shape body (FB) and ellipsoid body (EB). An interesting copy and shift function already described in (Sun et al 2020, eLife) seems to be well suited to generate the appropriate motor commands of the heading in response to a difference between the current heading and the measured heading (sensory feedback control). To summarize, this copy and shift function tends to minimize the heading error by making ant turn left or right. It is worth noting that the simulated responses of the ant have been compared to the real data published in previous papers by others. 

I have the following main concerns about this work: 

- First, the steering function as regard of the shift and copy mechanism should be recalled and carefully explained (figure 2B of the previous paper published in eLife) 

- About figure 1C and the on-off response of the ant: authors argue that their model replicates faithfully the ant's response: however to be absolutely convinced by this statement, authors must take into account in their simulation the following parameters used by Alavarez-Salvada et al. : ground speed, angular velocity, curvature and turn probability. If the simulated and real responses are similar, we should observe an ON response consisting of upwind orientation coupled with faster and straighter trajectories, and an OFF response consisting of slower and more curved trajectories. It is not clearly the case in the current version of the paper due to a lack of thorough analysis between the parameters listed previously. I can not see more curved trajectories in the OFF response. 

- About the simulated behaviour shown in figure 2C: this is a very interesting and critical point because here a conflict is produced between two sensory modalities: path integration and chemotaxis. Authors must clarify how is this conflict processed/managed by the ring attractor? Is it due to changes in the dynamics of the measurements (odor and path integration) or due to changes in the ring attractor itself? By the way, I strongly encourage authors to provide temporal simulation of the ring attractor state: plot the input and ring's output signals at different time steps to see clearly a shift in the bump output. 

- About the angular resolution of the PB, FB and EB: how many different angular directions are coded? How many neurons are simulated in each structure? In MM section (equation 10 page 13) it is indicated that the angular resolution (shifting accuracy) has been improved by a factor 10 (from 45{degree sign} to 4.5{degree sign}) to achieve better performance. This point must be indicated and discussed in the main text because it is related to the accuracy of the heading measurement and thus to the behaviour of the simulated ant. How can the ant improve its heading accuracy despite a coarse resolution of central complex in the heading measurement?

Reviewer #1 (Public Review):

Guo et al. report that tissue-specific inactivation of Kdm6b in the neural crest lineage, but not in the epithelium, cause high penetrance of cleft palate and complete penetrance of neonatal lethality. They show that the Wnt1-Cre;Kmd6bfl/fl embryos had apparent defect in palatogenesis by E14.5 and hypoplasia of the palatal processes of the maxillary and palatine bones at later stages.

Reviewer #2 (Public Review):

The paper aims to understand how palatogenesis is epigenetically controlled by the lysine-specific demethylase Kdm6b, and that loss of Kdm6b in the cranial neural crest (CNC) causes cleft palate by disrupting the p53 pathway. In the authors' Wnt1-Cre;Kdm6bfl/fl mouse model, CNC-specific loss of Kdm6b causes cleft palate with 90% penetrance. The mutant CNC cells (CNCC) migrate properly to the first pharyngeal arch but then exhibit hyperproliferation with evidence of increased DNA damage and inhibited differentiation and osteogenesis. The RNA-seq data indicates p53 pathway involvement, corroborated by the finding that p53 expression is reduced in the mutants' palatal region. Prenatal treatment with Nutlin-3, an MDM2 inhibitor known to increase p53 levels, rescues the cleft palate phenotype of the Wnt1-Cre;Kdm6bfl/fl mice. This is a very interesting result with potential future clinical application. Elevated levels of H3K27me3 methylation in the single mutant are decreased to normal levels in the double mutant Wnt1-Cre;Kdm6bfl/fl;Ezh2fl/+, and the cleft palate phenotype is rescued with 70% efficiency in the double mutant, suggesting that the methyltransferase Ezh2 and Kdm6b have opposing functions during palatogenesis and that increased H3K27me3 methylation contributes to cleft palate. The p53 promoter is shown to bind the transcription factor Tfdp1 and to be affected by H3K27me3 methylation. Tfdp1 silencing with siRNA reduces p53 expression in control cells, whereas Tfdp1 overexpression elevates p53 levels in the control but not in the Kdm6b mutant. Kdm6b and Tfdp1 precipitate together in Co-IP (Tfdp1 levels are unchanged in the mutant). The authors conclude that Kdm6b removes the H3K27me3 modification introduced by Ezh2, which allows Tfdp1 to access the p53 promoter and increase p53 expression. Normal levels of p53 are, in turn, required to control CNCC proliferation in the palatal region and to allow differentiation and osteogenesis. These findings illuminate the role of Kdm6b-mediated epigenetic modification in the cleft palate etiology and provide new possible targets for pharmaceutical intervention.

Overall this is a real tour de force study, with elegant mouse genetics and potentially clinically relevant rescue results.

Reviewer #3 (Public Review):

In the present study, Tingwei Guo et al use the mouse secondary palate as a model to assess the function of Kdm6b, a H3K27me3 demethylase, in the regulation of embryonic development. Guo's study shows that Kdm6b plays an essential role in neural crest development, and that loss of Kdm6b perturbs p53 pathway-mediated activity, leading to complete clefting of the secondary palate along with cell proliferation and differentiation defects.

In addition, the study reveals that Kdm6b and Ezh2 control p53 expression in cranial neural crest cells and that Kdm6b renders chromatin accessible to the transcription factor TFDP1 to activate p53 expression during palatogenesis. Together, the findings presented in this manuscript highlight the important role of the epigenetic regulator KDM6B and how it cooperates with TFDP1 to achieve its functional specificity in controlling p53 expression, and further provide mechanistic insights into the epigenetic regulatory network during secondary palate organogenesis.

Over the last years, it has been reported by multiple groups that among the various layers of epigenetic regulation, DNA methylation and histone methylation are key drivers of diverse cellular events and developmental processes. In addition, it has been demonstrated that demethylation also plays important roles during development. For instance, demethylation of H3K4 is required for maintaining pluripotency in embryonic stem cells, and the demethylases KDM6A and KDM6B are required for proper gene expression. Indeed, the concept that failure to maintain epigenomic integrity can cause deleterious consequences for embryonic development has been extensively explored by various groups and is not novel per se. In addition, both lysine methyltransferase Kmt2a and demethylase Kdm6a have been recently shown to be essential for cardiac and neural crest development. For example, Shpargel reported that mice carrying neural crest deletion of Kdm6a exhibit craniofacial defects, including cleft or arched palate, cardiac abnormalities, and postnatal growth retardation, modeling the clinical features of Kabuki syndrome (Shpargel et al. PNAS, 2017). In summary, roles of these demethylases in neural crest development are already known. However, how these epigenetic changes lead to tissue-specific responses during neural crest fate determination and differentiation remains poorly understood and understudied, making the current manuscript of interest and timely.

The study is robust, detailed, and comprises a wealth of original results and data of high quality, illustrated through many elegant figures. There are only some points of concern that need to be addressed, mainly related to additional quantitative analyses that are required for some of the experiments discussed in the manuscript and the need for clarifications regarding the regulation of the p53 pathway.

Reviewer #1 (Public Review):

This work provides a set of reference models based on a large population sample that combines neuroimaging data from over 80 different scan sites. Collective modelling of these data results in representative trajectories of brain development and the effects of ageing covering the full human lifespan. The models are not restricted to a certain clinical condition and model outputs can be used to assess variability on the subject level as well as differences between groups.

It provides a set of more detailed models than other comparable approaches modelling over 180 distinct brain regions and thus allowing to investigate patterns of spatial variability in individuals and across different clinical conditions.

The conclusions of this paper are well supported and the mathematical details of the employed methodology have been presented elsewhere. The authors provide well documented and easy to use software tools that can be used for further, independent validation.

If maintained and updated regularly, this has the potential to become a very valuable resource for a wide range of future studies.

Strengths:

Including (at present) over 50k structural MRI scans from 82 different scan sites, the data cover, essentially, the full human lifespan. Careful automated and manual quality control for a substantial part of the data should further rule out any strong effects due to artefacts.

Instead of whole-brain summaries, the models applied in this work consider a wide range of anatomically distinct brain regions and clinically relevant outcome measures. In this sense, this is the largest resource of its kind providing lifespan reference trajectories and allowing for both individual and group-based predictions.

The accompanying code and documentation is truly accessible through comprehensive tutorial pages and example scripts that can easily be executed locally or in a browser.

Weaknesses:

The assessment of model outputs is mainly focused on train-test splits stratified by site. Results from a transfer experiment to a new site are presented briefly, albeit with little detail. To better understand (and ideally quantify) scanner and other bias or confounding effects, a more comprehensive analysis would be needed. For the community to use this as a reference, it should be clear how generalisable it is to new data. One way to do this would be to train on one set of sites and test on another (disjoint) set of sites.

The underlying data are dominated by data from the UK Biobank, which means that, in effect, only few samples for the 25-50 age group are available. This may not be a big issue in terms of estimating smooth trajectories, but may limit comparisons to the reference model in certain cases (e.g. early disease onset) where this age range may be of particular interest.

The manual QC data is somewhat limited as it is based on a predominantly younger cohort (mean age ~30yrs). Furthermore, the number of outcome measures (cortical thickness and subcortical volume) and the number of data modalities (only structural MRI) are limited. However, as the authors also state, these limitations can hopefully be addressed by incorporating new/additional data sets into the reference models as they become available.

Reviewer #2 (Public Review):

This manuscript presents a herculean effort in extracting and modeling Imaging Derived Phenotypes (IDPs; Alfaro-Almagro, 2018) from anatomical MRI data of the human brain from a large sample of 58,836 images corresponding to individuals of ages 2-100, compiled from a total of 82 previously existing datasets. The objective of this modeling was to define standard (authors refer to this as "normative" and add a disclaimer in the discussion that the term should be avoided) distributions of a set of target IDPs across the lifespan. The manuscript shows the clinical utility of the model on a transdiagnostic psychiatric sample (N=1,985), in that "individual-level deviations provide complementary information to the group effects." The full extent of this application is however left for further work based on this paper. Finally, in the discussion, it is highlighted that the model "can easily be transferred to new sites," which indeed is a fundamental aspect, as the model should generalize to data coming from new (unseen by the model) acquisition sites - a major culprit of almost every current MRI study.

## Strengths<br> 1. The problem is important, and the establishment of these standard distributions of IDPs across time is critical to better describe the healthy brain development trajectories while providing clinicians with a powerful differential tool to aid diagnosis of atypical and diseased brains.<br> 2. The proposed analysis entails a massive feature extraction approach that, albeit based on the widely-used FreeSurfer software (i.e., not implemented by the authors themselves), requires very careful tracking of the computational execution of neuroimaging workflows and their outcomes.<br> 3. The choice of model seems reasonable and it is effectively shown that it indeed resolves the problem at hand.<br> 4. The manual quality control (QC) by author SR also deserves recognition, visually assessing and (I'm assuming as nothing is said otherwise) manually bookkeeping the QC annotations of thousands of subjects.<br> 5. Prior work noted by the Editors is referenced, and the results are discussed in relation to that prior work.<br> 6. The manuscript is well-written - the organization, accessibility, length, clarity, and flow are overall very adequate.

## Weaknesses<br> 1. The evidence that the model will generalize ("transfer" as per the authors) to new, unseen sites, is very limited. To robustly support the claim that the model generalizes to data from new sites, a cross-validation evaluation with a "leave-one-site-out" (or leave-K-sites-out) folding strategy seems unavoidable, so that at each cross-validation split completely unseen sites are tested (for further justification of this assertion, please refer to Esteban et al., (2017)). The "transferability" of the model is left very weakly supported by figures 3 and 4, which interpretation is very unclear. This point is further developed below, regarding the over-representation of the UK Biobank dataset.<br> 2. If I understand the corresponding tables correctly, it seems that UK biobank data account for roughly half of the whole dataset. If the cross-validation approach is not considered, at the very (very) least, more granular analyses of the evaluation on the test set should be provided, for example, plotting the distribution of prediction accuracy per site, to spot whether the model is just overfitted to the UKB sample. For instance, in Figure 4 it would be easy to split row 2 into UKB and "other" sites to ensure both look the same.<br> 3. Beyond the outstanding work of visually assessing thousand of images, the Quality Control areas of the manuscript should be better executed, and particularly lines 212-233):<br> 3.a. The overall role of the mQC dataset is unclear. QC implies a destructive process in which subpar examples of a given dataset (or a product) are excluded and dropped out of the pipeline, but that doesn't seem the case of the mQC subset, that seems a dataset with binary annotations of the quality of the FreeSurfer outcomes and the image.<br> 3.b The visual assessment protocol is insufficiently described for any attempt to reproduce: (i) numbers of images rated by author SR and reused from the ABCD's accept/reject ratings; (ii) of those rated by author SR, state how the images were selected (randomly, stratified, etc.) and whether site-provenance, age, etc. were blinded to the rater; (iii) protocol details such as whether the rater navigated through slices, whether that was programmatic or decided per-case by the rater, average time eyeballing an image, etc; (iv) rating range (i.e., accept/reject) and assignment criteria; (v) quality assurance decisions (i.e., how the quality annotations are further used)<br> 3.c Similarly, the integration within the model and/or the training/testing of the automated QC is unclear.

## Additional comments<br> - Repeated individuals: it seems likely that there are repeated individuals, at least within the UKB and perhaps ABCD. This could be more clearly stated, indicating whether this is something that was considered or, conversely, that shouldn't influence the analysis.<br> - Figure 3 - the Y-axis of each column should have a constant range to allow the suggested direct comparison.<br> - Tables 5 through 8 are hard to parse - They may be moved to CSV files available somewhere under a CC-BY or similarly open license, and better interpreted with figures that highlight the message distilled from these results.<br> - Lines 212-214 about the QA/QC problem in neuroimaging are susceptible to misinterpretation. That particular sentence tries to bridge between the dataset description and the justification for the mQC sample and corresponding experiments. However, it fails in that objective (as I noted as a weakness, it's unclear the connection between the model and QC), and also misrepresents the why and how of QC overall.<br> - The fact that the code or data are accessible doesn't mean they are usable. Indeed, the lack of license on two of the linked repositories (https://github.com/predictive-clinical-neuroscience/braincharts and https://github.com/saigerutherford/lifespan\_qc\_scripts) effectively preempts reuse. The journal guidelines Please state a license on them. We provide some food for thought about how to choose a license, and why we set the licenses we use in our projects here: https://www.nipreps.org/community/licensing/.<br> - Figure 1 - caption mentions a panel E) that seems missing in the figure.<br> - There is no comment on the adaptations taken to execute FreeSurfer on the first age range of the sample (2-7 yo.).<br> - Following up on weakness 3.c, while scaling and centering is a sensible thing to do, it's likely that those pruned outliers actually account for much of the information under investigation. Meaning, EC is a good proxy for manual rating - but Rosen et al. demonstrate this on human, neurotypical, adult brains. Therefore, general application must be dealt with care. For example, elderly and young populations will, on average, show substantially more images with excessive motion. These images will go through FreeSurfer, and often produce an outlier EC, while a few will yield a perfectly standard EC. Typically, these cases with standard ECs are probably less accurate on the IDPs being analyzed, for example, if prior knowledge biased more the output for the hidden properties of this subject. In other words, in these cases, a researcher would be better off actually including the outliers.<br> - Title: "high precision" - it is unclear what precision this is qualifying as high. Is it spatial granularity for a large number of ROIs being modeled or is it because the spread of the normative charts is narrow along the lifespan and as compared to some standard of variability.

Reviewer #3 (Public Review):

This work develops reference cohort models of lifespan trajectories of cortical thickness and subcortical volume from an impressively large sample of 58k subjects. Providing reference models as the authors do here is a significant advantage for the field. Providing sex specific models is important. This work is focused on regional cortical thickness and subcortical volumes which is excellent. The data shown in Figure 2 is quite impressive and in my mind somewhat unexpected. Significant differences are apparent for each of the patient categories ADHD, ASD, EP, etc (Fig. 2c) and the results indicate clear differences as a function of category. This work forms an excellent basis for further work on differences between and within these populations. Overall this is the type of work we need to see coming out of these very large datasets.

Reviewer #1 (Public Review):

The manuscript by Zhang et al. helps to establish the neural mechanisms underlying a well-established phenomenon in the cognitive literature on mind-wandering: mind-wandering while reading. This phenomenon is an interesting one because the brain's default mode network (DMN) has been proposed to support reading, as well as perceptually-decoupled thoughts such as autobiographical memories unrelated to reading that may underpin the experience of mind-wandering while reading. How the brain deals with these conflicting processes remains an open question. To examine this question, the authors designed a clever task in which some trials directed participants to focus on reading a sentence, while other trials asked participants to retrieve an autobiographical memory. Critically, in some of the trials, each of these "primary" tasks was accompanied by a distracting stimulus from the opposite domain (e.g. unrelated reading material when asked to retrieve an autobiographical memory and a distracting memory cue when asked to read expository text). The authors found that when the primary task was autobiographical memory retrieval, a set of DMN regions aligning with the DMN "core" and the DMN "medial temporal (MT) subsystem" became engaged to a greater degree than when the primary task was reading. Conversely, when the primary task was reading, there was greater involvement of regions overlapping with the "dorsal medial" DMN subsystem. Further, brain-behavior relationships show that increased involvement of the DMN core and MT subsystem during reading correlates with less perceived task focus. In Study 2, conducted in an independent sample of participants, regions recruited during reading in Study 1were more functionally coupled with ventral visual regions at rest than regions than regions recruited during autobiographical memory retrieval. Finally, individual difference analyses revealed that individuals who mind-wandered more while reading in a separate lab-based task exhibited decreased connectivity at rest between reading-related regions identified in Study 1 and a dorsal occipital region showing preferential involvement in autobiographical memory in the same study.

Overall, these findings are important because they point to neuroanatomical differences between autobiographical memory and reading, and highlight a means by which autobiographical thoughts may pull one's primary attention away from reading, leading to impaired reading comprehension and memory. There are numerous strengths of this manuscript, including:

Strengths<br> 1) Exploration of an important and pertinent topic. The topic explored by the authors bridges research on language, memory, and attention and will likely be of interest to a broad audience. Engaging in unrelated autobiographical thinking while reading may impair comprehension and memory for the reading material, and impairments in attention may be more pertinent in today's society given challenges to attention that have arisen from the COVID pandemic. Furthermore, heterogeneity of the DMN has been a matter of debate in recent years, which this study sheds light on.

2) Largely converging findings across studies. The task-related results from Study 1 converge nicely with the resting state analyses from Study 2. The extension of the task-related findings to resting state and individual differences in behavior is a strength of the manuscript (although the individual difference analyses in Study 2 are a bit perplexing).

3) The development of a clever set of tasks. It is logistically difficult to capture mind-wandering during reading in the scanner, so instead, the authors create a task which differentially emphasizes reading vs. autobiographical memory and include in these tasks distracting stimuli from the opposite task. Although I point out below that this task can also be seen as a "weakness" of the study because of its inability to fully capture the mind-wandering process, overall I think the authors do an excellent job given the difficulties of capturing an elusive process and the logistical issues with running the study in the MRI scanner.

4) Clarity in conceptual organization and writing. The manuscript is very well written, well-motivated, and overall, a pleasure to read.

5) A nice set of figures. The figures are clear, relevant and visually attractive.

6) Sample size. The sample size in Study 1 (n=29) seems sufficient given the analyses performed, while the sample size in Studies 2 (n=244) and 3 (n=69) are sizeable.

7) Manuscript data available for public use. The authors make their manuscript data available for public use, which promotes transparency and facilitates efforts toward reproducibility.

However, in addition to the numerous strengths of this manuscript, there are also some notable weaknesses, including:

Weaknesses<br> 1) Some of the authors' claims do not appear to be fully supported by the data presented. Most notably, there are likely many aspects of naturally-occurring mind-wandering during reading that may not be accurately captured by the autobiographical memory task. For example, naturally-emerging mind-wandering during reading is likely to be much more spontaneous in nature than the autobiographical memory task. The autobiographical memory task asks participants to deliberately recall and elaborate on a personal memory from the past related to a cue word. This is a deliberate, goal-directed process requiring participants to sustain attention to a mnemonic representation, which likely involves executive function resources. In contrast, autobiographical memories that occur while reading are more likely to occur spontaneously in nature. It is unclear how this fundamental difference influences the results and whether there are any discrepancies between brain regions highlighted during the autobiographical memory task and brain regions that may correspond to spontaneous memory recall during naturally-occurring reading.

Relatedly, the phenomenological content of mind-wandering occasions during reading may be much broader than autobiographical memory. Participants may be distracted away from the reading by "external distractions," or they may engage in autobiographical or nonautobiographical thought pertaining to the future, the present, or non-temporal content. Indeed, the mind wandering content reports in Study 2 suggest that thoughts tend to only be somewhat past focused. Without measuring the variety of brain activity patterns as participants start to lose focus on the reading, the findings in the present manuscript are somewhat limited in scope to the role of autobiographical memory retrieval while reading, and may warrant more speculative conclusions considering the deliberate nature of the autobiographical memory task.

Finally, the authors presented words serially in the naturalistic reading task and in short passages, yet naturalistic reading involves a self-paced process with longer length passages and a much more complex pattern of attentional focus on the text. One's natural pace of reading sometimes slows down or speeds up as one's attention, interest, or complexity waxes and wanes, and people also often re-read earlier parts of text, etc.. All of these differences point to an important discrepancy between the reading task and the natural reading which is not discussed as a possible limitation in the manuscript.

2) The authors examined differences in fMRI activity during conflict reading trials as compared to "pure reading" trials, which would theoretically point to the role of automatically surfacing autobiographical memories acting as a source of distraction. Conflict reading trials are trials in which the primary task is to read, yet the participants are presented with a distracting autobiographical memory previously linked to an autobiographical memory in an earlier lab session. The behavioral data suggests that the autobiographical memory cue is indeed distracting to the reading process, suggesting it may facilitate autobiographical retrieval in more of an automatic way, possibly addressing the concern noted in comment #1. However, the fMRI contrast between reading with autobiographical memory conflict vs. pure reading did not yield any significant findings (data reported in Supplementary Information), raising some questions that the authors do not address in the discussion. Notably, the authors DO find that activity within the core and MT subsystems relates to less perceived task focus during the reading task, although the contrasts used in Figure 3C are unclear and I suspect don't focus on conflict > pure reading. These negative relationships are interesting, although it is surprising to me that the authors did not observe that the DM subsystem showed a positive relationship with task focus.

3) The authors emphasize neural differences between autobiographical memory and reading, yet there may be neural similarities between the two processes that do not receive as much attention in the manuscript as I think should be warranted. The differences between autobiographical memory and reading emerged from a contrast showing brain regions with fMRI activity that is elevated to a greater degree for one task as compared to the other. It may well be that both autobiographical memory and reading recruit a more similar set of brain regions than highlighted by the difference analysis, yet do so to varying degrees. Indeed, prior research has emphasized the role of other regions in the DMN in reading and conceptual processing (e.g. Binder & Desai, 2011; Mar, 2011). Neural similarities between the processes seem important to emphasize, yet they may be difficult to fully test in this experiment because the baseline letter string control condition for reading and autobiographical memory is a passive task that may also recruit brain regions within the DMN due to the likelihood of mind-wandering.

4) Along these lines, the authors could have more comprehensively discussed prior work linking narrative comprehension, conceptual processing, and reading to the DMN, including the dorsal medial PFC subsystem. Earlier and more recent meta-analyses seem important to incorporate here, and the link between reading and the dorsal medial PFC subsystem of the DMN (in particular) is also not a novel link in the literature.

5) To me, the negative relationship between mind-wandering during laboratory reading tasks and connectivity with the dorsal occipital cortex region engaged during autobiographical memory seems to be the opposite direction to what I would have predicted given results from Study 1, raising questions that I would have liked to see be discussed in more detail in the discussion.

Reviewer #2 (Public Review):

The authors use three fMRI datasets (one with task data, n = 29, and two with rest, ns = 243 and 69) to test the hypothesis that mind wandering during reading disrupts the integration of visual input. In Experiment 1, they contrasted univariate activity observed while individuals read sentences one word at a time while ignoring a memory cue or retrieved autobiographical memories promoted by a memory cue while ignoring the sentences. Activity in lateral prefrontal default mode network (DMN) and visual regions was greater during reading, whereas activity in medial DMN regions was greater during memory retrieval. In Experiment 2 dataset 1, they showed that lateral DMN regions (linked to reading) are more strongly coupled with visual regions than are medial DMN regions (linked to memory) during rest. In Experiment 2 dataset 2, they relate individual differences in mind wandering to functional connectivity between lateral DMN regions (linked to reading) and a dorsal occipital region (linked to memory).

Strengths of the paper include a rich theoretical motivation and framing, use of three independent fMRI datasets, complementary analyses of relationships between visual and DMN regions, and open data. Areas of potential improvement include demonstrating specificity of the observed results to mind wandering (rather than distraction from reading in general) and strengthening the evidence for replication across the three datasets.

The reading comprehension and autobiographical memory task provides a unique opportunity to study the impact of off-task thought on reading in a controlled experimental setting. However, it leaves open the possibility that the same pattern of results would be observed in a reading task with any dual-task demand. It would be helpful to consider evidence about whether Experiment 1 findings are specific to reading disrupted by memory, or whether they reflect distraction from reading more generally.

It is not readily apparent how precisely the findings agree across datasets because the brain regions described in each experiment and dataset partially but do not fully overlap, and it's not clear what degree of overlap should be considered a replication or what the likelihood of seeing that degree of overlap by chance would be.

Finally, the manuscript frames mind wandering as detrimental to reading comprehension and operationalizes it as irrelevant to the text being read. For longer narratives than those used here, however, mind wandering could hypothetically facilitate comprehension. For example, imagine trying to recall clues in a mystery novel or an author's obscure literary reference. In this case would decoupling between DMN and sensory regions always impair comprehension and memory?

Reviewer #1 (Public Review):

Mika and colleagues reconstruct the evolution of uterine endometrial transcriptomes during pregnancy from 23 diverse species of mammals that differ with respect to their degree of placental invasiveness. Through this analysis the authors infer that the eutherian mammal ancestor had an invasive mode of placentation and that the degree of invasiveness of placentation is reflected on uterine endometrial gene expression during pregnancy. Thus, phylogenetic analysis of gene expression profiles of different mammals groups them on the basis of degree of placental invasiveness, a quite striking finding.

The study is soundly performed and the results clearly presented. I particularly appreciated the authors' statement of caveats and limitations of this type of analysis and the necessity of interpreting these data in the context of known differences in anatomy and physiology.

The work is an important contribution to our understanding of the evolution of mammalian pregnancy. My only disagreement is the authors' interpretation that "placental invasiveness is regulated by gene expression profiles in the maternal endometrium rather than the fetal portion of the placenta." I believe that one would have to conduct the same analysis on the fetal placenta and examine the evolution of the placental transcriptome with respect to degree of placental invasive. It could be very well be that the degree of convergence of the maternal uterine endometrial transcriptomes is larger than that of the fetal placental transcriptomes, but the opposite could also turn out to be true.

Reviewer #2 (Public Review):

The authors present a novel phylogenetic and clustering analysis of uterine endometrium transcriptomes to test hypotheses of the evolution of placental invasiveness.

The authors make three distinct claims in their manuscript: 1) epitheliochorial-like placentas evolved early in mammalian stem-lineage, 2) the Eutherian ancestor had an invasive hemochorial placenta, and 3) there is maternal control of placental invasiveness.

To come to these conclusions the authors analyzed a collection of uterine endometrium transcriptomes sampled across placental invasiveness types.

The authors present several lines of evidence to support their claims. First, that maximum-likelihood phylogenetic reconstruction of expression data groups species by placenta-type. The discordance between the known species taxonomy and expression-based phylogeny is interpreted as evidence for convergence in gene expression between species with similar placentas.

While this conclusion is supported by the topology of the phylogenies, the authors do not test models of co-evolution between traits (expression and placental invasiveness). The authors determine that there must be "significant convergence" based on observations from the tree topology but do not explicitly test this model. I recommend adding in alternative possibilities in this section, or explicitly testing trait co-evolution.

The authors conduct ancestral state reconstruction and FCM to test the evolutionary origins of placental invasiveness. The ancestrally reconstructed Ancestral Eutherian transcriptional program clusters with extant hemochorial and ancestral thererian and mammalia transcriptomes cluster with non-invasive epitheliochorial placentas. This supports the author's claim that epitheliochorial placentas evolved early in mammalian lineages and that hemochorial placenta is ancestral for eutherians. I am curious, however, how the sampling of species influences this result-especially the inclusion of the armadillo. It appears the armadillo sample may be a significant driver of these results (especially the ancestral eutheria reconstruction). Given the sampling limitations, which the authors discuss, the authors should be cautious in their use of statements like "our results resolve several evolutionary transformations."

Finally, the authors discuss how their results suggest maternal control of placental invasion. Their support for this hypothesis is that endometrial gene expression (maternal) is associated with placental invasiveness. It is unclear to me how the authors determine directionality from the correlation observed. They present relevant findings that support their conclusions, but do not address alternative hypotheses such as fetal controlled placental invasion shaping maternal transcriptomics.

Overall, the analyses conducted here make a significant contribution to our understanding of the evolution of pregnancy in mammals. The methods used are appropriate and the analyses include novel findings on the evolution of placental invasion. I would suggest, however, that the authors ensure that alternative hypotheses are discussed and that they avoid language such as "resolve."

Reviewer #3 (Public Review):

In this work the authors use available endometrial transcriptomic data from a range of 23 different species covering a range of placental invasiveness to reconstruct an ancestral transcriptome. This was then used as a basis for phylogenetic comparison that could resolve major transitions in the the evolution of the placenta.

A major strength of this work is the use of fuzzy clustering to explore the ancestral transcriptome. This approach allows for the existence of a specific gene in multiple clusters and thus reflects the underlying functional interactions in a manner that accounts for changing function over evolutionary time.

A weakness of the the approaches used is that as yet single cell resolution cannot be achieved in the endometrial data so the "bulk" transcriptome over the different cell types has to be considered. Whilst representing a problem in the assessment of the action of evolution on specific cell types this does not impact the general conclusions presented.

This work will galvanise more detailed analysis of comparative transcriptomic data in the field of endometrial biology. The methods presented are useful over a range of other investigations.

Reviewer #1 (Public Review):

The authors generate a fluorescently-tagged SAC9 that can complement sac9 mutants (Figure 1) and is localized to the cytosol and to small intracellular puncta (Figure 2) that colocalize with markers for the trans-Golgi network (TGN) (Figure 3). They also demonstrate that mutation of C459, a predicted catalytic residue in SAC9, causes SAC9(C459A) mislocalization and fails to complement the growth phenotype of sac9 mutants (Figures 1-3). Further characterization of sac9 subcellular phenotypes reveals that PI(4,5)P2 is mislocalized from the PM into intracellular structures and that PI(4)P and PS localization are also affected (Figure 4). They document endocytosis defects in sac9 mutants, including changes to FM4-46 tracer dye uptake, synergistic interactions with endocytosis inhibitors (Figure 6), and changes to the localization of a clathrin-mediated endocytosis marker (Figure 8). Finally, the authors present preliminary data that SAC9 might interact with SH3P2, a protein that may be involved in autophagy, endocytosis, and other intracellular trafficking processes (Figure 7).

The authors present a model (Figure 8) in which SAC9 could associate with PI(4,5)P2 on endocytic vesicles through its interaction with SH3P2 to convert PI(4,5)P2 to PI(4)P, thus allowing endocytic vesicles to fuse with target membranes.

The main strength of this work is the detailed documentation that loss of sac9 affects PI(4,5)P2, which provides the most direct evidence to-date that SAC9 affects PIPs.

The main weaknesses are that the results are somewhat predictable given what is known about PI(4,5)P2 and endocytosis in other organisms, and that some of their other major claims of their model are not fully supported by the data presented here:

1) The authors claim that "the phosphoinositide phosphatase activity of SAC9 is required for its function" but they have not documented any biochemical activity of SAC9. They say that these experiments were unsuccessful, but no supporting data are presented and instead, they rely on the CIT-SAC9(C459A) mutant, which they assume is catalytically inactive. It would support their claims that CIT-SAC9(C459A) is catalytically inactive to document similar changes to PI(4,5)P2 distribution in sac9 mutants carrying CIT-SAC9(C459A) and to document similar levels of CIT-SAC9(C459A) or CIT-SAC9 protein in their complementation lines.

2) The authors claim that SAC9 labels "a subpopulation of endosomes close to the plasma membrane" but they have not documented this PM proximity and there are clearly SAC9-labelled puncta throughout the cytoplasm (e.g. Figure 2D).

3) The interaction with SH3P2 is not well supported, relying on only Y2H data of truncated versions of both proteins. No attempts to verify this interaction using full-length clones or any independent method are documented. It is also unclear what role SH3P2 plays in plants, or even whether it is directly involved in endocytosis, since it has also been implicated in multivesicular body formation (Nagel et al 2017 PNAS), cell plate formation (Ahn et al 2017 Plant Cell), autophagy (Zhuang et al 2013 Plant Cell), which the authors do not assess in the sac9 mutants.

Reviewer #2 (Public Review):

Using a combination of genetic and imaging approaches Doumane et al. provide evidence that 1) SAC9 localizes to the trans-Golgi network/early endosomal system (TGN/EE), 2) SAC9 plays a role in maintaining the subcellular distribution of PI(4,5)P2 to the plasma membrane and 3) loss of SAC9 results in impaired endocytosis of the lipophilic dye, FM4-64 and the auxin efflux carrier, PIN2-GFP. Consistent with these endocytic defects, the sac9 loss-of-function mutant displayed impaired dynamics of the TPLATE endocytic adapter protein complex, as well as the localization of the SH3P2, which was identified here as an interactor of SAC9. Overall, this study provides new insights into our understanding of SAC9 function in phosphoinositide metabolism and trafficking. Intriguingly, the findings suggest that SAC9-mediated PI(4,5)P2 hydrolysis following (or concomitantly with) its internalization via endocytosis serves to spatially restrict PI(4,5)P2 to the plasma membrane.

The strengths of this manuscript include the quantitation of confocal microscopy images and subsequent determination of statistical significance, as well as the use of multiple marker lines to localize specific phospholipids and organelles in wild-type and sac9 backgrounds. Co-localization of wild-type SAC9 with internalized FM4-64 and clathrin light chain (CLC2) as well as its association with BFA bodies shown in Figure 3 support the authors' conclusion that SAC9 is associated with the TGN/EE. The use of multiple phosphoinositide marker lines to demonstrate that intracellular pools of PI(4,5)P2 and PI(4)P, as well as PS, but not other phospholipids, is well-done (Figures 4A-4B). Furthermore, the authors clearly demonstrate that PI(4,5)P2 and PI(4)P do not localize to the same intracellular puncta (Figures 4D-4F). The authors effectively explain the broader significance of their work and describe the limitations (i.e. the authors recognize the caveats that the biochemical activity of SAC9 in vitro and function of SH3P2 in endocytosis remain to be defined).

One weakness of the manuscript is the authors' claim that mCIT-SAC9 and the catalytically inactive mCIT-SAC9C459A localize to a spatially restricted TGN/EE subpopulation in the cell cortex (Figures 2 and 3). The qualitative and quantitative analysis, however, appear to have been only conducted on a single plane of focus. Also, it is not clear from the methods whether the fluorescent 'cortical' signal described by the authors excludes all fluorescent signal from the interior of the cell. The authors should present quantitative imaging of SAC9 fusion protein distribution at multiple regions (Z planes of focus) of the cell to support their claim that SAC9 distribution is spatially restricted.

As stated above, the results of the BFA and FM4-64 colocalization experiments in Figures 3A and 3C are consistent with the authors' conclusion that the wild-type SAC9 is associated with the TGN/EE. However, there is a disconnect between the representative images of the colocalization analyses of mCIT-SAC9 and mCIT-SAC9C459A with various endomembrane marker proteins and the quantitation shown in Figure 3E-3H which undermine the confidence in the authors' conclusion that the intracellular compartments labeled by SAC9 are TGN/EE. On one level, colocalization has been described using markers which are not quantitated (e.g. RabF2a in panel 3H) or which are quantitated but not depicted (e.g. VTI12, 3E-3F, line 137). Furthermore, the images in panel H showing the merged co-localizations of RabD1 and Got1p with mCIT-SAC9C459A show a number of puncta with signal from both fluorophores, which is not evident in the quantitative data shown in Figure 3G. I understand that the colocalization studies are challenging due to the high signal of mCIT-SAC9 in the cytosol. Nevertheless, given the high cytosolic signal it would be nice to see imaging and quantitation of a control cytosolic protein (e.g. mCIT alone) for comparison in the mCIT-SAC9 colocalization studies. In addition, the authors should present representative images of mCIT-SAC9 and mCIT-SAC9C459A colocalization analysis that more convincingly reflect the quantitative colocalization analyses.

It is certainly plausible that the endocytic defects including reduction in levels of PM- associated SH3P2 and TPLATE in the sac9 mutant cells are directly related to the loss of SAC9 and/or altered PI(4,5)P2 distribution (as implied in lines 286-288). However, the authors cannot rule out that loss of SAC9 and/or altered metabolism of PI(4,5)P2 may indirectly affect SH3P2 localization and/or TPLATE recruitment/dynamics at the plasma membrane due to alterations in the organization/function of the TGN/EE and/or general inhibition of post-Golgi trafficking. The authors have the necessary tools and expertise including the TGN/EE markers and assays to address these questions (e.g. analysis of PIN2-GFP recycling +/- cycloheximide or quantitating secretion of marker sec-GFP in sac9 vs wild-type).

Reviewer #3 (Public Review):

Doumane and colleagues describe in this paper how the phosphatase enzyme SAC9 is involved in controlling the homeostasis of the PI(4,5)P2 lipid levels at the plasma membrane and during the formation of the endocytic vesicles in root meristematic epidermal cells. They show how SAC9 is localized mainly in the cytosol and in structures close to the plasma membrane. Furthermore, their data indicate that SAC9 phosphatase activity (its catalytic cysteine 459) is necessary for an adequate clathrin-mediated endocytosis, and to keep a proper PI(4,5)P2 and PI4P lipids balance. Additionally, the authors show SAC9-SH3P2 interaction via Y2H, based on which they hypothesize an interaction of these proteins at the plasma membrane and their potential cooperation to regulate clathrin-mediated endocytosis and membrane phosphoinositide homeostasis.<br> Overall, this is potentially a very interesting and high-quality work that try to unravel part of the endocytosis mechanism and to clarify the different role of the lipids that form the vesicles during the process.

However, there are some open questions and important points for the authors to consider:<br> a) Describe with images and numbers the focal planes used in the imaging,<br> b) Clarify the exact cells and focal planes used to quantify the cortical early endosomes when cells in different focal planes are shown in the images (see as example cells 1 and 2 of Fig 2G),<br> c) Re-analyze endocytosis events considering the intensity of signal internalized vs PM-localized in at least 3/4 cell sides in focus, and<br> d) Describe the methodology followed to select the analyzed particles.

Reviewer #1 (Public Review):

The paper has a number of strengths: The basic question of whether individual ORNs drive behavior is an important one, and the authors test ~90% of the different ORN classes which is very extensive. The presentation of the data is beautiful and well-conceived. And the paper is extremely readable. The core observation that only 10/45 tested ORNs can drive locomotor behavior on their own is an important result, as it addresses some labelled-line ideas that have been prevalent in the field.

There are a few results which are rather unexpected, and one may wonder whether they are somewhat unique to the behavioral apparatus the authors use:

The absence of an influence of wind on odor responses may not be general (i.e. the authors show it doesn't change single-ORN-elicited behavior, that is likely not true with odor-guided behavior where wind direction is an important cue for the animals to localize the odor source). It is possible that the narrow corridors in the assay used here promotes locomotor behavior in flies (as opposed to in an open arena where flies locomote much less, and may need wind stimulation to promote movement).

The counter-intuitive effect of starvation on behavior driven by food-sensing ORNs may reflect the fact the optogenetic ORN stimulation is very strong, as the authors discuss. This result can only be interpreted if the spike rates elicited with their optogenetic stimulation were known.

Knowing where in the dynamic range of ORN firing the optogenetic stimulation lies will also be important for interpreting the pairwise interactions between ORNs. For example summation may be more apparent when lower ORN firing rates are being combined. While analyzing ORN pairs, it would also be more informative to examine each pair individually across a range of stimulation intensities, since some pairs may summate and others may max pool etc.

Also in Fig 5G-I the authors analyze all ORN pairs together to look for summation etc. It is more informative to examine each pair individually across a range of stimulation intensities, since some pairs may summate and others may max pool etc. So this data, currently in SuppFig8. should be moved to the main text.

Reviewer #2 (Public Review):

In this manuscript, Tumkaya et al mapped the effects of optogenetically activating olfactory sensory neuron types on place preferences in adult flies, used as an index of odor valence. The authors include an impressive number of experimental and control flies tested, and analyzed their data with sophisticated statistical methods. However, I struggled to judge whether the authors achieved their aims, for two main reasons. First, too little information is given on the behavior. It is not clear that reducing the behavior to one number, wTSALE, adequately captures the relevant range of behavioral variation, so the mostly neutral effects reported here may simply reflect an insufficiently sensitive assay. Second, the statistical methods, however innovative they may be, are not in common usage and will be unfamiliar to most readers (including this one). To be understood, these methods should be clearly explained and logically justified over simpler and more intuitive options. Deeper explanation and perhaps simplification would be well worth the effort, given the impact these findings could have on the field.

Reviewer #3 (Public Review):

In the current manuscript, Tumkaya et al., set to examine two questions:

1. What proportion of primary olfactory sensory neurons can individually drive avoidance or attraction?<br> 2. What are the rules that govern behavioral responses to receptor combinations?

To this end, they optogenetically activated single ORN classes and examined the behavioral output. They found that only a fifth of ORN-types drove avoidance or attraction. They also found that wind and hunger had no effect on single-ORN class behavioral responses. Finally, they examined several pooling rules that failed to predict behavior. They conclude that the majority of primary olfactory sensory neurons have neutral behavioral effects individually, but participate in broad, odor-elicited ensembles with potent behavioral effects arising from complex interactions.

The amount of work presented in this manuscript is truly impressive and the authors should be commended for their efforts.

The conclusion that the majority of olfactory receptor neurons do not individually drive avoidance or attraction is not unsurprising, however, it addresses the still open question of labelled lines in olfaction, and is thus of importance. Some additional experiments will more thoroughly address the second conclusion that receptor combinations are more complex than using either summation or pooling strategies.

Reviewer #1 (Public Review): 

Authors introduce a deep learning-based toolbox (ELEPHANT) to provide ease in annotation and tracking for 3D cells across time. The study takes two datasets (CE and PH) to demonstrate the performance of their method and compare it with two existing 3D cell tracking methods on segmentation and accuracy metrics. 3D U-Nets are shown to be performing well in segmentation tasks in recent years, authors also utilize 3D U-Net for segmenting cells as well as linking the nuclei across time through optical flow. The variation in selected datasets is shown to be in the shape, size and intensity of cells. Beyond segmentation, authors also demonstrate the performance of ELEPHANT in exploring the tracking results with and without optical flow and regenerating their fate maps. A complete server-based implementation is provided with detailed codebase and docker images to implement and utilize ELEPHANT. 

Strengths: 

The paper is technically sound with detailed explanation of each methodological step and results. 3D U-Nets are optimized for the segmentation task in hand with large training sessions, efficiency of the pipeline is nicely demonstrated which serves this as a useful toolbox for real-time annotation and prediction of cell structures. The detailed implementation on a local and remote server is presented which is a need while handling and analyzing large scale bio-imaging datasets. Beyond smoothing, SSIM-based loss is effectively applied to make the model robust against intensity and structural variations which definitely helps in generalized performance of the segmentation and tracking pipeline. 

Segmentation results are validated on a large set of nuclei and links which is helpful to understand the limitation of the models. The advantage of using optical flow-based linking is clearly shown on top of using nearest neighbors. Spatio-temporal distribution of cells on a given data guides the users in using the framework for several biological applications such as tracking the lineage of newly born cells - a hard task in stem cell engineering. 

