Reviewer #3 (Public Review):

The role of maternal sleep apnea on neurological and physiological function in the offspring is of substantial interest and the investigators have contributed significantly to this emerging field via prior publications. Recent work has evidenced that recurrent bouts of gestational intermittent hypoxia (GIH) result in life-long changes in cardiovascular, cognitive, and metabolic function in the offspring. Recently, investigators have shown that GIH reprograms the neuroinflammatory response of neonates, such that the newborn offspring's normal immune response is attenuated following a Lipopolysaccharides (LPS) exposure and respiratory rhythm generation is considerably altered (Johnson et al. Respir Physiol Neurobiol. 2018). The present study by Mickelson et al. substantially extends these previous findings by showing that GIH results in region and sex-specific changes in the microglial activation of adult rats. In male rats, these changes are indicative of an increased pro-inflammatory profile and contribute to the blunted ability to elicit respiratory neuroplasticity following apneic challenge-induced breathing instability. While a robust attenuation of key inflammation-related genes was observed in spinal and brainstem regions of GIH-exposed female rats, these results were not pursued further and present another exciting area of investigation. Nonetheless, the primary goal of these studies was to elucidate the potential role of spinal microglial activation in decreasing respiratory neuroplasticity in adult rats, which has been investigated in-depth using clever and appropriate experimental approaches.

The respiratory motor system employs homeostatic neuroplastic mechanisms at the spinal level to increase phrenic motor output in response to reduced neural activation of respiratory pathways (also called inactivity-induced inspiratory motor facilitation (iMF)). Under carefully controlled conditions, lowering inspired CO2 levels causes cessation of phrenic inspiratory output (central apnea). The authors have previously utilized a protocol of recurrent central apneas to elicit iMF in phrenic motor output. In the present study, authors utilize this neurophysiological outcome to test the impact of GIH on altering the neuroplastic capacity of adult rats. A key finding of this study is that GIH attenuates iMF in male rats. This attenuation is not observed in female rats. To test the role of inflammation (in particular microglia-driven inflammation), the authors employ two approaches to inactivate spinal inflammatory pathways or deplete microglia in adult male rats. Building upon the 29 out of 12982 differentially expressed genes in cervical spinal cord microglia in GIH vs GNX (control exposure rats), the authors targeted the NF-κB pathway using intrathecally delivered TPCA-1 (NF-κB inhibitory subunit (IκB) inhibitor). Indeed, spinal TPCA-1 application restored iMF in GIH-exposed male rats. The second approach employed global microglial depletion using an orally delivered CSF1R inhibitor Pexidartinib (PLX3397) to show that iMF could be provoked in GIH-exposed male rats. It is important to note that although the authors do not report changes in microglial expression in GIH vs. GNX rats, they conclude that there are alterations in microglial activation that contribute to the GIH-induced attenuation of the neuroplastic capacity of respiratory motor networks.

A few questions emerge from this study. In the previous study by the group investigating changes in the inflammatory profile of newborns exposed to GIH, Cox-2 mRNA expression was shown to be elevated in the spinal cords of male rats. This is an interesting finding that has not been tested in GIH-exposed adult male rats in this study and it would be interesting to follow up on whether these changes in microglial profiles are conserved from newborn to adult stages. Indeed, the authors identify additional changes in hypoxia-responsive signaling pathways of GIH rats whose role in impaired respiratory plasticity would be an exciting follow-up to the current study.

The authors emphasize that the reduction in iMF capacity is due to changes in local spinal microglia activation. They do also report that 4 genes were upregulated in the brainstem region of GIH rats as compared to GNX rats. Without an appropriate anatomical control (such as hypoglossal motor output), it would not be appropriate to conclude that microglial activation resulting from GIH has no impact on respiratory networks. Further, the inclusion of bursting frequency data could provide some insight into neural drive originating in brainstem regions.

In summary, this study by Mickelson et al. provides a valuable framework for mechanisms imposing long-lasting changes in respiratory motor control following gestational exposure to episodes of sleep apnea. Furthermore, the work completed here may very well be relevant to other motor systems in which spinal microglia modulate the capacity to elicit homeostatic plastic changes. These changes are particularly important in the context of disease and injury and may impair the capacity of GIH-exposed individuals to elicit neuroplastic changes at the motor neuron level.

Reviewer #3 (Public Review):

This is a clearly written, straightforward, resource paper describing the creation of several new cell lines that may prove useful to the Drosophila community. They are to be distributed through the Drosophila Genomics Resource Center and might be put to use at the Drosophila RNAi screening center.

Reviewer #3 (Public Review):

The manuscript by Kleynhans et al analyzes data from household contacts of SARS-CoV-2 cases at two sites in South Africa. Proximity sensors were distributed to household members following diagnosis of the "index case" and measured the frequency and duration of close contacts (defined as being face-to-face within 1.5 meters for at least 20 seconds). The authors then examined the association between the duration, frequency, and average duration of contacts and the risk of a diagnosis of SARS-CoV-2 among household members in the subsequent two weeks, for both contact with the index case and all cases within the household. The risk of infection among household members was high (~60%), but was not significantly associated with the contact metrics examined. The findings may indicate that aerosols may be the predominant mode of SARS-CoV-2 transmission within households; however, there are also a number of limitations associated with the design and analysis of the study, which the authors acknowledge and which may limit the interpretability of the conclusions of this study.

One important study limitation has to do with the design of the study: Sensors were not distributed to household members until a day or two after the diagnosis of the index case. Since individuals are most infectious with SARS-CoV-2 just prior to symptom onset, contact patterns were measured only after most transmission from the index case likely occurred. Furthermore, household members may have limited their contact with the index case, particularly if the index case attempted to isolate following their diagnosis, so the contact patterns measured are unlikely to be representative of typical mixing within the household.

Another important limitation has to do with the analytical approach: The logistic regression model assumes that the first person in the household to test positive for SARS-CoV-2 (i.e. the index case) infected all subsequent cases within the household. However, this approach does not account for chains of transmission within the household or transmission from outside the household (possibly from the same source that infected the index case). While this concern is partially addressed by also assessing the association between the risk of infection and contact with all infected household members, more sophisticated methods could be used to infer the most likely infector of each case. The possibility of multiple introductions of the virus from outside the household is also only partially addressed by excluding households in which more than one variant was detected. While these limitations (and others) are appropriately acknowledged by the authors in the Discussion, nevertheless they limit the conclusions that can be drawn from the study results.

It is also worth noting that the contact metrics as defined and analyzed in the model may not be the measures that are most relevant to transmission. The authors examined three different contact measures: the median daily duration of contact, the median daily frequency of contact, and the median daily average duration of contact (i.e. the ratio of the two previous measures). They chose to examine the median daily values because contact duration was heavily skewed and the number of days of follow-up varied after data cleaning, but it may be that longer-duration contacts important to transmission are not appropriately captured by these metrics. Indeed, the median daily duration of the contact is quite short (only ~18 minutes on average). It would be useful to also evaluate a measure such as the total cumulative duration of contact and frequency of contacts divided by the number of days of follow-up, which differs from the measures they calculate and would take into account more prolonged and frequent contacts.

Lastly, the measures of association reported in the manuscript are the odds ratios (ORs) associated with one additional second of contact per day. This is not a very biologically meaningful unit of measure, and when rounded to two significant digits, the ORs are not surprisingly 1.0 with 95% confidence intervals that also round to 1.0. It would be more interpretable to report the ORs associated with a 1-minute (rather than 1-second) increase in the duration of contact, and the biological interpretation of the ORs should be described in the text.

Reviewer #3 (Public Review):

Gene regulation at the single cell level can appear in two fundamentally different modes: a digital mode, in which a certain gene is either ON or OFF, and an analog mode, where a gene can gradually modulate its expression in a range of values. Yet, it is unclear how such two modes might operate together. In the work by Antoniou-Kourounioti et al, the authors argue that the Arabidopsis floral repressor FLOWERING LOCUS C (FLC) exhibits such two regulatory modes in the Arabidopsis root before cold exposure, with analog preceding digital.

This work has the strength of performing an elegant combination of experimental and modelling approach to solve a non-trivial and fundamental question on gene regulation. At the experimental level, the authors are able to quantify the number of FLC transcripts as well as their protein levels at the single cell level in the studied Arabidopsis lines, and they elegantly recapitulate some of their experimental results with an in silico root model.

Although this work has a very high potential, I find there are several important aspects that require some attention.

I think further explanations and clarity are needed to help the readership understand the differences between digital and analog regulation, beyond the explanations illustrated by Fig 1. In my understanding, digital regulation will involve observing some kind of bimodality when quantifying expression levels at the single cell level (see Bintu et al 2016), but from the definitions of ON and OFF cells the authors did in this work (see below), and the modelling they propose, it seems not to be the case. Given the authors derive very strong conclusions from their quantifications on what is digital and what is analog, I think it is important to be clearer in this regard. Also, to clarify the possible scenarios of interplay between analog and digital, I believe it would help to emphasize and better connect the modelling part to the experimental part.

Another major concern to me is whether the extracted conclusions rely too much on certain choices the authors made when doing the quantifications from the experimental data. In particular,

1) The way the authors define ON and OFF cells sounds a bit arbitrary to me and, in my understanding, can affect a lot the outcomes and derived conclusions. The authors define ON cells to those cells having more than one transcript, or when they are above the value of 0.5 of the Venus intensity measure - what would it happen if the thresholds are slightly above these levels? And why such thresholds should be the same for the studied lines Ler, fca-3 and fca-1? By looking at the distributions of mRNAs and Venus intensities in Ler and fca-3 plants, one could argue that all cells are in an OFF, 'silent' state, and that what is measured is some 'leakage', noise or simply cell heterogeneity in the expression levels. If there is a digital regulation, I would expect to see this bimodality more clearly at some point, as it was captured in Berry et al (2015) - perhaps cells in fca-1 show at a certain level of bimodality? When seeing bimodality, one could separate ON and OFF states by unmixing gaussians, or something in these lines that makes the definition less arbitrary and more robust.

2) The authors use means in all their plots for histograms and data, and perform tests that rely on these means. However, many of these plots are skewed right distributions, meaning that mean is not a good measure of center. I think using median would be more appropriate, and statistical tests should be rather done on medians instead of means. If tests using medians were performed, I believe that some of the pointed results will be less significant, and this will affect the conclusions of this work.

3) Some data might require more repeats, together with its quantification. For instance, the expression levels for fca-1 in Fig 2E and Fig 3D at 7 days after sowing look qualitatively different to me - not just the mean looks different, but also the distribution; fca-1 in Fig 3D looks more monomodal, while in Fig 2E it looks it shows more a bimodal distribution. Having these two different behaviours in these two repeats indicates that, more ideally, three repeats might be needed, together with their quantification. Fig. 2C would also need some repeats. In Fig 1S1 C and D, it would be good to clarify in which cases there are 2 or more repeats -3 repeats might be needed for those cases in Fig 1S1 C-D that have large error bars.

Also, when doing the time courses, I find it would be very beneficial to capture an earlier time point for all the lines, to see whether it is easier to capture the digital nature of the regulation. Note that the authors have already pointed that 7 days after sowing might be too late for Ler line to capture the switch.

If the above comments are addressed and the authors manage to clarify how the digital and analog regulation are integrated in the chosen system, I believe this work would have a strong impact on a very wide scientific community, given it tackles a very fundamental question in gene regulation.

Reviewer #3 (Public Review):

This article aims at investigating the genetic and developmental basis underlying colour pattern polymorphism in the wood tiger moth. It combines GWAS and QTL data pointing at a candidate gene from the yellow gene family. The pattern of gene expression during wing disk development is then consistent with a potential role of this gene in the control of colour pattern variation but functional validation is lacking. The pigment analyses reveal the presence of pheomelanin on the wings, whose synthesis is known to be controlled by a pathway regulated by genes from the yellow family. The identification of these pigments suggests that variations in the colours of the wings in this species could indeed be caused by the regulation of the yellow pathway. Although functional validation establishing the exact role of the valkea gene is lacking, the data provided are in line with a pleiotropic effect of controlled by a small region of the genome enabling the series of phenotypic variations associated with the white coloration. The duplication event restricted to a single haplotype also provides a convincing mechanism for the restriction of recombination in this genomic region. However, the fact that the valkea gene is truncated questions its functionality. It remains possible that the developmental switch could be rather caused by the variations detected in the non-coding part of the duplicated region, causing differential patterns of expression in different genes, including yellow-e. Some deeper discussion is needed on the putative role of the valkea gene vs. of the regulatory regions in controlling the developmental switch between yellow and white morphs.

Altogether, this interesting study provides original and important results on the genetic architecture underlying balanced polymorphism in the wild.

Reviewer #3 (Public Review):

In order to address their study question of a potential shared genetic predisposition to both smoking and DNA methylation level, they indicate that a MZ discordant pair analysis would be very powerful.

The authors draw on the well-characterized and very large prospective study of twins and family members from the Netherlands, the Netherlands Twin Register (NTR). Over 3000 cohort members have DNA methylation assessed by arrays (450k and Epic). Monozygotic twin pairs discordant and concordant for smoking are included in epigenome-wide analyses, and followed-up using enrichment and gene expression studies.

The results demonstrate that the strongest associations that have been seen in unrelated individuals (such as for AHRR) are seen in the discordant pairs but do not have the statistical power to confirm or reject weaker (yet consistently seen) associations

Some mention of the effect of second-hand smoke (SHS) could be made as it is an exposure to smoke not due to one's own active smoking. As twin pairs often reside together or are in frequent contact/visiting - MZs more than DZ and females more than males, SHS may be attenuating differences between current and non-current smokers in discordant pairs rather than shared genetics. Likewise twin pairs often have the same or related occupation, and if smoking is common at their typical workplace (even if they work at different companies/employers), the non-smoking twin may be exposed to more tobacco than an unrelated never-smoker.

The study sample should be better described, especially with regard to how smoking behavior was assessed, and whether the twins in pairs discordant for smoking differ in characteristics that can affect DNA methylation. These details would be essential for understanding to what degree the observed findings are attributable to smoking.

The study provides important information on the smoking methylation relationship and supports the generally held view that smoking has a direct effect on methylation. Hence, methylation changes are a useful biomarker of current and past smoking. The current results indicate that confounding due to shared genetics is unlikely to be a major factor but some role cannot be excluded.

Reviewer #3 (Public Review):

The manuscript entitled "Hippo signaling impairs alveolar epithelial regeneration in pulmonary fibrosis" is a rigorous and timely report detailing the significance of Hippo signaling, Taz and Yap in AT2/AT1 differentiation and the subsequent impact on the progression of lung fibrosis versus repair/ regeneration. The authors experimental design and results support their conclusions. The identification of the distinct effects of Taz and Yap in these processes highlight the pathway and specific molecules as potential therapeutic targets.

The major strengths of these studies lie in the rigor of the elegant transgenic developmental/adult injury-repair mouse models combined with spatial transcriptomics and analyses. The weaknesses include a lack of detail presented in the methods, some legends and discussion.

Reviewer #3 (Public Review):

This is an interesting manuscript with an important subject pertaining to the impact of COVID-19 pandemic on various delayed schedules of population-based cancer screening, leading to the reduction of screen-detected cancers and the possible upstaging cancers. The results were assessed by simulation model (Policy I modelling) with the demonstration of Australia scenarios including three major cancers, including breast cancer, colorectal cancer, and cervical cancer.

Assess the impacts of COVID-19 disruption to population cancer screening for three major cancers on short-term and long-term outcomes for policy analysis.

The merit of this study is to provide a series of simulated results under disruption scenarios but the weakness are several-fold including lacking of mortality estimates, inadequate assessments and inaccurate reports on missed cancers (interval cancers) and upstaging.

Policy analysis based on disruption scenario through the simulation model would be very informative to guide policy-makers for designing a salvage program to minimize the impacts of COVID-19 disruptions.

Direct reporting data on the empirical disruption scenario instead of relying on the sensitivity analysis of disruption scenario is more transparent and convincing for the public.

Reviewer #3 (Public Review):

Here, Buglak and coauthors describe the effect of Sun1 deficiency on endothelial junctions. Sun1 is a component of the LINC complex, connecting the inner nuclear membrane with the cytoskeleton. The authors show that in the absence of Sun1, the morphology of the endothelial adherens junction protein VE-cadherin is altered, indicative of increased internalization of VE-cadherin. The change in VE-cadherin dynamics correlates with decreased angiogenic sprouting as shown using in vivo and in vitro models. The study would benefit from a stricter presentation of the data and needs additional controls in certain analyses.

1. The authors implicate the changes in VE-cadherin morphology to be of consequence for "barrier function" and mention barrier function frequently throughout the text, for example in the heading on page 12: "SUN1 stabilizes endothelial cell-cell junctions and regulates barrier function". The concept of "barrier" implies the ability of endothelial cells to restrict the passage of molecules and cells across the vessel wall. This is tested only marginally (Suppl Fig 1F) and these data are not quantified. Increased leakage of 10kDa dextran in a P6-7 Sun1-deficient retina as shown here probably reflects the increased immaturity of the Sun1-deficient retinal vasculature. From these data, the authors cannot state that Sun1 regulates the barrier or barrier function (unclear what exactly the authors refer to when they make a distinction between the barrier as such on the one hand and barrier function on the other). The authors can, if they do more experiments, state that loss of Sun1 leads to increased leakage in the early postnatal stages in the retina. However, if they wish to characterize the vascular barrier, there is a wide range of other tissue that should be tested, in the presence and absence of disease. Moreover, a regulatory role for Sun1 would imply that Sun1 normally, possibly through changes in its expression levels, would modulate the barrier properties to allow more or less leakage in different circumstances. However, no such data are shown. The authors would need to go through their paper and remove statements regarding the regulation of the barrier and barrier function since these are conclusions that lack foundation.<br /> 2. In Fig 6g, the authors show that "depletion of GEF-H1 in endothelial cells that were also depleted for SUN1 rescued the destabilized cell-cell junctions observed with SUN1 KD alone". However, it is quite clear that Sun1 depletion also affects cell shape and cell alignment and this is not rescued by GEF-H1 depletion (Fig 6g). This should be described and commented on. Moreover please show the effects of GEF-H1 alone.<br /> 3. In Fig. 6a, the authors show rescue of junction morphology in Sun1-depleted cells by deletion of Nesprin1. The effect of Nesprin1 KD alone is missing.

Reviewer #3 (Public Review):

Genomic imprinting is a striking example of epigenetic inheritance in mammals with profound influence on growth and development. A powerful experimental approach to the study of imprinting involves reciprocal mouse F1 crosses; it allows direct assessment of the parent-of-origin effects in a genetically uniform setting that is also an order of magnitude richer in polymorphism than human samples. Use of RNA sequencing is a natural fit to systematic quantitative analysis of allele-specific expression; however, multiple RNA-seq studies of imprinting in F1 mouse tissues wildly disagree in the estimated numbers of novel imprinted genes and in the extent of allelic bias in these genes. In their study, Edwards et al. start with an observation that existing studies varied in their experimental design and data analysis procedures. To assess to what extent disagreements between findings are due to different data processing, they re-analyzed several published datasets using a single pipeline. Furthermore, they performed experimental validation of a number of the novel candidate imprinted genes using primer extension on RT-PCR products (pyrosequencing), to estimate the number of false positives.

Between re-analysis of RNA-seq datasets and the validation experiments, this study presents convincing evidence that most candidate novel imprinted genes are artefactual. The discordant predictions between studies remain even after processing all the data following ISoLDE protocol. Importantly, validated candidate genes tended to be on the periphery of known imprinted domains, suggesting that their boundaries are yet to be finalized.

This work brings into focus an important issue of reproducible analysis and interpretation of RNA sequencing data, especially the analysis of allele-specific expression, including in the specific case of imprinted genes. With novel molecular mechanisms described recently (such as H3K27me3-related parent-of origin gene regulation) and greater accuracy of measuring subtle allelic bias afforded by deep sequencing, the authors' suggested classification (canonical, weak canonical, non-canonical, and weakly biased) is a useful pragmatic step in dealing with the confusing terminology in different studies.

The authors make a strong case that the data analysis methods used in the analyzed studies are prone to false positives. However, the approaches they use are more of an invitation to further dialogue than a definitive recipe to follow. For example, the authors mention that combining the results of several analytical approaches should increase accuracy. However, if those approaches are erroneous, this could lead to two types of error: (1) tools might be erroneous in a similar way, then consistency of results might be taken as confirmation of correctness, (2) averaging results from tools with opposite biases would lead to loss of signal. In the long run, there is no substitute to developing statistically accurate tools and validating that they correctly deal with noise in the data. On the experimental side, Pyrosequencing also involves PCR. This does not change the main conclusions of this study but going forward, it is worth focusing on the methods less affected by amplification (such as allele-specific FISH, ddPCR, or direct RNA sequencing).

Reviewer #3 (Public Review):

In this manuscript, the authors studied the erythropoiesis and hematopoietic stem/progenitor cell (HSPC) phenotypes in a ribosome gene Rps12 mutant mouse model. They found that RpS12 is required for both steady and stress hematopoiesis. Mechanistically, RpS12+/- HSCs/MPPs exhibited increased cycling, loss of quiescence, protein translation rate, and apoptosis rates, which may be attributed to ERK and Akt/mTOR hyperactivation. Overall, this is a new mouse model that sheds light into our understanding of Rps gene function in murine hematopoiesis. The phenotypic and functional analysis of the mice are largely properly controlled, robust, and analyzed.

A major weakness of this work is its descriptive nature, without a clear mechanism that explains the phenotypes observed in RpS12+/- mice. It is possible that the counterintuitive activation of ERK/mTOR pathway and increased protein synthesis rate is a compensatory negative feedback. Direct mechanism of Rps12 loss could be studied by ths acute loss of Rps12, which is doable using their floxed mice. At the minimum, this can be done in mammalian hematopoietic cell lines.

Below are some specific concerns need to be addressed.

1. Line 226. The authors conclude that "Together, these results suggest that RpS12 plays an essential role in HSC function, including self-renewal and differentiation." The reviewer has three concerns regarding this conclusion and corresponding Figure3. 1) The data shows that RpS12+/- mice have decreased number of both total BM cells and multiple subpopulations of HSPCs. The frequency of HSPC subpopulations should also be shown to clarify if the decreased HSPC numbers arises from decreased total BM cellularity or proportionally decrease in frequency. 2) This figure characterizes phenotypic HSPC in BM by flow and lineage cells in PB by CBC. HSC function and differentiation are not really examined in this figure, except for the colony assay in Figure 3K. BMT data in Figure4 is actually for HSC function and differentiation. So the conclusion here should be rephrased. 3) Since all LT-, ST-HSCs, as well as all MPPs are decreased in number, how can the authors conclude that Rps12 is important for HSC differentiation? No experiments presented here were specifically designed to address HSC differentiation.

2. Figure 3A and 5E. The flow cytometry gating of HSC/MPP is not well performed or presented, especially HSC plot. Populations are not well separated by phenotypic markers. This concerns the validity of the quantification data.

3. It is very difficult to read bone marrow cytospin images in Fig 6F without annotation of cell types shown in the figure. It appears that WT and +/- looked remarkably different in terms of cell size and cell types. This mouse may have other profound phenotypes that need detailed examination, such as lineage cells in the BM and spleen, and colony assays for different types of progenitors, etc.

4. For all the intracellular phospho-flow shown in Fig7, both a negative control of a fluorescent 2nd antibody only and a positive stimulus should be included. It is very concerning that no significant changes of pAKT and pERK signaling (MFI) after SCF stimulation from the histogram in WT LSKs. There are no distinct peaks that indicate non-phospho-proteins and phospho-proteins. This casts doubt on the validity of results. It is possible though that Rsp12+/- have very high basal level of activation of pAKT/mTOR and pERK pathway. This again may point to a negative feedback mechanism of Rps12 haploinsufficiency.

5. The authors performed in vitro OP-Puro assay to assess the global protein translation in different HSPC subpopulations. 1) Can the authors provide more information about the incubation media, any cytokine or serum included? The incubation media with supplements may boost the overall translation status, although cells from WT and RpS12+/- are cultured side by side. Based on this, in vivo OP-Puro assay should be performed in both genotypes. 2) Polysome profiling assay should be performed in primary HSPCs, or at least in hematopoietic cell lines. It is plausible that RpS12 haploinsufficiency may affect the content of translational polysome fractions.

Reviewer #3 (Public Review):

Cumplido-Mayoral and colleagues' study focused on the brain-age paradigm in the context of Alzheimer's disease risk. The goal was to valid brain-age 'deltas' by assessing how they relate to Alzheimer's biomarkers and related neurodegenerative measures. They did this by training a new brain-age model on FreeSurfer phenotypes (cortical and subcortical) using the UK Biobank dataset. They then tested multiple datasets including ALFA, ADNI, OASIS, and EPAD, focusing on cognitively unimpaired people and people with mild cognitive impairment. Using brain-age deltas calculated in the test sets, the authors then tested associations with a range of dementia-related measures, including the presence of MCI, APOE e4, amyloid and tau positivity, white matter hyperintensity volume and NfL levels from plasma or CSF.

Strengths include using multiple independent datasets from different sources. This provides large sample sizes and access to different data types. Another strength is the efforts to understand drivers of brain age prediction, by using the SHAP technique. The authors include a newly trained brain-age prediction model, which appears to work as well as existing alternative methods.

A weakness is the number of tests conducted and the absence of multiple comparison corrections. A problem with the SHAP analysis is that it does not account for the correlated nature of the input features.

Overall, the study met the stated aims, and I anticipate the results to make a positive contribution to the research field. The results tended to support the conclusions, particularly regarding the relationship between brain-age delta and the markers of neurodegeneration, AD risk, and cerebrovascular health. The only concern around this is whether the number of tests conducted has inflated the type I error rate and resulted in some false positives. This could have been explored further. The conclusions are sex differences are less well supported by the evidence. While some delta-by-sex interactions were significant, others were not (e.g., Figure 3), however, the interpretation focuses only on the significant ones to support blanket statements about the differences between males and females with regard to neurodegeneration. Given the issues about multiple comparisons, this seems premature and somewhat uneven.

Reviewer #3 (Public Review):

The period that is examined is in the range (21 to 37GW) and uses tractography to delineate five distinct thalamocortical pathways. The paper generates anatomically constrained whole-brain connectomes for each gestational week. The authors parcellate the thalamus according to to streamline connectivity that has been published about two decades ago. The authors delineate the developing thalamocortical pathways and parcellate the fetal thalamus according to its cortical connectivity using diffusion tractography. The study included the primary motor cortex, primary sensory cortex, posterior parietal cortex, dorsolateral prefrontal cortex, and primary visual cortex. With the limitations of the method, the authors delineated five major thalamocortical pathways in each gestational week.

The study finds consistent and distinct origins of different tracts, resembling the adult topology of thalamic nuclei as early as 23W gestation. The study monitors the transient compartment of the subplate and intermediate zone, internal capsule, and establishes references to complement histological knowledge.

The paper's hypothesis is straightforward: "the biological processes occurring in different fetal compartments leads to predictable changes in diffusion metrics along tracts, reflecting the appearance and resolution of these transient zones." Study transient structures, such as subcortical plate or subplate. The authors predict that as subplate neurons disappear the tissue fraction is becoming relatively higher in the deep grey matter and the cortical plate and lower in the subplate. The authors investigate this by characterising the entire trajectory of tissue composition changes between the thalamus and the cortex, to explore the role of transient fetal brain developmental structures on white matter maturational trajectories. The authors demonstrate that along-tract sampling of diffusion metrics can capture temporal and compartmental differences in the second to the third trimester, reflecting the maturing neurobiology of the fetal brain described in histology studies.

Reviewer #3 (Public Review):

The manuscript approaches an important problem associated with mannose challenge and subsequent changes in metabolism and DNA replication. The researchers employed MPI-KO human cancer cells to explore the key mechanism behind the anti-cancer activity of mannose, and demonstrated that the large influx of mannose exceeding the capacity to metabolize it, that is, the onset of 'honeybee syndrome', induced dramatic metabolic remodeling that led to dNTP loss.

• They established MPI-KO human cancer cells using the CRISPR-Cas9 system, and exploited the mannose auxotrophy and sensitivity observed in MPI-KO mouse embryonic fibroblasts (MPI- KO MEFs) (DeRossi et al., 2006). The addition of a physiological concentration of mannose (50 μM, unchallenged) to culture medium supported the proliferation of MPI-KO MEFs. However, mannose challenge increased the sensitivity of MPI-KO HT1080 cells to DNA replication inhibitors (i.e., cisplatin and doxorubicin) when the cells had been preconditioned with excess 5 mannose prior to the drug treatment.<br /> • Thus, induction of honeybee syndrome suppresses cell proliferation and increases chemosensitivity in MPI-KO human cancer cell models.<br /> • These results suggest that mannose challenge severely impairs the entry of the cells into S phase and its progression to mitotic phase. Strikingly, however, switching of the mannose-challenge medium to the mannose-unchallenged medium after long-term mannose challenge (6 days) resulted in robust cell proliferation.<br /> • The researchers observed downregulation of proteins related to the cell cycle and DNA replication in mannose-challenged cells (Fig. 3A), which were enriched with the mini-chromosome maintenance 2-7 (MCM2-7) complex.<br /> • Together, these results indicate that mannose challenge disengages dormant origins from DNA synthesis during replication stress, thus exacerbating DNA damage.<br /> • Our finding that DNA synthesis from dormant origins during replication stress is highly sensitive to the dNTP pool size is in good agreement with the therapeutic advantages of RNR inhibition in enhancing the efficacy of radiochemotherapy (Kunos and Ivy, 2018).<br /> The work is of potentially great importance in understanding the action of mannose on cancer cells and the resulting sensitization to anti-cancer agents.

Reviewer #3 (Public Review):

The authors report on an interesting study that addresses the effects of a physical and dietary intervention on accelerated/decelerated brain ageing in obese individuals. More specifically, the authors examined potential associations between reductions in Body-Mass-Index (BMI) and a decrease in relative brain-predicted age after an 18-months period in N = 102 individuals. Brain age models were based on resting-state functional connectivity data. In addition to change in BMI, the authors also tested for associations between change in relative brain age and change in waist circumference, six liver markers, three glycemic markers, four lipid markers, and four MRI fat deposition measures. Moreover, change in self-reported consumption of food, stratified by categories such as 'processed food' and 'sweets and beverages', was tested for an association with change in relative brain age. Their analysis revealed no evidence for a general reduction in relative brain age in the tested sample. However, changes in BMI, as well as changes in several liver, glycemic, lipid, and fat-deposition markers showed significant covariation with changes in relative brain age. Three markers remained significant after additionally controlling for BMI, indicating an incremental contribution of these markers to change in relative brain age. Further associations were found for variables of subjective food consumption. The authors conclude that lifestyle interventions may have beneficial effects on brain aging.

Overall, the writing is concise and straightforward, and the langue and style are appropriate. A strength of the study is the longitudinal design that allows for addressing individual accelerations or decelerations in brain aging. Research on biological aging parameters has often been limited to cross-sectional analyses so inferences about intra-individual variation have frequently been drawn from inter-individual variation. The presented study allows, in fact, investigating within-person differences. Moreover, I very much appreciate that the authors seek to publish their code and materials online, although the respective GitHub project page did not appear to be set to 'public' at the time (error 404). Another strength of the study is that brain age models have been trained and validated in external samples. One further strength of this study is that it is based on a registered trial, which allows for the evaluation of the aims and motivation of the investigators and provides further insights into the primary and secondary outcomes measures (see the clinical trial identification code).

One weakness of the study is that no comparison between the active control group and the two experimental groups has been carried out, which would have enabled causal inferences on the potential effects of different types of interventions on changes in relative brain age. In this regard, it should also be noted that all groups underwent a lifestyle intervention. Hence, from an experimenter's perspective, it is problematic to conclude that lifestyle interventions may modulate brain age, given the lack of a control group without lifestyle intervention. This issue is fueled by the study title, which suggests a strong focus on the effects of lifestyle intervention. Technically, however, this study rather constitutes an investigation of the effects of successful weight loss/body fat reduction on brain age among participants who have taken part in a lifestyle intervention. In keeping with this, the provided information on the main effect of time on brain age is scarce, essentially limited to a sign test comparing the proportions of participants with an increase vs. decrease in relative brain age. Interestingly, this analysis did not suggest that the proportion of participants who benefit from the intervention (regarding brain age) significantly exceeds the number of participants who do not benefit. So strictly speaking, the data rather indicates that it's not the lifestyle intervention per sé that contributes to changes in brain age, but successful weight loss/body fat reduction. In sum, I feel that the authors' claims on the effects of the intervention cannot be underscored very well given the lack of a control group without lifestyle intervention.

