Reviewer #1 (Public Review):

The authors demonstrate that modest oscillatory changes in the E-I ratio occur throughout the day and are linked to changes in both synaptic excitation and inhibition. These conclusions were based on adequately sampled electrophysiological data of stimulation-driven and non-evoked excitatory and inhibitory currents. For these studies, a fixed stimulation was not used across slice recordings but was limited to intensities where the E-I ratio was stable. Two points may need further clarification in the text. Firstly, authors might comment on whether current magnitudes plateau at these stimulation intensities. Secondly, make clear why the cause of E-I balance changes was not elucidated from convergent, evoked measurements in the same cell, but instead relied on non-evoked measures of spontaneous miniature excitatory postsynaptic currents and miniature inhibitory postsynaptic currents (mEPSCs and mIPSCs) that were recorded separately in different cell populations. mEPSCs and mIPSCs data analysis relied on statistical scrutiny within genotype and could gain additional rigor and benefits to study reproducibility by applying tests ( e.g. two- way repeated measures (RM) ANOVA) that consider the influence of both genotype and time of day. With this approach, the authors could determine in figures 3 and 4 whether control (B6) mice exhibit the predicted increase in mEPSCs and reduction in mIPSCs at ZT0 when compared to its ASD mouse model. In a noteworthy experiment, the authors connect abnormalities in inhibitory oscillations to altered endocannabinoid signaling using measurements of spontaneous (s) IPSCs, where changes in sIPSC charge were noted. The measurements used to make the paper's conclusion lacked consistency and the authors can bridge these differences by testing whether WIN agonist treatment can restore normal daily E/I oscillation in FMR1 KO and BTBR mice using the stimulation-evoked measurements from figure 1. The study used male and female BTBR mice and only male Fmr1 KO mice. Sex- effects in the study were not disclosed, so it is unclear whether daily E/I oscillation changes were similar in male and female BTBR mice or occur at all in female Fmr1 KO mice. Lastly, numerous studies have noted significant changes in the magnitude of the E-I ratio in an autism mouse model and causally linked these changes to alterations in disorder-related behavior or homeostatic regulation of circuit activity. However, in this current study, neither the loss nor reversal of daily E/I ratio oscillation were causally linked to alterations in sleep timing and architecture or any change in behavioral phenotype. On a promising note, the authors did find a slight decrease in NREM delta power in the Fmr1 KO and a larger decrease in the BTBR mice. Future mechanistic studies on this topic may aim to buttress support for E/I oscillations rather than alterations to the overall E/I level in causing autism-related phenotypes by providing supporting examples of biological significance.

Reviewer #1 (Public Review):

Pathological conformation and aggregation of tau protein are involved in several neurodegenerative diseases such as tauopathies and Alzheimer's disease. Identifying drug-candidates capable of interfering with pathological transformation of tau remains a challenge for which sensitive and specific assays are needed. This article describes the development and characterization of tau biosensors based on NanoBit technology (nanoluciferase complementation). It is a well-designed and precise study providing very interesting new tools.

Strengths<br /> 1/ The authors have developed a variety of tau biosensors: some that can be used for basic research to monitor pathological tau transformation and others with properties suitable for drug screening. All biosensors make it possible to evaluate the action of different agents (chemical products, purified proteins, cell or tissue extracts) in a living cell.

2/ The intermolecular biosensors developed from a shortened version of tau (K18), or the full-length tau, and carrying the P301L mutation possess sensibility and specificity allowing their further development to identify drug-candidates interfering with tau self-interaction.

3/ The characterization of the tau probes confirmed the physiological and pathological knowledge concerning the tau protein: - proximity of full-length tau when bound to microtubules, - conformational changes of tau during its phosphorylation and - tau-self interaction induced by pathological seeds.

Weaknesses<br /> 1/ The suitability of tau biosensors for high-throughput screening needs to be further developed as experiments were only performed in 96-well plates. No scaling up in 384- or 1536-well plates was attempted. Moreover, the transfection of the biosensors could be tedious.

2/ The models used are basic (HEK-293T cells associated with tau aggregates, Aβ oligomers or mouse brain lysates). The number of drugs tested is also quite low.

Collectively, the conclusions drawn by the authors are supported by the results. These new biosensors will be easily usable by the scientific community in fundamental research and could also be of interest to pharmaceutical laboratories wishing to carry out screenings of molecules capable of impacting the pathological transformation of tau.

Reviewer #1 (Public Review):

The authors use a newly developed object-space memory task comprising of a "Stable" version and "Overlapping" version where two objects are presented in two locations per trial in a square open field. Each version consists of 5 training trials of 5-min presentations of an object-space configuration, with both object locations staying constant across training trials in the Stable condition, and only one object location staying fixed in the Overlapping condition. Memory is tested in a test trial 24 hours later where the opposite configuration is presented - overlapping configuration presented for the Stable condition and stable configuration presented for the Overlapping condition - with the thesis that memory in this test trial for the Overlapping condition will depend on the accumulated memory of spatial patterns over the training trials, whereas memory for the test trial in the Stable condition can be due to episodic memory of last trial or accumulated memory. Memory is quantified using a Discrimination Index (DI), comparing the amount of time animals spend exploring the two object locations.

Here, animals in other groups are also presented with an interference trial equivalent to the test trial, to test if the memory of the Overlapping condition can be disrupted. The behavioral data show that for RGS14 over-expressing animals, memory in the Overlapping condition is diminished compared to controls with no interference or controls where over-expression is inhibited, whereas memory in the Stable condition is enhanced. This is interpreted as interference in semantic-like memory formation, whereas one-shot episodic memory is improved. The authors speculate that increased cortical plasticity should lead to increased and larger delta waves according to the sleep homeostasis hypothesis, and observe that instead increased cortical plasticity leads to less non-REM sleep and smaller delta waves, with more prefrontal neurons with slower firing rates (presumably more plastic neurons). They further report increased hippocampal-cortical theta coherence during task and REM sleep, increased NonREM oscillatory coupling, and changes in hippocampal ripples in RGS14 over-expressing animals.

While these results are interesting, there are several issues that need to be addressed, and the link between physiology and behavioral results is unclear.

1) The behavioral results rely on the interpretation that the Overlapping condition corresponds to semantic-like memory and the Stable condition corresponds to episodic-like memory. While the dissociation in memory performance due to interference seen in these two conditions is intriguing, the Stable condition can correspond not just to the memory of the previous trial but also accumulated memory of a stable spatial pattern over the 5 testing trials, similar to accumulated memory of a changing spatial pattern in the Overlapping pattern.

Here, it is puzzling that in the behavioral control with no interference (Figure 1D), memory in the Stable and Overlapping condition is unchanged in the test trial, with the DI statistically at 0 in the test trial. In the original description of the Object Space task by the authors in the referenced paper, the measure of memory was a Discrimination Index significantly higher than 0 in both the Stable and Overlapping conditions. This discrepancy needs to be reconciled. Is the DI for the interference trial shown in Fig. S1 significantly different than 0? No statistics or description is provided in the figure legend here.

2) The physiology experiments compare Home cage (HC) conditions to the Object Space task (OS) throughout the manuscript. While some differences are seen in the control and RGS14 over-expressing animals, there is no comparison of the Stable vs. Overlapping condition in the physiology experiments. This precludes making explicit links between physiological observations and behavioral effects.