A detailed implementation on both remote and server as well as open-source codebase on Github is well provided for the scientific community which will help the users to easily use ELEPHANT for specific datasets. Although CE and PH datasets are used to demonstrate the performance, however, similar implementation can also be performed on neuronal datasets that would be of much use in exploring neurogenesis. 

Weaknesses: 

Authors use ellipse-like shapes to annotate the data, however, many cells are not elliptic or circular in shape but consist of varying morphology. If the annotation module is equipped with drawing free annotations then it will be better useful to capture the diverse shapes of cells in both training and validation. This also limits the scope of the study to be used only for cells' datasets that are circular/elliptical in shape. 

Authors use 3D U-net for segmentation which is a semantic segmenter, perhaps, an instance-based 3D segmenter could be a better choice to track the identity of the cells across time and space. However, an instance-based segmenter may not be ideal for segmenting the cells boundaries but a comparison between a 3D U-Net and an instance-based 3D segmenter on the same datasets will be helpful to evaluate. 

The selected datasets seem to be capturing the diversity in shape and intensity, however, the biological imaging datasets in practice often have low signal to noise ratio, cell density variation and overlapping, etc. It seems like the selected datasets lack these diversities and a performance on any other data of such kind would be useful for performance evaluation as well as providing a pre-trained model for the community usage. Moreover, it would also be useful to demonstrate the performance of the framework in segmenting+tracking any 3D neuronal nuclei dataset which will broaden the scope of the study. 

The 3D U-Nets are used for linking by using the difference between two consecutive images (across time) as labels. However, this technique helps to track the cell in theory but may also result in losing cell identity when cells are overlapping or when boundary features are less prominent, etc. Perhaps, a specialized deep neural network such as FlowNet3D could be a better choice here.

Reviewer #2 (Public Review): 

The authors created a cell tracking tool, which they claimed was user-friendly and achieved state-of-the-art performance. 

Would a user, particularly a biologist, be able to run the code from a set of instructions clearly defined on the readme? This was not possible for me. I am not familiar with Java or Mastodon, but I'm not sure we can expect the average biologist to be familiar with these tools either. I was very impressed by the interface provided though. 

Did the authors achieve state-of-the-art performance? It is unclear from the paper. It would be helpful to see comparisons of this tool with modern deep learning approaches such as Stardist. Stardist for instance reports performance on the parhyale dataset in their paper. Many people in the field are combining tools like Stardist with cell tracking tools like trackmate (e.g. see https://www.biorxiv.org/content/10.1101/2020.09.22.306233v1). It would be important to know whether one can get performance comparable to Stardist (at e.g. a 0.5 IoU threshold) on a single 3D with this sparse labelling and interactive approac. I still think this approach of using sparse labelling could be very useful for transferring to novel datasets, but it is difficult to justify the framework if there is a large drop in performance compared to a fully supervised algorithm.

Reviewer #3 (Public Review): 

This work describes a new open source tool (ELEPHANT, https://elephant-track.github.io/) for efficient and interactive training of a deep learning based cell detection and tracking model. It uses the existing Fiji plugin Mastodon as an interactive front end (https://github.com/mastodon-sc/mastodon). Mastodon is a large-scale tracking and track-editing framework for large, multi-view images. The authors contribution is an extension of Mastodon, adding automated deep learning based cell detection and tracking. Technically, this is achieved by connecting the Mastodon as a client (written in Java) to a deep learning server (written in Python). The server can run on a different dedicated computer, capable of the GPU based computations that are needed for deep learning. This framework makes possible the detection and tracking of cells in very large volumetric data sets, within a user friendly graphical user interface. 

Strengths: 

1) It is great to reuse an existing front-end framework like Mastodon and plug in a deep learning back-end! Such software design avoids reinvention of the wheel and avoids that users need to learn too many tools. 

2) The idea to use sparse ellipsoids as annotations for cell detection is in my view fantastic as it allows very efficient annotation. This is much faster than having to paint dense 3D ground truth as is required for most deep learning algorithms. 

3) It is great that the learning is so fast that it is essentially interactive! 

Opportunities for improvements: 

The software in its current form had a view issues that made it a little hard to use. It would be great if those could be addressed in future versions. 

1)There are several options for how to set up the ELEPHANT server. In any case this requires quite some technical knowledge that may prevent adoption by a broader user base. It would thus be great if this could be further streamlined (I shared so me specific ideas with the authors). 

2) For a GUI based software it is becoming state-of-the-art to provide recorded videos that demonstrate how to use the software. This is much more telling than written text. The authors added very nice short videos to the documentation, but I think it would be essential to also provide a longer video (ideally with voice over) where the authors demonstrate the whole workflow in one go. 

3) As a user one interacts with the Mastodon software which sends requests to the ELEPHANT client. It would be great if the feedback for what is going on server side could be improved. For example adding progress bars and metrics for the process of the deep learning training that are visualized within Mastodon would be, in my view, very important for the usability.

Reviewer #1 (Public Review):

In the present manuscript, the authors investigate regulatory roles of class IIa histone deacetylases (HDACs) in Schwann cells on developmental myelination, as well as on myelin repair after acute nerve injury. The study directly builds on previous observations (Gomis-Coloma et al., 2018) where the authors have shown that the primary HDACs of Schwann cells, HDAC4 and HDAC5, have redundant functions and cause only a mild delay in myelination in a double knock out (dKO), suggesting compensatory mechanisms by other HDACs. In the present study the authors indeed show compensatory upregulation of HDAC7 in HDAC4/5 dKO. They furthermore show by ablating all three HDACs that, next to a induction of HDAC9 expression, myelination is further delayed and the architecture of Remak bundles even permanently altered. The authors provide high quality data employing a broad spectrum of methodology, including conditional mutagenesis in mice, electrophysiology, immunofluorescence, electron microscopy, RNAseq, ChIP, cell culture, qPCR and Western blotting to justify their hypothesis of a regulatory and compensatory role of HDACs in Schwann cells during development and regeneration. The physiological relevance of this compensatory network, however, is not intuitive. Better discussion and elaboration of central findings in triple KOs in comparison to single KOs (and vice versa) would strongly improve the manuscript.

In detail, the following points may improve the strength of the manuscript:

1) With regard to the triple mutants (HDAC4,5 and 7) the authors present a data set from P2 to P21 and another at P60. Here, the manuscript would benefit from more comparable data sets for the respective timeline. E.g. the authors show an increased SC number at P21. What happens to these Schwann cells? Are they still present at P60? In line, the authors show that even in the triple mutants the expression of certain genes including cJun remains upregulated. How do the authors explain this upregulation? It would be helpful to know whether these genes remain upregulated in myelinating SC or whether persisting supernumerary SC are responsible for the expressio of cJun and others at later timepoints (e.g. by IHC)?

2) An important point is the description of the Remak- SC phenotype, which, in contrast to the only transient myelination phenotype, seems to persist in triple mutants. The authors suggest a defect of axonal segregation independent of a sorting defect and link this to a ectopic expression of genes of the melanocytic lineage. Given the importance of the Remak phenotype, a more detailed elaboration of this aspect also in dKO and cKO would be a strong benefit for the manuscript. In addition, the proposed ectopic expression of the melanocytic lineage genes would profit from a more extensive discussion and description with regard to their potential (transient) expression in wildtype Schwann cells and their functional relevance in relation to the observed Remak SC pathology. Moreover, the EM image in figure 2E suggests not only an increased number but also size of axons in the Remak bundles of triple mutants, in contrast to the respective quantification. As this point is crucial with regard to a potential sorting defect, the authors should carefully reevaluate the discrepancy between the presented image and data.

3) Regarding the expression changes of HDAC7 and HDAC9 in mutant mice: The authors only show HDAC7 expression at P60, while the proposed role of HDAC7 concerns early postnatal development. Could the authors comment on the expression of HDAC7 at earlier timepoints?<br> Furthermore, within the manuscript, the authors suggest a "de novo" expression of HDAC9 in triple mutants. However, the authors show a small, but significant upregulation of HDAC9 already in single cKO4 nerves (Fig S1A) as well as in single cKO7 mice (Fig. 9A), hence a more careful usage of the term "de novo" may be advisable.

4) In general, the discussion of the single HDAC knockout mutants is sometimes too sparse. This applies especially to the description of the cKO4 mice, which show a number of, albeit subtle, important differences with regard e.g. to the number of unmyelinated axons at P2 and P8 as well as with regard to the number of Schwann cell nuclei. However, the authors conclude that the single KO does not show a prominent phenotype. Though, given the compensatory mechanisms between HDACs in SC and the fact that the double HDAC4 (in SC) and HDAC5 (global) knockout display a similar phenotype to single HDAC4 mutants, this point requires more discussion. This dKO dataset, however, is redundant to the previously published study by the authors (Gomis-Coloma et al., 2018).

5) The authors then tested the mutants after injury. The presentation of data from these experiments, however, is a bit confusing as it is going back and forth between nerve crush and cut, different mutants (cKO4, KO5, dKO, tKO) and time points of analysis (10dpi, 20dpi, 21dpi, 30dpi). All mutants show a decreased remyelination after crush, the dKO and tKO further present increased c-Jun mRNA and protein at 10dpi and reduction of Krox20, Mbp, Mpz, Periaxin. The sequencing results are said to be obtained after nerve injury, however, it is not clear whether this was a cut or crush. Four days after nerve cut in tKO, the authors report increased expression of genes typical for repair Schwann cells, as well as a more rapid myelin debris clearance, although it is unclear how this was measured. Only by quantifying the number of still intact myelin profiles early after injury as in figure 5A? If the authors would like to stress the point of myelin clearance, additional information on degeneration profiles and autophagy (LC3bI-II, p62 Western blots) or data on macrophage abundance is needed and would gain meaningful insight.

6) Mechanistically, the authors investigated the genes that respond to HDACs or to which HDACs bind. It is nicely shown that HDAC4 can bind the c-Jun promoter, thereby repressing its expression, but also to the TSS of Mcam, belonging to the melanocyte lineage. However, a potential role of this finding is not further clarified. In addition, the generalized conclusion that "class IIa HDACs bind to and repress the expression of melanocyte lineage genes and negative regulators of myelination allowing myelination and remyelination proceed in a timely fashion" may be revised, considering that only HDAC4 has been tested. On the other side, it is nicely shown that c-Jun can bind to the HDAC7 promoter, inducing its expression. This is well analyzed both in vitro and in vivo using conditional c-Jun gain and loss of function in SC development. Here, although ectopic c-Jun overexpression in mice artificially increases HDAC7 expression in development, adding a more (patho-)physiological relevant experiment using c-Jun cKO in a nerve injury paradigm would be an asset.

7) The final hypothesis from the authors is, that upon lack of the functionally redundant HDAC4/5 and the concomitant de-repression of c-Jun, HDAC7 is upregulated upon binding of c-Jun to compensate for the loss and ensure myelination, although delayed. If HDAC7 is also lost, Mef2d expression increases and induces "de novo" expression of HDAC9. The data presented in the manuscript indeed provide evidence of a role for HDAC4, HDAC5 and HDAC7 in developmental myelination and nerve repair with compensatory potential for each other. However, the physiological relevance of this compensatory functions is, although interesting, not quite clear and the manuscript may profit from a discussion of this point.

Reviewer #2 (Public Review):

The classIIa Histone De-Acetylases (HDAC) play important roles in the transcriptional control of differentiation of a wide range of cell types. This class of HDACs is regulated by different signalling pathways and it involves the shuttling of the protein into the nucleus. Indeed, previous work from this lab has demonstrated that increased levels of cAMP shuttles HDAC4 into the nucleus of Schwann cells where it recruits NcoR1/HDAC3 to repress c-Jun expression and allows commencement of a myelin-related gene expression program. Thus, HDAC4 links cAMP signalling to repression of a 'repressor' to stimulate cell differentiation. However, genetic deletion of HDAC4 (or HDAC5 and HDAC4/HDAC5) does not have a significant effect on Schwann cell differentiation and myelination in vivo, suggesting that other compensatory mechanisms might exist.

Building upon their previous work, Velasco-Aviles and colleagues now demonstrate the existence of a genetic compensatory mechanism that relies on functional redundancies among the ClassIIa HDACs and the transcription factors c-Jun and Mef2d.

Using genetic ablation of multiple HDAC genes, extensive morphological analysis of developing and regenerating nerves combined with gene expression analysis, provide a description of the gene regulatory mechanisms that maintain adequate levels of ClassIIa HDACs required for peripheral nerve development and repair.<br> Their data are of high quality and support their major finding.

One interesting finding is that in the tKO, in which myelination eventually appears to progress normally, Remak Schwann cells are deficient in segregating lower calibre axons into cytoplasmic cuffs (Figure 2E). The authors interpret this a segregation defect and not as a sorting defect (page 5). Now, it is difficult to see how these two cellular mechanisms can be distinguished or whether they are different mechanisms to begin with. Notably, the unsorted bundle of axons presented in Figure2E also contains larger calibre axons that should normally be myelinated. Therefore, a simpler interpretation is that tKO Schwann cells are moderately impaired in axonal segregation, which results in the failure to sort out the occasional larger calibre axons from bundles and ensheathment of the smaller calibre axons into mature Remak bundles. There is no justification for proposing a 'segregation' mechanism different from the 'sorting' mechanism. As the sorting process critically depends on the elaboration of a basement membrane, it would be of interest to have a closer look at the basement membrane in EM and by IF in nerve sections and maybe WB. Is there any evidence for reduced laminin/collagen (or their receptors) expression in tKO nerves?

It is argued throughout the manuscript that classIIa HDACs are involved in the repression of repressors of myelination. It is stated that in injured nerves a strong upregulation of such negative regulators of developmental myelination is observed (page 17). Regulators such as c-Jun, Runx2, Sox2 etcetera. To avoid confusion, it is important to clearly distinguish between developmental and repair functions (exemplified by c-Jun) and in Schwann cells cultured in the absence of axonal contact. Confusingly and erroneously, it is also stated that the POU domain transcription factor Oct6 blocks the transition from promyelinating Schwann cell into myelinating cells. The quoted paper does not support this idea at all. On the contrary, it demonstrates that Oct6 expression is required for the progression of promyelinating cells into fully myelinating cells.

Reviewer #1 (Public Review):

Tissue microarrays (TMAs) are a critical tool for conducting tissue-biomarker research. In this report, the authors investigated whether technical aspects involved in TMA-based investigations contribute to the presence of batch-effects (e.g., differences in the values of biomarkers measured in tumor samples due to non-biological factors) and tested multiple ways to correct for the measurement error resulting from batch-effects.

Using data generated from 20 prostate cancer biomarker investigations using 14 different TMAs that included tumor tissue from over 1400 men with prostate cancer, the investigators determined that tumor characteristics such as stage, grade, and date of diagnosis do not contribute to the batch-effects observed across the 14 TMAs. Though these findings may not be generalizable for all potential tissue-biomarkers investigated using TMAs, TMAs developed using different protocols for patient selection and tissue acquisition, preservation, and TMA construction as well as those with smaller sample size and for other cancer types.

The authors then evaluated six different statistical methods to correct the measurement error due to batch-effects. The strengths and limitations of each method investigated are discussed. An overall strength of this study is the availability of empirical data generated from 20 biomarker investigations using the same TMAs to identify which statistical method leads to the most valid (e.g., true) biomarker estimates. Data simulations were used to determine how each method used to correct the biomarker measurement error due to batch-effects influenced the biomarker-cancer outcome relationship. This is another strength of the investigation which provide a robust assessment of different statistical approaches to overcoming the influence of batch-effects using both empirical and simulated data.

The author's conclusion that bath-effects are not an error of an individual study, but a feature of this type of research utilizing TMAs is supported by the results reported. While the extent of potential bias introduced from batch-effects does vary between studies based on the data reported, the author's recommendations are well supported and will contribute to improving the validity of tissue-biomarker investigations using TMAs.

Reviewer #2 (Public Review):

Tissue microarrays have become a mainstay in clinical and basic research, for both discovery and validation of biomarkers. The authors approach the possible sampling variation in a thoughtful way, not only quantifying the issue systematically, but working towards a solution.

Major Comments:<br> o The authors split the variation in to two co-existing explanations, either intratumoral heterogeneity or batch effect (likely a degree of both play a role). Batch correction inherently reduces noise (the latter) at the cost of reducing signal (the former). It would be useful to know what approaches have been employed to test for overfitting. The authors claim in the introduction the use of different methods for maintaining "biological" variation, but that analysis seems limited.<br> o Were there considerations for the variability in Gleason scoring between members of the study team?<br> o The manuscript involves the processing of a number of different cohorts in the field of prostate cancer. It would be important to know how would the performance of the batchma approach would change in tumors with greater heterogeneity.

Reviewer #1 (Public Review):

Gallego-Carracedo and colleagues investigated the relationship between neural spiking activity and local field potentials (LFP) across three different sensorimotor areas (dorsal premotor (PMd), primary motor (M1), and area 2 of somatosensory cortex (S1)) during a well-trained motor behavior. In contrast to previous studies, where spiking-LFP relationships were studied at the level of single neurons, the authors explore whether and how mesoscopic signals like the LFP are related to population-level patterns in spiking activity (referred to as "latent dynamics"). This is a very interesting and potentially valuable revisiting of LFP-spiking relationships, since increasing evidence has shifted focus away from purely single-neuron-based analyses towards population perspectives. Insights into relationships between LFP and latent dynamics may also inform interpretations of these signals.

The largest strength of this paper is the large amount of data. The paper includes analyses of datasets from 3 brain areas (7 implanted arrays total) in 4 animals. This reveals that LFP - latent dynamics relationships vary across brain areas and opens possibilities to fully examine relationships of all signals. The wealth of data allows them to clearly show that LFP-latent relationships are frequency-dependent and vary across brain areas.

The primary weaknesses of the paper are that it skips some important preliminary analyses, does not fully describe/interpret the broad diversity of data they present, and their interpretation of "stable relationship" is somewhat unclear.

1) Given the frequency-based analyses presented, more detailed characterization of the LFP spectra will greatly benefit the paper. A key question the authors should address is whether the frequency-dependence (and its variance across areas) is related to differences in power spectra across areas. They present an analysis suggesting their results are not simply explained by variance differences across bands, but there are no analyses to address power differences (and deviations from the 1/f "noise" spectrum).

2) The data reveal some clear differences between subjects and across areas that are not fully elaborated on. The relationship between decoding performance and LFP-latent correlation appears to only be present in M1. The relationships in PMd and area 2 are not quantified or commented on in much detail. Similarly, across all areas there are notable differences in LFP-latent correlations in some frequency bands (primarily the lower frequencies) between animals that is not addressed.

3) One of the manuscript's primary claims is that LFP-latent correlations are "stable" within areas while being different between areas. These claims are the main basis of their interpretation that these relationships reflect biophysical properties of the cortical networks (e.g. cytoarchitecture). The claim of stable relationships focus on comparing between motor planning and execution task epochs. These task epochs appear to include partially overlapping time windows based on their methodological description, which seems like a potential confound that should be addressed. The time windows used are also different durations, which should be controlled for. Moreover, their results also show that LFP-latent relationships change (mostly disappearing) in inter-trial intervals. If these correlations truly reflect properties of circuit structure, I am unclear on why they would be task-dependent. This interpretational point needs significant clarification.

Reviewer #2 (Public Review):

In this paper, Gallego-Carracedo, Perich, Chowdhury, Miller, and Gallego set up an important question: In much of systems neuroscience, researchers record spiking data from populations of single neurons or multi-unit channels to estimate neural population state. Applying dimensionality reduction algorithms like PCA to the high dimensional neural population state yields an estimate of the lower-dimensional latent dynamics, which are commonly understood to be a compact representation of the patterns of activity in the brain. Understanding the relationship between these latent dynamics and behaviors, sensory inputs, or cognition, represents a central goal of systems neuroscience.

In most such experiments, local field potential (LFP), is often also recorded, as it is simple to do so and these complementary signals may also provide scientific utility. In many other studies, often including studies using human participants, only LFP recordings are possible due to constraints of the neural sensors or recording equipment. Understanding the relationships between the LFP signals and latent dynamics thus represents an important bridge for helping to contextualize studies relying on LFP alone. In addition, better understanding the link between the two recording modalities (i.e. understanding the relative information content contained of each), in principle, could help to elucidate the biophysical mechanisms by which LFP arises from the collective spiking activity of neural circuits.

The authors outline four central hypotheses: 1) That there should be a robust relationship between LFP and latent dyamics, 2) that this relationship should be frequency dependent, 3) that these relationships are similar between preparation and movement (for data recorded in PMd, M1, and S1), and 4) that different areas should have different relationships between LFP and latent dynamics.

The motivation for this work is strong, and the quality and breadth of the data sets collected and curated is impressive. With a narrow reading of these hypotheses, the analyses presented here support the authors conclusions. The author's explicit goal is to assess whether any relationship exists between LFP and latent dynamics. Their analysis reveals that for certain frequency bands, in certain brain areas, that information in LFP signals is also contained within the manifold of latent activity.

While the stated goals, hypotheses, and overall presentation of this paper are all clear, the primary analysis method limits the broader interpretability of the results. The main analysis method that the authors use to assess the relationship between LFP and latent dynamics is the distribution of correlation coefficients derived from applying canonical correlations analysis (CCA) between the latent dynamics and individual channels of LFP, for individual frequency bands on those channels. As described, this method produces a metric for how well signals within a specific LFP frequency band on one channel is represented within the manifold of latent dynamics, allowing for a rotation of that manifold.

This analysis, however, does not say anything, however regarding the information contained in the manifold of latent dynamics that is not present within LFP signals. An analysis capable of revealing these differences would provide a more actionable takeaway for contextualizing what information is lost when an experiment relies on LFP signals alone. For a concrete hypothetical example, if every individual LFP channel (within one frequency band) contained a signal that perfectly correlated with principal component 1 (or any other PC), then the metric would report a distribution clustered tightly around 1. While this metric cannot get any higher, suggesting a high degree of alignment between LFP and latent dynamics, it appears to ignore the fact that in this contrived scenario, no LFP channels have captured any information about PCs 2,3,4,...,n, and the metric tells us nothing about the information lost by only recording LFP. If we don't know what information is lost, it is difficult to know how to apply these results to contextualize other studies based on LFP recordings, which is one of the stated broader motivations for this paper.

These limitations aside, the authors have carefully shown that there appears to be a frequency dependence between which LFP bands share similar information with the latent dynamics. In addition, they establish that LFP recordings in PMd, M1, and S1 show different relationships with the latent dynamics, and that the degree of LFP correlation with latent dynamics is stable between the movement preparation and execution. This paper is well written, with extraordinary attention to detail and clarity throughout.

Reviewer #1 (Public Review):

Agarwal and Przeworski have performed a very timely and interesting study of the distribution of fitness effects (DFE) of new mutations. This study is timely because modern human population genetic datasets have finally achieved sample sizes for which a certain class of nucleotide sites, i.e. methylated CpG sites, when neutrally evolving, should approach near complete polymorphism saturation. If every neutral site is expected to carry at least one variant, the classic problem of distinguishing sites that are monomorphic due to chance (no mutation) versus sites that are monomorphic due to selective constraint (removed mutations) is greatly simplified. The point at which population genomic datasets are saturated with polymorphisms should represent a major advance in understanding the DFE at individual sites and is what immediately piqued my interest.

Overall, this manuscript is a thorough and thoughtful examination of this topic; to my enjoyment there were several times where a question came to mind that was addressed shortly later in the paper. I believe the authors have made a compelling case for why methylated CpG sites provide an entry point for understanding the site-specific DFE. I found the section "Interpreting monomorphic and polymorphic sites in current reference databases" particularly insightful as a guide to thinking about future datasets; similarly, I thought the comparison with CADD scores (Fig S9) provided important food for thought regarding confounders to maps of constraint generated from vast numbers of species using modern genomic datasets.

While the study is addressing an interesting topic, I also felt this manuscript was limited in novel findings to take away. Certainly the study clearly shows that substitution saturation is achieved at synonymous CpG sites. However, subsequent main analyses do not really show anything new: the depletion of segregating sites in functional versus neutral categories (Fig 2) has been extensively shown in the literature and polymorphism saturation is not a necessary condition for observing this pattern. Similarly, the diminishing returns on sampling new variable sites has been shown in previous studies, for example the first "large" human datasets ca. 2012 (e.g. Fig 2 in Nelson et al. 2012, Science) have similar depictions as Figure 3B although with smaller sample sizes and different approaches (projection vs simulation in this study). There are some simulations presented in Fig 4, but this is more of a hypothetical representation of the site-specific DFE under simulation conditions roughly approximating human demography than formal inference on single sites. Again, these all describe the state of the field quite well, but I was disappointed by the lack of a novel finding derived from exploiting the mutation saturation properties at methylated CpG sites.

Similarly, I felt the authors posed a very important point about limitations of DFE inference methods in the Introduction but ended up not really providing any new insights into this problem. The authors argue (rightly so) that currently available DFE estimates are limited by both the sparsity of polymorphisms and limited flexibility in parametric forms of the DFE. However, the nonsynonymous human DFE estimates in the literature appear to be surprisingly robust to sample size: older estimates (Eyre-Walker et al. 2006 Genetics, Boyko et al. 2008 PLOS Genetics) seem to at least be somewhat consistent with newer estimates (assuming the same mutation rate) from samples that are orders of magnitude larger (Kim et al. 2017 Genetics). Whether a DFE inferred under polymorphism saturation conditions with different methods is different, and how it is different, is an issue of broad and immediate relevance to all those conducting population genomic simulations involving purifying selection. The analyses presented as Fig 4A and 4B kind of show this, but they are more a demonstration of what information one might have at 1M+ sample sizes rather than an analysis of whether genome-wide nonsynonymous DFE estimates are accurate. In other words, this manuscript makes it clear that a problem exists, that it is a fundamental and important problem in population genetics, and that with modern datasets we are now poised to start addressing this problem with some types of sites, but all of this is already very well-appreciated except for perhaps the last point.

At least a crude analysis to directly compare the nonsynonymous genome-wide DFE from smaller samples to the 780K sample would be helpful, but it should be noted that these kinds of analyses could be well beyond the scope of the current manuscript. For example, if methylated nonsynonymous CpG sites are under a different level of constraint than other nonsynonymous sites (Fig. S14) then comparing results to a genome-wide nonsynonymous DFE might not make sense and any new analysis would have to try and infer a DFE independently from synonymous/nonsynonymous methylated CpG sites.

Reviewer #2 (Public Review):

This manuscript presents a simple and elegant argument that neutrally evolving CpG sites are now mutationally saturated, with each having a 99% probability of containing variation in modern datasets containing hundreds of thousands of exomes. The authors make a compelling argument that for CpG sites where mutations would create genic stop codons or impair DNA binding, about 20% of such mutations are strongly deleterious (likely impairing fitness by 5% or more). Although it is not especially novel to make such statements about the selective constraint acting on large classes of sites, the more novel aspect of this work is the strong site-by-site prediction it makes that most individual sites without variation in UK Biobank are likely to be under strong selection.

The authors rightly point out that since 99% of neutrally evolving CpG sites contain variation in the data they are looking at, a CpG site without variation is likely evolving under constraint with a p value significance of 0.01. However, a weakness of their argument is that they do not discuss the associated multiple testing problem-in other words, how likely is it that a given non synonymous CpG site is devoid of variation but actually not under strong selection? Since one of the most novel and useful deliverables of this paper is single-base-pair-resolution predictions about which sites are under selection, such a multiple testing correction would provide important "error bars" for evaluating how likely it is that an individual CpG site is actually constrained, not just the proportion of constrained sites within a particular functional category.

The paper provides a comparison of their functional predictions to CADD scores, an older machine-learning-based attempt at identifying site by site constraint at single base pair resolution. While this section is useful and informative, I would have liked to see a discussion of the degree to which the comparison might be circular due to CADD's reliance on information about which sites are and are not variable. I had trouble assessing this for myself given that CADD appears to have used genetic variation data available a few years ago, but obviously did not use the biobank scale datasets that were not available when that work was published.

Reading this paper left me excited about the possibility of examining individual invariant CpG sites and deducing how many of them are already associated with known disease phenotypes. I believe the paper does not mention how many of these invariant sites appear in Clinvar or in databases of patients with known developmental disorders, and I wondered how close to saturation disease gene databases might be given that individuals with developmental disorders are much more likely to have their exomes sequenced compared to healthy individuals. One could imagine some such analyses being relatively low hanging fruit that could strengthen the current paper, but the authors also make several reference to a companion paper in preparation that deals more directly with the problem of assessing clinical variant significance. This is a reasonable strategy, but it does give the discussion section of the paper somewhat of a "to be continued" feel.

Reviewer #3 (Public Review):

Agarwal et. al combine a few well-known ideas in population genetics - diminishing returns in sampling new alleles with increasing sample size and the enrichment of invariant sites for sites under strong purifying selection - and point out the exciting result that sample sizes of modern human data sets are sufficiently large that, for highly mutable sites, saturation mutation has been reached. This is my favorite kind of result - one that is strikingly obvious in retrospect but that I had never considered (and probably wouldn't have). The manuscript is well written, and a number of my concerns or questions while reading were resolved directly by the authors later on. I have no major concerns, but a few potential suggestions that might strengthen the presentation.

The authors emphasize several times how important an accurate demographic model is. While we may be close to a solid demographic model for humans, this is certainly not the case for many other organisms. Yet we are not far off from sufficient sample sizes in a number of species to begin to reach saturation. I found myself wondering how different the results/inference would be under a different model of human demographic history. Though likely the results would be supplemental, it would be nice in the main text to be able to say something about whether results are qualitatively different under a somewhat different published model.

On a similar note, while a fixed hs simplifies much of the analysis, I wondered how results would differ for 1) completely recessive mutations and 2) under a distribution of dominance coefficients, especially one in which the most deleterious alleles were more recessive. Again, though I think it would strengthen the manuscript by no means do I feel this is a necessary addition, though some discussion of variation in dominance would be an easy and helpful add.

There's some discussion of population structure, but I also found myself wondering about GxE. That is, another reason a variant might be segregating is that it's conditionally neutral in some populations and only deleterious in a subset. I think no analysis to be done here, but perhaps some discussion?

Maybe I missed it, but I don't think the acronym DNM is explained anywhere. While it was fairly self-explanatory, I did have a moment of wondering whether it was methylation or mutation and can't hurt to be explicit.

Reviewer #3 (Public Review):

In this manuscript the authors describe the biogenesis and the mechanism of action of a pair of cis-encoded sRNAs: CJnc190 and CJnc180. Both RNAs are being processed by RNase III. 5' and 3' ends mapping together with in vitro and in vivo experiments using purified RNase III and rnc deletion mutant demonstrated that the processing of CJnc190 sRNA depended on the formation of an intramolecular duplex, while CJnc180 sRNA processing required the presence of the antisense CJnc190 sRNA. The mature CJnc190 and CJnc180 sRNA specious are 69 and 88 nt long respectively. They also show that mature CJnc190 sRNA represses translation of ptmG via base-pairing and CJnc180 sRNA antagonizes CJnc190 repression acting as a sponge, scavenging CJnc190 sRNA. In addition, they find that two promoters are responsible for the synthesis of CJnc190 sRNA and both transcripts are subject to RNase III processing.

The study represents an enormous amount of work. The data are solid and generally support the overall conclusions. Having said that the manuscript is overwhelming, loaded with too many details which make the reading difficult and in the absence of a bigger picture many times uninspiring.

Reviewer #1 (Public Review):

The manuscript describes the mechanisms of biogenesis of two antisense sRNAs by RNase III in C. jejuni, CJnc180 and CJnc190, as well as the specific post-transcriptional activity of CJnc190 on ptmG. The study provides thorough experimental support of (i) binding of CJnC190 to repress translational of ptmG, (ii) RNAse III processing to produce mature CJnc190 and CJnc180 transcripts, (iii) location and contribution of CJnc180/190 promoters and 3' ends, and (iv) mechanisms of RNase III cleavage of CJnc180 and CJnc190. Notably, this study proposes a novel cis-sRNA processing mechanism of CJnc180 in which base pairing with antisense sRNA CJnc190 facilitates proper cleavage by RNase III. Overall, this well constructed and informative study provides impactful knowledge that furthers the field of regulatory RNAs.

Reviewer #2 (Public Review):

Campylobacter jejuni is serious food-borne pathogen and understanding how the various products necessary for pathogenesis are regulated is a key step in preventing its growth and/or treating disease. Here, Sharma and coworkers demonstrate the complex pathway that leads to the maturation of two complementary regulatory RNAs and how one of the RNAs antagonizes the other to relieve repression of a virulence-related gene. The work is detailed and convincing, and provides a reference point for the roles of regulatory RNAs in C. jejuni as well as other bacteria. Future work will be needed to better understand when each of these RNAs is best expressed and processed into active form, and to fully support the idea that one RNA acts as an antagonist for the other.

Reviewer #1 (Public Review):

In this manuscript, the authors use extensive pharmacologic manipulations to examine a pathway by which oligomeric amyloid beta assemblies bind to a sodium potassium pump subunit and lead to an increased proportion of inactive endothelial nitric oxide synthetase. They speculate that this reduced eNOS activity in endothelial cells could underlie changes in cerebral perfusion in Alzheimer's disease. While the idea that eNOS activity is altered in Alzheimer's is not novel, having been described elsewhere in relation to amyloid beta, the authors clearly outline a new pathway involving NAKalpha3 providing mechanistic insight into eNOS changes. Further, their data uses immunofluorescence, western blotting, and qPCR to show rat aorta and cultured human brain microvessel endothelial cells express NAKalpha3--a protein previously believed to be a neuron-specific.

Reviewer #2 (Public Review):

The current study by Sasahara et al. examined the cerebrovascular effects of amylospheroids (ASPD), highly neurotoxic ~30-mer assemblies of β-amyloid (Aβ), which the author's group purified from human brains of AD patients and characterized in previous studies. The authors propose that the aberrant interaction of ASPD with NAKalpha3 in endothelial cells induces production of reactive oxygen species (ROS) from mitochondria and activates protein kinase C (PKC). In turn, PKC phosphorylates inactive form of eNOS, reduces NO production, and attenuates carbachol-induced vasorelaxation. These conclusions were based on ASPD immunostaining of brain sections from AD patients, the ASPD effects on carbachol (a muscarinic M3 receptor agonist)-induced vasorelaxation in rat aortic rings, and in vitro studies in primary human brain endothelial cells, including the effect of ASPD on carbachol-induced eNOS phosphorylation and NO production and ASPD-induced ROS production. These data add a new class of mechanisms by which Aβ impairs neurovascular regulation in the brain, and the manuscript could make an interesting contribution to the field of vascular contributions to cognitive impairment and dementia.

Reviewer #1 (Public Review):

The authors present an interesting concept for the mechanism of rash induction in EGFR inhibitor (EGFRi) treated rats. EGFRi causes production of pro-inflammatory factors in epidermal keratinocytes which may induce dedifferentiation and reduction of the dWAT compartment, presumably mediated via PPAR. Factors produced by dedifferentiated FB then recruit monocytes thereby inducing skin inflammation. This work is aiming to improve targeted cancer therapy efficiency and is therefore of potential clinical relevance.

However, most of the conclusions drawn by the authors are based on correlations, e.g. between the amount of dWAT and rash intensity. Mechanistic data have been mainly generated in vitro. The exact order of events to formulate a definitive mechanistic proof in vivo for this hypothesis is missing. In particular, it is not clear which cells in the skin, apart from keratinocytes, are specifically targeted by EGFR inhibitors and/or by Rosiglitazone. The authors also do not show EGFR staining in adipocytes and its inhibition by Afa. The effects of Afa and Rosi on monocytes / macrophages are completely ignored by the authors. Additionally, some of the presented results are overinterpreted and not really supporting what is claimed.

Most importantly, the whole study is based on inhibitor treatments. Afatinib for example is not only inhibiting EGFR but all other erbB family members and as such it represents a panErbB inhibitor and it is not clear whether the observed effects are induced by inhibition of EGFR of other erbB receptors which have been shown to have also effects in the skin. For further specification of the role of EGFR, other, more specific inhibitors should be used to confirm the basic concept along with genetic proof either in genetically engineered mice or by Crispr-mediated-deletion.

To further support the hypotheses of the authors, the study needs to be further substantiated by mechanistic experiments and the clinical relevance should be strengthened by performing histologic analysis of skin samples of patients treated with EGFRi and respective analysis of rash and e.g. BMI etc.

Reviewer #2 (Public Review):

Leying Chen et al. investigated the mechanism of EGFR inhibitor-induced rash. They find that atrophy of dermal white adipose tissue (dWAT), a highly plastic adipose tissue with various skin-specific functions, correlates with rash occurrence and exacerbation in a murine model. The data indicate that EGFR inhibition induces the dedifferentiation of dWAT and lipolysis , finally lead to dWAT reduction which is a hallmark of the pathophysiology of rash. Notably, they demonstrate that stimulating dermal adipocyte expansion with a high-fat diet (HFD) or the pharmacological PPARγ agonist rosiglitazone (Rosi) ameliorated the severity of rash. Therefore, PPARγ agonists may represent a promising new therapeutic strategy in the treatment of EGFRI-related skin disorders pending to be confirmed in further study.

The conclusions of this paper are mostly well supported by data, but some results need to be clarified and verified.

1) PPAR signaling in the pathology of EGFRI-induced skin toxicity.<br> In figure 2 , the results show Rosi reversed the dedifferentiation of dermal adipocytes induced by Afa. This may due to PPARγ upregulation but not be confirmed in the results. The relative genes expression in dWAT after treated with Afa and ROSi were not demonstrated in the results.

2) the effect of PPAR signaling on PDGFRA-PI3K-AKT pathway<br> The AKT pathway is a key downstream target of EGFR kinase, so it is reasonable to see p-AKT1 and p-AKT2 levels were decreased by Afa (figure 3C) However, addition of Rosi to Afa significantly activated both AKT1 and AKT2 . What is the underlying mechanism for the results and whether it is related to the PPAR signaling pathway.

3) According to figure 3 F , 3G and 3H., authors draw a conclusion that " a lack of APs and mature dWAT impairs the maintenance of the host defense and hair growth in the skin" In my opinion, there are no results can directly prove this. According to figure 3H, the impairment of hair growth may be caused by EGFR inhibition of hair follicles.

4) EGFRI stimulates keratinocytes (HaCaT cells) to produce lipolytic cytokines (IL-6) (Figure 4G). IL6 enhanced the lipolysis of differentiated dFB (Figure S4M) and C18 fatty acids were supposed to be released the cell matrix during lipolysis.<br> In figure 4H, HaCaTcells supernatants and dFB supernatants were collected. IL-6 was supposed to increase in HaCaTcells supernatants and was confirmed in Figure 4SK and S4L.However, C18 fatty acids were not showed to be in the dFB supernatants in the study directly.

Reviewer #1 (Public Review):

Rinkenberger et al. take a forward genetics ORF overexpression approach to identify human interferon (IFN)-inducible gene (ISG) products driving host defense to the protozoan pathogen Toxoplasma. The screen encompassing approximately 500 ISG identifies 3 ISG candidates and is able to validate 2 of these 3, namely the transcription factor IRF1 and the retinoic acid receptor responsive gene RARRES3, which becomes the focus of the study. Using gain- and loss-of-function approaches the study demonstrates that RARRES3 promotes the reduction of parasitic burden in human cell lines. Importantly, the study provides evidence linking RARRES3 functionally to the previously reported interferon-inducible defense mechanism of host-mediated parasite extrusion. Overall, the discovery of RARRES3 as an anti-parasitic factor is potentially of broad interest to the field of innate immunity, parasitology and, more generally, microbial pathogenesis, although its physiologically importance or its role in host defense to pathogens other than Toxoplasma was not explored in this study.

Strengths:

The paper takes an unbiased genetics approach to identify novel human genes that execute cell-autonomous host defense against the parasite Toxoplasma<br> The study is well controlled and convincingly demonstrates that RARRES3 limits parasitic burden in human cell lines, using both gain- and loss-of-function approaches.

The study provides indirect evidence that RARRES3 mediates the expulsion of parasites from infected cells

The study shows that some clonal lineages of Toxoplasma are resistant to RARRES3-mediated immunity suggesting that some Toxoplasma strains may have evolved mechanisms to counteract the host defense pathway(s) regulated by RARRES3.

Weaknesses:

The physiological relevance of RARRES3-mediated parasite egress during the course of Toxoplasma infections is unclear and not discussed.

Regarding the failure to see an IDO phenotype (Fig. 1F), the authors may consider that there standard media and serum contains relatively high concentrations of tryptophan (Materials and Methods doesn't provide any information on the exact trp concentration used) and that IDO cannot catabolize the excess amount of tryptophan present in media + serum to achieve tryptophan starvation conditions. I believe previous studies demonstrating IDO-mediated nutritional immunity in cell culture used trp-limited culture conditions. Without any careful experiments using titrated concentrations of trp, the conclusion that IDO cannot restrict Toxo in A549 cells does not seem justified

The authors state that RARRES3 deficiency was complemented with RARRES3 ectopic expression. However, it is unclear from the data presentation whether complemented KOs are statistically different from controls (KO + FLUC) under IFNgamma primed conditions (Fig. 5B) and thus whether complementation was actually achieved.

The paper lacks any direct evidence for RARRES3-mediated parasite egress.

Reviewer #2 (Public Review):

The manuscript by Rinkenberger et al. titled "Over-expression Screen of Interferon-Stimulated Genes Identifies RARRES3 as a Restrictor of Toxoplasma gondii Infection" describes a series of experiments to investigate the role of IFNg-induced genes, or 'ISGs', in T. gondii restriction in human cells. In humans, mechanisms of Toxoplasma gondii restriction are both cell-type specific and diverse, not relying solely on the IRG system observed in mice. Hence there are many unanswered questions as to how humans control and ultimately clear this widespread parasite of warm-blooded animals. Importantly, the authors use an unbiased over-expression ISG library to understand what additional host genes and mechanisms are employed by human cells to control parasitic infection.

The initial screen and experimental validation, using ectopical expression, reveal IRF1 and RARRES3 as important host factors capable to restrict T. gondii infection in human cells. Importantly, RARRES3 induces premature parasite egress which can be blocked by Compound 1, a parasite egress inhibitor. Moreover, RARRES3 acts independently of host cell death pathways and appears to work autonomously in several contexts suggesting a new mode of parasite restriction not yet described.

The manuscript is well written. The methods employed to test the hypothesis and RARRES3 function are adequate and relevant. Little is known about RARRES3. The discussion is informative and addresses why so few ISGs were found to impact parasite restriction, whereas similar screens for viral pathogens appear to turn up many ISGs with anti-pathogen capabilities. Potential mechanisms are discussed.

Reviewer #1 (Public Review):

In their manuscript entitled "PBN-PVT projection modulates negative emotions in mice", Zhu et al. combine circuit mapping techniques with behavioral manipulations to interrogate the function of anatomical projections from the parabrachial nucleus (PBN) to the paraventricular nucleus of the thalamus (PVT). The study addresses an important scientific question, since the PVT and particularly the posterior PVT is known to be mostly sensitive to aversive signals, but the neural circuit mechanisms underlying this process remain unknown. Here the authors contribute important evidence that PBN inputs to the PVT may be critical for this process. Specifically, the authors identify that the PVT receives glutamatergic projections from the PBN that promote aversive behavioral responses but do not modulate nociception. The latter finding is intriguing considering that the PBN is an important node in pain processing and that the PVT has recently emerged as a modulator of pain. Overall, the study includes an impressive array of techniques and manipulations and offers insight to an important scientific question. The authors' conclusions will be significantly strengthened by the inclusion of some additional experiments and controls.

It is in my view problematic that the authors used different genetic strategies to target the PBN-PVT pathway. For example, in Figure 1 the authors used Vglut2-cre mice for the anterograde tracings but later on in the same figure used constitutively expressed ChR2 in the PBN to assess functional connectivity with the PVT using ex-vivo patch-clamp electrophysiology. In Figure 2 the authors once again employed Vglut2-Cre mice to target PBN projections to the PVT and manipulate these projections optogenetically during behavioral tests. However, in the following figure (Fig. 3) the authors then use a retro-Cre approach and chemogenetics. The interchangeable use of these different manipulations is not warranted by data presented by the authors. For example it is unclear whether all PBN neurons projecting to the PVT are glutamatergic and express VGLUT2. When using the constitutively expensed ChR2 in the PBN to demonstrate glutamatergic projections to the PVT, the authors may be faced by potential contamination from adjacent brain stem structures like the LC and DRN, which project to the PVT and are known to contain glutamatergic neurons (vglut1 and vglut3, respectively). Another example, for figure 4 why did the authors not use Vglut2-cre mice and inhibited PBN terminals in the PVT as in Figure 2?