Another major weakness is that no rationale is provided for why the authors use functional connectivity data instead of structural scans for their age estimation models. This gets even more evident in view of the relatively low prediction accuracies achieved in both the validation and test sets. My notion of the literature is that the vast majority of studies in this field implicate brain age models that were trained on structural MRI data, and these models have achieved way higher prediction accuracies. Along with the missing rationale, I feel that the low model performances require some more elaboration in the discussion section. To be clear, low prediction accuracies may be seen as a study result and, as such, they should not be considered as a quality criterion of the study. Nevertheless, the choice of functional MRI data and the relevance of the achieved model performances for subsequent association analysis needs to be addressed more thoroughly.

Reviewer #3 (Public Review):

This paper argues that it has developed an algorithm conceptually related to chemotaxis that provides a general mechanism for goal-directed behaviour in a biologically plausible neural form.

The method depends on substantial simplifying assumptions. The simulated animal effectively moves through an environment consisting of discrete locations and can reliably detect when it is in each location. Whenever it moves from one location to an adjacent location, it perfectly learns the connectivity between these two locations (changes the value in an adjacency matrix to 1). This creates a graph of connections that reflects the explored environment. In this graph, the current location gets input activation and this spreads to all connected nodes multiplied by a constant decay (adjusted to the branching number of the graph) so that as the number of connection steps increases the activation decreases. Some locations will be marked as goals through experiencing a resource of a specific identity there, and subsequently will be activated by an amount proportional to their distance in the graph from the current location, i.e., their activation will increase if the agent moves a step closer and decrease if it moves a step further away. Hence by making such exploratory movements, the animal can decide which way to move to obtain a specified goal.

I note here that it was not clear what purpose, other than increasing the effective range of activation, is served by having the goal input weights set based on the activation levels when the goal is obtained. As demonstrated in the homing behaviour, it is sufficient to just have a goal connected to a single location for the mechanism to work (i.e., the activation at that location increases if the animal takes a step closer to it); and as demonstrated by adding a new graph connection, goal activation is immediately altered in an appropriate way to exploit a new shortcut, without the goal weights corresponding to this graph change needing to be relearnt.

Given the abstractions introduced, it is clear that the biological task here has been reduced to the general problem of calculating the shortest path in a graph. That is, no real-world complications such as how to reliably recognise the same location when deciding that a new node should be introduced for a new location, or how to reliably execute movements between locations are addressed. Noise is only introduced as a 1% variability in the goal signal. It is therefore surprising that the main text provides almost no discussion of the conceptual relationship of this work to decades of previous work in calculating the shortest path in graphs, including a wide range of neural- and hardware-based algorithms, many of which have been presented in the context of brain circuits.

The connection to this work is briefly made in appendix A.1, where it is argued that the shortest path distance between two nodes in a directed graph can be calculated from equation 15, which depends only on the adjacency matrix and the decay parameter (provided the latter falls below a given value). It is not clear from the presentation whether this is a novel result. No direct reference is given for the derivation so I assume it is novel. But if this is a previously unknown solution to the general problem it deserves to be much more strongly featured and either way it needs to be appropriately set in the context of previous work.

Once this principle is grasped, the added value of the simulated results is somewhat limited. These show: 1) in practical terms, the spreading signal travels further for a smaller decay but becomes erratic as the decay parameter (map neuron gain) approaches its theoretical upper bound and decreases below noise levels beyond a certain distance. Both follow the theory. 2) that different graph structures can be acquired and used to approach goal locations (not surprising) .3) that simultaneous learning and exploitation of the graph only minimally affects the performance over starting with perfect knowledge of the graph. 4) that the parameters interact in expected ways. It might have been more impactful to explore whether the parameters could be dynamically tuned, based on the overall graph activity.

Perhaps the most biologically interesting aspect of the work is to demonstrate the effectiveness, for flexible behaviour, of keeping separate the latent learning of environmental structure and the association of specific environmental states to goals or values. This contrasts (as the authors discuss) with the standard reinforcement learning approach, for example, that tries to learn the value of states that lead to reward. Examples of flexibility include the homing behaviour (a goal state is learned before any of the map is learned) and the patrolling behaviour (a goal cell that monitors all states for how recently they were visited). It is also interesting to link the mechanism of exploration of neighbouring states to observed scanning behaviours in navigating animals.

The mapping to brain circuits is less convincing. Specifically, for the analogy to the mushroom body, it is not clear what connectivity (in the MB) is supposed to underlie the graph structure which is crucial to the whole concept. Is it assumed that Kenyon cell connections perform the activation spreading function and that these connections are sufficiently adaptable to rapidly learn the adjacency matrix? Is there any evidence for this? As discussed above, the possibility that an algorithm like 'endotaxis' could explain how the rodent place cell system could support trajectory planning has already been explored in previous work so it is not clear what additional insight is gained from the current model.

Reviewer #3 (Public Review):

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

Reviewer #3 (Public Review):

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

Overall, the manuscript provides new insights into the beneficial effects of the type I PRMT inhibitor MS023 for skeletal muscle regeneration. The description of the MS023-induced transcriptional and metabolic changes in MuSC is interesting and the effects on MuSC transplantation and muscle strength are stunning. However, the proposed underlying mechanism, which is claimed to rely on the expansion of MuSC and 'reprograming' of MuSCs towards a "unique and previously uncharacterized identity" is not sufficiently supported. The extent of the description of scRNA-seq data is inappropriate. Some conclusions from the scRNA-seq data appear to be overinterpreted or are rather trivial. It remains completely unclear whether the MS023-stimulated increase of metabolic pathway activity (OXPHOS, glycolysis) plays any role for preserving stem cell properties of MuSC during expansion and improves engraftment. Additional functional and mechanistic studies are required to explore the underlying molecular processes. Furthermore, it remains completely unclear whether the acclaimed increase in grip and tetanic strength of mdx mice after MS023 treatment relies on enhanced expansion of MuSC mediated by PRMT1 inhibition.

Reviewer #3 (Public Review):

The strength of this article is that the experiment performed was successfully validated by previously published results. However, it would be useful to determine whether changes in protein levels correlated with changes in mRNA levels and whether or not the protein present was functional, and whether Stac3 was in fact stoichiometrically depleted in relation to Cacna1s. The authors suggest that the change in RyR1 protein levels may have a knock-on effect on the levels of other proteins, which is a reasonable claim, but no experiments (such as using RNAi) were performed to confirm this. The authors also claim that an adaptive response exists to compensate for deleterious mutations, which is indeed well-established (see dosage compensation in x-linked disorders between XX women and XY men, for example), and their experiment is consistent with this finding but does not itself show this on the level of cells, tissues, or the RyR itself.

Minor concerns.<br /> 1) In the abstract, the authors stated that skeletal muscle is responsible for voluntary movement. It is also responsible for non-voluntary. The abstract needs to be refocused on the mutation and on what we learn from this study. Please avoid vague statements like "we provide important insights to the pathophysiological mechanisms..." mainly when the study is descriptive and not mechanistic.<br /> 2) The author should bring up the mutation name, location and phenotype early in the introduction. This reviewer also suggests that the authors refocus the introduction on the mutation location in the 3D RyR1 structure (available cryo-EM structure), if there is any nearby ligand binding site, protomers junction or any other known interacting protein partners. This will help the reader to understand how this mutation could be important for the channel's function.

Reviewer #3 (Public Review):

This important study continues the development of normative models of neuroimaging-derived features initiated by themselves (Rutherford et al., 2022a) in two directions. First, the existing models - which were developed on structural imaging features - are complemented with features derived from functional networks. Second, these models are compared with the utilization of the features themselves in three different inference settings. Overall, the evaluation of the functional networks modeling yielded similar benchmarking metrics in agreement with their previous structural modeling. The study delivers strong evidence that normative models efficiently increased the statistical power in mass univariate group difference testing. The improvement in the other two inferential scenarios was less evident. However, normative modeling was not comparatively detrimental and should continue to be investigated.

The study showcases several major strengths:<br /> - The methodological approach is robustly supported by previous work and protocol definitions by the authors, mainly (Rutherford, 2022a; 2022b).<br /> - The intent of the manuscript is very clear, structured first with a confirmation of the soundness of their functional-networks model and second the "head-to-head" comparison (a term used in the abstract which effectively describes the aim) to alternative inference approaches.<br /> - The results of task 1 are very compelling. The other two tasks, while perhaps less robust, are definitely relevant to be part of the communication and help draw a more accurate picture of the role of normative models in years to come.<br /> - The manuscript is accompanied by a comprehensive set of tutorials, examples, documentation, and the sharing of code, models, and data. Sharing all these resources is a decisive effort toward research transparency that deserves full recognition as scientific scholarship.

As major weaknesses, I will speculate that some researchers could understand this work as incremental. Although there's continuity with the previous work of the authors (otherwise would be a weakness, in my opinion), my assessment is that the science in this manuscript should be considered new results and hence deserve independent communication.

Finally, I would like to highlight how normative modeling outperformed its "direct" (saving the removal of confounding factors) inference counterpart in task 1, providing solid evidence of the usefulness of normative models beyond the classical application in "easy" clinical decisions (I refer the readers to the manuscript, which elaborates on these aspects more appropriately and comprehensively).

Reviewer #3 (Public Review):

The manuscript presented the identification of an herbal drug combination via the approach of knowledge graph for the treatment of plasma cell mastitis (PCM), a breast inflammation with severe and intense clinical symptoms. The authors evaluated the efficacy of the herbal drug combination in clinical trial, which recruited 160 patients thus far (Trial number: NCT05530226). The clinical trial results showed that the herbal drug combination could significantly reduce the recurrence rate and reverse the clinical symptoms of PCM patients.

The manuscript provides strong evidence for the following,<br /> 1. The authors showed that, for the first time, knowledge graph is a useful approach for the identification of herbal drug combination towards plasma cell mastitis. This is novel because in the past, the design of formulae in TCM is solely based on the principle of 'syndrome differentiation'.<br /> 2. The herbal drug combination identified by knowledge graph can markedly suppress various inflammatory cytokines in serum and restore clinical symptoms of PCM patients.<br /> 3. The herbal drug combination could reduce the recurrence rate of PCM, a major obstacle for PCM treatment.

The major merit of the manuscript is that the authors introduced the concept of knowledge graph into the domain of herbal drugs or TCM. Namely, the authors designed a knowledge graph towards systematic immunity or immunotherapy based on massive data mining techniques. The authors successfully identified an herbal drug combination for PCM with the help of a scoring system. Moreover, the authors conducted a clinical trial study and the clinical data showed that the herbal drug combination holds great promise as an effective treatment for PCM. The weakness of the manuscript is that some details for the herbal drug combination and the clinical trial study are missing.

Reviewer #3 (Public Review):

In the proposed paper, the authors use a combination of case data and genetic data to characterise the impact of a dog vaccine campaign on rabies transmission on Pemba island. This represents an impressive set of data to answer key questions linked to rabies control. It is rare to see a combination of detailed genetic and epidemiology data from the same disease system. Overall, I thought it was an impressive paper. My only major concerns were with the phylogenetic analyses.

The phylogenetic analyses were difficult to understand. The authors use a phylogenetic framework to estimate the underlying number of rabid dogs per outbreak (171 in the first outbreak and 140 in the second one), but it was unclear to me where the information was coming from. From the supplementary material, it seems the authors build transmission trees consistent with the phylogenies. However, these are reliant on (a) a serial interval and (b) a dispersal kernel. There is no reference as to what serial interval distribution was used and how it was calculated. Similarly, there is no information on the dispersal kernel, including what data was used to fit it. I suspect that the serial interval for rabies (and probably the dispersal kernel) has a long tail, which would lead to substantial uncertainty in the transmission chains, however, I could not see uncertainty in the outbreak sizes.

Reviewer #3 (Public Review):

The authors previously reported a daily oscillation of the excitation/inhibition ratio occurs normally in layer 2/3 cortical neurons in wild-type mice. In this manuscript, they examined the E/I ratio in the primary visual cortex in two different autism mouse models and showed that the daily oscillation was disrupted in both, albeit in different ways. They further demonstrated that complementary changes in excitatory and inhibitory synaptic transmissions were underlying the disrupted E/I ratio, which is also accompanied by alterations in the endocannabinoid signaling but not sleep time in general.

Disruption of the E/I ratio (or balance) has been a major theme of proposed mechanisms underlying sensory and behavioral abnormalities observed in autism spectrum disorder patients and animal models. The demonstration and characterization of the shift/flattening of the daily oscillation of E/I in the two mouse models provide strong evidence for a disruption of the daily dynamic regulation of the E/I ratio instead of an overall change in the absolute level of E/I, at least in layer 2/3 pyramidal neurons in the visual cortex examined here. These results call for a re-visit of previous studies and offer a potential explanation to reconcile conflicting prior reports regarding the valence of E/I ratio changes in different autism models and brain areas, taking the recording time during the day into consideration. It also raises the question of how the dysregulated daily E/I oscillation affects brain functions. On the other hand, the dissociation of sleep and E/I oscillation observed in the autism models may also provide an opportunity to better understand the functional relevance of sleep-dependent E/I oscillation in a normal brain in the future.

Reviewer #3 (Public Review):

The paper by Cecon et al. presents a novel biosensor approach designed to study aspects of Tau aggregation that employ the luciferase-based NanoLuc Binary Technology (NanoBiT). The last decade has seen a rise in the number and variety of Tau biosensor systems, each with its own strengths and weaknesses to study various aspects of Tau aggregation. So far, these have proven to be extremely useful tools for the detection of proteopathic Tau molecules from different origins, by virtue of their capacity to induce easily detectable aggregation of the "endogenous" reporter Tau proteins in the intracellular environment, enabling for example to interrogate the structural features that render the protein pathogenic; in addition, they have been employed for screening of therapeutic candidates that can inhibit or slow down the aggregation process. As regards the study of the aggregation process itself, such systems encounter important limitations in that the modifications done to the protein likely impact reaction rates (both intramolecular and intermolecular interactions) and the aggregation mechanism itself. Additionally, the majority of them rely on overexpression systems, further altering the dynamics of physiological interactions. This paper implements a recently developed and commercially available technology based on nano-luciferase complementation, which has been used to study transient protein-protein interactions but not yet for Tau, and reports on its utility to study both inter- and intra-molecular interactions of Tau in live-cells and seeding activity of exogenously added Tau.

Strengths<br /> The field of Alzheimer's will benefit greatly from cellular models that enable faithful replication of aggregation mechanisms that occur intracellularly involving Tau. The elucidation of high-resolution molecular structures of Tau fibrils from cryo-electron microscopy and the realisation that fibrils from different tauopathies display characteristic folds point to altered cellular states that drive the intrinsically-disordered protein (IDP) Tau to adopt specific conformations that spur pathological aggregation processes. The aggregate burden is known now to correlate well with disease progression. Tau has otherwise been described as a highly soluble protein, yet under certain circumstances it adopts a misfolded conformation that in the proximity of other monomers can template further misfolding and spur aggregation. Several biosensor systems have been developed that detect proteopathic Tau with high sensitivity, most notably those that consist of cell lines expressing intracellular FRET pairs. These have been invaluable to the field and have served to demonstrate that seeding activity strongly correlates with disease aggressiveness in Alzheimer's patients (see Dujardin et al. Nat Med 2021), among other important contributions. There are however major limitations in using these models to study aggregation mechanisms in a cellular context in that they rely on significant structural modifications to the protein that alter the aggregation energy landscape, among other artefactual concerns (e.g., protein overexpression).<br /> This paper sets out to showcase the applicability of the NanoBiT technology on the strength of the considerably smaller size of the fusion proteins. which comprise one large BiT fragment of 17.6 kDa and a small complementation peptide of only 11 amino acids, compared to for instance the popular Tau RD P301S FRET biosensor line that relies on CFP and YFP (both ~27 kDa) Tau-fused constructs as FRET pairs. This is important for interrogating intracellular inter- and intra-molecular interactions as steric effects impact reaction rates and mechanisms. This, coupled with high sensitivity of the bioluminescence signal and amenability for high throughput, comprise the most important advantages of this approach.

Weaknesses<br /> Perhaps the most significant advantage (conceptually) of the NanoBiT technology in this context is the ability to create intramolecular interaction sensors by fusing the fragments to opposite termini. This is especially useful for the N- and C- termini of Tau which are known to be in proximity in certain conformations. The same can be achieved with fluorescence complementation yet with the caveat of introducing larger molecules. Nevertheless, regardless of the smaller dimensions of the fusion protein, the modifications are likely to still alter protein interaction dynamics - this is relevant to both intra- and inter-molecular sensors. While this may not always be a major concern when working with globular proteins, it should be a key consideration when studying Tau aggregation. The energy landscape of intrinsically disordered proteins is highly sensitive to even small structural changes, as exemplified by conformational changes in Tau that render this otherwise highly-soluble protein aggregation-prone. The interaction between the complementary small and large fragments of NanoBiT is reversible and weak (reported as 190 uM), but may still stabilise non-intrinsic conformations. Demonstrating that interaction and aggregation kinetics are not affected significantly compared to the native protein in vitro would be required to support the physiological relevance of the claims related to inter- and intra-molecular interactions.

An additional concern with the intramolecular sensor is the ability to discriminate whether interactions are indeed intramolecular and not intermolecular, this introduces a confound for instance in the interpretation that a reduction in signal with the WT Tau conformation sensor after treatment with colchicine suggest that microtubules stabilise Tau in a conformation where N- and C- termini of a Tau monomer are in proximity, when this could also well be due to intermolecular interactions, or a combination of both (see the continuous stretch of density of Tau along protofilaments in Kellogg et al. Science 2018). Furthermore, the colocalization data is not of high enough quality to support the claims regarding microtubule interactions, in fact there seems to be stronger colocalization with the intramolecular sensor than with the intermolecular one. Better quality images and co-localization analysis are needed to support these interpretations. The paper thus falls short of providing compelling data to regard this method as a physiologically-relevant approach to study Tau molecular interactions.

Artefactual problems stemming from the aforementioned alterations are likely not as important for their applicability as sensors, as other Tau biosensors have shown the ability to detect proteopathic forms in a way that reflects the severity of pathology in various contexts, regardless of whether the ensuing aggregates faithfully replicate those encountered in pathology. It would then be of interest to assess how the NanoBiT technology fares compared to alternative cell models in regard to sensitivity. The paper provides a response curve with tissue extracted from a mouse model of tauopathy. The extracts are not purified for tau which makes comparison with other data difficult given that the degree of tauopathy is model and mouse dependent. A more extensive evaluation of the sensing capacity would be needed to establish sensitivity in a meaningful way, for instance with Tau forms for which concentration can be more appropriately estimated, e.g., recombinant Tau and IP-purified extracts from mouse and human tissues, or a direct comparison with other methods.

Reviewer #3 (Public Review):

The authors set out to test the idea that memories involve a fast process (for the acquisition of new information) and a slow process (where these memories are progressively transferred/integrated into more-long term storage). The former process involves the hippocampus and the latter the cerebral cortex. This 'dual-learning' system theoretically allows for new learning without causing interference in the consolidation of older memories. They test this idea by artificially increasing plasticity in the pre-limbic cortex and measuring changes in different learning/memory tasks. They also examined electrophysiological changes in sleep, as sleep is linked to memory formation and synaptic plasticity.

The strengths of the study include a) meticulous analyses of a variety of electrophysiological measurements b) a combination of neurobiological and computational tools c) a largely comprehensive analysis of sleep-based changes. Some weaknesses include questions about the technique for increasing cortical plasticity (is this physiological?) and the absence of some additional experiments that would strengthen the conclusions. However, overall, the findings appear to support the general idea under examination.

This study is likely to be very impactful as it provides some really new information about these important neural processes, as well as data that challenges popular ideas about sleep and synaptic plasticity.

Reviewer #3 (Public Review):

The study by Silva et al details the discovery and evaluation of a third class of broadly cross-reactive anti-Spike antibody that binds a conserved hinge region in the S2 domain. After immunizing mice with a stabilized S2 protein from MERS and generating scFv phage libraries, the authors were able to identify antibody 3A3, which showed broad cross-reactivity with SARS2 (including Omicron BA.1), SARS1, MERS, and HKU1 spike proteins. Using a combination of a low-resolution cryo-EM structure and HDX mass spectrometry, the authors were able to map amino acids in the antibody paratope and spike epitope, the latter of which is the hinge region of the Spike S2 domain (residues 980-1005) that plays a critical role in pre- to -post-fusion conformational changes. Through well-executed and comprehensive mutagenesis, binding, and functional assays, the authors further validated critical residues that lead to antibody escape, which centered around the 2P residues and diminished viral entry. While 3A3 and an affinity-enhanced engineered version, RAY53, did not show potent in vitro neutralization against the authentic virus, the antibody was shown to recruit Fc effector functions for viral clearance, in vitro.

Overall, the conclusions of this paper are well supported by the data, but the usefulness of such antibodies is likely limited. The work can be strengthened by extending the analysis of 3A3-like antibodies in the context of human immune responses and in vivo effectiveness.

1. Isolation of 3A3 was achieved after the generation of scFv-phage libraries following immunization with a MERS S2-domain immunogen in a mouse model. The fact that 3A3 binds well to 2P-stabilized sequences and binding/neutralization is diminished upon reversion of 2P mutations back to the native spike sequence (Figures 3a, 4c, and 5b), suggest that such antibodies would likely not arise from natural infection. This contrasts the isolation of fusion peptide and stem helix-directed antibodies, which were isolated from both immunized animals and convalescent individuals. To make their results more solid regarding the use of such antibodies in future vaccine strategies, the authors should provide evidence that 3A3-like antibodies can be identified in human donors. For example, they could enrich donor-derived S2-specific antibodies that bind both MERS and SARS2 S2 domains and evaluate the fraction of antibodies that recognize the hinge-epitope using competition binding assays (either ELISA or BLI), which have commonly been used to map epitope-specific sera responses. This could also be achieved with nsEMPEM of polyclonal IgGs bound to S2 proteins.

2. The authors speculate in the discussion that strategies to enhance access to the hinge epitope, which may include ACE2-mimicking antibodies, could promote enhanced viral clearance. In addition to ACE2-mimicking antibodies, several antibodies have been described that bind the RBD and promote S1 shedding (see for instance mAb S2A4 - Piccoli et al, 2020, Cell). Several 2nd generation vaccine platforms utilize RBD-only immunogens that are likely to induce high titers of ACE2-mimicking and cross-reactive S1-shedding antibodies. Thus, adding in vitro neutralization and ADCC experiments to assess synergy between 3A3/RAY53 and such antibodies would booster this speculative claim and be of interest to many in the field developing strategies for pan-coronavirus therapies.

3. The authors provide in vitro evidence in Figure 5c,d for Fc-mediated viral clearance. While in vivo data to show effectiveness in animal models is ideal, additional in vitro data that utilize engineered constructs that modulate effector function (e.g., DLE (+) or LALA (-)) would boost the authors' claims regarding Fc-mediated viral clearance mechanisms by EA3/RAY53.

Reviewer #3 (Public Review):

In this manuscript, Li et. al, investigate whether epithelial or stromal Nphp2 loss, a gene causative of nephronophthisis (NPHP), drives polycystic kidney disease (PKD) and kidney fibrosis in a novel floxed model of Nphp2. The authors found that only epithelial and not stromal Nphp2 loss results in NPHP-like phenotypes in their mouse model. In addition, the authors show that concurrent cilia, via Ift88 loss, and Nphp2 loss within the kidney epithelium as well as HDAC inhibition results in less severe PKD/kidney fibrosis, as has been shown in mouse models of other non-syndromic forms of PKD, such as autosomal dominant PKD caused by mutations to Pkd1 or Pkd2.

The authors aimed to understand (1) whether the published NPHP phenotype (kidney cysts and fibrosis), known from the global Nphp2 knockout mouse, is driven by the function of NPHP2 in the kidney epithelium or stromal cells; (2) if kidney fibrosis in NPHP is linked to kidney damage caused by cysts, or independent and preceding of the PKD phenotype; (3) whether cilia are required, causative, or prohibitive of NPHP cystogenesis; and (4) if a broad spectrum HDAC inhibitor is a potential therapeutic approach for NPHP.

With the provided results, the authors established that epithelial Nphp2 loss is likely a predominant driver of PKD in their model; however, they cannot exclude that stromal NPHP2 does not play a role in cysts growth post-initiation because the authors failed to directly compare their cell type-specific models to a global cre knockout (e.g. Cagg-cre). In addition, it is possible that cyst initiation/growth upon stromal Nphp2 loss occurs substantially slower compared to epithelial Nphp2 loss. However, it seems the authors did not look at kidney phenotypes beyond 28 days of age. Publications from the ADPKD field suggest, that stromal Pkd1 loss initiates cystogenesis much slower than epithelial Pkd1 loss. Further, while the authors suggest that kidney fibrosis precedes cyst development, the results supporting this conclusion are limited to one time point, analyzing IF staining of a single marker that can be compared between non-cystic and cystic time points. These analyses need to be extended to make any firm conclusions.

The most interesting finding of the manuscript, and likely most impactful to the field, is, that loss of cilia within the setting of epithelial Nphp2 loss reduces PKD severity. This finding parallels published findings for Pkd1 and Pkd2 which are suggested to function in a cilia-dependent cyst-activation mechanism. Unfortunately, the here shown studies, do not add to the mechanistic insight beyond showing the descriptive finding. Most importantly, it remains unclear whether NPHP2 functions in the same pathway as polycystin-1 or -2 (the Pkd1, Pkd2 gene products) or in a separate independent pathway.

With respect to the HDAC preclinical studies, the authors show supporting data that a broad-spectrum HDAC inhibitor may be suitable for slowing cyst growth in their model of NPHP. Overall, these studies are not novel to the field, as HDAC inhibition has been shown to slow PKD progression in various models of PKD al while not in NPHP specifically. Further, the studies seem like an add-on, which does not directly link to the prior cell type-specific studies of NPHP2, and no mechanisms linking the two concepts are provided.

Reviewer #3 (Public Review):

Software UX design is not a trivial task and a point-and-click interface may become difficult to use or misleading when such design is not very well crafted. While Phantasus is a laudable effort to bring some of the out-of-the box transcriptomics workflows closer to the broader community of point-and-click users, there are a number of shortcomings that the authors may want to consider improving. Here I list the ones I found running Phantasus locally through the available Bioconductor package:

1. The feature of loading in one click one of the thousands of available GEO datasets is great. However, one important use of any such interfaces is the possibility for the users to analyze his/her own data. One of the standard formats for storing tables of RNA-seq counts are CSV files. However, if we try to upload from the computer a CSV file with expression data, such as the counts stored in the file GSE120660_PCamerge_hg38.csv.gz from https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE120660, a first problem is that the system does not recognize that the CSV file is compressed. A second problem is that it does not recognize that values are separated by commas, the very original CSV format, giving a cryptic error "columnVector is undefined". If we transform the CSV format into tab-separated values (TSV) format, then it works, but this constitutes already a first barrier for the target user of Phantasus.

2. Many RNA-seq processing pipelines use Ensembl annotations, which for the purpose of downstream interpretation of the analysis, need to be translated into HUGO gene symbols. When I try to annotate the rows to translate the<br /> Ensembl gene identifiers, I get the error

"There is no AnnotationDB on server. Ask administrator to put AnnotationDB sqlite databases in cacheDir/annotationdb folder"

3. When trying to normalize the RNA-seq counts, there are no standard options such as within-library (RPKM, FPKM) or between-library (TMM) normalization procedures. If I take log2(1+x) a new tab is created with the normalized data, but it's not easy to realize what happened because the tab has the same name as the previous one and while the colors of the heatmap changed to reflect the new scale of the data, this is quite subtle. This may cause that an unexperienced user to apply the same normalization step again on the normalized data. Ideally, the interface should lead the user through a pipeline, reducing unnecessary degrees of freedom associated with each step.

4. 4. Phantasus allows one to filter out lowly-expressed genes by averaging expression of genes across samples and discarding/selecting genes using some cutoff value on that average. This strategy is fine, but to make an informed decision on that cutoff it would be useful to see a density plot of those averages that would allow one to identify the modes of low and high expression and decide the cutoff value that separates them. It would be also nice to have an interface to the filterByExpr() function from the edgeR package, which provides more control on how to filter out lowly-expressed genes.

5. When attempting a differential expression (DE) analysis, a popup window appears saying:

"Your dataset is filtered. Limma will apply to unfiltered dataset. Consider using New Heat Map tool."

One of the main purposes of filtering lowly-expressed genes is mainly to conduct a DE analysis afterwards, so it does not make sense that the tool says that such an analysis will be done on the unfiltered dataset. The reference to the "New Heat Map tool" is vague and unclear where should the user look for that other tool, without any further information or link.

6. The DE analysis only allows for a two-sample group comparison, which is an important limitation in the question we may want to address. The construction of more complex designs could be graphically aided by using the ExploreModelMatrix Bioconductor package (Soneson et al, F1000Research, 2020).

7. When trying to perform a pathway analysis with FGSEA, I get the following error:

"Couldn't load FGSEA meta information. Please try again in a moment. Error: cannot open the connection In call: file(file, "rt")

Finally, there have been already some efforts to approach R and Bioconductor transcriptomics pipelines to point-and-click users, such as iSEE (Rue-Albrecht et al, 2018) and GeneTonic (Marini et al, 2021) but they are not compared or at least cited in the present work. One nice features of these two tools that I missed in Phantasus is the possibility of generating the R code that produces the analysis performed through the interface. This is important to provide a way to ensure the reproducibility of the analyses performed.

Reviewer #3 (Public Review):

The authors present in great detail a novel transfer of learning AI model architecture called diffRBM, which is based on the original RBM papers [Hinton, 2002, Hinton and Salakhutdinov, 2006]. They further show how this tool can be used to assess the immunogenicity of TCR positions and the importance of different by-position amino acid usages in creating this immunogenicity. They show that this novel method identifies all known important positions at least as well as existing analytical and structural methods, potentially in a more explanatory way.

Reviewer #3 (Public Review):

This manuscript uses novel techniques to examine the intracellular trafficking and membrane insertion of AMPA receptors to dissect the molecular mechanism involved in regulating these processes in neuronal cultures under basal conditions and during the induction of a chemical form of long-term potentiation (LTP). Specifically, they examine the role of the interaction of the GluA1 subunit with two neuronal proteins SAP97 and 4.1N. The manuscript uses a novel approach to synchronize and temporally control the release of GluA1-containing receptors from the ER and examine its trafficking through the Golgi and dendrites to the plasma membrane. This assay can measure the number of GluA1-containing intracellular vesicles, their speed of trafficking, and the delivery of newly synthesized GluA1 to the surface.

First, the authors use shRNA knockdown (KD) techniques to decrease the expression of SAP97 and 4.1 and found dramatic effects on the number of GluA1-containing vesicles and plasma membrane insertion of GluA1. SAP97 had a larger effect on trafficking while 4.1N had a larger effect on plasma membrane insertion. The authors then went on to use mutants of GluA1 that lack the whole C-terminal domain or mutations in the SAP97 and 4.1N biding sites in GluA1 C-termini and examine the trafficking of these mutants. These mutations decreased the intracellular trafficking and the membrane insertion of GluA1. In addition, the authors mutated phosphorylation sites that have been reported to regulate the interaction of GluA1 with 4.1N. Mutations in these sites that eliminated phosphorylation inhibits membrane insertion while the phosphomimetic mutations did not affect membrane insertion. Finally, mutations in the SAP97 and 4.1N binding sites including mutations in the phosphorylation sites also inhibited chemical-induced LTP increases in the regulation of GluA1 ER-Golgi exit, intracellular transport, and membrane insertion.