3) The authors speculate that learning will result in larger and more delta waves as per the synaptic homeostasis hypothesis. It should be noted here that an alternative hypothesis is that there should also be a selective increase in synaptic plasticity for learning and consolidation. The authors do observe that control animals show more frequent and higher-amplitude delta waves, but rather than enhancing this process, RGS14 animals with increased plasticity show the opposite effect. How can this be reconciled and linked with the behavioral data in the Stable and Overlapping condition? Similarly, there is an increase in slower-firing neurons in RGS14 over-expressing animals. Slower-firing neurons have been proposed to be more plastic in the hippocampus based on their participation in learned hippocampal sequences, but appropriate references or data are needed to support the assertion that slower-firing neurons in the prefrontal cortex are more plastic.

4) It is noted that changing cortical plasticity influences hippocampal-cortical coupling and hippocampal ripples, suggesting a cortical influence on hippocampal physiological patterns. It has been previously shown that disrupting prefrontal cortical activity does alter hippocampal ripples and hippocampal theta sequences (Schmidt et al., 2019; Schmidt and Redish, 2021). The current results should be discussed in this context.

Reviewer #1 (Public Review):

The authors push a fresh perspective with a sufficiently sophisticated and novel methodology. I have some remaining reservations that concern the actual make-up of the data basis and consistency of results between the two (N=16) samples, the statistical analysis, as well as the "travelling" part.

I previously commented on the fact that findings from both datasets were difficult to discern and more effort should be made to highlight these. Also, a major conclusion "the directionality effect [effect of attention on forward waves] only occurs for visual stimulation" only rested on a qualitative comparison between studies. The authors have improved on this here, e.g., by toning down this conclusion. One thing that is still missing is a graphical representation of the data from Foster et al. (the second dataset analysed here) that would support the statistical results and allow the reader a visual comparison between the sets of findings.

Also, for any naive reader, the concept of travelling waves may be hard to grasp in the way data are currently presented - only based on the results of the 2D-FFT. Can forward and backward-travelling waves be illustrated in a representative example to make this more intuitive?

Finally, the way Bayes Factors from the Bayesian ANOVA are presented, especially with those close to the 'meaningful boundaries' ⅓ and 3, as defined in the 'Statistical analysis' section, requires some unification/revision. For example, here: "We found a positive correlation between contra- and ipsi- lateral backward waves, and occipital (all Pearson's r~=0.4, all BFs 10 ~=3) and -to a smaller extent- frontal areas (all Pearson's r~=0.3, all BFs 10 ~=2).", where the second part should strictly be labelled as inconclusive evidence. In the same vein, there is occasional mention of "negative effects", where it should say that evidence favours the absence of an effect.

Reviewer #1 (Public Review):

The manuscript by Silva et al. "Evaluation of the highly conserved S2 hairpin hinge as a pan-coronavirus target" seeks to evaluate a new epitope target on the S2 domain of SARS-CoV2 Spike protein and evaluate its potential as a pan-coronavirus target. This is an impressive combination of extensive structural, HDXMS-based dynamics and antibody engineering approaches. What is missing is a detailed correlation of HDXMS with Spike dynamics. The authors have not examined the allosteric effects of 3A3 binding to the Spike trimer, specifically cooperativity in antibody binding. Does binding of one Fab positively or negatively impact the subsequent binding of antibody? In this regard, readers would benefit from HDXMS spectral envelopes in figures, at least for the epitope locus peptides. Further, what is the effect of the intrinsic ensemble behavior of the Spike protein on 3A3 interactions? In a broader sense antibody binding is assisted by intrinsic trimer ensemble behavior, as observed by the lowered binding to the omicron variant- but are there induced binding effects? It would help to better integrate HDXMS with cryo-EM and antibody engineering. It is a novel, less explored epitope target on the S2 domain. Overall, a more definitive mechanistic conclusion for how targeting the S2 hinge can advance future pan-coronavirus strategies is missing.

Major Comments:

1) Given that the authors have demonstrated ensemble switching behavior from 4 ℃ to 37 ℃ (Costello et al. (2021)) why is this not factored in how the HDXMS is carried out? The samples were stored, frozen at -80 ℃, thawed, and equilibrated for 20 min at 20 ℃ with or without antibody present and analyzed by HDXMS. However, the reported t1/2 for trimer tightening at 37 ℃ is t1/2 = 2.5 h (Supplementary Fig. 7). The samples should ideally be analyzed under standardized conditions with the stable conformer. Sample heterogeneity from HDXMS is likely due to any of the following contributing factors:<br /> i) Intrinsic ensemble heterogeneity (Costello et al. (2021)), Kinetics of RBD- up and down conformational switching<br /> ii) Cooperativity of Fab binding.<br /> iii) Partial occupancy of trimer epitopes with bivalent IgG.<br /> iv) Combination of cooperativity effects and partial binding effects

I would predict for any of the above reasons, it is intriguing why are there no bimodal kinetics of deuterium exchange reported. Partial occupancy should be evident from HDXMS paratope analysis.

2) Pan-coronavirus neutralization potential is clearly evident. It is intriguing that the antibodies were isolated after immunization with an authentic MERS S2 domain but showed better selectivity to full-length 6P-engineered Spike. How is cooperativity built into antibody binding, given that the epitope site is occluded to various extents by the S1 domain and access is contingent upon RBD up-down kinetics?

3) I am surprised that there is no allostery described for 3A3 (Supplementary figures 5, 6).

Reviewer #1 (Public Review):

Nephronophthisis (Nphp) is a multigenic, recessive disorder of the kidney presenting in childhood that is characterized by cysts predominantly at the cortico-medullary junction and progressive fibrosis. An infantile form of the disease presents earlier with more diffuse cystic change. The condition is considered a ciliopathy because most of the genes linked to the condition encode proteins involved in ciliary biogenesis or function. Germline mutations in NPHP2 are associated with a particularly severe, infantile form of the disease. Given that interstitial fibrosis is a more prominent feature of Nphp compared to many other forms of polycystic kidney disease, the authors sought to determine the mutant cell types responsible for the phenotype.

In the current study, the authors generated and characterized mouse lines with Nphp2 selectively inactivated in either renal epithelial cell or stromal cell lineages and found that inactivation in renal epithelial cells was both necessary and sufficient to cause disease. They further showed that markers of interstitial fibrosis and proliferation increase in mutants prior to the onset of histologically evident cystic disease, suggesting that aberrant epithelial-stromal cell signaling is an early and primary feature of the condition (Figures 1-4). The study design was straightforward and appropriate to address the question, and the results support their conclusions.

They next tested whether the cilia-dependent cyst-activating pathway (CDCA) that is "unmasked" by loss of other PKD-related genes is similarly active in Nphp2 mutants by generating Nphp2/Ift88 double mutants. Their studies found that the severity of cystic disease and markers of proliferation and fibrosis was attenuated in double-mutants (Fig 5, 6). These studies were also appropriate for testing the hypothesis and the results were similarly consistent with their interpretation.