Related to the previous point, in the retrograde labeling experiment (Fig. 1) it would be useful if the authors determined what fraction of retrogradely label cells are indeed VGLUT2+. For behavioral experiments employing the retro-Cre approach the authors may be manipulating a heterogenous population of PBN neurons which could be influencing their behavioral observations. In general, the authors should ensure that a similar population of PBN-PVT neurons is been assessed throughout the study.

The authors' grouping of the behavioral data into the first vs the last four minutes of light stimulation in the OF does not seem to be properly justified an appears rather arbitrary. Also related to data analysis, the unpaired t-test analysis in the fear conditioning experiment in Figure 4J seems inappropriate. ANOVA with group comparisons is more appropriate here.

Considering the persistency of the effect in the OF following optogenetic stimulation of PBN-PVT afferents, the lack of such persistent effect in the RTPA is hard to reconcile. By performing additional experiments the authors attempt to settle this discrepancy by proposing that the PBN-PVT pathway promotes aversion but does not facilitate negative associations. I find this conclusion to be problematic. If the pathway is critical for conveying aversive signals to the PVT, one expects that at the very least it would be require for the formation of associate memories involving aversive stimuli. However, the authors do not show data to this effect. Instead they show that animals decrease their acute defensive reactions to aversive stimuli (2-MT and fear conditioning), but do not show whether associative memory related to this experience (e.g. fear memory retrieval) is impacted by manipulations of the PBN-PVT pathway.

A similar lack of connection between aversive signals within the PVT and the PBN pathway is found in the photometry data presented in Figure 5. While importantly the authors' observation of aversive modulation of the pPVT reproduces data from other recent studies, the question here is whether the increased activity of PVT neurons is mediated by input from the PBN. The cFos experiment included in this figure attempts to draw this connection, but empirical evidence is required.

Reviewer #2 (Public Review):

Zhu et al. investigated the connectivity and functional role of the projections from the parabrachial nucleus (PBN) to the paraventricular nucleus of the thalamus (PVT). Using neural tracers and in vitro electrophysiological recordings, the authors showed the existence of monosynaptic glutamatergic connections between the PBN and PVT. Further behavioral tests using optogenetic and chemogenetic approaches demonstrated that activation of the PVT-PBN circuit induces aversive and anxiety-like behaviors, whereas optogenetic inhibition of PVT-projecting PBN neurons reduces fear and aversive responses elicited by footshock or the synthetic predator odor 2MT. Next, they characterized the anatomical targets of PVT neurons that receive direct innervation from the PBN (PVTPBN). The authors also showed that PVTPBN neurons are activated by aversive stimuli and chemogenetically exciting these cells is sufficient to induce anxiety-like behaviors. While the data mostly support their conclusions, alternative interpretations and potential caveats should be addressed in the discussion.

Strength:

The authors used different behavioral tests that collectively support a role for PBN-PVT projections in promoting fear- and anxiety-like behaviors, but not nociceptive or depressive-like responses. They also provided insights into the temporal participation of the PBN-PVT circuit by showing that this pathway regulates the expression of affective states without contributing for the formation of fear-associated memories. Because previous studies have shown that activation of projection-defined PVT neurons is sufficient to induce the formation of aversive memories, the differences between the present study and previous findings reinforce the idea of functional heterogeneity within the PVT. The authors further explored this functional heterogeneity in PVT by using an anterograde viral construct to selectively label PVT neurons that are targeted by PBN inputs. Together, these results connect two important brain regions (i.e., PBN and PVT) that were known to be involved in fear and aversive responses, and provide new information to help the field to elucidate the complex networks that control emotional behaviors.

Weakness:

The authors should avoid anthropomorphizing the behavioral interpretation of the findings and generalizing their conclusions. In addition, there is a series of potential caveats that could interfere with the interpretation of the results, all of which must be discussed in the article. For example, the long protocol duration of laser stimulation, the possibility of antidromic effects following photoactivation of PBN terminals in PVT, and the existence of collateral PBN projections that could also be contributing for the observed behavioral changes. Additional clarification about the exclusive glutamatergic nature of the PBN-PVT projection should be provided and the present findings should be reconciled with prior studies showing the existence of GABAergic PBN-PVT projections.

Reviewer #3 (Public Review):

Zhu YB et al investigated the functional role of the parabrachial nucleus (PBN) to the thalamic paraventricular nucleus (PVT) in processing negative emotions. They found that PBN send excitatory projection to PVT. The activation of PBN-PVT projection induces anxiety-like and fear-like behaviors, while inhibition of this projection relieves fear and aversion.

Strengths:

The authors dissected anatomic and functional connection between the PBN and the PVT by using comprehensive modern neuroscience techniques including viral tracing, electrophysiology, optogenetics and pharmacogenetics. They clearly demonstrated the significant role of PBN-PVT projection in modulating negative emotions.

Weaknesses:

The PBN contains a variety of neuronal subtypes that expressed distinct molecular marker such as CGRP, Tac1, Pdyn, Nts et al. The PBN also send projections to multiple targets, including VMH, PAG, BNST, CEA and ILN that could mediate distinct function. What's the neuronal identity of PVT-projecting PBN neurons, how is the PVT projection and other projections organized, are they overlapping or relative independent pathway? Those important questions were not examined in this study, which make it hard to relate this finding to other existing literature.

Reviewer #1 (Public Review):

Authors found that Dwn1 suppressed circadian Npas2 expression, and increased fibroblast cell migration, and decreased collagen synthesis in vitro. Then, they applied Dwn1 to full-thickness murine wounds and showed that Dwn1-treated dermal wounds healed faster and developed less granulation tissue than the controls. It was suggested that Dwn1 treatment might control the hypertrophic scaring .

Authors showed the effectiveness in vito, but their in vivo skin defect model had too narrow skin defects to compare the scarring and the observation period seemed to be short.

Reviewer #2 (Public Review):

Building upon prior work in which the authors identified Npas2 as a key suppressor of wound healing, they perform a high-throughput drug screen to identify a compound called Dwn1 as a pharmacologic strategy to improve wound healing. A major strength of this paper is that it translates recent genetic work to a potential new therapy for wounds. The data support their conclusions that Dwn1 should be investigated further in as a new treatment. This paper also implicates peripheral circadian biology to the process of wound healing. Thus, this work has the potential to further unveil how circadian biology intersects with wound biology. They have also developed a unique way to assess linear incisional wounds which may be a useful technique for other investigators.

Reviewer #1 (Public Review):

The manuscript by Rai et al., presents a straightforward approach to identify key transcriptional changes occurring as Candida glabrata infects macrophages. This is based on the premise that the changes occurring early on, as part of the pathogen response, are key in determining the progression of the infection process. Candida species are important opportunistic fungal pathogens, posing a relevant problem among immunocompromised populations. While for decades C. albicans has been responsible for most candidiasis infections, in recent years reports have indicated an upsurge in infections caused by Candida glabrata. The capacity of the latter to survive and divide within immune cells, and its increased resistance to drugs like fluconazole makes of this pathogen an organism of interest. Therefore, new information that can help to molecularly dissect aspects related to its infectious process is relevant both from the clinical and scientific points of view.

In this study, based on CHiP-seq assays directed to elongating polymerase the authors identified a series of DEGs displaying different expression profiles over a time course during macrophage infection. The authors identify several hundred of genes that show distinct profiles, from increased expression at early times, to ones becoming more active later on in the process. Based on GO analyses several correlations are drawn regarding key physiological changes that may be key for survival and virulence. Such chronological study of transcriptional changes (with a good resolution) over the first hours of macrophage infection represents an important dataset from where different testable hypotheses can emerge. The authors paid special attention to several transcription factors encoding genes which expression was high during early time points. Among them, they focused on a homolog of the S. cerevisiae transcriptional repressor Xbp1. Thus, they generated a KO of CgXBp1 and interrogated the resulting strain regarding its gene expression profile, through an equivalent time-course. The RNAPol II-Chipseq analysis showed a series of genes which expression was accelerated relative to WT, which can be interpreted as many of them being directly repressed by CgXBp1. To assess the latter, they attempt to conduct a Chip-seq of a tagged version of CgXBp1 as C. glabrata infects macrophages, nevertheless, the correlation between replicates was low and further analyses were not conducted (data not shown). Therefore, the conditions of the assay were changed and CgXbp1 Chip-seq was performed in quiescent cells, a condition where Xbp1 is known to play important roles in S. cerevisiae. This data indicated that among direct targets there are several genes encoding TFs, which suggest an important transcriptional cascade where CgXbp1 plays an important role. Such data are correlated with the RNAPolII data obtained early on in the study, and a mechanistic model is proposed. Importantly, CgXbp1 appears to recognize different types of cis-elements in the bound promoters: one similar to the reported one in yeast and another one displaying a quite different DNA logo. Additional analyses focus on determining the consequences on growth, virulence, and fluconazole resistance of the CgXbp1 (and complemented strain). Three aspects stand out: increased resistance to fluconazole, decreased proliferation in macrophages, and decreased virulence. Such phenotypes are not discussed in extenso, since most of that section focuses on the transcriptional aspects of the work.

While the datasets are valuable and several observations are interesting, it is important to be cautious as the direct targets of CgXbp1 were characterized under one particular condition and the transcriptional analyses were obtained in another condition, one shown to be highly dynamic. Therefore, several inferred targets may or may not be under CgXbp1 control during macrophage infection. Most importantly, as it is, the study does not provide a clear parallel between one list of genes and the other one, to get a glimpse of such concepts. Since CgXbp1 shows to recognize distinct binding motifs, it becomes relevant to understand whether one group behaves differently from the other one in the absence of CgXbp1.

Reviewer #2 (Public Review):

This manuscript describes the temporal transcriptional response of Candida glabrata during macrophage infection and characterizes the role of the transcriptional repressor CgXbp1 the process. The manuscript is well written, the experiments were well conducted and the subject is very interesting.

However, a few issues should be addressed to improve the quality of the manuscript. Particularly, it will be important to: 1) Either repeat the experiment or discuss further the unexpected failure to obtain reliable ChIP-seq results for Xbp1 within the macrophage microenvironment. during macrophage infection". The option for defined media makes it difficult to compare with the RNA PolII dataset; 2) Validate experimentaly the proposed consensus sequence recognized by Xbp1; 3) Use standard MIC determination, to have a clear notion on the impact of Xbp1 on fluconazole resistance.

These extra experiments will provide a stronger basis for the author's claims and increase the foreseen impact of this work.

Reviewer #3 (Public Review):

The paper by Rai and colleagues examines the transcriptional response of Candida glabrata, a common human fungal pathogen, during interaction with macrophages. They use RNA PolII profiling to identify not just the total transcripts but instead focus on the actively transcribing genes. By examining the profile over time, they identify particular transcripts that are enriched at each timepoint, and build a hierarchical model for how a transcription factor, Xbp1, may regulate this response. Due to technical difficulties in identifying direct targets of Xbp1 during infection, the authors then turn to the targets of Xbp1 during cellular quiescence.

The authors have generated a large and potentially impactful dataset, examining the responses of C. glabrata during an important host-pathogen interface. However, the conclusions that the authors make are not well supported by the data. The ChIP-seq is interesting, but the authors make conclusions about the biological processes that are differentially regulated without testing them experimentally. Because Candida glabrata has a significant percent of the genome without GO term annotation, the GO term enrichment analysis is less useful than in a model organism. To support these claims, the authors should test the specific phenotypes, and validate that the transcriptional signature is observed at the protein level.

Additionally, the authors should also include images of the infections, along with measurements of phagocytosis, to show that the time points are the appropriate. At 30 minutes, are C. glabrata actually internalized or just associated? This may explain the difference in adherence genes at the early timepoint. For example, in Lines 123-132, the authors could measure the timing of ROS production by macrophages to determine when these attacks are deployed, instead of speculating based on the increased transcription of DNA damage response genes. Potentially, other factors could be influencing the expression of these proteins. At the late stage of infection, the authors should measure whether the C. glabrata cells are proliferating, or if they have escaped the macrophage, as other fungi can during infection. This may explain some of the increase in transcription of genes related to proliferation.

An additional limitation to the interpretation of the data is that the authors should put their work in the context of the existing literature on C. albicans temporal adaptation to macrophages, including recent work from Munoz (doi: 10.1038/s41467-019-09599-8), Tucey (doi: 10.1016/j.cmet.2018.03.019), and Tierney (doi: 10.3389/fmicb.2012.00085), among others.

When comparing the transcriptional profile between WT and xbp1 mutant, it is not clear whether the authors compared the strains under non-stress conditions. The authors should include an analysis of the wild-type to xbp1 mutants in the absence of macrophage stress, as the authors claims of precocious transcription may be a function of overall decreased transcriptional repression, even in the absence of the macrophage stress. The different cut-offs used to call peaks in the two strain backgrounds is also somewhat concerning-it is not clear to me whether that will obscure the transcriptional signature of each of the strains. Additionally, the authors go on to show that the xbp1 mutant has a significant proliferation defect in macrophages, so potentially this could confound the PolII binding sites if the cells are dying.

In the section on hierarchical analysis of transcription factors, at least one epistasis experiment should have been performed to validate the functional interaction between Xbp1 and a particular transcription factor. If the authors propose a specific motif, they should test this experimentally through EMSA assays to fully test that the motif is functional.

The jump from macrophages to quiescent culture is also not well justified. If the transcriptional program is so dynamic during a timecourse of macrophage infection, it is hard to translate the findings from a quiescent culture to this host environment.

Overall, there is a strong beginning and the focus on active transcription in the macrophage is an exciting approach. However, the conclusions need additional experimental evidence.

Reviewer #1 (Public Review):

The manuscript of Zaydman et al. proposes a spectral analysis of phylogenetic profiles, which allows to identify signals of protein-protein interaction or association at different scales, from direct PPI over pathways to phenotypes and finally to phylogenetic relationships.

The paper reports some potentially very interesting results:

- Different scales are related to different (even if overlapping) windows in the spectrum of the phylogenetic profiles, with the most global scale (phylogeny) related to the largest singular values, and the most local scale (physical PPI) to much smaller singular values.

- Using this observation, and the correlation of proteins (projections of groups of orthologs to the SVD) across windows in the spectrum, the authors are able to extract a hierarchy of protein networks, which get refined from some general phenotype (bacterial mobility in the paper) to several pathways and complexes (e.g. chemotaxis, flagellum).

- This allows to associate proteins of unknown function to some pathways or complexes; the paper shows a case of experimental validation for one new association.

- Using a supplementary layer of supervised machine learning (interacting and non-interacting proteins), they claim to have more precise results than some recent PPI networks reconstructed using amino-acid coevolution (Cons et al.).

While these results seem to be highly interesting and, in some cases, potentially spectacular, the paper is very hard to read and to understand. It is written in a semi-technical jargon mixing spectral analysis, machine learning and information theory. Even having expertise in these fields, I had to continuously jump between the main text, the methods and the figure (including the supplementary figures - a total of 86 pages) to follow the argumentation of the paper. The authors should make a serious effort to ensure that the main messages become more accessible.

Reviewer #2 (Public Review):

From its inception comparative genomics has held the promise of predicting protein-protein interactions using the phyletic patterns of proteins. The current work represents another iteration in the long series of such attempts, which aims to use the increasingly popular applications of machine learning to this classic problem. The authors start by using the phyletic pattern matrix for orthologous proteins and perform singular value decomposition on it to obtain the successive SVD components. They observed that the higher ranked SVD components were dominated by information from the phylogenetic relationships between organisms. However, there was a large unaccounted variance contained in the lower components, which they sought to further query for potential biologically relevant information such as indirect interactions and direct interactions, such as PPIs. They assembled benchmarks using known biological databases for assessing the inferred interactions which were derived from the "spectral correlation" which they obtained from row correlations in the U and V matrices of the decomposition of their ortholog phyletic pattern matrix. Given that the correlations can be a mix of all kinds of signals, including phylogenetic, indirect and direct interactions, they used a gold-standard set of well characterized E. coli K12 protein pairs to train random forest models for learning direct PPIs.

The attractive aspects of this work include: 1) the use of a comprehensive phyletic pattern matrix for orthologs; 2) A reliable training set for the random forest method; 3) the assembly of multiple benchmarking sets with thorough benchmarking of the method. 4) Recovery of subsystems of bacterial flagellar motility and other systems.

Weaknesses: 1) Bacteria tree is not uniformly sequenced. There is an overrepresentation of certain lineages, e.g., of gammaproteobacteria and terrabacteria (Bacillus group) in the starting matrix. This could potentially bias the quality of the correlations that are obtained in the ``mid-range' SVD components; 2) The actual biological inferences drawn for the role of the tested gene in twitching mobility might be over-interpreted. Briefly, the authors recover 4 uncharacterized proteins (Q9I5G6, Q9I5R2, Q9I0G2, Q9I0G1) as part of their T4 pilus sub-graph and infer a general function for them in the twitching mobility. They chose Q9I5G6 because it was the only one with a supposed domain of unknown function (DUF4845). However, it should be noted that Q9I5R2 also contains another such domain DUF805 along with a Zn-ribbon domain. Further, Q9I0G2 is a T2SS secretion platform protein and Q9I0G1i is the ATPase engine for the pilus. Genomic neighborhood analysis by this referee revealed that DUF4845 likely functions with the signal peptidase in secretion. Thus, given the role of the pilus in secretion and mobility, the best one could infer is a role for DUF4845 in pilus function perhaps with a greater intersection with secretion. This could even indirectly affect the mobility function which the authors' experiments are said to support. However, the authors state right in the abstract they have uncovered a twitching mobility effector. At best they could say they have uncovered a potential component that might be functionally linked to the T4 pilus which might affect secretion or twitching mobility. Indeed, the phyletic pattern of DUF4845 does not immediately suggest that all organisms with it also possess definitive twitching mobility.

While methods of this kind have the promise to serve biological functional inference, the actual example provided does not appear to be the strongest. That said, I do think the work presents a method that might have utility in computational inferences of function, especially if combined with other forms of information from comparative genomics.

Reviewer #3 (Public Review):

The authors describe a computational prediction framework aimed at connecting individual genes into progressively larger units of function: from protein complexes to higher-order pathways. The framework is based on the tracking of the presence and absence of orthologous genes across a large number of genomes; the authors' method is demonstrated to work well, albeit only for prokaryotic organisms. The basic evolutionary signal used by the authors has been described previously, and has been used previously to predict protein-protein associations, but the authors take it a step further by carefully deconstructing the signal into multiple components: a phylogenetic component, a direct protein-protein interaction component, and a more indirect association component. They then construct a hierarchical model of functional linkages, for any prokaryotic genome of interest. Finally, they use this to predict and experimentally verify the function of a previously uncharacterized protein in Pseudomonas aeruginosa.

This is a well-written and carefully executed study, taking a known prediction technique to a new level. It has broad applicability, and should be of interest to a wide readership.

Reviewer #2 (Public Review):

The manuscript of Kunze et al. aimed at finding how different kinds of fluctuations in temperature affect the disease outcome. The authors used Daphnia magna - Ordospora colligate host - parasite system exposed to a range of temperatures which were either stable, regularly fluctuating, or included a single heat wave, and measured fitness of the host (as reproductive output) and the parasite (infection rate and spore burden). The experiment is very well designed, and the methods of data analysis are sound and well suited to address the questions stated by the authors.

The authors found that the unstable thermal conditions change the fitness of the host and the parasite. Temperature fluctuations narrowed thermal breadth for infection and spore burden of the parasite, whereas the heat wave caused shift in thermal optimum and a strong increase of maximal spore burden of the parasite. Both thermal variation treatments resulted in shifts in thermal optimum and maximal performance of the host. The most interesting (and surprising) result was the spectacular increase in spore burden of the parasite exposed to heat wave in comparison to fluctuating temperature treatment and stable temperature treatment, obtained in 16°C.

Authors rightfully conclude that the outcome of infection could be strongly altered by variations in thermal regime. This context dependency might to some extent explain the limited accuracy of disease spread models. This is critical especially in the face of climate change, which is expected to result in more frequent and more rapid thermal variation events. Moreover, the narrowed thermal performance curve of the parasite (especially in the high temperatures range) under fluctuating temperature regime indicates, that the thermal tolerance of some organisms to warming might be overestimated, when tested under (less realistic) stable thermal conditions.

I think the paper of Kunze et al. is a very strong contribution to the field of disease ecology, and I find no major weaknesses. The Introduction and Discussion sections are well written and provide some extensive overview of the relevant literature. The study design and results are described clearly and the conclusions are well supported. I have no major criticism to this manuscript.

Reviewer #1 (Public Review):

Kunze et al. provide an interesting experiment aimed to understand the effects of variable temperature regimes in host-pathogen interactions. This is one of the most complete experiments to date, that goes beyond exploring increasing but constant temperature regimes. The experimental setup is strong, exposing Daphnia magna to the natural range of temperature variability and realistic fluctuating (+-3C) and extreme (6C pulse) regimes. Daphnia exposure to Odospora colligata pathogens was also rightly tested against a placebo control. Aided by their experimental approach Kunze et al. explore their results with clear figures and fine text, getting deep into our understanding of the thermal performance of important host and pathogen life history traits (such as reproductive output) and setting them in the larger picture of global warming. In short, I am impressed by the quality of the new information provided by this ms.

Joint Public Review:

Gupta et al. investigate the mechanism of Mcm2-7 helicase loading using an in vitro reconstituted S. cerevisiae system by single molecule colocalization spectroscopy and sm-FRET. Previous biochemical and single-molecule studies have led to contrasting models as to whether one or two ORCs are involved in recruiting and loading both Mcm2-7 hexamers in a double hexamer. How the transitions between OCCM and MO loading intermediates are coordinated was likewise unknown. Using COSMOS and sm-FRET, the authors convincingly show that a) a single ORC recruits both the first and second Mcm2-7 hexamer in the majority of observed loading events, b) ORC recruits the first and second Mcm2-7 hexamer using similar ORC-MCM interactions, c) ORC is retained at the origin by the formation of an MO complex and these interactions stabilize the first Mcm2-7 hexamer on DNA and d) Cdt1 release coordinates the transition between OCCM and MO complexes. These data are consistent with the proposed ORC-flip model, which posits that ORC is released from the original DNA site and rebinds on the opposite side of the first loaded Mcm2-7 for loading of the second Mcm2-7. This work provides important new insights into understanding the mechanisms of bidirectional replicative helicase loading.

The paper is an important contribution and the reviewers asked for a number of clarifications and explanations about the data.

Joint Public Review:

Fowler et al. report on hits of a CRISPR-Cas9 FACS-based screen for chromatin associated RPA in quiescent murine pre-B cells that lack DNA ligase 4 (to prevent NHEJ) identifying component required for DSB resection or inhibiting DSB resection in G0 cells. The screen is well validated by previously published results (Chen et al. 2021 eLife) and controls reported in this manuscript. Unexpectedly, the authors identify all components of the DNA-dependent protein kinase complex, Ku70, Ku80 and DNA-PK catalytic subunit, as being required for DSB resection in G0 cells. This was surprising as DNA PK inhibits DSB resection in G1 and G2 cells, which was confirmed in this work. The results are verified by END-seq, showing strand specificity, and processing is dependent on MRE1 and CtIP, assuring that the RPA signal reports on DSB resection. Independent confirmation is derived from results with FBXL12, an DNA-PK-specific ubiquitin E3 ligase, which leads to DNA-PK turnover and counteracts DSB resection is G0 cells. The genetic dependencies were established by gene knockouts, and key results confirmed in a human cell line (MCF-10A). The specificity of the effect for DNA-PK was confirmed using inhibitors against ATM, which showed no effect on DSB resection, whereas DNA-PK inhibitors mimicked the genetic dependency. 

The manuscript is well structured and describes an interesting finding for the DNA repair community, speculating that DSBs repair in quiescent cells functions differently than in cycling cells. This has implications on how non-cycling cells in the body, such as neurons, could handle DNA damage, but remains to be validated in the corresponding model. In addition, more experiments need to be performed to adequately support the key conclusions of the manuscript with respect to the applicability in human cells and with regards to the distinction between resection in G0 and G1 cells. Some data are lacking a precise description of the methods which need to be extended.

Reviewer #1 (Public Review): 

Homeostatic plasticity is a process that helps to confine neural network activity within limits. While our understanding of the expression mechanisms of homeostatic plasticity have considerably advanced, very little is known of how the bounds of permissive activity levels are set and kept in check. In this study, the authors present data indicating that activation of PAR bZIP family of transcription factors help confine the extent of homeostatic synaptic plasticity, illustrating the existence of a negative regulator of homeostatic plasticity. 

The study addresses a timely topic of general interest and the key findings are important. The data as presented leave some questions concerning the conclusion that Par bZIP proteins act as negative regulators of homeostatic synaptic plasticity. Although TTX treatment substantially increases the expression of both HLF and TEF, HLF is dramatically upregulated in PV+ neurons compared to pyramidal cells. In addition, when whole cortical lysates are examined, the kinetics of upregulation of HLF and TEF appear to differ. Given that slice cultures from mice lacking HLF, TEF and DBP show a strong reduction in mIPSC amplitude, which is otherwise compensated presumably via mechanisms independent of Par bZIP transcription factors, additional characterization of the expression properties and the respective roles of HLF and TEF in pyramidal and PV+ neurons might help provide a clearer view of when and how HLF and TEF are engaged to regulate network activity.

Reviewer #2 (Public Review): 

Valakh et al. report increased transcript abundance of PAR bZIP transcription factors after treating organotypic cortical mouse brain slice cultures with TTX for five days. Triple-knockout neurons lacking the transcription factors Hlf, Dbp and Tef displayed a more pronounced increase in calcium spike frequency and mEPSC frequency upon TTX treatment than controls, suggesting that these transcription factors limit homeostatic compensation. The study addresses a very interesting and almost completely unresolved question - the mechanisms that may constrain homeostatic plasticity. In principle, this paper presents highly relevant data for the field of synaptic transmission and synaptic plasticity.

Reviewer #3 (Public Review): 

The manuscript by Valakh et al. discovered transcription factors (TFs) belonging to the PAR bZIP family that normally limit upward homeostatic response without affecting baseline activity levels. The authors show that long-term blockade of spikes by TTX activates Hlf, Tef, and Dbp TFs in both excitatory and inhibitory neurons. The authors demonstrate that chronic silencing in TKO slice cultures that lack all 3 TFs causes over-compensation at the presynaptic level, reflected by larger increase in mEPSC frequency, but not at the postsynaptic level or at the level of intrinsic excitability in excitatory neurons. In addition, homeostatic plasticity of inhibitory synapses at excitatory neurons was disturbed by TKO. At the network level, over-compensation of average activity level was observed in TKO following prolonged network silencing. In contrast, no deficits in downward homeostasis from hyperactive state were detected. 

These are exciting results that demonstrate a novel transcriptional program that normally restricts upward homeostatic plasticity and prevents over-compensation. While previous studies revealed transcriptional regulation that enables downward firing rate homeostasis by REST (Pozzi et al., EMBO 2013), this work is the first one to identify transcriptional regulation that restricts upward firing rate homeostasis. Hlf, Tef, and Dbp TFs are regulated by circadian clock and may be implicated in many types of physiological regulations across light-dark phases. The knockout mice lacking all 3 TFs show epilepsy phenotype and short lifespan that can be related to a novel mechanism discovered by the authors. The paper is of high significance for both basic neuroscience and neuropathology related to homeostatic deficits, such as epilepsy, neuropsychiatric disorders and many more.

Reviewer #1 (Public Review):

In the paper "Coiled coil control of growth factor and inhibitor-dependent EGFR trafficking and Degradation", Mozumdar et al investigate the cellular role of the juxtamembrane region in the EGF receptor. The juxtamembrane segment is a poorly understood motif in the EGFR cytosolic domain that physically connects the transmembrane segment to the kinase domain. It can form two kinds of coiled-coil dimers depending on whether the ligand it is bound to is TGF-a or EGF. EGFR traffics either down the endolysosomal pathway, or via the recycling pathway depending on the ligand it binds, and previous studies linked the trafficking route it adopted to the stability of the ligand-EGFR complex, or EGFR dimer strength. Through a series of well-designed experiments, this paper shows that the endocytic trafficking route of EGFR following its activation is determined by the juxtamembrane coiled coil conformation in a model cell line.

This finding is important for three reasons. First, it identifies a critical role for the ill-understood juxtamembrane region. Second, it resolves the discrepancy that TGF-α is thought to dissociate from EGFR before EGF yet the EGFR-TGFα complex continues to signal from endosomes. Finally, it pinpoints the mechanism of EGFR inhibition by a new class of tyrosine kinase inhibitors, which downregulate EGFR activity by funnelling the EGFR-inhibitor complex for lysosomal degradation.

Reviewer #2 (Public Review):

The premise of this study is that a juxtamembrane coiled-coil structure in EGFR exists in two isomeric orientations depending on ligand occupancy, mutations or tyrosine kinase inhibitors. The authors show a plausible correlation between the orientation of this coiled-coil domain and receptor fate which could be important in driving tumor phenotypes.

In this study, the authors use three sets of molecular tools to manipulate the conformation of the coiled-coil domain and capture receptor trafficking and fate. First, they design mutations in the coiled coil helices that favor either the EGF or TGF type conformations, that feature either a Leu rich or charged interface, respectively. The effect of the mutations on conformation is convincingly validated using a Cys binding fluorescence reporter and recombinant EGFR. However, the sorting of mutant receptors in response to either EGF or TGF, although shifted in the predicted direction, is not perfectly correlated for clear conclusions to be made. For example, E661R receptor gains the ability to associate with Rab7 endosomes in response to TGF binding, however, it also loses much of the original association with Rab11 (ideally, this should not have changed). The KRAA mutant appears to be non-selective for ligand in association with Rab11 although it results in poor association with Rab7 endosomes for both ligands. In any case, these experiments are incomplete without evaluation of receptor fate in lysosomal degradation and inconclusive as presented.

In contrast, the next set of tools consisting of mutations in the GXXG motif (previously validated in Sinclair et al., 2018) yield results that are much easier to interpret. Mutation G628F sends the receptor to Rab7 endosomes and on to lysosomal degradation in response to TGF. Conversely, mutation G628V sends the receptor to Rab11 endosomes where it escapes degradation in response to EGF. In each case, there is a significant and convincing gain of function phenotype that correlates a shift in endosomal localization to receptor fate.

The last set of tools are tyrosine kinase inhibitors used in conjunction with constitutively active and endocytosed EGFR. Here, the authors make a nice case for endosome association and receptor fate that is uncoupled from the inhibition of phosphorylation. Again, there is good correlation between Rab7 protein association and receptor degradation, irrespective of the kinase inhibitor activity.

Overall, the authors make a convincing case that sorting of receptor to Rab7 endosomes results in effective lysosomal degradation. However, the argument that conformation of the coiled-coil motif drives endosomal sorting and fate is not well supported. Mutations in the coiled-coil domain had confusing outcomes, and no information on coiled-coil conformation was presented for the tyrosine kinase inhibitors. Only the G628 mutants present the complete set of correlations, although not all in this manuscript (some of the pertinent experiments are already published).

Reviewer #3 (Public Review):

The Schepartz lab have previously shown that the binding of growth factors results in the formation of two distinct coiled coil dimers within the juxtamembrane (JM) segment. These two isomeric coiled coil structures are also allosterically preferred by point mutations within transmembrane (TM) helix. In this manuscript, authors demonstrate that the JM coiled coil is a binary switch, governing the trafficking status of EGFR, either towards degradative or recycling pathway.

They design novel variants of EGFR (E661R and KRAA) that mimic the two distinct coiled coil types, EGF-type and TGF-α-type. These variants are further validated using bipartite tetracysteine- ReAsH system. In order to assess the trafficking of these variants, authors use confocal imaging to measure colocalization with respective organelle markers. In addition, authors also use variants with point mutations at TM segment that controls the JM coiled coil state to demonstrate that the trafficking is dependent on JM segment and not growth factor identity. EGFR signaling is of prime importance in cancer biology and trafficking plays a major role, where the degradative pathway decreases the signaling, in contrast to recycling pathway that sustains the signaling. The authors clearly demonstrate this switch in EGFR lifetime using relevant variants and show how well-known tyrosine kinase inhibitors regulate this in a drug resistant non-small cell lung cancer model.

The model proposed by the authors is mostly well supported by data, but few points require clarification.<br> i) The authors need to address why the switch is incomplete when JM mutants are used but appears complete with TM mutants. A) Does this mean recycling requires other criteria in addition to JM segment? B) Is it possible that TM mutants cause other changes in addition to controlling JM segment? C) Would it be better if organelle transmembrane markers were used (Tf, Lamp1, NPC1 etc.).<br> ii) It would be helpful to represent data as a distribution or scatter points instead of bar plot. Did authors observe any expression level dependence on their colocalization and lifetime assays?<br> iii) Did authors investigate the lifetime of JM variants? Like it was shown with TM variants in Fig 4.

Reviewer #1 (Public Review):

In this manuscript, Cai and authors offer a new and important discovery demonstrating the persistence of a clade on non-caballine equids, Sussemionus, well into the later millennia of the Holocene in northern China. My expertise does not lie with the genomics analysis, so I will not offer detailed comment - but as an outsider, the arguments seemed well-supported and convincing.

The primary weakness of the article lies in the omission of detailed archaeological context, and in the failure to consider implications for and from human societies. All specimens were taken directly from archaeological sites, but no information is given about the archaeological sites and cultures the specimens were derived from. In early China, ca. 3500 BP, the persistence of wild equid taxa is a very significant finding. This time period was a very dynamic period across northern East Asia, with the first introduction of domestic horses and the first spread of other livestock pastoralism (see Brunson et al, https://www.sciencedirect.com/science/article/abs/pii/S2352409X20300535). And, as summarized in Yuan and Flad (2006), many of the earliest sites speculatively linked with domestic horses that predate the final Shang Dynasty are isolated equid bones from archaeological sites, without definitive archaeological data to determine domestic or wild status. Therefore, the archaeological context of these finds is really important - how were each of the bones originally identified in archaeological reports? Is there associated evidence that the equids were hunted and eaten? The authors must add a section describing the archaeological context in greater detail, and considering the possible implications of the finds. For example, the persistence of sussemione equids through the 2nd millennium BCE implies that researchers must be exceedingly careful in zooarchaeological identifications prior to this period. Moreover, the result might also warrant a discussion about the role of pastoral cultures, or the introduction of domestic horses, in the final extinction of the sussemiones. Without such a summary, it is incomplete to suggest that their final extinction is a result of inbreeding and reduced genetic diversity.

Reviewer #2 (Public Review):

Dawei Cai and colleagues present a series of firsts and new discoveries including (1) the first high coverage genome from an equid that is unequivocally an extinct species and (2) demonstrating that Equus (Sussemionus) ovodovi survived into the late Holocene, belonged to a lineage sister to all extant non-caballine equids, and underwent extensive admixture soon after its divergence from non-caballine equids.

The manuscript is clearly laid out and well written. The analyses are conducted logically and to a high standard, which includes testing the impacts of reference genome choice and DNA misincorporations in nearly all analyses. The conclusions are mostly supported by the data but some methodological clarifications and discussion of conflicting results are required.

Strengths/weaknesses of the five main findings:

(1) Sussemiones survived into the late Holocene.<br> Strengths: It is remarkable that Sussemiones survived so late into the Holocene, but the authors present radiocarbon evidence from multiple skeletal elements and sites supporting the late survival hypothesis. Combined with the genomic evidence, there is very strong support for this assertion.<br> Weaknesses: The manuscript does not describe the radiocarbon methods, such as which laboratory these analyses were conducted in and whether samples were ultrafiltered or not. A description of the calibration methods and curve version used is also lacking.

(2) Equus (Sussemionus) ovodovi is a sister lineage to all extant non-caballine equids.<br> Strengths: The authors construct both exome and candidate neutral loci phylogenies from across the nuclear genome, including testing the impact of two different reference genomes. All analyses support the same placement of E. ovodovi with 100% bootstrap support. The assertion is therefore strongly supported.<br> Weaknesses: No weaknesses identified.

(3) The early evolution of the lineages leading to the E. ovodovi and the three main extant equid groups was characterised by extensive admixture.<br> Strengths: The authors use three different methods to infer the presence, extent, and/or direction of admixture.<br> Weaknesses: A major weakness here is the incongruence between the TreeMix models and the D-statistics and G-PhoCS analyses (the latter two give a coherent story). Given the large admixture events determined by G-PhoCS, it seems concerning that these events are not recovered as migration edges in the TreeMix analyses.

(4) Population size of E. ovodovi over the past 2 Myr.<br> Strengths: The authors correct for differences in genome coverage to allow for the PSMC profiles between four equid taxa to be comparable, allowing for comparison of population size trajectories.<br> Weaknesses: In Figure 4, the presented PSMC profiles are a mix of those with or without transitions (comparing profiles to Figure - 4 figure supplement 1). Given that the exclusion of transitions impacts the PSMC profiles, these should be standardized in Figure 4 to give a fair comparison.

(5) Inbreeding was a contributing factor to the extinction of E. ovodovi.<br> Strengths: The authors determine heterozygosity and runs-of-homozygosity in E. ovodovi and compare these to all living equids, and find that E. ovodovi had low heterozygosity although not excessive runs-of-homozygosity.<br> Weaknesses: The authors should be more cautious with their interpretation/phrasing on L383-384, given that inbreeding and/or reduced genetic diversity has not been demonstrated as the extinction driver.

Reviewer #1 (Public Review):

This study fused images from CMR and T1 mapping to reconstruct 3D anatomical models of the heart for HCM patients. Using the model, they investigated potential contributions of diffusive fibrosis to arrhythmogenesis of the heart model in response to focal stimulus. They found that the diffusive fibrosis contributed to increased incidence of ventricular arrhythmias.

The study is of some interest. However, there are some concerns regarding its publication in its present form.

1) Details are unclear about how the imaging segmentation and alignment were conducted. Especially when CMR and T1-mapping data were fused together, how the slice images were aligned as mismatch is of a challenge and can affect the simulation results and conclusion.

2) It is unclear what is the spatial resolution of the CMR, and how the spatial resolution of about 330 micrometre was achieved for the finite element model.

3) It is unclear how the incorporation of fibre structures was done and validated. Given that fact that at different stages of HCM and individual differences, the fibre structures are different in different subjects. Without consideration of this, conclusions based on the diffusive fibrosis are non-conclusive.

4) It is also unclear how the physiological model for the HCM was developed and validated for the patient-specific model.

Reviewer #2 (Public Review):

The overall aims of this work are to use computer models of electrical activation to (i) understand how remodelling of structure and function in hypertrophic cardiomyopathy promotes ventricular arrhythmias, and (ii) to assess whether a model-based approach could be used to predict the risk of arrhythmias in specific patients.

The approach taken by the authors builds on previous work by this group, where a personalized mesh representing the ventricles is constructed from automated analysis of cardiac MRI. Models of human electrophysiology are then solved on this mesh with simulated pacing, to identify vulnerability to arrhythmias.

The major strength of this approach is that it presents an environment within which an investigation that may be technically difficult, time-consuming, or unethical in a patient can be undertaken to guide treatment or assess risk. It is very promising.

However, although the methodology used is sound, there are important assumptions that underpin this approach and limit the extent to which the outcomes are trustworthy. These include:

1. MRI physics. The MR signal is produced from a finite volume of tissue, which is about an order of magnitude larger than the size of finite elements used in the computer model. Thus, the personalised mesh may not capture small scale features that could be important for initiation of arrhythmias.

2. Cardiac mechanics. The mesh used to solve the computer model is static, whereas the heart contracts with every beat. Mechanical contraction not only changes the shape of the heart and the thickness of the ventricular wall, but also feeds back into electrical activity.

3. Population variability. The electrical model used in this study is a standard representation for the human ventricles. This is adjusted to capture some features of electrical activity in fibrotic regions, but these are not well characterised so assumptions are made. The patterns of electrical activation and recovery in the human heart vary from place to place within the human ventricles, with time within the same patient in response to external effects including autonomic activity, and from one patient to another. Hypertrophic cardiomyopathy is usually a progressive disease, so patterns of fibrosis may change over time.

Nevertheless, this study has found evidence that diffuse fibrosis plays a role in the vulnerability to arrhythmias in hypertrophic cardiomyopathy, and found that, in this group of 26 patients, a model-based approach can provide a more accurate risk stratification than other methods based on patient clinical data.

Reviewer #3 (Public Review):

In their paper the authors set out to develop a novel ventricular arrhythmia risk assessment in the setting of hypertrophic cardiomyopathy.

The authors combine contrast enhanced MRI with T1 mapping data to construct computer models of HCM patient hearts that include patient specific distribution of fibrosis. They then use these personalized hearts to assess the propensity for ventricular arrhythmias.

The others demonstrate using a computational approach that diffuse fibrosis increases vulnerability to ventricular arrhythmia. It's an important finding especially because diffuse fibrosis is not a parameter that is typically tracked in HCM patients.

The potential impact of the work described in the study is high. By accounting for diffuse fibrosis as a risk factor for ventricular arrhythmias in HCM patients, the authors demonstrate improved sensitivity, specificity and accuracy compared to other risk predictive parameters. It is feasible that as a result of the study that diffuse fibrosis may be tracked in these patients as an indicator of propensity to deadly arrhythmias.

Reviewer #1 (Public Review):

Understanding the effects of cystic fibrosis-causing mutations in CFTR channel function, stability or expression is important because this determines the choice of treatment for the disease. The negative effects on gating of a common disease-causing mutation, R117H, were puzzling because it is located at an extracellular loop, away from the sites that control ATP-dependent gating in the channel. In the present manuscript, Simon and Csanády identify a hydrogen bond between the side chain of R117 and the main-chain carbonyl of E1124 that is present in a structure that is thought to closely represent the open state and absent in a structure representative of the closed state of the protein. The authors perform molecular modeling to identify that a residue deletion at E1124 is predicted to disrupt this interaction, and show that CFTR channels with the deletion behave very similar to those carrying the single R117H mutation in regards to channel closure kinetics in a mutant background lacking ATP hydrolysis, consistent with the proposed interaction found in the structures. Using two different mutant backgrounds to disrupt ATP hydrolysis, and channels carrying either the R117H mutation or the E1224 deletion, or both perturbations, the authors measure the rates of channel opening and closure to both the resting state and a short-lived flickering closed state that occurs within open bursts of ATP-bound channels. From their measurements, the authors perform mutant cycle analysis and find that the two perturbations have non-additive effects consistent with a disruption of a stabilizing interaction that occurs only in the open state but not in the deactivated state or the short-lived closed state that occurs within open bursts. By comparing the predictions from kinetic models of channel function, the authors find that the energetics of disrupting the open state-stabilizing interaction can fully explain the major effects of the R117H mutation in the background channels utilized in the study, and suggest that a similar mechanism operates in WT CFTR channels carrying the R117H mutation. The data is of high quality, the analysis is carefully done, and the conclusions are well supported by the evidence that is provided, and are of both clinical and mechanistic relevance. Importantly, the finding the interaction established by R117H occurs only in the open state provides a relevant constraint for associating structures to specific functional states of the channel.

Although the conformational changes associated with formation or disruption of the interaction involving R117 are evident in the published structural models, it would be important to confirm whether these are supported by the experimental maps. Few details and data are provided in relation to the molecular dynamics simulations/molecular modeling that were carried out, which precludes evaluating the robustness of the calculations. The authors utilize the measurements in the D1370N background (Figure 3A) to calculate gating parameters from the kinetic models, but the burst-length in the R117H, E1124Δ, and R117H+E1124Δ appear to short in the recordings, raising concerns about the robustness of the parameters associated with the intra-burst transitions. Also regarding these intra-burst transitions, whereas the observed effects for the gating equilibrium constant are consistent with the authors' interpretation, the effects of the structural perturbations on the associated rate constants are intriguing: if the interaction occurs only in the open state, then the transition from the Cf state to the open state should not be affected by any of the perturbations, but this rate seems to also become altered, perhaps suggesting some degree of stabilization by the interaction in the Cf state or a destabilization of the transition state.