These studies are well done and novel and provide support for the role of the GluA1 C-termini and its protein interactors in the trafficking of the AMPA receptor under basal and plasticity conditions. This contributes new data using a novel approach to the controversy over the role of the C-termini of AMPA receptors in the regulation of AMPA receptor function. It supports the role of these interactions in AMPA receptor function.

Reviewer #3 (Public Review):

Darunavir (DRV) has been shown to be a potent HIV-1 protease inhibitor in individuals, has pM binding to the protease active site, has activity to protease inhibitor resistant HIV-1s, and has been reported to be difficult to develop resistance to individuals and in tissue culture. The authors argue that given published studies of generating HIV-1 resistance to DRV in tissue culture was not accomplished and all published studies started with either a drug-resistant virus or a combination of drug-resistant viruses for selection, new information can be gleaned as to the viral mutational pathways leading to drug-resistant viruses from HIV-1 wild type (no pre-existing drug mutations) NL4-3.

To better understand the development of HIV-1 wild-type DRV resistance, Spielvogel and colleagues detail their studies on characterizing HIV-1 protease genomic and structural alterations and viral fitness before and during the development of tissue culture resistance to DRV, as well as 10 new compounds (UMass compound series) based on DRV. The UMass compounds have distinct R1 and R2 groups as compared to DRV, which provides for a comprehensive chemical toolset to probe protease genetics and structural changes and alterations in viral fitness resulting during HIV protease drug resistance development in tissue culture. Differences in HIV protease resistance patterns developing over time combined with the potency of the protease inhibitors to HIV mutants resulting from inhibitor selections provide insights as to how DRV chemical groups impact resistance development. The manuscript is comprehensive, well-written, and informative, yet dense and with some figures that readers may not find informative.

Protease inhibitor tissue culture selection of wild-type NL4-3 was based on increasing protease inhibitor concentrations over time. Generally, the DRV resistance mutations that came up early de novo from wild-type NL4-3 virus were, 84V, followed by the acquisition of accessory mutations, predominately 54L and 82I, with 84V, 85V, 46I, 47V, 63P, and others as well, which became entrenched over time. The 84V mutational series have been reported for DRV as the authors noted. To determine the DRV selection pattern from pre-existing HIV single drug-resistant population a pool of 26 single mutant viruses was used for selection. Similar patterns were seen as for wild-type viruses, starting with 84V.

Interestingly, when the UMass compound series was used to select wild-type NL4-3 in tissue culture, 3 mutational series resulted, a protease mutational pattern similar to DRV (UMass 1, and 4, a protease mutational pattern starting with 50V, and followed by the predominate accessory mutations 10F, 13V, 33F, 46I, 63P, and 71V, but not 84V (UMass 3,6,7,8,9, and 10) and a mixture of both populations (UMass 2 and 5). When the HIV single drug-resistant population pool was used, which didn't contain 50V, was used for selection, UMass 2,4,7, and 8 retained the same mutational patterns as the original wild-type HIV selection, where, interestingly, UMass 6 utilized the 84V mutational pathway, rather than 50V, when the 84V mutation was pre-existing.

The results pointed out that modification of the DRV R2 and R1 groups alters selection patterns. It appears that a smaller hydrophobic side chain at the P1' position appears to drive towards 84V selection, whereas a larger side chain selects for the 50V pathway. UMass compounds 2, 5, 7, and 10 demonstrate the highest potency to both 50V/71V and 84V mutant viruses. Interestingly, UMass 2 and 5 were selected for both 50V/71V and 84V resistance mutational pathways, whereas 7 and 10 were selected for 50V/71V pathways.

Based on entry/replication studies, the authors argue that pushing viruses to select 50V/71V mutational pathways in protease, vs 84V mutational pathways in protease, promotes a higher genetic barrier to overcome resistance. This would be due to the reduction in fitness for the 50V/71V protease mutant and the large number of accessory mutants required to regain fitness. However, more in-depth analyses of the various mutants are warranted to support this point, such as head-to-head viral replication studies. A further limitation to the general conclusions is whether mutations in Gag provide for compensatory mutations to augment protease (and viral) fitness for the UMass compound findings.

Reviewer #3 (Public Review):

This manuscript provides a remarkably simple, yet effective, model of hippocampal replay. A replay event is stitched together as a chain of reactivated experiences. Individual experiences are prioritized for reactivation according to three intuitive measures: the spatial proximity of an experience to that previously reactivated, the frequency of and reward associated with an experience, and an inhibitory term that propagates the replay across space. Under certain conditions, their model can produce replays that are nearly as optimal--in terms of teaching a reinforcement learning agent to successfully navigate to a reward--as those produced by Mattar and Daw's 2018 model which, by design, generates the most behaviorally useful replays.

The authors assert that their model can recapitulate the replay statistics observed in a subset of experimental works, including the ability of replay to generate novel 'short cuts' from segments of past experience, the resemblance of replay to Brownian diffusion following random exploration, the ability of replay to steer around environmental barriers, and the observation of pre-play. These claims are generally well supported by the data presented (in particular, the model seems to be quite robust to different parameters).

One important caveat is that the proposed model requires two modes ('default' and 'reverse') to simultaneously account for empirical findings and provide behavioral utility (the performance of the agent is poor when using the default mode, but comparable with that of Mattar and Daw in the reverse mode). The authors suggest that the brain could dynamically switch between modes (dubbed the 'dynamic' mode). I feel that the paper would be strengthened by focusing on this dynamic mode throughout and demonstrating that it produces replays with statistics matching empirical data. For example, what is the distribution of forward and reverse replays produced by the default model (figure 3D)? Since neither mode by itself is adequately consistent with experimental findings, showing that the model appropriately switches between modes would strengthen its plausibility.

The authors state that their model is able to recapitulate the finding that replay in sleep following random exploration can be described by Brownian diffusion. A key point in that paper was that the preceding behavior was not diffusive. The authors go some way to address this point by showing that their model produces diffusive replays even if the strength of experience across space is not uniform. However, it isn't clear to me that modeling non-uniform experience strength is equivalent to modeling non-diffusive behaviorally trajectories. A more convincing test would have been to simulate realistic behavioral trajectories and show that subsequent replay events are still diffusive.

In my view, the fact that the model can generate 'pre-play' (in this case, replay of a visually cued, but unvisited arm of the maze) is not particularly informative. In order to generate pre-play, the authors allow the agent to 'visually explore' the cued arm. The implementation of this visual exploration is equivalent to allowing the agent a limited amount of real physical experience on the cued arm. Thus, the finding of replay for the cued arm is unsurprising. It would have been more useful to show that the model over-represents the rewarded arm on a T-maze, given equal exploration of the arms (as in Mattar and Daw).

Also debatable is the authors' assertion that their model is biologically plausible, while that of Mattar and Daw is not. While the former model is certainly computationally less expensive, little experimental data exists that could definitively point to the biological plausibility or implausibility of either model.

Overall, this model is impressive in its ability to generate replay events with realistic and varied statistics, using only a few simple rules. It will be a welcomed addition to the fields of replay, learning and memory, and reinforcement learning.

Reviewer #3 (Public Review):

Zhang, Q. et al. developed a two-photon fluorescence microscope (2PFM) by incorporating direct wavefront sensing adaptive optics (AO), which is optimized for mouse in vivo retinal imaging. By using the same 2PFM with the option of using or not using the incorporated AO system, this team compared the in vivo retinal images and convincingly demonstrated that AO correction acquired brighter and higher resolution images of retinal ganglion cells (RGCs) and their axons in both densely and sparse labeled transgenic mouse lines, normal and defected capillary vasculatures, and RGC spontaneous activities detected by genetic Ca2+ sensor. Interestingly and importantly, this team found that a global correction by removing the common aberration from the entire FOV enhances imaging signals throughout the entire large FOV, indicating a preferable AO imaging strategy for large FOVs. The potential applications of the in vivo retinal imaging techniques and strategies developed by this study will certainly inspire further investigation of the dynamic morphological and functional changes of retinal vasculatures and neurons during disease progression and before and after treatments.

It would be beneficial to the manuscript and the readers if the authors can elaborate on optic design a little bit more. For example, whether the incorporation of AO adversely affects the 2PFM optic design? If the 2PFM can be further optimized by uncompromised optic design without incorporating AO, the quality of in vivo images will comparable to the AO-2PFM or not?

Reviewer #3 (Public Review):

In this manuscript Zhao et al investigated how multiple Rab27 effectors work to regulate insulin secretion by murine pancreatic b-cells. They do this by comparing the phenotypes of b-cells/islets lacking effectors doubly or singly. Their main findings/contributions are that:

Mlph works downstream of Myrip/exophilin-8 to mobilise granules for fusion from the actin network to the plasma membrane after stimulation.

Mlph and exophilin-8 interact via the exocyst

Down-regulation of exocyst affects exocytosis in cells expressing exophilin-8

Exophilin-8 promotes fusion of granules docked by granuphilin at the membrane

Exophilin-8 not required for Grph related granule docking at the plasma membrane

A model for how the three effectors coordinate ISG secretion. According to this model there are 2 insulin secretion pathways in b-cells; a) where Exo8 acts upstream of Mlph and with actin/Myosin Va/VIIa, exocyst and syntaxin 4 to move dock granules in actin and promote exocytosis, and b) where Exo8 works in an antagonistic manner with Grph promoting secretion of granules docked at the membrane by Grph.

This is an interesting/important question and the authors make important contributions (above). In general experiments are well designed and controlled but there are some questions that remain open that could have been included to make the study a more comprehensive analysis of Rab27 effectors in insulin secretion.

Reviewer #3 (Public Review):

By popular single-cell RNA-seq, the authors identified FOXC2 as an undifferentiated spermatogonia-specific expressed gene. The FOXC2+-SSCs can sufficiently initiate and sustain spermatogenesis, the ablation of this subgroup results in the depletion of the uSPG pool. The authors provide further evidence to show that this gene is essential for SSCs maintenance by negatively regulating the cell cycle in adult mice, thus well-established FOXC2 as a key regulator of SSCs quiescent state.

The experiments are well-designed and conducted, the overall conclusions are convincing. This work will be of interest to stem cell and reproductive biologists.

Reviewer #3 (Public Review):

In this manuscript, the authors examine the role of VAPA in focal adhesion (FA) turnover and cell motility via effects on ER-PM contact site functions. The authors show that VAPA KO CaCo2 cells form larger FA and have aberrant migration behavior and spreading. Those cells show lower levels of PI(4,5)P2 at PM, but no change in PI(4)P at Golgi and endosomes. PI(4)P is not tested at the PM. The authors show that VAPA KO cells have a similar number but less stable GFP-MAPPER positive ER-PM contact sites as compared to control cells. In contrast, FA are more stable over time in VAPA KO. The authors also aimed to evaluate GFP-MAPPER proximity with vinculin spots and concluded that ER-PM contacts partially overlap with FA, whereas they are more distant in VAPA KO. Thus, a correlation between stable contact sites near FA and FA disassembly likely exists. From this set of data, the authors suggest that VAPA has a key role at ER-PM contacts near FA by mediating lipid transfer, which ultimately enables internalization of integrins and FA disassembly.

The approach in the paper is innovative and interesting because VAPA is a major tether at contact sites and the link between contact sites and cytoskeleton dynamics and cell motility remains little explored. This can potentially lead to significant advances in the field. The experiments presented are technically well executed, but most of the results and hypotheses arising from VAPA KO cells are not tested by rescue experiments with exogenous VAPA and VAPA mutants. Although the proposed role for VAPA might fit with the data, the final model is not experimentally tested and is thus highly speculative. The role of VAPA at ER-PM contact sites near FA, and the direct link between VAPA, PI(4,5P)2, and FA disassembly, are not established. VAPA is not shown at ER-PM contacts in the manuscript. Some controls are missing and statistics must be improved. In summary, this work seems to be on the right track, but looks quite preliminary.

Reviewer #3 (Public Review):

Macrophage colony-stimulating factor (M-CSF) plays key roles in the differentiation of myeloid-lineage cells, including monocytes, macrophages and osteoclasts. The latter mediate bone resorption, which is important for physiological bone remodelling but, unrestrained, contributes to bone loss in conditions such as in post-menopausal osteoporosis. M-CSF production within the bone marrow is implicated in the maintenance of myeloid and skeletal homeostasis, but the cellular source of bone marrow M-CSF has remained elusive. In this study, Inoue et al address this issue through advanced transcriptomic and gene targeting approaches. They conclude that a population of Adipoq-expressing progenitors within the bone marrow, designated "AdipoQ-lineage progenitors", is the key cellular source of M-CSF. Consistent with this, they find that transgenic deletion of M-CSF from these cells disrupts macrophage and osteoclast development, leading to osteopetrosis and possibly preventing bone loss following ovariectomy. However, they have not adequately addressed the possibility that M-CSF production from other cell types, particularly adipocytes in peripheral adipose tissues, may also be influencing these phenotypes. Specific strengths and weaknesses are as follows:

Strengths:

1. The manuscript is written in a clear, succinct manner and the data are generally nicely presented. It is therefore a pleasure to read.

2. The analysis of single-cell transcriptomic data is clear and convincing, and the skeletal phenotyping has been done to a high standard.

Weaknesses:

1. The authors underplay the potential contribution of M-CSF production from other cell types, particularly from adipocytes in peripheral adipose tissues. They show that M-CSF expression from these cells is lower than from the bone marrow progenitors that they focus on; however, based on this they allude to "no expression" of M-CSF from these other adipocytes. This overlooks the findings of other studies showing that peripheral adipocytes produce M-CSF and that this has biological functions. Whether their knockout model alters M-CSF expression in peripheral adipose tissue, whether for whole tissue or for isolated adipocytes, has not been tested.

2. The decreases in M-CSF have been assessed at the transcript level, but not for M-CSF protein. Whether their knockout model

3. It is also unclear if the Adipoq-lineage progenitors consist exclusively of adipogenic cells, or if osteogenic progenitors are also part of this population.

If these weaknesses are addressed then this work has potential to yield firm conclusions and new insights into the regulation of myeloid and skeletal homeostasis, both in normal physiology and in clinically relevant conditions.

Reviewer #3 (Public Review):

In this study, the authors sought to develop an ex vivo organ culture system that would allow for long-term (>12 hours) live imaging of the lymph gland (LG), the hematopoietic organ in Drosophila, in order to gain insights into the process of differentiation during hematopoiesis. The authors successfully built such a system through trial and error and showed that the LG could survive for over 12 hours and that it recapitulated many of the aspects seen in in vivo LGs.

The authors also developed sophisticated quantitative image analysis tools that allowed them to identify new modes of differentiation that may help explain the cellular heterogeneity previously seen by other groups. Furthermore, they were able to follow mitosis in real-time and showed evidence that not only can progenitors undergo symmetric cell division but that mitosis shows some orientation bias which may help explain the overall structure of the organ. The authors went on to show that upon infection, modes of differentiation and mitosis orientation seem to shift, but they did not provide any mechanistic insight into how this may occur or whether these shifts would impact the final cell fate or function of the mature hemocytes. Nevertheless, the identification and description of these patterns are in itself helpful and informative and provide a basis for future studies delving into these mechanistic questions.

The major strengths of the methods include the advancement in live-imaging technology and the development of quantitative image analysis tools. Weaknesses of the results include small sample sizes (and relatively high p-values), which limit the strength and breadth of some conclusions. This is to be expected as there is a trade-off between long-term live imaging of individual samples and sample number, nevertheless, it represents a minor weakness. Overall this weakness is overshadowed by the strength of the advancements afforded by live imaging and following in real-time the process of differentiation and mitosis. Furthermore, the quantitative analysis tools developed and used in this study can be applied across multiple subfields and represents an important step forward in the field.

The evidence presented here is generally solid and the results tend to support their conclusions although some specific conclusions are supported by data with no p-values noted or relatively high p-values and low correlation coefficients, and so should be interpreted with this in mind.

This study represents a compelling and convincing theoretical and technical advance in efforts to understand hematopoiesis in flies. This is a powerful and versatile system that will allow for not only genetic manipulation of the LG but also of the tissues co-cultured with the LG to elucidate the mechanisms that control various signaling pathways during homeostasis. In fact, which additional tissues (like the fat body and brain) that had to be included in the co-culture system in order for the LG to survive recapitulate what past studies have shown about where key signals come from that help maintain homeostasis in the LG.

One caveat of the work is that because the authors used Eater-DsRed to follow differentiation, these modes may only apply to the formation of plasmatocytes and not necessarily crystal cells, which the authors noted do not tend to go through an Eater-DsRed intermediate state. Future work using this live-imaging system and image analysis tools to study the formation of the various mature cell types in flies will be a valuable addition to the field.

The methods developed here will be highly useful to both the specific subfield and to the general scientific community and will likely spark new insights into the process of hematopoiesis when combined with different markers and genetic manipulations, as outlined by the authors in the discussion. Future studies that explore whether the different modes of differentiation identified here ultimately result in divergent cell fates for the mature hemocytes will be important for understanding the significance of the findings more generally. But the identification of changes in the ratios and rates of the modes of differentiation upon infection with E.coli suggests functional ramifications of the different modes. It will be interesting to see if other types of infection or systemic stresses cause similar or different changes in differentiation modes.

Reviewer #3 (Public Review):

The method of ATRAP provides a useful workflow for processing and analysing single-cell sequencing data of TCRs and barcoded pMHC. The method addresses an important subfield of research, as the availability of these datasets is increasing substantially due to the wider availability of commercial reagents and tools.

Overall the study is highly technical and can be considered almost a "user manual" to assist researchers who pursue this TCR-pMHC specificity experiments by single-cell sequencing. Convincing experimental work, data analysis, appropriate controls, and technical details are provided throughout.

Reviewer #3 (Public Review):

Cancer cell populations co-evolve under the pressure exerted by the recognition of tumor-associated antigens by the adaptive immune system. Here, George and Levine analyze how cancers could dynamically adapt the rate of tumor-associated antigen loss to optimize their probability of escape. This is an interesting hypothesis that if confirmed experimentally could potentially inform treatments. The authors analyze mathematically how such optimally adapting tumors gain and lose tumor-associated antigens over time. By simplifying the complex interplay of immune recognition and tumor evolution in a toy model, the authors are able to study questions of practical interest analytically or through stochastic simulations. They show how different model parameters relating to the tumor microenvironment and immune surveillance lead to different dynamics of tumor immunogenicity, and more immunologically hot or cold tumors.

Simple models are important because they allow an exhaustive study of dynamical regimes for different parameters, such as has been done elegantly in this study. However, in this quest for simplification, the authors have not considered biological features that are likely to be of importance for understanding the process of cancer immune co-evolution in generality: tumor heterogeneity and immune recognition that only stochastically results in cancer elimination. In this sense, this paper might be seen as the opening act in a series of more sophisticated models, and the authors discuss avenues towards such further developments.

Reviewer #3 (Public Review):

Overall, this is an interesting and well performed study that described a comprehensive landscape of m1A modification in primary neuron and investigated the role of m1A in the circRNA/lncRNA‒miRNA-mRNA regulatory network following OGD/R. The focus on the two different complex regulatory networks for differential expression and differential methylation is important and it will be a valuable resource for the research community that focuses on epitranscriptomics and central nerve system diseases. Collectively, the authors present an exciting piece of work that certainly adds to the literature regarding epitranscriptomic features in neuron. While interesting results obtained and the paper is nicely written, I have the following suggestions for minor revisions to improve the paper.

1. The authors have explored the role of m1A modification in neuron, but it would have been helpful if the authors described the significance of these findings in depth in some sections (Figure 5 and Figure 6) to enhance the value of the article.<br /> 2. The authors should describe in detail the current research state of m1A modification and the significance of this study to the field of epitranscriptomics in the introduction and discussion section.

Reviewer #3 (Public Review):

In this work, the authors find that similar to mammals, sialylation is critical in neurons within flies, yet in flies the critical substrate for sialylation, CMP-Neu5Ac, is 'outsourced' to glial cells. These findings are shown through an extensive array of knockout, knockdown, and transgenic flies where CMP-Neu5Ac biosynthesis and sialyltransferase expression is modulated in either glial cells or neurons. The importance of sialylation in neurons is demonstrated by showing that sialylation impacts the expression levels of a critical voltage-gated ion channel.

This elegant work dissecting sialylation in the fly brain convincingly demonstrates the requirement for glial cells in the process of sialylation of neurons and deserves to be published. The major unaddressed question remaining is precisely how the CMP-Neu5Ac is delivered from the glial cells to neurons with several possibilities that merit further discussion including (but not limited to): extracellular vesicles, receptor-mediated uptake (unlikely but can't be ruled out), or exocytosis. The authors could make the point stronger that CMP-Neu5Ac should not be able to cross the neuronal membrane (or the Golgi membrane for that matter), requiring specific transport mechanisms.

Reviewer #3 (Public Review):

This work compares transcriptional responses of shoots and roots harvested from four plate-based assays that simulate drought and from plants subjected to water deficit in pots using the model plant Arabidopsis thaliana with the aim to select a plate-based assay that best recapitulates transcriptional changes that are observed during water-deficit in pots. Polyethylene glycol (PEG), mannitol, and sodium chloride (salt) treatments that are commonly used by molecular biologists to simulate drought were used for the plate-based assays as well as a new assay that uses increased concentrations of agar and nutrients to elicit drought which was developed by the authors and termed a 'low-water agar' assay since the amount of water added to the media mix and plates was lowered. Plants in pots were grown on vermiculite with the same nutrient mix as used in the plates and drought was induced by withholding watering for five days. Additionally, treatment with abscisic acid was conducted to study whether growth on plates itself led to artifacts compared to water deficit in pots. Shoot and root samples were harvested from all treatments for RNA sequencing analysis and differentially expressed genes were called against control samples.

The authors observed that gene expression responses of roots in their 'low-water agar' assay resembled more closely the water deficit in pots compared to the PEG, mannitol, and salt treatments (all at the highest dose). In particular, 28 % of PEG led to the down-regulation of many genes that were up-regulated under drought in pots. Through GO term analysis, it was pointed out that this may be due to the negative effect of PEG on oxygen solubility since downregulated genes were over-represented in oxygen-related categories. The data also shows that the treatment with abscisic acid on plates was very good at simulating drought in roots. Gene expression changes in shoots showed generally a high concordance between all treatments at the highest dose and water deficit in pots, with mannitol being the closest match. This is surprising, since plants grow in plates under non-transpiring conditions, while a mismatch between water loss by transpiration on water supply via the roots leads to drought symptoms such as wilting in pot and field-grown plants. The authors concluded that their 'low-water agar' assay provides a better alternative to simulate drought on plates.

Strengths:

The development of a more robust assay to simulate drought on plates to allow for high-throughput screening is certainly an important goal since many phenotypes that are discovered on plates cannot be recapitulated on the soil. Adding less water to the media mix and thereby increasing agar strength and nutrient concentration appears to be a good approach since nutrients are also concentrated in soils during water deficit, as pointed out by the authors. To my knowledge, this approach has not specifically been used to simulate drought on plates previously. Comparing their new 'low-water agar' assay to popular treatments with PEG, mannitol, salt, and abscisic acid, as well as plants grown in pots on vermiculite led to a comprehensive overview of how these treatments affect gene expression changes that surpass previous studies. It is promising that the impact of 'low-water agar' on the shoot size of 20 diverse Arabidopsis accessions shows some association with plant fitness under drought in the field. Their methodology could be powerful in identifying a better substitute for plate-based high-throughput drought assays that have an emphasis on gene expression changes.

Weaknesses:

While the authors use a good methodological framework to compare the different drought treatments, gene expression changes were only compared between the highest dose of each stress assay (Fig. 2B, 3B). From Fig. 1F it appears that gene expression changes depend significantly on the level of stress that is imposed. Therefore, their conclusion that the 'low-water agar' assay is better at simulating drought is only valid when comparing the highest dose of each treatment and only for gene expression changes in roots. Considering how comparable different levels of stress were in this study leads to another weakness. The authors correctly point out that PEG, mannitol, and salt are used due to their ability to lower the water potential through an increase in osmotic strength (L. 45/46). In soils, water deficit leads to lower water potential, due to the concentration of nutrients (as pointed out in L. 171), as well as higher adhesion forces of water molecules to soil particles and a decline in soil hydraulic conductivity for water, which causes an imbalance between supply and demand (see Juenger and Verslues, The Plant Cell 2022 for a recent review). While the authors selected three different doses for each treatment that are commonly used in the literature, these are not necessarily comparable on a physiological level. For example, 200 mM mannitol has an approximate osmotic potential of around -5 bar (Michel et al. Plant Physiol. 1983) whereas 28 % PEG has an osmotic potential closer to -10 bar (Michel et al. Plant Physiol. 1973). It also remains unclear how the increase in agar concentration versus the increase in nutrient concentration in the 'low-water agar' affect water potentials. For these reasons it cannot be known whether a better match of the 'low-water agar' at the 28% dose to water deficit in pots for roots in comparison to the other treatments is due to a good match in stress levels with the 'low-water agar' or adverse side-effect of PEG, mannitol, or and salt on gene regulation. Lastly, since only two biological replicates for RNA sequencing were collected per treatment, it is not possible to know how much variance exists and if this variance is greater than the treatments themselves.

Reviewer #3 (Public Review):

This study focuses on the role of the chromatin remodeller ISWI in Cryptococcus. The authors show that a) ISWI modulates Cryptococcus' ability to grow in the presence of antifungal drugs and b) ISWI post-translational modifications (Acetylation and Ubiquitination) regulate ISWI protein stability. The observation that post-translational modifications regulate ISWI activity and stability is exciting and it could unveil novel mechanisms to rapidly and reversibly regulate the response to antifungal drug treatments. However, the study lacks a fundamental characterisation of ISWI. This information is essential to understand the mechanistic regulations of ISWI in Cryptococcus and how it mediates drug response. The following are questions that should be addressed:

1. ISWI chromatin remodellers are well-characterised in many organisms. How many ISWI proteins does Cryptococcus contain? Why did the authors focus on ISWI?<br /> 2. What is the ISWI protein complex(es)? The Mass-Spec analysis should reveal this.<br /> 3. Is Cryptococcus ISWI a transcriptional activator or repressor?<br /> 4. Is ISWI function in drug resistance linked to its chromatin remodelling activity?<br /> 5. Does ISWI interact with chromatin? If so, which are ISWI-target genes? Does drug treatment modulate chromatin binding?

Reviewer #3 (Public Review):

The work from Dupuy et al aims to characterize the mutagenic effects of two DinB homologs of Mycobacteria, DinB2, and DinB3. The manuscript shows solid and convincing biochemical data about slippage promoted by DinB 2 on various homopolymeric templates. Overall, this study makes a solid contribution to the understanding of the properties of polymerases from the different DinB subfamilies of bacteria, although some points of the in vivo experiments should be critically evaluated by the readers as described below.

In vivo DinB2 is the more mutagenic of the two and is toxic when overexpressed. Nevertheless, these results are obtained with the overexpression of the polymerases and should be interpreted with caution. In this sense, it would have been interesting to have a quantification of how much overexpression the plasmids constructs achieve in the conditions used in the experiments, for a better assessment of the relevance of the data. For example, a physiological 10-fold increase in the expression of DinB2 is mentioned in the discussion - would that be close to what is achieved with plasmid-based overexpression?

The finding of kanR CFUs without any detectable mutations in the kan marker is worrisome and should be better discussed in the text. The same for sacB data in supplementary material. The explanation given in lines 216-218 does not make sense. Markers 7G and 8G clearly are barely measuring any mutagenesis. I think that the experiments in which most of the supposed KanR revertants actually have no Kan mutation should either be removed from the manuscript or better discussed, because it is uncertain what they are measuring, therefore no conclusion can be drawn from them. For the Kan markers, one possible explanation is that translational frameshifts are occurring and allow residual growth of some of the cells. Gene amplification as seen in the lac system of Cairns and Foster in E. coli could also promote growth without actual mutations. Is the KanR phenotype of these colonies heritable and stable?

Also, spontaneous mutagenesis should have been more precisely measured by using fluctuation analysis of larger sample sizes. In many instances, the results shown are the means of a few cultures with very large differences in mutant frequencies (several hundred-fold - e.g. Figures 4C, D and E, 5C and F, S3). Authors could discuss/explain their choice of statistical analysis and sample sizes.

Reviewer #3 (Public Review):

In this manuscript, Kidwell & Casalini, et al. use cell biology and functional approaches to investigate the dynamics and consequences mitochondrial transfer from macrophages to breast cancer cells. Unlike prior studies that emphasize the metabolic benefits of mitochondrial reconstitution in cells with defective mitochondrial DNA, they ask how mitochondrial transfer affects breast cancer cells with intact mitochondria. They observe that macrophage co-culture or "bathing" breast cancer cells in isolated mitochondria from macrophages results in low frequency mitochondrial transfer, which increases cell cycling, ERK signaling, and cell proliferation rate of recipient cells. Interestingly, fluorescent dyes and sensors were used to determine that transferred mitochondria had low mitochondrial membrane potential and were highly oxidized, suggesting dysfunctional mitochondria with elevated ROS. In addition, activation of mitochondrial ROS by photobleaching a region of mitochondria in cells expressing mito-KillerRed was sufficient to similarly increase cell cycling, and mitochondrial targeted antioxidants could mitigate the proliferative benefits of mitochondrial transfer. Finally, the authors used several in vitro and in vivo models to demonstrate that M2-like macrophages had more fragmented mitochondria, had higher mitochondrial transfer rates, and promoted cell cycling in tumors.

Overall, a strength of the study is the usage of creative cell biology techniques and rigorous mouse models to provide compelling support for their primary claims, many of which go against the grain of current thinking in mitochondrial transfer research. While the discrepancies with the literature are by no means the fault of the authors, this study could nonetheless improve its reach by directly seeking resolution to these differences. In addition, the study raises some important questions how mitochondrial ROS from transferred dysfunctional mitochondria might be beneficial and at what doses, which should be further investigated to contextualize the findings.

Reviewer #3 (Public Review):

This manuscript reveals opioid suppression of breathing could occur via multiple mechanisms and at multiple sites in the pontomedullary respiratory network. The authors show that opioids inhibit an excitatory pontomedullary respiratory circuit via three mechanisms: 1) postsynaptic MOR-mediated hyperpolarization of KF neurons that project to the ventrolateral medulla, 2) presynaptic MOR mediated inhibition of glutamate release from dorsolateral pontine terminals onto excitatory preBötC and rVRG neurons, and 3) postsynaptic MOR-mediated hyperpolarization of the preBötC and rVRG neurons that receive pontine glutamatergic input.

This manuscript describes in detail a useful method for dissecting the relationship between the dorsolateral pons and the rostral medulla, which will be useful for various researchers. It's also great to see how many different methods have been applied to improve the accuracy of the results.

1. Relationship between the dorsolateral pons and rostral ventrolateral medulla.

The method of this paper is a good paper to show a very precise relationship between the presence of opioid receptors and the dorsolateral pons and rostral ventrolateral medulla, and for opioid receptors, based on the expression of Oprm1, the use of genetically modified mice with anterograde or retrograde viruses with additional fluorescent colors showed both anterograde and retrograde projections, revealing a relationship between the dorsolateral pons and rostral ventrolateral medulla.

For example, to visualize dorsal pontine neurons expressing Oprm1, Oprm1Cre/Cre mice were crossed with Ai9tdTomato Cre reporter mice to generate Ai9tdT/+ oprm1Cre/+ mice (Oprm1Cre/tdT mice) expressing tdTomato on neurons that also express MOR at any point during development, and the retrograde virus encoding Cre-dependent expression of GFP (retrograde AAV-hSIN-DIO-eGFP was injected into the respiratory center of Oprm1Cre/+ mice and into the ventral respiratory neuron group, showing that KF neurons expressing Oprm1 project to the respiration-related nucleus of the ventrolateral medulla.

However, although the authors have also corrected it, the virus may spread to other places as well as where they thought it would be injected, and it is important to note that it is injected accordingly to mark the injection site with an anterograde virus encoding a different fluorescent color mCherry, and the extent of the injection is quantified, which is excellent as a control experiment.