In the last set of studies, they tested whether valproic acid (VPA), a drug that has multiple modes of action including acting as a broad inhibitor of HDACs and previously used by the investigators in other forms of polycystic kidney disease, would have similar effects in Nphp2 mutants. The authors tested daily injection from days P10 through P28 in both control and Nphp2 mutant mice with VPA or an appropriate vehicle control and found that VPA was beneficial (Fig 7). The study design was acceptable and the results generally support their conclusions. The one perplexing result is shown in Fig 7B. The Nphp2 mutants, regardless of treatment status, have body weights (BW) that are significantly lower than the controls, with treated mutants even trending lower than their untreated mutant counterparts. This is unexplained and should be addressed. In the mutants with more widespread epithelial cell knock-out of Nphp2 (Ksp-Cre, Fig 1), total body weight decreased as mice became more severely cystic with renal impairment. In the milder form of disease produced with the Pkhd1-Cre (Fig 7), total body weight is inexplicably approx. 2g lower on average despite having much more modestly elevated KBWs and BUNs. Moreover, one might have expected that mutants treated with VPA would have had BWs intermediate between untreated mutants and controls since the severity of the disease was moderately attenuated. These differences raise the question as to whether body weight differences are due to factors independent of disease status, the most likely of which would be that the controls were not littermates. This prompted a careful review of the text for descriptions of the control mice. Throughout the study, the investigators describe selecting animals from the same "cohort", but this term is imprecise. There is little information provided about background strains, whether any of the lines were congenic, or whether any of the studies were done using littermate controls. This must be addressed. It would help if the investigators identified the litter status in their plots. This would clearly show relationships between animals and the number of litters that had animals with these properties. If littermates were not used for each study, the authors must explain both why they didn't do so and how they then selected which animals to use. This information is especially important for interpreting the results of their genetic interaction and drug treatment studies.

Several other considerations. The authors state that the effects of VPA are mediated through the drug's inhibition of HDACs and suggest that future studies could be directed at refining the specific HDAC. While this is certainly possible, the authors should acknowledge that VPAs have been reported to have numerous pharmacologic effects and targets and which of these is mediating the effects in their model is unknown. They would need mechanistic studies to show this, though it doesn't discount their possible efficacy as a therapy for PKD. The authors also state in their abstract that their double knock-out studies "support a significant role of cilia in Nphp2 function in vivo." It is not clear to me how their studies show this nor how they can exclude that ciliary activity is operating in an Nphp2-independent, parallel fashion that modulates some common downstream pathways.

Reviewer #1 (Public Review):

The paper reports important work in which the Fub-1 boundary of the Drosophila bithorax complex is characterized in detail. Fub-1 separates the bxd/pbx regulatory domain, which is active in PS6/A1, from the abx/bx regulatory domain, which is active in PS5/T3. The work presented provides compelling evidence that Fub-1 consists of two key elements: an insulating boundary region called HS1, which is regulated by an adjacent region called HS2. HS2 contains a promoter that is activated in PS6/A1 by enhancers in the bxd/pbx region. Read-through of HS1 by transcripts from the HS2 promoter blocks the insulating activity of HS1, allowing the bxd/pbx regulatory regions to activate Ubx transcription in PS6/A1. It has long been appreciated that boundary elements within the BX-C are regulated in a segment-specific fashion. The work presented in the Ibragimov manuscript provides a very nice example of how this segment-specific regulation can take place. For the most part, the work is very thorough and the conclusions are well-supported. However, there are a few important issues that should be addressed.

First, throughout the manuscript, it is stated that the read-through transcription of HS1 eliminates its blocking activity. Missing, however, is a test of whether the direction of transcription of HS1 is important. That is, no construct is tested in which HS1 is inverted so that RNAs from the HS2 promoter are transcribed from the opposite strand of HS1. If read-through transcription of HS1 is all that is required to abrogate its blocking activity, such a construct should behave identically to constructs in which HS1 is not inverted. However, if the structure of the F1HS2 RNA is critical to preventing the blocking activity of HS1, inversion of HS1 relative to HS2 may render it immune to inactivation by transcripts initiated at HS2.

Second, the terminology used to designate the constructs tested is very hard to follow and needs simplification. Since the orientation of HS1 in isolation is unimportant, perhaps just HS1 HS2, HS1 Inv(HS2), HS2 HS1, and Inv(HS2) HS1 could be used.

Third, in many places in the manuscript genotypes are shown in which the HS1 insulator is placed into F7attP50. For these genotypes, H1 is said to block the interaction between iab-6 and iab-7, but not to support bypass activity. Readers need some help here, as they will not understand why A5 and A6 tergites are black in these genotypes, as this implies that iab-5 is able to act over the HS1 element to activate Abd-B. One explanation may be that iab-5 can promote pigmentation by acting on abd-A.

Fourth, a more complete description of the HS1248 HS2505R genotype is needed. In this genotype, the H1 insulator is constitutively active, as H2 is inverted. Do animals of this genotype show a bxd phenotype in the larval cuticle? Do adults show a transformation of the halteres like that shown by classical bxd mutations? Answers to these questions would shed light on when H1 is active as an insulator, and whether it is active throughout PS6/A1.

Reviewer #1 (Public Review):

Maksim Kleverov et al. developed the tool called Phantasus, a web application for matrix visualization and analysis of gene expression data generated by either microarray or RNA-seq technologies. By Phantasus, the users can load, normalize, and plot their own data or those available in public databases and investigate the samples to remove outliers before the differential expression analysis.

Phantasus can be accessed on-line or can be installed locally from Bioconductor.<br /> One of the advantages of the web application is that it combines an interactive graphical user interface with access to various R-based analysis methods. For the methods that rely on functions that are already available in the existing R packages, for such practices, only wrapper R functions are implemented. The tool was developed focusing on being helpful to both expert and non-expert users in bioinformatic gene expression analysis.

Reviewer #1 (Public Review):

In this work, the authors propose a "transfer learning" approach for modeling the properties of sequences that are selected from larger sequence pools on the basis of biophysical or functional properties, where the source populations may themselves be biased in composition. Examples include the set of immunogenic peptides, considered as a subset of all HLA-presented peptides, or the set of TCRs that are specific for a given peptide epitope, as selected from within the much larger pool of all peripheral TCRs. The motivation for transfer learning is that there may only be small numbers of selected sequences available for training and many more examples of the background sequences. Rather than directly fitting a single model on the selected sequences, the idea is to first fit a background model that captures the properties of the source/background population of sequences, using the many examples available for training, and then train a "differential" model that specifically seeks to capture the differences between the selected and background populations. This differential model is trained using the subset of selected sequences, by optimizing their likelihood under a composite model that combines the background model (whose parameters are frozen) and the differential model. The specific architecture used here is the "restricted Boltzmann machine" (RBM), which can be thought of as a generalization of the position-weight matrix approach that can capture pairwise and higher-order interactions between positions. The applications are the two mentioned above, prediction of immunogenic peptides and prediction of TCRs specific for a given peptide-MHC epitope. This work builds on previous work by the authors applying the RBM architecture to peptide-MHC binding [Bravi et al., 2021b] and T-cell responses [Bravi et al., 2021a]. The advance here is in formalizing the "differential" framework and testing immunogenicity prediction and epitope specificity. Considering the field and the current state of the art, the main contributions of the manuscript appear to be theoretical/conceptual, in introducing the "diffRBM" method and providing a range of evaluations of its performance, for example, the use of contact prediction to assess the model. For TCR-epitope prediction, it does not look like the method improves over methods like TCRex or TCRdist, though an advantage is that the parameters may be more interpretable than some black box machine learning approaches. Also for epitope prediction, as noted by the authors, the model may be learning features that differentiate TCRs expressed by CD8+ T cells from the background of all TCRs (which is probably weighted toward CD4+ T cells). This would explain the poorer performance discriminating TCRs specific for one MHC class I epitope from those specified for a different class I epitope. For immunogenicity prediction, evaluations are so dependent on the specifics of the datasets, and the feature itself is so murky, that it's hard to say whether there is a performance advance here.