Reviewer #2 (Public Review):

Cystic Fibrosis (CF) is the most common fatal genetic disease in Caucasian populations. Disease-associated mutations of the CFTR gene often result in defects in opening/closing (or gating) of the CFTR channel. Recent breakthroughs in the development of drugs that target the CFTR protein itself pave the way for structure-based drug design, the success of which depends on our comprehensive understanding of how mutations cause functional abnormalities and how pharmaceutical reagents may act on CFTR channel folding and gating dynamics. Current studies by Simon and Csanady were meant to address the former by focusing on one mutation R117H commonly found in CF patients with less severe symptoms.

Major strengths of the manuscript include diligent utilization of the mutant cycle analysis, high-quality single-channel recordings and detailed data interpretations in the context of gating energetics.

This reviewer is more concerned with authors' structural interpretations of the data as there is no direct evidence for the assumed mutation-induced disruption of the hydrogen bond (e.g., E1124Δ) because it is the backbone carbonyl, not the side-chain, at position 1124 that is involved in hydrogen bond formation. Some molecular dynamic simulations were carried out to support this assumption, but the reported change of the hydrogen-bond distance by E1124Δ seems quite small. It is questionable if this change is adequate to explain quantitatively the reported 2.6 kT enthalpy change. Moreover, despite the fact that the hydrogen bond is found in the phosphorylated, ATP-bound structure of human CFTR, it is noted that this structure does not show a patent anion conduction pathway. Thus, some precautions are warranted when this structure is taken literally as the "open" channel conformation. Indeed, there are major discrepancies regarding pore-lining residues shown in this structure and those based on functional studies, suggesting that additional conformational changes in the transmembrane segments likely take place for the channel to sojourn to the true open state.

A few minor discrepancies between the current report and previous publications, although not necessarily affect their conclusions, may need clarification.

Reviewer #3 (Public Review):

The cystic fibrosis transmembrane conductance regulator (CFTR) is an anion channel crucial for salt and water transport across epithelial cells. CFTR mutations causes its dysfunction, and the dysfunction causes cystic fibrosis.

R117H is one of the most common mutations in cystic fibrosis. It was known that the R117H mutation affect ion channel gating and reduce conductance of the channel, but the molecular mechanism underlying is unclear. In this paper, the authors produced high-quality data through a very robust electrophysiology and thermodynamic approaches, and the data showed that a hydrogen bond between the arginine 117 side chain and the glutamate 1124 main chain carbonyl group on the extracellular side of CFTR stabilizes the open state of the ion channel. Therefore, the R117H mutation lowers the conductivity of the ion channel by breaking the hydrogen bond and induces a malfunction of CFTR.

There are five classes of cystic fibrosis mutations. By elucidating the molecular mechanism of these mutations, we can consider their application in therapeutics. Since the R117H mutation is a representative of Class IV CFTR mutations, which induce malfunction of ion conductance through the channel, researches on it, like presented in this paper, will guide the development of therapeutics targeting Class IV mutation.

Reviewer #3 (Public Review):

1) The two algorithms presented are essentially a low-pass and high-pass filter on binarized odor. As such, it may not be so surprising that there is a tradeoff between which algorithm works better depending on the frequency content of different environments. The low-pass filter (algorithm 1) works better in environments with mostly low-frequency fluctuations (boundary layer plume, low wind-speed, high diffusivity) while the high-pass filter (algorithm 2) works better in environments with mostly high-frequency fluctuations (high windspeed, low diffusivity). To understand what is essential in these algorithms I think it would be useful to (1) compare the two algorithms to a "null" algorithm that drives upwind orientation whenever odor is present (i.e. include thresholding and binarization but no filtering), (2) compare navigation success metrics directly to the frequency content of different environments, (3) examine how navigation success depends on the filtering cutoff of the two algorithms (tau_on and tau_w). Comparing to the null algorithm with no filtering I think is important to determine whether there is actually a tradeoff to be made, or whether a system that can approximate a flat transfer function (or at least capture all relevant frequencies in the environment) is ideal and must be approximated with biological parts.

2) While the two algorithms presented here present a nice conceptual division, biological filtering algorithms are likely to incorporate elements of both. For example, the adaptive compression algorithm of Alvarez-Salvado (which is eliminated in the simplification used here) provides some sensitivity to odor onsets and is based on well-described adaptation at the olfactory periphery. Synaptic depression algorithms likewise provide sensitivity to derivatives as well as integration over time, and synaptic depression with multiple timescales has been described in detail at various stages of the olfactory system. A productive extension of the work done here would be to explore the utility of biophysically-motivated filtering algorithms for navigation in different environments.

3) It would be helpful in the Discussion to present a clearer picture of what the frequency content of natural environments is likely to be. For example, flies stop walking at windspeeds above ~70cm/s (Yorozu 2009). In contrast, flies in flight are likely to encounter much sparser and high frequency plume encounters, as they are moving through the air at much faster speeds and because odors encountered here would be away from the boundary layer. Therefore the best test of the tradeoff hypothesis would likely be to compare temporal filtering of odor plumes by neural circuitry in flying vs walking flies. This would connect to the literature in motion detection as well, where octopamine release during flight causes a speeding of the motion detection algorithm.

Reviewer #1 (Public Review):

The authors used microelectrode recordings in patients with drug-resistant epilepsy, automatically detected interictal and ictal epleptiform discharges, and measured the directions of travel of both. They found that most interictal discharges are traveling waves with two opposite-facing predominant directions of travel. Furthermore, they found that the direction of travel for interictal traveling waves was similar to that for ictal discharges. They conclude that studying interictal discharge propagation can reveal information about seizure propagation. This is an elegant approach to answering the important question of whether the spatiotemporal propagation of IEDs can elucidate seizure propagation.

The strengths of this paper are that it addresses an important question and uses elegant quantitative techniques to try to answer it in human subjects. It is relevant to epilepsy clinical care as well as our understanding of how information spreads in the brain.

The authors' aims are largely met, but there are some questions about the methods and results that would be important to address to be sure that their conclusions are supported. These are:<br> 1. To be sure to demonstrate validation of their discharge detection methods. It would be important to report on positive predictive values for a random subset of detections, particularly on a testing set of data not used for training?

2. The authors write in the abstract and discussion that interictal discharges (IEDs) traverse the same path as ictal discharges (SDs), but the angle between the SDs and IEDs was 24 degrees, and the IEDs weren't exactly opposite the ictal wavefront (150 degrees). This raises questions as to whether these two classes of abnormal activity really follow the same path.<br> It would be important to account for the sizable angle between their observed paths. In some ways this seems to refute more than support the main conclusions.

3. The influence of sampling error and method of recording are important to discuss, and how they might alter the brain and its conduction of abnormal activity. It would be important to be clear about what effects if any these factors have on the conclusions and information recorded.

4. It would be important to explain how seizures were defined and the rationale for this definition. This is an elusive topic and one in great debate, so this would be helpful to understand your thinking and also to assess the paper's relevance to clinical epileptic events.

Overall this is a very interesting study on an important topic, and one that is relevant to both basic science research and the clinical evaluation and care of patients with epilepsy.

Reviewer #2 (Public Review):

In this manuscript, Smith et al analyze a dataset comprising multi-day microelectrode recordings acquired for the purpose of surgical planning in human epilepsy patients. The authors evaluate the propagation of 3 activity modes: 1) interictal epileptiform discharges (IEDS); 2) the ictal wavefront (IW), which is a slowly expanding wave of tonic neural firing; and 3) Seizure discharges (SD), which are rapidly travelling waves of activity that follow the IW. Specifically, the key findings are: 1) IED propagation direction is non-uniform and most commonly bimodal, with the two modes being antipodal. 2) SDs and IEDs propagate in approximately the same direction, which is approximately antipodal from the IW 3) there is a strong relationship between the predominant IED direction and recruited SD direction, also between the auxillary IED direction and pre-recruited SD direction. These findings support the potential utility of interictal spikes in surgical planning for refractory epilepsy. The ability to use IEDs would be particularly beneficial as they are considerably more frequent than seizures and typically occur without withdrawal of patients from their medication. This work is interesting and potentially important, however some additional analysis is necessary to support the potential translational utility of this approach as well as revision to improve readability.

Reviewer #3 (Public Review):

Interictal epileptiform discharges, known to occur between seizures in patients with epilepsy are not thought to provoke an ictal event. Pre-ictal epileptiform or seizure discharges on the other hand, occur prior to a seizure. However, it is unclear if these two pathological events are connected spatiotemporally or at single- or multi-unit level. This question is fundamental to our understanding of epileptogenesis, because there exists a subgroup of patients who have interictal epileptiform discharges on their scalp electroencephalogram (EEG) and seldom have a seizure. At the same time, there are patients who have completely normal EEGs but have frequent unprovoked epileptic seizures.

Smith et al., using complex computational methods were able to model epileptiform discharges in the seizure core versus away from the core. They showed that interictal discharges are predominantly bidirectional, whether in the seizure core or away from it. The technique to model MUA is difficult because the units can be heterogenous in the seizure core. The process to sort through MUAs and IED waveforms from multiple days of data, is labor intensive and requires intense dedication. Their strengths are using a technique that they have showed to be reproducible from multiple groups. In this paper, the authors employ their traveling waveform modeling from their prior well published work to interictal discharges that were collected over multiple days with or without seizures. The strengths of being able to study traveling ictal wavefront and compare seizure discharges to interictal discharges can only be achieved on a Utah array. This is also their weakness. The Utah array is implanted on the neocortex and close to the seizure zone, it is hard not to imagine the bias that exists when they model the traveling wavefront of the IEDs to that of their seizure discharges. It is unclear if this bidirectional IED hypothesis applies to mesial temporal lobe seizures where the ictal onset is far from the neocortex.

Reviewer #1 (Public Review):

This is an impressive structural and molecular mechanistic study in the modes of action of a series of SERMs / SERDs. Such large scale comparative studies on ligands binding to ER (or proteins in general) and of great value to the field as these studies greatly surpass studies on isolated singular examples in terms of impact and relevance. It is in the comparison of a large set of compounds that trends and design principles can be extracted.

Also of high relevance is the study of these SERMs/SERDs in the context of the resistance acquiring ER537/538 mutations. Molecular understanding of the underlying mechanisms is lacking here and the authors make a valuable contribution.<br> The data on the pyrrolidine methyl-substituted Lasofoxifenes are fascinating.

Reviewer #2 (Public Review):

Fanning and Greene present an important addition to the idea that one of the two major classes of ER antagonists, selective estrogen receptor degraders (SERDs), do not require degradation for efficacy. They used a chemical biology approach to develop derivatives of lasofoxifene analogs that have a single methyl group added and identified isomers that either stabilize or destabilize the receptor. They profiled these compounds and a panel of clinical and preclinical antagonists for effects on receptor levels, post-translational modifications, coactivator recruitment, transcriptional activity and proliferation. They generated two major conclusions: degradation does not correlate with efficacy; and that the patterns of pharmacology across the panel of ligands are markedly different in the WT ER or in two constitutive mutants that drive metastatic disease and treatment resistance. They presented a series of crystal structures with 8 ligands bound to the WT or Y537S ER. A more careful interpretation of these structures might be needed to make conclusions regarding the structural basis of efficacy, but the structures are of high interest in revealing how the Y537S mutant changes how the ligands interact with the receptor to drive differences in pharmacology from the WT receptor.

Reviewer #1 (Public review)

The paper demonstrates the role of Pou domains for various sensory cells. Using CRISPR to delete the gene, the authors show an incomplete deletion of sensory cells.  Further evidence shows problems with the formation of mechanosensory cells.<br> Overall, the presentation is clear but can be expanded by adding the role of bHLH genes (Atoh1 is upstream of Pou4f3).  If possible, I suggest expanding the role of TMC as it is the main receptor in mammalian hair cells that connects to the stereocilia.  Please note that the cnidarian organization is a central kinocilium surrounded by microvilli, comparable to choanoflagellates. This paper is a great original presentation but it could provide a broader perspective by expanding on the evolution of Pou IV and by adding a discussion of the evolution of bHLH, Myc and TMC in order to provide this broader perspective.

Reviewer #2 (Public Review):

Whereas the role of POU-IV for the differentiation of cnidocytes and other neurons of Nematostella has been previously characterized (Tourniere et al., 2020), the present study extends previous reports by specifically addressing the role of POU-IV for the so-called "hair cells" of Nematostella (not to be confused with the hair cells of the vertebrate inner ear and lateral line). These presumably mechanosensory hair cells are identified here as postmitotic neurons, which are ciliated and carry a collar of stereovilli - actin-filled microvilli with a long actin-rich rootlet. Using CRISPR/Cas9 based gene editing, the study shows that transgenic animals, in which the POU-IV gene has been disrupted, become touch insensitive. While hair cells can still be identified in these POU-IV mutants, they lack the stereovillar rootlets suggesting that POU-IV is required for proper hair cells maturation, but dispensable for early steps of hair cell specification and differentiation. The study then uses ChIP-Seq to identify direct target genes of POU-IV in Nematostella and to characterize a POU-IV binding motif, which turned to be evolutionarly highly conserved with POU-IV binding motifs in bilaterians. Comparison of the ChIP-Seq data with published bulk and single-cell transcriptome data indicated that POU-IV activates substantially different sets of effector genes (but no regulatory genes) in hair cells and cnidocytes, and identified polycystin1 as a hair cell-specific direct target of POU-IV. Taken together, this suggests that POU-IV had an evolutionary ancient role as a terminal selector gene for mechanosensory neurons, which predated the split between cnidarian and bilaterian lineages but that its function diverged (e.g. by the acquisition of new target genes) during the evolution of cnidocytes as a novel cell type in cnidarians.

Combining gene editing with sequencing and with careful morphological and behavioral characterisation of cellular phenotypes, the study provides valuable new insights into the evolution of sensory neurons. POU-IV class transcription factors have previously been implicated in the specification of mechano- and chemosensory neurons in bilaterians. The present study together with the previous study of Tourniere et al. (2020) now suggests an even deeper evolutionary origin of this cell type in the last common ancestor of eumetazoans. The paper is very well written and the results are beautifully documented. The authors are overall cautious and conservative in the conclusions drawn from their findings. However, two points deserve a more critical discussion, first, the question of which sensory modality is mediated by the hair cells (are these dedicated mechanoreceptors or possibly multimodal cells?), and second, the question whether POU-IV serves as transcriptional activator or repressor in cnidocytes.

Reviewer #3 (Public Review):

In this manuscript, Ozmet et al. investigated the developmental genetics of mechanoreceptor cells (hair cells) in the cnidarian model N. vectensis. They used CRISPR-Cas9-mediated mutagenesis to showed that POU-IV homeodomain transcription factor regulates the differentiation of hair cells in this organism. The authors applied behavior assay, EM observations, and various types of fluorescence labeling to show that pou-iv -/- polyps exhibit defects in touch-sensitive behavior, likely due to the failure of forming the complete stereocilliary rootlet structure near the apical side of the hair cells in those mutant polyps. The authors went on to apply ChIP-seq in N. vectensis and showed that the POU-IV-binding motifs are conserved across Cnidaria and Bilateria. They also used this ChIP-seq dataset to screen for possible POU-IV downstream targets and identified one of the candidate genes, PKD1, as a conserved effector gene that has been shown playing important functions in hair cells across different bilaterian animals. Furthermore, by cross-checking their results with the newly published single-cell transcriptome data from N. vectensis adults, the authors identified the putative cell cluster (c79) of mechanosensory hair cells and confirmed that pou-iv and PKD1 are indeed co-expressed in this cell type. This approach also enabled the identification of additional candidate POU-IV downstream targets, and based on the GO term analysis, it appears that many of these genes are involved in ion transport and sensory perception functions. In summary, the authors provide strong evidence to support that POU-IV likely functions as a terminal selector factor of hair cell development in the sea anemone N. vectensis. Comparing their findings with other animals, the authors suggested that POU-IV factor plays a conserved role in regulating mechanoreceptor differentiation across Cnidarians and Bilaterians and that this regulatory mechanism may represent an ancestral trait dated back to their common ancestor.

This is a detailed study on the role of POU-IV factor during cnidarian mechanoreceptor cell development. In general, the manuscript is well written, most of the data presented are of great quality, and the conclusions of the paper are supported by the data. This study is a significant advancement to our understanding on the evolutionary origin of sensory neurons and the possible genetic mechanisms underlying the diversification of neuronal cell types in animals.

Strengths:<br> The authors applied multiple approaches to examine the developmental process of hair cells in N. vectensis and analyze the molecular genetic functions of POU-IV factor during this process. The generation of gene-specific KO animals with CRISPR-Cas9 mediated mutagenesis in N. vectensis and the characterization of the sensory ability of those mutant animals with behavior assays provide compelling data to show that POU-IV factor is involved in the final maturation of mechanoreceptor hair cells. The ChIP-seq data generated by this study further enabled the authors to analyze the POU-IV factor binding sequences across animals, and the data also help to identify candidate downstream targets of POU-IV factor in N. vectensis system. Because POU-IV factor is likely involved in the development of multiple cell types in N. vectensis (as shown by previous publications and this study), this dataset would be highly valuable in the future for analyzing the differentiation process of different neuronal cell types in N. vectensis. In fact, by comparing with the recently available scRNA-seq resources, the authors have demonstrated the usefulness of this dataset and pointed out several interesting future research directions. Because N. vectensis is one of the few experimentally tractable systems within Cnidarian, which represents the sister group of bilatarian animals, experimental data from N. vectensis would give important mechanistic insights to infer the possible developmental characters in the common ancestor of Cnidarians and Bilaterians.

Weakness:<br> 1. The specificity of the POU-IV antibody staining. It appears that the signals of the POU-IV immune-staining are distributed quite extensively, especially near the basal part of the epithelia ectoderm (Figure 2A-L). And in their Western blot, the authors also noticed an extra band that might represent another protein in the N. vectensis sample that cross-reacted with their anti-POU-IV antibody. Although the authors provided controlled experiment showing that the immunostaining signals disappeared after they pre-absorbed the antibody with the POU-IV antigen (Figure 2 - supplement 2), this result can only demonstrate that indeed their antibody reacts specifically with this antigen. This cannot rule out the possibility that other N. vectensis protein(s) may possess peptide motifs similar to this antigen region and can be recognized by their antibody. Therefore, it would be nice if the authors can do double staining using in situ hybridization with pou-iv anti-sense riboprobe and immunostaining with their POU-IV antibody, to examine whether these two different methods would give overlapping results, so that they can be more confident about the specificity of their POU-IV antibody staining.<br> 2. The electron microscopy data (Figure 5J-L) are not as clear as one would expect showing the differences in rootlet structure between the wildtype and mutant polyps, given that the phalloidin staining results (Figure 5B, E, H) show quite noticeable reduction in the mutant polyp tip. It is very hard to see the stereovillar rootlets (rlst) in Figure 5J, and thus it is very difficult to assess whether these structures are indeed affected in Figure 5K and 5L. In addition, the rootlet structure of the apical cilium in Figure 5K and L (presumably underneath "ci") appears to be less prominent compared to that shown in Figure 1I (labeled as "rlci"). I am not sure whether this is due to differences of the section angle, or whether it really reflects some differences between the wildtype and mutant.

Public Review:

In "Label-free imaging of macrophage phenotypes and phagocytic activity in the human dermis in vivo using two-photon excited FLIM", Kroger et al attempt to visualize macrophages and distinguish their phenotypes using FLIM within the skin of live animals. This study provides data characterizing the fluorescence lifetime signatures of macrophages derived from peripheral blood mononuclear cells or dermal macrophages stimulated with IFNg or IL4, to polarize them towards more M1-like and M2-like phenotypes, respectively. The FLIM signatures are compared to macrophages in other conditions or other cell types, including macrophages ex vivo in skin cryosections, macrophages in forearm of healthy human individuals, as well as mast cells, dendritic cells, fibroblasts, and neutrophils in vitro. The authors then use immunohistochemistry to correlate the FLIM signatures with phenotype markers, CD68 and CD163. Finally, the authors visualize phagocytosis through morphological changes and identify FLIM signatures of phagocytic macrophages.

Strengths:

Using optical methods to non-invasively detect cells has significant interest for clinical and basic studies, and the impact of this study is considered high.

The authors have identified different FLIM signatures for macrophages polarized towards different phenotypes in vitro, and were able to compare these signatures to those of other cell types and macrophages in the skin.

They identified a few cells in the skin that expressed markers associated with macrophage polarization, and also exhibited the FLIM signatures that were established from the in vitro polarization studies.

Weaknesses:

There are some significant technical concerns given that macrophages are a highly heterogeneous population of cells, particularly in vivo during an activation event such as injury. The few cells analyzed in Figure 3 are not sufficient given the heterogeneity of macrophages in vivo. Mixed phenotypes are common in vivo, and it is unclear how the FLIM signatures would correlate to such mixed signatures.

Visualizing a single phagocytosing cell in Figure 5 is also not sufficient to conclude that the method is capable of detecting phagocytosis events.

Finally, the reporting of lifetime alone does not offer insights into the function of the macrophages. The study would be strengthened with further analysis that correlates FLIM signatures with metabolic state (free vs. bound NADPH).

Reviewer #1 (Public Review): 

Although the authors make persuasive arguments regarding the effect of cholesterol being indirect, much is based on comparison of what to expect for a classic allosteric modulator, where the binding affinity is typically far higher. By comparison, the local concentration of cholesterol in the bilayer is extremely high, and may even involve an interplay between different low-affinity sites that have been identified by prior structural studies. 

One argument used by the authors that if cholesterol is an allosteric modulator, its binding to A2AR should depend on whether A2AR is in the active or inactive state (Fig. S4). The observation that the effect is more pronounced at low F7-chol may point to a not-insignificant effect, in particular when noting that the effect decreases at higher F7-chol. The observation that the chemical shift change is in the same direction for agonist and inverse agonist would be incompatible with a single binding site, but perhaps not with the existence of two binding sites. The fact that there is any effect at all on the F7-chol NMR spectrum suggests that F7-chol senses the state of A2AR and therefore must involve transient binding. It appears likely that, as the authors point out, "subtle direct interaction with cholesterol" are in play at the same time as "indirect effects through the membrane".

Reviewer #2 (Public Review): 

What the authors attempt to achieve, and their approaches: 

The author attempt to establish by which mechanisms cholesterol influences the function of the GPCR A_{2A}R, an adenosine receptor. The role of cholesterol on GPCRs has been reported in a number of studies, primarily in cellular experiments, and the authors set out here to clarify the molecular mechanisms. 

To this end, they build upon their recent achievements to produce this protein and reconstitute it in nanodiscs, i.e. discoidal objects comprised of the membrane protein (here: A_{2A}R), lipids (here: POPC, POPG and cholesterol) and a membrane-scaffold protein (MSP) which wraps around this disc of protein+lipid. Nanodiscs allow studying proteins in solution, and are thought to be much more native-like than e.g. detergent micelles. 

The authors first use GTP hydrolysis experiments to quantify the basal activity and agonist potency at cholesterol concentrations from 0 to 13%. The cholesterol effects are weak but detectable. Then they use a single 19F label that reports on the protein's conformation (active, inactive) to show that the protein populates slightly more active states with cholesterol. (again, weak effects). Then they investigate G-protein binding to A_{2A}R in the nanodisc, and find (very!) weak enhancement at 13% cholesterol. These data point to weak positive allosteric modulation by cholesterol. <br> They then use molecular dynamics simulations to probe the allosteric communication, using a recently proposed framework (Rigidity-transmission allostery). Doing these simulations in the presence of cholesterol (postions of cholesterol from X-ray structure) and absence. This analysis shows again only very weak effects of cholesterol, and this time the effect is opposite, i.e. negative allosteric modulation by cholesterol. Then they use 19F-labeled cholesterol analogues to probe by NMR the state of cholesterol (bound to protein?). Lastly, they use Laurdan fluorescence experiments and pressure NMR to establish that (i) the lipids become more ordered when cholesterol is present, and (ii) if one achieves such ordering even without cholesterol - namely by pressure - one may achieve similar effects as those that cholesterol has. 

Collectively, these data lead them to conclude that cholesterol has a (weak) positive allosteric effect on this receptor, and this effect is not a direct one, but goes via modulation of the membrane properties. 

Major strengths and weaknesses of methods and results: 

The study addresses an important question, which inherently is difficult to answer: the effect of cholesterol is poorly understood and such studies require to work in an actual membrane. The authors do a careful combination of different methods to achieve their goal of identifying the mechanisms. 

Despite combining several methods, several of them have their inherent problems: 

(i) the nanodisc is too small to properly mimic the membrane environment, and it does not allow reaching relevant cholesterol concentrations. Moreover, it is not clear (to me) if one can exclude e.g. interactions of the protein with the surrounding MSP, or of cholesterol with MSP (see (iii) below). 

(ii) the state of the protein (inactive, active) is probed with a single NMR-active site. The effects are small and I am not convinced that one shall interpret changes as small as the ones in Figures 3 and 4. In particular, how does this single probe behave at high pressure? Does it reflect an active state at 2000 bar pressure - where possibly other effects (unfolding?) may occur? 

(iii) the data in Figure 6 (19F of cholesterol analogs) are hard to interpret. Is cholesterol bound to the protein? Does the 19F shift reflect binding to the protein? or interactions within the confined space of the disc? or with MSP? The two analogs do not tell a coherent story. 

(iv) the pressure NMR study (Fig 7D) has weaknesses. The authors implicitly assume that pressure acts on the membrane, leading to more ordering. (They do recognize the possibility that pressure may have an effect on the protein directly, but consider that this direct effect on the protein is minor.) I think that their arguments are possibly incorrect: they apply here pressure onto a sample of nanodiscs, but all studies they cite to justify the use of pressure on membranes dealt with extended lipid bilayers (liposomes). To me it is not clear what is the lateral effect of pressure onto a nanodisc. Can water laterally enter into the bilayer and thus modify the lipid structure? I also note that previous pressure-NMR studies on a GPCR in micelles (rather than nanodiscs) showed a shift toward the active state. As a micelle is a very different thing than a nanodisc, this suggests that the pressure effect is, at least in part or predominantly, on the protein itself. 

On top of the weakness of the pressure NMR experiment to identify what actually happens to the disc, it is not clear either how to interpret the 19F shift at very high pressure (Fig 7D). Given that there is only a single NMR probe, far out in an artificial side chain, it is difficult to assess the state of the protein. 

Appraisal of whether the authors achieved their aims, and whether the results support their conclusions:

The manuscript presents a number of observations which can be interpreted in the way that is proposed here, but as stated above, several experiments have their own problems: from the small disc with little cholesterol to questions in interpreting 19F of cholesterol analogs and high-pressure NMR data. Collectively, the interpretations are somewhat tentative in my view. 

Likely impact of the work on the field, and the utility of the methods and data to the community: 

In my view, the conclusions of this manuscript are fairly tentative (see above). Nonetheless, given the difficulty of studying these effects by any experimental method, this work may have an impact in the GPCR field, and more broadly in the membrane-protein field. I hope that reviewers from those fields can clarify this question. From my NMR perspective I would say that the paper is a nice combination of methods, I feel that the authors carefully assembled the methods and thought about pitfalls -- but a number of issues remain, as listed above. They make it hard to reach final conclusions. Some of the data appear contradictory (e.g. negative allosteric modulation seen in MD, contradicts the positive allostery seen by other methods; the two cholesterol analogs are not consistent).

Reviewer #3 (Public Review): 

There has been much interest in whether cholesterol modulates the function of G protein-coupled receptors and, if so, how. Here Huang, Prosser and colleagues examine this question for the Adenosine A2 receptor using purified receptor, biophysical measurements in nanodiscs of well-defined composition, and a variety of functional measurements. They convincingly show that cholesterol does modulate A2AR function, but not via direct binding. Instead cholesterol exerts its impact by altering the thickness and dynamics of the membrane milieu. 

This paper is extremely well written and seems to be typo free. State-of-the-art methods are used and this paper is thorough and rigourous. While there have been many papers on whether and how cholesterol interacts with and modulates GPCR function, this paper distinguishes itself in it thoroughness and the use of NMR spectroscopy not only to quantitate the functional states of the receptor under various conditions, but also to look directly at fluorinated cholesterol analogs as they are titrated into the protein, an approach that revealed no evidence for long lifetime binding of these analogs to the receptor. I found this paper to be mostly compelling in its conclusions but recommend that the authors address concerns regarding the cholesterol analog data.

Reviewer #1 (Public Review):

The model proposed here is the first large-scale model that actually performs a cognitive task, which in this case is working memory but could easily extend to decision making in general as is acknowledged by the authors. Briefly, each of the 30 areas are simulated as a rate, Wong-Wang circuit (i.e. two excitatory pools inhibit each other through a third, inhibitory population). The authors use previously collected anatomical data to constrain the model and show qualitatively match with the data, in particular how mnemonic activity emerges somewhat abruptly along the brain hierarchy.

Strengths:

Previous models have focused on neural dynamics during the so-called "resting state", in which subjects are not performing any cognitive task - thus, resting. This study is therefore an important improvement in the field of large-scale modelling and will certainly become an influential reference for future modelling efforts. As typically done in large-scale modelling, some anatomical data is used to constrain the model. The model shows several interesting characteristics, in particular how distributed working memory is more resilient to distractors and how the global attractors can be turned off by inhibition of only top areas.

Weaknesses:

Some of these results are not clear how they emerge, and some "biological constraints" do not seem to constrain. Moreover, some claims are slightly exaggerated, in particular how the model matches the data in the literature (which in some cases it does not) or how somatosensory working memory can be simulated by simply stimulating the "somatosensory cortex".

This paper has two different models, one being a simplified version of the main model. However, it is not very clear what the simplified model adds the main findings, if not to show that the empirical anatomical connectivity does not constrain the full model.

Reviewer #1 (Public Review):

Hickey et al. studied chromatin landscape changes in early Zebrafish embryos at three distinct stages: preZGA, ZGA and postZGA. Using ChIP-seq on these time-course samples, they examined developmental genes at their regulatory elements, including promoters and enhancers, that carry nucleosomes enriched with histone variant H2A.Z, as well as post-translational modifications H3K4me1 and H3K27ac, but with low DNA methylation, in early-stage embryos prior to turning on zygotic gene expression. During embryogenesis, this group of elements recruit a Polycomb Repressive Complex 1 (PRC1) component Rnf2 to "write" the ubiquitinated H2A or H2A.Z. The mono-ubH2A/Z then recruits a PRC2 component Aebp2 to further "write" the H3K27me3 repressive mark to silent these developmentally regulated genes in later stage embryos. Using a small molecule to inhibit Rnf2 abolishes H3K27me3 and leads to ectopic gene expression.

Most of the data for the first half of this manuscript are presented in a clear and logic manner. The conclusions based on these correlation assays are quite obvious and well supported (except a few minor points raised below for clarifications, #2-#3). The major concern is for the second half of the manuscript where a drug is used to draw causal relationships (see point #1 below).

1. Using small molecule could have secondary effects. It also seems that the drug-induced defects cannot be reversed after being washed away. Furthermore, this drug treatment eliminates almost all H3K27me3 genome-wide, regardless of their occupancy status with mono-ubH2A/Z, making it difficult to make the causal connection between the prerequisite mono-ubH2A/Z occupancy and the subsequent de novo H3K27me3. I think it is important for the authors to address this point more directly as this is the main conclusion of this work. Could the authors perform genetic analyses to confirm the specificity of the phenotypes?

2. Page 8, line 160-163: "Curiously, enhancer cluster 5 (Figure 2A) was unique - displaying high H3K4me1, very high H3K27ac, and open chromatin (via ATAC-seq analysis; Figure 2 - figure supplement C, D) - but bore DNA methylation - an unusual combination given the typical strong correlation between high H3K4me1 and DNA hypomethylation." I suspect that the authors are talking about the chromatin state at pre-ZGA stage as this is the only stage DNA methylation pattern was included, but it is hard to tell that this cluster displays high H3K4me1 at all.

3. Page 10, line 206-207: "PRT4165 treatment also conferred limited new/ectopic<br> Aebp2 peaks (Figure 4C, clusters 4, 6, 7,8)", it seems that clusters 4, 6, 7, 8 together are not "limited" compared to clusters 1, 3, and 5, and could be even more abundant.

4. In the context of studying the chromatin state of developmental genes in early vertebrate embryos, there are two recent publications in mouse embryos which also investigated the crosstalk between mono-ubH2A and H3K27me3 at the ZGA transition in mouse (https://doi.org/10.1038/s41588-021-00821-2 and https://doi.org/10.1038/s41588-021-00820-3). It would be informative to add some discussion for comparisons between these two vertebrate organisms.

Reviewer #2 (Public Review):

One model for polycomb domain establishment suggests that PRC2 adds H3K27me3 first, and then recruits PRC1 for silencing. The key evidence for this model is the H3K27me3-binding module CBX proteins in canonical PRC1 complexes. This model has been revised by recent studies, and it is now well recognized that the polycomb domains can be de novo established in a different order. In other scenarios, including X inactivation, a non-canonical PRC1 complex that lacks CBX proteins catalyzes ubH2A first, and PRC2 complex is subsequently recruited through recognizing ubH2A modification by its Jarid2 and Aebp2 subunits.

In this manuscript, Hickey and co-workers analyzed the temporal change of various epigenetic marks around ZGA stages during zebrafish early embryo development. Based on their experimental data and bioinformatic analysis, they suggest that polycomb establishment in zebrafish embryo is following the 'non-canonical' order, in which H3K27me3 establishment is dependent on ubH2A pre-deposition and the following recruitment of Aebp2-PRC2 complex. Moreover, they suggest that polycomb-silenced developmental genes are solely repressed by ubH2A, independent of H3K27me3. Overall, the functional analysis (RNF2 inhibitor experiments) conducted in the current study highlights the critical function of PRC1 and ubH2A in silencing developmental genes during early embryo development. Moreover, this study provides clues that could reconcile with the earlier observations that H3K27me3 seems largely dispensable for silencing developmental genes in zebrafish early embryo (e.g. PMID: 31488564).

The main concern is two similar studies have just been published in Nature Genetics using mouse early embryos, and the observation of this manuscript largely agree with the two mouse studies, rendering the novelty of this study.

In addition, certain conclusions in the manuscript requires further experimental support:

1. While the authors claim that H3K27me3 is established after ZGA, it is quite surprising to me that they did NOT analyzed the H3K27me3 pattern before ZGA. While IF staining suggests a minimal level of H3K27me3 before ZGA (Fig1 S2B), previous ChIP-seq analysis demonstrate that H3K27me3 are present (e.g. PMID: 22137762).<br> 2. While the RNF2 inhibitor experiment clearly demonstrates that PRC1 is required for the deposition of both ubH2A and H3K27me3, that does not necessarily mean that PRC1-mediated ubH2A deposition precedes H3K27me3. The establishment and maintenance of polycomb domain usually requires the crosstalk and reinforcement between polycomb complexes. Therefore, the deficiency in either PRC1 or PRC2 complex may lead to the decreased level of both marks. To clarify a hierarchical order of the polycomb domain establishment, a phenotypic analysis of PRC2 deficiency is also necessary.<br> 3. Parental difference. As shown in Fig.1B, ubH2A level varies greatly in sperm and egg, which suggests that the reprogramming process of ubH2A (and perhaps H3K27me3) distribution could be significantly different for the two parental alleles. It would be interesting to analyze the ubH2A and H3K27me3 distribution in germ cells before fertilization.<br> 4. The role of Aebp2 subunit. Given the well-characterized function of Aebp2 in recognizing ubH2A, an involvement of Aebp2-PRC2 complex in establishing H3K27me3 on PRC1 pre-deposited regions is not unexpected. Indeed, Aebp2 co-localized well with ubH2A marked regions (Fig.3). However, an issue not clarified in the manuscript is whether Aebp2 is the sole subunit for the recruitment of PRC2 to ubH2A marked regions. Paralleled analysis of the changes for Aebp2 and H3K27me3 upon RNF2 inhibitor treatment is necessary, and Aebp2-dependent and -independent regions should be separately classified for analysis.<br> 5. Role of PRC1 on the temporal regulation of gene expression during early development. The authors only analyzed the RNA-seq results for RNF2i treated embryos post ZGA. Therefore, it is currently not clear if the role of PRC1 in transcriptional repression is restricted to post-ZGA stages. RNA-seq analysis of RNF2i treated embryos on those stages are also warranted.

Reviewer #3 (Public Review):

Hickey et al. continue their groups investigation of how vertebrate embryos establish the proper chromatin regulatory context around the time of zygotic genomic activation for both housekeeping genes, which are spatially and temporally widely transcribed, and for developmental genes, which must remain off but must remain available for later lineage-restricted expression. Employing an array of ChIP-Seq analyses of chromatin marks and specific chromatin modifiers (e.g. Rnf2) on pre ZGA, ZGA, and post ZGA zebrafish embryos, they find increased deposition of H2Aub1 by Rnf2-PRC1 at Placeholder nucleosomes (enriched at developmental genes) which, in their model, serves to recruit Aebp2-PRC2 to lay down H3K27me3 marks to repress these developmental genes until their appropriate time of expression later in development post ZGA. Overall, the conclusions are largely well-supported by the data, the proposed models provide a thoughtful interpretation and roadmap forward for future work. Overall, the study also opens a number of interesting questions including how tissue specific transcription factors fit into the overall process as well.

Strengths of the paper include the quality of the epigenetic profiling across multiple informative chromatin marks with chromatin accessibility and DNAme, with multiple highly concordant replicates for the profiling experiments. The resulting data is necessarily very dense and feature-rich, and the authors are largely successful in conveying the meaning of the many "metagene" displays and browser tracks and developing a coherent story of how these show the transcriptional regulatory events captured around ZGA. The high quality of this data will also be of great value for reanalysis across the field. The 'non-canonical' order of PRC component recruitment further broadens our understanding of the multiple mechanisms by which epigenetic regulators can function.

Perhaps the main weakness, and one which is not in an obvious way technically surmountable for the present study but is worth considering going forward, is the reliance on the single drug PRT4165 to block Rnf2 activity and prevent H2Aub1 deposition. An orthologous genetic tool producing similar results would strengthen this mechanistic insight, but seems challenging given the likely wide-ranging effects of loss-of-function in chromatin modifiers (as noted in the Discussion with death at 3 dpf with Rnf2 LOF) and narrow window of time around the ZGA over just a few hours making inducible (e.g. Cre/lox or CRISPR) approaches likely challenging. The use of MZ mutants for Rnf2 might allow for further understanding of the precise temporal requirements for Rnf2 activity by removing maternal contribution that might function even earlier.

Reviewer #3 (Public Review):

Todesco et al undertake an ambitious study to understand UV-absorbing variation in sunflower inflorescences, which often, but not always display a "bullseye" pattern of UV-absorbance generated by ligules of the ray flowers.

The authors first characterize the extensive variation across the range of two Helianthus species to set the stage for their questions. One of their main goals was to then identify genetic mechanisms of UV-absorbance variation. This portion of the paper is strong, and combines many different methods to arrive at a full picture of what is appears to be the primary genetic mechanism for UV-absorbance variation.

Specifically, the authors grow GWAS panels for two species in BC, Canada. In H. annuus, the GWAS identified a region where genotype was very strongly associated with phenotype, and further analysis in F2-populations confirmed the association. Most variation was in the promoter near a gene that is expected to regulate flavonol production, and the authors found that expression patterns of this gene indeed match those of a known downstream flavonol pathway gene. The authors also verified this function by moving a sunflower copy into an Arabidopsis thaliana line that is null-mutant for the homolog. The sunflower copy restored normal flavonol production. In sunflowers, expression of this flavonol regulator was greater in UV-absorbing regions at the stage when UV-absorbance develops, and it was higher in plants with greater UV-absorbance. Further sequencing revealed that while little coding sequence variation correlates with phenotypes, upstream variation in the promoter region both covaries with alleles at the SNP highlighted by the GWAS and the phenotypes - clearly identifying cis-regulatory variation of this gene as an important driver of phenotypic variation.

Next, the authors focus on what processes might maintain this phenotypic variation, and genetic variation at this locus, across the range of H. annuus. This portion of the work is not as conclusive, but does develop likely explanations that are consistent with the evidence.

Specifically, plants with intermediate and large absorbing UV-phenotypes received more pollinator visits in a field trial. This is suggestive, but not conclusive evidence of fitness consequences via the pollinator pathway for several reasons: visits by pollinators may not translate directly to fitness (pollen limitation is not measured), relative preference for plants with larger UV-absorbance could be due to other phenotypes that also vary among populations (i.e. due to population structure, the effect was not tested within populations or F2 panels), and may or may not hold true in the in their local sites (where pollinator genotype, species composition, or background abiotic conditions could alter preferences). The authors also find ligules that are highly UV-absorbing retain water better, which they argue could be beneficial in stressfully dry sites, or costly in sites that are very hot and humid, where heat-dissipating effects of transpiration would be beneficial. With the current analysis and data, it is unclear if this difference in transpiration is in fact driven by the UV-absorbing pigments (it could be due to i.e. any other phenotype that co-varies due to population structure such as ligule stomata density) though the authors' explanation seems most likely. It also isn't clear how much ligule transpiration increases inflorescence transpiration (the authors may be able to elaborate), or whether ligule transpiration influences fitness in dry environments (though again, I agree with the authors that this seems likely).

The potential effect of UV-absorbance on transpiration fits with the results of phenotypic-environment correlations, which are very tight for average temperature and relative humidity. It also fits with genotypic-environment correlations (for the region identified by GWAS): associations between temperature or relative humidity and variants in the region identified by GWAS are stronger than those between the environmental variables and putatively neutral genomic variation. These results tantalizingly suggest that abiotic environmental variation may select on UV-absorbing phenotypes, though as yet no conclusive link has been made between fitness and genotype, or between fitness and UV phenotype.

In sum, Todesco et al identify the primary genetic mechanism underlying UV-absorbance variation across the range of H. annuus, and provide insight into mechanisms that most likely maintain this phenotypic and genotypic variation across the range of H. annuus and possibly in Helianthus generally. Not only do Todesco et al provide a nearly complete understanding of an interesting and potentially agronomically or horticulturally important phenotype, they also provide a great model of highly collaborative, creative science that combines expertise across fields. I think this manuscript has high potential impact on science on both of these fronts.

Reviewer #1 (Public Review):

Todesco et al. investigate the genetic causes of variation in UV pigmentation in sunflowers as well as the possible biotic and abiotic factors that play a role in natural variation for the trait among populations. Overall I am very enthusiastic about this manuscript as it does an elegant job of going from phenotype to a key locus and then presenting a solid foray into the factors causing variation. I have only a fe relatively minor comments.

The introduction felt a bit short. I was hoping early on I think for a hint at what biotic and abiotic factors UV could be important for and how this might be important for adaptation. A bit more on previous work on the genetics of UV pigmentation could be added too. I think a bit more on sunflowers more generally (what petiolaris is, where natural pops are distributed, etc.) would be helpful. This seems more relevant than its status as an emoji, for example.

The authors present the % of Vp explained by the Chr15 SNP. Perhaps I missed it, but it might be nice to also present the narrow sense heritability and how much of Va is explained.

A few lines of discussion about why the Chr15 allele might be observed at only low frequencies in petiolaris I think would be of interest - the authors appear to argue that the same abiotic factors may be at play in petiolaris, so why don't we see this allele at frequencies higher than 2%? Is it recent? Geographically localized?

Page 14: It's unclear to me why there is any need to discretize the LUVp values for the analyses presented here. Seems like it makes sense to either 1) analyze by genotype of plant at the Chr15 SNP, if known, or 2) treat it as a continuous variable and analyze accordingly.

Page 14: I'm not sure you can infer selection from the % of plants grown in the experiment unless the experiment was a true random sample from a larger metapopulation that is homogenous for pollinator preference. In addition, I thought one of the Ashman papers had actually argued for intermediate level UV abundance in the presence of UV?

I would reduce or remove the text around L316-321. If there's good a priori reason to believe flower heat isn't a big deal (L. 323) and the experimental data back that up, why add 5 lines talking up the hypothesis?