In addition, the respiratory center seems to be related not only to preBötC but also to pFRG recently, so if the relation with it is described, it is important from the viewpoint of the effect on the respiratory center and the effect on the rhythm.

2. Electrophysiological approaches and useful methods for target neurons

Oprm1Cre/+ mice), the authors found abundant Oprm1 + projections in the preBötC region of the medulla oblongata (respiratory center) and sought to determine whether presynaptic opioid receptors inhibit glutamate release from KF terminals to excitatory preBötC and rVRG neurons, since KF neurons in the dorsolateral pons projecting to the ventrolateral medulla oblongata had been shown to be glutamatergic and to have opioid receptors. The authors injected a channelrhodopsin-2-encoding virus (AAV2-hSin-hChR2 (H134R) -EYFP-WPRE-PA) into the dorsolateral pontine KF of vglu2Cre / tdT mice and performed whole-cell voltage-clamp recordings from td tomato-expressing, excitatory vglu2-expressing preBötC and rVRG neurons, contained in acute brain slices. Moreover, both opioid-sensitive and opioid-insensitive KF neurons that project to preBötC and rVRG were visible and recorded using FluoSpheres which are much more visible in acute brain sections than retrograde tracers of viruses.

1) Optogenetic stimulation of the KF terminus was blocked by the AMPA-type glutamate receptor antagonist DNQX. In excitatory pre-BötC and rVRG neurons, the terminals from the dorsal pontine KF were activated by optogenetic stimulation, and the KF synapses to the medullary respiratory neurons were found to be monosynaptic because oEPSCs(optical stimulated EPSCs) were removed by TTX but were subsequently restored by the application of K-channel blocker 4AP. Thus, KF neurons have been shown to send monosynaptic glutamatergic projections to excitatory ventrolateral medullary neurons using terminal optogenetic stimulation and receptor and channel inhibitors.

2) To determine whether opioids inhibit glutamate release from KF terminals to medullary respiratory neurons, we recorded a pair of oEPSCs (50 ms stimulus interval) from excitatory preBötC and rVRG neurons and applied an endogenous opioid agonist, [Met5] enkephalin (ME), to the perfusion solution. ME is preBötC and rVRG neurons, indicating inhibition of glutamate release by presynaptic MOR PPR. Thus, presynaptic opioid receptors have been shown electrophysiologically to inhibit glutamate release from KF terminals to excitatory pre-BötC and rVRG neurons.

3) Whether excitatory pre-BötC or rVRG neurons themselves receiving opioid-sensitive glutamatergic synaptic inputs from KF are hyperpolarized by opioids can be determined by monitoring their retention currents.

4) Since FluoSpheres are much more visible in acute brain sections than retrograde tracers of viruses and do not spread to injection sites, they chose to record from retrogradely labeled KF neurons with FluoSpheres injected into preBötC or rVRG in wild-type mice, allowing us to label KF neurons regardless of Oprm1 expression status and determine the projection patterns of both Oprm1 + and Oprm1- neurons. Whole-cell voltage-clamp recordings from fluorescent KF neurons contained in acute brain slices show that the presence of ME-mediated outward current can identify KF neurons that express functional MORs and are opioid-sensitive compared to neurons that lack ME-mediated outward current (insensitive). This suggests that both opioid-sensitive and opioid-insensitive KF neurons project to preBötC and rVRG.

Although much has been written about the relationship between KF neurons and medulla oblongata neurons and their being glutaminergic neurons, detailed descriptions of the recorded neuronal firing patterns are lacking. You should describe what firing pattern the recorded neurons had. If we don't do that, we won't be able to tell whether it's a respiratory neuron or another tonic firing neuron, so I don't think we can discuss whether it's involved in the respiratory rhythm.

3. Compare the distribution of neurons

To examine the distribution of Oprm1 + and Oprm1- dorsolateral pontine neurons projecting to the ventrolateral medulla, we injected retrograde AAV-hSin-DIO-eGFP and retrograde AAV-hSin-mCherry into preBötC and rVRG of Oprm1Cre/+ mice and found a neuronal distribution in which Oprm1-expressing projection neurons expressed GFP and mCherry, but not Oprm1-expressing projection neurons expressed only mCherry.

In addition, rostral glutamatergic KF neurons express FoxP2, while MOR-expressing glutamatergic neurons in the lateral parabrachial region that project to the forebrain express the CGRP-encoding gene, Calca. In view of this, the authors performed immunohistochemistry for FoxP2 and CGRP on Oprm1 + KF neurons projecting to the ventrolateral medulla, and Oprm1 + medulla oblongata projecting KF neurons expressed FoxP2 but not CGRP. The expression of CGRP was not observed in rostral KF and medullary projection Oprm1 + neurons and neurites but was strong in lateral parabrachial neurons and their axonal fiber projections. Can you describe the relationship between CGRP and FoxP2 and recorded neurons?

Reviewer #3 (Public Review):

In their study, Purandare & Mehta analyze large-scale single unit recordings from the visual system (LGN, V1, extrastriate regions AM and PM) and hippocampal system (DG, CA3, CA1 and subiculum) while mice monocularly viewed repeats of a 30s movie clip. The data were part of a larger release of publicly available recordings from the Allen Brian Observatory. The authors found that cells in all regions exhibited tuning to specific segments of the movie (i.e. "movie fields") ranging in duration from 20ms to 20s. The largest fractions of movie-responsive cells were in visual regions, though analyses of scrambled movie frames indicated that visual neurons were driven more strongly by visual features of the movie images themselves. Cells in the hippocampal system, on the other hand, tended to exhibit fewer "movie fields", which on average were a few seconds in duration, but could range from >50ms to as long as 20s. Unlike the visual system "movie fields" in the hippocampal system disappeared when the frames of the movie were scrambled, indicating that the cells encoded more complex (episodic) content, rather than merely passively reading out visual input.

The paper is conceptually novel since it specifically aims to remove any behavioral or task engagement whatsoever in the head-fixed mice, a setup typically used as an open-loop control condition in virtual reality-based navigational or decision making tasks (e.g. Harvey et al., 2012). Because the study specifically addresses this aspect of encoding (i.e. exploring effects of pure visual content rather than something task-related), and because of the widespread use of video-based virtual reality paradigms in different sub-fields, the paper should be of interest to those studying visual processing as well as those studying visual and spatial coding in the hippocampal system. However, the task-free approach of the experiments (including closely controlling for movement-related effects) presents a Catch-22, since there is no way that the animal subjects can report actually recognizing or remembering any of the visual content we are to believe they do. We must rely on above-chance-level decoding of movie segments, and the requirement that the movie is played in order rather than scrambled, to indicate that the hippocampal system encodes episodic content of the movie. So the study represents an interesting conceptual advance, and the analyses appear solid and support the conclusion, but there are methodological limitations.

Major concerns:

1) A lot hinges on hinges on the cells having a z-scored sparsity >2, the cutoff for a cell to be counted as significantly modulated by the movie. What is the justification of this criterion? It should be stated in the Results. Relatedly, it appears the formula used for calculating sparseness in the present study is not the same as that used to calculate lifetime sparseness in de Vries et al. 2020 quoted in the results (see the formula in the Methods of the de Vries 2020 paper immediately under the sentence: "Lifetime sparseness was computed using the definition in Vinje and Gallant").

To rule out systematic differences between studies beyond differences in neural sampling (single units vs. calcium imaging), it would be nice to see whether calculating lifetime sparseness per de Vries et al. changed the fraction "movie" cells in the visual and hippocampal systems.

2) In Figures 1, 2 and the supplementary figures-the sparseness scores should be reported along with the raw data for each cell, so the readers can be apprised of what types of firing selectivity are associated with which sparseness scores-as would be shown for metrics like gridness or Raleigh vector lengths for head direction cells. It would be helpful to include this wherever there are plots showing spike rasters arranged by frame number & the trial-averaged mean rate.

3) The examples shown on the right in Figures 1b and c are not especially compelling examples of movie-specific tuning; it would be helpful in making the case for "movie" cells if cleaner / more robust cells are shown (like the examples on the left in 1b and c).

4) The scrambled movie condition is an essential control which, along with the stability checks in Supplementary Figure 7, provide the most persuasive evidence that the movie fields reflect more than a passive readout of visual images on a screen. However, in reference to Figure 4c, can the authors offer an explanation as to why V1 is substantially less affected by the movie scrambling than it's main input (LGN) and the cortical areas immediately downstream of it? This seems to defy the interpretation that "movie coding" follows the visual processing hierarchy. Relatedly, the hippocampal data do not quite fit with visual hierarchical ordering either, with CA3 being less sensitive to scrambling than DG. Since the data (especially in V1) seem to defy hierarchical visual processing, why not drop that interpretation? It is not particularly convincing as is.

5) In the Discussion, the authors argue that the mice encode episodic content from the movie clip as a human or monkey would. This is supported by the (crucial) data from the scrambled movie condition, but is nevertheless difficult to prove empirically since the animals cannot give a behavioral report of recognition and, without some kind of reinforcement, why should a segment from a movie mean anything to a head-fixed, passively viewing mouse? Would the authors also argue that hippocampal cells would exhibit "song" fields if segments of a radio song-equally arbitrary for a mouse-were presented repeatedly? (reminiscent of the study by Aronov et al. 2017, but if sound were presented outside the context of a task). How can one distinguish between mere sequence coding vs. encoding of episodically meaningful content? One or a few sentences on this should be added in the Discussion.

Reviewer #3 (Public Review):

The authors study the performance, generalization, and dynamics of artificial neural networks trained on integration tasks. These types of tasks were studied theoretically in the past, and comparisons have also been made between artificial and biological networks. The authors focus on the effect of short-term plasticity on the networks. This is modeled as a multiplicative modulation of synaptic strengths that decays over time. When not decaying, this modulation is driven by Hebbian (or anti-Hebbian) activity-dependent terms. To isolate the effects of this component of the networks, the authors study a feedforward architecture, thereby rendering the synaptic modulations the only dynamical variables in the system. The authors also compare their network (MPN) with RNNs (gated and vanilla).

Perhaps not surprisingly, the information on the integration task is encoded in the dynamic variables of the networks - which are hidden units for RNNs and synaptic modulations for MPNs. The authors also study the dynamics of MPNs in the presence of noise or longer-than-trained input sequences. Finally, context-dependent integration is also studied.<br /> Biological neurons are far more complex than their artificial counterparts. This implies that there are computations that can be "outsourced" to these complexities, instead of being handled by a vanilla-rnn-like network that only has connectivity and hidden states. Given the recent rise in applications of trained RNNs as models of biological systems, it is thus timely to ask what are the consequences of integrating some of these complexities. The current study falls under this broad question, with a focus on short-term synaptic plasticity.<br /> I am worried, however, by two issues: the relation between integration tasks and the plasticity mechanism introduced, and the relation to existing work.

Because the MPN is essentially a low-pass filter of the activity, and the activity is the input - it seems that integration is almost automatically satisfied by the dynamics. Are these networks able to perform non-integration tasks? Decision-making (which involves saddle points), for instance, is often studied with RNNs.

The current work has some resemblance to reservoir computing models. Because the M matrix decays to zero eventually, this is reminiscent of the fading memory property of reservoir models. Specifically, the dynamic variables encode a decaying memory of the input, and - given large enough networks - almost any function of the input can be simply read out. Within this context, there were works that studied how introducing different time scales changes performance (e.g., Schrauwen et al 2007).

Another point is the interaction of the proposed plasticity rule with hidden-unit dynamics. What will happen for RNNs with these plasticity rules? I see why introducing short-term plasticity in a "clean" setting can help understand it, but it would be nice to see that nothing breaks when moving to a complete setting. Here, too, there are existing works that tackle this issue (e.g., Orhan & Ma, Ballintyn et al, Rodriguez et al).

One point regarding biological plausibility - although the model is abstract, the fact that the MPN increases without bounds are hard to reconcile with physical processes.<br /> To summarize, the authors show that plastic synapses can perform integration tasks in a manner that is dynamically distinct from RNNs - thereby strengthening the argument to include such synapses in models. This can be of interest to researchers interested in biologically plausible models of neural circuits.

Schrauwen, Benjamin, Jeroen Defour, David Verstraeten, and Jan Van Campenhout. "The Introduction of Time-Scales in Reservoir Computing, Applied to Isolated Digits Recognition." In Artificial Neural Networks - ICANN 2007, edited by Joaquim Marques de Sá, Luís A. Alexandre, Włodzisław Duch, and Danilo Mandic, 471-79. Lecture Notes in Computer Science 4668. Springer Berlin Heidelberg, 2007. http://link.springer.com/chapter/10.1007/978-3-540-74690-4_48.

Orhan, A. Emin, and Wei Ji Ma. "A Diverse Range of Factors Affect the Nature of Neural Representations Underlying Short-Term Memory." Nature Neuroscience 22, no. 2 (February 2019): 275-83. https://doi.org/10.1038/s41593-018-0314-y.

Ballintyn, B., Shlaer, B. & Miller, P. Spatiotemporal discrimination in attractor networks with short-term synaptic plasticity. J Comput Neurosci 46, 279-297 (2019). https://doi.org/10.1007/s10827-019-00717-5

Rodriguez, H.G., Guo, Q. & Moraitis, T.. (2022). Short-Term Plasticity Neurons Learning to Learn and Forget. Proceedings of the 39th International Conference on Machine Learning, in Proceedings of Machine Learning Research 162:18704-18722 Available from https://proceedings.mlr.press/v162/rodriguez22b.html.

Reviewer #3 (Public Review):

This study has the strengths of novelty and significance across multiple fields, including bone marrow biology, skeletal health, hematopoiesis, and protein posttranslational modification (PTM). It establishes the role of protein O-GlcNAcylation in bone development and bone marrow niche. The cooperative O-GlcNAcylation on Runx2 and C/EBPb to prime BMSCs toward osteoblast differentiation over adipogenesis is a very interesting and sounding molecular mechanism. The employment of an inducible OGT conditional knockout mouse model with appropriate Osx-Cre controls is conclusive and rigorous. The in vitro experiments were carefully designed in support of strong rationales. The overall flow of the story is logical and clear. Last, the conclusions are drawn from concrete evidence in an accurate way.

Reviewer #3 (Public Review):

The authors describe the method, PrEDiCT, which helps identify disease affected cell types based on gene sets. As I understand it, the method is based on finding which "disease genes" (from an annotation) are relatively highly expressed. The idea is nice, however, I have concerns about how "significance" is assessed and the relative controls.

Overall, I find the idea interesting, but the execution raises some concerns.

1. From a causal perspective, there is an association of high expression of these genes within these cell types, but without also assessing individuals with those specific diseases, I do not it is fair to say "disease affected" cell types. It is possible that these genes might behave completely fine but are highly expressed in those cell types while being affected another in other cell types.

2. It is unclear to me what the "null" comparison is in the method and if there is one. For example, by chance, would I expect this gene to be highly expressed because other genes are also highly expressed in this cell type? Some way to assess "significance" or "enrichment" beyond simply using ranks and thresholds would be helpful in deciding whether these associations are robust.

3. Additionally, it is unclear to me, but I suspect that there are unequal cell numbers in the scores computed as well as between relevant tissues. This is related to point (2) above, but as a result, the estimates of the scores will inherently have different variances, thus making comparisons between them difficult/unreliable unless accounted for. If I understand correctly, the score is first the average expression within a tissue, _then_, the Z-score? If so, my comment applies.

4. There is a large set of work done in gene enrichment sets which appears to not be mentioned (e.g. GSEA and other works by the Price group). It would be helpful for the authors to summarize these methods and how their method differs.

5. Additionally, it should be noted that a caveat of this analysis is that the comparisons are all done only relative to the cell types sampled and the diseases which have Mendelian genes associated with them. I would expect these results to change, possibly drastically, if the sampled cell types and diseases were to be changed.

6. Finally, I would appreciate a more detailed explanation in the methods of how the score is computed. Some equations and the data they are calculated from would be helpful here.

In summary, the general idea is an interesting one, but I do think the issues above should be addressed to make the results convincing.

Reviewer #3 (Public Review):

In this manuscript Fujino and colleagues used C9-ALS/FTD fly models to demonstrate that FUS modulates the structure of (G4C2) repeat RNA as an RNA chaperone, and regulates RAN translation, resulting in the suppression of neurodegeneration in C9-ALS/FTD. They also confirmed that FUS preferentially binds to and modulates the G-quadruplex structure of (G4C2) repeat RNA, followed by the suppression of RAN translation. The potential significance of these findings is high since C9ORF72 repeat expansion is the most common genetic cause of ALS/FTD, especially in Caucasian populations and the DPR proteins have been considered the major cause of the neurodegenerations.

1) While the effect of RBP as an RNA chaperone on (G4C2) repeat expansion is supposed to be dose-dependent according to (G4C2)n RNA expression, the first experiment of the screening for RBPs in C9-ALS/FTD flies lacks this concept. It is uncertain if the RBPs of the groups "suppression (weak)" and "no effect" were less or no ability of RNA chaperone or if the expression of the RBP was not sufficient, and if the RBPs of the group "enhancement" exacerbated the toxicity derived from (G4C2)89 RNA or the expression of the RBP was excessive. The optimal dose of any RBPs that bind to (G4C2) repeats may be able to neutralize the toxicity without the reduction of (G4C2)n RNA.

2) In relation to issue 1, the rescue effect of FUS on the fly expressing (G4C2)89 (FUS-4) in Figure 4-figure supplement 1 seems weaker than the other flies expressing both FUS and (G4C2)89 in Figure 1 and Figure 1-figure supplement 2. The expression level of both FUS protein and (G4C2)89 RNA in each line is important from the viewpoint of therapeutic strategy for C9-ALS/FTD.

3) While hallmarks of C9ORF72 are the presence of DPRs and the repeat-containing RNA foci, the loss of function of C9ORF72 is also considered to somehow contribute to neurodegeneration. It is unclear if FUS reduces not only the DPRs but also the protein expression of C9ORF72 itself.

4) In Figure 5E-F, it cannot be distinguished whether FUS binds to GGGGCC repeats or the 5' flanking region. The same experiment should be done by using FUS-RRMmut to elucidate whether FUS binding is the major mechanism for this translational control. Authors should show that FUS binding to long GGGGCC repeats is important for RAN translation.

5) It is not possible to conclude, as the authors have, that G-quadruplex-targeting RBPs are generally important for RAN translation (Figure 6), without showing whether RBPs that do not affect (G4C2)89 RNA levels lead to decreased DPR protein level or RNA foci.

Reviewer #3 (Public Review):

Mahlandt et al. report the design and proof of concept of Opto-RhoGEF, a new set of molecular tools to control the activation by light of the three best-known members of the Rho GTPase family, RhoA, Rac1, and Cdc42.

The study is based on the optogenetically-controlled activation of chimeric proteins that target the plasma membrane guanine nucleotide exchange factors (GEFs) domains, which are natural activators specific for each of these three Rho GTPases. Membrane-targeted GEFs encounter and activate endogenous Rho proteins. Further investigation into the effect of these tools on RhoGTPase signaling would have strengthened the report.

These three Opto-RhoGEFs are reversible and enable the precise spatiotemporal control of Rho-regulated processes, such as endothelial barrier function, cell contraction, and plasma membrane extension. Hence, these molecular tools will be of broad interest to cell biologists interested in this family of GTPases.

Mahlandt et al. design and characterize three new optogenetic tools to artificially control the activation of the RhoA, Rac1, and Cdc42 by light. These three Rho GTPases are master regulators of the actin cytoskeleton, thereby regulating cell-cell contact stability or actin-mediated contraction and membrane protrusions.

The main strength of this new experimental resource lies in the fact that, to date, few tools controlling Rho activation by reversibly targeting Rho GEFs to the plasma membrane are available. In addition, a comparative analysis of the three Opto-RhoGEFs adds value and further strengthens the results, given the fact that each Opto-GEF produces different (and somehow expected) effects, which suggest specific GTPase activation. The design of the tools is correct, although the membrane targeting could be improved, since the Lck N-terminus used to construct the recombinant proteins contains myristoylation and palmitoylation sites, which have the potential to target the chimeric protein to lipid rafts. As a consequence, this may not evenly translocate these Rho-activating domains.

An additional technical feature that must be highlighted is an elegant method to activate Opto-RhoGEFs in cultured cells, independent of laser and microscopes, by using led strips, which notably expands the possibilities of this resource, potentially allowing biochemical analyses in large numbers of cells.

The experimental evidence clearly indicates that the authors have achieved their aim and designed very useful tools. However, they should have taken more advantage of this remarkable technical advance and investigated in further detail the spatiotemporal dynamics of Rho-mediated signaling. Although the manuscript is a "tool and resource", readers may have better grasped the potential benefits of tuning GTPase activity with this tool by learning about some original and quantitative insights of RhoA, Rac1, and Cdc42 function.

One of such insights may have come from the set of data regarding the contribution of adherens junctions. The effect of other endothelial cell-cell junctions, such as tight junctions, may also contribute to barrier function, as well as junctional independent, cell-substratum adhesion. These optogenetic tools will undoubtedly impact these future studies and help decipher whether these other adhesion events that are important for endothelial barrier integrity are also under the control of these three GTPases. Overall, the manuscript is sound and presents new and convincing experimental strategies to apply optogenetics to the field of Rho GTPases.

Reviewer #3 (Public Review):

The chromosomal passenger complex (CPC) is an important regulator of mitotic progression, e.g. controlling kinetochore-microtubule attachment and cytokinesis. In this manuscript, Segura-Peña and colleagues investigated how the enzymatic core complex of the CPC, Aurora B and IN-box (the C-terminal part of INCENP), is structurally and functionally regulated by multiple (auto)phosphorylations. By doing so they are providing an insightful, dynamic picture of how the coordinated phosphorylations of the Aurora B T-loop and two serines in IN-box act cooperatively in order to fully activate the kinase.

Previously, several structures of Aurora B/IN-Box (missing the C-terminus of IN-box with two important phosphorylation sites or being unstructured, Sessa et al. 2005, Sessa and Villa et al. 2015, Elkins et al., 2012) and phosphorylated Aurora C/IN-Box (Abdul Azeez et al., 2019) had provided numerous structural insights and highlighted the role of the phosphorylated residues in T-loop and IN-box. Here, the authors now reveal the dynamic dimension of how the activity of this complex is regulated by using a compelling combination of H/D exchange mass spectrometry (HDX), molecular dynamics simulation and elegant biochemistry. Using HDX they demonstrate that upon Aurora B/IN-box autophosphorylation several regions of the complex become more structured. Using molecular dynamics, they explore the different conformational states of the complex and in particular how the phosphorylation and interactions of the phosphorylated C-terminal tail of IN-box coordinates and rigidifies Aurora B. To dissect the contributions of the phosphorylations on T-loop and IN-box, the authors create differentially phosphorylated versions of the complex using a sophisticated, intein-based protein engineering approach. The biochemical assays performed with these versions reveal not only the synergistic nature of these phosphorylation sites but also establish the nature of the autophosphorylation (cis for Aurora B, trans for IN-box) and show that Aurora B autophosphorylation in cis is rate-limiting. The data is convincing and intriguing, and remaining criticisms have been addressed extensively during the rewriting of the manuscript. In my opinion no additional experiments are required.

In summary, this is a well-executed study that provides new detailed molecular insights into the regulation of an important cell cycle complex. The findings and approaches will be of great interest to both the kinase and the cell cycle community.

Reviewer #3 (Public Review):

Melo et. al. sought to characterize the neuronal basis for the breathing modulation of nasal dilation (mystacial pad activity). The hypothesis is that a subset of breathing pacemaker neurons (preBötC) are specialized to relay a breathing signal to modulate the nares instead of contributing to pacing breathing. The authors identify that a subset of neurons within the anatomical region of the preBötC project to the facial motor nucleus and are required for the respiratory modulation of the nares. Furthermore, they show these neurons are partially required for breathing. The authors do this by using an intersectional genetic approach to selectively inhibit the preBötC neurons that project to the facial motor nucleus while measuring the impact of this manipulation on the breathing-related movement of the nares and breathing. As a control, the authors broadly silence the preBötC. The simplicity of the experiments makes the results robust and the correct positive control is used. The manuscript's conclusion contributes to the logic for the breathing modulation of the nares and the notion that subsets of neurons in the preBötC play distinct roles in breathing-related behaviors. Although the data are compelling for this conclusion, alternative models cannot be completely ruled out, like that these neurons are important for breathing rhythm generation and a secondary cell type from other premotor centers (Kurnikova 2019) are those that relay this signal to the motor neurons for the nares. The role of the preBötC as a "master clock" for orofacial activity (nose movement, swallowing, chewing, vocalizing; Kurnikova 2017) is an important line of research and this work contributes to understanding the cellular mechanisms.

Reviewer #3 (Public Review):

This is an excellent manuscript, describing a few lines of discoveries:<br /> 1. Establishment of a structural biological pipeline for iterative structural determination of an engineered Nav1.7;<br /> 2. Illumination of the novel compound binding mode;<br /> 3. Structure-based development of the hybrid compounds, which led to the novel Nav1.7 inhibitor;

The cryo-EM study on the engineered Nav1.7 consistently reveals the map at the mid to low 2 Å range, which is unprecedented and impressive, thus, demonstrating the high value of this workflow. The further strength of this study is that the authors were able to develop a new compound by combining structural information gained from the two Nav1.7 structures complexed to two different compounds with different binding modes. Overall, the depth and quality of this study are excellent.

Reviewer #3 (Public Review):

The authors use a combination of computational and experimental analyses to study how Pyrin-only proteins (POPs) could regulate either the abundant ASC effector protein or the PYDs of ALRs AIM2 and IFI16 or NLRs NLRP3 and NLRP6. This systematic approach shows differences in the free energy of binding interfaces within the potential filament assemblies. Fluorescence anisotropy experiments are performed on PYD filament formation, using FRET-donor and -acceptor labeled recombinant PYDs (e.g., ASC) and increasing concentrations of unlabeled POPs. These experiments indicate how the lag phase of PYD nucleation and the kinetics of the filament elongation phase is perturbed. Fluorescence microscopy images of HEK cells co-transfected with, e.g., mCherry-tagged ASC-PYD and eGFP-labelled POPs indicate co-localization and overall filament content (as % puncta). Finally, negative stain EM imaging shows assemblies into ordered filaments or aggregates for the recombinant PYD proteins in the presence or absence of POPs. In conclusion, the authors propose a decoy receptor mechanism for the POPs and NLRs/ALRs with different specificities for each individual PYD.

Reviewer #3 (Public Review):

McQuate et al have succeeded in reconstructing 3D images of mitochondria and discovered unique structural features of mitochondria in zebrafish hair cells. Compared to the other cell types, such as central and peripheral support cells, Hair cells have many elongated and connected mitochondria and they seem to be involved in hair cell and ribbon synapses development. These findings will contribute to understanding the mechanisms for mitochondrial network regulation.

Using the SBFSEM technique, the authors provide clear 3D images of hair cells and the technique improves the resolution of the image to understand the structural parameters of not only mitochondria but also ribbon synapses compared to typical fluorescent imaging. These results are very attractive and have the high potential to broadly apply to 3D imaging of any type of organelles, cells, and tissues. On the other hand, however, the authors provide the data from a small sample size, and the functional experiments to make a conclusion are lacking. Some missing representative images and the nonunified methods of grouping for the analysis make the reviewer concerned.

Reviewer #3 (Public Review):

The authors present analyses of cryo-plasma FIB/SEM hardware for practical use in the field of cell and tissue biology at microscopic resolutions. The results include several practical analyses and considerations for structural biologists when imaging their specimens; details are provided for optimizing imaging parameters and some image processing. Several examples of pFIB-milling cells and tissues are shown. The authors also introduce a method for quantifying curtaining, one of the major artifacts in FIB/SEM imaging, and software for reducing streaking artifacts in images. The analyses in the manuscript appear to come to conclusions that are experimentally justified. I see no major weaknesses in this manuscript.

Reviewer #3 (Public Review):

With this work, the authors build on their previous findings on the role of the long non-coding RNA, Charme. Here, the authors show that the nuclear isoform of Charme ncRNA, pCharme, is specifically expressed in cardiac myocytes from the earliest stages of cardiac development and persists in postnatal life too. The authors perform phenotypic and molecular analysis on Charme knockout hearts to demonstrate abnormal cardiogenesis in the form of cardiac hyperplasia during development which persists postnatally. pCharme also localizes with the nuclear matrix protein MATR3 to form puncta in cardiomyocytes during development, similar to what was observed in skeletal muscle and the authors provide data to show that this punctated form of MATR3 is lost in Charme KO hearts. Finally, by CLIP-seq, the authors identify other transcripts that can interact with MATR3, including pCharme, and a percentage of these are involved in cardiac development. This paper is of interest since it highlights a new non-coding player in cardiac development which could further inform how non-coding RNAs govern gene expression during specific developmental processes. However, the authors have previously shown similar studies identifying the role of pCharme and its interaction with MATR3 in skeletal muscle. While it is important to show that a similar process is occurring in a different muscle cell-type, a more in-depth analysis and discussion especially of the CLIP-seq data would further elevate the paper. Overall, these findings do extend the authors' previous work. However, the manuscript would greatly benefit from a more nuanced and in-depth discussion of their findings as to how this non-coding RNA is regulating cardiac development at a more mechanistic level.

Reviewer #3 (Public Review):

The authors present an association study geared to examine how epigenetic regulation of sexual commitment, immune responses and parasite growth change within a region that has undergone dramatic changes in transmission patterns over time. The work builds on previous epidemiological studies suggesting lower transmission settings result in parasites increasing sexual commitment, and most notably, examines mechanisms underlying these trends. The work shows the first in vivo association between LysoPC and gametocyte commitment (previously shown in vitro) in a large patient cohort. It also shows some very interesting trends relating LPC and parasite epigenetic markers to patient immune reactions.

The strengths of this paper include the use of a large patient cohort from a single geographic region, across distinct transmission intensities - an intrinsically exciting way of studying.<br /> The combination and integration of Luminex, RT-PCR, lipidomics, and clinical data provide a rich dataset for understanding host and parasite factors and provide novel in vivo evidence to support a role for LysoPC in commitment to gametocytogenesis.

In terms of weaknesses, by its nature as an association study it is difficult to ascribe causation to the patterns of seen. However, the work is built around testing of clearly defined hypothesis (based on both in vitro and clinical data) and has enabled the development of sound and exciting models for testing in future work.

The work is well-designed and written, and the conclusions fully align with the data presented. The one minor contention with the description of data is the discussion of Fig 4C-E. The manuscript states "Indeed, LPC species showed a negative association with both ap2-g and Pfsir2a transcription levels (Fig.4C-E). The association was only significant in our data when inflammation is highest (and LPC level lowest), which is at low transmission (i.e., post decline)." There is in fact only an association in post decline samples and very clearly no association pre decline. This could be made clearer here and also in the discussion (L217). This is a minor point of clarity - the work remains a compelling addition to our understanding of sexual commitment of malaria parasites.

Reviewer #3 (Public Review):

The manuscript by Hall et al., first describes the global and multi-organs phenotype of PCM1-/- mice and then focus on the role of PCM1 in the process of basal body production/maturation in multiciliated cells and finally on the role of PCM1 in primary ciliogenesis on RPE1 and MEF cells. In multiciliated cells, they show that the absence of PCM1 delays basal body formation and that PCM1 is required for the formation of structurally normal cilia, and for their consecutive coordinated beating. As regards to primary ciliogenesis, they show that PCM1 is required to allow efficient ciliation in RPE1 but not in MEF cells. Notably, they reveal defects in the formation of the preciliary vesicle in RPE1 cells and propose that PCM1 restricts CP110 and Cep97 at the centrosomal centriole in both MEFs and RPE1.

The study presented here represents a lot of nice work and highlights original data. However, in its present form, the study, which covers many aspects of the PCM1 mouse phenotype, is too fragmentary and does not allow to have, either a global view of the diversity of the phenotypes, or give mechanistic insight into one of the phenotypes. I would recommend the authors make two different papers on multiciliation and primary ciliogenesis, or try to test whether both type of ciliation are affected in a common way by the absence of PCM1. For instance, the title focuses only on the last part of the paper. Below are my comments.