One nice feature of the diffRBM model is that scores ("single-site factors") can be assigned to individual amino acids in a peptide (or TCR) sequence that captures the contribution of that amino acid at that position to the overall score of the sequence, taking into account the sequence context. The authors show that these single-site factors, for the diffRBM model trained on immunogenic peptides, highlight positions that tend to be involved in TCR contacts as well as specific amino acids, such as "W at position 5", that have been found in previous studies to enhance TCR recognition. The single-site factors for a diffRBM model trained on epitope-specific TCRs appear to do a reasonable job of predicting CDR3 positions that contact the peptide.

Overall, the conclusions of the study are well-supported and the descriptions of the method's performance are balanced. The manuscript is well-written, and the supporting information nicely addresses minor questions that come up in reading the main text. One minor quibble I have is with the description of the method as "unsupervised", especially in the TCR-epitope prediction setting, since the sequences provided to the diffRBM for training, and which the model is tasked with learning differences between, is exactly the positive and negative sequences for the AUROC calculations (up to train/test sampling). It is also confusing to me that the overall selection factors for TCR-epitope binding are so very modest (0.19 for Flu M158, for example; Figure S20D, this is the "effective fraction of sequences retained in selected data compared to background ones"). This doesn't seem like it can be correct, given how focused some of these epitope-specific repertoires are. Overall, though, the study and associated software tools are likely to be useful contributions to the field.

Reviewer #1 (Public Review):

CD73 is a promising biomarker in cancer and has been characterized as having an immunosuppressive role in the tumor microenvironment. However, many cancer cell-intrinsic roles of CD73 are still under investigation. In this work, the authors explore the immune-independent roles of CD73 in cancer and demonstrate a function in maintaining metabolic fitness in cancer cells. The authors utilize genetic and pharmacological inhibition of CD73 to characterize metabolic changes in a panel of cancer cell lines and assess tumor growth in vivo. Furthermore, the authors demonstrate that the impaired metabolic fitness due to CD73 inhibition rendered cancer cells more susceptible to DNA-damaging agents. Overall, this work demonstrates the new roles of CD73 in cancer and provides a rationale for combination therapies including CD73 inhibition.

Reviewer #1 (Public Review):

This important study by Bonnet et al addresses the question of how AMPA receptor numbers at the synapse are regulated during basal conditions and during chemically induced Long Term Potentiation (cLTP). Specifically, the study aims to determine which molecular mechanisms contribute to export from Golgi/the ER, intracellular trafficking of AMPA receptors, and insertion into the synaptic plasma membrane, respectively. The authors had previously established an approach to separately measure these distinct events: to enable a high-fidelity measurement of the Golgi/ER release and subsequent speed of GluA1-containing vesicles, the release of vesicles is synchronized. Finally, the insertion into the plasma membrane is measured by immunolabelling.<br /> The authors set out to specifically understand the contributions of two auxiliary proteins in AMPA receptor expression: 4.1N and SAP97. Namely, the authors find that under basal conditions, binding of SAP97 to GluA1 is necessary for the GluA1 release from the Golgi/ER and intracellular trafficking. In turn, binding of 4.1N to GluA1 is necessary for the exocytosis of the receptor at the plasma membrane at basal conditions. Following induction of cLTP, the authors find that the role of SAP97 remains similar to that observed under basal conditions but, interestingly, 4.1N significantly grows in influence and is required for all stages of GluA1 expression - from release from the Golgi/ER to exocytosis and insertion into the plasma membrane.

In summary, using convincing methodology, the authors are able to dissect the distinct roles of two proteins that bind to the C-terminal domain of the AMPA receptor subunit GluA1: 4.1N and SAP97.<br /> The scientific rigor is high in this work. For example, the question of whether the expression of GluA1 depends on physical interaction with 4.1N and/or SAP97 is nicely addressed by several, well-considered experiments. Overall, the authors' claims are well justified by the data presented.

I did not find any major scientific weaknesses in this manuscript. The approach developed by the group appears to be a good tool for studying the molecular choreography at the synapse under different conditions and the results will be of interest to a wide range of neuroscientists.

DOI: 10.1016/j.cub.2023.01.039

Resource: ZFIN_ZDB-ALT-160830-1

Curator: @evieth

SciCrunch record: RRID:ZFIN_ZDB-ALT-160830-1

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DOI: 10.1016/j.cub.2023.01.039

Resource: (ZFIN Cat# ZDB-ALT-160120-1,RRID:ZFIN_ZDB-ALT-160120-1)

Curator: @evieth

SciCrunch record: RRID:ZFIN_ZDB-ALT-160120-1

What is this?

DOI: 10.1016/j.cub.2023.01.039

Resource: (ZFIN Cat# ZDB-ALT-150324-1,RRID:ZFIN_ZDB-ALT-150324-1)

Curator: @evieth

SciCrunch record: RRID:ZFIN_ZDB-ALT-150324-1

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Reviewer #1 (Public Review):

The authors used viral replication assays to select for and define the resistance pathways against ten developmental Protease Inhibitors (PIs) and their parent drug, Darunavir (DRV), which is one of the leading antiretrovirals used to treat people living with HIV/AIDS. There are two specific regions of the small molecule inhibitors that are actively being modified to increase potency against drug-resistant mutants, the P1' region, and the P2' region, which protrude into pockets of PR occupied by I84 / I50, and a neighboring region containing D29-D30, respectively. Selections using drugs containing small modifications of the P1' region led to primary mutations at PR position I184V, but not I150V. In contrast, selections using drugs containing larger modifications at the P1' region led to primary mutations at PR position I150V, a pathway that is less fit. Furthermore, having modifications at the P2' position added additional potency to the inhibitors, most evident within the I184V pathway. The authors rationalize their findings using previously published structural biology data. These results provide the first evidence for de novo pathway selection using state-of-the-art drugs based on the DRV scaffold and provide an atomic basis for designing compounds that are highly active against DRMs. The comprehensive nature of the analysis of drug resistance to the latest generation PIs, and the insights gained that can be rationalized based on atomic structure, are the major strengths of the paper. The weakness is the lack of commentary on the accessory mutations, which frequently arise in the selections but are not well-explained. It would also be useful to provide some concrete suggestions for minimizing drug resistance using 5th generation PIs, as part of a discussion.

Reviewer #1 (Public Review):

Thakkar et al describe the immune effects of 3rd and 4th doses of COVID-19 monovalent vaccines in a diverse cohort of immunocompromised cancer patients. They describe augmentation of anti-Spike antibodies after dose 3, especially seroconversion in 57% of patients, followed by a durable response over six months. The fourth dose was associated with increased anti-Spike antibodies in 67% of patients. T-cell responses were seen in 74% and 94% of patients after the third and fourth doses respectively. Strikingly, neutralization of Omicron was absent in all patients after the third dose but increased to 33% after the fourth dose.

Strengths:<br /> Diverse cohort (34% Caucasian, 31% AA, 25% Hispanic 8% Asian) including 106 cancer patients after dose 3, of which 47 patients were longitudinally assessed for six months, as well as eighteen patients assessed after the fourth dose.<br /> Seronegative as well as seropositive patients benefit from a third dose of vaccination.<br /> Assessment of cellular (T cell) immune responses and viral neutralization against wild-type as well as Omicron variant is commendable.