Page 17: The discussion of flower size is interesting. Is there any phenotypic or genetic correlation between LUVP and flower size?

Reviewer #2 (Public Review):

The works seeks to understand the genetic basis and functional significance of variation in bullseye sizes in accessions and near relatives of Helianthus annuus.

Strengths:

The manuscript is very well written and referenced.

The genetic analysis is rigorously conducted with multiple Helianthus species and accessions of H. annuus. The same QTL was inputed in two Helianthus species, and fine mapped to promotor regions of HaMyb111. The allelic variation of the TF was carefully mapped in many populations and accessions. Flavonol glycosides were found to correlate spatially and developmentally in ligules and correlate with Myb111 transcript abundances, and a downstream flavonoid biosynthetic gene. Heterologous expression in Arabidopsis in Atmyb12 mutants, showed that HaMyb111 to be able to regulate flavonol glycoside accumulations, albeit with different molecules than those that accumulate in Helianthus. Several lines of evidence are consistent with transcriptional regulation of myb111 accounting for the variation in bullseye size.

Functional analysis examined three possible functional roles, in pollinator attraction, thermal regulation of flowers, and water loss in excised flowers (ligules?), providing support for the first and last, but not the second possible functions, confirming the results of previous studies on the pollinator attraction and water loss functions for flavonol glycosides. The thermal imaging work of dawn exposed flower heads provided an elegant falsification of the temperature regulation hypothesis. Biogeographic clines in bullseye size correlated with temperature and humidity clines, providing a confirmation of the hypothesis posed by Koski and Ashmann about the patterns being consistent with Gloger's rule, and historical trends from herbaria collections over climate change and ozone depletion scenarios. The work hence represents a major advance from Moyers et al. 2017's genetic analysis of bullseyes in sunflowers, and confirms the role established in Petunia for this Myb TF for flavonoid glycoside accumulations, in a new tissue, the ligule.

Weakness:<br> The authors were not able to confirm their inferences about myb111 function through direct manipulations of the locus in sunflower.

Given that that the flavonol glycosides that accumulate in Helianthus are different from those regulated when the gene is heterologously expressed in Arabidopsis, the biochemical function of Hamyb111, while quite reasonable, is not completely watertight. The flavonol glycosides are not fully characterized (only Ms/Ms data are provided) and named only with cryptic abbreviations in the main figures. This and the differences in metabolite accumulations between Arabidopsis and Helianthus becomes a bit problematic for the functional interpretations. And here the authors may want to re-read Gronquist et al. 2002: PNAS as a cautionary tale about inferring function from the spatial location of metabolites. In this study, the Eisner/Meinwald team discovered that imbedded in the UV-absorbing floral nectar guides amongst the expected array of flavonoid glycosides, were isoprenilated phloroglucinols, which have both UV-absorbing and herbivore defensive properties. Hence the authors may want to re-examine some of the other unidentified metabolites in the tissues of the bullseyes, including the caffeoyl quinic acids, for alternative functional hypotheses for their observed variation in bullseye size (eg. herbivore defense of ligules).

The hypotheses regarding a role for the flavonoid glycosides regulated by Myb111 expression in transpirational mitigation and hence conferring a selective advantage under high temperatures and low and high humidities, are not strongly supported by the data provided. The water loss data from excised flowers (or ligules-can't tell from the methods descriptions) is not equivalent to measures of transpiration rates (the stomatal controlled release of water), which are better performed with intact flowers by porometry or other forms of gas-exchange measures. Excised tissues tend to have uncontrolled stomatal function, and elevated cuticular water loss at damaged sites.

The putative fitness benefits of variable bullseye size under different humidity regimes, proposed to explain the observed geographical clines in bullseye size remain untested.

Alternative functional hypotheses for the observed variation in bullseye size in herbivore resistance or floral volatile release could also be mentioned in the Discussion. Are the large ligules involved in floral scent release?

Reviewer #2 (Public Review):

Kluger and colleagues investigated the influence of respiration on visual sensory perception in a near-threshold task and argue that the detected correlation between respiration phase and detection precision is liked to alpha power, which in turn is modulated by the phase of respiration. The experiments involved detecting a low-contrast visual stimulus to the left or right of a fixation point with contrast settings adjusted via an adaptive staircase approach to reach a desired 60% hit rate, resulting in an observed hit rate of 54%. The main findings are that mutual information between the discrete outcome of hit-or- miss and the continuous contrast variable is significantly increased when respiration phase is considered as well. Furthermore, results show that neuronal alpha oscillation power is modulated in phase with respiration and that perception accuracy is correlated with alpha power. Time resolved correlation analysis aligned on respiration phase shows that this correlation peaks during inspiration around the same phase where the psychometric function for the visual detection task reaches a minimum.<br> The experimental design and data analysis seem solid but there are several concerns regarding the novelty of the findings and the interpretation of the results.

Major concerns:<br> The finding that visual perception is modulated by the respiration cycle is not new (see e.g. Flexman et al. 1974 or Zelano et al. 2016).

There are multiple studies going back decades that show alpha oscillation power to be modulated by breathing (e.g. Stancák et al., 1993, Bing-Canar et al. 2016). Also, as the authors acknowledge, it is well-established that alpha power correlates with neuronal excitability and perception threshold. What seems to be new in this study is the use of a linear mixed effect model to analyze the relationship between alpha power, respiration phase and perception accuracy. However, the results mostly seem to confirm previous findings.

Magnetoencephalography captures broad band neuronal activity including gamma frequencies. As the authors show (Fig. 4) and other studies have shown, the power of neuronal oscillations across multiple frequency bands is modulated by respiration phase. Gamma and beta oscillations have been implicated in sensory processing as well. Support for the author's hypothesis that the perception threshold modulation with respiration is due to alpha power modulation would be strengthened if they could show that the power of oscillations in other frequency bands are not or only weakly linked to perception accuracy.

In the discussion the authors speculate that respiration locked modulation of alpha power and associated neuronal excitability could be based on the modulation of blood CO2 levels. Most recent studies of respiratory modulation of brain activity have demonstrated significant differences between nasal and oral breathing, with nasal breathing (through activation of the olfactory bulb) typically resulting in a stronger influence of respiration on neuronal activity and behavioral performance than oral breathing. The authors only tested nasal breathing. If blood CO2 fluctuations are indeed responsible for the observed effect, there should be no difference in outcome between nasal and oral breathing. Comparing the two conditions would thus provide interesting additional information about the possible underlying mechanisms.

Minor concerns:<br> Figures 1, 3 and 4: label fonts are too small on some panels.

Supplementary figure 3: labels are illegible.

Reviewer #1 (Public Review):

The main finding - that the moment-to-moment relationship between excitability and perception is coupled to the body's slower respiratory oscillation - is novel, interesting, and important for advancing our understanding of how the brain-body system works as a whole. The experiment is simple and elegant, and the authors strike the right level of making the most of the data without doing too much and obscuring the main findings. The primary weakness, in my opinion, is the inability to distinguish between the possibility that respiration modulates excitability and the possibility that respiration modulates something boring like signal-to-noise ratio. In terms of conclusions, I thought the authors stuck pretty well to the data. The one place where the conclusions felt a little bold was in terms of the respiration <> alpha <> behavior relationship, where it felt the authors had already made up their minds re: causality. I agree that it probably makes more sense for respiration to influence something about the brain than vice versa, and the background presented in the Intro/Discussion supports this. However, the analysis only tells us that the behavioral performance was modulated by both alpha and respiration (and their interaction, but this is no way causal). Overall, it will be necessary to differentiate the current interpretation from the possibility that breathing and alpha are two unrelated time courses that influence behavior at the same time (and even interact in how they influence behavior, but just not interact with each other), and I do not believe the phase-amplitude coupling analysis is sufficient for this.

Reviewer #3 (Public Review):

The topic is timely, the study is well-designed, and the work has been performed in a highly competent manner. The authors relate three variables: respiration, alpha power and perceptual performance, constituting a link between somatic and neuronal physiology and cognition. A particular strength is the temporal resolution of respiration effects on cognition (continuous analysis of the respiration cycle). Furthermore, results are well contextualized by very comprehensively written introduction and discussion sections (which, nevertheless, could be slightly shortened).

I have three points of criticism, all meant in a constructive way:

1. I wonder whether the authors could have gone one step further in the analysis of causal mechanisms, rather than correlations. The analysis of timing (Fig. 4d) and the last sentence of the abstract suggest that they imagine a causal role of respiratory feedback on cognitive performance, mediated via coordination of brain activity (in the specific case, by increasing excitability in visual areas). This could be made more explicit by appropriate experiments and data analysis:

1.1. Manipulating the input signal: former studies suggest that nasal respiration is crucial for effects on brain oscillations and/or performance (e.g. Yanovsky et al., 2014; Zelano et al., 2016). Thus, the causal inference could be easily checked by comparing nasal versus oral respiration, without changing gas- and pH-parameters of activity of brainstem centers. Admittedly, this experiment may add significant work to the present data which, by themselves, are already very strong.

1.2. Temporal relations: The authors show that respiration-induced alpha modulation precedes behavioral modulation (Fig. 4d and related results text). Again, this finding suggests a causal influence of respiration on performance, mediated by alpha suppression (see results, lines 318-320). Could the data be directly tested for causality (e.g. by applying Granger causality, dynamic causal modelling or other methods)? If this is difficult, the question of causality should at least be discussed more explicitly.

2. At various instances, the authors suggest that respiration-induced changes in pH may be responsible for the changes in cortical excitability which, in turn, affect behavioral performance. In the discussion, they quote respective literature (lines 406-418). I glanced through the quoted papers by Feldman, Chesler, Lee, Dulla and Gourine - as far as I could see none of them suggests that the cyclic process of respiration induces significant cyclic shifts of pH in the brain parenchyma (if at all, this may occur in specialized chemosensory neurons in the brainstem). Moreover, recent real-time measurements by Zhang et al. (Chem. Sci 12:7369-7376) do also not reveal such cyclic changes in the cortex. Finally, translating oscillatory extracellular pH changes (if existent) into changes in inhibitory efficacy would require some time, potentially inducing delays and variance onto the cyclic changes at the network level. I feel that the evidence for the proposed mechanism is not sufficient, notwithstanding that it is a valid hypothesis. Please check and correct the interpretation of the cited literature if necessary.

3. Finally, some illustrations should be presented in a clearer way for those not familiar with the specifics of MEG analysis. I add some specific suggestions below.

Reviewer #2 (Public Review):

There is a lot to like about this manuscript. It provides a large-scale model of a well-known phenomenon, the "delay activity" underlying working memory, our oldest and most enduring model of a cognitive function. The authors correctly state that despite the ubiquity of delay activity, there is little known about the macro and micro circuitry that produces it. The authors offer a computational model with testable hypotheses that is rooted in biology. I think this will be of interest to a wide variety of researchers just as delay activity is studied across a variety of animal models, brain systems, and behavior. It is also well-written.

My main concern is the authors may be self-handicapping the impact of their model by not taking into account newer observations about delay activity. For a number of years now, evidence has been building that working memory is more complicated than "persistent activity" alone. Stokes, Pasternak, Dehaene, Miller and others have been mounting considerable evidence for more complex dynamics and for "activity-silent" mechanisms where memories are briefly held in latent (non-active) forms between bouts of spiking. There is also mounting evidence that the thalamus plays a key role in working memory (and attention). In particular, higher thalamic nuclei are critical for regulating cortical feedback. Cortical feedback plays a central role in the model presented here. The model presented in this manuscript just deals with persistent attractor states and the cortex alone.

This is not to say that this manuscript does not have good value as is. No one disputes that some form of elevated, sustained, activity underlies working memory. This work adds insights into how that activity gets sustained and the role of, and interactions between, different cortical areas. The observation that the prefrontal and parietal cortex are more critical than other areas, that there are "hidden" attractor states, and "counterstream inhibitory bias" are important insights (and, importantly, testable). They will likely remain relevant even as the field is moving beyond persistent attractor states alone as the model for working memory. The new developments do not argue against the importance of delay activity in working memory. They show that it is more to the story, as inevitably happens in brain science.

The authors do include a paragraph in the Discussion referencing the newer developments. Kudos to them for that. However, it presented as "new stuff to address in the future". Well, that future is now. These "newer" developments have been mounting over the past 10 years. The worry here is that by relying so heavily on the older persistent attractor dynamics model and presenting it as the only model, the authors are putting an early expiration date on their work, at least in terms of how it will be received and disseminated.

Reviewer #1 (Public Review):

Rawle, Le et al. investigated the influence of genetic background of murine models of granzyme A absence or inactivity on the arthritic foot swelling phenotype induced by chikungunya virus. By using CRISPR/Cas knockouts, the authors show that the reduced foot swelling previously attributed to the absence of granzyme A (GzmA-/-), was in truth due to the presence of intact nicotinamide nucleotide transhydrogenase in GzmA-/-, since the experimental controls used in previous studies bear a truncated Nnt, which results in the lack of activity of the corresponding enzyme. It is worth noting that these results challenge the previously stated role of granzyme A in the arthritic foot swelling phenotype induced by chikungunya virus infection. Moreover, an interesting analysis of previous literature reveals a significant number of studies comparing mice with truncated Nnt to mice with full length Nnt. Concerns over Nnt genotype in experimental settings are not new and have been raised in several studies since the description of the truncated Nnt in C57BL/6J by Toye et al. (PMID: 15729571). However, this is the first study to analyze Sequence Read Archive (SRA- NCBI) data in order to demonstrate that inappropriate comparisons regarding the Nnt genotype also occur due to listing errors and inadequate backcrossing in knock-out mice. Thus, this study emphasizes the importance of characterizing genetic backgrounds when conducting experiments on mice.

Strengths

By using CRISPR/Cas knockouts, the authors provide reliable data that strongly support the conclusions. Experimental controls and comparisons are well-conceived and suitable for the aim of the study.

Weakness

Despite the presence of a section dedicated to the re-investigation of the physiological role of granzyme A, the authors did not reach a conclusion on this subject. Experimental settings carried out to investigate the role of granzyme A were poorly explored. Moreover, the possible role of granzyme A in other chikungunya virus-induced phenotypes was not investigated, as acknowledged by the authors. Additionally, the analysis of SRA-NCBI data did not comprise the presence of heterozygous carriers of the truncated Nnt allele, which could also generate a distinct phenotype due to gene-dose effects.

Reviewer #2 (Public Review):

In a previous paper, the authors demonstrated that the chikungunya virus (CHIKV)-induced arthritis (foot swelling) was attenuated in Gzma gene knock-out (C57BL/6J-Gzma-/-) mice compared to C57BL/6J (B6J) mice. In this paper the authors created C57BL/6J-GzmaS211A knock-in mice that have a mutation at GzmA active site to verify the role of GzmA in CHIKV-induced arthritis. The authors observed that foot swelling following CHIKV infection was not different between B6J-GzmaS211A and B6J. The authors then conducted whole-genome sequencing of B6-Gzma-/-and found that this mouse has a mixed (mosaic) genetic background of C57BL/6J and C57BL/6N (B6N). Of note, B6J uniquely has a loss-of-function deletion mutation of exons 8-12 of the nicotinamide nucleotide transhydrogenase (Nnt) gene that plays a critical role in scavenging mitochondrially generated ROS, while both B6-Gzma-/-and B6N have an intact (functional) Nnt gene. The authors then created B6N-NntΔexon8-12 mice and demonstrated that CHIKV arthritic foot swelling was ameliorated in the mice, reinforcing the contention that the amelioration of foot swelling in B6-Gzma-/-mice was rather due to functional Nnt gene introgressed into the strain from B6N. The authors also conducted RNA-seq analysis of the CHIKV foot and revealed that signatures of CHIKV arthritis in B6-Gzma-/-mice are reduced ROS and more importantly reduced cell (leukocyte) migration. The authors then re-evaluated the physiological role of GzmA by administration of polyinosinic:polycytidylic acid (poly(I:C)) to C57BL/6J-GzmaS211A knock-in mice and concluded that one of the roles of circulating GzmA is to activate monocyte/macrophages. Finally, the authors undertook a k-mer mining approach to transcriptome data deposited in the public database to find that ~27% of NCBI Sequence Read Archive (SRA) Run accessions and ~38% of BioProjects are labeled erroneously as collected from C57BL/6J. Based on these data the authors concluded that widespread discrepancy in Nnt genotypes complicates granzyme A and other knockout mouse studies.

This is a well-written paper containing interesting results. The conclusions of this paper are well supported by ample data obtained from an appropriately and carefully designed methodology.

This reviewer agrees with the authors' contention that "the C57BL/6J-GzmaS211A knock-in mice should allow assessment of the physiological function of GzmA without the confounding influence of differences in Nnt or other genes associated with the mixed genetic background" (Line 516). However, this reviewer considers that the more appropriate model would be C57BL/6N-GzmaS211A knock-in mice with the functional Nnt gene, given that most humans have a functional NNT gene (Line 547).<br> Also, the authors may be able to describe how C57BL/6J-GzmaS211A (or C57BL/6N-GzmaS211A) knock-in mice can be used to resolve the critical issues concerning granzyme A, such as target(s) of the extracellular target of the enzyme and molecular mechanisms of the enzyme on activation of monocyte/macrophage for the benefit of the reader.

Reviewer #1 (Public Review):

This manuscript describes single molecule measurements of rotation of the C10 ring of E. coli ATP synthase in intact complexes embedded in lipid nanodiscs. The major point of the work is to identify the mechanisms by which protonation/deprotonation steps produce torque between the a-subunit and the C10 ring, which is subsequently conveyed to F1 to couple to ATP synthesis. The work explores the pH-dependent of the "transient dwell" (TD) phenomenon of rotation motion to identify likely intermediates, showing a likely step of 11o in the "clockwise" (ATP synthase-related) direction. The results are then interpreted in the context of detailed structural information from previous cryo-EM and X-ray crystallographic reports, to arrive at a more detailed model for the partial steps for coupling of proton translocation to motion. The effects of site-specific mutations in the c-subunits appears to support the overall model.

While the detailed structural arguments seem, at least to this reader, to be plausible, the text is not structured for any hypothesis testing, and one might imagine that alternative models are possible. No alternative models were presented, so it is not clear to what extent the 11o rotation step rules out such possibilities. This leaves the reader with the feeling that a lot of speculation occurs in the Discussion, but it is very difficult to figure out which parts are solid and which parts are speculation.

The Discussion also tries to pack in too many concepts, going well beyond the advances enabled by the TD results themselves. For example, the proton "funnel" concept is quite interesting, but it is not easy to see how the TD leads up to it. This overpacking makes it difficult to pinpoint the real advances, and dilutes the message sets the reader up to ask for more support for such extensive modeling. Do the mechanistic details set up good testable hypothesis for future experimental tests?

Overall, the text would be far more impactful if it focused more tightly on the implications of the TD results themselves, testing specific sets of models, and taking more care to guide the readers through the interpretation.

Reviewer #2 (Public Review):

This brilliant, beautiful and important study provides the essential kinetic framework for the recent, static high-resolution cryo-EM structures of F1FO ATPases from bacteria, chloroplasts and mitochondria. The elegantly conducted single-molecule work is necessarily complex, and its analysis is difficult to follow, even for someone who is intimately familiar with F1FO ATPases. Some more background and better explanations would help.

For F1FO ATPases, CCW rotation has little if any biological relevance, whereas CW rotation is centrally important. Evidently, the CW ATP synthesis mode is not accessible to the approach taken in this manuscript, since the ATP synthase is reconstituted into lipid nanodiscs rather than liposomes. This critical fact should be stated more clearly in the introduction.

The central concepts of "transient dwells", "dwell times" and "power strokes" need to be introduced more fully for a general, non-expert audience.

The manuscript describes the power stroke and dwell times in CCW ATP hydrolysis mode in unprecedented detail. Presumably the dwell times and power strokes apply equally to the physiologically relevant CW ATP synthesis mode, but are they actually the exact reverse? Is there evidence for transient dwells and 36{degree sign} power strokes divided into 11{degree sign}+25{degree sign} substeps during ATP synthesis?

The meaning of low, medium and high efficiency of transient dwell formation (Figure legend 2; lines 189/190; Figure 3; line 365) is not obvious and not well explained. How are these efficiencies defined? Why are they important? What would be 100% efficiency? And what would be 0%?

Why is it important whether transition dwells do or do not contain a synthase step? Is this purely stochastic? If not, what does it depend on?

The formation of a salt bridge between aR210 of subunit-a and cD61 of the c-ring rotor would seem to be counter-productive for unhindered rotary catalysis. What is the evidence for such a salt bridge from the cryo-EM structures or molecular dynamics simulations?

Reviewer #3 (Public Review):

Yanagisawa and Frasch utilise a gold nanorod single molecule method to probe the pH dependency of F1FO rotation. The experimental setup has been previously used to investigate both F1-ATPase and FO function in multiple studies. In this study, clockwise rotations are observed in transient dwells which may correlate to synthesis sub-steps. Mutations along the proposed proton path modify the pH dependency of the transient dwells.

The strength of this manuscript can be seen in the rigorous way in which the problem has been explored. Testing the pH dependence of mutants along the proposed proton path and linking this to potential sub-steps using the known atomic structure.

In my view, the main weakness of this study is the experimental design (shown in Fig. 1C). Strictly, the measurements show rotation of the c-ring relative to subunit-Beta rather than relative to subunit-a. Recent structures of E. coli F1FO ATP synthase inhibited by ADP (doi: 10.1038/s41467-020-16387-2) have shown that the peripheral stalk is flexible and can accommodate movements of the c-ring relative to the F1 (AlphaBeta)3-subunit ring. For example, comparison of PDB entries 6PQV and 6OQS shows that FO (the c-ring and subunit-a) can rotate 10 degrees as a rigid body relative to the F1 (AlphaBeta)3-subunit ring - with no relative rotation between the c-ring and subunit-a, or rotation of subunit-gamma. The authors discuss structures from this study related by a 25 degree rotation of the c-ring relative to subunit-a, but I do not believe they have ruled out the possibility that their observations show rotation of the FO as a rigid body. A preprint investigating E. coli F1FO ATP synthase in the presence of ATP has proposed that the complex becomes more flexible during ATP hydrolysis (doi: 10.1101/2020.09.30.320408), with the central stalk twisting by up to 65 degrees. The small CW movements seen in the transient dwells in this study could be attributed to 36 degree FO sub steps, facilitated by central stalk flexibility, with counter rotation facilitated by peripheral stalk flexibility.

It is also unclear what causes the stochastic nature of transient dwells. Are these related to inhibition of F1-ATPase? Could increased drag in FO increase the likelihood of F1-ATPase inhibition?

Reviewer #1 (Public Review): 

Summary:

Moody et al. presented a comprehensive investigation into the choice of marker genes and its impact on the reconstruction of the early evolution of life, especially regarding the length of the branch that separates domains Bacteria and Archaea in the phylogenetic tree. Specifically, this work attempts to resolve a debate raised by a previous work: Zhu et al. Nat Commun. 2019, that the evolutionary distance between the two domains is short as estimated using an expanded set of marker genes, in contrast to conventional strategies which involve a small number of "core" genes and indicate a long branch. 

Through a series of analyses on 1000 genomes, Moody et al. defended the use of core genes, and reinforced the conventional notion that the inter-domain branch (the AB branch) is long, as inferred by the core gene set. They proposed that with the 381 marker genes (the "expanded" set) used by Zhu et al., the observed short branch length is an artifact due to inter-domain gene transfer and hidden paralogy. Through topology tests, they ranked the markers by "verticality", and showed that it is positively correlated with the AB branch length. They also conducted divergence time estimation and showed that even the most vertical genes led to an implausible estimate of the origin of life. 

In parallel, Moody et al. surveyed the best marker genes using a set of 700 genomes. They recovered 54 markers, and demonstrated that ribosomal markers do not indicate a longer AB branch than non-ribosomal markers do. With the better half (27) of these marker genes, they conducted further phylogenetic analyses, which shows that potential substitutional saturation and the use of site-homogeneous models could contribute to the underestimation of the AB branch. Using this taxon set and marker set, they reconstructed the prokaryotic tree of life, which revealed a long AB branch, a basal placement of DPANN in Archaea, and a derived placement of CPR in Bacteria. 

Prokaryotic tree of life:

The scope(s) of the manuscript is somehow split. First, it is posed as a point-to-point rebuttal to the Zhu et al. paper, on the long vs. short AB branch question. Second, it introduces a new phylogeny of prokaryotes using 27 "good" marker genes, and demonstrates that DPANN is basal to Archaea, and CRP is derived within Bacteria. 

The second scope has inadequate novelty. A recent paper (Coleman et al. Science. 2021), which was from a partially overlapping group of authors, was dedicated to the topic of CPR placement, and indicated the same conclusion (CPR being derived and sister to Chloroflexi) as the current work does, albeit using more sophisticated approaches. The paper also addressed the debate of CPR placement (including citing the Zhu et al. paper). Additionally, the basal placement of DPANN has also been suggested by previous works (such as Castelle and Banfield. Cell. 2018). Therefore, re-addressing these two topics using a largely well-established and repeatedly adopted method on a relatively small taxon set does not constitute a significant extension of current knowledge. 

The debate:

The first scope appears to be the more important goal of this manuscript, as it extensively discusses the claims made by Zhu et al. and presents a point-to-point rebuttal, including counter evidence. This may narrow the interest of this work to a small audience of specialists. Nevertheless, to best evaluate the current work, it is necessary to review the Zhu et al. paper and compare individual analyses and conclusions of the two studies. 

In doing so, I found that the two articles have distinct scopes that appear similar but not actually inline. To a large extent, the current work does not constitute actual rebuttal to the points made by Zhu et al. In contrast, some of the analyses presented in the current work support those by Zhu et al., despite being interpreted in a different way. For the claims that directly contest Zhu et al., I do not see sufficient evidence that they are supported by the analyses. 

Below is a summary of the comparison, which I will explain point-by-point in later paragraphs. 

- Moody et al. assessed AB branch length, while Zhu et al. assessed AB evolutionary distance (which is different). <br> - Moody et al. evaluated the phylogeny indicated by a small number of core markers, while Zhu et al. evaluated the genome average using hundreds of global markers. <br> - Zhu et al.'s results also showed that gene non-verticality, substitutional saturation, and site-homogeneous models shorten the AB distance, which is consistent with Moody et al.'s. <br> - However, Zhu et al. found that some core markers are outliers in the genome-wide context, and the long AB distance indicated by them cannot be compensated for by the aforementioned effects. Moody et al. hasn't addressed this. <br> Therefore, the novelty and potential impact of the current work is less compelling: It used a classical method (a few dozen core genes) and found a pattern that has been found many times by some of the same authors and others (including Zhu et al., who also analyzed core genes). 

AB distance metric:

There is a subtle but critical difference between the scopes of the two papers: The Zhu et al. paper "reveals evolutionary proximity between domains Bacteria and Archaea". By stating "evolutionary proximity", it investigated two metrics: <br> The length of the branch separating Archaea from Bacteria in the phylogenetic tree, i.e., the "AB branch". This was the main focus of the current work. 

The average tip-to-tip distance (sum of branch lengths) between pairs of Archaea and Bacteria taxa in the tree. A significant proportion of the Zhu et al. work was discussing this metric, and it led to several important conclusions (e.g., Figs. 4F, 5). The current work has not explored this metric. 

These two metrics implicate distinct research strategies: For 1), HGTs and paralogy are usually considered problematic (as the current and many previous works argued). However, 2) is naturally compatible with the presence (and prevalence) of HGTs and paralogy. 

Authors of the current work equate "genetic distance" to "branch length" (line 70), and only investigated the latter. This equation is misleading. If organism groups A and B diverged early, but then exchanged many genes post-divergence, then this is indisputable evidence that their "genetic distance" is close. This point needs to be clearly explained in the manuscript. 

Core vs genome:

This difference between "AB distance" and "AB branch length" is relevant to a more fundamental question: What defines the "evolutionary distance" between two groups of organisms? Both papers did not explicitly discuss this topic. It likely cannot be resolved in one article (as many scholars have continuously attempted on related topics in the past decades). But the discordance in understanding led to very different research strategies in the two papers, and rendering them incongruent in methodology. 

Specifically, the current work (and multiple previous works) based phylogenetic inference on only genes that demonstrate a strong pattern of vertical evolution. HGTs were considered deleterious, and needed to be excluded from the analysis. This left a few dozen genes at most, and many are spatially syntenic and functionally related (e.g. ribosomal proteins). In this work, the final number is 27. Previous critiques of this methodology have suggested that this is not a tree of life, but a "tree of one percent" (Dagan and Martin, Genome Biol. 2006). 

In contrast, Zhu et al. (and related previous works) attempted to evaluate the evolution of whole genomes by "maximizing the included number of loci.". They used a "global" set of 381 genes. They faced the challenge of "reconciling discordant evolutionary histories among different parts of the genome", because "HGT is widespread across the domains". To resolve this, they adopted the gene tree summary method ASTRAL. 

Therefore, the "AB distance" estimated by Zhu et al. is a genome-level distance, calculated by merging conflicting gene evolutions (which itself can be disputed, see below). Whereas the "AB branch" evaluated in this work is strictly the branch length in the core gene evolution. Therefore, the results presented in the two papers do not necessarily conflict, because of the different scopes. 

The expanded marker set:

The authors made a valid critique (line 121-135) that many of the 381 genes in the "expanded marker set" adopted by Zhu et al., are under-represented in Archaea. According to the PhyloPhlAn paper (Segata et al. Nat Commun. 2013) which originally developed the 400 markers (a superset of the 381 markers), these genes were selected from ~3,000 bacterial and archaeal genomes available in IMG at that time time (note that it was 2013). Zhu et al. also admitted, in the discussion section, that this marker set falls short in addressing some questions (such as the placement of DPANN). What is important in the current context, is that they were not specifically selected to address the AB distance question. 

However, note that Zhu et al.'s Fig. 5A, B presented the AB distance informed by 161 out of the 381 genes. These genes have at least 50% taxa represented in both domains - the same threshold discussed in the current work (line 132). Even with those sufficiently represented genes, they still found that ribosomal proteins and a few other core genes are "outliers" in the far end of the AB distance spectrum. 

Domain monophyly in gene trees:

The authors' efforts in manually checking the gene trees are appreciable (Table S1), considering the number and size of those trees. They found (line 147) "Archaea and Bacteria are recovered as reciprocally monophyletic groups in only 24 of the 381 published (Zhu et al., 2019) maximum likelihood (ML) gene trees of the expanded marker set." 

The domain monophyly check was valid, however the result could be misleading because any sporadical A/B mixture was considered evidence of non-monophyly for the entire gene tree. As the taxon sampling grows, the opportunity of observing any A/B mixture also increases. For example, in Puigbò et al. J. Biology. 2009, 56% (a much higher ratio) of nearly universal genes trees had perfect domain monophyly based on merely 100 taxa. This is because even the "perfect" marker genes (such as ribosomal proteins) are not completely free from HGTs (e.g., Creevey et al. Plos One. 2011), let alone the fact that there are many artifacts in the published reference genomes (Orakov et al. Genome Biol. 2021). 

Therefore, to have an objective assessment of this topic, it would be better to have a metric that allows some imperfection and reports an overall "degree" of separation (also see below). 

AB branch by gene: correlation and outliers

Figure 1 is the single most important result in this work, because it argues that the short AB branch observed in Zhu et al. is an artifact due to "inter-domain gene transfer and hidden paralogy" (line 202). This argument is based on the observation that the indicated AB branch length is negatively correlated with "verticality" (measured by ΔLL and split score) of the gene. 

However, Zhu et al. also investigated the impact of verticality on AB distance, and they also found that they are negatively correlated (Fig. 5E). Therefore, the current result does not appear to deliver new information (as do multiple other analyses, see below). 

An important finding in Zhu et al., which is largely not discussed in the current work, is that a handful of "core" genes are outliers in the spectrum of AB distance, as compared to the majority of the genome (Fig. 5A). The AB distance indicated by these core genes is so long compared with the genome average that it cannot be compensated for by the impact of non-verticality, substitutional saturation, site-homogeneous model, etc (see below). 

Fig. 1A of the current work also clearly shows that many long-AB branch genes are outliers compared with the majority of the genome (the bottom of the blue bar). 

Figs. 3 and 4 attempted to show that ribosomal proteins are not outliers, but that analysis was based on a very small set of core genes, and the figures clearly show that there are outliers even in this small set (to be further discussed below). 

Verticality is not causative of short AB branch:

In spite of the outlier question, there is an important logic problem in these analyses: The authors observed that gene verticality (measured by negative ΔLL) is correlated with AB branch length (Fig. 1), and concluded that HGTs and paralogy shortened the AB branch (line 202). However, they did not directly assess the rate of evolution in this model. It is totally possible that the most vertical genes happen to be those that evolved faster at the AB split. In order to support the claim made in this work, it is important to separate the effect of the rate of evolution from the effect of HGT / paralogy. 

The ideal solution would be to include ALL genes (not just "good" ones), build gene trees, identify parts of the gene trees that once experienced HGT or paralogy, and prune off these PARTS, instead of excluding the entire gene tree. The remaining data are thus free of HGT / paralogy, based on which one can quantify the "true" AB branch length, and further assess how much it is correlated with "verticality", and whether there are still "outliers". This solution is likely not trivial in implementation, though. However, without such assessment, the observed short AB branch still only applies to the "tree of one percent", not the "tree of life". 

Differential metric for verticality:

In spite of the similarity between the current result and Zhu et al.'s (see above), the two works approached this goal using different metrics. 

First, the authors attempted to quantify the AB branch length in individual gene trees, including those that do not have Archaea and Bacteria perfectly separated. To do so they performed a constrained ML search (line 210). I am wary of this treatment because it could force distinct sequences (due to HGT or paralogy) to be grouped together, and the resulting branch length estimates could be highly inaccurate. 

In contrast, Zhu et al. quantifies the average taxon-to-taxon phylogenetic distance between the two domains, regardless of the overall domain monophyly. That method was free of this concern, although it computed a different metric. 

Second, the authors assessed "marker gene verticality" using two metrics: a) AU test result (rejected or not) (Fig. 1A), c) ΔLL, the difference in log likelihood between the constrained ML tree and ML gene tree (line 222, Fig. 1B, C). I am concerned that they are sensitive to taxon sampling and stochastic events, as I explained above regarding domain monophyly. It is possible that a single mislabeling event would cause the topology test to report a significant result. In addition, they evaluate how severely domain monophyly is violated, but they do not account for intra-domain HGTs and other artifacts, which are also part of "verticality", and they can potentially distort the AB branch as well. 

I did not find the ΔLL values of individual markers in any supplementary table. I also did not find any correlation statistics associated with Fig. 1B. 

Statistical test:

Line 157: "For the remaining 302 genes, domain monophyly was rejected (p < 0.05) for 232 out of 302 (76.8%) genes." Did the authors perform multiple hypothesis correction? If not, they probably should. 

Line 217: "This result suggests that inter-domain gene transfers reduce the AB branch length when included in a concatenation." and Fig. 1A. If I understand correctly, this analysis was performed on individual gene trees, rather than in a concatenated setting. Therefore, the result does not directly support this conclusion. 

Line 224: "Furthermore, AB branch length decreased as increasing numbers of low-verticality markers were added to the concatenate (Figure 1(c))". While this statement is likely true, Zhu et al. also presented similar results (Fig. 5) despite using a different metric, and they concluded that the impact is moderate and cannot explain the status of some core genes as outliers. 

Concatenation and branch length:

The authors pointed out that "Concatenation is based on the assumption that all of the genes in the supermatrix evolve on the same underlying tree; genes with different gene tree topologies violate this assumption and should not be concatenated because the topological differences among sites are not modelled, and so the impact on inferred branch lengths is difficult to predict." (line 187). 

This argument is valid. In my opinion, this is the one most important potential issue of Zhu et al.'s analysis. In that work, they inferred genome tree topology through ASTRAL, which resolves conflicting gene evolutions. However ASTRAL does not report branch lengths in the unit of number of mutations. Therefore, they plugged the concatenated alignment into this topology for branch length estimation, hoping that it will "average out" the result. That workaround was apparently not ideal. 

However, the practice of molecular phylogenetics is complicated. Theoretically, every gene, domain, codon position and site may have its unique evolutionary process, and there have been efforts to develop better partition and mixture models to address these possibilities. But there is a trade off; these technologies are computationally demanding and have the risk of overfitting. It is plausible that in some scenarios, the gain of concatenating many loci (despite conflicting phylogeny) may outweigh the cost of having unpredictable effects. 

But this dilemma needs to be analyzed rather than just being discussed. The Zhu et al. paper did not assess the impact of such concatenation on branch length estimation. The best answer is to conduct an analysis to show that concatenating genes with conflicting phylogeny would result in an AB branch that is shorter than the mean of those genes, and the reduction of AB branch length is correlated with the amount of conflict involved. The current work has not done this. 

Divergence time estimation:

The manuscript dedicates one section (line 230-266) to argue that the divergence time estimation analysis performed by Zhu et al. was not good evidence for marker gene suitability. Zhu et al. showed congruence of the expanded marker set with geological records whereas ribosomal proteins were conflicting with the geologic record.To support their argument, the authors estimated divergence times using the top 20 most "vertical" genes measured by ΔLL. 

It would be good to clarify which genes they are, and it would be important to check whether they include some of the most "AB-distant" ones found by Zhu et al. Their Fig. 5A shows that there are genes that divide the two domains several folds further than the ribosomal proteins (such as rpoC). If they are among the 20 genes, it will not be surprising that the estimated AB split is older than it should be. 

Overall, I think this section is logically questionable. Zhu et al. suggested that "They show the limitation of using core genes alone to model the evolution of the entire genome, and highlight the value in using a more diverse marker gene set.". The current work showed that using another set of a few genes (I do not know if they include multiple "core" genes, as discussed above, but it is plausible) also did not work well. This does not refute Zhu et al.'s claim. 

What's important in Zhu et al.'s analysis is this: they demonstrated that using a small set of genes in DTE may cause artifacts due to them significantly violating the molecular clock at certain stages of evolution. Instead, using a larger set of markers that represent a portion of the entire genome would help to "smooth out" these artifacts. This of course is not the ideal solution, likely because concatenating conflicting genes and modelling them uniformly is not the best idea (see above). But as an operational workaround, it was not challenged by the analysis in the current work. 

Finally, I agree with the authors' statement that more and reliable calibrations are the best way to improve divergence time estimation. 

AB branch by ribosomal and non-ribosomal genes:

Two figures (Figs. 3 and 4) are two sections (line 270-303) dedicated to the argument that ribosomal markers do not indicate a longer AB branch than a non-ribosomal one. However, this is a small scale test (38 ribosomal markers vs. 16 non-ribosomal markers) compared with the similar analysis in Zhu et al. (30 ribosomal markers vs. 381 global markers). A closer look at Figs. 3 and 4 suggests that while the AB lengths indicated by the ribosomal markers are within a relatively narrow range, those by the non-ribosomal ones are very diverse, including ones that are several folds longer than the ribosomal average. This result is in accordance with that of Zhu et al.'s Fig. 5A, although the latter was describing a different metric. Do these genes also overlap the ones found by Zhu et al.? 

Nevertheless, this analysis does not falsify Zhu et al.'s, because it compared a different, much smaller, and deliberately chosen group of genes. 

Substitutional saturation:

The comparative analysis of slow- and fast-evolving sites is interesting. The result (Fig. 5) is visually impactful. In my view, this analysis is valid, and the conclusion is supported. It would be better to explain the rationale with more detail to facilitate understanding by a general audience. 

Zhu et al. also tested the impact of substitution saturation on the AB branch, using a more traditional approach (Fig. S19). They also found that the inter-domain distance is more influenced by potential substitution saturation, but the difference is minor. They concluded that (AB distance) "is not substantially impacted by saturation." 

Like other analyses, these two analyses involved very different locus sampling (27 most "vertical" genes vs. 381 expanded genes). They also differ by the metric being measured (AB branch length vs. average distance between AB taxa). Therefore, the analysis in the current work does not falsify the analysis by Zhu et al. In contrast, it is inline with (though not in direct support of) Zhu et al. and others' suggestion that there was "accelerated evolution of ribosomal proteins along the inter-domain branch" (line 25) in the 27 core genes (of which 15 are ribosomal proteins). 

Evolutionary model fit:

The authors compared the AB branch length indicated by the standard, site-homogeneous model LG+G4+F vs. the site-heterogeneous model LG+C60+G4+F, and found that the latter recovered a longer AB branch (2.52 vs. 1.45). The author's reasoning for using a site-heterogeneous model is valid, and this analysis is sound. 

However, Zhu et al. also analyzed their data using the site-heterogeneous model C60 -- the same as in this work, but through the PMSF (posterior mean site frequency) method. Zhu et al. also compared it with two site-homogeneous models (Gamma and FreeRate). The results were extensively presented and discussed (Figs. 3, 4E, F, S23, S24, Note S2). They also found that C60+PMSF elongated the AB branch compared with the site-homogeneous models (Fig. S24A). As for the average AB distance (another metric evaluated by Zhu et al., as discussed above), C60+PMSF increased this metric when using ribosomal proteins, but not much when using the expanded marker set (Fig. S25A). And overall, the elongation by C60+PMSF with the expanded markers cannot compensate for the long branch indicated by the ribosomal proteins. 

Therefore, similar to the point I made above, this analysis is sound but it does not logically falsify the conclusion made by Zhu et al., as it only concerns a small set of markers, and it recovered a previously described pattern. 

The manuscript also did not clarify what the phrase "poor model fit" refers to (line 34 and line 304). If this is addressing the Gamma model evaluated by the authors, then this claim is valid though not novel (but see my previous comment on the trade-off). If that is a general reference to Zhu et al.'s methodology, then the authors should at least include the C60+PMSF model in the analysis, and show that C60 indicates a significantly longer AB branch than C60+PMSF does (if that's the case, which is doubtful). Admittedly, C60+PMSF is cheaper than the native C60 in computation, but "In some empirical and simulation settings PMSF provided more accurate estimates of phylogenies than the mixture models from which they derive." (Wang et al. Syst Biol. 2018). 

Finally, Zhu et al. also performed an analysis using the native C60 model on a further reduced taxon set. That result was not presented in the published paper, but it can be found in the "Peer Review File" posted on the Nature Communications website. That tree also recovered a short AB distance, and placed CPR at the base of Bacteria, and showed that this placement was not impacted by the removal of Archaea. 

Taxon sampling:

My final comment is about taxon sampling. Zhu et al. developed an algorithm for less biased taxon sampling, and they argued that extensive taxon sampling is important in resolving the early evolution of life. They presented evidence showing that reduced taxon sampling changed overall topology and basal relationships (Figs. S13, S14, S23, Note S2). The analyses were performed in combination with the assessment of site sampling, locus sampling, substitution model and other factors. The importance of less biased and/or extensive taxon sampling was also noted by previous works, especially in a phylogenomic framework (e.g., Hedtke et al. Syst Biol. 2006; Wu and Eisen. Genome Biol. 2008; Beiko. Biol Direct. 2011). The current work is based on a smaller set of taxa, and it has not addressed the impact of taxon sampling. As I suggested above, some results may be sensitive to taxon sampling.

Reviewer #2 (Public Review): 

Williams and colleagues address a fundamental questions in phylogenetics, the suitability of different sets of gene markers for resolving the deepest branchings in the Tree of Life. They define an optimal set of marker genes that apparently underwent minimal horizontal transfer. Phylogenetic analysis of this gene set is applied to determine the length of the branch separating archaea and bacteria, the two primary domains of life. They show that the branch separating these domains is very long, in contrast to recent claims of a short branch based on analysis of much larger set of markers that. Apparently, the latter analyses have bene thrown off by frequent horizontal gene transfer and hidden paralogy. The analysis of Williams and colleagues is very careful, and the result is compatible with the multiple, qualitative differences between archaea and bacteria. 

Additionally, Williams and colleagues determine the positions in the tree of tow recently defined superphyla of archaea and bacteria, DPANN and CPR, respectively. Both these superphyla include microbes with small genomes that are apparently parasite or symbionts of other archaea or bacteria. In the phylogenetic trees reported in this paper, DPANN occupies the basal position in the archaeal subtree whereas CPR comes across as the sister group of Chloroflexi. These are interesting observations, but given the well know fast evolution of parasites and symbionts, additional analysis seems to be required for confident placement of these superphyla in the tree.