Global phenotype

The authors convincingly show that the absence of PCM1 during development leads to perinatal lethality, hydrocephalus, cerebellar hypoplasia, oligospermia and cystic kidneys.

Role of PCM1 in multiciliation

The authors convincingly show that the absence of PCM1delays centriole amplification and therefore multiciliation which has never been shown before to my knowledge.

They also propose that the basal bodies produced in absence of PCM1 show a problem of rotational polarity. This is not fully supported by the data. To confirm this observation, the authors should look at later time points as P3 is very early and the rotational polarity is progressively established after BB docking and the beginning of cilia beating. Also many more cells should be analyzed. Since this is a lot of work by EM, one should consider doing it by immunostainings as done in some other papers. Same comment for the absence of ciliary pocket in PCM1 KO. P3 is too early and since some cilia do not show a clear ciliary pocket, one should look in a sufficient number of EM sections.

The defect in translational polarity is interesting and has never been described before. This phenotype is analyzed at P5 and should also be confirmed at later time point since the delay in multiciliation in the PCM1 KO may affect the number of cells with a terminal differentiated state and therefore bias the result. In fact, migration of BB is the last event occurring during multiciliation.

The phenotype of cilia beating uncoordination is convincing and confirms what has been also described by Zhao et al., in 2021. The authors seem to propose a causality link between this phenotype and the proteomic study between WT and PCM1 KO in another MCC cell type: mTEC at ALID7. Since the difference resolve in these mTEC at ALID21, do the authors think the delay in cilia motility protein expression could explain a consecutive permanent problem of cilia beating coordination seen at later stages ? Also it is difficult to link these results with motility since motility is assessed in ependymal cilia and proteomic study in mTEC. One would like to know if motility is also affected in mTEC. And to use the proteomic study to propose an additional explanation of the one proposed by Zhao et al. showing that PCM1 depletion also deregulates the centriolar and ciliary targeting of satellites client proteins, a process that could affect cilia beating. The structural defects of cilia seen by the authors and by Zhao et al., are also one important piece of explanation.

In vitro, MCC in PCM1 KO seem to display less cilia. Is this true in vivo in the brain? Since it is not obvious in vivo in the trachea, it would be nice to just address qualitatively whether this is the case in vivo in the brain. Also, are the number of BB affected ? Zhao et al., counted the number of BB in PCM1 siRNA treated cells and show no difference. If one would address how PCM1 affect the number of cilia, this is important to know whether less centrioles are produced or whether they fail to dock correctly at the plasma membrane. Since formation of the preciliary vesicle is affected in in RPE1 cells, it is tempting to speculate that a similar defect could arise in MCC and affect motile ciliogenesis. If the « number of cilia » phenotype is not true in vivo, one should also consider a culture artefact.

Altogether, the phenotype on multiciliation needs to be strengthened to confirm the original results and to be put into the context of the previous study done in vitro (Zhao et al., 2021).

Role of PCM1 in primary ciliogenesis

Knockdown of different satellite components have been shown to affect primary ciliogenesis (Conkar et al., 2017; Kim et al., 2008; Klinger et al., 2014; Lee and Stearns, 2013; Mikule et al., 2007; Staples et al., 2014, Kurtulmus et al., 2016). More particularly cell type dependent variability of PCM1 suppression on ciliogenesis has previously been described (Odabasi et al., 2019; Wang et al., 2016). It appears necessary to clarify in one paragraph in the introduction this bibliographic context and to put forward the unresolved questions the present study proposes to address as well as the new insights it provides on the question.

First, the two main phenotypes described here, e.g. defect in ciliary vesicle formation and defect in CP110 and Cep97 removal from the mother centrioles, are very similar to the phenotype described in WDR8 knock down (Kurtulmus et al., 2016). Is there any reason why the authors did not cite this study ? If not, and since WDR8 and PCM1 are interacting partners and are interdependent for their localization, I would suggest assessing whether PCM1 acts upstream or downstream of the WDR8-Cep135 axis. For example, I would suggest testing if WDR8 expression in PCM1 KO rescue the ciliary vesicle and CPP110/Cep97 phenotypes.

The phenotype of preciliary vesicle formation defect in PCM1 KO is convincing in RPE1 cells. I would suggest to reproduce the MyoVa staining in MEFs to detect whether, in cells forming cilia in the absence of PCM1, the ciliary vesicles are forming properly. It may be a good control and also give insight into how PCM1 affects differentially ciliogenesis in different cell types. Also, the extent of TEM analysis is difficult to assess (I did not find the « n »). TEM is important to confirm the phenotype since MyoVa is an actin-based molecular motor that plays several roles in the final stages of secretory pathways.

Then the authors propose that PCM1 promotes the transition zone formation and IFT recruitment. The data presented here support that PCM1 promotes TZ formation. However, since PCM1 absence compromises preciliary vesicle formation, one could conclude that TZ alterations are just a consequence of this defect. This needs to be discussed. Regarding recruitment of IFT and TZ components, the data presented here do not support that PCM1 promotes TZ components and IFT recruitment. In fact, TZ components are not absent in non ciliated RPE1 KO cells, just decreased, and they are present at normal levels in ciliated MEFs in absence of PCM1.

The authors propose that centriolar satellites restrict CP110 and Cep97 levels at centrioles, which promotes ciliogenesis. Defect in the removal of CP110 and Cep97 from the mother centriole are very convincing in PCM1 KO both in RPE1 and MEFs. However, the causality link between this mother centriole maturation and ciliogenesis still needs to be tested since MEFs are able to ciliate in the absence of PCM1 and in the presence of CP110. Knock down of CP110 in PCM1 KO would be needed to accurately test this hypothesis. For example, in absence of WDR8, CP110 knock down does not rescue ciliogenesis defect probably because of the upstream defect of preciliary vesicle docking (Kurtulmus et al., 2016). This could be the case also here.

Finally, the authors propose that PCM1 satellites transport CP110 and Cep97 away from the centriole. They nicely show that CP110 colocalize with satellites. By IP, they suggest that PCM1 and CP110 coIP which need to be further confirmed by another IP since the signal is really weak. They show that CP110 does not colocalize anymore to the satellites as soon as 1h after serum deprivation. If satellites were involved in removing CP110 from the mother centriole for ciliation, I would expect to see an increase in CP110 localization to the satellites, and not a decrease at this time point. The authors also measure an increase of CP110 and Cep97 at the centrioles in PCM1 KO, which would go in line with their hypothesis. However, this phenotype is the opposite of what was shown in Quarantotti 2019 in the same cell type where they show that upon PCM1 loss, CP110 was decreased at the centrosome. Together with the fact that the overaccumulation of CP110 and Cep97 illustrated by IF and measured is weak, more data are needed to support this phenotype. Altogether, the hypothesis that satellites are transporting CP110 and Cep97 away from the centrioles needs more data to be convincing.

Reviewer #3 (Public Review):

The manuscript by Scinicariello and collaborators examines the mechanisms regulating the cellular accumulation of the RNA-binding protein Tristetraprolin (TTP). This factor is a well-described regulator of mRNA stability. TTP binds to RNA AU-rich sequences localized in mRNA 3'Untranslated regions. As AU-rich elements are abundant in mRNA encoding pro-inflammatory factors, TTP has been described as a negative regulator of the inflammatory response.

Previous reports have described that the cellular level of TTP is modulated by phosphorylation and proteasome-dependent process (see several references in the introduction of the manuscript). Non-degradative phosphorylation-dependent ubiquitination of TTP has also been reported (Schichl et al. 2011 JBC 286:38466). This publication is not cited in the current version of the manuscript. The results of Schichl et al. seem particularly relevant for the interpretation of some of the results presented here and should be considered in the final discussion and conclusions of the present work.

In the first part of the results section, Scinicariello et al. evaluate the degradation and ubiquitination of TTP and conclude that TTP is degraded in a ubiquitin-dependent manner. By a pharmacological approach, they observed, as previously shown, that endogenous TTP is degraded by the proteasome (Fig1a). They also show that an overexpressed tagged version of TTP is degraded by the proteasome and ubiquitinated on lysine residues (Fig. 1B, C). The general conclusion of this paragraph seems premature in relation to the results presented. The ubiquitination of endogenous TTP has not been demonstrated. The type of ubiquitination detected on the overexpressed version of TTP is not characterized. This seems important in view of the results of Schichl et al. who showed non-degradative ubiquitination (K63) of TTP. The half-life of the non-ubiquitinated mutant of TTP (K→R) was not precisely compared to the half-life of the wild-type TTP protein (similar to the experiment presented in 1B). The effect of the E1 ubiquitin ligase TAk-243 on endogenous TTP levels was not tested.

In the second part, the authors identified the E3 ligase HUWE1 as a major determinant of cellular TTP protein abundance. This demonstration is first based on the identification of HUWE1 in an unbiased CRISPR/cas9 screen to identify modulators of mCherry-TTP fusion reporter accumulation upon activation of RAW 264.7 cells by LPS. While they demonstrate that TTP-HA is efficiently degraded after 3 to 7h of LPS stimulation (Fig 1B) and that the stronger decrease in mCherry-TTP fusion level occurs between 4 and 6h of LPS stimulation the screen for identification of TTP modulators is performed 16h of LPS stimulation (Fig 2A). The rationale behind this experimental setting is not explicitly described. Nevertheless, the authors convincingly demonstrate that HUWE1 is involved in the controls of TTP cellular abundance. This demonstration mainly relies on the fact that HUWE1 inactivation induced a strong increase of both mCherry-TTP fusion and endogenous TTP (Fig. 2B and C). Ablation of HUWE1 selectively decreases the abundance of a limited number of proteins including TTP (Fig. 5A). The specificity of Huwe1 effect is confirmed by the detection of a constant level of the co-expressed BFP protein upon HUWE1 depletion (fig sup. 2E). The effect of HUWE1 depletion on TTP accumulation is observed in different cell lines and primary cells (murine, human) (Fig. sup. 2G, Fig2F).<br /> In this paragraph, the demonstration that Huwe1 specifically affects the stability of TTP protein appears less robust. The authors did not directly test the effect of HUWE1 inactivation on endogenous TTP accumulation after blocking protein synthesis. This control seems important as data presented in figure 2E could result both from an effect of Huwe1 level on LPS-induced TTP synthesis and TTP degradation.

In the data presented in figure 2, it is not entirely clear what exactly the authors are referring to as "endogenous TTP". In Figure 2C endogenous TTP is detected by western blot on cells transfected with an mCherry-TTP fusion. In this case, the size difference allows unambiguous identification of the endogenous form of TTP (although one could not exclude that overexpressing a TTP fusion protein might affect the level of the endogenous protein). However, TTP and mCherry-TTP cannot be distinguished by FACS (Fig2 D and E). If cells used in the experiments shown in 2C and 2D-E are distinct, this should be mentioned more explicitly in the legend of Fig. 2. Otherwise, the detection of endogenous TTP should be performed on cells that do not express mCherry-TTP.

The third part of the manuscript aims to demonstrate that loss of Huwe1 decreases the half-life of pro-inflammatory mRNAs controlled by TTP. In my opinion, this conclusion is reliably supported by the data presented in Figure 3 and Supplementary Figure 3. As the conclusion of this paragraph refers to the effect of TTP on the stability of these mRNAs, the measurement of TNF mRNA stability (Fig. sup. 3C) should be presented in the main part of Fig. 3.

The authors then aim to demonstrate that HUWE1 regulates TTP phosphorylation and its increase is responsible for increased TTP stability. Taken together, data from fig. 1F, 2C, and 2F clearly show that a phosphorylated form of TTP is accumulated in Huwe1 deficient cells. The authors state that Fig 4E aims to identify kinases and phosphatases potentially involved in TTP stability (line 277, line 298). However, the approach used here (a measure of intracellular TTP level) cannot distinguish between increased production of TTP or a decrease in TTP degradation. Also, the result presented in fig. 4E, are not totally consistent with the results presented in 4A. Fig4D shows a similar level of endogenous TTP accumulating after 2h of LPS stimulation in Huwe1 KO and control cells while a clear difference in TTP level is observable in the same condition in fig. 4A. Could the difference in the TTP detection method (Western vs intracellular FACS) be responsible for this discrepancy? In addition, the absence of positive control for the various pharmacological treatments renders difficult the interpretation of these results, especially when the inhibitor shows no effect on TTP level (ex: CalyculinA). On this basis, the authors' conclusions for this paragraph seem partially over-interpreted.

From the data presented in figure 5, the authors conclude that HUWE1 controls only a small fraction of proteasome targets and regulates the stability of TTP paralog ZFP36L1.<br /> A comparison of protein levels in Huwe1 and Psmb7 Ko cells reveals that Huwe1 ablation significantly changes the concentrations of only a limited number of proteins (Fig. 5A). The reliability of these data is confirmed by the identification as increased proteins in the huwe1 ko of factors previously identified as targets of HUWE1 (Fig. sup. 5C). These experiments and data presented in Fig.5D show that the level of the TTP paralog ZFP36L1 accumulates in huwe1 KO cells but do not demonstrate that HUWE1 affects ZFP36L1 protein stability.

The next conclusion of the manuscript describes residues in the TTP234-278 region as important for their stability. Based on data presented in fig. 6 B and sup. 6B the authors conclude that residues S52 and 178, previously identified as regulators of TTP stability, are unlikely to be involved in HUWE1-dependent TTP accumulation. The data are only based on 2 independent experiments, one of which (fig 6B) shows a difference in TTP S52/S178 mutant in Huwe1 deficient cells as compared to wt TTP. These results seem therefore too preliminary to reliably exclude the implication of S52 and 178 on the HUWE1 accumulation of TTP.

Other data from Fig. 6 further analyze the effect of deleting different regions of the TTP protein on the accumulation of this factor in HUWE1 KO and control cells. From these data, the authors conclude (line 416) that N-terminal deletion does not affect the TTP protein level. However, TTP accumulation in Huwe1 KO cells seems mostly lost in mutant N4. As mentioned above the limited number of replicates (n=2) and the absence of a statistical test makes the interpretation of this result difficult.

Several TTP C-terminal mutants show a HUWE1-independent accumulation when compared to the wt protein (Fig6. D). Is this region identical to the unstructured region identified by Ngoc (line 1255) as a potent regulator of TTP degradation? If relevant this point should be discussed.

Reviewer #3 (Public Review):

In this work, the authors attempt to probe the constraints on the early evolution of nitrogen fixation, the development of which presented a key metabolic transition. Given that life on Earth evolved only once (to our knowledge) which aspects were necessary and which may have taken a different course are open questions. Are there alternative forms of life, metabolic networks, or even enzymatic mechanisms that could have replaced the ones we see today, or is the space of possible biologies limited? This manuscript tests the ability of ancestrally-reconstructed molybdenum-dependent nitrogenase complexes to support diazotrophic growth in Azotobacter vinelandii, as well as in vivo and in vitro activity, which all point towards a conserved mechanism for nitrogen reduction at least since proteobacteria divergence.

This is an ambitious project, requiring multiple techniques, systems, and approaches, and the successful combination of these is one of the major strengths of this work. Using parallel techniques is an important way to be certain that the overall results are robust, and an appropriate mix of in vivo and in vitro experiments is chosen here. The manuscript should serve as a useful model for how to combine phylogenetics and biochemistry.

The nature of ASR means that a solid phylogeny and/or understanding of how robust the results are to uncertainty in reconstructed states is essential since all results flow from there. The overall phylogenetic methods used are appropriate and the system is an apt one for the technique, but there is not quite enough detail in the methods to be certain of the results. Given that only the single maximum a posteriori sequence is assayed at every 3 nodes, this may have compounding results in that the sensitivity to uncertainty in the reconstruction is increased. The authors appropriately make qualitative rather than quantitative inferences, but some hesitation towards the overall results still exists.

The assumption that the Anc1A/B and Anc2 nodes correspond to ancestral states might be undermined by horizontal gene transmission, which has been reported for nif clusters. In particular, there may be different patterns of transmission for each element of the cluster. By performing reconstruction with a concatenated alignment, the phylogenetic signal is potentially maximized, but with the assumption that each gene has an identical history. Discordant transmission may cause an incorrect topology to be recovered.

Finally, I am unsure if ASR is the most appropriate approach to answer questions of contingency and alternative pathways for protein evolution. ASR may tell what nitrogenase millions or billions of years ago looked like, but it can only say what has already existed. If there are different mechanisms or metabolic pathways enabling nitrogen fixation that simply never came to pass, via contingency and entrenchment or simple chance, ASR would say nothing about them. It is true that a conserved mechanism would point towards a constrained space for evolving nitrogen fixation, but that does not directly address it.

Overall, despite these issues, the manuscript is compellingly written and the figures are attractive and clear, and help get the major narrative across. This work will be of interest to protein biochemists of evolutionary bent and microbial physiologists with an interest in the origins of life.

Reviewer #3 (Public Review):

In this work, Z. Kliesmete, L. Wange and colleagues investigate TRNP1 as a gene of potential interest for the evolution of the mammalian cortex. Previous evidence suggests that TRNP1 is involved in self-renewal, proliferation and expansion in cortical cells in mouse and ferret, making this gene a good candidate for evolutionary investigation. The authors designed an experimental scheme to test two non-exclusive hypotheses: first, that evolution of the TRNP1 protein is involved in the apparition of larger and more convoluted brains; and second, that regulation of the TRNP1 gene also plays a role in this process alongside protein evolution.

The authors report that the rate of TRNP1 protein evolution is strongly correlated to brain size and gyrification, with species with larger and more convoluted brains having more divergent sequences at this gene locus. The correlation with body mass was not as strong, suggesting a functional link between TRNP1 and brain evolution. The authors directly tested the effects of sequence changes by transfecting the TRNP1 sequences from 5 different species in mouse neural stem cells and quantifying cell proliferation. They show that both human and dolphin sequences induce higher proliferation, consistent with larger brain sizes and gyrifications in these two species. Then, the authors identified six potential cis-regulatory elements around the TRNP1 gene that are active in human fetal brain, and that may be involved in its regulation. To investigate whether sequence evolution at these sites results in changes in TRNP1 expression, the authors performed a massively parallel reporter assay using sequences from 75 mammals at these six loci. The authors report that one of the cis-regulatory elements drives reporter expression levels that are somewhat correlated to gyrification in catarrhine monkeys. Consistent with the activity of this cis-regulatory sequence in the fetal brain, the authors report that this element contains binding sites for TFs active in brain development, and contains stronger binding sites for CTCF in catarrhine monkeys than in other species. However, the specificity or functional relevance of this signal is unclear.

Altogether, this is an interesting study that combines evolutionary analysis and molecular validation in cell cultures using a variety of well-designed assays. The main conclusions - that TRNP1 is likely involved in brain evolution in mammals - are mostly well supported, although the involvement of gene regulation in this process remains inconclusive.

Strengths:<br /> - The authors have done a good deal of resequencing and data polishing to ensure that they obtained high-quality sequences for the TRNP1 gene in each species, which enabled a higher confidence investigation of this locus.<br /> - The statistical design is generally well done and appears robust.<br /> - The combination of evolutionary analysis and in vivo validation in neural precursor cells is interesting and powerful, and goes beyond the majority of studies in the field. I also appreciated that the authors investigated both protein and regulatory evolution at this locus in significant detail, including performing a MPRA assay across species, which is an interesting strategy in this context.

Weaknesses:<br /> - The authors report that TRNP1 evolves under positive selection, however this seems to be the case for many of the control proteins as well, which suggests that the signal is non-specific and possibly due to misspecifications in the model.<br /> - The evidence for a higher regulatory activity of the intronic cis-regulatory element highlighted by the authors is fairly weak: correlation across species is only 0.07, consistent with the rapid evolution of enhancers in mammals, and the correlation in catarrhine monkeys is seems driven by a couple of outlier datapoints across the 10 species. It is unclear whether false discovery rates were controlled for in this analysis.<br /> - The analysis of the regulatory content in this putative enhancer provides some tangential evidence but no reliable conclusions regarding the involvement of regulatory changes at this locus in brain evolution.

Reviewer #3 (Public Review):

This paper dissects the molecular mechanisms of diet induced taste plasticity in Drosophila. The authors had previously identified two proteins essential for sugar-diet derived reduction of sweet taste sensitivity - OGT and PRC2.1. Here, they showed that OGT, an enzyme implicated in metabolic signaling with chromatin binding functions, also binds a range of genomic loci in the fly sweet gustatory receptor neurons where binding in a subset of those sites is diet composition dependent. Furthermore, a minority of OGT binding sites overlapped with PRC2.1 recruiter Pcl, where collectively binding of both proteins increased under sugar-diet while chromatin accessibility decreased. The authors demonstrate, that the observed taste plasticity requires catalytic activity of OGT, which impacts chromatin accessibility at shared OGT x Pcl but not diet induced occupancy. In an effort to identify transcriptional mechanisms that instantiate the plastic changes in sensory neuron functions the authors looked for transcription factors with enriched motifs around OGT binding sites and identified Stripe (Sr) as a transcription factor that yielded sugar taste phenotypes upon gain and loss of function experiments. In follow-up overexpression experiments, they show that this results in reduced taste sensitivity and reduced taste evoked spiking in gustatory receptor neurons. Notably the effects of Sr on taste sensitivity also depend on OGT catalytic activity as well as PRC2.1 function. Finally, they explore the function of rolled (rl) - an extracellular-signal regulated kinase (ERK) ortholog in Drosophila, suggested to function upstream of Sr - in diet induced gustatory plasticity. The authors showed that the overexpression of the constitutively active form of rl kinase results in reduced neuronal and behavioral responses to sucrose which was dependent on OGT catalytic activity. In sum, these findings reveal several new players that link dietary experience to sensory neuron plasticity and open up clear avenues to explore up- and downstream mechanisms mediating this phenomenon.

Strengths:<br /> • Good genetically targeted interventions<br /> • Thorough exploration of the epistatic relationships between different players in the system<br /> • Identification of several new signaling systems and proteins regulating diet derived gustatory plasticity

Weaknesses:<br /> • The GO term enrichment analyses with little functional follow up has limited explanatory power<br /> • ERK/rl data is a bit hard to interpret since any imbalance in this system appears to reduce gustatory sensitivity.

The conclusions in this manuscript are mostly well or at least reasonably supported by data. Below are a few recommendations for improvement:<br /> • The paper claims to address cell-type-specific nutrigenomic regulatory mechanisms. However, this work only explores nutrigenomic mechanisms in a single cell type (Gr5a+ sweet sensing cells) and we don't really learn whether these nutrigenomic mechanisms exist in all other cell types or just Gr5a+ cells. It would be valuable to see how specific OGT and PRC2.1 binding locations and effects on chromatin accessibility are in a different cell type - e.g. bitter sensing Gr66a. This would reveal how global in nature these findings are and or which aspects of nutrigenomic signaling are specific for sweet sensory cells.<br /> • Behavioral data from the screen identifying Sr is missing. Which other candidates were screened and what were the phenotypes?

Reviewer #3 (Public Review):

Fozard et al. presented a new model explaining the distribution of the pro-crossover factor HEI10 and its effect on the formation of crossovers in the absence of a functional synaptonemal complex (SC). The creation of such a model is important considering recent results showing that in Arabidopsis and possibly many other plants (perhaps all plants), the major crossover pathway may function independently of the SC. Crossover modeling can help to better understand crossover formation dynamics and facilitate the prediction of crossover distribution.

The new model assumes the possibility of loading HEI10 directly from the nucleoplasm, which of course is logical considering the phenotype of the zyp1 mutant in Arabidopsis. However, in a situation where the SC is fully functional, should not we expect some level of nucleoplasmic coarsening in addition to the dominant SC-mediated coarsening? Should the original model not be corrected, and if it is not necessary (e.g., because it included this effect from the very beginning, or the effect is too weak and therefore negligible), the authors should discuss it. With reference to this observation, it would be worthwhile to compare different characteristics of both types of coarsening (e.g., time course).

Recently, a preprint from the Raphael Mercier group has been released, in which the authors show a massive increase in crossover frequency in zyp1 mutants overexpressing HEI10. I think this is a great opportunity to check to what extent the parameters adopted by the authors in the nucleoplasmic coarsening model are universal and can correctly simulate such an experimental set-up. Therefore, can the authors perform such a simulation and validate it against the experimental data in Durand et al. doi.org/10.1101/2022.05.11.491364? Can CO sites identified by Durand et al. be used instead of MLH1 foci for the modeling?

Reviewer #3 (Public Review):

In this manuscript, Zhang and Schekman investigated the mechanisms underlying intercellular cargo transfer. It has been proposed that cargo transfer between cells could be mediated by exosomes, tunneling nanotubes or thicker tubules. To determine which process is efficient in delivering cargos, the authors developed two quantitative approaches to study cargo transfer between cells. Their reporter assays showed clearly that the transfer of Cas9/gRNA is mediated by cell-cell contact, but not by exosome internalization and fusion. They showed that actin polymerization is required for the intercellular transfer of Cas9/gRNA, the latter of which is observed in the projected membrane tubule connections. The authors visualized the fine structure of the tubular connections by electron microscopy and observed organelles and vesicles in the open-ended tubular structure. The formation of the open-ended tubule connections depends on a plasma membrane fusion process. Moreover, they found that the endogenous trophoblast fusogens, syncytins, are required for the formation of open-ended tubular connections, and that syncytin depletion significantly reduced cargo Cas9 protein transfer.

Overall, this is a very nice study providing much clarity on the modes of intercellular cargo transfer. Using two quantitative approaches, the authors demonstrated convincingly that exosomes do not mediate efficient transfer via endocytosis, but that the open-ended membrane tubular connections are required for efficient cargo transfer. Furthermore, the authors pinpointed syncytins as the plasma membrane fusogenic proteins involved in this process. Experiments were well designed and conducted, and the conclusions are mostly supported by the data. My specific comments are as follows.

1. The authors showed that knocking down actin (which isoform?) in both donor and acceptor cells blocked transfer, and more so in the acceptor cells perhaps due to the greater knockdown efficiency in these cells. However, Arp2/3 complex knockdown in donor cells, but not recipient cell, reduced Cas9 transfer. It would be good to clarify whether the latter result suggests that the recipient cells use other actin nucleators rather than Arp2/3 to promote actin polymerization in the cargo transfer process. Are formins involved in the formation of these tubular connections?<br /> 2. The authors provided convincing evidence to show that the tubular connections are involved in cargo transfer. Intriguingly, in Figure 4-figure supplement video (upper right), protein transfer appeared to occur along a broad cell-cell contact region instead of a single tubular connection. How often does the former scenario occur? Is it possible that transfer can happen as long as cells are contacting each other and making protrusions that can fuse with the target cell?<br /> 3. The requirement of MFSD2A in both donor (HEK293T) and recipient (MDA-MB-231) cells is consistent with a role for syncytin-1 or 2 in both types of cells. Since HEK293T cells contain both syncytins and MFSD2A but cargo transfer does not occur among these cells, does this suggest that syncytins and/or MFSD2A are only trafficked to the HEK293T cell membrane in the presence of MDA-MB-231 cells?

Reviewer #3 (Public Review):

The manuscript 'Connectomics of the Octopus vulgaris vertical lobe provides insight into conserved and novel principles of a memory acquisition network' by Bidel et al. uncovers the connectivity of the vertical lobe (VL) of the octopus' central brain. Using serial section electron microscopy, the authors report several cell types and connectivity patterns consistent with their previous work and the classic work of Young and Gray. They also uncover novel cell types, including a set of complex amacrine cells (CAMs), with far less abundance compared to simple amacrine cells (SAMs). Importantly, CAMs are proposed to be GABAergic and inhibitory and plausibly suggested to be involved in pattern sharpening - while SAMs are cholinergic and excitatory. SAMs receive single inputs from diverging SFL input, while CAMs receive multiple afferent inputs and additionally pool inputs from SAMs. Both SAMs and CAMs converge onto LNs that form the output layer of the VL. Finally, the authors describe putative neuromodulatory connections.

This study is equally impressive as important - using high-resolution anatomy it uncovers putative computational motifs at high resolution. The described network reveals a novel computational logic and highlights how different biological computational networks can be made up. Indeed, comparison to the Drosophila mushroom bodies - a structure following a fan-out, fan-in logic - will allow more in-depth cross-species comparisons in the future, both regarding commonalities and differences in network architecture. Importantly, this study additionally describes, at high resolution, synaptic motifs (palms) that appear quite different from motifs in other systems, including putative direct feedforward connections via SAMs to CAMs and organelle distributions.

Reviewer #3 (Public Review):

The Odorant Receptor and Gustatory Receptor families of 7 Transmembrane domain Ion channels were previously believed to have no family members in vertebrates. This paper uses the recent advances in protein folding prediction tools to first validate previous discoveries and confirm their approach with genes of known function. They then search for new family members and discover additional related genes in insects, where both ORs and IRs were previously known to exist. The most striking finding of the paper is that they identify genes related to these protein families in vertebrates, including humans. They propose a model for the evolution of this gene family based on their data.

Overall, the data in this paper is strong, the data presentation is clear and the text is well-written and scholarly. The main weaknesses of the paper are that they have no functional analysis of any of their newly discovered proteins. This paper would benefit from experimental evidence that these are functional ligand-gated ion channels. The authors discuss this limitation at the end of the paper and note the challenges that conducting a functional analysis of these channels would represent. We agree that this could take years and that it is beyond the scope of the current paper, although we eagerly await a follow-up study where those experiments might be done.

Reviewer #3 (Public Review):

In this study, the authors present the first comprehensive transcriptome map of the human locus coeruleus using two independent but complementary approaches, spatial transcriptomics and single nucleus RNA sequencing. Several canonical features of locus coeruleus neurons that have been described in rodents were conserved, but potentially important species differences were also identified. This work lays the foundation for future descriptive and experimental approaches to understand the contribution of the locus coeruleus to healthy brain function and disease.

This study has many strengths. It is the first reported comprehensive map of the human LC transcriptome, and uses two independent but complementary approaches (spatial transcriptomics and snRNA-seq). Some of the key findings confirmed what has been described in the rodent LC, as well as some intriguing potential genes and modules identified that may be unique to humans and have the potential to explain LC-related disease states. The main limitations of the study were acknowledged by the authors and include the spatial resolution probably not being at the single cell level and the relatively small number of samples (and questionable quality) for the snRNA-seq data. Overall, the strengths greatly outweigh the limitations. This dataset will be a valuable resource for the neuroscience community, both in terms of methodology development and results that will no doubt enable important comparisons and follow-up studies.

Major comments:

Overall, the discovery of some cells in the LC region that express serotonergic markers is intriguing. However, no evidence is presented that these neurons actually produce 5-HT.

Concerning the snRNA-seq experiments, it is unclear why only 3 of the 5 donors were used, particularly given the low number of LC-NE nuclear transcriptomes obtained, why those 3 were chosen, and how many 100 um sections were used from each donor. It is also unclear if the 295 nuclei obtained truly representative of the LC population or whether they are just the most "resilient" LC nuclei that survive the process.

The LC displays rostral/caudal and dorsal/ventral differences, including where they project, which functions they regulate, and which parts are vulnerable in neurodegenerative disease (e.g. Loughlin et al., Neuroscience 18:291-306, 1986; Dahl et al., Nat Hum Behav 3:1203-14, 2019; Beardmore et al., J Alzheimer's Dis 83:5-22, 2021; Gilvesy et al., Acta Neuropathol 144:651-76, 2022; Madelung et al., Mov Disord 37:479-89, 2022). It was not clear which part(s) of the LC was captured for the SRT and snRNAseq experiments.

The authors mention that in other human SRT studies, there are typically between 1-10 cells per expression spot. I imagine that this depends heavily on the part of the brain being studied and neuronal density, but it was unclear how many LC cells were contained in each expression spot.

Regarding comparison of human LC-associated genes with rat or mouse LC-associated genes (Fig. 2D-F), the authors speculate that the modest degree of overlap may be due to species differences between rodents and human and/or methodological differences (SRT vs microarray vs TRAP). Was there greater overlap between mouse and rat than between mouse/rat and human? If so, that is evidence for the former. If not, that is evidence for the latter. Also would be useful for more in-depth comparison with snRNA-seq data from mouse LC: https://www.biorxiv.org/content/10.1101/2022.06.30.498327v1.