Weaknesses:<br /> The efficacy of the bivalent vaccine (Omicron specific) is not studied here, since the fourth dose of vaccine was a monovalent vaccine. This should be clarified in the discussion.<br /> The authors describe an increase in anti-S titers after monoclonal antibodies. Were any of the patients receiving IVIG, and what was the effect, if any on Anti-S antibodies?<br /> Characteristics of breakthrough infections, particularly if they had prolonged duration, would be important to include.

Reviewer #1 (Public Review):

In this work, Diekmann and Cheng have proposed a new computational model for hippocampal replay. The new model is based on the linear RL work by Piray and Daw 2021, and addresses a fundamental problem in the seminal replay model of Mattar and Daw 2018 (M&D). The new model is based on the default representation, which is a realistic account for state closeness in model-based RL.

This study addresses an important problem in neuroscience at the computational level. The proposed theory is a significant normative computational model that captures important aspects of experimental data in the replay literature. The paper is very well-written (a difficult task for a pure computational work) and figures illustrate the main concepts very well. I have only one question/suggestion:

I believe that there is important data in the literature that cannot be explained by the current model, especially regarding representation of the goal. That is fine; no model is complete, but it is important that authors discuss those caveats in the discussion.

Reviewer #1 (Public Review):

The manuscript by Zhang et al. titled "Retinal microvascular and neuronal pathologies probed in vivo by adaptive optical two-photon fluorescence microscopy" reports a custom-designed two-photon fluorescence microscope coupled with adaptive optics (AO-2PFM) that allows in vivo imaging of mouse retinal structures at a lateral resolution of ~0.8 μm and axial resolution of ~6.7 μm. The authors provided two examples of applications for in vivo imaging of mouse retinal structure and function. In the first example, AO-2PFM has been used to visualize capillary lesions in a mouse model of retinal angiomatous proliferation (RAP), a form of age-related macular degeneration characterized by capillary proliferation and focal vascular leakage. Using AO-2PFM, the authors observed capillary disruption, with which dye leakage was associated. In the second example, the authors performed in vivo functional imaging of Ca2+ signals in RGCs of the rd1 mouse - a model of retinal degeneration with a mutation in the Pde6B gene. They interpreted the elevated Ca2+ signals in RGCs of rd1 mouse as an indication of RGC hyperactivity that has been reported in ex vivo electrophysiological recordings. They further observed dampened Ca2+ signals in RGCs of rd1 mouse upon retro-orbital injection of lidocaine.

The authors carefully documented the technical features of this state-of-the-art in vivo mouse retina imaging system. The manuscript is very well written and, needless to say, the images presented are of superb quality. There is no doubt that the system will be of great value to many retinal researchers studying the normal structure and function of the retina as well as tracking the pathophysiology of retinal disease models longitudinally.

Reviewer #1 (Public Review):

This manuscript investigates the question of how polylysogeny impacts competition with a sensitive non-lysogen, and how this is shaped by phage resistance. This is an important and timely question, as lysogeny can be a strategy to invade new niches, and prophages are important vehicles for the acquisition of a range of virulence factors by pathogens including Klebsiella. The authors use a polylysogenic Klebsiella clone in competition with a non-lysogen that is sensitive to at least some of the prophages produced by the polylysogen. They compete these strains over a 30-day period and measure host population dynamics and evolution of phage resistance and lysogenic conversion in the (initially) sensitive competitor. Overall, the experiment shows that lysogen formation is relatively rare and short-lived. Instead, phage resistance through complete loss of the capsule is the primary mechanism evolving, but other resistant capsule mutants, with more subtle mutations affecting capsule expression, emerge as well. The authors have collected a very impressive amount of data and made some very interesting observations.

My main problem with this paper is that the manuscript lacks a clear narrative, making it very hard to extract the key message this paper wants to convey. Related to this, (some of) the conclusions that the authors make do not appear to be well supported by the data. For example, the authors conclude that selection favours more subtle capsule mutations because they are less costly than capsule-loss mutants (lines 497-500). However, there are no data to support this conclusion, as fitness costs of the various resistance phenotypes analysed were not measured. Apart from the genotypes, the data that are presented in this show that these subtle mutants have more subtle decreases in capsule production compared to the mutants that show a complete loss of capsule. But this does not tell us their relative cost. It also doesn't tell us how the emergence of these different mutants relates to phage pressure, because whilst bacterial population dynamics data are monitored meticulously, phage dynamics data are missing (I have not found them in the supplemental information either). This makes it impossible to directly relate the emergence of the various resistance mechanisms to phage infection pressure during the coevolution experiment, even though this appears to be a hypothesis the authors wish to test.

Overall I think the overarching question of the manuscript is important and the model system is a very relevant one to study this question, but in my view, the current data don't support the conclusions of the paper. Apart from these criticisms, the manuscript is very well written and the figures are overall easy to interpret.

Reviewer #1 (Public Review):

Rab27 is a major regulator of insulin granule exocytosis from beta cells, and it acts via (at least) three distinct effector proteins; Granuphilin, Melanophilin and Exophilin-8. Although the role of each of these three Rab-effectors in the regulation of insulin secretion is fairly well-established from studies of KO mice, the functional hierarchy between the effectors remains largely unknown. This study by Zhao et al addresses this question by investigating how simultaneous loss of two these effectors influence insulin granule exocytosis and also provide an explanation for their differential regulation of this process. They propose that Exophilin-8 acts upstream of Melanophilin, which in turn is involved in crash-fusion of granules with the plasma membrane, and that the interaction between these two effectors require the exocyst complex. This mode of exocytosis is relatively rare and only accounts for around 20% of all fusion events. The majority of fusion events instead involves exocytosis of granules stably docked at the plasma membrane. The authors propose that this mode of exocytosis also depends on Exophilin-8, now acting by removal of a Granuphilin-mediated exocytic clamp.

Technically, this is a superb study where the authors use primary mouse islets isolated from both single and double KO mice and perform both bulk secretion assays and single-cell granule imaging to elucidate the role of Rab27 effectors in glucose-stimulated insulin secretion. Unfortunately, while visualization of granule dynamics is performed in living cells, visualization of the Rab27 effectors and the ecoxyst components is restricted to static immunofluorescence imaging. It is therefore difficult to reconcile granule dynamics with effector action. While the results are clearly presented and largely consistent with previous work, I feel that many of the conclusions are based on over-interpretation of data and that important control experiments are missing. The authors are able to confirm their and others' previous observations that each of the three Rab27 effectors have distinct functions during insulin secretion. A connection between insulin granule exocytosis and the Exocyst complex has also been established in previous studies. The most intriguing finding in this study is that the Exocyst complex function in cooperation with Rab27 and its effectors, thus connecting these two pathways, and that there appears to be a functional hierarchy amongst the Rab27 effectors where Exophilin-8 act upstream of the other two. What remains unclear to me is how this entire process is regulated and how it relates to prevailing models of insulin granule pools and modes of exocytosis.

Understanding the mechanism that regulate insulin secretion is imperative for understanding how this process fails in certain types of diabetes. This study reinforces the concept that the secretion of insulin granules is a very heterogenous process that involves multiple pools of granules and modes of exocytosis and provides important new information on how cross-talk between these pathways help to shape the secretory response and give it robustness.