Reviewer #3 (Public Review): 

Moody and coworkers principally address a recent paper presented by Zhu et al. (Nature Communications, 2019). In their paper, Zhu and coworkers claim that (i) ribosomal protein genes, commonly used in resolving deep phylogenies, have experienced an increased rate of evolution right after LUCA, and (ii) that an expanded set of markers show that the branch separating archaea from bacteria (AB-branch) is 10-fold shorter than previously thought. Moody et and coworkers first demonstrate flaws in the Zhu et al. analysis: first, the expanded gene set is biased towards bacteria, with 25% of the single-gene trees having very few archaeal counterparts. Second, that over 75% of the single-gene trees from Zhu et al are not monophyletic at domain level, suggesting a large influence of horizontal gene transfers (HGT), inter-domain exchanges, and inclusion of paralogous sequences in the original datasets. Third, they show that genes with fewer HGT display longer AB-branches. Fourth, they show that the argument by Zhu et al. that the longer AB-branch yields absurd LUCA datation is not relevant. Fifth, and maybe most important, they show that the shorter AB-branches recovered by Zhu et al in their expanded dataset result from inadequate substitution models, which lead to underestimating rates and thus branch lengths. 

Going further, they select a set of 54 manually curated markers (showing mostly monophyletic archaea and bacteria), both from ribosomal proteins (36) and non-ribosomal proteins (18) and retrieve these in a balanced set of 350 archaea and 350 bacteria. With this set, they show that ribosomal protein markers do not display longer AB-branches than non-ribosomal ones. They also show that diversity among Archaea and Bacteria, as measured as the total tree length within each domain, is very similar, when sampling equal number of genomes in both domains. 

Strengths:

The paper is well-written and well structured. In general, the methodology chosen here is adapted to the question at hand and very rigorously followed. The balanced dataset (with equal amounts of bacteria and archaea) of 54 carefully selected genes is also appropriate to explore diversity differences between the two domains of life. 

Although all arguments presented in Zhu et al are carefully re-evaluated, the part where Moody et al show that substitutional saturation and poor model fit is artifactually producing short AB-branches is quite compelling and elegantly presented. 

Weaknesses:

One potential weakness, more in terms of significance than in terms of scientific soundness is that the paper is mostly "reactive", responding to a single other paper. The authors might have used the data and methodology presented here to give the paper a broader scope. An example would be to provide the audience with a solid protocol or general guidelines on how to avoid artifacts in making deep phylogenies. I believe that the authors have demonstrated that they have the authority to do that. 

The authors use the difference in log-likelihood between the constrained and unconstrained gene trees as a proxy for verticality and thus marker gene quality (Figure 1b). However, they don't demonstrate that that metric is actually appropriate. Could the monophyly (or split score) be also involved here? The authors might want to comment on that. 

The argument about the age of LUCA an ad absurdum one, showing that using better suited genes one gets impossible time estimates. However, the argument presented by Zhu et al is also a "just so" argument (if we get a time estimate that doesn't make sense then the phylogeny must be wrong), which doesn't give it much weight. The authors themselves note well that this part of the paper is more revealing of the limitations of the strict clock method, or of the relaxed clock with one single calibration point, than of the quality or appropriateness of the dataset. 

Another small weakness (or loose end) is that manual curation of the 95 genes dataset is not consistently reducing the percentage of non-monopyhletic genes (e.g. 62 to 69% from the 95 to the 54 genes dataset for non-ribosomal genes; 21 to 33% from the 95 to the 27 genes dataset for ribosomal genes). The author don't discuss how this impacts the manual curation they perform on the datasets; however, they state that "manual curation of marker genes is important". The authors might want to discuss that aspect further. 

In summary and despite the small weaknesses listed above, my opinion is that the authors reach their goal of showning that the AB-branch is indeed a long one, and that the results support the conclusion. 

Impact:

The main point addressed by the authors here, the time of divergence between Archaea and Bacteria, is crucial to our understanding of early evolution. The long branch separating Bacteria and Archaea has long been thought to be a long one, and the paper by Zhu et al casted a doubt about the validity of this long-standing hypothesis. Here, Moody et al convincingly establish that the divergence between archaea and bacteria is a profound one. The paper also has profound implications on the validity of the commonly used core-gene phylogenies, particularly those based on ribosomal protein genes. Indeed, it shows that the these proteins are appropriate for deep phylogenies. They also show the impact of model violations on deep phylogenies, and how to avoid them.

Reviewer #1 (Public Review):

This paper by Gallagher et al (Carthew lab with M. Mani) addresses how the regular patterning of ommatidial preclusters emerges from the morphogenetic furrow (MF) during eye development in Drosophila. The study uses a novel live imaging protocol that allows the visualization of cell movements and associated contractions and dilations as the MF moves from posterior to anterior across the eye discs and leaves the evenly spaced preclusters in its wake. The highly regularly spaced ommatidia, compose of photoreceptors, has served as a paradigm for many studies involving cell signaling pathways, lateral inhibition, and a reaction-diffusion model has been proposed to be at the center of the even spaced out ommatidia. Here the authors show a very unexpected discovery that the regular spacing is generated by a mechanical mechanism, generated by evenly spaced region of cell flow. It is proposed that the cell flow is generated by a pressure gradient, which is caused by a zone of cellular dilation posterior to the MF, and the existing "template" of spaced R8-centerd preclusters. It is further shown that the spacing indeed depends on the anterior "template" and that this is depending on the role of the Scabrous gene.

This is overall an exciting paper and it will make an excellent contribution to the field and developmental patterning in general.

Reviewer #2 (Public Review):

This is an impressive study, that characterizes, for the first time, the dynamic cell behavior in the differentiating eye imaginal disc, that was hitherto believed to be static. The cell movements that are uncovered are not uniform or random, but rather correspond to, and perhaps dictate, the regular spacing of photoreceptor clusters. The identification and characterization of these cell movements is clearly valuable and should be considered in future analyses of cell differentiation in this tissue.

The major claim of the paper is that this patterned cell movement is sufficient to generate the regular spacing in the developing eye, by separating clusters of Atonal-expressing cells. The suggestion is that the signaling pathways that were identified over decades of work are executing the determination that was initially triggered by cell movement. Therefore, a critical review of the paper should examine if this provocative claim is indeed substantiated by the results.<br> We believe that the causal role of cell movement has not been conclusively demonstrated. While the study is novel and will have a broad impact on future studies, the claims regarding the causal role of cell movement in dictating ommatidial spacing cannot be made until critical experiments will be carried out.<br> In conclusion, this paper examines, for the first time, the movement of cells within the differentiating eye disc. The patterns of movement identified are highly correlated to the emerging pattern of photoreceptor clusters. Therefore, they will certainly have to be taken into account in future analyses of this system. The current data cannot distinguish between cause and consequence vis a vis the role of cell movements in photoreceptor spacing. The data is of high quality, however, the conclusions need to be toned down.

Reviewer #1 (Public Review):

The manuscript describes the mechanisms of biogenesis of two antisense sRNAs by RNase III in C. jejuni, CJnc180 and CJnc190, as well as the specific post-transcriptional activity of CJnc190 on ptmG. The study provides thorough experimental support of (i) binding of CJnC190 to repress translational of ptmG, (ii) RNAse III processing to produce mature CJnc190 and CJnc180 transcripts, (iii) location and contribution of CJnc180/190 promoters and 3' ends, and (iv) mechanisms of RNase III cleavage of CJnc180 and CJnc190. Notably, this study proposes a novel cis-sRNA processing mechanism of CJnc180 in which base pairing with antisense sRNA CJnc190 facilitates proper cleavage by RNase III. Overall, this well constructed and informative study provides impactful knowledge that furthers the field of regulatory RNAs.

Reviewer #2 (Public Review):

Campylobacter jejuni is serious food-borne pathogen and understanding how the various products necessary for pathogenesis are regulated is a key step in preventing its growth and/or treating disease. Here, Sharma and coworkers demonstrate the complex pathway that leads to the maturation of two complementary regulatory RNAs and how one of the RNAs antagonizes the other to relieve repression of a virulence-related gene. The work is detailed and convincing, and provides a reference point for the roles of regulatory RNAs in C. jejuni as well as other bacteria. Future work will be needed to better understand when each of these RNAs is best expressed and processed into active form, and to fully support the idea that one RNA acts as an antagonist for the other.

Reviewer #3 (Public Review):

In this manuscript the authors describe the biogenesis and the mechanism of action of a pair of cis-encoded sRNAs: CJnc190 and CJnc180. Both RNAs are being processed by RNase III. 5' and 3' ends mapping together with in vitro and in vivo experiments using purified RNase III and rnc deletion mutant demonstrated that the processing of CJnc190 sRNA depended on the formation of an intramolecular duplex, while CJnc180 sRNA processing required the presence of the antisense CJnc190 sRNA. The mature CJnc190 and CJnc180 sRNA specious are 69 and 88 nt long respectively. They also show that mature CJnc190 sRNA represses translation of ptmG via base-pairing and CJnc180 sRNA antagonizes CJnc190 repression acting as a sponge, scavenging CJnc190 sRNA. In addition, they find that two promoters are responsible for the synthesis of CJnc190 sRNA and both transcripts are subject to RNase III processing.

The study represents an enormous amount of work. The data are solid and generally support the overall conclusions. Having said that the manuscript is overwhelming, loaded with too many details which make the reading difficult and in the absence of a bigger picture many times uninspiring.

Reviewer #1 (Public Review):

In this manuscript, authors develop a computational model of migrating pairs of cardiopharyngeal progenitors in Ciona model to describe their properties during migration. Most of the predictions drawn from the simulations are corroborated experimentally in vivo. The polarized migrating pair of progenitors represent a minimum unit of collective migration showing essentially that "two cells are better than one".

The simulation of the migrating cell pair is based on Cellular Potts Model and uses cell morphology as a proxy to model different mechanical and cellular forces that generate specific cell shapes during migration. The model seems to faithfully reproduce the in vivo observations. The arguments describing the model are well explained. The model thus provides a number of predictions pertaining the migrating cell collectives that can be tested in vivo using genetic and molecular tools. The authors then test some of the predictions prompting them to conclude that the migrating progenitor pair present the simplest model of collective migration, in which the two cells are intercellularly coupled and their polarization occurs across the two-cell continuum, thus forming a supracellular collective. This cell collective displays hierarchy, which favors linear arrangement conferring directionality and persistence in migratory behavior. While some conclusions are well justified and the experimental evidence corroborates the simulations, at few points the conclusions are more speculative; the figure panels not always match the manuscript text. The concept that the migrating pair can deform the overlaying endoderm is the least developed and not probed in vivo. In summary, the computational model represents a powerful tool to examine directional migration of polarized cell collectives with similar characteristics allowing for studying how biomechanical cues integrate with molecular signals across group of cell collectives to coordinate their behavior.

Reviewer #2 (Public Review):

In this study, the authors develop a model to understand the collective migration of migrating pairs of cardiopharyngeal progenitor cells. This represents an simplest form of collective migration with two cells. They propose that the collective migrates as a "supracell", with leader cells assuming a greater protrusive capability and trailer cells assuming greater retractive capability. They meticulously study the effects of leader-trailer and cell-matrix adhesivity, intracellular force distributions and noise on robustness of cell migration. They corroborate their simulation results with experiments. Overall, this study comprehensively demonstrates that migrating as a collective leads to more mechanically efficient and persistent migration than as a single motile cell. A particular strength of the paper is that the authors have done an excellent job of explaining how the Cellular Potts Model works and how they chose to represent specific biological details using this modelling environment. The model is also likely to provide a useful framework for development of models of more complex examples of collective migration.

Reviewer #1 (Public Review):

In the manuscript by Jayaram and coworkers, the authors model how temporal features of the olfactory environment impact the navigation of walking fruit flies. The authors find that under certain stimulus conditions, utilization of both intermittency and odor encounter rate increases navigational success in the agent-based model.

The strengths of the study lie in the examination of distinct aspects of the features of the plume as the animal navigates. The authors build on their previous and important work in this domain that used spatiotemporal complex plumes and navigating flies.

The weaknesses of the study are concerns about the oversimplification of the spatiotemporal dynamics of the plume and its encounters with the navigating fly and the other features of the plume (intensity). The authors describe other processes that were excluded from the model (bilateral sensing, learning, adaptation) - the model results that include these features would more realistically define the model results. Testing only two different plumes (environments; high frequency, high intermittency) may also over-simply the processes at play and results.

Of course, the authors are the experts, but in atmospheric sciences and past odor plume studies, the term intermittency is related to the stimulus (conditional statistics on the presence of the volatile chemical signal), rather than the reference frame of the navigating organism. Similarly, "rate" has been used in the more powerful characterization of "flux" (C/time) as the animal navigates to the plume. Defining these terms at the onset, and incorporating intensity in the model, would be helpful.

Reviewer #1 (Public Review):

The authors use an array of in vitro experiments to evaluate the rules that govern 23/24-nt sRNA production by DCL3. The authors conclude that DCL3 measures from only one end of a dsRNA precursor, prefers ends with overhangs and terminal A or U nucleotides, and does not require ATP. The authors also conclude that RDR2 generates overhangs not only via its terminal transferase activity, but by initiating synthesis 1 or 2 nucleotides from the end of the molecule synthesized by Pol IV. This enables 24-nt production from both the Pol IV and RDR2 strands of the dsRNA template.

The paper's conclusions are generally well-supported.

Reviewer #2 (Public Review):

Loffer et al. investigated the activity of recombinant DCL3 against substrates with overhangs or blunt ends and substrates with different 5' nucleotide. They found that DCL3 preferentially cuts dsRNAs with 3' overhangs and dsRNAs with 5' AU pair and monophosphate group. DCL3 measures 24 nt from the 5' end of one strand to make the first cut and chooses a position 2 nt offset on the opposite strand to make the second cut. Investigation of the activity of mutant forms of DCL3 revealed that RNase III domain B of DCL3 cuts the measured strand, while domain A cuts the non-measured strand. Interestingly, they found that 3' overhangs are produced at both ends of dsRNAs because RDR2 initiates transcription internally and also has terminal transferase activity and 23-nt siRNAs and 24-nt siRNAs can be produced both from the Pol IV strand and the RDR2 strand. It should be noted that end point assays were used for dicing assays in Fig. 4A-C (three time points) and Fig. 5 (one time point). The results from the end point assays might be misleading, especially when the substrates were not excessive (Fig. 4C and Fig. 5). Extra care needs to be taken to draw the conclusion that ATP is not required for DCL3 activity. In the discussion, the authors proposed that Pol IV, RDR2 and DCL3 activities can collectively account for much of the 5' A-bias among siRNAs that become loaded into AGO4, independent of AGO4-mediated selection. A better interpretation could be that the initiation (production of 5' A siRNAs) and effector (specific binding to AGO4 that preferably accepts 5' A siRNAs) stages of RdDM have co-evolved to achieve the specificity of RdDM pathway.

Reviewer #3 (Public Review):

RdDM is the most important mechanism for understanding plant epigenetics. In order to maintain the plant genome integrity, heterochromatin regions containing transposons and near centromeres are essentially stabilized by RdDM. Although a large number of genes (proteins) involved in RdDM have been genetically identified, the functions of these proteins have not been characterized in detail. In this paper, biochemical analyses using purified recombinant DCL3 show that DCL3 efficiently cleaves short dsRNAs with 1- to 2-nt 3' overhang (OH) and the 5' phosphate. The authors also show that DCL3 preferentially cleaves dsRNAs with adenosine (A) or uridine (U) at the 5' phosphate end. And they show that RNA-dependent RNA polymerase 2 (RDR2) initiates second strand RNA synthesis 1- to 2-nt internal to the RNA polymerase IV (PolIV) transcript and has terminal transferase activity to add one nucleotide at the 3' end of its transcripts. These dsRNAs with the 3'overhangs at both ends are preferred DCL3 substrates. Finally, they propose that these RDR2 and DCL3 activities shown here together with the results of their previous study (Pol IV initiating nucleotide choice, Blevins et al., 2015; Singh et al., 2019) can collectively account for much of the 5' A-bias among siRNAs that become loaded into argonaute 4 (AGO4).

Strengths:<br> The Pikaard's group has achieved excellent research by analyzing enzymatic activities of proteins involved in RdDM such as PolIV, RDR2 and DCL3, though it is very difficult to purify these high-molecular-weight proteins to analyze their enzymatic activities. In this paper, the excellent biochemical results are shown by using purified recombinant proteins and polyacrylamide gel electrophoresis (PAGE) with high resolution.

Public Review:

This is a well-executed study looking at the association of urinary metabolites to the types of diets consumed by European children. They focus on four analytes that have opposing patterns from a "good" KIDMED Mediterranean style diet versus a "bad" diet with processed foods and high sugars. They then create an association with levels of C-peptide, which has in turn been linked to health outcomes.

Overall there is extensive data provided in the supplementary data to justify their findings. The one omission is the effects of activity levels and total caloric consumption. There is an attempt to link body weight to C-peptide associations, but in a minor revision, it would be nice to also include MBI as a parameter for the concentrations of metabolites.

Reviewer #1 (Public Review):

Prisco et al. detail how the GABAergic APL neuron normalizes olfactory input to the Drosophila mushroom body, reporting that the APL neuron normalizes olfactory responses in Kenyon cells via scaled inhibition. Silencing APL neurons increases variability in the magnitude of KC olfactory responses. Furthermore, APL provides localized feedback in the MB calyx that may shape the patterns of KC responses locally. This is straightforward manuscript describing a high-quality data set that lays out clear conclusions about the role of GABAergic feedback in shaping olfactory responses in a brain region that is critical for olfactory learning. The conclusions of the paper are well supported by the data for the most paper, though a few additional analysis and experiments would help to clarify the role of the APL neuron in modulating KC responses across the calyx.

Reviewer #2 (Public Review):

The principal neurons of the fly mushroom body, the Kenyon cells, receive input at their dendrites in the calyx: excitatory input from projection neurons and inhibitory input from APL. The anatomical and physiological interaction between these two inputs in the calyx has not yet been examined in detail, and the authors do so here. The study confirms and extends previous findings showing that APL normalizes KC activity by reducing the difference in amplitude in KC activity for odors of different strength, by showing that these effects also extend to KC post-synaptic claws (as opposed to acting only on KC spiking), and by directly comparing the activity of KCs, PNs and APL for different odors. Although the conclusion would be more robust if a larger range of odors was tested and some aspects of the quantification methods could be clarified, the data are well-controlled and persuasive. A particular strength is the use of targeted GCaMP indicators to confine their analysis to pre-synaptic and post-synaptic compartments. The findings improve our understanding of the integration of excitatory and inhibitory inputs in Kenyon cells.

Reviewer #1 (Public Review):

Pillet et al. describe a novel feedback mechanism that coordinates the expression of ribosomal proteins RPL3 and RPL4 with the pace of ribosome assembly through the co-translational regulation of their mRNA stability mediated by the Ccr4-Not deadenylase complex. The authors present a wealth of compelling data in support of a novel regulatory mechanism regulating the production of ribosomal proteins in yeast. Their conclusions are justified based on corroborating evidence generated from appropriately controlled experiments.

Reviewer #2 (Public Review):

Ribosomal proteins are prone to aggregation, and 9 or the 79 r-proteins in yeast are known to associate with a dedicated chaperone protein required for their function. The authors report here on a continuation of their study of two r-proteins, Rpl3 and Rpl4, whose chaperones are Rrb1 and Acl4, respectively. The present study was motivated by the observation that acl4Δ strains, which grow extremely slowly, give rise to spontaneous suppressor mutations, 48 of which were identified by whole-genome sequencing. One suppressor is a point mutation in the RPL4 gene, whereas all the others are in genes linked to the well-studied Ccr4-Not complex (CAF130 and NOT1), known to be involved, among other things, in cytoplasmic mRNA degradation. They also identified suppressor mutations in YJR011C, which they show interacts with Caf130, and thus dub CAL4 (Caf130-associated regulator of Rpl4). Using RNA-seq, they show that all of caf130Δ and cal4Δ Interestingly, they point out that both Caf130 and Cal4 have been physically connected with nascent polypeptide-associated complex (NAC) proteins Btt1 and Egd1/2, and they go on to show that ablation of the genes encoding Btt1 and Egd2 also leads to some level of acl4Δ suppression. The authors then present evidence indicating that Caf130 connects both Cal4 and Btt1 to Ccr4-Not through an N-terminal domain of Not1 that is not required for its essential function(s). They thus identify a previously unknown function of the Not1 N-terminus which they show, Interestingly, is expressed only in a minor Not1 isoform, with the majority isoform initiating from a downstream ATG. The working model that emerges at this point is that RPL3 and RPL4 mRNA levels are regulated by targeted Ccr4-Not degradation at the ribosome, directed by an interaction with the emerging r-protein and some as-yet-unidentified component of the system. In what follows, the authors map regions of both Rpl3/4 that are required for their mRNA regulation and show that they are adjacent to but distinct from their chaperone binding sites. They then show that overexpression of either chaperone gene increases mRNA levels of the corresponding r-protein gene, and that deregulated expression of the RPL3 and 4 genes induces aggregation of their encoded proteins and lethality in the absence of the Tom1 E3 enzyme, known to be involved in r-protein degradation.

The experiments reported here are well designed and the data are consistent with the authors' molecular model for regulation of RPL3/4 mRNA levels. The strength of this work lies in (1) its molecular (Y2H) and biochemical (co-IP) characterization of a network of protein-protein interactions linking parts of the Ccr4-Not complex to the nascent peptide associated complex (NAC), (2) its detailed mapping of sequences within both Rpl3 and Rpl4 that are required for regulation, and (3) its demonstration that these sequences can confer mRNA regulation to an otherwise heterologous transcript.

Reviewer #3 (Public Review):

This paper follows from previous analyses of the regulation of Rpl3 and Rpl4 expression and pre-ribosome incorporation. Starting from the observation that suppressors for loss of the Rpl4 chaperone Acl4 are relatively common, the authors discovered that RP abundance is linked to mRNA stability via the ribosome-associated nascent polypeptide-associated complex (NAC). In the absence of specific Rlp4 or Rpl3 chaperones, NAC-beta components Edg1 and Btt1 recruit the NOT-complex via the non-essential, sub-stoichiometric, associated proteins Caf130 and Yjr011c (now Cal4). Caf130 binds the N-terminus of Not1 and Cal4 binds Caf130. Crr4-NOT then promotes mRNA degradation - presumably cotranslationally, although this is not shown. Finally, they show that tight regulation of Rpl3 and Rpl4 is important in avoiding toxic protein aggregation.

Overall, the work reports a quite complete body of research - from initial genetic observations, through interaction mechanisms, to biological significance. The logical links between the different steps in the analysis are clear, sound and well set out. The findings are unexpected but convincingly supported and seem very likely to be relevant and important for other regulatory systems and conserved in eukaryotes.

Reviewer #3 (Public Review):

This manuscript aims to assess the main factors that drive disease in crabs across Europe. Crabs are critical members of the trophic chain in marine environments and also serve as food source for human consumption. Therefore, understanding the health of this group of organisms is very important for ecosystem health as well as human health.

This is a well written manuscript that identifies important knowledge gaps in the field. The authors evaluate whether crabs are infected by an important parasitic dinoflagelate, Hematodinium, and question several current dogmas in the field. The first one is whether or not presence of Hematodinium infection is a significant factor driving co-infections by other pathogens including trematodes, bacteria or fungi. The authors, based on their data set, conclude that this is not the case, since co-infections are observed in similar proportions in Hematodinium free animals. The authors perform in depth assessment of other drivers of infection and identify geographical location as the main factor driving community structure.

A second important aspect of Hematodinium biology is the previous notion that this parasite immunosuppresses the crab host (and therefore co-infections may be found). However, there is a paucity of studies to confirm or reject this hypothesis, and therefore, the current study is important to expand our current knowledge of marine invertebrate immunobiology and specifically in the mechanisms by which Hematodinium is harmful to the host. The current study provides some evidence that, in fact, this parasite is not an immune suppressor but rather evades the cellular immune response of the host. In particular, the authors claim that the host hemocytes (immune cells) fail to phagocytose (engulf) and encapsulate (surround and fend off) this parasite. These two cellular immune responses are the most common in invertebrates such as crabs.

Overall, this study is important because very few studies evaluate wild marine diseases, especially in invertebrate hosts and because it contributes with a large data set from a wide geographical range.

Reviewer #1 (Public Review):

The study would have benefited from qPCR instead of presence or absence. It is interesting to note that differences were observed when CFUs were compared. I understand that for most organisms, in the absence of a draft genome, assigning copy numbers to cells is not yet possible; however, it would have provided a more robust dataset for the statistical analysis.

Similarly, histology relies upon one of a hundred possible for every single specimen. With this caveat, scoring the presence of the parasites rather than (+) (-) would also have been more informative. Without knowing the intensity of the infection for each of the other pathogens makes it more difficult to reject the hypothesis. The authors discuss other papers with cellular immune data, which is lacking in this manuscript. Observing fresh hemolymph and histology are just not enough. The data strongly support that parasite community composition is affected by the location. Smaller crabs also appear to be more likely to display co-infections compared to disease-free crabs.

Reviewer #2 (Public Review):

Strength:<br> 1. The authors looked into the prevailing idea that parasitic infection make crab immune-compromised although evidence to support this idea is lacking except one study by where immune gene expression was found to be modulated upon Hematodinium infection in Japanese blue crab. Apparently, the authors studying gene expression in Japanese blue crab upon parasitic infection did not identify any effort molecules of parasitic origin.<br> 2. It was interesting to note that haplotype diversity analysis revealed higher genetic diversity in the host compared to the Hematodinium parasite in two locations examined.

Weakness:<br> 1. The authors should clarify how the sample sizes were selected.<br> 2. It is also recommended that details on sample collections are included (for example the times in which samples from the surrounding waters of infected crabs)

Reviewer #1 (Public Review):

William-Beuren syndrome (WBS) is a rare genetic disease characterized by manifestation affecting the central nervous system and cardiovascular system. This disease is caused by the hemizygous deletion of several adjacent genes within a specific region of chromosome 7 (7q11.23). Specific treatments for this syndrome are unavailable until now.

Researchers in this study found an alteration in density and signaling of CB1R in several areas of CD mice brain compared to WT mice by [3H]CP55,940 radioligand binding assay and [35S]GTPγS autoradiography. This result suggested an important role of endocannabinoid signaling in the pathophysiology of WBS. Further experiments showed that administration of monoacylglycerol lipase inhibitor JZL184 ameliorated social behavioral phenotype and cardiac clinical and transcriptional phenotype in CD mice compared to WT mice. Overall, this manuscript provided us new information about a potential target for the treatment of WBS.

Strength<br> This manuscript showed a comprehensive analysis of WBS in CD mice by several methods including CD mice behavioral phenotype and cardiac phenotype, endocannabinoid signaling alteration, and amelioration of CD mice phenotype after administration of JZL184. Especially for cardiac phenotype, they did cardiac RNA-seq which provides in-depth analysis.

Weakness<br> Change CD mice cardiac phenotype in this study seemed not representative for WBS in humans. CD mice only showed mild cardiac hypertrophy and slight reduction of cardiac ejection fraction, while in humans WBS is characterized by congenital cardiovascular phenotype such as pulmonary artery stenosis, supravalvular aortic stenosis, ventricular septal defects, etc.

Reviewer #2 (Public Review):

The authors of this manuscript explored the ECS properties in a mouse model for WS. This is a novel research field in WS, with high interest and relevancy. The authors improved behavioral aspects by modulating the ECS, and demonstrate partial improvements in cardiovascular properties. The advances in our understanding of the ECS in WS is of high interest, and the authors indeed improve our understanding by this work. Given the fact that WS treatment nowadays is relatively limited, this manuscript has high potential to be groundbreaking.

The use of the MAGL inhibitor, based on the ECS characterization and the alterations found in CD mice compared to control, is elegant and well-designed.

Nevertheless, it is unclear how exactly the drug affect behavior and cardiovascular properties. Only partial evidence is demonstrated, and therefore the authors do not show enough results to fully present the full picture of this mouse model following drug administration.

I believe that in its current formation, this manuscript has some major gaps. Some of these issues are:

- In the abstract, the statement saying "Nowadays, there are no available treatments to ameliorate the main traits of WBS" is not accurate, as there are treatments and procedures to ameliorate the main traits of WBS. The authors indeed mention those in the introduction, but not in the abstract. Authors should moderate this statement, perhaps by changing to "no pharmacological treatment to directly ameliorate main traits of WBS".

- In general, I have difficulties with the test mice age range, being 8-16 weeks of age. This is relatively a wide range, that can lead to the unclear behavioral and other phenotypes described in the manuscript. Many studies showed dynamic changes in the developmental trajectory of the endocannabinoid system in the mouse brain. It might be that the 8-16 weeks of age range is responsible for the different stages of the endocannabinoid system maturation. This is a major issue in this study in my opinion.

- In the novel object test, why did the authors decided to exclude from the analysis mice that explored <10s both objects? How many mice were excluded based on this criteria? Raw data should be included to allow the reader better judgement of the raw data (with all excluded mice included) and the presented final graphs.<br> - In the novel object test, Fig.1c,d make no sense to me: the CD mice should have higher (!) discrimination index than WT based on Fig.1c, right, while Fig.1d shows the opposite.

- The behavioral characterization in this study is very limited. Why the authors did not characterize other aspects related to WS, such as anxiety and motor capabilities? Tests such as elevated zero maze, open field exploration, rotarod, etc. will better define mice properties and the effectiveness of the drug to modulate these. Especially given the fact that previous article from this group showed motor deficits in CD mice (Segura-Puimedon et al., 2014) , Figure 5a-b). Also contextual fear conditioning can add more insights on fear and cognition in these mice with and without the drug. Similarly, short (i.e. 1 hour) and long-term memory (i.e. 24 hour) should be studied in the NOR test, to better define the results in Fig.1c,d (see comment below).

- Can the authors show that the i.p. administration of the drug resulted in increased drug concentration in the brain of treated mice control to placebo? The reported alterations following drug administration might be secondary to other drug-related responses.

- From Fig.3c in this manuscript it seems that the drug has negative effects on NOR discrimination index in WT animals (together with the positive effects on CD mice). This should be discussed in the manuscript to emphasize the potential negative effects of the drug.

- Why did the authors not characterize the signaling of the CB1R following drug treatment in Fig.3 as they did in Fig.2? This should be demonstrated, to further study the drug's effect on the ECS.

- The effect of the drug on the cardiovascular phenotype is not clear. There is no mechanistic explanation for this change. RNAseq is only partial aspect of this surprising effect, and is not sufficient to understand and support the mechanistic explanation of how the drug affected cardiovascular properties in only a few days. Why should it affect heart properties, especially given such a short treatment of only several days? This should be further demonstrated and explained, with more histological and molecular and cellular evidence to support this surprising effect. Moreover, did these changes in weight etc. were specific to the heart only? The different number of mice in each of the tests presented in Fig.4 is unjustified and makes me feel uncomfortable. Why did the author have to use 20 mice in Fig.4b, but used 7 in Fig.4c? Can they show statistical power calculation to support the huge difference in mice number between the tests?

Reviewer #3 (Public Review):

This paper describes an interesting research with many meaningful observations to study the etiology and therapeutics for WBS. In particular, the authors report evidence supporting that the endocannabinoid system is impaired in an animal model for WBS, the CD mice. Furthermore, a sub-chronic pharmacological treatment with JZL184 normalized all tested phenotypes of CD mice, that are relevant to human disorder, to the level similar to normal mice. These findings should provide deep insights into developing strategies for WBS therapeutics.

Strength<br> 1. The authors studied behavioral phenotypes of CD mice that are reminiscent of the problems that WBS individuals have. In addition to the CNS phenotype, cardiac hypertrophy was observed in the CD mice model, and the effect of JZL-184 for the cardiac phenotype was evaluated.<br> 2. Dysfunctional endocannabinoid system in the amygdala was observed by various molecular assessments including CB1 receptor density and its G protein coupling. Brain levels of endocannabinoids and related molecules were also measured.<br> 3. Results from transcriptomic profiling of heart gene expression supports the idea that subchronic JZL184 treatment may restore the normal cardiovascular function in CD mice. The correlation analysis of differentially expressed genes shown in Fig 5E is appropriate and nicely display the beneficial effects of JZL184 on abnormal cardiac gene expression in CD mice.

Weakness<br> 1. It is interesting that the authors tested the effect of subchronic JZL, rather than CB1 inverse agonists, in order to reverse the abnormal elevation of the CB1 receptor in CD mice. The rationale by which they selected prolonged MAGL inhibition-induced CB1 desensitization over more straightforward method of CB1 inhibition is not discussed explicitly.<br> 2. Endocannabinoid levels were quantified from whole brain homogenate (Table 1), not from separated brain regions such as amygdala. Considering the complex structure of the brain and brain region-selective abnormality of CB1 receptors observed in CD mice (Fig 2a), not much conclusion can be made from current dataset regarding endocannabinoid molecules deficits in WBS.<br> 3. Although it is shown that subchronic JZL184 induces normalization of CB1 receptors density in basolateral amygdala, it is not clear whether this molecular recovery occurs also in other brain regions where increased CB1 availability had been observed in CD mice. Therefore, it is not clear if JZL184-induced normalization of CB1 receptors mediates a part or all beneficial effects on behaviors of CD mice.

Reviewer #1 (Public Review):

Several questions have remained regarding the characteristics of these cells:

1. Based on the transcriptome data in Figure 2, the authors inferred that thymic macrophages are "specialized in lysosome degradation of phagocytosed material and antigen presentation" yet did not show functional data to support these claims. Functional assays such as phagocytosis and antigen presentation are desirable, especially in comparison to other well characterized macrophage populations.

2. Do transcriptomes of CX3CR1+ thymic macrophages in old mice significantly differ from those of young mice?

3. It would be helpful to better graphically show the compositions (both cell number and cell ratio) of thymic macrophage subsets (TIM4+, CX3CR1+, and others) in mice at different ages (1 week, 6 weeks, and 4 months old). It is not straightforward to deduce all the information based on the current data presentation.

4. The description of the gating strategy of thymic macrophages for Figure 1 is quite verbose. Adding a step-wise gating strategy of thymic macrophages as a figure panel would be helpful for readers to follow the experimental details.

Reviewer #2 (Public Review):

This work provides by far the most thorough characterization of thymic macrophages. The authors used bulk RNA-seq, single-cell seq and fate mapping animal models to demonstrate the phenotype, origin and diversity of thymic macrophages. Overall the manuscript is well written and the conclusions of the paper are mostly well supported by data.

Some aspects of data acquisition and data analysis need to be clarified.<br> 1) The authors should state what does row min row max in figure2 b,d refer to. is this expression value on log scale? In figure 2d, the authors compared their own RNAseq data with ImmGen seq data, what kind of normalization did the authors apply?

2) The authors used immunofluorescent to identify the localization of two populations of macrophages, where they used merTK staining to indicate all macrophages. However, MerTK expression may not restrict to immune cells. The authors are encouraged to confirm that MerTK only labels macrophages in thymus by co-staining with F4/80 or CD45. Tim4 can also be used in immunofluorescence.

3) The data of Cx3cr1+ cells accumulation with age in thymus is very interesting, and as the author has discussed, might indicate their contribution to thymus involution. However, the authors only showed change of percentage. As the total macrophages numbers decreased with age, it is not clear whether these cells actually "accumulate" with age. It will help us to assess if this increased percentage of Cx3Cr1+ cells is an actual increase of "influx" or due to the decrease of the self-maintain Tim4+ macrophage subsets.

Reviewer #3 (Public Review):

This study by Zhou et al. focuses on thymic macrophages and shows that two populations can be distinguished with different identities, localization and origin. Authors use several murine reporter and fate-mapping models, coupled with flow cytometry and transcriptomics approach to support their claims.

Overall, the question tackled by this study is interesting, thymic macrophages having a bit being forgotten in the last decade which has seen many studies similar to the one presented here in other organs. So, the stated aim to closing this gap is relevant. But the actual version of the study suffers from many defects, more or less severe, which affect the clarity and the persuasiveness of it.

- About the plan, authors study the origin of the thymic population and provide data in fig 2, 3 & 4 assuming that thymic macs form a homogeneous population. But from fig 5, they distinguish 2 populations and study them separately. So the end of the paper renders obsolete the beginning, that asks for a revision of the whole plan.

- The figure 1 is not very clear. The backgating should be added in 1a. Or why not using the color map axis mode from FlowJo to show 3 parameters at a glance? The gating strategy should be more clearly displayed on the figure. On fig 1S3, there are clearly 2 pops in the CX3CR1-GFP mice. Why not starting from this to introduce the two populations?

- The figure 2 could be revised also. First, the panel 2a is useless and should be removed. A PC analysis of all the macs would be more useful here. Also, the color code used for the genes is confusing. Why genes up in ThyMacs are red in 2b but only half of them in 2d? Info can be found in the legend but it should be more clear on a graphical point of view.

- For figure 3, what is the timepoint of the panel 3b? Here, authors should show microglia and ThyMacs for both timepoints and conclude based on the comparison. If ThyMacs are as stable as the microglia, no replacement. If not, replacement. For the panel 3f, n=3 is too low to be convinced notably with the standard variation here. And displaying the dot plot with 11% of blood mono from donor while the median being around 20 is not fair, authors should present the most representative plot. For the panel 3h, there are more GFP (in term of MFI) for TEC and ThyMacs than for total cells. How is it possible? TECs and ThyMacs should be in the total cells? Or the gating is not clear enough?

- For figure 4, the EdU staining (4e) is not convincing at all. The signal is very low (as compared to 4c for example.

- For figure 7, the interpretation of the data and the way to present them are not clear. Authors use an inducible fate-mapping model. The fact that Tim4- loose their signal with time argue for a replacement by non-labelled cells (blood monocytes) whereas Tim4+ ones are stable meaning they self-maintain. It is what authors claim. But how it fits with previous data where they say that Tim4+ derived form CX3CR1+? The explanation that is a bit subtended here but not enough clearly shown is that CX3CR1+ give rise to Tim4+ during embryonic development but is stops after, Tim4 self-renew independently, and CX3CR1+ are slowly replaced by monocytes. As this is the central claim of the paper, it should be most clearly reported and for this, a substantial change of the whole plan is required.

Reviewer #1 (Public Review):

Overall, this study provides new and compelling evidence for how methylated RBPs control RNA processing and flow through the nucleus and into the cytoplasm. One strength of the study is the use of orthogonal approaches of chemical inhibition and genetic knockdown to deplete cells of type I or type II PRMT activity. Moreover, the study presents extensive transcriptomic and proteomic data sets and analysis that will likely be useful to the community in promoting other studies. Although the manuscript does not provide a precise mechanistic investigation of how methylation controls the activity of RBPs, they do go beyond mere cataloguing of gene expression defects and link the impact of PRMTs on RNA processing to the activity of specific RBPs.

Reviewer #2 (Public Review): <br> Previous work has established that inhibition or knockdown of the Type II (symmetric) arginine methyl-transferase PRMT5 has global effects on splicing, and although it is less well-characterized, loss of the major Type I (asymmetric) enzyme PRMT1 affects both splicing and RNA export activities. In both cases, inhibition or depletion of the enzymatic activities of these proteins has been shown to negatively impact cancer cells, but the specific targets that are required for the RNA processing effects have not been identified. The key findings here are that levels of transcripts containing unspliced introns were inversely affected by the two classes of inhibitor, with intron retention increasing upon PRMT5 inhibition, and decreasing relative to the control in the case of PRMT1 inhibition. The affected introns were shown to be localized to the nucleus, indicating that they belong to the class of 'detained' introns (DI). Using kinetic assays to measure transcriptional elongation and splicing rates, the authors concluded that PRMT inhibition affects DI levels post-transcriptionally. They found that spliceosome component SNRPB and nuclear RNA export factor CHTOP were both enriched in chromatin-associated, poly(A) RNA fractions, that SNRPB was specifically demethylated by PRMT5 inhibitors while PRMT1 inhibition demethylated CHTOP in the chromatin associated fractions, and that both knockdown of the methyltransferases as well as replacement of the modified arginine residues in each protein recapitulated the effects of the inhibitors. Together, these experiments provide strong evidence supporting a coherent mechanism of differential arginine methylation on RNA processing. They support and significantly extend previously published observations implicating the PRMT enzymes in gene expression. These findings are of broad interest to those who study RNA processing and transcription, cancer biology, and signaling through post-translational modifications.

Reviewer #3 (Public Review):

This manuscript focuses on addressing the contribution of two classes of arginine methyltransferases - Types I and II PRMTs - make to determining alternative splicing (AS) patterns. This question has biochemical and disease relevance because 1) the Sm proteins of snRNPs are symmetrically dimethylated by Type II (PRMT 5 and 9) enzymes, providing binding sites for SMN protein during snRNP assembly (deficiency in SMN leads to SMA), 2) many RNA binding proteins that could potentially regulate AS have arginine-rich regions that are known to be or may be modified by asymmetric or symmetric dimethylarginine (aDMA or sDMA), and 3) arginine demethylation affects biomolecular condensates that control splicing and mRNA fates.

The major strengths are the high-end sequencing strategies taken to compare transcriptomes of cells mock treated or treated with inhibitors of the two PRMT classes; exhaustive data analyses suggest that changes in AS outcomes are due to post-transcriptional splice site choices rather co-transcriptional ones. This implies that alternative splicing choices may be predominantly post-transcriptional, an appealing idea that could help rationalize the functionalities of fast, efficient co-transcriptional splicing with slow, post-transcriptional splicing. Another strength is the isolation of proteins bound to nuclear mRNA in the presence and absence of PRMT inhibitors. The identified protein that are modified by dimethyl-arginine are candidates for mediating the effects of PRMTs.

The major weaknesses include the descriptive nature of the data, in spite of the presented evidence that dimethylated Sm and CHTOP proteins are major components accounting for the mechanistic details that must exist between the activities of Type I PRMTs (PRMT 1, 2, 3, 4, 6, 8) and Type II PRMTs. The connections between the activities of these proteins and mRNA isoforms and fates remain unclear, because post-transcriptional splicing was tested in the presence of transcription inhibition, while protein isolation was conducted by subcellular fractionation that excluded nucleoplasmic proteins. In addition, the potential roles of PRMT9 are ignored.

The authors do achieve their goal of characterizing splicing changes upon inhibition of the two classes of PRMTs. However, their conclusions regarding mechanism are limited by their preliminary nature.

The major impact for the field could be an understanding of how proteins modified by dimethylarginine bridge the gap between co-transcriptional and post-transcriptional splicing regulation. The dataset already created will be of interest to the field, as would additional datasets.

Reviewer #1 (Public Review): 

The results shown by Grobben et al. are basically important and should be published for the post-vaccination era. However, the cross-reactivity is mostly evaluated by only the binding affinity to the S proteins. The authors should evaluate the neutralizing activity of these antibodies using live viruses and/or pseudoviruses. Using all coronaviruses might be difficult, but some (e.g., seasonal coronaviruses such as 229E and NL63) should be used.

Reviewer #2 (Public Review):

Here the authors were seeking to assess whether SARS-CoV-2 infection elicited antibodies that cross react with diverse human CoVs using a Luminex platform. The major conclusions are that cross-binding antibodies are elicited by SARS-CoV-2 infection, and that there is a positive association between the magnitude of the response and the sequence similarity to SARS-CoV-2. As a result, most cross-reactive antibodies target the S2 region which is the most conserved among diverse CoVs. The study also shows that immunization with a SARS-CoV-2 spike derived nanoparticle vaccine similarly elicits cross reactive CoV antibodies in non-human primates. Overall, the data suggests that the S2 domain might be important for the development of pan CoV vaccines. 

The study is largely observational, and conclusions are supported by the data. The major limitation is that it is not clear what role the measured cross-reactive antibodies might play in protective immunity. If a function could be assigned to the cross-reactive antibodies ie neutralization or ADCC, then these findings might be more impactful for guiding the design of pan CoV vaccines.