The finding of ACHE expression in LC neurons is intriguing, especially in light of work from Susan Greenfield suggesting that ACHE has functions independent of ACH metabolism that contributes to cellular vulnerability in neurodegenerative disease.

High mitochondrial reads from snRNA-seq can indicate lower quality. It was not clear why, given the mitochondrial read count, the authors are confident in the snRNA-seq data from presumptive LC-NE neurons.

Reviewer #3 (Public Review):

The manuscript by Sidhaye et al. aims to integrate proteomic and transcriptomic analyses of human stem cell-derived cortical brain organoids to identify post-transcriptional regulatory mechanisms during human cortical development. The authors use an innovative and useful dual-reporter strategy to isolate NPCs and neurons separately and integrate proteomic and transcriptomic analyses in each cell type. The data analysis is robust and identifies gene modules with cell class specificity.

While there is no large overlap between the proteomic and transcriptomic datasets, the authors focus additional experiments on one candidate pathway, mTOR-mediated regulation of translation in progenitors, and validate this pathway's role in progenitor development.

The authors also identified a stress-related role for processes in corticogenesis, although, without comparison to human tissue, it's possible that some of the results are due to the artificial nature of the organoids as they have been reported to have elevated stress (Bhaduri et al.,).

The data is from organoids from one human stem cell line, the female H9 human embryonic stem cell line and so it is critical to validate the results on 1-2 additional stem cell lines, to rule out the possibility that these results are unique to this one cell line.

The major concerns in this paper can be addressed through validation of the results in other systems (e.g. human tissue) or in additional cell lines.

The results provide a valuable resource and address some of the limitations of current organoid and tissue single-cell data by focusing on proteomics.

Reviewer #3 (Public Review):

The paper by Rahsepar et al. employed a closed-loop optogenetic approach to stimulate mouse dentate gyrus (DG) 'engram cells' at different phases of the ongoing theta rhythm. While stimulation of DG engram cells in fear conditioning paradigms has been conducted several times before (with similar results to those presented here), the current approach constitutes a significant methodological improvement over typical 'open loop' designs. The authors first characterize the performance of their closed-loop theta phase prediction method and show that it outperforms constant frequency stimulation in achieving a theta phase-specific stimulation, albeit with some limitations. A prominent theory in the field has proposed that memory encoding and recall preferentially take place at the peak and trough of theta respectively. Based on this framework, the authors compared the behavioral and physiological effects of stimulating engram cells at either the theta peak or trough as well as with constant frequencies. They found that, as predicted by the theory, stimulation at the theta through was the most effective in inducing enhanced fear memory recall (measured as freezing during re-exposure to a neutral context). Finally, the authors examined theta-gamma hippocampal LFP dynamics to provide physiological support for the observed behavioral differences of the different stimulation patterns.

Overall, this work illustrates an interesting methodological development that will be of relevance for future studies conducting manipulations of engram cells and provides additional experimental support for an influential theory in the memory field. Experiments are well conducted and the results presented support the main interpretation of the authors, but several aspects of the interpretation and discussion of the work need to be improved. Likewise, several aspects of data analysis and interpretation, in particular in reference to hippocampal oscillations and regional differences need to be improved.

Reviewer #3 (Public Review):

Elimination of aberrant cells from epithelial tissues is important for normal tissue physiology. Here the authors study a specific type of cell elimination that is dedicated to the removal of miss-specified cells. This type of elimination is dependent on interface contractility. The authors now identified an important role for JNK signaling, which is activated at this interface, where contractility is highest.

Strength: The authors use a large variety of cell specification mutants and different drivers to manipulate cell specification. Together, this shows that the observed phenotypes are of a general nature and not dependent on single signaling pathways.<br /> Weakness: Quantitative characterization of much of the data is missing. Only single representative images are shown for many of the experiments. The manuscript would strengthen massively when these images are supported with a quantitative measurement. For example (but not limited to), TRE-GFP in correctly vs mis-specified clones in Figure 2K-L, TRE-GFP intensity in Figure 3, clonal analysis in Figure 5.

Type of elimination:<br /> The authors describe a very distinct and specific phenotype of smooth rounded clones with high contractility. It is obvious that this is, on a phenotypic scale, different from other types of cell elimination, such as live extrusion and cell-cell competition. Throughout the manuscript the authors emphasize that the underlying nature of interface contractility is different to cell competition. Because cell competition "responds to a clearly defined fitness gradient between two neighbouring cells, which ensures that always the aberrant loser cell dies, independent of spatial context." And "linking apoptosis to a fixed loser genotype". However, this only holds true for the classical types of cell competition (e.g. Minute), while many examples of cell competition have been reported where elimination of cells is not set in stone, but also highly context dependent. For example, HRasV12 expressing cells are eliminated from epithelia in mice on a normal diet, while a high fat diet prevents their elimination (Sasaki et al, Cell Reports 2018). Without the experimental support that relative differences in cell specification do not cause a difference in cellular fitness it is hard to grasp the conceptual difference. Instead, the concept reported by the authors is better described as a variety of cell competition.

Clone size<br /> The authors claim that remove aberrant cells by interface contractility is dependent on clone size and only occurs when aberrant cells are the minority compared to the surrounding tissue. Currently, there is no data in the manuscript that supports this claim. The only analysis of tissues containing a majority of miss-specified cells (Figures 2I-2J) shows a bilateral activation of JNK, similar to a minority of miss-specified cells. To support the claim that the phenotype is size dependent further analysis of clone size in relation to apoptosis and JNK activation is essential.

JNK and cell autonomous regulation:<br /> The authors validate that expression of TRE-GFP is dependent on JNK signaling, through over-expression of a dominant negative variant of the JNK kinase (BSKDN) in clones of miss-specified cells (ey or tkv). This experiment nicely shows that activation of JNK in surrounding WT cells is not altered. This furthermore illustrates that JNK signaling in the miss-specified cells is not needed for activation of JNK in their neighbors. However, this does not support the conclusion that JNK is activated in a cell autonomous fashion in either of these populations. The interaction of the two cell types can still cause signaling, but through inhibition of one of the kinases within the pathway, this just does not lead to downstream activation of TRE-GFP. In fact, one could argue that the expression of TRE-GFP is not cell-autonomous, because tkvCA clones that are not mis-specified (within dad4-LacZ regions) do not show induction of TRE-GFP (Fig 2L). The only way to untangle cell autonomous vs non-autonomous effects is through manipulation of upstream communication between the different cell populations. Such experiments, for example manipulation of contractility, are likely beyond the scope of this study. Therefore, I would suggest rephrasing this paragraph.

Apoptosis:<br /> A large part of the manuscript is dedicated to the characterization of elimination of miss-specified cells through apoptosis. This process is important for maintenance of tissue integrity and a crucial part of the manuscript. Some conclusions are not fully supported by the data represented in the current form of this manuscript;<br /> The authors claim that fkh- and ey-expressing cells are not eliminated when apoptosis is blocked by expression of p35. This is based on analysis of apical vs basal clone count (Figure 1T). This analysis reflects a combination of induction efficiency and clone retention. Therefore, information on the cellular behavior within clones is lacking and only provides information on survival of cells when complete clones are eliminated. The conclusion should be supported by additional analysis on clone size and total clone area, ideally based on cell number. In addition, statistical analysis of conditions with and without expression of p35 should be included.<br /> Furthermore, the analysis of apoptosis at clonal interfaces does not support the conclusion that "many, but not all apoptotic events occur at interfaces". Overall, there is increased apoptosis within clones compared to wild-type tissue. However, the rates of apoptosis are higher (ey, Fig S5B) or similar (fkh and tkvCA, Fig 5B-C) in clonal cells compared to clonal interface cells. The authors should revise these statements or provide more compelling analysis.

Reviewer #3 (Public Review):

This manuscript describes a villin-2a-Flp-based intersectional strategy for selectively targeting EEC in the intestine and uses it to examine the function of subsets. The approach for targeting select subsets of enteroendocrine cells described here will be important for neuroscientists, endocrinologists, microbiologists, and other scientists studying nutritional biology. Here single-cell sequencing is used, primarily, to confirm what was already known about EEC classes at a transcriptomic level. The intersectional approach described here has the potential to provide broad access to EECs. However, from the relatively limited characterization of targeted EEC cells, it appears that the genes that have been combined with the villin driver largely fail to selectively target transcriptomically defined cell types. Thus, at present, this manuscript fails to convincingly target transcriptome-defined enteroendocrine cell types, and conclusions on gut motility, feeding behavior, and flavor avoidance are overstated.

Some aspects of the study are compelling including the use of villin drivers as a means to restrict recombination to the epithelium containing EECs. The single-cell data (although not unique to this study) proved a basis for a better understanding of EECs and also their developmental specification. The charcoal-based gut motility assay appears valuable (although the results are perhaps not surprising given what was already known). In addition, some of the care taken characterizing extra-EEC expression is commendable. However, the manuscript is difficult to read with important details scattered in different figures and text (e.g., the characterization of expression patterns of the various lines). Moreover, whereas some things like the genetic makeup of the lines are always specified in full (excruciating) details, the expression patterns of the various lines are often casually dealt with e.g., describing separate targeting of L and I cells despite no evidence that this is actually being done. I would hope that the authors will address these issues and devote significant attention to making the paper more accessible to its readers.

Reviewer #3 (Public Review):

Porter, Li et al. investigate the roles of SA1 and SA2 in cohesin loading, and as well as roles that are independent of the cohesin ring. Using co-IP and imaging approaches, they show that both SA1 and SA2 interact with CTCF and they use auxin-induced degradation of Rad21 to show that this is only partially dependent on cohesin. The authors next use IP followed by mass spectrometry to identify additional SA binding partners, which include many RNA binding proteins including factors involved in RNA modification, export, splicing, and translation. Unlike the interaction with CTCF, these interactions are enhanced in cohesin depletion conditions. In fact, CLIP experiments show that SA binds RNA directly, in an R-loop-dependent manner. This co-localisation of SA with R-loops is confirmed by STORM.

To address whether SA proteins are involved in cohesin loading, the authors measure chromatin-bound cohesin levels after auxin washoff in the presence and absence of NIPBL and SA. They find that SA knockdown has a comparable impact on cohesin binding to chromatin compared to NIPBL knockdown, and that combining the knockdowns reduces cohesin loading further. This newly synthesised cohesin co-localises with R-loop domains by STORM, and this localisation is sensitive to RNAse H. The authors propose that SA promotes cohesin loading at R-loops, and that SA1 is the main contributor to this. Finally, they provide evidence that differential usage of a conserved exon between SA1 and SA2 may be responsible for differences between SA1 and SA2 in this system, as SA2 with this exon included has higher RBP binding and is more enriched at R-loops.

This paper provides convincing evidence that SA proteins associate with R-loops and various RNA-binding proteins, suggesting that they may have a cohesin-independent role related to RNA processing or R-loops specifically. Additionally, the paper provides evidence for a NIPBL-independent role of SA proteins at cohesin loading, which may occur at R-loops. These results will be of broad interest in relation to chromatin organisation and the role of SA proteins/cohesin in cancer.

Overall, the experiments are thorough and well-controlled, including some nice validations such as the use of siRNA-mediated cohesin depletion and a different cell line to confirm the SA-CTCF interactions. In many cases STORM imaging is used to provide complementary evidence to western blots / IP experiments.

However, one weakness is that imaging approaches can only address co-localisation. Although the vast majority of cohesin complexes will be bound to DNA, imaging approaches cannot distinguish between chromatin-bound and unbound nuclear proteins. For example, although cohesin co-localises with R-loops and SA after auxin washoff, and this is dependent on R-loops, it is not possible to tell from imaging whether this cohesin is chromatin bound and whether this is bound to specific genomic loci that contain R-loops or just associated with them in 3D space. Therefore it would be preferable to have a clearer distinction in terminology depending on whether the evidence discussed can demonstrate chromatin binding (e.g. chromatin fractionation experiments), or just co-localisation.

Reviewer #3 (Public Review):

Although initially discovered as axon guidance molecules in the nervous system, Semaphorins, signaling through their receptors the Neuropilins and Plexins, regulate a variety of cell-cell signaling events in a variety of cell types. In addition, cells often express multiple Semas and receptors. Thus, one important question that has yet to be adequately understood about these important signaling proteins is: how does specificity of function arise from a ubiquitously expressed signaling family?

This study addresses that important question by investigating the role of cysteine palmitoylation on the localization and function of the Neuropilin-2 (Nrp-2) receptor. It was already known that Sema3F signaling through a complex of Nrp-2 and Plexin-A3 regulates pruning of dendritic spines in cortical neurons while Sema3A signals through Nrp-1/PlexA4 to regulate dendritic arborization. The major finding of this study which is well-supported by the data is that palmitoylation of Nrp-2 regulates its cell surface clustering and dendritic spine pruning activity in cortical neurons. Interestingly, palmitoylation of Nrp-1 at homologous residue does not appear to regulate its localization or known neuronal function.

A clear strength of this manuscript is the many techniques that are utilized to examine the question: this study represents a tour de force of biochemical, molecular, genetic, pharmacological and cell biological assays performed both in vitro and in vivo. The authors carefully dissect the function of distinct palmitoylated cysteine residues on Nrp-2 localization and function, concluding that palmitoylation of juxtamembrane cysteines predominates over C-terminal palmityolyation for the Nrp-2 dependent processes assayed in this study. The authors also demonstrate that a specific palmityl transferase (DHHC15) acts on Nrp-2 but not Nrp-1 and is required for Nrp-2 clustering and dendritic spine pruning. These findings are important because they demonstrate one mechanism by which different signaling pathways, even from a related family of proteins, can achieve signaling specificity in the cell.

A minor weakness of the paper is that one would like to see a connection between palmitoylation-dependent cell membrane clustering of Nrp-2 on the cell surface and Nrp-2 regulation of dendritic spine pruning. Although the two phenotypes frequently correlate in the data presented, there are a few notable exceptions: e.g. Nrp-2TCS forms larger clusters in cortical neurons while Nrp-2FullCS is diffuse on the cell surface; both mutants affect spine pruning. In the future, it would also be interesting to know if increased clustering of Nrp-2 was observed at spines that were eliminated, for example. Nonetheless this manuscript represents an important advance in our understanding of synaptic pruning and cellular mechanisms that constrain protein surface localization and signaling pathways.

Reviewer #3 (Public Review):

This manuscript builds upon prior work showing that alpha-actinin-2 binds to the regulatory domain of the major postsynaptic protein kinase, CaMKII. The authors report the structure of a complex between the relevant domain in alpha-actinin-2 and a peptide based on the CaMKII regulatory domain. Data are presented indicating that the interaction of the NMDA receptor GluN2B subunit with the CaMKII catalytic domain stabilizes the complex with alpha-actinin-2. Furthermore, the authors present proximity ligation assay (PLA) data obtained in cultured neurons demonstrating that NMDA receptor activation strongly enhances the colocalization of CaMKII with alpha-actinin-2. Data obtained using mutated proteins indicate that this co-localization is mediated by the interaction characterized structurally.

Strengths:

Significant strengths of this work are:<br /> 1. The high-quality structures of the complex that are reported.<br /> 2. Integration of these findings with the much better-studied complex of CaMKII and GluN2B.<br /> 3. The convincing PLA analyses show that NMDA receptor activation increases CaMKII colocalization with alpha-actinin-2.<br /> 4. The careful comparisons of data from these new studies with data reported in previous publications.

Weaknesses:

Despite the significant strengths of the work, there are some gaps/weaknesses.<br /> 1. Although there is abundant published evidence that activated CaMKII colocalizes with NMDA receptors, the evidence for the involvement of GluN2B in the CaMKII-alpha-actinin-2 complex in neurons is lacking.<br /> 2. The evidence supporting a role for the EF1 and EF2 domains of alpha-actinin-2 in binding to CaMKII is not very convincing.<br /> 3. CaMKII autophosphorylation at multiple sites plays an important role in regulating the subcellular localization of CaMKII, but the role of autophosphorylation is not explored here.

Taken to together the manuscript describes novel data that provide a significant extension to prior work, and the data convincingly, but perhaps only partially, support an interesting proposed model for the control of CaMKII targeting in spines.

This more sophisticated delineation of the mechanisms underlying CaMKII targeting synapses will be of interest to the broader field of investigators studying the molecular basis for the regulation of excitatory synaptic transmission, learning, and memory.

Reviewer #3 (Public Review):

This manuscript entitled "PASK relays metabolic signals to mitotic Wdr5-APC/C complex to drive exit from self-renewal" by Xiao et al presents an interesting story on the role of PASK in the control of muscle stem cell fate by controlling the decision between self-renewal and differentiation. While the biochemistry presented is fairly compelling, the experiments revolving around the myogenic cells are lacking in quality and data.

Major concerns:

1. The isolation method used by this group to isolate muscle stem cells is inappropriate for the experiments used and may contribute to the misinterpretation of some of the results. It is simply a preplating method that results in a very heterogenous cell population in terms of cell type, comprised of numerous fibroblasts. While preplating can be used to isolate muscle stem cells and culture them as myoblasts, it takes days of growth and multiple rounds of passaging that are not used in this paper in order to get a more pure population of myogenic cells. This would also explain the high number of Pax7 negative cells in their primary myoblast experiments (~50% in some conditions) as they are most likely fibroblasts, which the authors could show by staining for fibroblast markers. The increase in Pax7 cells in certain conditions could also simply be due to the loss of contaminating cell types due to the treatment. Every single experiment that was performed on myoblasts must be redone using a more appropriate cell isolation method (i.e. FACS) or by culturing these isolated cells for a much longer period of time to eventually get a more pure cell population. As it stands, none of the data from the primary myoblast experiments are trustworthy.<br /> 2. The authors possess a genetic mouse model where PASK is knocked out. However, the mouse model is never described and the paper that is referenced also does not describe it. Please detail your mouse model.<br /> 3. The majority of experiments are performed on C2C12 cells. While C2C12s are adequate for biochemistry and proof of concepts, when it comes to biological significance primary myoblasts should be used. While the authors try to explain this use by claiming that primary myoblasts undergo precocious differentiation that can be avoided by using an appropriate growth media (F10, 20% FBS, 1% P/S, 5ng/mL of bFGF).<br /> 4. The authors possess a genetic mouse model, yet performed RNA-Seq on C2C12 myoblasts that were either untreated or treated with a PASK inhibitor. It would be much more informative and valuable to sequence the primary myoblasts from WT and PASK KO mice, thereby providing a more biologically relevant model.<br /> 5. The KO mouse model is rarely used and the cells isolated from it would be very useful in determining the biological role of PASK in muscle cells. The authors should isolate WT and KO cells and perform basic muscle functional experiments such as EDU incorporation for proliferation, and fusion index for differentiation to see whether the loss of PASK has an effect on these cells.<br /> 6. The authors never look at quiescent muscle stem cells and early activated muscle stem cells in terms of PASK protein expression and dynamics. The authors should isolate EDL myofibers and stain for PASK and PAX7 at 0, 24, 48, and 72-hour post isolation. This would allow the authors to quantify the changes in PASK expression and cell localization, as well as confirm the number of muscle stem cells in WT and KO mice, during quiescence and during the process of muscle stem cell activation, proliferation, and differentiation in a near in vivo context.<br /> 7. Contrary to their claim, MyoD is not a stemness/self-renewal gene.<br /> 8. The authors state that PASK is necessary for exit from self-renewal and establishment of a progenitor population but this is a vast overstatement. In the genetic KO mouse model, the mice are able to regenerate their muscle after injury, therefore PASK cannot be a necessary protein for the formation of progenitor cells.<br /> 9. In numerous figure panels, the y-axis represents the # of cells, rather than a percentage or ratio. This is uninformative as the number of cells will never be the same between conditions and experiments. These panels need to be replaced with a more appropriate y-axis.

Reviewer #3 (Public Review):

In this paper, the authors model brain responses for visual objects and the effect of attention on these brain responses. The authors compare three models that have been studied in the literature to account for the effect of attention on brain responses to multiple stimuli: a normalization model, a weighted average model, and a weighted sum model.

The authors presented human volunteers with images of houses and bodies, presented in isolation or together, and measured fMRI brain activity. The authors fit the fMRI data to the predictions of these three models, and argue that the normalization model best accounts for the data.

The strengths of this study include a relatively large number of participants (N=19), and data collected in a variety of different visual brain regions. The blocked design paradigm and the large number of fMRI runs enhance the quality of the dataset.

Regarding the interpretation of the findings, there are a few points that should be considered: 1) The different models that are being studied have different numbers of free parameters. The normalization model has the highest number of free parameters, and it turns out to fit the data the best. Thus, the main finding could be due to the larger number of parameters in the model. The more parameters a model has, the higher "capacity" it has to potentially fit a dataset. 2) In the abstract, the authors claim that the normalization model best fits the data. However, on closer inspection, this does not appear to be the case systematically in all conditions, but rather more so in the attended conditions. In some of the other conditions, the weighted average model also appears to provide a reasonable fit, suggesting that the normalization model may be particularly relevant to modeling the effects of attention. 3) In the primary results, the data are collapsed across five different conditions (isolated/attended for preferred and null stimuli), making it difficult to determine how each model fares in each condition. It would be helpful to provide data separately for the different conditions.

Reviewer #3 (Public Review):

Mann and colleagues have generated a knock-in mouse model carrying a recently identified mutation in the Mfn2 gene that leads to a syndrome of severe upper body adipose overgrowth in humans (Mfn2R707W). The goal was to gain a better mechanistic understanding on how this mutation leads to such a dramatic phenotype in humans. The authors consistently demonstrate how the knock-in mutation leads to abnormalities in mitochondrial shape, mtDNA content, as well as in the abundance of some mitochondrial proteins, most notably in brown adipose tissue. The authors detect some stress response signatures, which could explain the decreased leptin and adiponectin levels observed in the knockin mice.

The authors have to be praised for their effort in trying to provide mechanistic insights to such a rare condition. This work constitutes a real tour de force in the characterization of Mfn2R707W mice. The path, however, was full of surprises. On one side, the knockin mouse model fails to recapitulate multiple aspects of the human syndrome. This is, of course, beyond the control of the researchers, but somehow tells us that there are some elements missing in our understanding of the effects of this Mfn2 mutation at the cellular level (not just organismal), and on why it impacts so much adipose tissues. A second layer of complexity is that the authors find an interesting connection between Mfn2R707W, the integrated stress response and a severe decrease in the expression of leptin and adiponectin. However, whether these elements have any causal role in the human syndrome or in the phenotypes observed in the mice, remains an open question.

Reviewer #3 (Public Review):

The manuscript by Barthe et al compares the effects derived from the application of isoprenaline (Iso) or isoprenaline covalently linked to PEG (PEG-Iso) on adult rat ventricular myocytes (ARVM). Iso is a well-characterized β-AR agonist and the authors work under the assumption that PEGylation of Iso prevents it from accessing the T-tubules. Therefore, due to its larger size, PEG-Iso is only able to activate β-ARs located on the outer surface membrane (OSM), and any additional effect observed by Iso stimulation is attributed to the activation of β-ARs located in T-tubules. First, the authors determined that the affinity of PEG-Iso for β-ARs is about 100 times lower than the one of Iso. Then, they analyze the effects of Iso (10 nM) and PEG-Iso (1 µM) on calcium channel currents, contractility, calcium transients, and cytosolic and nuclear PKA activity. They only found a stronger effect of Iso on nuclear pKA activity. Therefore they conclude that, while OSM β-ARs stimulation mainly results in positive inotropy and lusitropy, T-tubules ARs stimulation mainly results in increased nuclear pKA activity.

Overall the manuscript is well written and the findings are biologically important from the perspective of understanding the mechanism of β-AR stimulation as well as in assigning the functional contribution of β-ARs in the OSM and in the T-tubules. However, the major conclusion is not strongly supported by the data. The interpretation of the results is all based on the assumption that PEG-Iso is excluded by the T-tubules, but no experiment presented here rigorously demonstrates this.

1. The only indication that PEG-Iso may be excluded by the T-tubules is one confocal image in which FITC or PEG-FITC were applied on ARVM. No experiment has been performed to assess if PEG-Iso is indeed not able to enter the T-tubules.<br /> The treatment of ARVM with neuraminidase made the T-tubules accessible to PEG-FITC. If the authors could demonstrate that neuraminidase treatment followed by PEG-Iso would result in similar nuclear pKA activity as Iso, this would strengthen their conclusion.<br /> 2. The fact that PEG-Iso treatment resulted in a lower increase of intracellular cAMP (Figure 3) could also be due to the activation of a smaller fraction of β-ARs, independent of their localization.

Reviewer #3 (Public Review):

The authors use hydrogen-deuterium exchange mass spectrometry (HDXMS) to assess the dynamics of several relevant mutant forms of SARS-CoV 2 Spike protein including the most recent Omicron variant. The Spike protein is heavily glycosylated and is a trimer so is a very difficult protein to study by HDXMS. The authors confirm the glycosylation sites, which can't be covered by the HDXMS experiment, yet they still manage to cover nearly 50% of the sequence revealing many interesting changes in dynamics in the prevalent circulating mutant forms. The beautiful HDXMS data reveal consistent trends as SARS-CoV2 mutates to survive including stabilization of the stalk and increased dynamics of the N-terminal domain where ACE2 receptor binding occurs. The authors incubate the protein at 37C and discover additional stabilization of the trimer occurs under these conditions explaining a lot of conflicting data in the literature done at different temperatures. These results have profound implications for the development of small molecule inhibitors of the Spike protein-ACE2 interaction.

Reviewer #3 (Public Review):

This paper shows that RecA-mediated recombination between two insertion sequence elements can drive the duplication of a large (~200 kb) region that leads to a growth advantage in biofilms, but a disadvantage during planktonic growth. The experiments presented are incisive and definitive. While IS elements are more commonly implicated in gene inactivation, this paper reveals that they can provide a benefit by driving a reversible genome modification in the form of a large-scale duplication. The paper should appeal to readers interested in mechanisms of genome evolution, phase variation, biofilms, and bacterial pathogenesis. The final model is convincing and also lays the foundation for future studies aimed at identifying which gene(s) in the duplicated region are ultimately responsible for the biofilm growth benefit. The paper also serves to correct this lab's prior interpretation of related data in which they concluded that the genomic region being investigated excised and circularized. They very nicely lay out what led them to conclude this previously and how their new data led to a revised model, as well as many additional, important new insights. To be clear, there were no issues with the prior data, just the interpretation/model. So in my view, this is exactly how science should unfold - new data can and should lead to revised models. I applaud the authors for laying this trajectory out in such a straightforward, open manner.

Reviewer #3 (Public Review):

In this manuscript, the authors consider a rate model with recurrently connections excitatory-inhibitory (E-I) modules coupled by distance-dependent excitatory connections. The rate-based formulation with adaptive threshold has been previously shown to agree well with simulations of spiking neurons, and simplifies both analytical analysis and simulations of the model. The cycles of beta oscillations are driven by fluctuating external inputs, and traveling waves emerge from the dephasing by external inputs. The authors constrain the parameters of external inputs so that the model reproduces the power spectral density of LFPs, the correlation of LFPs from different channels and the velocity of propagation of traveling waves. They propose that external inputs are a combination of spatially homogeneous inputs and more localized ones. A very interesting finding is that wave propagation speed is on the order of 30 cm/s in their model which is consistent with the data but does not depend on propagation delays across E-I modules which may suggest that propagation speed is not a consequence of unmylenated axons as has been suggested by others. Overall, the analysis looks solid, and we found no inconsistency in their mathematical analysis. However, we think that the authors should discuss more thoroughly how their modeling assumptions affect their result, especially because they use a simple rate-based model for both theory and simulations, and a very simplified proxy for the LFPs.

The authors introduce anisotropy in the connectivity to explain the findings of Rubino et al. (2006), showing that motor cortical traveling waves propagate preferentially along a specific axis. They introduce anisotropy in the connectivity by imposing that the long range excitatory connections be twice as long along a given axis, and they observe waves propagating along the orthogonal axis, where the connectivity is shorter range. Referring specifically to the direction of propagation found by Rubino et al, could the authors argue why we should expect longer range connections along the orthogonal axis? In fact, Gatter and Powell (1978, Brain) documented a preponderance of horizontal axons in layers 2/3 and 5 of motor cortex in non-human primates that were more spatially extensive along the rostro-caudal dimension as compared with the medio-lateral dimension, and Rubino et al. (2006) showed the dominant propagation direction was along the rostro-caudal axis. This is inconsistent with the modeling work presented in the current manuscript.

The clarity and significance of the work would greatly improve if the authors discussed more thoroughly how their modeling assumptions affect their result. In particular, the prediction that external inputs are a combination of local and global ones relies on fitting the model to the correlation between LFPs at distant channels. The authors note that when the model parameter c=1, LFPs from distant channels are much more correlated than in the data, and thus have to include the presence of local inputs. We wonder whether the strong correlation between distant LFPs would be lower in a more biologically realistic model, for example a spiking model with sparse connectivity and a spiking external population, where all connections are distant dependent. While the analysis of such a model is beyond the scope of the present work, it would be helpful if the authors discussed if their prediction on the structure of external inputs would still hold in a more realistic model.

Reviewer #3 (Public Review):

Ras mutations are found in almost 25 percent of cancer patients. It has been difficult to directly target Ras proteins due to the lack of druggable pockets on the surface of the protein and the extremely high binding affinity of nucleotides to Ras proteins. Recently a mutant specific irreversible drug that targets the mutation G12C has been FDA approved. This drug binds to a shallow pocket on the surface of Ras and attacks the G12C mutation irreversibly. Another approach is to compete with the nucleotides bound to Ras. An attempt to generate nucleotide competitors that can take advantage of the G12C mutant has been proposed. Nevertheless, these published competitors had much lower affinities compared to endogenous nucleotides which would hinder the covalent modification in the presence of other nucleotides.

To overcome this, the authors propose to introduce a warhead in the ribose ring. Indeed, this modification did not affect the reversible binding affinity of these nucleotides to Ras wild type, in comparison to GDP and GTP. This finding represents a new opportunity to target G13C ras by competing with the nucleotides in cells. The authors support their claims with the appropriate in vitro experiments. Nevertheless, these experiments were performed at non physiologically high pH (9.5) and those compounds were not able to cross the cellular membrane. Thus, it is too early to draw conclusions regarding the appropriateness of the approach and whether it will prove successful in cells or if it will have medical application.

Reviewer #3 (Public Review):

This study by Aggad, Pujol, and colleagues provides some exciting new insights into a largely overlooked organelle/structure present in C. elegans epidermial cells, the "meiosome". Although noted by several previous researchers, this folded-membrane structure was never fully characterized. In particular, the authors provide an important and thorough characterization of meiosome morphology during development. The authors also provide data suggesting that meiosomes may function to provide attachment points between the epidermis and overlying cuticle, although this portion was less clear cut. In addition, the authors show that certain cuticle collagens can affect the morphology and position of meiosomes in addition to the formation of molting-associated actin cables. Some of these latter results, which suggest an 'outside-in' type of patterning regulation, run counter to certain previous models.

The major strengths of the paper are the novelty of describing a 'new organelle' and the thoroughness and clarity of the morphological analysis. The various EM studies were particularly well done and likely required a good deal of technical development, which may be of use to others in the field. One clear weakness is that it's not currently clear if the reported cuticle detachment defect is due to altered meiosomes, to the altered cuticle composition, or perhaps both, and thus the exact function(s) of meiosomes is left open. Other concerns include the use of extrachromosomally expressed VHA-5::GFP as a meiosome-specific marker. Although this could certainly be the case, it wasn't proven.

Reviewer #3 (Public Review):

In this paper, the authors examine the fate of exophers ejected from C. elegans neurons overexpressing a presumably aggregated mCherry protein. They show that exophers are taken up by adjacent hypodermal cells, split into smaller fragments, and eventually degraded by lysosome fusion. They identify a number of small GTPases and accessory components, as well as the phagocytic receptor (CED-1) and the likely eat-me signal (phosphatidylserine).

The manuscript follows up on previous exopher work from some members of the current collaboration, and provides a detailed analysis of exopher fate, that will likely be useful for understanding similar events in other settings. The studies are well done, the images and data are convincing, and the interpretations are generally appropriate.