Reviewer #1 (Public Review):

Decidualization, denoting the transformation of endometrial stromal cells into specialized decidual cells, is a prerequisite for normal embryo implantation and successful pregnancy in humans. Abnormal cytokine-associated inflammation during decidualization can alter the endometrium's receptivity to healthy embryo implantation. Jiang and colleagues present an important analysis of the role and function of the Gaq axis on the inflammatory response during decidualization essential for early pregnancy, and present preliminary data on its clinical relevance.

The data narrative provides solid evidence of the mechanisms suggested by Jiang and colleagues. The study is highlighted both by the in vitro analysis and also by the study of human samples and subjects impacted by Recurrent Pregnancy Loss (RPL). Overall, the data seems to justify the conclusions taken, although some of the methodology and data interpretation require further clarification and justification.

Reviewer #1 (Public Review):

The expression and localization of Foxc2 strongly suggest that its role is mainly confined to As undifferentiated spermatogonia (uSPGs). Lineage tracing demonstrated that all germ cells were derived from the FOXC2+ uSPGs. Specific ablation of the FOXC2+ uSPGs led to the depletion of all uSPG populations. Full spermatogenesis can be achieved through the transplantation of Foxc2+ uSPGs. Male germ cell-specific ablation of Foxc2 caused Sertoli-only testes in mice. CUT&Tag sequencing revealed that FOXC2 regulates the factors that inhibit the mitotic cell cycle, consistent with its potential role in maintaining a quiescent state in As spermatogonia. These data made the authors conclude that the FOXC2+ uSPG may be the true SSCs, essential for maintaining spermatogenesis. The conclusion is largely supported by the data presented, but two concerns should be addressed: 1) terminology used is confusing: primitive SSCs, primitive uSPGs, transit amplifying SSCs... 2) the GFP+ cells used for germ cell transplantation should be better controlled using THY1+ cells.

Reviewer #1 (Public Review):

The fields of ancient and environmental DNA have many similarities. Practitioners are constantly tinkering with methods to extract as much information from biological samples as possible. Both fields of research also have to deal with the fact that only a tiny fraction of the DNA is 'on target' and that the background DNA (largely bacterial) is often immense.

In this research Urban et al tackle the question of individual identification of a flightless New Zealand parrot (the kakapo) using shotgun eDNA (from soil) within a study system where reference genomes exist for most of the animals within a population. Most eDNA studies stay in the relative safety of metabarcoding (typically on mitochondrial DNA) thus Urban et al are breaking new ground.

In this small-scale (and highly controlled) study, Urban et al. use shotgun eDNA from a gram of soil and then match kakapo reads to reference genomes. Using some innovative Bayesian inference the researchers are able to identify individuals within the populations.

There are a number of innovations in this study that have relevance to the conservation sector. The idea that we can identify individuals in a population in a non-invasive manner is an exciting prospect. It immediately conjures up the possibility of genetic mark-recapture applications. In the case of highly endangered populations, the work shows the value of building reference genomes for the whole population.

At its core, this is a proof-of-principal study that arguably leaves the reader with more questions than answers. I was left wondering (i) why didn't nanopore's adaptive sampling function enrich targets? (ii) how would short-read platforms compare (iii) could genomic signatures of other taxa (e.g. bats) identified by metabarcoding be detected in shotgun data? And (iv) is sediment the best substrate for this work?

Sedimentary DNA methods have been around for ~20 years and it is exciting to see the field continue to innovate. The speed and portability of nanopore devices may, with time, see real-time genotyping become a reality in conservation biology. I welcome these innovations as, on the global stage, we need all the tools we can get to battle the biodiversity crisis.

Reviewer #1 (Public Review):

Medwig-Kinney et al perform the latest in a series of studies unraveling the genetic and physical mechanisms involved in the formation of C. elegans gonad. They have paid particular attention to how two different cell fates are specified, the ventral uterine (VU) or anchor cell (AC), and the behaviors of these two cell types. This cell fate choice is interesting because the anchor cell performs an invasive migration through a basement membrane. A process that is required for correct C. elegans gonad formation and that can act as a model for other invasive processes, such as malignant cancer progression. The authors have identified a range of genes that are involved in the AC/VC fate choice, and that imparts the AC cell with its ability to arrest the cell cycle and perform an invasive migration. Taking advantage of a range of genetic tools, the authors show that the transcription factor NHR-63 is strongly expressed in the AC cell. The authors also present evidence that NHR-63 is could function as a transcriptional repressor through interactions with a Groucho and also a TCF homolog, and they also suggest that these proteins are forming repressive condensates through phase separation.

The authors have produced an extensive dataset to support their two primary claims: that NHR-67 expression levels determine whether a cell is invasive or proliferative, and also that NHR-67 forms a repressive complex through interactions with other proteins. The authors should be commended for clearly and honestly conveying what is already known in this area of study with exhaustive references. But absent data unambiguously linking the formation and dissolution of NHR-67 condensates with the activation of downstream genes that NHR-67 is actively repressing, the novelty of these findings is limited.

Reviewer #1 (Public Review):

Vaparanta et al propose a new bioinformatic algorithm for pathway discovery from multi-omics data sources at one time point, and validate some of their algorithm's predictions using functional experiments. The authors should be commended for their detailed experimental work and comprehensive data collection around TYRO3 signaling in melanoma, which will likely be of value to that field. They also provide a mature software package that is well documented for implementing their bioinformatic methods. The reviewer's experience with the software was that it is computationally efficient/fast with well written code. The biological data (both multiomics and functional validation studies) will be of interest to melanoma research as well as scientists interested in TYRO3 signaling.

At this time, however, the bioinformatics algorithm proposed is of unclear utility to the broader multiomics community for the following reasons:

First, the algorithm itself has numerous hyperparameters, which can make it challenging to use and potentially highly sensitive to these user inputs. Just the regulatory complex inference step has 10 hyperparameters/settings required to be selected.

Second, the algorithm is presented in an ad hoc manner without mathematical/statistical justifications of the many design decisions and steps in the analysis. For example, the authors write "The inference of regulatory complexes from the combined score follows the nearest neighbor principle, assuming that while a single high combined score can be random chance, the combination of combined scores between 3 cell signaling molecules would be predictive". It is mathematically unclear that this is true, and thus this reviewer attempted to test the algorithm using simulated uncorrelated Gaussian noise (see code/outputs at end of the review) in 10K genes and 10 samples using a best attempt at hyperparameter selection per the code comments and documentation. It appears that nearly 1/3 of all genes (i.e., 3205 of 10K) were erroneously grouped into complexes (assuming no mistakes in reviewer's usage of the code). In general, "unbiased" pathway analysis in multiomics that is not relying on prior knowledge will require solving the extraordinarily challenging task of estimating a very large covariance matrix from statistically small sample sizes. This puts the method at high risk of producing spurious results.