Reviewer #1 (Public Review): 

This manuscript explores the role of ER and PR in the endometrial cancer cell model called the Ishikawa cell line. The authors conduct a series of detailed experiments to assess the estrogen (E2) and progesterone (R5020) response in this model and show that E2 can promote cell growth which is subsequently inhibited by co-treatment with R5020. RNA-seq revealed E2 or R5020 gene targets with most differential genes being unique to the treatment condition. The functional role of PR was assessed and confirmed on a specific locus of interest and using a reporter assay. ChIP-seq was conducted revealing gained PR binding events following R5020 and some that were already present prior to treatment, as well as sites that were lost. Substantially more PR binding sites were observed in the T47 breast cancer model and the authors mimic the elevated levels of PR, by expressing exogenous PR in Ishikawa cells and conducting a series of ChIP-seq experiments. Analysis of specific binding regions revealed the enrichment of motifs for the Pax family of transcription factors and the authors assess the hormonal regulation of PAX2 cellular expression. PAX2 ChIP-seq was conducted, revealing very few binding peaks and these were partially integrated with the PR and ER binding peaks. Finally, a Hi-C experiment was conducted, revealing that ER, PR and PAX2 binding occurs in genomic compartments and specific gene signatures were derived from this analysis. 

This is a topical area and the work is of potential interest, but several key issues need to be addressed:

- The role for PAX2 (over other family members) is inferred by the enrichment for motifs specific to that family member, but the motif enrichments are not good as defining individual family members that share a common motif. What are the expression levels of the PAX family members in the Ishikawa cell line and in primary endometrial cancers, to support the role for PAX2 over other family members? This wouldn't require any experiments and could simply involve analysis of public expression datasets. 

- The authors conclude that PAX2 binding overlaps with PR in pre-treated cells, but data in Figure 5C and 5D could simply represent co-binding at open enhancers, which are notorious for recruiting many transcription factors that are expressed in that cell type. What is the overlap in peaks between PAX2 and PR/ER, ideally via a Venn diagram or some visual that allows for a comparison of the total number of peaks for each factor and the common ones? Is there a statistically enriched co-binding of PAX2 to PR/ER sites, at levels that are more than expected? 

- The link with PAX2 is potentially exciting but is not convincing in its current form. The only real evidence linking PAX2 to ER/PR is that PAX2 cellular location can be altered and there are some binding peaks where there is co-enrichment, but this would likely happen with any transcription factor expressed in that cell line model. What is currently missing (and essential), is some evidence providing a functional link between ER/PR and PAX2. Is PAX2 required for PR and/or ER function, either ER/PR binding or induction of target genes? If not, then the data on PAX2 is circumstantial and isn't really relevant to the transcriptional pathways regulated by PR or ER. 

- There is no explanation put forward to the 307 lost PR sites.

- The GEO dataset indicates that only one replicate was conducted for the ChIP-seq experiments. This does not meet the minimum ENCODE requirement and many of the differential peaks (i.e. the 307 lost peaks) are potentially false positives that result from having only one replicate. 

- The authors claim that the motif enrichment supports a conclusion where monomer PR could bind at 30 min and dimers at 60 min, but there is no direct evidence that this is the case. Unless the authors plan to pursue this functionally and can show dimeric vs monomeric binding, this statement should be removed, as it is not backed up by data and the presence of a half site vs a full palindromic motif does not provide evidence for the genuine mode of binding. 

- All the work is conducted in a single cell line model. I understand that there are few endometrial cancer cell line models and I also acknowledge that the authors have conducted a complicated series of genomic experiments and it would be unrealistic to repeat these in another model. However, the findings from this one model should reveal new insight that can be validated in either another model or in a cohort of clinical samples of the cancer types. But, in its current state, neither are done. The authors attempt to extract gene signatures from the genomic data to assess in patient cohorts, but the data (see my next comment) is not compelling or convincing and the only conclusion I can make, is that out of the hundreds of somatic mutations and hundreds of PgCR genes, only a handful of genes correlate with outcome. I suspect the same conclusion could be made with a random set of several hundred genes.

Reviewer #2 (Public Review): 

Alejandro La Greca and co-workers have used the well-studied ER+/PR+ endometrial cancer cell line (Ishikawa cells) to model the genomic actions of ER and in particular PR in hormone-naïve and hormone-treated conditions using a variety of complementary techniques for probing global regulation of the genome (RNAseq, ChIPseq, ATACseq, Hi-C, CNV, and virtural 4C). They demonstrate that in contrast to the T47D breast cancer model, ER and PR mostly bind to cell-specific independent binding sites that are in close proximity to PAX2 binding sites. They define these regions as "progestin control regions" that exhibit an open chromatin state prior to hormone exposure. Hormone treatment results in the recruitment of PR and PAX2 as well as ER (at selected genes regulated by ER/PR/PAX2) to already formed loops in order to initiate hormone-dependent transcription. The major strengths include that these studies reveal important differences between ER and PR crosstalk in uterine relative to breast cancer models and provide a deeper understanding of the mechanisms of PR-dependent regulation of the genome in well-studied endometrial cancer cells relative to well-studied breast cancer cell counterparts. Limitations of this work include the lack of additional ER+/PR+ models of endogenous steroid hormone receptor action to support a tissue-specific vs. cell line-specific role of PAX2 as a newly discovered partner with PR or ER/PR at hormone-regulated target genes. In addition, a requirement for PAX2 in PR-dependent gene regulation/gene selection has not been demonstrated.

Reviewer #3 (Public Review): 

The manuscript by La Greca et al explores the relationship between estradiol (E2) and progesterone (Pg) treatment with ER and PR binding and their crosstalk in the Ishikawa endometrial cancer cell line. They find that PR binding is enhanced upon treatment with the ER ligand, E2, and that overlap between ER and PR binding sites also corresponds with binding by the PAX2 transcription factor. The regions of PR and PAX2 binding, present in open chromatin regions inside TADs, are designated progestin control regions. 

Although there is a high volume of high quality genomic data for a variety of hormone treatment conditions, there is an overall lack of functional relevance. The overlap and co-occupancy of ER and PR and PR and PAX2 is merely circumstantial. There are no experiments to show that PAX2 is important for any of the transcriptional changes observed, or that loss of PAX2 would result in reduced PR binding or progestin-responsive genes. There is also very little explanation of what the function of PAX2 is in occupying adjacent chromatin sites to ER and PR. Additionally, many of the conclusions are based on small subsets of overlapping genes. There needs to be more exploration of what the consequences are of these overlaps in binding and adjacent binding sites. Finally, there is almost no discussion of what this genomic data means for endometrial cancer disease progression. It is primarily a description of ER, PR, and PAX2 binding in the Ishikawa cell line.

Reviewer #1 (Public Review): 

The manuscript by Huang et al. reports the cryo-EM structures of EGF and TGFalpha bound to full-length EGFR. As for other receptor tyrosine kinases like the insulin and IGF1 receptors, the transmembrane helices and cytoplasmic kinase domains are not resolved in the cryo-EM maps. 

3D classification of EGF-EGFR revealed multiple, closely-related conformational states of the ligand-bound ectodomain, in which a "scissor-like" rotation of the EGF binding portion of the ectodomain (D1-3) was correlated with a separation of the ends of the membrane-proximal domain (D4); the larger the scissor angle (~25{degree sign}), the closer the ends of D4 (~5 Å), and vice versa. For the smaller scissor angle of ~10{degree sign}, the two-fold symmetry of the EGF+EGFR complex breaks down, such that one of the D4 domains pivots from D1-3 further than the other one, resulting in a D4 separation of ~20 Å. 

The authors utilized previous NMR data on the isolated TM helices of EGFR, which indicated that there are two mutually exclusive crossovers points between the TM helices, one closer to the N-termini of the helices and one closer to the C-termini. Molecular dynamics simulations performed by the authors showed that, in general, the "tips-separated" configuration of the D4 domains was correlated with the N-terminal apposition of the TM helices, and the "tips-juxtaposed" configuration was correlated with the C-terminal apposition. 

Previous biochemical data had indicated/suggested that the N-terminal dimerization of the TM helices results in higher kinase activity (through formation of the asymmetric kinase dimer) than the C-terminal dimerization, even though the C-terminal dimerization places the cytoplasmic juxtamembrane (JM) regions (leading into the kinase domains) closer together. 

The authors determined cryo-EM structures of EGF bound to an EGFR mutant, L834R, which is a gain-of-function substitution in the activation loop of the kinase domain, and found that D4 in the tips-separated conformation was stabilized vs. in wild-type EGFR, indicating that a stabilized asymmetric kinase dimer is conformationally coupled to the tips-separated ectodomain conformation. 

The authors determined cryo-EM structures with TGFalpha bound to EGFR and found that, in this ensemble of structures, D4 in the tips-separated conformation was destabilized (vs. in the EGF-EGFR structures) because of slight differences in the ligand-binding head of EGFR induced by TGFalpha vs. EGF binding. 

All of these data - theirs and others - led to the hypothesis that EGF is a higher activity ligand than TGFalpha (despite both being high-affinity binders to EGFR) because of the conformational coupling between the ligand-binding head of EGFR, the distal tips of D4, the TM helices, the cytoplasmic JM region, and the asymmetric kinase dimer. To test this hypothesis, the authors performed in vitro and in-cell activity assays and, indeed, found that the level of EGFR phosphorylation was higher when stimulated with EGF vs. TGFalpha. 

To provide evidence that the conformational coupling described above was responsible, the authors generated mutant EGFRs -a point mutation in D4 (W492G) and insertion in and replacement of the extracellular JM region - and measured phosphorylation levels upon stimulation with EGF or TGFalpha. These data showed that increasing the flexibility in these regions (through mutation) abrogated the phosphorylation difference in the two cases (EGF vs. TGFalpha), consistent with their hypothesis. 

In summary, this is an impressive study providing solid evidence for a molecular mechanism by which two related, high-affinity growth factors, binding in exactly the same site, can achieve differential signaling outputs through a dimerized receptor tyrosine kinase, and represents an important advance in the field.

Reviewer #2 (Public Review): 

EGFR can be activated by several extracellular ligands. The molecular mechanisms of EGFR in differentiating extracellular signals from these ligands and transforming them into distinct intracellular signaling outputs are not fully understood. In this manuscript, Huang et al. carried out structural analysis of the full-length human EGFR (with ligand EGF or TGF-α) using cryo-EM and MD simulation. The authors reported that the dimeric structure of the two extracellular modules is not rigid at the dimeric interface, resulting in conformational fluctuations of individual domains. One interesting observation was the membrane-proximal tip of the extracellular module in representative two conformations, "separated" and "juxtaposed" states. The authors next tried to correlate the structural dynamics of EGFR to its signaling outputs. 

Major concern: 

Cryo-EM 3D classification was widely used to analyze the dynamics of protein complexes. It could be efficient in probing new intermediate states. But it is not strictly a quantitative method unless certain "unbiased" procedures are applied throughout the complete workflow of image processing. For example, the authors only showed classification results of one dataset (Figure 2). The particles appeared to be evenly distributed in the ten classes, which was a sign that the classification was not efficient and the resulting reconstructions are still mixtures of multiple states. To support the main conclusion of the manuscript, the authors should show the classification results of all datasets: (1) To our knowledge, cryoSparc is less efficient in separating particles into different conformations compared to Relion. The authors could try Relion and multiple rounds of 3D classification could be implemented. (2) Mask-based classification should be applied. (3) The authors could provide an analysis on the changes of particle distribution among different datasets.

Reviewer #3 (Public Review): 

This will be a landmark work in the RTK and EGFR fields. Huang et. al reported a series of cryo-EM structures of full-length EGFR/EGF complexes in different conformations. The major difference among these structures is the distance between the membrane proximal domains IV of EGFR. Although the TM and kinase domains of EGFR were not resolved in the cryo-EM maps, through comprehensive structural analysis and MD simulations, the authors proposed that, the EGFR/EGF complex with separated domains IV would induce N-terminal associated dimeric TM domain and high activity; whereas the EGFR/EGF complex with juxtaposed domains IV would promote C-terminal associated dimeric TM domain and low activity. Such claim is strongly supported by two structure evidences: (1) In the cryo-EM structure of EGFR L834R mutant/EGF complex (a mutant that is supposed to have higher activity than EGFR WT), the separated domains IV is captured in a more stable state. (2) In the cryo-EM structure of EGFR with a weaker ligand TGF-a bound, the separated domains IV is in a more flexible conformation. In addition, the authors also introduced some mutations to EGFR, designed to break the structural coupling between domain IV and TM domain. These EGFR mutants can't response to EGF and TGF-a differently, which further supports the major conclusion of this work that the conformation of ECD determines the conformation of TM as well as the downstream signaling. Overall, the experiments were well designed, and the structural and functional works are of great quality.

Reviewer #2 (Public Review): 

By using modern high-throughput sequencing this paper demonstrates the antibody mediated immune responses that are elicited by vaccination are improved by pre-existing memory CD4 T cell responses. This is important, interesting and novel. These results can only be obtained by interdisciplinary collaborations between clinicians, immunologists and bioinformaticians.

Reviewer #1 (Public Review): 

George Elias et al investigated the response of a cohort of individuals to Hepatitis B vaccination and analysed the role of preexisting vaccine-reactive CD4+ memory T cell receptors in the immune response. They found that the presence of these cross-reactive receptors elicits a faster and stronger response in the vaccines. This is an extremely interesting result, as it suggests that a better understanding of the immune receptor repertoire of an individual can be used to predict and analyse its response to vaccination. 

Strengths: 

The study presents a detailed experimental analysis of the role of CD4+ T cells in the immune response to vaccination. 

The authors show clearly that the dynamics of expansion of memory CD4+ vaccine-specific clones follows the immune response, corroborating the results of previous studies that analysed effector CD4+ cells. 

The authors asked also whether the presence of preexisting vaccine-specific clones impacts the response to vaccination. They found that this is the case. They defined an estimator of a normalized number of putative vaccine-specific clones and showed that can be used to classify individuals into early or late responders. This result has the potential to be extremely impactful in the way we understand immune response to vaccination. 

Weaknesses: 

This central result follows the definition of the R_{hbs} measure. It is not completely clear how much the numerator and denominator of R_{hbs} contribute to the results and how those bystander and putative receptor sequences have been chosen. Some additional explanations could help reinforce the trust in this specific analysis. 

It is also not clear if multiple testing correction has been performed in the presentation of the results of Fig5. 

The correlation between the number of putative vaccine-reactive CD4+ T cells at day 60 and antibody titers is an interesting and robust result. This however does not support the claim of the authors that preexisting vaccine-specific CD4+ memory cells are associated with stronger immune response. This could be the case only if a similar correlation would be observed at day 0.

Reviewer #3 (Public Review): 

In this paper, the authors aim to understand what are the general computational principles that the brain uses for predicting stochastic environments ruled by underlying latent variables. For this, they analyze a particular class of artificial neuronal networks (ANNs) trained to predict stochastic environments. The authors compare the network performance with the optimal solution derived from Bayesian analysis as well as several heuristic algorithms. Importantly, the authors also perform several 'perturbation experiments' in which they take out specific elements of the network and study its performance. In particular, they study the role of gating variables, the recurrent connections, and the trained weights. By doing that authors can causally understand the role of these three mechanisms in the network's computations. The authors establish causal relationships of several important aspects of the flexibility of this network with these three network elements. 

This paper has several strengths. First, the authors systematically define stochastic environments based on graphical models. Second, the authors compared the network performance with several alternatives. Third, the authors perform thorough perturbation and decoding analyses establishing the connection between the particular elements of the network with the important characteristics of the network performance as changes in learning rate or adjustment to changes in baseline probability. 

However, even though the setup of the problem is interesting, as well as the analysis of the ANN seems correct there are three major weaknesses I see in this work that hinders the support to the importance of gaing and the relationship with neuroscience: 

1) The authors study a particular ANN (GRU) neuronal network that includes both the dynamic of the activity of the units as well as the dynamics of two 'gate variables'. It is unclear to me how much their conclusions -- constrained by choosing this particular network -- teach us about the brain. In particular, the relationship of these gate variables with actual synapses, neurons, or populations of neurons is at best speculative at this point. Additionally, claiming that 'gating is necessary' might be the result of using a GRU network in this particular task. Training RNNs have been a productive avenue for understanding neural computations in the past years, in many studies of this class networks are constrained or contrasted by experimental data (Mante and Sussillo et al, 2013, Rajan et al, 2016 or Finkelstein and Fontolan et al, 2021 as some examples), the present study is not constrained either contrasted with data. In most studies, comparison with population activity is relatively straightforward since they trained a network of rate units, which correspond to the 'mean field' description of the ensemble of neurons. In contrast, the present study uses a network where the relationship with biophysical elements of the brain is unknown, hindering the interpretation of their results. Additionally, it might be that other classes of machine learning networks, as for example LSTMs are also able to perform the tasks studied. Actually, LSTMs are able to perform similar computations like the ones in this study here as is shown in Wang and Kurt-Nelson et al, 2019. Different machine learning networks might use different computational strategies, which also hinders the generality claimed in the present paper for understanding computations in the brain. 

2) Although the authors analyze the network by performing several statistical analyses and numerical experiments. The authors did not try to understand the dynamics using standard mathematical tools from dynamical systems and statistical physics that have been used for study trained neuronal networks and understanding their computational mechanisms (see Susillo and Barak, 2013 or Dubreuil and Valente et al, bioRxiv as examples). Their network is 11-dimensional, it might be possible to 'open the box' and understand quantitatively the network dynamics and computations after training. In particular, the authors didn't try to understand at least numerically the geometry of neural representations of latent variables in network dynamics and how it is learned and depends on the environment. Additionally, by performing standard dynamical system analysis it might be possible to understand the role of gating in the network computations. 

3) There are several aspects of the writing of their analysis that clarified. In particular, key points as the optimal solution (which is used as a benchmark to all the other algorithms/networks) or the definition of precision is not fully clear.

Reviewer #3 (Public Review): 

The main purpose of the study was to examine the neural computations underlying working memory in the crow, and to compare these computations to those underlying working memory in primates. Through a series of careful analyses, the authors conclude that the neural computations underlying crow working memory, divisive normalization, is similar to that found in the primate brain. 

What makes these findings particularly interesting, of course, is that the architecture of the avian brain differs significantly from that of the mammalian brain. Namely, the primate brain has a layered cortical architecture, whereas the avian brain has a nuclear cluster architecture. Yet despite these divergent mechanisms at the architectural level, birds show convergent functions at the behavioural level and convergent computational functions at the neural level. 

The detailed and careful computational analyses are a strength of the paper. What is more difficult to know, owing to the fact that it is hard to track eye movements in birds, is how the findings relate to the receptive field of the NCL neurons. That said, what the authors have clearly shown is that neurons in a species very different from primates nevertheless display very primate-like neural computations. 

In terms of a larger picture, the finding that the neural computation underlying crow working memory is similar to that of primates despite a different neural architecture raises another interesting point. The clustering architecture, along with the fact that the neurons in the avian brain are generally smaller than neurons in the primate brain, results in birds having more neurons in their brains than a primate of the same size. Whether the greater number of neurons means the avian brain is computationally more efficient than the primate brain remains to be seen. One thing appears certain from the findings of this study: it is certainly no less computationally efficient.

Reviewer #1 (Public Review): 

In this study, Hahn et al. taught crows to perform a working memory task designed to mimic traditional monkey tasks, where the birds had to use a touch screen and remember different numbers of stimuli across a delay. They recorded single neurons in the crow nidopallium caudolaterale (NCL) during task performance, and found changes in tuning with working memory load similar to those observed in the monkey dorsolateral prefrontal cortex. At the neural population level, increasing loads decreased stimulus information, and these changes could be explained as arising from divisive normalization. The authors conclude that there may be common neural mechanisms, that include stimulus tuning and divisive normalization, that have evolved in both primate and avian species to support multi-item working memory. 

Overall, I think that the premise of the study is interesting, the design and analyses are appropriate, and the conclusions drawn seem well-founded. The most interesting contributions of the paper are the comparative conclusions, particularly because they are counter to a common idea about primate dorsolateral prefrontal cortex and working memory. It's commonly believed that some neurophysiological properties of monkey dorsolateral prefrontal are unique, since a clear homolog doesn't exist in rodents, and it's often suggested that these properties arise from the cytoarchitecture of the region. This study shows that this is not the case, at least with respect to load-dependent effects on working memory, and the authors do an excellent job of elaborating on this point.

Reviewer #1 (Public Review):

Through sequencing the genomes of nearly 650 individuals of a single species of cichlid fish within a lake, this study provides an unprecedented molecular view of the surprising extent of variation in sex-determination systems that can exist within a single species. The authors further find evidence for a complex interaction between genetic background, location along the depth gradient and sex determination system on male body size. Although the authors speculate that these interactions are relevant for fitness in the different habitats and the maintenance of the polymorphism in sex determination within the species, these arguments are mostly indirect and somewhat weaker. Still, this is a unique study with an impressive dataset that provides the foundation for gaining further insights into how variation in sex determination systems evolves and is maintained.

Here I will summarize the strength of the evidence that supports each of the major conclusions of the manuscript:

1. Multiple Y alleles determine sex in Lake Masoko: Here the data are very strong; the GWAS analyses is well-done, and the data presented to support the duplication of the gsdf locus in the most prevalent Y-allele is convincing.

2. Molecular nature of the three sex-determination loci: In all three cases, there is circumstantial evidence supporting the molecular nature of the sex determination gene; i.e. duplication of and insertion of a transposable element next to an excellent candidate sex determination gene (gsdf) and insertion of a transposable element next to a gene (id3) previously not associated with sex determination. Nonetheless, there is no functional data (beyond expression data for gsdf) showing that these mutations are causative, and so the authors should be more circumspect in their claims that they have identified the molecular nature of these alleles.

3. Differential use of Y alleles in Lake Masoko: It is clear that the different Y-alleles are present at different frequencies in the different "genetic clusters" but these are somewhat arbitrarily defined, and PC1 only explains 2.16% of the overall variation in genomic variation. The argument that the benthic and littoral populations are genetically distinct and reproductively isolated, with a bias in gene flow from the benthic into the littoral population, is not so clear from the data presented and seems to rely somewhat on data presented in Malinsky et al. 2015, but this previous evidence is not clearly summarized in the present manuscript.

4. Antagonism between Y alleles and admixture: These are interesting results but not so clear to interpret. Males in the admixture zone with high levels of benthic ancestry and the benthic Y allele (gsdf-dup) are smaller, particularly if caught in the deeper water. But, all of these conditions would seem to favor the gsdf-dup allele, which is fixed in the deeper water benthic population. Furthermore, the frequency of gsdf-dup males does not differ between low and middle PC1 males, suggesting that there is not a selective difference between them. The authors argue that patterns of LD on the other two alleles are suggestive of selection on them, but they do not show patterns of LD on the gsdf-dup allele, which could also be under selection in the benthic population. This hypothesis would be consistent with the results of Malinsky et al. 2015, suggesting that the benthic population is derived from the littoral population. So, one could also argue that the littoral population is segregating for ancestral variation, and there has been directional selection for the gsdf-dup allele in benthic population.

5. Distribution of sex-determining alleles across the Lake Malawi radiation: These are quite nice data supporting that these three alleles are widespread either across species (gsdf-dup) or geographically within A. callipterus (chr19-ins, chr7-ins).

Reviewer #2 (Public Review):

The manuscript by Munby et al. presents genomics studies from a crater lake population of the Eastern happy, Astatotilapia calliptera. The data support convincingly the presence of three different Y-chromosomes in the population and also show a differential presence of the Ys among the benthic and littoral ecomorphs. While previous studies showing multiple sex determination systems in African cichlid species were based on captive-breeding experiment, the work by Munby et al. now is the first investigation on how sex determination acts in a natural population. Thus, this manuscript provides novel information, which is not only relevant for understanding the evolutionary ecology of this important group of fishes, but which is also very interesting for all those working on the evolution of sex determination mechanisms and sex chromosomes.<br> Mapping of the GWAS to the high-quality reference genome of A. calliptera uncovered candidate genes for being the master regulators of male sexual development and even potential mechanisms of action of the sex determining gene. However, here I see a problem with the current version of the manuscript and how this part of the work is presented. I fully concur with the authors that their data suggest these genes as likely candidates and also a way how they are activated. But much more work is needed to provide the full experimental evidence to call them bona-fide sex determining (SD) genes. What the authors have found is the genetic evidence for association of these genes with the sex phenotype, but this is only half of the story. A SD gene has to be shown to be expressed at the right time at the right place, and a molecular mechanism has to act accordingly. Functional studies by genome modifications then bring final clarity. As this is not available for the gsdf and id3 candidates in the current study, a much more careful wording is necessary - or the experimental proof provided by inclusion of additional experiments.

Reviewer #3 (Public Review):

Munby and Linderoth et al. examine genetic sex determination in East African cichlids in four contexts: 1) Genome-wide association mapping in a study population of the species Astatotilapia calliptera from Lake Masoko that identified three putative Y alleles, two at the gsdf gene, and one at the id3 gene, 2) Analysis of expression of these two genes in various tissues by sex genotype, 3) Comparison of body size as a fitness measure by sex genotype and ecomorph, and 4) A survey of A. calliptera populations and numerous Lake Malawi cichlid species for a broader view of use of the Y alleles in the species radiation. Taken together, the findings seem sufficient to make the claim that the gsdf is the first sex determination gene identified for a non-tilapiine cichlid, noteworthy because cichlids have complex and rapidly evolving sex determination systems and thus provide a model for evolution of sex determination. Identification of this widespread sex determination gene is extremely valuable for future studies re-examining its impact in various contexts, including where it interacts with other sex determination systems. Accompanying gene expression analysis presented here demonstrates that Y alleles of gsdf produce higher levels of gsdf transcript in somatic tissues, but does not provide appropriate comparisons in gonadal tissue to provide direct support for a role for gsdf in sex determination. The lack of gonadal expression analysis does not seriously impact the claim that gsdf is the master sex determination gene however, given replicated association with the gsdf Y alleles and sex in other cichlid species surveyed here, and the known role of gsdf as a sex determiner in other fish species. Genetic mapping results are also used to compare inferred fitness of males of different sex genotypes in a natural population, with results suggesting antagonism between different Y alleles and genetic background. These latter findings are noteworthy as providing an empirical example of interactions between sex determination, genetic variation, and ecology that could support polygenic sex determination as an evolutionarily stable strategy. Below I provide strengths and weaknesses of each of the four contexts listed above.

Association mapping

Strengths: Association mapping used a sufficiently large population to allow a stepwise mapping strategy to identify three putative sex determination alleles. The authors provide a careful analysis of genetic variation at the two loci involved including analysis involving use of long-read technology and analysis of coverage to identify copy number variants and transposable element insertions associated with sex determination. The associations with gsdf fall within a previously mapped interval for an XY system in the species, so there is concordance with previous work.

Weaknesses: Potential sequence variation for the associated genes does not appear to be described. It is unclear if there are sequence variants in the coding or untranslated regions of gsdf or id3 associated with X vs. Y alleles, or between the two copies of gsdf in the case of the gsdf duplication allele. Such polymorphism could impact protein function, transcription and translation, or provide landmarks for additional gene expression or gene evolution analyses.

Gene expression

Strengths: Expression is assessed for gsdf in four different male genotypes in four somatic tissues, with results consistent with upregulation of gsdf on the two Y alleles identified on chr7. Their findings suggest that evolution of gsdf as the master sex determination gene resulted in the gene being upregulated in numerous tissues throughout the organism, which could have unknown but significant pleiotropic effects.

Weaknesses: Analysis of expression of gsdf and id3 in the gonad is lacking, though gonad is the primary tissue of interest for sex determination via gsdf. Only two females and two males of unknown sex genotype are assayed for adult gonadal expression of gsdf, which provides little information; sexually dimorphic gsdf expression in the gonad would be expected in any fish, regardless of their master sex determination gene. For the somatic tissue comparisons, gsdf expression levels for females are not included, which would importantly demonstrate if, and to what degree, gsdf expression is sexually dimorphic. Allele-specific expression analysis is not included, which would more definitively support claims of regulatory evolution. There is an in-text mention that no differences in id3 expression were found, but no data is provided and the comparisons made are unclear. Overall, the gene expression data supports links between the described Y-linked gsdf alleles and gsdf expression in somatic tissues, but does not by itself indicate a role for gsdf in gonadal sex determination.

Genetic sex and fitness

Strengths: Sex genotype is shown to impact body size (a proxy of male fitness) in nature in a manner that varies by ecology and genetic background, supporting a scenario where there are different optimal sex genotypes for males depending on genetic background associated with ecology (depth). These findings were only possible through careful and extensive sampling in a natural population, and contextualizing sex genotype and trait information within population structure, using whole genome analysis. Why and how polygenic sex determination systems are evolutionary stable remain standing questions, and the findings presented here could provide a straightforward example for how multiple XY systems can be maintained along an ecological gradient in nature.

Weaknesses: Body size differs by sex genotype when partitioned by genetic background, using a principle component score; however, it is unclear how the specific PC1 score was chosen to partition the littoral fish into low and middle groups, or how differences in that choice might impact results. Though male size is used as a standard proxy for fitness in cichlids, the actual impact of potential pleiotropic effects of sex genotype and/or body size on lifetime fitness in the study population is unclear; admittedly this is also not trivial to measure, to say the least.

Species survey

Strengths: The author provide a broad survey of the identified Y alleles in numerous populations and species, providing an important catalog of sex determination systems present across the Lake Malawi radiation, and showing evolutionary dynamics of sex determination in the species radiation. The apparent continued association of the Y alleles with phenotypic sex in numerous species helps provide confirmation of gsdf as a master sex determination gene.

Weaknesses: The results of the survey could be better presented and summarized to allow readers to understand the evolutionary dynamics of the Y alleles. It is difficult to extract much insight from the current supplemental table. For example, presenting all species surveyed in phylogenetic context with frequency of each Y by sex would provide an extremely valuable overview for readers, and reveal evolutionary patterns not clear in the current presentation. Also, some species are unexpectedly listed as having greater than four copies of gsdf, with as many as eleven copies; the authors do not discuss these results or their interpretation.

Reviewer #1 (Public Review):

The authors succeeded in providing data-based models (including both lab and non-lab data) to predict the risk of diabetes type 2 development. The major strength of this manuscript is the sound methodology and robust statistical methods used. However, the link between the developed items with more clinical aspects of such tools is partly missing in this manuscript and a further evaluation by and recruitment of the clinicians and epidemiologists would help enrich the proposed material. In this effort, authors almost reached their study aim; however, final improvements and amendments are needed to advantage the high-risk part of the population.

Reviewer #2 (Public Review):

Developing simple methods that can predict onset of T2D is an important research area. I think that transforming a machine learning model to a scorecard that can be used without computational resources has the potential to be very useful. However, my fear is that the authors have jumped the gun to early on this. Before the prediction model is clinically validated in a relevant cohort, it has little value in my opinion. This is a process that can be performed to a machine-learning model that has been validated in multiple settings, and now a scorecard can be implemented to ease it use.

The predictive results presented in this study are very strong - a highly simplistic model that is based only on age, sex and body measurements achieves AUC=0.82, a blood tests-based model with only 5 blood tests achieves 0.89. As far as I am aware, those are better than previous studies. However, since I didn't observe methodological advancements in the is study, these results raise doubts whether the analysis is consistent with previous studies, and we can really compare apples to apples.

Reviewer #3 (Public Review):

The authors analyzed several models for predicting the early onset of T2D, where they trained and tested on a UKB based cohort, aged 40 - 69 and suggest two simple logistic regression models: the anthropometric and the five blood tests models in reference to FINDRISC and GDRS models. Their models achieved better auROC, APS, and decile prevalence OR, and better-calibrated predictions.

Strengths:

1. The authors have neatly explained their objectives and performed well-justified analyses.

2. The authors highlight how using both features - HbA1C% measure and reticulocyte count may provide a better indication of the average blood sugar level during the last two-three months than using just the standard HbA1C% measure.

3. Further verification of the proposed anthropometric-based and 5 blood-test results-based models, i.e. if they are capable of discriminating within a group of normoglycemic participants and within a group of pre-diabetic participants resulted in outperforming the FINDRISC and the GDRS based models.

Weaknesses:

1. As the authors point out in the manuscript that these models are suited for the UKB cohort or populations with similar characteristics. It limits the extrapolation of these findings onto another cohort from a different background until analyzed on another country/continent-based cohort.

2. In the methods section, an additional explanation of how the T2D prevalence bins were formed would be useful to a reader.

3. The authors have mentioned that the prevalence of diabetes has been rising more rapidly in low and middle-income countries (LMICs) than in high-income countries and the objective of the present research was to develop clinically usable models which are easy to use and highly predictive of T2D onset. As lifestyle is also one of the contributory factors for T2D, additional analysis that includes a comparison of groups between low-income and high-income subjects within UKB-based cohort provided such metadata available would help understand if the prevalence for T2D differs or not between such groups.

Overall, the authors achieve their aims and the results clearly support their conclusions.

The data analyses performed on a large UKB cohort show promising outcomes, however, the research in its current form is on a pilot scale, if the suggested models work similarly better than the reference highly-esteemed control models on other country-based cohorts, the impact of this study will serve to be a useful tool for the clinicians and to a broader community.

Reviewer #2 (Public Review):

Summary:

The authors used large volume complete EM reconstruction to analyze and quantify the synapses around AIS of 153 pyramidal cells from mouse visual cortex. Totally 1127 putative axon-axon synapses from 122 axon fragments were revealed. They found that the number of axon-axon synapses per AIS is highly variable and correlates with target cell properties. Monitoring the activity of ChCs by genetic expression of calcium indicator in ChCs revealed increased and collective activity during spontaneous walking bout in mice. This is an important work which provide the ultimate resolution and ground truth data on chandelier cell innervation patterns. However, there are several important issues that the author should address and clarify in Results and Discussion. In particular, it is unfortunate and surprising that the work used P36 rather than P56 or older mice. There is no good evidence that P36 visual cortex in mice is fully mature; most likely it is not. Therefore, some of the observations may not be describing the mature state of ChC-PyN connectivity, but rather a developing intermediate state. This point or possibility should be highlighted in the Results and Discussion, otherwise it will mislead the field.

Review:

The authors used large volume complete EM reconstruction to analyze and quantify the synapses around AIS of 153 pyramidal cells from mouse visual cortex. Totally 1127 putative axon-axon synapses from 122 axon fragments were revealed. They found that the number of axon-axon synapses per AIS is highly variable and correlates with target cell properties. Monitoring the activity of ChCs by genetic expression of calcium indicator in ChCs revealed increased and collective activity during spontaneous walking bout in mice. This is an important work which provide the ultimate resolution and ground truth data on chandelier cell innervation patterns. However, there are several important issues that the author should address and clarify in Results and Discussion. In particular, it is unfortunate and surprising that the work used P36 rather than P56 or older mice. There is no good evidence that P36 visual cortex in mice is fully mature; most likely it is not. Therefore, some of the observations may not be describing the mature state of ChC-PyN connectivity, but rather a developing intermediate state. This point or possibility should be highlighted in the Results and Discussion, otherwise it will mislead the field.

Main concerns:

It is puzzling why the authors used a P36 mouse. For such a demanding and effort intensive work, they should have made sure that they would be studying the "end product" of ChC-PyN connectivity. They cited Inan et al., 2013 for evidence of "mature ChCs" at P36, but Inan et al did not study older mice. We have unpublished evidence from high resolution complete single cell reconstruction that ChC axons and synapse "cartridges" continue to increase in complexity even at 2 months of age in the frontal cortex in mice; this timeline may also apply to visual cortex. If this were the case, then some of the results may pertain to a developing rather mature state of ChC -PyN connectivity. For example, the sparse non-ChC synapses at AIS may be further reduced or eliminated. I am not suggesting the authors to do another mouse at P60 for this paper, but this should be done at some point. For now, the authors should discuss the possibility that their results may not be describing the mature ChC circuit, and certain result could be pertaining to postnatal maturation.

Reviewer #1 (Public Review):

All of the experiments and modeling described are beautifully executed and the data and analyses are clearly presented. All conclusions are well supported by the data presented. The introduction and discussion provide a good overview of the prior literature and put the new observations in context. Some aspects of the introduction could be improved by providing a more nuanced view of the prior work on functional interactions between different inhibitory cell types in the cortex, but this does not impact the quality or novelty of the results and analyses that are presented.

Reviewer #3 (Public Review):

Schneider-Mizell et al., investigate the structure and function of chandelier cells, a class of inhibitory neurons in the superficial layers of the cerebral cortex that is characterized by specially forming synaptic connections with the axon initial segments (AISs) of excitatory pyramidal neurons (PNs). For this purpose, the authors employ dense electron-microscopy to reconstruct all inhibitory synapses along the AISs of PNs within a relatively large volume of mouse primary visual cortex. These data provide an unprecedented quantitative account of the number of axo-axonic synapses per PN, their distributions along the AISs, whether they originate from chandelier or other inhibitory cells, and how these connectivity parameters depend on morphological properties of the targeted PNs. Simulations of activity patterns in PNs with anatomically-inspired synaptic input from chandelier cells appear to be more effective in suppressing excitatory output compared to alternative scenarios for axo-axonic connectivity. Moreover, functional imaging in behaving animals revealed highly correlated activity across genetically identified chandelier cells, which correlated with pupil dilation. Taken together, the authors propose that arousal drives collective activation of chandelier cells, which is then passed onto PNs such that cell-specific structural differences of the PNs are compensated by heterogeneous axo-axonic connectivity patterns.

The major strength of this study are the electron-microscopic reconstructions of axo-axonic connections for a representative sample of 153 PNs. The authors show convincingly that they have solved many of the challenges for reconstructing such a complex dataset by combining automated image segmentation with manual proof-reading. The reconstructions provide an unprecedented quantitative account for the number and distributions axo-axonic connections, and their cell-cell variability. Moreover, the simulations provide first insight for how these empirically observed structural configurations of axo-axonic connections could impact the function of PNs. However, the simulation predictions remain to be tested empirically. While the functional imaging experiments performed here indicate how such testing might be achieved in future studies, direct evidence for how chandelier cells impact PN activity is not provided. In sum, the key claims of the paper are well supported by the data, and the approaches used are thoughtful and rigorous.

This study will have a strong impact on neuroscience research for many reasons. For example, the quantitative reconstruction data is available online, which will allow others to constrain more comprehensive simulations of cortex function. Moreover, the study reports a novel approach that allows imaging specifically the activity of chandelier cells, setting the stage for future studies to explore how these cells are embedded into local and long-range circuits, and how they are engaged during different behavioral states. Finally, the methods used here will facilitate dense reconstructions of larger cortical volumes, as emphasized by the authors in the last paragraph of the discussion section.

Reviewer #1 (Public Review):

This study investigates calcium signalling in Toxoplasma gondii bradyzoites, which form persistent cysts in brain and muscle tissues in more than a quarter of the world's human population. Unlike the fast growing (and intensively studied) tachyzoites, relatively little is known about the signalling pathways that regulate the intracellular growth and host cell egress of these slow growing, metabolically quiescent stages. The authors use a variety of different approaches to stimulate calcium signalling and monitor calcium transients in bradyzoites stages and show that every aspect of calcium signalling is repressed in these stages. They show that repressed calcium signalling is associated with reduced motility and microneme secretion (important for infection of new host cells) and that calcium uptake and replenishment of stores occurs rapidly after egress and exposure to exogenous calcium and either glucose or glutamine carbon sources. Based on these findings, it is proposed that repression of calcium signalling might be intimately linked to metabolic quiescence (or visa versa). This is a very nice and carefully performed study which utilizes multiple agonists/inhibitors, reporter lines and calcium sensors together with advanced imaging techniques to undertake the comparative analysis of calcium signalling in tachyzoites and bradyzoite stages. A major strength of the study is the use of bradyzoites from different sources e.g. in vitro differentiated in fibroblasts to more 'physiologically' relevant bradyzoites formed in myoblasts or isolated from murine brain lesions. The conclusions are generally justified. One point that is not addressed is the source of energy used by bradyzoites to egress naturally (before they are exposed to high concentrations of extracellular glucose/glutamine). The possibility that bradyzoites mobilize internal carbohydrate stores is strongly suggesed by the finding that bradyzoite motility is increased in vitro in the presence of exogenous calcium but no glucose/glutamine. In this context, it would be important to discuss the possible role of calcium signalling in amylopectin mobilization and activation of glycolysis/OxPhos from internal stores.

Reviewer #2 (Public Review):

Yu et al provide a comprehensive set of experiments to determine that bradyzoites have much slower cytosolic Ca2+ parameters, which impact on gliding motility, a key process of Toxoplasma spread and persistence.

The only main criticism that I have is the use of the MIC2-GLuc reporter to measure microneme secretion in bradyzoites. Do bradyzoites have any appreciable level of MIC2 and its associated protein M2AP?? This is important that may affect the outcome. If bradyzoites do not, then the MIC2-GLuc reporter might not have appropriate levels of M2AP to correctly traffic to the micronemes. I recommend that the authors quantitate, either by western blot or IFA, the levels of MIC2 and M2AP in bradyzoites versus tachyzoites and also show that M2AP co-localises with MIC2-GLuc to give confidence that MIC2-GLuc is trafficked correctly and thus the low readings of secretion are not just a result of the reporter mistrafficked. It would also be pleasing to see, that 1hr incubation leads to restoration of MIC2-GLuc secretion.

Reviewer #3 (Public Review):

This is a first study that looks in detail at Ca-controlled gliding motility and ATP supply in bradyzoites. A comparison of such different parasite stage by manipulating Ca and ATP metabolism is challenging. Intervention by chemical compounds needs to overcome a prominent cyst wall and the usage of genetic tools needs to consider the broad changes in protein expression between tachyzoites and bradyzoites as well as a heterology between individual bradyzoites. The authors used excysted bradyzoites to exclude the cyst wall as a diffusion barrier as a major factor in the efficacy of different Ca agonists. To address differences in expression levels between tachyzoites and bradyzoite stages the authors developed a ratiometric Ca sensor based upon an autocleaved GCaMP6f-BFP dimer protein.

Overall the conclusions are well supported but there are methodological questions that need to be addressed.

Bradyzoites show a heterogenous expression of Bag1 / Sag1 markers as well as heterologous proteins. This is shown in Fig 1A and Fig 2b for example.

However, in most time-dependent measurements of Ca-dependent fluorescence (Fig 2G, 3D the authors only average three cells. This appears to be insufficient to represent the bradyzoite population. How is the variance between the three measured cells?

In addition, the Mic2 promoter driven Gluc-myc protein is not expressed in all bradyzoites. This is perhaps not suprising as Mic2 seems to be downregulated in bradyzoites according to Pittman and Bucholz et al dataset in ToxoDB. If interpreted correctly the lower expression of Gluc in some bradyzoites would favour an underestimation of the RLUs in Fig 2D.

The maturation of bradyzoite takes several weeks. This cannot be accomplished with currently available system in vitro and the authors use 1 week matured bradyzoites. To facilitate comparability to data from other manuscripts it would be helpful if the authors could quantify the differentiation stage of the in vitro bradyzoites. This could be done by measuring the fractions of Bag1-positive and Sag1-negative bradyzoites.

The mcherry and GCaMP6f signal in fig 3B seem mutually exclusive. This may be due to difference in calcium signalling between Bag1 pos or neg parasites or due to expression differences of GCaMP6f.

The authors use syringe, trypsin-released and FACS sorted bradyzoites in multiple Ca assays. How can it be excluded that this procedure affects (depletes) Ca stores? In my opinion several experiments in this manuscript would benefit from clarification of this point. For example: In Fig 7A Fu et al measure Ca for 5min during trypsin digestion, however, for gliding assays cysts are digested for 10min. The Ca monitoring should cover the complete 10min off trypsin digest.

In Fig 2B Fu et al digest infected monolayers with trypsin to release mcherry from cysts matrices. How can the authors exclude that trypsin is not digesting mCherry protein in this assay?

Fig 7 E,F: the authors measure shorter gliding distances of bradyzoite as compared to tachyzoites. Trails of both parasites however, are detected by visualizing using different antigens that may have different shedding behavior on the FBS-coated glass surface. The Bag1 trail also depends on Bag1 expression, which is shown in numerous images to not be equal among individual bradyzoites. This point is very challenging to address but should at least be discussed.