Reviewer #3 (Public Review):

The manuscript " S-adenosylmethionine synthases specify distinct H3K4me3 populations and gene expression patterns during heat stress " by Godbole et al proposes a novel mechanism by which different S-adenosylmethionine (SAM) synthase enzymes exhibit specificity towards target sequences, thereby providing a layer of control over H3K4 trimethylation (H3K4me3) in Caenorhabditis elegans. The authors detail an extensive investigation of the function of two C. elegans SAM synthase enzymes, SAMS-1 and SAMS-4. They provide evidence that mutation or knockdown of these two enzymes affected gene expression of distinct gene sets and that loss of these enzymes has opposite effects on survival under heat stress. These differential effects are linked to differential effects on histone modification H3K4me3 of specific target gene sets. It is unclear from this work how exactly this specificity may be achieved and some of the data regarding the role of other components of the methylation machinery are somewhat superficial and confusing. Nevertheless, the study suggested a novel mechanism by which H3K4me3 of specific gene sets may be controlled and this mechanism is novel and potentially important.

Reviewer #3 (Public Review):

In this article, the authors examined color evolution in the kingfishers, a group of birds that have achieved a spectacular diversity of colors and color patterns as they have diverged across the continents and island chains of the globe. Like many other avian taxa, kingfishers on islands often exhibit color patterns distinct from their close relatives. The authors focus here on putting this informally recognized pattern of evolutionary change to a formal test, asking if plumage color diversity and evolutionary rate are elevated on islands. They also explore whether a notable characteristic of some kingfishers - their simultaneous use of many of the coloration mechanisms available in birds - contributes to the evolutionary lability of their color patterns.

The authors have previously explored how when color varies in birds it is not just in dimensions of color, but also in the distribution of those colors in patches on the body. Summarizing this variation is challenging, and there are statistical obstacles to comparing it in a holistic manner. In this study, the authors use an exceptional set of analyses to study color in total as a multivariate trait. These are the major strengths of the paper. The authors' efforts are somewhat less convincing when they pursue a univariate model fitting on a small number of principal components, but these analyses are not central to the study. And as with all studies using ancestral state reconstruction to test hypotheses, it's an important tool and one that contributes to this study's effectiveness, but we should acknowledge some level of uncertainty with its results.

The authors report two important relationships in this study. They provide convincing evidence that rates of color evolution are elevated in island kingfishers, without convergence towards a particular island phenotype. They also describe a relationship between the complexity of plumage patterns and the rate at which they evolve, which has fundamental implications for our understanding of the tempo of trait evolution.

Islands make up a tiny portion of the earth's surface but are home to a seemingly disproportionate amount of life's diversity. This paper makes an important contribution to our understanding of how this diversity is generated, by showing that the evolutionary rate is elevated on islands for traits relevant to mate choice and recognition. The authors find that "plumage complexity, rather than uniformity, provides more phenotypic traits for natural selection to act upon". Given the number of different coloration mechanisms they express, the kingfishers are a unique group in which to study this issue, so I look forward to reading and hearing more from the authors on this issue in the future.

Reviewer #3 (Public Review):

Activity-based anorexia (ABA), which combines access to a running wheel and restricted access to food, is a most common paradigm used to study anorexic behavior in rodents. And yet, the field has been plagued by persistent questions about its validity as a model of anorexia nervosa (AN) in humans. This group's previous studies supported the idea that the ABA paradigm captures cognitive inflexibility seen in AN. Here they describe a fully automated touchscreen cognitive testing system for rats that makes it possible to ask whether cognitive inflexibility predisposes individuals to severe weight loss in the ABA paradigm. They observed that cognitive inflexibility was predictive of resistance to weight loss in the ABA, the opposite of what was predicted. They also reported reciprocal effects of ABA and cognitive testing on subsequent performance in the other paradigm. Prior exposure to the ABA decreased subsequent cognitive performance, while prior exposure to the cognitive task promoted resistance to the ABA. Based on these findings, the authors argue that the ABA model can be used to identify novel therapeutic targets for AN.

The strength of this manuscript is primarily as a methods paper describing a novel automated cognitive behavioral testing system that obviates the need for experimentalist handling and single housing, which can interfere with behavioral testing, and accelerate learning on the task. Together, these features make it feasible to perform longitudinal studies to ask whether cognitive performance is predictive of behavior in a second paradigm during adolescence, a peak period of vulnerability for many psychiatric disorders. The authors also used machine learning tools to identify specific behaviors during the cognitive task that predicted later susceptibility to the ABA paradigm. While the benefits of this system are clear, the rigor and reproducibility of experiments using this paradigm would be enhanced if the authors provided clear guidelines about which parameters and analyses are most useful. In their absence, the large amount of data generated can promote p-hacking.

The authors use their automated behavioral testing paradigm to ask whether cognitive inflexibility is a cause or consequence of susceptibility to ABA, an issue that cannot be addressed in AN. They provide compelling evidence that there are reciprocal effects of the two behavioral paradigms, but do not perform the controls needed to evaluate the significance of these observations. For example, the learning task involves sucrose consumption and food restriction, conditions that can independently affect susceptibility to the ABA. Similarly, the ABA paradigm involves exercise and restricted access to food, which can both affect learning.

In the Discussion, the authors hypothesize that the ABA paradigm produces cognitive inflexibility and argue that uncovering the underlying mechanism can be used to identify new therapeutic targets for AN. The rationale for their claim of translational relevance is undermined by the fact that the biggest effect of the ABA paradigm is seen in the pair discrimination task, and not reversal learning. This pattern does not fit clinical observations in AN.

In summary, the significance of this manuscript lies in the development of a new system to test cognitive function in rats that can be combined with other paradigms to explore questions of causality. While the authors clearly demonstrate that cognitive flexibility does not promote susceptibility to ABA, the experiments presented do not provide a compelling case that their model captures important features of the pathophysiology of AN.

Reviewer #3 (Public Review):

The authors have developed a new form of transparent surface multielectrode integrated into an imaging window, enabling simultaneous recording of electrical activity at the surface of the cortex combined with two-photon imaging through the window and electrode. The authors characterise the electrical signals and use simulations to argue that they reflect the activity of axons in layer 1. This is then correlated with calcium imaging signals from layer 2/3 pyramidal cells. A subset of these displayed strong correlations with the layer 1 activity.

The raw electrical recordings appear to be contaminated by large movement artefacts. The authors attempt to decompose the signal into neuronal activity and artefact. The independent component analysis (ICA) employed yields plausible results. However, there is no definitive validation of this procedure.

The simulations strongly suggest that only layer 1 axons will generate significant neuronal signals at the surface, but the authors have not attempted to reconstruct the multiunit activity in the simulations, which could provide additional assurance for their interpretation.

A small fraction of pyramidal cells has activity strongly correlated with the signal at the surface electrode. However, the authors have not examined whether the distance from neuron to the electrode influences the strength of correlation. It remains possible that the differential correlation reflects a distance effect rather than the existence of two populations.

Reviewer #3 (Public Review):

Dominant pathogenic variants of the Aac2/Ant1 ATP transporter cause disease by an unknown mechanism. In this manuscript the authors aim to reveal how these gain of function mutants impair cellular and mitochondrial health. To characterize the phenotype of Aac2 mutants in yeast, the authors use a series of single and double Aac2 mutations, within the 2nd and 3rd transmembrane domains that are associated with human diseases. Aac2A128P,A137D mutant, which caused high toxicity and damaged the mitochondrial DNA was selected for further analysis. This mutant was not imported efficiently into mitochondria and exhibited an increased association with TOM, suggesting that it clogs the TOM translocase. As a result, expression of Aac2A128P,A137D led to impaired import of other mitochondrial proteins. Several findings suggested that the single mutant Aac2A128P impaired mitochondrial import in a similar manner: 1. mass spec analysis revealed its increased association with cytosolic chaperones, TOM and TIM22 subunits, 2. Aac2A128P overexpression led to global mitochondrial protein import deficiency, demonstrated by HSP60 precursor accumulation and activation of stress responses (transcription of chaperons, proteosome induction, and CIS1).<br /> Parallel mutants of human Ant1 (AntA114P and Ant1A114P,A123D) were ectopically expressed in HeLa cells. The mutants were demonstrated to clog TOM and cause a global defect in mitochondrial protein import. This was confirmed in tissues from Ant1A114P,A123D/+ knock-in mice. The Ant1A114P,A123D/+ mice exhibited decreased maximal mitochondrial respiration in muscles. Examination of the skeletal muscle myofiber diameter and COX and SDH activity revealed that Ant1A114P,A123D expression in heterozygous mice acts dominantly and causes a myopathic phenotype and in some case neurodegeneration.

Major strengths -

The ability of proteins to clog TOM and sequentially disrupt protein import into mitochondria was demonstrated in recent years. However, till now this was achieved using chemicals, artificial cloggers and overexpression of mitochondrial proteins. This study reveals, for the first time, that disease associated variants of native mitochondrial proteins can clog the entry into the organelle. Thus, this work demonstrates that TOM clogging is a physiological relevant phenomenon that is involved in human diseases.

The manuscript is well-written and the experiments are well-designed, presenting convincing data that mostly support the conclusions. The methods used are well-establish and suitable techniques that are often used in the field. This work took advantage of 3 different biological systems/model organism, yeast, cell culture, and mice tissues, to validate the results, show conservation, and exploit the strengths of each system.

Overall, this study is impactful, greatly contributes to the field and should be of interest to the general scientific community. The work sheds light of the mechanisms by which Ant1 pathogenic mutants impact cellular health and provides evidence for the involvement of translocases clogging and impaired protein import in human diseases. The gain of function Aac2/Ant1 mutants will provide a new and powerful tool for future studies of mitochondrial quality control and repair mechanisms.

Major weaknesses -

1. The evidence for clogging of mitochondrial translocases and for general defect in protein import are solid. However, there are not enough evidence to conclude that all phenotype seen in mice and yeast are directly connected to clogging.

2. This work implies that Aac2/Ant1 variants can clogg TOM, TIM22, or both. Clogging of TIM22 is novel and interesting but is not fully discussed in the manuscript, as well as the possibility that clogging of different translocases can result in different defects.

Reviewer #3 (Public Review):

In this paper, Yeung et al., use patch-clamp electrophysiology measurements combined with structural analyses and mutagenesis to compellingly reveal how the tubular aggregate myopathy (TAM)-associated Orai1 L138F mutation leads to the gain of CRAC channel function. They discover that L138F not only constitutively activates Orai1-composed channels but also enhances Ca2+-dependent inactivation (CDI). The authors find that the L138F gain of function occurs due to a steric clash with T92 from an adjacent subunit and that introduction of a bulky residue at the T92 position similarly activates CRAC channels and enhances CDI in the absence of STIM1. Nevertheless, co-expression of STIM1 with strongly activating T92W or L138F mutants regularized the CDI to wild-type levels. Collectively, the work represents an important conceptual advancement, exposing that STIM1 is not necessary for CDI and that Orai1 likely contains the Ca2+ sensor intrinsically for this phenomenon.

Strengths:<br /> The authors use rigorous and careful electrophysiological measurements to probe how the TAM-related mutation (L138F) affects the biophysical properties of CRAC channels. The extensive and systematic mutagenesis (i.e. substitution to every possible amino acid at the T92 and L138 sites) coupled with these functional assessments reveal a steric clash between L138F and T92 and provide a complete picture of how any residue type at the so-called T92/L138 lever point may contribute to constitutive CRAC and CDI activity. The use of available high-resolution structural data to interpret functional data, rationalize the consequence of new mutations related to the mechanisms of L138F dysfunction, and generate new hypotheses is a strength of the research. Overall, the work provides a considerable conceptual advance in terms of understanding the molecular requirements for CRAC and CDI activity; in particular, the discovery that CDI can occur independently of STIM1 and the notion that Orai1 may contain an intrinsic Ca2+ sensor that regulates CDI are important steps forward for the field.

Weaknesses:<br /> While the work provides a phenomenological advancement regarding CRAC channel regulation and pinpoints new important residues for function, some aspects of the study appear incomplete. It was shown that STIM1 can normalize the enhanced CDI caused by the T92W mutation, but it is not clear how this happens. Further, the authors propose a "push" - "pull" mechanism for the complementary roles L138 and H134 in channel regulation but do not provide any structural dynamics data to support this idea. The authors provide a mathematical explanation for chelator-specific differences in CDI observed for the T92W compared to WT Orai1 but do not show any fitted data to accompany and support the model. Finally, the authors show that a considerable portion of the CDI can be eliminated after a C-terminal Orai1 deletion (i.e. residues 267-301) and probe the idea that N-terminal W76, Y80, and R83 residues may contribute to the residual CDI effect; however, after W76E, Y80E, R83E mutations showed enhanced CDI (rather than suppressed) in the context of the T92W mutation, no further experiments were pursued to account for the residual CDI.

Overall, the strengths far outweigh the weaknesses of this study, and the conclusions drawn based on the data are compelling. The work represents an important conceptual advancement as future studies can now steer towards identifying the STIM-independent Ca2+ sensor underlying the CDI of CRAC channels and revealing structural mechanisms by which Ca2+ sensing leads to pore closure.

Reviewer #3 (Public Review):

The authors start by examining the COOLAIR promoter and identifying a CRT/DRE motif that is bound by the CBF transcription factor family that is involved in the short-term cold. This is confirmed by gel shift assays and chromatin immunoprecipitation. However, it should be noted that the gel shift assays are an in vitro assay and the chromatin immunoprecipitation is carried out with plants over-expressing CBF3-myc from the pSuper promoter and so do not necessarily reflect the native state. The authors then examine COOLAIR expression in lines over-expressing each of the three CBF proteins of Arabidopsis and found COOLAIR expression elevated in the warm in all three, but with small differences in the variants of COOLAIR that are expressed. Examination of the expression of COOLAIR after short-term cold shows that transcript abundance increases after 6 hours, this expression was not observed in the cbfs-1 where all three CBFs are knocked out. Taken together this provides good evidence that COOLAIR transcription is rapidly induced via CBFs on exposure to cold.

The authors then go on to look at the roles of CBFs in longer-term cold. COOLAIR has previously been shown to increase during long-term cold (multiple weeks duration), so the question was whether this increase is CBF-dependent. The increase in COOLAIR abundance is similar to other CBF targets but does begin to decline with 40-day cold periods, presumably reflecting the shutdown of the FLC locus. The lack of COOLAIR expression in the cbfs-1 mutant is good evidence that increased COOLAIR expression is CBF-dependent. The authors also present evidence that CBFs are required for COOLAIR induction by the first seasonal frost, which is consistent with this being a short-term cold response.

The authors then examine deletions of the COOLAIR promoter. In agreement with the hypothesis that CBFs regulate COOLAIR transcription via the CRT/DREs in the COOLAIR promoter, deletions that include the two elements do not show cold induction of COOLAIR, while one that contains them does. It should be noted that these deletions are relatively coarse so could include other elements than the CRT/DREs.

The authors then use the finding that COOLAIR is not induced in the cbfs-1 mutant or in the deltaCOOLAIR1 and 3 lines to ask whether COOLAIR is required for the repression of FLC in the vernalization response. The data in Figures 6 and 7 show that these lines don't show different responses to vernalization treatment at the FLC expression, FLC chromatin modifications, or flowering time/leaf number to flowering. This supports the conclusion that the COOLAIR transcript does not play an essential role in the vernalization response.

The Discussion is well-balanced and considers previous publications in this area and highlights differences with this study. The conservation of COOLAIR in other brassica species suggests that it does have a biological function, but the data here suggest it isn't an essential component of the vernalization response. Whether there is a function in more natural conditions where the temperature fluctuates in a diurnal manner during the vernalization period is a possibility that is considered. When the data presented here are taken with other publications, the precise biological role of COOLAIR remains enigmatic.

Reviewer #3 (Public Review):

Based on studies over the last two decades, tomosyns participate in processes as diverse as synaptic SNARE complex stability (Yu H et al., 2014), dendritic spine density (Saldate JJ et al., 2018), mossy fiber synaptic plasticity (Ben-Simon Y et al., 2015), inhibition of mast cell degranulation (Madera-Salcedo IK et al., 2018), insulin-stimulated GLUT4 exocytosis by adipocytes (Wang S, et al., 2020), and both basal and stimulated secretion by PC12 cells (Williams et al., 2011). In yeast, which lacks storage granules, two tomosyn orthologs control the formation of post-Golgi vesicles. The actions of tomosyn are cell-type specific and subject to regulation by phosphorylation and the ubiquitin-proteasome system (Saldate JJ et al., 2018; Williams et al., 2011; Madera-Salcedo IK et al., 2018). In beta-cells, the ability of tomosyn to decrease insulin secretion by binding syntaxin1A requires its SUMOylation (Ferdaoussi M, et al., 2017). The carefully designed and validated mouse line developed by the authors will facilitate detailed, mechanistic studies of the diverse, cell-type specific actions of tomosyns.

Using cultures derived from the hippocampi of this new mouse strain, multiple differences were observed between two-week-old WT and DKO (double knockout of tomosyn-1 and -2) cultures. Analysis of dense core vesicle release by single neurons revealed no change in their exocytosis, but identified a decrease in levels of the dense core vesicle reporter, leading to the discovery of a decrease in levels of two endogenous dense core vesicle proteins, BDNF and IA-2. In contrast, levels of two lysosomal/endocytic markers were unaltered, demonstrating granule specificity.

WT and DKO cultures were compared using mass spectrometry. Significant changes in the levels of 3% of the proteins were identified. Strikingly, levels of several additional dense core vesicle proteins were decreased in DKO cultures. In contrast, levels of multiple mitochondrial proteins were greatly increased in DKO cultures. In addition, significant increases in VGLUT2 (a marker of glutamatergic neurons) and in GAD67, GAT1, and GAT3 (GABAergic markers) confirmed the presence of widespread differences in hippocampal cultures that matured in the absence of tomosyns. Focusing on BDNF and other dense core vesicle proteins, qPCR studies revealed decreases in mRNA levels for a subset of dense core vesicle proteins.

The use of multiple culture systems allowed the authors to employ different approaches, ranging from monitoring the release of single granules expressing a dense core vesicle reporter to quantifying the accelerated trafficking of a tagged cargo protein from the ER through the TGN and into DCVs in the absence of tomosyns. While no changes in synaptic complex formation were observed, both electron microscopy and analysis of single vesicles expressing a dense core vesicle reporter revealed a decrease in granule diameter.

Weaknesses of methods and results. Within 8 h of plating, hippocampal cultures prepared from a single litter were transduced with a lentivirus encoding active or inactive mCherry-tagged Cre-recombinase, generating WT and DKO cultures; expression of Cre-recombinase was limited to neurons using the synapsin promoter. Cultures were generally examined after two weeks. Culture conditions were varied to allow comparison of dense core vesicle exocytosis by single neurons (a neuron on a glial microisland) or protein and mRNA levels in dense neuronal networks plated on coated plastic without a glial feeder layer in WT vs. DKO cultures. Whether cultures allowed to develop under these vastly different conditions respond to the absence of tomosyns in a different manner is unknown. No attempt was made to rescue any of the differences observed by expressing tomosyn in DKO neurons. Successful rescue experiments would alleviate concerns about the effects of developmental differences on the phenotypes observed.

Immunocytochemical studies revealed an approximately two-fold drop in BDNF protein levels in the soma and neurites of DKO neurons. In contrast, BDNF, which was detectable in WT cultures using mass spectrometry, was not detectable using mass spectrometry to analyze DKO cultures. No explanation for this discrepancy between immunocytochemistry and mass spectrometry is offered. Despite the fact that neither BDNF secretion nor BDNF degradation was assessed, the authors state in their Abstract that "tomosyns regulate neuropeptide and neurotrophin secretion via control of DCV cargo production".

The authors do not adequately refer to the rich literature discussing the many secretory pathways used by different cell types, referring only to synaptic vesicles and dense core vesicles. Golgi by-pass pathways are known to take membrane proteins to dendrites and tomosyns are known to play a role in the trafficking of GLUT4 from endocytic compartments to the plasma membrane. Soluble cargo proteins such as BDNF are released both constitutively and in response to stimuli. Cargo proteins (proinsulin, proANP, and growth hormone, for example) can drive the appearance of dense core vesicles.

The mass spectrometry data presented in Fig. 3 are not well incorporated into the Discussion. KIF6, which plays a role in retrograde Golgi to ER traffic, is detectable in DKO cultures, but not in WT cultures and could contribute to the accelerated trafficking phenotype observed using RUSH. Coordinate control of the expression of dense core vesicle genes has been studied in a variety of systems, ranging from mammals to C. elegans to D. melanogaster. Levels of these gene products could have been assessed using existing mass spectrometric data or by additional qPCR studies. The diminished levels of dense core vesicle reporters observed in Fig.1 remain unexplained. Intracellular degradation and increased basal secretion, neither of which was assessed, could contribute to this observation.<br /> The authors did not take advantage of the structure/function studies used to dissect the roles of the beta-propeller and SNARE-domains of tomosyns. In yeast, loss of SR07/SR077, tomosyn orthologs which lack a SNARE-like domain, causes a defect in the exocytosis of post-Golgi vesicles and the accumulation of secretory vesicles with altered composition (Forsmark et al., 2011).

Are claims and conclusions justified by data: The title of the manuscript, "SNARE protein tomosyn regulates dense core vesicle composition but not exocytosis in mammalian neurons" is misleading. The authors present no evidence that the SNARE-domain of tomosyn is necessary for its effects on dense core vesicle composition. The yeast orthologs of tomosyn, which lack a SNARE domain, affect post-Golgi vesicular trafficking via their beta-propeller domains. Hippocampal neurons are not representative of all "mammalian" neurons. In rat sympathetic neurons, tomosyn depletion results in a decrease in neurotransmitter release. A key conclusion is that tomosyns regulate neuropeptide and neurotrophin secretion by controlling cargo production, not cargo release - this conclusion is not supported by the data presented.

Likely impact of work on the field: The mouse line developed for these studies will be of great use in mechanistic studies of the multiple roles of tomosyns. The authors identified a range of parameters that are altered in hippocampal neurons which develop in the absence of tomosyns. Additional mechanistic studies are needed to directly assess the manner in which the absence of tomosyns contributes to these changes.

Reviewer #3 (Public Review):

This is a well-designed and well conducted study on the effect of 4 months sustained exercise on atrioventricular function and cardiac remodeling in a clinically relevant large animal (canine) model. All methods are well described with proper controls. The findings support the conclusion. Potential limitations are the study are clearly stated. The findings advance the field and provide clear evidence for the susceptibility of ventricular arrhythmia in the canine model of endurance training.

Reviewer #3 (Public Review):

In this work, Bachschmid-Romano et al. propose a novel model of the motor cortex, in which the evolution of neural activity throughout movement preparation and execution is determined by the kinematic tuning of individual neurons. Using analytic methods and numerical simulations, the authors find that their networks share some of the features found in empirical neural data (e.g., orthogonal preparatory and execution-related activity). While the possibility of a simple connectivity rule that explains large features of empirical data is intriguing and would be highly relevant to the motor control field, I found it difficult to assess this work because of the modeling choices made by the authors and how the results were presented in the context of prior studies.

Overall, it was not clear to me why Bachschmid-Romano et al. couched their models within a cosine-tuning framework and whether their results could apply more generally to more realistic models of the motor cortex. Under cosine-tuning models (or kinematic encoding models, more generally), the role of the motor cortex is to represent movement parameters so that they can presumably be read out by downstream structures. Within such a framework, the question of how the motor cortex maintains a stable representation of movement direction throughout movement preparation and execution when the tuning properties of individual neurons change dramatically between epochs is highly relevant. However, prior work has demonstrated that kinematic encoding models provide a poor fit for empirical data. Specifically, simple encoding models (and the more elaborate extensions [e.g., Inoue, et al., 2018]) cannot explain the complexity of single-neuron responses (Churchland and Shenoy, 2007), and do not readily produce the population-level signals observed in the motor cortex (Michaels, Dann, and Scherberger, 2016) and cannot be extended to more complex movements (Russo, et al., 2018).

In both the Introduction and Discussion, the authors heavily cite an alternative to kinematic encoding models, the dynamical systems framework. Here, the correlations between kinematics and neural activity in the motor cortex are largely epiphenomenal. The motor cortex does not 'represent' anything; its role is to generate patterns of muscle activity. While the authors explicitly acknowledge the shortcomings of encoding models ('Extension to modeling richer movements', Discussion) and claim that their proposed model can be extended to 'more realistic scenarios', they neither demonstrate that their models can produce patterns of muscle activity nor that their model generates realistic patterns of neural activity. The authors should either fully characterize the activity in their networks and make the argument that their models better provide a better fit to empirical data than alternative models or demonstrate that more realistic computations can be explained by the proposed framework.

Major Comments<br /> 1. In the present manuscript, it is unclear whether the authors are arguing that representing movement direction is a critical computation that the motor cortex performs, and the proposed models are accurate models of the motor cortex, or if directional coding is being used as a 'proof of concept' that demonstrates how specific, population-level computations can be explained by the tuning of individual neurons.<br /> If the authors are arguing the former, then they need to demonstrate that their models generate activity similar to what is observed in the motor cortex (e.g., realistic PSTHs and population-level signals). Presently, the manuscript only shows tuning curves for six example neurons (Fig. S6) and a single jPC plane (Fig. S8). Regarding the latter, the authors should note that Michaels et al. (2016) demonstrated that representational models can produce rotations that are superficially similar to empirical data, yet are not dependent on maintaining an underlying condition structure (unlike the rotations observed in the motor cortex).<br /> If the authors are arguing the latter - and they seem to be, based on the final section of the Discussion - then they need to demonstrate that their proposed framework can be extended to what they call 'more realistic scenarios'. For example, could this framework be extended to a network that produces patterns of muscle activity?

2. Related to the above point, the authors claim in the Abstract that their models 'recapitulate the temporal evolution of single-unit activity', yet the only evidence they present is the tuning curves of six example units. Similarly, the authors should more fully characterize the population-level signals in their networks. The inferred inputs (Fig. 6) indeed seem reasonable, yet I'm not sure how surprising this result is. Weren't the authors guaranteed to infer a large, condition-invariant input during movement and condition-specific input during preparation simply because of the shape of the order parameters estimated from the data (Fig. 6c, thin traces)?

3. In the Abstract and Discussion (first paragraph), the authors highlight that the preparatory and execution-related spaces in the empirical data and their models are not completely orthogonal, suggesting that this near-orthogonality serves an important mechanistic purpose. However, networks have no problem transferring activity between completely orthogonal subspaces. For example, the generator model in Fig. 8 of Elsayed, et al. (2016) is constrained to use completely orthogonal preparatory and execution-related subspaces. As the authors point out in the Discussion, such a strategy only works because the motor cortex received a large input just before movement (Kaufman et al., 2016).

Reviewer #3 (Public Review):

This work provides a new tool, a comprehensive PhIP-seq library, containing 238,068 individual 62-amino acids peptides tiled every 25-amino acid peptide covering all known 8,980 proteins of the deadliest malaria parasite, Plasmodium falciparum, to systematically profile antibody targets in high resolution. This phage display library has been screened by plasma samples obtained from 198 Ugandan children and adults in high and moderate malaria transmission settings and 86 US controls. This work identified that repeat elements were commonly targeted by antibodies. Furthermore, extensive sharing of motifs associated with seroreactivity indicated the potential for extensive cross-reactivity among antigens in P. falciparum. This paper provides a new proteome-wide high-throughput methodology to identify antibody targets that have been investigated by protein arrays and alpha screens to date. Importantly, only this methodology (PhIP-seq library) is able to investigate repeat-containing antigens and cross-reactive epitopes in high resolution (25-amino acid resolution).

Strengths:<br /> 1) Novel technology<br /> Firstly, the uniqueness of this study is the use of novel technology, the PhIP-seq library. This PhIP-seq library in this study contains >99.5% of the parasite proteome and is the highest coverage among existing proteome-wide tools for P. falciparum. Moreover, this library can identify antibody responses in high resolution (25 amino acids).<br /> Secondly, the PhIP-seq converts a proteomic assay (ie. protein array and alpha screen) into a genomic assay, leveraging the massive scale and low-cost nature of next-generation short-read sequencing.<br /> Thirdly, the phage display system is the ability to sequentially enrich and amplify the signal to noise.<br /> Finally, a high-quality strategic bioinformatic analysis of PhIP-seq data was applied.

2) Novel findings<br /> The major findings of this study were obtained only by using this novel technology because of its full-proteome coverage and high resolution. Repeat elements were the common target of naturally acquired antibodies. Furthermore, extensive sharing of motifs associated with seroreactivity was observed among hundreds of parasite proteins, indicating the potential for extensive cross-reactivity among antigens in P. falciparum.

3) Usefulness for the future research<br /> Importantly, plasma samples from longitudinal cohort studies will give the scientific community important insights into protective humoral immunity which will be important for the identification of vaccine and exposure-marker candidates in the near future.

Weaknesses:<br /> Although the paper does have strengths in principle, the weaknesses of the paper are the insufficient description of the selected parasite proteins and seroreactivity ranking of the selected proteins such as TOP100 proteins.

Reviewer #3 (Public Review):

The general objective of this work is the dissection of osteoclast diversity; in particular, the authors intend to identify the specific features and properties that distinguish inflammatory and steady-state (tolerogenic) osteoclasts. To this end, the authors perform a transcriptional analysis of inflammatory and tolerogenic osteoclasts and identify the pattern recognition receptors TLR2, Dectin-1, and Mincle as differentially expressed genes. Agonists of these receptors or yeast probiotics regulating the elicited mechanisms in vitro and in vivo caused a specific inhibition of the differentiation of inflammatory rather than tolerogenic osteoclasts, thus highlighting the preferential use of different differentiation pathways by the two distinct osteoclast populations.

The project is based on the previous knowledge and know-how of the authors on this peculiar skeletal cell population. The work is well conceived; the experiments are clearly designed and exploit state-of-the-art technologies. The results confirm the heterogeneity of osteoclasts and provide new insights in this respect. The in vitro and in vivo studies suggest that osteoclast heterogeneity can be purposedly modulated; which might be useful and advisable for therapeutic purposes. Overall, the work provides hints for further implementation and future broad applications to diseases featuring pathological bone loss.

Reviewer #3 (Public Review):

This is a well-conducted phase 2 randomized trial testing outpatient therapeutics for Covid-19. In this report of the platform trial, they test ivermectin, demonstrating no virologic effect in humans with Covid-19.

Overall, the authors' conclusions are supported by the data.

The major contribution is their implementation of a new model for Phase 2 trial design. Such designs would have been ideal earlier in the pandemic.

Reviewer #3 (Public Review):

The authors provide a molecular dynamics (MD)-based detailed evaluation of the contribution of the two elongated loops (alpha3-beta7 and beta12-alpha5), present near each active site of the tetrameric Stenotrophomonas maltophilia class B Metallo-beta-lactamase (MBL) L1, towards the L1's lactamase activity with the premise that a better understanding of the categorical conformational states sampled by the loops would ultimately help in the design of a better lactamase inhibitor. This is to then ultimately alleviate the public health crisis arising from β-lactam antibiotic resistance. Using enhanced sampling MD, Markov state modeling (MSM), and convolutional variation autoencoder (CAVE)-based deep learning, the authors identify five key interacting residues in these two loops which contribute to the conformational states of loops.

The major strength of the study is that the authors carry out a detailed study (e.g., enhanced sampling MD, Markov state modeling, and convolutional variation autoencoder-based deep learning) of the conformational landscape of an important enzyme as these findings would help further experimental studies (e.g., NMR dynamics) for ligand binding, better design of inhibitory ligands of an important class of enzyme. One weakness would be that MBL L1 is a good representative of the class of MBL enzymes or not needs clarification.

The authors achieve the goal of capturing the various conformational states of the L1 enzyme loops and their computational results support the conclusion about the various loop conformations sampled during the dynamics. However, how the mutagenesis experiment supports the existence of different conformational states will likely benefit from more clarification. Further clarification on how detecting the existence of multiple conformers benefits better inhibitor design will be very beneficial.

Since details on macromolecular motion are often neglected in macromolecular experimental studies, the detailed MD methods described here will be a very useful companion in experimental studies of proteins and their interactions.