Third, pathway analysis has long been a bioinformatic goal in the literature, with the authors citing a landmark paper for the WGCNA method from 2008. As such, there are numerous and long-standing discussions in the literature regarding challenges of pathway analysis (i.e., omics data often has dimensionality D far larger than sample size N, and correlation matrix estimation requires D^2 >> N parameters to be estimated) and its potential for spurious correlations. Some authors use sophisticated statistical tools (e.g., "Biological network inference using low order partial correlation" 2014, "Learning Large‐Scale Graphical Gaussian Models from Genomic Data" 2005, "Incorporating prior knowledge into Gene Network Study" 2013) to attempt to deal with this issue. Furthermore, the authors indicate that their approach is the first to attempt pathway analysis in multi-omics setting, stating "Integrative approaches combining more than one robust molecular association measure, however, have not been explored", but one can find attempts such as "An Integrative Transcriptomic and Metabolomic Study of Lung Function in Children With Asthma" to build on WGCNA for work in multiomics datasets. The 2020 review paper "Metabolomics and Multi-Omics Integration: A Survey of Computational Methods and Resources" seems to identify multiple published methods dealing with pathway estimation in multiomics datasets. As the paper stands, this reviewer cannot adequately assess the impact of the proposed bioinformatic algorithm and its results against the existing body of literature for pathway inference.

Reviewer #1 (Public Review):

Siegfried et al. study a very interesting and timely topic in cell biology: the connection between ER-PM membrane contact sites (MCS) and cell migration. In brief, the authors use the polarized epithelial model cell line (CACO-2) to study this process. They routinely compare parental cells (Control) with a clonal CACO-2 cell line knocked out (KO) for the ER tether protein VAP-A. They convincingly show that KO cells move faster but in a less directional manner, leading to slower monolayer migration. Interestingly, they showed that KO cells have larger focal adhesions (FAs), a phenotype that was reverted upon expression of the wild-type VAP-A but not of a VAP-A mutant (VAP-A-KDMD, mutation in the MSP domain) defective in binding to FFAT-containing partner proteins. Some observations regarding the role of VAP-A's MSP domain on the regulation of the actin cytoskeleton, although the evidence for this was incomplete. Furthermore, VAP-A depletion was shown to have an impact on PI(4,5)P2 levels at the PM (but not on PI(4)P levels at the Golgi membranes), to stabilize the dynamics of ER-PM MCS, and to increase FA lifetime by decreasing FA disassembly rate. Finally, they showed that there is a correlation between the appearance of ER-PM MCS at FAs with FA disassembly, however, how VAP-A plays a role in this effect is unclear. The authors put their findings in the context of the literature in the field to propose a working hypothesis by which VAP-A at ER-PM MCS could impact FA dynamics and cell motility.

Reviewer #1 (Public Review):

The authors' conclusions presented herein are supported by a well-established mouse genetic conditional approach and an extensive array of phenotypic analyses.

Strengths:

1. The authors utilized well-described genetic tools, AdipoQCre, to target preadipocyte-like progenitor cell populations in bone marrow, as well as Csf1 floxed alleles. They further sifted through the cell population by showing that mature lipid-laden adipocytes express Csf1 at a much lower level, and determined that AdipoQCre-marked progenitor cell population presents a major cellular source of M-CSF,

2. The reanalysis of published scRNAseq datasets in Figure 1, as well as the following phenotypic analyses of the mutant mice are well-conducted. The analyses include a broad range of experiments both in vivo (3DmicroCT, histology, flow cytometry) and ex vivo (osteoclastogenesis assay in bone marrow cell culture). The confidence of the reported findings is high.

3. The data presented in this manuscript are of very high quality.

Weaknesses:

1. The role of AdipoQ-lineage progenitors as a source of M-CSF is overstated. The authors claim in many instances that "mature bone adipocytes do not express M-CSF", "These cells however do not produce Csf1", "...these peripheral AdipoQ+ cells nearly do not produce M-CSF". However, the authors' qPCR experiments only show four times differences in Csf1 expression. Therefore, the claim that AdipoQ-lineage progenitors are an exclusive source of M-CSF is not well substantiated. In line with this, some of the recent literature reporting conditional deletion of M-CSF in other bone cells (JBMR Plus. 4:e10080., Nature. 590:457-462) are not included.

2. Some of the phenotypic analyses are still incomplete. The authors did not report whether CHet (AdipoQCre Csf1(flox/+)) showed any bone phenotype. Further, the authors did not show that Csf1 mRNA or M-CSF protein is expressed in AdipoQ-lineage progenitors using histological methods. Current evidence is only based on scRNAseq and qPCR of isolated cells. Whether there was any change in circulating bone resorption markers in CKO mice was not shown. Cortical bone parameters were not included in the 3D-microCT analyses. These missing pieces of information would be important to correctly interpret the phenotypes.

3. Which bone marrow cell population(s) are marked by AdipoQCre remain largely unclear. It is possible that AdipoQCre also marks at least part of MSPC-osteo cluster in addition to MSPC-adipo. Adipo-lineage progenitors may not stay entirely as adipoprogenitors and drift toward osteoblasts or their precursor cells.

4. The OVX data in Figure 5 are not very well explained. The data do not seem to support the authors' conclusion that M-CSF deficiency in AdipoQ-lineage progenitors alleviates estrogen-deficiency induced osteoporosis. The CKO mice lose bone mass almost to the same extent as WT mice upon OVX.

Reviewer #1 (Public Review):

Although COVID-19 primarily causes an inflammatory response in the lungs, there is growing evidence that other organs are also affected by SARS-Cov-2, and that some patients continue to receive long-term effects of the disease sequelae even after treatment. We are not clear at this time about the effects of COVID-19 in organs other than the lungs. In this study, the authors presented the COVID Tissue Atlas (CTA) that comprises scRNA-seq data across six human organs of severe COVID-19. This study provides a valuable data resource to study the systemic effects of severe COVID-19, especially the common and specific transcriptional response to COVID-19 in multiple organs. Specifically, the authors identified dysregulated insulin and HIF signaling and prominent macrophage-endothelial interactions. This study will obviously help us to understand the pathogenesis of long-COVID.

Reviewer #1 (Public Review):

In this study, Tanentzapf and colleagues have developed a new live-imaging technique for the lymph gland hematopoiesis over 12 hours, which is enough to visualize changes in the cell state or cell division by tracking the same cell. With the new method, the authors successfully cultured the lymph gland for a long period without modifying cell viability or stress responses and detected a continuous cell cycle and division. Moreover, the authors showed that lymph gland progenitors divide when they reach a certain size and regrow upon division, supporting previous findings and providing a new concept in lymph gland biology. The authors moved on to resolve the spatial distribution of progenitor mitosis in 3D and found that progenitors divide in a polarized manner which contributes to the typical shape of lymph glands. In addition to developing lymph glands, the authors observed the lymph gland following oral infection and found that progenitors divide less upon infection but significantly increase the number of differentiations at the MZ-CZ boundary. Furthermore, the authors found two different modes of differentiation in the lymph gland: sigmoid and linear, which are altered during infection.

Studies in the lymph gland hematopoiesis have heavily relied on snapshots of the lymph gland phenotypes although stem-progenitor differentiation is a continuous process. In this regard, the method developed in this study is extremely valuable to the fly community and will help improve the ex vivo culture and analysis techniques of fly organs as well as the lymph gland. The authors rigorously took advantage of numerous measures to validate the new method, including cell death, oxidative stress response, cell viability, and cell cycle, and observed biologically significant phenomena of the correlation between cell size and cell division, cell division polarity, and changes in the mode of differentiation during development or infection. This study provides a useful system for Drosophila immunologists and developmental biologists and will help explore the real-time mechanisms underlying blood development and immune reactions.