Fig 7E: Bradyzoites are considered to satisfy their ATP needs mostly via glycolysis and the data shown do support this capability. I find the ability of OligomycinA to block glucose-dependent gliding surprising as this suggests a necessary mitochondrial transport chain for ATP-production from glucose. This result should be mentioned clearly in the text and its implications discussed.

Figure 8: The authors claim a recovery of bradyzoite ATP and Ca levels after 1hr incubation with carbon sources and Ca, that together enable efficient gliding. However, the elevation of bradyzoite ATP occurs after the parasites spend 2 hours in glucose-free and Ca-free conditions, whereas gliding assays are done after a short 10min trypsin digest. I am not entirely convinced that low ATP levels post-egress are responsible for the low gliding activity. Ideally gliding assays should be done after a similar purification procedure to correlate the two experiments.

Reviewer #1 (Public Review):

The aim of this paper to reveal the mechanisms that establish the Wnt gradient combining a mathematical model and experiments is of general importance. The results of computer simulations and biological experiments are interesting because they consider multiple extracellular components. They successfully demonstrated that the ligand/receptor feedback and the other extracellular components shape the morphogen gradient of Wnt ligand so that the fine patterning found in heart development can be explained. However, I feel that quantification of the experimental data, explanation of the mathematical model and discussion of the results are not sufficient in the current manuscript.

Major points<br> 1. Experimental validation of the results of computer simulations is very important in this study. However, many of experimental data were not properly quantified or statistically tested. The authors would need to quantify the experimental results when appropriate and perform statistical tests (e.g. Figs. 1E, 2A, 4A-B, Supplemental Figs. 6, 7).

2. Design of the mathematical model is not sufficiently explained in the main text. Besides details in the method section, the basic design of the model and simulation should be briefly explained. For example, initial distribution of Fzd7, regions that produce Wnt6 and sFRP1, and interpretation of the simulation results should be added for Fig. 3 (page 10, line 11-16).

3. The authors demonstrated the roles of Wnt6/Fzd7 feedback and sFRP/Heparan sulfate binding. A typical simulation data showing the roles of sFRP and Heparan sulfate would need to be shown in the main figure. Unfortunately, they did not sufficiently discuss their actions using the mathematical model. They would need to at least qualitatively discuss these points. How do they control Wnt gradient? What are the roles of these two mechanisms? What are the difference? How do they influence with each other? Simplified models may be necessary to reveal the relationship between these two mechanisms and to gain mechanistic insights.

Additionally, the situation studied in this paper would need to be compared with the other examples of ligand/receptor feedback, and the similarity and difference should also be discussed (e.g. Hedgehog/Patched and Wingless/Frizzled2 in the fly wing).

Reviewer #2 (Public Review):

In this work, the authors tried to understand the effect of receptor and diffusible inhibitors on the Wnt morphogen gradient during heart development by combining experiment and computational modeling. The experimental part seems to be a solid contribution to this academic field, and I appreciate the interdisciplinary attempt to combine the results with the computational model. However, their results may be interpreted more clearly using classical mathematical models.

1. Classical models may be enough.

Previous *mathematical* models provided stronger predictions than numerical simulations, and I am not sure numerical results provided by the authors give us new insights. For example, Eldar et al. (2003) have provided analytical results on why the concentration becomes robust. In normal SDD model

u'(x,t) = -d_1 u(x,t) + d_u \Delta u(x,t),

the steady-state solution is exponential function,

u_s(x) = u_0 exp(- \sqrt (d_1/d_u)x)

, and the amount of morphogen production at the boundary critically affects the result (If the production becomes 1/2, the concentration becomes 1/2 everywhere). On the other hand, if the degradation is promoted by the morphogen itself (in this case, by the upregulation of the receptor expression), the governing equation becomes

u'(x,t) = -d_2 u(x,t)^2 + d_u \Delta u(x,t),

the solution is

u_s(x) =A/(x+x_b)^2

($A$ and $x_b$ are constants determined by $d_u$ and $d_2$). It converges to

u_s(x) =A/x^2

and the morphogen gradient profile does not change much *when the morphogen production is relatively high* (that means there is a condition to be robust).

Similarly, a linear approximation is enough to understand the diffusion length change -<br> diffusion length of the morphogen gradient (the length necessary to become morphogen concentration 1/e) is in general $\sqrt{D_u /d_1}$, and feedback mechanism should increase d_1 in first-order estimation, hence decreasing the diffusion length. Binding to HSPG may have a similar effect (in the case of FGF, HSPG is necessary to the binding of FGFR, and the situation is very different).

2. Biological example of Wnt fluctuation

The authors examine the effect of Wnt production fluctuation, but their motivation is not clear. Eldar et al. (2003) is motivated by the fact that the Shh heterozygote knockout has no phenotype, although the amount of mRNA is halved. Theoretically, it should have a major effect on the organs utilizing the Shh morphogen gradient (actually, haploinsufficiency is observed, but the phenotype is mild). The authors would need to provide some argument why they are interested in the robustness to the Wnt expression fluctuation.

3. Wnt signal distribution

It is difficult for general readers to understand why the Wnt signal distribution in the simulations (0 around 0-10 µm, Sudden disappearance at 40 µm) is appropriate. The authors can provide the profile plot of the actual measurement, which corresponds to the modeling result.

4. Variable "Wnt signal"

It is not clear what the variable "Wnt signal" means. As far as I understand, the signal inside the cell changes quickly (in the case of FGF, the ERK phosphorylation state changes within a minute). The author should provide a concrete example of this "Wnt signal" (maybe mRNA expression of some marker gene?).

5. Use of BMP measurement values.

In addition, I am not sure whether using BMP values for the estimate of Wnt dynamics is appropriate. I have an impression that BMP is a fast-diffusing molecule that has a less binding affinity to ECM compared to FGFs. Although I have not dealt with Wnts, they are reported to bind strongly to ECM.

Reviewer #3 (Public Review):

A summary of the study and the strengths of this manuscript:<br> The authors found several new molecular interactions that may be essential for understanding the mechanism of steep gradient formation of Wnt ligands in the prospective cardiac field.

One of the new findings is that expression of a Wnt receptor, Frizzled7, in the prospective heart field is activated by Wnt/b-catenin signaling, as well as by Wnt6 ligands, which is involved in the patterning of this field. They also found that the diffusing Wnt6 ligand is trapped at the surface of cells in which Frizzled7 is ectopically expressed. It seems reasonable that the combination of signal-dependent receptor expression and receptor-dependent ligand capture would result in a steep gradient of morphogen molecules. In fact, this idea is supported by mathematical modeling. In addition, this modeling suggests that the receptor feedback mechanism provides robustness to morphogen-mediated patterning against fluctuations in morphogen production.

Another highlight of their study is that the soluble Wnt antagonist, sFRP1, specifically binds to N-acetyl HS, and this modification of HS is specifically detected in the outer of the cardiogenic field. The localized N-acetyl HS may also be involved in Wnt gradient formation by inhibiting Wnt signaling around myocardium region.

The weaknesses of this manuscript:<br> Although the issue they address in this manuscript is very important for understanding the mechanism of morphogen-based tissue patterning, most of the experimental data presented in this manuscript are preliminary. Therefore, interpretations other than the ones they have argued for in this manuscript are quite possible. any other interpretations except those they claimed in this manuscript are still possible.

For example, the authors argue that receptor feedback is essential for the formation of steep Wnt gradients (lines 8-9 in the abstract), but their model does not rule out an alternative possibility that high levels of receptor expression in the cardiogenic field form steep gradients. Furthermore, they have not succeeded in directly examining the effect of receptor feedback on Wnt6 gradient formation. Although the data shown in Supplementary Figure 6E appear to support the contribution of feedback mechanisms to patterning, the results do not exclude another interpretation that an increase in Wnt trapper molecules simply inhibits the receptor-mediated clearance of Wnt ligands from the extracellular space in the pericardial region, resulting in an increase of extracellular Wnt ligands and their long-range transport.

With regard to the restriction of sFRP1 diffusion, no evidence has been presented to show that N-acetyl modification of HS is actually involved in the restriction of sFRP1 diffusion, the formation of Wnt gradient, and the patterning of prospective cardiac fields. This lack of data significantly undermines the credibility of the conclusions presented in this paper.

Reviewer #1 (Public Review):

Inflammation has been recently found as a crucial factor underlying the pathogenesis mechanism of obesity and the metabolic syndrome. Plant-derived celastrol was identified as an important therapeutic agent for obesity; however, the drug target of celastrol was totally unknown. Luo Dan et al employed affinity isolation, protein identification and biochemical validation to study the molecular mechanism by which celastrol attenuates inflammation and lipid accumulation in diet-induced obese mice. As result, the authors found that celastrol formed covalent conjugate with endoplasmic reticulum chaperone GRP78 and thereby disconnected the interactions between ER stress, inflammation and lipid metabolism. The findings are very significant to the field of natural product chemistry, pharmacology and metabolic diseases.

Reviewer #1 (Public Review):

The reviewer believes that there is a fundamental problem with the approach of the current MS. Dense reconstruction from serial EM images is a powerful tool for revealing the connectivity matrix in many brain area, where the majority of synaptic connections are made by glutamatergic pyramidal and GABAergic interneurons. Many studies have convincingly demonstrated that the site of synaptic communications among these cells is the well-known EM defined synapses with a presynaptic cloud of vesicles, a rigid presynaptic active zone membrane facing a rigid postsynaptic membrane that either has or does not have a pronounced postsynaptic density. We know from many EM localization results that e.g. the active zone contains the essential molecules of the release sites, the presynaptic Ca2+ channels and the PSD contains the appropriate receptors. Thus, with this information, when the connectome is created from serial EM sections, the sites of communication can be defined based on the EM images. To the knowledge of the reviewer, such pre-existing information is lacking for the DA varicosities. The authors argue that almost all varicosities lack synapses. Verification of such a statement would require the molecular characterization of these varicosities, demonstrating that the molecules essential for vesicle docking/priming/release are lacking. However, if these molecules are present in these varicosities without forming an apparent active zone, then the conclusion of the MS is misleading.<br> The authors demonstrate the clear labeling of DA neuronal processes using the cytoplasmic-targeted Apex2. However, due to the well-known masking effect of DAB precipitate in the cytoplasm, which prevent the unequivocal identification of vesicles, the authors decided to use the mitochondria targeted Apex2 in the first half of the MS. However, for the cocaine part, they turned to the cytoplasmic version for some reasons. They then analyzed the axonal branching structure and the varicosities/contact points. The reviewer cannot see how this later was achieved with densely filled DAB containing structures.<br> The evidence for DA varicosities making synapses (Fig4) is not convincing. The presented EM images does not have the quality/resolution to see the opposing rigid pre- and postsynaptic membranes and the widening of extracellular space in the cleft.<br> Analyzing the structures immediately next to DA varicosities is questionable. If DA is indeed a volume transmitter, how would the authors know how far it can exert its effect. 1 or 5 microns? If 5 microns, there are many structures of all kinds (axon, glia, spine, dendrite) and only their DA receptor content will tell whether they are sensitive or not (and not necessarily their physical distance) to the released DA.

Reviewer #2 (Public Review):

This manuscript provides large-scale EM reconstructions of dopaminergic axons in the mouse striatum. These reconstructions are performed in five mice. One mouse had mitochondrial DAB in dopamine axons and was used for large scale, high-resolution reconstructions. Four mice had cytosolic DAB in dopamine axons, and they were used mostly for lower resolution analyses of effects of cocaine (2 mice) vs control (2 mice). Overall, the data are rigorously acquired and well presented. The paper is easy to follow.

Key findings of this study are that:<br> (1) only a very small subset of dopamine varicosities, 2%, forms synaptic contacts<br> (2) there is considerable heterogeneity in vesicle content with varicosities without vesicles, with small vesicles, with large vesicles, or with small and large vesicles<br> (3) cocaine induces strong changes in dopamine axon structure with extensive branching and large bulbs

The findings are very important for understanding dopamine axon biology and dopamine transmission. In particular, the non-synaptic nature of dopamine transmission has been debated for years. While the field mostly agrees that dopamine transmission is unlikely to rely on classical synaptic structure, it was not possible before to assess the actual number of synaptic contacts compared of non-synaptic varicosities, because previous EM studies did not reconstruct dopamine axons but instead relied on single sections. This is an important finding. Similarly, the finding of heterogeneity of vesicle size is important, and may be related to multiple release and signaling modes that have been proposed for dopamine neurons.

Altogether, the conclusions are supported by the data. Recommendations for improvement include a more detailed discussion of some of the technical limitations that are inherent to the method that is used and suggestions related to data presentation. One important point is that the morphological features used to distinguish synaptic and non-synaptic varicosities have to be described better, and non-DA axons should be assessed in the same dataset by a blind experimenter using the same morphological parameters for comparison.

Reviewer #3 (Public Review):

Fundamental questions about the connectivity of neuromodulatory circuits remain to be solved. The present study focuses on the dopaminergic (DA) system that controls a variety of brain functions including learning, decision making, reward assessment, and social behaviors, and whose dysfunctions being associated with neurological and neuropsychiatric diseases such as Parkinsons's disease, attention-deficit/hyperactivity disorder, and schizophrenia. Specifically, the authors have sought to address the basis by which DA axons might communicate with their target(s) and identify features of structural plasticity of DA axons upon drug exposure. To this end, the authors capitalized on a genetic method to label DA axons that is amenable to electron microscopy (EM), and 3D reconstructions of high-resolution EM images obtained by automated sequential sampling of contiguous sections. Several key findings are presented. Most importantly, the authors present a striking observation that majority of DA axons have varicosities that are largely devoid of synaptic vesicle-like structures and do not form typical morphologically identifiable synapses with target (contacting) cells, which are important in shedding light on the mode by which dopaminergic signaling is implemented. As the manuscript stands, some of the analyses are incomplete to fully support the conclusions.

Reviewer #1 (Public Review):

In their manuscript, Sengupta et al. describe a developmental mechanism that positions a single neuron across multiple layers in the hierarchical C. elegans nerve ring. The authors show that neighborhood placement of the interneuron AIB is established during embryogenesis and is maintained throughout development. AIB is one of the few C. elegans neurons that are divided into distinct pre- and post-synaptic regions, and its axons curiously occupy two physically separated neighborhoods or layers. How this occurs is not known. This study uses time-lapse imaging to show that unlike canonical axon tip outgrowth mediating fasciculation in a target region, AIB's axon occupies two neighborhoods by first growing completely into one, and then gradually unzippering from the first, switching, and zippering onto the second neighborhood. Importantly, axon outgrowth and neighborhood choice are continuously visualized during embryogenesis, an impressive experiment typically constrained by lack of cell-specific reporters during early development as well as the struggle of imaging embryos.<br> The authors posit that zippering is mediated by temporally regulated differential adhesive forces between AIB's neighboring pre- and post-synaptic neurons. How this differs from differential adhesion in classic fasciculating neurons is described but could be made much clearer. They proceed to identify the immunoglobulin syg-1/syg-2 receptor-ligand pair to be necessary and sufficient for AIB's axon switch; in syg-1/syg-2 mutants, AIB is not able to position itself in the second neighborhood and remains fasciculated with the first one, suggesting that adhesive forces are dampened in syg-1/syg-2 mutants. Lastly, the authors show that pre-synapse assembly follows zippering, linking AIB axon placement with synaptogenesis, and that this is also compromised in syg mutants.<br> The pipeline used to study axon outgrowth at a single-cell level in the embryo at relevant time points is commendable and will be useful to people studying C. elegans nervous system establishment. Although the overall manuscript and data are well-presented, we think the mechanism of retrograde zippering could be better described. Also, syg-1/syg-2 expression needs to be delineated to support the notion of differential adhesion between neighborhoods.

Reviewer #2 (Public Review):

A large amount of data is presented in this paper. The experiments are carefully documented and support the conclusions. Of particular importance is the live imaging of the outgrowth of the AIB neurite in the embryo. This is challenging and required the development of a new marker for labelling and the adaptation of a new type of microscope. This enabled the initial and surprising observation that part of the neurite relocates after outgrowth. I'm not sure that the mathematical modeling adds much. The main conclusion is that the modeling is consistent with a "net increase of adhesive forces in the anterior neighbourhood", which is to be expected. The authors then try to identify the relevant adhesion molecules and find that a pair of IgCAMs (syg-1 and syg-2), which are known to act as receptor-ligand pair, are involved. A series of experiments establishes that syg-2 act in the AIB neurons, whereas syg-1 does not. The neurite positioning defects in syg-1 and syg-2 mutants are partially penetrant, suggesting that other adhesion molecules must be involved. While a large percentage of mutant animals show defects, the defects within an individual animal are surprisingly low with only 21.5% +/- 4% of the neurite detached. This would suggest that syg-1/syg-2 aren't even the major adhesion molecules involved here. In further studies, where the authors ablate the RIM neurons (which express syg-1), the authors use a different measure to quantify the defects (minimal distance between neurite segments, Suppl Figure 7). This makes it difficult to compare the results to those of the syg-1 mutants. For the ectopic expression experiments with syg-1 the authors only report the percentage of animal with defects and not the extent of the defects (how much of the neurite was in an abnormal position).<br> Overall, this is a very detailed study describing an important novel mechanism for neurite positioning within an nerve bundle.

Reviewer #3 (Public Review):

This is a very interesting manuscript describing the changes of neurite position in a complex neuropil during development. The experimental system is well chosen because AIB's function within the circuit requires its neurite to be in two different neuropil "neighborhoods". The manuscript included some technically difficult experiments of imaging neurite outgrowth in C. elegans embryos which are very hard to do. The surprising finding here is that neurite position is not sole dependent on its growth cone navigation. In the case of the AIB neuron, the growth cone is anchored after it reaches its destination point and then a segment of the neurite shift direction towards its final position through a zippering action. They also show that this shift in position is driven by adhesion molecules SYG-1 and SYG-2. Overall, I think this is a strong candidate for eLife. I have one main point and a few minor points.

My main point is about the relationship between synapse formation and neurite zippering. In my opinion, this is an interesting point because it would tell us if the zippering behavior is a consequence of synapse formation or it is a distinct specificity step before synapse formation. From the time course that was described in the paper, it seems that the accumulation of RAB-3 only starts after the zippering has completed. I would suggest the authors to examine at least another synaptic marker like SNB-1 or SYD-2. We have created cell specific endogenous labeling of several active zone markers that can be used for these experiments. If the results hold, then, I think the authors should make it clear in the text that the zippering takes place before synapse formation and serves as a distinct step in achieving the neighborhood specificity.

Minor points<br> 1. The schematic diagram is somewhat misleading because in the axial view, the anterior and posterior segment of the nerve ring should appear on top of each other. The lateral view is the right view to show the anterior and posterior segments.<br> 2. Describe the screen that led to the mutant alleles of syg-1 and syg-2 better. Any other mutants?<br> 3. "Consistent with the importance of adhesion-based mechanisms in the observed phenotypes, ectopic expression of the SYG-1 endodomain in the posterior neighborhood did not result in mislocalization of AIB (Figure 6-figure supplement 1A,B). " This statement is wrong. I suspect the authors meant in syg-2 mutants.<br> 4. For Fig. 7-figure supplement 1, please quantify this phenotype.

Reviewer #1 (Public Review):

Summary<br> Moncunill et al set out to investigate a very important question: why are half of children vaccinated three times with RTS,S AS01 protected from clinical malaria - and half not? To do so they isolated PBMCs before vaccination and one month after third vaccination and stimulated them in vitro with DMSO (vehicle control), two malaria antigens (CSP (part of RTS,S) & AMA1) or HBS (hepatitis B antigen - part of RTS,S). They then assessed their transcriptional response by blood transcriptional module analysis and correlated those results with previous published data on antibody titers and T cell cytokine production to find associations. To assess risk of clinical malaria, responses were compared between RTS,S vaccinated children who developed clinical malaria in the one year follow-up (cases) and those who received RTS,S or a comparator vaccine and did not (controls). They found that responses after RTS,S vaccination did not predict protection from clinical malaria. Instead a blood transcriptional module signature related to dendritic cells, inflammation, and monocytes before vaccination may be associated with clinical malaria risk.

Strengths<br> Immune correlates of protection are evaluated in African children (who are the RTS,S target population) in a natural transmission setting.<br> Excellent set of controls: children (same age) vaccinated with RTS,S or comparator vaccine alongside each other -> retrospectively stratified by whether the did or did not develop clinical malaria : controls for the effect of a developing immune system and would allow to disentangle RTS,S specific and clinical malaria specific response patterns.

Weaknesses<br> RTS,S is composed of CSP & HBS. yet when PBMCs from children vaccinated three time with RTS,S are stimulated with these peptides no transcriptional differences compared to children receiving a rabies or meningitis vaccine were detected (Figure 2). this lack of recall response impacts all downstream conclusions and comparisons made in the paper.<br> Transcriptional responses 1 month after the final RTS,S vaccination do not predict clinical malaria risk (Figure 3) - this is a key finding, which should be central to the conclusion of this paper.<br> The take-home message put forward in the title/abstract (that a monocyte and DC related pre-vaccination signature predicts risk of clinical malaria in RTS,S vaccinated children) is not strongly supported by the data. It is based on blood transcriptional modules related to monocytes being picked out when comparing RTS,S vaccinated cases and controls. Many other modules are picked out as well e.g. cell cycle (Figure 6B). An in-depth analysis of the genes in these module and what their up and downregulation can tell us about their function is warranted to support the conclusions.

Impact<br> This paper will inform future studies looking for correlates of RTS,S induced protection from clinical malaria in a variety of ways:<br> It validates the blood transcriptional module approach (as published by Li S, Rouphael N, Duraisingham S, Romero-Steiner S, Presnell S, Davis C, Schmidt DS, Johnson SE, Milton A, Rajam G, et al: Molecular signatures of antibody responses derived from a systems biology study of five human vaccines. Nat Immunol 2014) to find target cell populations which can then be investigated in much more detail.<br> It shows that studying PBMC recall responses after peptide stimulation post final vaccination is not the way forward, since no response is detected (Figure 2). future studies can now take an alternative approach. e.g. since unstimulated PBMCs (vehicle control) from RTS,S vaccinated children were different from those who received a comparator vaccine (Figure 2) RTS,S vaccine signatures could be picked up much more easily by whole blood RNAseq.<br> It implicates innate immune cells in shaping an individuals response to a vaccine - an exciting basis for future functional and mechanistic studies.

Reviewer #2 (Public Review):

This paper reports a sub-study of the RTS,S/AS01 malaria vaccine Phase 3 trial, which aimed to identify groups of genes (blood transcriptional modules, BTMs) for which expression in DMSO or antigen-stimulated PBMCs was associated with clinical malaria during a 12-month follow-up period. Study subjects were infants and children who received either RTS,S/AS01 or comparator vaccines (meningococcal C for infants, rabies vaccine for children), enrolled in the study in Tanzania and Mozambique (with some additional analyses using samples from Gabonese infants).

Using PBMCs collected at baseline before vaccination and 3 months later (a month after the third vaccine dose), stimulated with DMSO or parasite antigens, the authors used RNA-sequencing to identify BTMs which were different between recipients of RTS,S/AS01 vs comparator vaccines; which were different between baseline and month 3 in RTS,S/AS01 recipients; and which differed between RTS,S/AS01 recipients with a malaria episode and those without a malaria episode during the follow-up period. This combination of analyses might help to distinguish BTMs specifically associated with RTS,S/AS01 vaccine efficacy from those associated with other factors influencing susceptibility to malaria. To further aid mechanistic understanding the authors examined correlations between BTMs and measures of cellular and humoral immune responses. To try to establish generalisability the authors examined whether BTMs identified in African children were also associated with developing malaria in RTS,S/AS01-vaccinated malaria-naïve adults in the United States who underwent controlled human malaria infection (CHMI).

Strengths of the study include:<br> 1) The relatively large number of subjects, the large amount of transcriptomic and immunological data which has been generated (and made publicly accessible), and the extensive analysis to evaluate associations between BTMs and numerous immunological variables.<br> 2) Clear explanation of both the rationale and methods for most of the analyses<br> 3) The attempt to validate findings in the CHMI studies<br> 4) Matching of subjects to try to eliminate the confounding effects of age, study site, and time of vaccination

Weaknesses of the study include:<br> 1) Despite the relatively large size of the study, it is hard to know whether it had sufficient power to achieve its main objective, and we are not presented with data to demonstrate how successfully the authors managed to match subjects for age, timing of vaccination and follow-up duration<br> 2) The comparator group to the RTS,S/AS01 vaccine is not a single vaccine, but two vaccines, but the presentation of the data makes it difficult to identify what effect this may have had on the results<br> 3) A very "liberal" false-discovery rate (FDR) threshold has been used throughout to define significant associations. An FDR of 0.2 indicates that 20% (or 1 in 5) results which are considered significant will be false-discoveries. This means that the "significant" results must be interpreted with a high degree of caution. Typically researchers use lower FDR thresholds, like 0.05 or 0.01, although one may argue for different thresholds under different circumstances<br> 4) A perplexing finding, which is not addressed in detail, is the large number of BTMs which differ between RTS,S and comparator vaccine groups after DMSO stimulation of PBMCs, but these are not seen when PBMCs are stimulated with parasite antigens in DMSO (and a similar finding for month 3 vs month 0 samples from RTS,S recipients). This raises some concern about the stimulation experiments, because one might expect that the DMSO vehicle in the antigen preparations would trigger a similar response to DMSO alone.

The authors partly achieved their aims. They identified BTMs differentially expressed between RTS,S/AS01 and the comparator vaccines, and between baseline and month 3 in RTS,S/AS01 recipients. They also identified BTMs at month 3 associated with developing malaria, and BTMs at baseline associated with developing malaria. These latter BTMs were partly replicated in the CHMI study subjects. Higher expression of BTMs associated with monocytes and dendritic cells were most consistently identified across the different analyses and their expression in stimulated baseline samples was most consistently associated with development of clinical malaria in RTS,S/AS01 recipients. However there were inconsistencies in associations between some of the studies, and it is possible that the "consistent" monocyte and dendritic cell BTMs would not be so consistent if a more stringent FDR threshold was used. However the authors conclusions are largely quite measured and for the most part they do not over-interpret the significance of their findings.

Overall the work provides some evidence that baseline immunological status, particularly related to monocyte and dendritic cell responses and possibly their role in or response to baseline inflammation, may be a determinant of how well the RTS,S vaccine works to prevent malaria. This provides a basis for further work to optimise the effectiveness of the vaccine. The usefulness of PBMC stimulation to predict an individual's response to vaccination will be limited because this is not a method which can be used at scale in resource limited settings, but the concept that vaccine response could be enhanced by modifying pre-vaccine immunological or inflammatory status is potentially important. The data published with this study will be a valuable resource and will undoubtedly be used by others to address similar questions. Increasing the efficacy of malaria vaccines remains an extremely important goal, and identifying possible mechanisms which restrict the efficacy of RTS,S is important.

Reviewer #1 (Public Review): 

In this study, the authors seek to understand the target and mechanism of action of two structurally related orally available antibiotic drug candidates active against Neisseria gonorrhoeae (Ng). The experimental approach involves a detailed investigation of drug efficacy in bacterial culture experiments and a mouse model for gonorrhea infections, along with biochemical experiments to identify the drug target. The latter experiments include discovery of resistance-inducing mutations in class Ia ribonucleotide reductase (RNR), in vitro validation of the ability of the Ng inhibitors to diminish enzyme activity, and structural studies to evaluate the effects of the compounds on Ng RNR structure. The work succeeds in providing convincing evidence for inhibition of the RNR but it does not fully explain how the drug candidates bind to the enzyme. Although the findings represent an important advance that could motivate other work in exploiting bacterial RNRs for antibiotic drug development, conclusions about the mechanism of action could be better supported by more thorough understanding of inhibitor-enzyme interaction. This insight would be important for improving drug design and broad expansion of the approach to other pathogens. Additionally, the inhibitors are billed as narrow-spectrum antibiotic candidates, but these claims are based on analysis of a small and specialized group of bacteria that are not likely to contain or exclusively rely on a class Ia RNR. It is not clear from this study if the inhibitors could affect growth of commensal organisms that contain aerobic RNRs.

Reviewer #2 (Public Review): 

This study by Narasimhan et al. describes the identification of ribonucleotide reductase (RNR), a critical enzyme in all organisms, as a new target for treatment of antibiotic-resistant gonorrhea, via a novel mechanism for RNR inhibition. The authors begin with the identification of two inhibitors that selectively target Neisseria gonorrhoeae, including multidrug resistant strains, over other pathogens and microbiota. They then show that these inhibitors target the synthesis of DNA, but not by the mechanism of other members of this class of compounds; instead, isolation of resistant mutants indicates that the class Ia ribonucleotide reductase of this organism is the target of the molecules. These results are supported by in vitro activity assays of the RNR, along with electron microscopy characterization of the RNR, showing that the resistance mutations disrupt the protein's ability to form a ring-like inhibited state, implying that the mechanism of action of the compounds involves that ring-like state. While other compounds have been shown to induce formation of the inhibited state of the human RNR, the mechanism of inhibition of the gonorrheal RNR evidenced here is distinct. Finally, the authors present data from a mouse infection model showing efficacy of the compounds. The comprehensive nature of this study, from small molecule to in vitro analysis to in vivo efficacy, is compelling, and the results are of interest both from an enzymological perspective and from the development of new strategies to combat important pathogens. There are two issues that I believe the authors should address to support two important aspects of their work, the mechanisms of inhibition and resistance: 

1) The major question that sticks in my mind after reading this manuscript is the mechanism by which the molecules inhibit the RNR. They act as potent inhibitors in vitro, and the identification of the resistance mutations, H25R and S41L, which interfere with the ability of the RNR to form the inactive a4b4 form, are strong pieces of evidence in favor of the authors' proposal that the inhibitors "potentiate conversion of its active a2b2 state to an inactive a4b4." The clincher of this argument would be EM evidence that the presence of the inhibitors leads to a4b4 formation, just as the H25R and S41L variants do, or (perhaps simpler) size exclusion chromatography or direct evidence from another analytical method pointing to formation of the a4b4 species. 

Relatedly, it would also be helpful if the dependency of inhibition on dATP would be clarified. Figure S6 suggests that dATP is not required for this state, but on p. 14, line 12, the authors write "the dependency on a dATP-induced inactive a4b4 state also explains why the Ng and Ec Ia RNRs are both sensitive to these inhibitors..." and on p. 20, line 27, it is also implied that dATP could be involved. Please clarify this point. 

2) The observation that the resistance mutant strains have lower fitness is an interesting and important one. I suggest that the authors determine whether this decreased fitness might be the result of the mutations in the RNR leading to lower activity - the authors should give the activities of the H25R and S41L with the normal substrate, vs. wild-type alpha. If the activities are similar to wild-type, perhaps the authors could suggest another potential explanation. One that seems possible to me is that the loss of dATP inhibition (see Fig S5) might lead to loss of fitness via misregulation of (deoxy)nucleotide pools.

Reviewer #1 (Public Review):

Capraz et al extend previous studies of ACE2 protein as an antiviral agent against SARS-CoV-2, which acts by binding to spike. Their work indicates that ACE2 with engineered decreased glycosylation has increased binding (as measured by BLI) and antiviral activity against SARS-CoV-2, without decreasing enzymatic activity of ACE2. Glycosylation of ACE2 is decreased using point mutations and enzymatically.

Reviewer #2 (Public Review):

Summary: The authors provide a succinct review of Spike and ACE2 glycosylation and its apparent importance in modulating the interaction of these two proteins. Of particular translational value is the idea that understanding the configurations and effects of ACE2 glycans could inform efforts to develop soluble ACE2 variants as SARS-CoV-2 therapeutics. The authors make a good case for studies to understand the functional roles of glycosylation in this context. For their analysis, the authors begin with 3D modeling of the Spike trimer in complex with one molecule of ACE2. The interaction is mediated by a single RBD (receptor binding domain) in Spike that is modeled in the "up" conformation previously shown to be competent for receptor binding. They then generated a fully glycosylated model and then performed molecular dynamics simulation. Post-simulation structural analysis focused primarily on identifying effects of the glycans on the Spike:ACE2 interaction. Two types of apparent effects were concluded: (1) direct interactions between glycans and proteins, and (2) indirect effects. Finally, the authors present functional data on glycan effects on protein stability, binding, and inhibitory activity of soluble ACE2.

Critique: Data and analysis presented in this manuscript generally support the general idea that glycans can have direct and/or modulatory effects on the interaction between the SARS-CoV-2 Spike protein and its receptor ACE2. The molecular dynamics methodology and functional/biophysical experiments themselves appear to have been performed expertly. However, there are deficiencies in the both the experimental design and data analysis, at least some of which can be addressed by more detailed and clearer description of the what was done and how the data were analyzed.

Reviewer #1 (Public Review):

Previous studies have provided crystallographic snapshots of the autokinase domains of several sensor histidine kinases (HK) involved in signal transduction in bacteria. Nevertheless, the lack of a full-length structure of these HK hampered the understanding of the molecular mechanism of signaling. Moreover, how a stimuli perceived by the membrane-bound sensor domain is transmitted to the catalytic cytoplasmic domain of an HK, to modulate its activity is poorly understood. To probe the coupling between the sensor and autokinase domains Mensa et al. used cysteine cross linking and reporter gene assay to probe the signaling state of E. coli PhoQ in a set of several point mutations. Using these data they developed a 3-domain model in which the sensor, HAMP and catalytic domain are in allosteric communication to interconvert the kinase state in an "on" or "off" conformation. The authors conclude that signals transmit to the catalytic domain through intradomain allosteric transitions, rather than through a concerted conformational change.

This work represents an important and novel attempt to understand the mechanism of signal transduction by sensor kinases in two-component systems. This contribution challenges the concept that signal transmit via propagation of single concerted conformational changes of the sensor kinase. The authors, instead, propose that signal is transmitted by the sensor via an interdomain allosteric mechanism.

The way in that the paper is presented appears to be directed to enzymologist working in enzyme kinetics models, rather than to a wide audience. For example, the paper starts saying that "Fully cooperative two-state models are unable to explain the gamut of activities of mutants". This affirmation seems too abrupt without defining what kind of model they are talking about. Is a molecular model, a kinetic model or a thermodynamic model? They should explain these concepts before to show the results. After we start to read it seems clear that they propose a thermodynamic model to explain the coupling of the different domains.

For an enzymologist should be quite worrying to interpret data of activity assays with gene reporters without knowing the answers to the following: Do the mutations affect the PhoQ protein levels in cells? How accurate are the Western blots to quantify dimer formation in Y60C and establish the kinase "on" or kinase "off" states of PhoQ? The error bar of the of crosslinking experiments, shown in the different Figs, seems quite small for a Western blot quantification. Nevertheless, in Figure 8-figure supplement 2 panel, in the mutant I221F is obtained a poor fit which is not taken into account. Is it because the error is dismissed? Same for panels A and D. Is missing something the proposed model?

Reviewer #2 (Public Review):

This manuscript characterized the effects of 35 mutational substitutions in three domains the bacterial transmembrane two-component sensor protein for Mg2+, PhoQ, on the signaling state of the periplasmic sensor domain and the cytoplasmic histidine kinase domain. Signaling state was assayed by a diagnostic cysteine cross-link for the sensor domain and the expression of a coupled beta-galactosidase reporter for the kinase domain. The results of those characterizations were used to develop an allosteric coupling model of conformational signaling from sensor domain to kinase domain, with a key role played by the HAMP domain that connects sensor to kinase. Single-site mutational substitutions were at positions expected to be in the interior of the protein structure in the periplasmic, HAMP and the S-helix regions of the protein as well as at boundaries of transmembrane segments. In addition, the connections between the second transmembrane helix and the HAMP domain, and between the HAMP domain and the S-helix were disrupted by introduction of a sequence of seven glycines. Each mutant protein was assayed for the signaling states of the sensor and kinase domains at five different concentrations of Mg2+. Some of the resulting dose-response curves showed patterns much like that for the wild-type receptor in which the signaling state of the sensor and kinase domain were correlated. However, a majority of the curves exhibited a variety of altered relationships between patterns for the two domains. Importantly, the effects of the glycine insertions before and after the HAMP domain indicated that this domain reduced the native "on" signaling state of both the sensor and kinase domain to be less extreme and thus in a more balanced state between on and off. Examination of the effects of similar glycine substitutions in two related two-component sensor kinases showed a similar negative influence of HAMP domain coupling on kinase domain signaling state. A global fitting using the allosteric coupling model between the three domains was performed for all 35 pairs of dose-response curves for PhoQ, allowing variation of one or a few individual parameters relevant to the position of the particular mutational substitution. An important validation of the resulting global parameters was a reasonable fit of the wild-type dose-response curves. The global parameters fit the experimental data for most but not all mutant receptors. Overall, the allosteric coupling model performed well, providing support for its validity.

Thus, this work provides support for concept of intra-receptor signaling via allosteric coupling between independent domains that each have their own intrinsic equilibrium between the "on" and "off" state. This allosteric coupling model introduces a third way of thinking about how ligand occupancy of a transmembrane receptor site facing the cell's exterior generates altered activity of a cytoplasmic domain inside the cell. Instead of considering that ligand binding "sends a signal" by sequential conformational changes that travel through the receptor structure or that ligand binding shifts a conformational equilibrium of the entire receptor in a concerted manner, the allosteric model suggests that signaling occurs by allosteric coupling between relatively independent domains of a multi-domain receptor protein. This constitutes an important contribution to our concepts of receptor conformational signaling.

However, the impact of this contribution is likely to be less than it could be because of the way the manuscript is written. Specifically, the devotion of a majority of the Results section to consideration of models for signaling obscures the most compelling parts of the work, the experimental observations of the striking effects of mutational substitutions throughout PhoQ on the signaling state of the sensor and kinase domains and the explanation of those disparate effects by the allosteric coupling model of conformational signaling. For many experimental scientists interested in mechanisms of signaling, this work would be much more accessible if the experimental results were presented first, the allosteric coupling model was introduced as a way to explain the results, and much of the consideration of other models and the development and details of the allosteric model were shifted to the Materials and Methods or provided as part of supplementary materials.

Reviewer #3 (Public Review):

This manuscript describes a comprehensive study of kinase activation and allosteric coupling in the sensor histidine kinase (SHKs) PhoQ. Quantitative assays for sensor domain activation and kinase response are used to evaluate a large number of variant proteins that display a range of properties with respect to ligand binding, interdomain coupling and kinase activity. The data is used to construct and fit a conceptually elegant model that provides a thermodynamic explanation for domain interactions, allostery and sensing responses in SHKs. The experiments also demonstrate that sensor kinase domains intrinsically favor their "on" states and that HAMP domains act to deactivate both the sensor and the kinase units. In all it is a very impressive study that sets the bar for enzymatic approaches aimed at understanding signaling by multidomain transmembrane kinases. Generality of key principles are explored by examining several SHKs related to PhoQ. The paper is well written and the complex data and their interpretation are for the most part clearly discussed. That said, there are some issues the authors should address:

The model applied for Mg binding should be described to a greater extent. The equations of Figs. 1, 2,3,6,7 represent a situation more complex than the accompanying schematics portray. Even the simplest equation of 1B implies sequential binding of 2 Mg ions to one PhoQ dimer (presumably 1 site per subunit). Furthermore, the binding sites are assumed to be independent and, importantly, there are no intermediate states in the model in which one subunit is "on" (in either its sensor or kinase domain) and the other is "off". Is it known experimentally that the two subunits act independently and what is the consequence of not allowing for hybrid activation states within the dimer?

In addition, there may be a factor of 2 missing in treating the relative dissociation constants for Mg binding to an empty PhoQ or to a singly Mg-occupied PhoQ. Because the multiplicity changes by a factor of 2 in going from both the empty to the half-occupied state and again by 2 in going from the half-occupied to the fully occupied states, the effective Kd for binding to the singly occupied state is 4x larger than for binding to the empty state. It appears that all of the models accommodate only a factor of 2. This issue affects the (1 + [Mg]/Kd)2 term, likely to a minor extent.

In a similar vein, for the final models of Fig. 6,7, why is Mg binding only considered to selected states (SenOFF/HAMP1/Akon/off, for example)? And in Fig. 6A what does AK "on/off" signify?

Line 549 - Discussion of the setpoint of the autokinase domain depends on the "reference point" given that KAK and alpha2 are correlated parameters. For example, one could view the intrinsic activity of the autokinase as being the fully uncoupled state, with KAK defined closed to 1.0 and alpha2 having a smaller value that currently modeled in the case of the Y60C (WT) protein. Could one fix KSen and KAK at the values for the Gly-decoupled systems and allow the shifts in equilibrium owing to HAMP coupling to be compensated solely for by alpha1 and alpha2? This framing might be more straightforward for understanding the HAMP coupling. Although the reference position is largely arbitrary and in any given fitting scheme likely depends on the choice of constraining and fixing parameters, it does alter how one views the role of kinase-activating mutations. i.e. with the fully decoupled state as the reference, the HAMP is always deactivating, with different variants (including the WT) deactivated to varying extents. Some additional comments on this issue may help readers understand the range of kinase behavior and how it is influenced by HAMP.

Related to the previous point, in Fig. 7 the alpha2 parameter seems to have a large amount of uncertainty, and appears biphasic in the fits, this behavior deserves a comment as to its impact in the model. How much would the interpretations change if alpha2 is considered to hold its extreme values?

p. 30 line 587 - It's unclear what is meant by the statement that the HAMP domain "serves to tune the ligand-sensitivity amplitude of the response" (p. 30 line 587). In this model, the HAMP domain does alter the sensitivity of the sensor domain by favoring the sensor OFF state (even though it does not directly modulate KdOFF), but what is meant by "sensitivity amplitude".

Reviewer #1 (Public Review):

In support of the hypothesis that electrons from intracellular metabolism can be diverted to compounds outside the cell, independent experiments quantify electrons transferred to both iron and electrodes (via soluble shuttles), link the process to specific genes, and finds evidence for the ability in related genomes. When poorly fermentable sugar is the substrate, use of external acceptors alters intracellular NAD:NADH and ATP levels dramatically, and at least 20% of the electrons produced from mannitol oxidation can be recovered at electrodes. Extracellular electron transfer also alters fermentation when extracts from raw food (kale) are the substrate. The paper supplies extensive supplementary experiments testing competing hypotheses and related strains.

Reviewer #2 (Public Review):

Tejedor-Sanz et al. describe the physiology of extracellular electron transfer in lactic acid bacteria. This work builds on previous work in Listeria that identified and characterized a flavin-dependent mechanism for exporting electrons to the cell exterior, which appears to be a widely conserved mechanism in many Gram-positive bacteria. Curiously, extracellular electron transfer does not seem to directly result in cell growth in experiments with L. plantarum, though some aspects of metabolism are accelerated. The ability to shift and/or accelerate metabolism of lactic acid bacteria capable of extracellular electron transfer may have interesting biotechnological applications, but if and to what extent this impacts their native physiology is unclear.

Reviewer #3 (Public Review):

The authors describe a metabolic strategy in the lactic acid bacterium Lactiplantibacillus plantarum, a primarily fermentative organism, wherein L. plantarum utilizes extracellular electron transfer (EET) to increase its fermentative flux, ATP yield, and growth. The primary claim of the paper is that a novel metabolic strategy has been observed that combines elements of respiratory and fermentative pathways. The authors recently published a study describing flavin-based EET ("FLEET") in the bacterium Listeria monocytogenes and also defined a gene cluster, which is found in diverse bacteria, that is required for this activity. In the current study, the authors describe several experiments, convincingly demonstrating L. plantarum EET, the consequential increase in fermentation, and the requirement for one of the genes in the FLEET cluster--ndh2 (encoding a membrane-bound NADH dehydrogenase)--for EET to occur. However, some of the other findings in the paper are less substantiated. For example, the claim that EET proceeds completely independently of respiration may be overstated, as the experimental methods do not rule out the possibility of other respiratory components playing some role. Additionally, we suggest that the authors minimize the use of the term "FLEET" to describe the L. plantarum system since only a single gene within the proposed FLEET locus (ndh2) is strictly necessary for the mechanism they are studying. Finally, we note that while the physiological relevance is questionable (due to the continuous requirement of an exogenous quinone, even in the kale juice fermentation assay), this study does have broad applications in food science and biotechnology.