A discussion on how the study of one particular enzyme could benefit in understanding the molecular properties of a class of enzymes would enhance the generality of the study.

Reviewer #3 (Public Review):

It is a brilliant idea to combine the MS2-MCP system with Suntag. As the authors stated, it reduces the copies of the MS2 stem loops, which can create challenges during cloning process. The Suntag system can easily amplify the signal by several to tens of folds to boost the signal for live RNA tagging. One of the best ways to claim that MASS works better than the MS2 system by itself is to compare their signal-to-noise ratios (SNRs) within the same model system, such as HeLa cells or the C. elegans epidermis. Because the authors' main argument is that they made an improvement in live RNA tagging method, it is necessary to compare it with other methods side-by-side. The authors claim that MASS can significantly improves the efficiency of CRISPR by reducing the size of the insert, it still requires knocking in several transgenes, which can be even more challenging in some model systems where there are not many selection markers are available. Another possible issue is that the bulky, heavy tagging (384 scFv-sfGFP along with 24xSuntag) can affect the mobility or stability of the target mRNAs. If it also tags preprocessed RNA in the nucleus, it may affect the RNA processing and nuclear export. A few experiments to address these possibilities will strengthen the authors' arguments. I am proposing some experiments below in detailed comments.

1. For the experiments with HeLa cells, it is not clear whether the authors used one focal plane or the whole z-stack for their assessment of mRNA kinetics, such as fusion, fission, and anchoring. If it was from one z-plane, it was possible that many mRNAs move along the z-axis of the images to assume kinetics. If the kinetics is true, is it expected by the authors? Are beta-actin mRNAs bound to some RNA-binding proteins or clustered in RNP complexes?<br /> 2. Some quantifications on beta-actin mRNA kinetics, such as a plot of their movement speed or fusion rate, etc., would help readers better understand the behaviors of the mRNAs and assess whether the MASS tagging did not affect them.<br /> 3. Using another target gene for MASS tagging would further confirm the efficacy of the system. Assuming the authors generated a parental strain of HeLa cell, where MCP-24xSuntag and scFv-sfGFP are already stably expressed (shown in Fig. 1B), CRISPR-ing in another gene should be relatively easy and fast.<br /> 4. Adding a complementary approach to the data presented in Fig. 1, such as qRT-PCR for beta-actin, with or without the MASS system would ensure the intense tagging did not affect the mRNA expression or stability.<br /> 5. For experiments with the C. elegans epidermis, including at least one more MASS movie clip for c42d4.3 and a movie for mai-1 would be helpful for readers to appreciate the RNA labeling and its dynamics.<br /> 6. The difference between Fig. 2D and Fig. 2-fig supp. 3 is unclear. The authors should address the different patterns of RNA signal propagation. Is it due to the laser power used too much, resulting in photobleach in Fig. 2D?<br /> 7. Movie 7 is the key data the authors are presenting, but there are a few discrepancies between their arguments and what is seen from the movie. The authors say the RNAs are "gradually spread" (the line 120 in the manuscript). However, it seems that the green foci just appear here and there in the epidermis and the majority of them stay where they were throughout the timelapse. This pattern seems to be different from the montage in Fig. 2-fig supp. 3, which indeed looks like the mRNA spots are formed around the lesion and spread overtime. Additional explanation on this will strengthen the arguments. Given the dramatic increase of c42d4.3 mRNA abundance 1 min. after the laser wounding, there must be a tremendous boost of transcription at the active transcription sites, which should be captured as much bigger and fewer green foci that are located inside the nucleus. Is this simply because those nuclear sites are out of focus or in a similar size as mRNA foci? Regardless, this should be addressed in the discussion.<br /> 8. One clear way to confirm that MASS labels mRNAs and does not affect their stability/localization is to compare the imaging data with single-molecule RNA fluorescence in situ hybridization (smFISH) that the Singer lab developed decades ago. The authors can target the endogenous c42d4.3 or mai-1 RNAs using smFISH and compare their abundance and subcellular localization patterns with their data.<br /> 9. One of the main purposes to live image RNAs is to assess their dynamics. Adding some more analyses, such as the movement speed of the foci, would be helpful to show how effective this system is to assess those dynamics features.

Reviewer #3 (Public Review):

In this paper, Zhang et al. investigated the regulation of the meiotic checkpoint kinase CHK-2, whose inactivation is a necessary step in ensuring that chromosomes have synapsed and received crossovers before progression to later events of meiotic prophase. Using mass spectrometry, biochemistry, and cytological analysis of mutant and transgenic strains, they show that CHK-2 is phosphorylated and that CHK-2 activity is attenuated in a manner dependent on recruitment of the kinase PLK-2 to a conserved docking motif on the synaptonemal complex, which forms between pairs of homologous chromosomes. The results plausibly explain how CHK-2 can remain active and prolong the events of early prophase chromosome dynamics in response to delays in synapsis since unsynapsed chromosomes will not recruit PLK-2 to inactivate CHK-2 locally. While molecular details remain to be worked out (e.g., why the loss of crossover intermediates can also extend CHK-2 activity; why PLK-2 does not inactivate CHK-2 at pairing centers), this work provides an elegant explanatory unification of several disparate observations.

The authors made extensive use of the auxin-inducible degron system combined with the spatiotemporal arrangement of the C. elegans germline to examine the effect of conditional depletion of proteins in cells where the depleted protein was required for earlier events. This is a powerful approach that can give stronger evidence than an examination of genetic mutant backgrounds, especially when, as in this paper, controls are performed to confirm the timing of depletion by loss of immunofluorescence signal. The method of measuring the proportion of the gonad occupied by nuclei with bright COSA-1 foci is generally robust, but the criteria for demarcation could be more strictly defined. For example, does a single nucleus with a single bright COSA-1 spot suffice to mark the beginning of a zone?

A weakness of this paper is that the non-phosphorylatable alleles constructed to provide a functional test of CHK-2 phosphorylation, unfortunately, had severe meiotic defects, so the importance of CHK-2 phosphorylation in its deactivation remains uncertain. While the results overall point towards direct phosphorylation of CHK-2 by PLK-2 (and possibly PLK-1), the authors are careful to point out that this is not the only possible explanation. In this regard, the mass spectrometry data should be given a statistical analysis to see whether they are best explained by in vitro phosphorylation of CHK-2 by PLK-2.

Reviewer #3 (Public Review):

The work by Olson and colleagues provides novel, fundamental insights into the role of HLA polymorphisms in the processing of exogenous antigens via the non-canonical vacuolar and cytosolic pathways. The choice of the two exemplar HLA-B allotypes leverages a significant amount of background work done both by the Raghavan lab and others, together with a series of novel and very elegant in vitro assays to elucidate a trend where differences in peptide binding preferences and other molecular features can have a drastic effect on non-canonical processing of exogenous antigens. Finally, using two related cell types (monocytes and monocyte-derived DCs) it highlights important differences in endo-lysosomal assemble within different cell types, an aspect of the non-canonical antigen processing that has not been sufficiently addressed in previous studies. While the number of allotypes and cell types utilized in this study is small (n=2 in each case), it provides an elaborate view into the vacuolar processing pathway and motivates further studies on a more expanded set of alleles in future studies. Finally, it underscores the importance of defining the expression of HLA expression levels in the context of specific cell types, setting a standard for future studies in the field.

Moreover, the work outlined in this study is technically sound, with sufficient attention to detail, adequate control experiments, and a rigorous statistical analysis of the resulting data when needed. Overall, the conclusions are well supported by the data. The manuscript is written in a clear, succinct manner to comprehend by a wide audience of readers.

One shortcoming of the paper is a lack of molecular characterization of the peptide-receptive MHC-I species at different stages of their assembly and trafficking process. For instance, while the authors utilize a monoclonal antibody (HC10) to probe empty MHC-I conformers and their dynamics, they don't provide further analysis of interactions with the light chain, a component of the complex that is known to be critical for regulating the internalization and peptide-loading process, both on the cell surface and at different intracellular compartments. Finally, while the overall effects on the cross-presentation of specific EBV antigens by the two allotypes are well described, what is lacking is a more quantitative analysis of the number of molecules, their densities, and distribution on the cell surface, all of which are known to have important consequences for T cell stimulation.

Reviewer #3 (Public Review):

Yeatman et al. tested whether the emergence of brain regions that selectively process novel visual stimuli like words occur at the expense of cortical representations of other stimuli like faces and objects. They conducted a randomized controlled trial with preschool children (five years of age) that were either taught reading or oral language skills. They found that being taught reading versus oral language skills induced different patterns of change in category-selective regions of the visual cortex. Their main conclusion is that reading instruction enhanced the response to text but did not diminish the response to other categories.

The main novelty of this study seems to be that they conducted a randomized controlled trial. The study is well crafted and executed. However, based on the current methodology, it is unclear if they shed novel light on the cortical recycling hypothesis.

Reviewer #3 (Public Review):

The research question is highly relevant as far too little is known about the efferent olivocochlear system, and the methods are state-of-the-art. This is high-quality work both for molecular analysis as well as for LOC physiology. The study is well-designed and executed, the manuscript is elegantly prepared. The high-quality gene expression data from a region of the ventral brainstem at 3 different postnatal time points (P1, P5, P26-28) is impactful in terms of development, heterogeneity, and physiological relevance of OCNs. I expect the data of this study to become instrumental for future functional studies on the lateral efferent olivocochlear system.

One issue inherent to transcriptomics studies is the challenge of linking RNA levels to protein levels for functional interpretation. I would ask the authors to acknowledge this and (still more) carefully draw conclusions. For example, name the differentiation of LOC from MOC based on collagen (Col4a4) expression or Gad2 vs. Htr2c for differentiating OCNs from FMNs. Moreover, the lack of physiological differences in soma recordings would seem to suggest a rather homogeneous phenotype but certainly does not exclude the postulated different presynaptic functions of LOC2 and LOC1 neurons.

I am worried that the NPY-based identity in the sparse labeling experiment meant to selectively report LOC2 might not be such a safe approach. This is even more concerning considering that the NPY identity of presynaptic terminals varies within a given axon. Therefore, wonder why the authors did not perform more immunohistochemical labeling of LOC2 and LOC1 markers in the cochlea. Also, it would be great to see how LOC subtype specification changes in genetically deaf and noise-deafened mice.

Reviewer #3 (Public Review):

In this study, the authors investigate the genetic and environmental causes of elevated Mitochondrial Membrane Potential (MMP) in yeast, and also some physiological effects correlated with increased MMP.

The study begins with a reanalysis of transcriptional data from a yeast mutant lacking the gene MCT1 whose deletion has been shown to cause defects in mitochondrial fatty acid synthesis. The authors note that in raffinose mct1del cells, unlike WT cells, fail to induce expression of many genes that code for subunits of the Electron Transport Chain (ETC) and ATP synthase. The deletion of MCT1 also causes induction of genes involved in acetyl-CoA production after exposure to raffinose. The authors therefore conduct a screen to identify mutants that suppress the induction of one of these acetyl-CoA genes, Cit2. They then validate the hits from this screen to see which of their suppressor mutants also reduce expression in four other genes induced in a mct1del strain. This yielded 17 genes that abolished induction of all 5 genes tested in an mct1del background during growth on raffinose.

The authors chose to focus on one of these hits, the gene coding for the phosphatase SIT4 (related to human PP6) which also caused an increase in expression of two respiratory chain genes. The authors then investigated MMP and mitochondrial morphology in strains containing SIT4 and MCT1 deletions and surprisingly saw that sit4del cells had highly elevated MMP, more reticular mitochondria, and were able to fully import the acetolactate synthase protein Ilv2p and form ETC and ATP synthase complexes, even in cells with an mct1del background, rescuing the low MMP, fragmented mitochondria, low import of Ilv2 and an inability to form ETC and ATP synthase complexes phenotypes of the mct1del strain. Surprisingly, the authors find that even though MMP is high and ETC subunits are present in the sit4del mct1del double deletion strain, that strain has low oxygen consumption and cannot grow under respiratory conditions, indicating that the elevated MMP cannot come from fully functional ETC subunits. The authors also observe that deleting key subunits of ETC complex III (QCR2) and IV (COX5) strongly reduced the MMP of the sit4del mutant, which would suggest that the majority of the increase in MMP of the sit4del mutant was dependant on a partially functional ETC. The authors note that there was still an increase in MMP in the qcr2del sit4del and cox4del sit4del strains relative to qcr2del and cox4del strains indicating that some part of the increase in MMP was not dependent on the ETC.

The authors dismiss the possibility that the increase in MMP could have been through the reversal of ATP synthase because they observe that inhibition of ATP synthase with oligomycin led to an increase of MMP in sit4del cells. Indicating that ATP synthase is operating in a forward direction in sit4del cells.

Noting that genes for phosphate starvation are induced in sit4del cells, the authors investigate the effects of phosphate starvation on MMP. They found that phosphate starvation caused an increase in MMP and increased Ilv2p import even in the absence of a mitochondrial genome. They find that inhibition of the ADP/ATP carrier (AAC) with bongkrekic acid (BKA) abolishes the increase of MMP in response to phosphate starvation. They speculate that phosphate starvation causes an increase in MMP through the import and conversion of ATP to ADP and subsequent pumping of ADP and inorganic phosphate out of the mitochondria.

They further show that MMP is also increased when the cyclin dependent kinase PHO85 which plays a role in phosphate signaling is deleted and argue that this indicates that it is not a decrease in phosphate which causes the increase in MMP under phosphate starvation, but rather the perception of a decrease in phosphate as signalled through PHO85. Unlike in the case of SIT4 deletion, the increase in MMP caused by the deletion of pho85 is abolished when MCT1 is deleted.

Finally they show an increase in MMP in immortalized human cell lines following phosphate starvation and treatment with the phosphate transporter inhibitor phosphonoformic acid (PFA). They also show an increase in MMP in primary hepatocytes and in midgut cells of flies treated with PFA.

The link between phosphate starvation and elevated MMP is an important and novel finding and the evidence is clear and compelling. Based on their experiments in various mammalian contexts, this link appears likely to be generalizable, and they propose and begin to test an interesting hypothesis for how MMP might occur in response to phosphate starvation in the absence of the Electron Transport Chain.

The link between phosphate starvation and deletion of the conserved phosphatase SIT4 is also interesting and important, and while the authors' experiments and analysis suggest some connection between the two observations, that connection is still unclear.

Major points

Mitotracker is great fluorescent dye, but it measures membrane potential only indirectly. There is a danger when cells change growth rates, ion concentrations, or when the pH changes, all MMP indicating dyes change in fluorescence: their signal is confounded Change in phosphate levels can possibly do both, alter pH and ion concentrations. Because all conclusions of the manuscript are based on a change in MMP, it would be a great precaution to use a dye-independent measure of membrane potential, and confirm at least some key results.

Mitochondrial MMP does strongly influence amino acid metabolism, and indeed the SIT4 knockout has a quite striking amino acid profile, with histidine, lysine, arginine, tyrosine being increased in concentration. http://ralser.charite.de/metabogenecards/Chr_04/YDL047W.html<br /> Could this amino acid profile support the conclusions of the authors? At least lysine and arginine are down in petites due to a lack of membrane potential and iron sulfur cluster export.- and here they are up. Along these lines, according to the same data resource, the knock-outs CSR2, ASF1, SSN8, YLR0358 and MRPL25 share the same metabolic profile. Due to limited time I did not re-analyse the data provided by the authors- but it would be worth checking if any of these genes did come up in the screens of the authors.

One important claim in the manuscript attempts to explain a mechanism for the MMP increase in response to phosphate starvation which is independent of the ETC and ATP synthase.

It seems to me the only direct evidence to support this claim is that inhibition of the AAC with BKA stops the increase of mitotracker fluorescence in response to phosphate starvation in both WT and rho0 cells (Figs 4B and 4C). It would strengthen the paper if the authors could provide some orthogonal evidence.

Introduction/Discussion The author might want to make the reader of the article aware that the 'reversal' of the ATP synthase directionality -i.e. ATP hydrolysis by the ATP synthase as a mechanism to create a membrane potential (in petites), has always been a provocative idea - but one that thus far could never be fully substantiated. Indeed some people that are very familiar with the topic, are skeptical this indeed happens. For instance, Vowinckel et al 2021 (PMID: 34799698) measured precise carbon balances for peptide cells, and found no evidence for a futile cycle - peptides grow slower, but accumulate the same biomass from glucose as peptides that re-evolve at a fast growth rate . Perhaps the manuscript could be updated accordingly.

In the introduction and conclusion there is discussion of MMP set points. In particular the authors state:

"Critically, we find that cells often prioritize this MMP setpoint over other bioenergetic priorities, even in challenging environments, suggesting an important evolutionary benefit."

This does not seem to be consistent with the central finding of the manuscript that MMP changes under phosphate starvation. MMP doesn't seem so much to have a 'set point' but rather be an important physiological variable that reacts to stimuli such as phosphate starvation.

The authors suggest that deletion of Pho85 causes an increase in MMP because of cellular signaling. However, they also state in the conclusion:

"Unlike phosphate starvation, the pho85D mutant has elevated intracellular phosphate concentrations. This suggests that the phosphate effect on MMP is likely to be elicited by cellular signaling downstream of phosphate sensing rather than some direct effect of environmental depletion of phosphate on mitochondrial energetics."

The authors should cite the study that shows deletion of PHO85 causes increased intracellular phosphate concentrations. It also seems possible that the 'cellular signaling' that causes the increase in MMP could be a result of this increase in intracellular phosphate concentrations, which could constitute a direct effect of an environmental overload of phosphate on mitochondrial energetics.

Related to this point, in the conclusion, the authors state:

"We now show that intracellular signaling can lead to an increased MMP even beyond the wild-type level in the absence of mitochondrial genome."

In sum, the data shows that signaling is important here- but signaling alone is only the message - not the biophysical process that creates a membrane potential. The authors then could revise this slightly.

The authors state in the conclusion that

"We first made the observation that deletion of the SIT4 gene, which encodes the yeast homologue of the mammalian PP6 protein phosphatase, normalized many of the defects caused by loss of mtFAS, including gene expression programs, ETC complex assembly, mitochondrial morphology, and especially MMP (Fig. 1)"

The data shown though indicates that a defect in mtFAS in terms of MMP, deletion of SIT4 causes a huge increase (and departure away from normality) whether or not mct1 is present (Fig 1D)

The language "SIT4 is required for both the positive and negative transcriptional regulation elicited by mitochondrial dysfunction" feels strong. SIT4 seems to influence positive transcriptional regulation in response to mitochondrial dysfunction caused by MCT1 deletion (but may not be the only thing as there appears to be an increase in CIT2 expression in a sit4del background following a further deletion of MCT1). In terms of negative regulation, SIT4 deletion clearly affects the baseline, but MCT1 deletion still causes down regulation of both examples shown in Fig 1B, showing that negative transcriptional regulation can still occur in the absence of SIT4. The authors might consider showing fold change of expression as they do in later figures (Figs 4B and C) to help the reader evaluate the quantitative changes they demonstrate.

The authors induce phosphate starvation by adding increasing amounts of potassium phosphate monobasic at a pH of 4.1 to phosphate dropout media supplemented with potassium. The authors did well to avoid confounding effects of removing potassium. The final pH of YNB is typically around 5.2. Is it possible that the authors are confounding a change in pH with phosphate starvation? One would expect the media in the phosphate starvation condition to have a higher pH than the phosphate replacement or control media. Is a change in pH possibly a confounding factor when interpreting phosphate starvation? Perhaps the authors could quantify the pH of the media they use for the experiment to understand how much of a factor that could be. One needs to be careful with Miotracker and any other fluorescent dye when pH changes. Albeit having constraints on its own, MitoLoc as a protein rather than small molecule marker of MMP might be a good complement.

Reviewer #3 (Public Review):

The authors' aim was to examine the early stages of the HIV-1 packaging process inside cells, with specific focus upon how the Gag protein and its cognate domains mediate the initial interaction with the packaging signal on the genomic RNA. The technique that has generated the majority of results in the paper is a modified version of CLIP. The authors have achieved this aim well, with data that clearly support the importance of Capsid, as well as the importance of two different aspects of RNA structure, the IP6 binding site, and various sites that help to form the dimer, trimer and hexamer interfaces on Gag. The major conclusions of the paper, that an immature Gag lattice is needed to form, that NC alone is insufficient to mediate specific recognition of the packaging signal within cells, and that various aspects of Capsid are necessary, are clearly supported by the data.

A particular strength of the paper is the way in which the viral protein and RNA are expressed within cells - these derive from the same construct, which is essentially the proviral genome with mutations to enable the authors to study the various truncations/mutations of Gag and/or the RNA structure. The authors could instead have transfected separate packaging signal/gRNA and viral protein plasmids, but in ensuring that the viral proteins are translated from the same RNA molecule that can also be packaged, they recapitulate the native viral situation in a state of the art experimental form. This is important in terms of the conclusions they can draw, because although HIV-1 can co-package some other lentiviruses, and HIV-1 packaging can occur in trans (ie where 2 gRNA molecules are packaged by molecules of Gag that have not been translated from them), the experiments determining copackaging ability are sometimes not performed in a competitive or limiting system, so it is difficult to say whether there is indeed some remaining importance of co-translational packaging in the very early stages of HIV-1 Gag-psi recognition. Expression of gRNA and protein from the same construct also ensures a balance in stoichiometry within the cytoplasm that is representative of a native infection.

The weakness within the paper is the lack of consideration of how Gag concentration within the cytosol may affects its binding kinetics, both with itself and with the RNA. The CLIP experiments are internally controlled in that they measure binding to the packaging signal relative to the rest of the genome; however, the authors do not appear to have checked that all constructs were expressing at roughly equivalent amounts. This is especially important when interpreting data from a protein such as Gag, which undergoes very complex multimerization, and when considering that the RNA also multimerizes. Both of these multi-step events may alter according to the actual concentrations of both Gag and RNA, and not just the stoichiometric ratio of the two. Some of the data that are needed to provide this evidence are present within the paper already, as western blots analysing multimerization of Capsid mutants, and look to broadly support the expression of the constructs at similar levels. More consideration of this point would strengthen the paper.

The authors place their findings in the context of the field very well. They appear to have considered multiple lines of evidence and to have accounted broadly for previous work done. I do find the discussion of Capsid mutants, and the dimer, trimer and hexamer interfaces quite protein-centric though. I wonder whether there might be a larger role for the RNA structure and structural changes in bringing together the precise Gag lattice structure in some sort of step-wise fashion.

Overall, the manuscript is of great value to the retroviral research community, as it provides data from a highly relevant biological setting. Such data has largely been lacking within the field.

Reviewer #3 (Public Review):

STRENGTHS

• This ambitious study is broad in scope, beginning with a bacterial GWAS study and extending all the way to in vivo guinea pig infection models.

• Numerous reports have attempted to identify Mtb strains with elevated mutation rates, and the results are conflicting. The present study sets out to thoroughly evaluate one such mutation that may produce a mutator phenotype, mutY-Arg262Gln.

WEAKNESSES

• While the authors follow-up experiments with the mutY-Arg262Gln allele are all consistent with the conclusion that this mutation elevates the mutation rate in Mtb and thus could promote the evolution of drug resistance, further work is needed to unambiguously demonstrate this link.

• The authors highlight five mutations in genes associated with DNA replication and or repair from their GWAS analysis:

o dnaA-Arg233Gln: as the authors note in the Discussion, Hicks et al. associate SNPs in dnaA with low-level isoniazid resistance, as a result of lowered katG expression. Since this is unrelated to their focus on DNA repair genes whose mutation could elevate mutation rates, I would consider removing this allele from the Table.

o mutY-Arg262Gln: querying publicly available whole genome sequences of clinical Mtb isolates, this SNP appears to be restricted to lineage 4.3 (L4.3). All of these L4.3 strains appear to be drug-resistant. How many times did the mutY-Arg262Gln mutation evolve in the authors dataset? If there is evidence of homoplastic evolution, this would strengthen their case. If not, it doesn't mean the authors findings are incorrect, but does elevate that risk that this mutation could be a passenger (i.e. not driver) mutation. To address this, the authors could attempt to date when the mutY-Arg262Gln arose. If it was before the evolution of drug-resistance conferring alleles in these L4.3 strains, that is consistent with (but not proof of) a driver mutation. If mutY-Arg262Gln arose after, this is much more consistent with a passenger mutation.

o uvrB-Ala524Val: curiously we don't see this SNP in our dataset of publicly available whole genome sequences of clinical Mtb isolates (~45,000 genomes).

o uvrA-Gln135Lys: this SNP also appears to be restricted to lineage 4.3. Same question as for mutY-Arg262Gln.

o recF-Gly269Gly: this is a very common mutation, is it unique to lineage 2.2.1? Same question as for mutY-Arg262Gln.

• The CRYPTIC consortium recently published a number of preprints on biorxiv detailing very large GWAS studies in Mtb. Did any of these reports also associate drug resistance with mutY? If yes, this should be stated. If not, the potential reasons for this discrepancy should be discussed.

• Based on the authors follow-up studies in vivo, MutY-Arg262Gln is presumed to be a loss-of-function allele. If the authors could convincingly demonstrate this biochemically with recombinant proteins, this would significantly strengthen their case.

• If the authors are correct and mutY-Arg262Gln strains have elevated mutation rates, presumably there would be evidence of this in the clinical strain sequencing data. Do mutY-Arg262Gln containing strains have elevated C→G or C→A mutations in their genomes? Presumably such strains would also have a higher number of SNPs than closely related strains WT for mutY- is this the case?

• While more work, mutation rates as measured by Luria-Delbruck fluctuation analysis are more accurate than mutation frequencies. I would recommend repeating key experiments by Luria-Delbruck fluctuation analysis. It is also important to report both drug-resistant colony counts and total CFU in these sorts of experiments. Given the clumpy nature of mycobacteria, mutation rates can appear to be artificially elevated due to low total CFU and not an increase in the number of drug-resistant colonies.

• Figure 4 would appear to measuring drug tolerance not resistance? Are the elevated CFU in the presence of drugs in the mutY-Arg262Gln strain due to an increase in the number of drug resistant strains or drug sensitive strains? This could be assessed by quantifying resulting CFU in the presence or absence the indicated drugs.

Reviewer #3 (Public Review):

Wang et al. show a new role for the small heat-shock protein Hsp47 in the assembly and plasma membrane trafficking of GABAA receptors and other heptameric neuroreceptors. Hsp47 (SERPINH1) is primarily known as a collagen-specific molecular chaperone, but it has been increasingly recognized as important for other protein clients. In a prior mass spectrometry study from the same group, Hsp47 was identified as the most enriched interaction partner of GABAA neurotransmitter-gated ion channels. In this study, the authors now follow up on the functional role of Hsp47 for the GABAA heteromer assembly and its cell-surface trafficking.

Strengths:<br /> The authors show convincingly that Hsp47 plays an important role in promoting the cell surface expression and activity of GABAA receptors. Knockdown of Hsp47 in rat primary neurons decreases endogenous GABAA protein subunits on the cell surface and GABA-induced currents. Overexpression of Hsp47 in HEK293T increases abundance and cell surface trafficking of exogenously expressed GABAA subunits. Importantly, the overexpression of Hsp47 also rescues cell surface expression and channel currents of epilepsy-associated mutant GABAA receptors (alpha1 A332D), which could point to a future avenue to ameliorate pathogenic misfolding. The authors use a variety of experimental approaches to glean the mechanism by which Hsp47 promotes GABAA cell surface expression. In vitro GST pulldown experiments confirm a direct interaction between Hsp47 and the alpha1 and beta2 subunits. Site-directed mutagenesis and DTT addition indicate that the formation of a disulfide bond in the alpha1 subunits is critical for the Hsp47 interactions, leading the authors to conclude that Hsp47 is likely to bind to a more folded state of the subunit. In contrast, the ER Hsp70 chaperone BiP binds more strongly when the disulfide bond is disrupted, which corresponds to a more misfolded state as indicative of more alpha1 in the insoluble fraction. FRET assays and non-reducing gels to monitor GABAA receptor assembly again show that Hsp47 overexpression promotes the formation of the alpha1-beta2 complex. However, while these experiments are generally carried out thoroughly and the data is presented well, the results are interpreted too narrowly to only support their proposed models without considering alternative possibilities (see more below). Lastly, the authors show that Hsp47 overexpression also enhances the cell-surface expression and peak currents of another heteropentameric Cys-loop superfamily neuroreceptor, namely the a4b2 nicotinic acetylcholine receptor.

Weaknesses:<br /> The authors propose a compelling model in Figure 7 by which Hsp47 binds to a late-stage, largely folded alpha1 or beta2 subunit essentially acting as a holdase to promote assembly into larger dimers or other folding intermediates. However, the data in the manuscript would also support alternative models that the authors should more carefully consider. For instance, Hsp47 overexpression leads to a buildup of additional alpha1 and beta2 subunits (as described in lines 256-258 and seen in Fig. 4C), suggesting that Hsp47 may instead prevent subunits from getting degraded. Conclusions about Hsp47 binding after BiP to a largely folded state are indirectly based on shifts in the steady population of WT or misfolded GABAA subunits, but Hsp47 overexpression may in turn influence this equilibrium. Without any experiments examining the kinetics of protein interactions, degradation, or cell surface expression conclusions are difficult to interpret. Lastly, most experiments are carried out in HEK293T, which does not endogenously express GABAA or other neuroreceptors. There is a disconnect between the knockdown studies in rat primary hippocampal neurons and the overexpression experiments in HEK293T cells. The loss of GABAA receptor trafficking and function in the neurons could result from the secondary effect of the Hsp47 knockdown.

Overall, the study provides valuable new insights into the client scope of the ER small heat shock protein Hsp47, advances our understanding of neuroreceptor proteostasis, and provides potential corrective strategies to enhance the expression of epilepsy-associated mutations through targeting Hsp47. Hence, the paper should have broader relevance for a readership interested in proteostasis, membrane protein trafficking, and neuroreceptor signaling. However, I recommend addressing the following comments, mainly because the study in its current form only incompletely corroborates the authors' conclusions about the mechanism by which Hsp47 facilitates the neuroreceptor subunit assembly:

• For the in vitro experiments in Fig. 1, it would be important to show controls that the recombinantly expressed alpha1(ERD) adopts a well-folded state. Similarly, how did the authors ensure that the alpha1- and beta2-GST proteins adopt a folded (or near-folded) conformation?<br /> • In several experiments (e.g. Fig. 2A, Fig. 4B-C, Fig. 5B) IF staining or Western blots for the alpha1 and beta2 subunits are taken as a proxy for full GABA receptor assembly. Are the other subunits (e.g. gamma2) present and can they be detected?<br /> • Does Hsp47 knockdown in the primary hippocampal neurons leads to other changes in proteostasis network composition, e.g. UPR activation? This will be important to quantify to ensure that the reduced GABAA function can be directly attributed to the loss of Hsp47.<br /> • How are the Hsp47 knockdown and overexpression phenotype in the 2 different cell lines connected? If Hsp47 abundance is a limiting factor for GABAA proteostasis, it would be helpful to show (e.g. by lentivirus transduction) that additional Hsp47 can increase GABAA surface expression in the primary neurons.<br /> • Increased alpha1 and beta2 monomers in Fig. 4C suggest that the increase in receptor complex formation is likely due to more subunits being present when Hsp47 is overexpressed. Does Hsp47 prevent the degradation of excess or misfolded subunits? This can be easily tested with cycloheximide-chase or pulse-chase assays.<br /> • Does Hsp47 overexpression lead to more alpha1(A332D) monomer build up in cells (similarly to the WT alpha1)? The total level of alpha1(A332D) should be quantified for Fig. 5B. Similarly in Fig. 6A, does Hsp47 overexpression stabilize the abundance of nAChR subunits? The authors could easily quantify the abundance of individual subunits by Western blot.<br /> • Did the authors test the effect of Hsp47 overexpression on the trafficking of other misfolding-prone GABAA subunit variants? For therapeutic purposes, it will be important to evaluate a broader set of variants. Even if Hsp47 only restores select variants, these results would be useful for pinpointing a mechanism by which Hsp47 binds to the receptor subunits.

Reviewer #3 (Public Review):