Reviewer #1 (Public Review):

Single-cell sequencing technologies such as 10x, in conjunction with DNA barcoded multimeric peptide MHCs (pMHCs) has enabled high throughput paring of T cell receptor transcript with antigen specificity. However, the data generated through this method often suffers from the relatively high background due to ambient DNA barcodes and TCR transcripts leaking into "productive" GEMs that contain a 10X bead and a T cell decorated with antigen-specific barcoded proteins. Such contaminations can affect data analysis and interpretation and have the potential to lead to spurious results such as an incorrect assessment of antigen-TCR pairs or TCR cross-reactivity. To address this problem, Povelsen and colleagues have described a data-driven algorithm called "Accurate T cell Receptor Antigen Pairing through data-driven filtering of sequencing information from single-cells" (ATRAP) that supplies a set of filtering approaches that significantly reduces background and allows for accurate pairing of T cell clonotypes with cognate pMHC antigens.

This paper is rigorously conducted and will be useful for the field - there are some areas where further clarifications and comparisons will benefit the reader.

Strengths:<br /> 1. Povelsen and colleagues have systematically evaluated the extent to which parameters in the experimental metadata can be used to assess the likelihood of a GEM to correctly identify the antigen specificity of the associated T cell clonotype.<br /> 2. Povelsen and colleagues have provided elegant data-driven scoring metrics in the form of concordance score, specificity score, and an optimal ratio of pMHC UMI counts between different pMHCs on a GEM, which allows for easy identification of poor quality data points.<br /> 3. Based on the experimental goals, ATRAP allows for customizable filters that could achieve appropriate data quality while maximizing data retention.

Weakness:<br /> 1. The authors mention that 100% of the 6,073 "productive" GEMs contained more than one sample hashing barcode, and 65% contained pMHC multiplets. While the rest of the paper elaborates on the steps taken to deal with pMHC multiplets issue, not much is said about the extent of multiplet hashing issue and how was it dealt with when assigning cells to individual donors. How is this accounted for? Even a brief explanation would be beneficial.

2. It would be helpful for the authors to describe how experimental factors such as the quality of the input MHC protein may affect the outputted data (where different proteins may have different degrees of non-specific binding), and to what degree the ATRAP approach is robust to these changes. As an example, the authors mention that RVR/ A03 was present at high UMI counts across all GEMs and RPH/ B07 was consistently detected at low levels. Are these observations the property of the pMHCs or the barcoded dextran reagent? Furthermore, are there differences in the frequency of each of these multimers in the starting staining library which manifests in consistent high vs low read counts for the pMHC barcodes?

3. It would be helpful for the authors to further explain how ATRAP handles TCRs that may be present in only one (or a small number) of GEMs, as seen in Figure 7b, and potentially for the large number of relatively small clonotypes observed for the RVR/A03 peptide in Figure 6 (it is difficult to know if the long tail of clonotypes for RVR is in the range of 1 or 10 GEMs based on the scale bar). Beyond that, is there any effect on expected (or observed) clonal expansion on these data analyses, for example, if samples are previously expanded with a peptide antigen ex vivo or not?

4. The authors mention a second method, ICON, for conducting these types of analyses, and that the approach leads to significantly more data loss. However, given there could be differences in dataset quality themselves, and given the dataset, ICON is publicly available, it would be helpful for a more explicit cross-comparison to be conducted and presented as a figure in the paper.

Reviewer #1 (Public Review):

George and Levine present in their manuscript a mathematical framework describing the evolution of tumor cells under immune surveillance. The adaptive immune system recognizes tumor associate antigens (TAAs) to eliminate the cancer cells, while the tumor evades it through an evolutionary process of clonal selection. Their framework describes how the TAAs are gained and lost from the tumor, as a discreet time-stochastic process. The authors construct and parametrize their model to fit different known regimes of tumor and its microenvironment and explore the consequences of different tumor behaviors. Specifically, they suggest that tumor cells might sense the action of the immune system and adapt their escape probability.

The mathematical analysis is clear and is an impressive attempt to find governing principles behind a complicated and messy process. While the model cannot give specific predictions at this point, it facilitates understanding real-world observations, like high and low mutation tumors. As such it can motivate further modeling of more realistic situations. In its current form, however, the manuscript is difficult to follow, with the many mathematical details and regimes confounding the message. Also, since the model simplifies the clonal nature of the evolution processes considerably, in its current form it has limited capability to make predictions or be more than supporting evidence to empirically known observations.

Reviewer #1 (Public Review):

N1-methyladenosine (m1A) is a rather intriguing RNA modification that can affect gene expression and RNA stability etc. The manuscript presented the exploration of RNAs m1A modification in normal and OGD/R-treated neurons and the effects of m1A on diverse RNAs. The authors showed that m1 modification can mediate circRNA/LncRNA-miRNA-mRNA mechanism and 3'UTR methylation of mRNAs can disturb miRNA-mRNA binding.

The manuscript provides evidence for the following,<br /> 1. The OGD/R can have impacts on various functions of m1A mRNAs and neuron fates.<br /> 2. The m1A methylation of mRNA 3'UTRs disturbs the miRNA-mRNA binding.<br /> 3. The authors identified three possible patterns of m1A modification regulation in neurons.

The main merit of the manuscript is that the authors identified some critical features and patterns of m1A modification and in neurons and OGD/R-treated neurons. Moreover, the authors identified m1A modifications on different RNAs and explored the possible effects of m1A modification on the functions of different RNAs and the overall posttranscriptional regulation mechanism via an integrated approach of omics and bioinformatics. The major weakness of the manuscript is that technique details for many results are missing. Moreover, language inconsistences can be found throughout the manuscript. My general feeling about the manuscript is that some conclusions are rather superficial and therefore require validation and discussion.

Reviewer #1 (Public Review):

Here, the authors generated a CSAS-LexA driver line to investigate the expression pattern of CSAS and showed that CSAS expression is confined to glia and does not overlap with DSiaT expression. DSiaT expression is presumed to be in neurons, but this was not evaluated with specific markers in this study.

The authors showed that restoring CSAS expression specifically in glia but not neurons could rescue the mutant phenotype of temperature sensitive paralysis and confirmed that glial (and not neuronal) CSAS expression could rescue excitatory junction potentials at neuromuscular junctions in CSAS mutants. In addition to rescue experiments, the authors also performed RNAi knockdowns in glia vs. neurons to show that CSAS function is required in glia and DSiaT in neurons for the same paralysis phenotype.

Next, the authors performed mass spec to analyse sialylated proteins in larval brains and found that sialylated proteins could not be detected in DSiaT and CSAS mutants. However, sialylated proteins were only barely detectable in wildtypes.

Of note, the authors show that CSAS functions normally in glia and cannot function in neurons due to low endogenous NANS activity (sialic acid synthase).

Finally, the authors explore the hypothesis that the temperature-sensitive paralysis CSAS phenotype is due to oxidative stress with a paraquat exposure paradigm. This could be strengthened by examining ROS levels in vivo in CSAS or DSiaT mutants. The specific genetic background of these experiments seemed to be a major factor in the results obtained and more stringent controls or backcrossing to isogenize the genetic background would be required to be fully confident in the conclusions drawn from these experiments.

The authors also demonstrate a link between sialylation and Para (protein) expression. Although intriguing, there is very little data provided on this aspect of the story, though it does not detract from the broader message of the manuscript.

Reviewer #1 (Public Review):

High-throughput genetic screening is a powerful approach to elucidate genes and gene networks involved in a variety of biological events. Such screens are well established in single-celled organisms (i.e. CRISPR-based K/O in tissue culture or unicellular organisms; screens of natural variants in response to drugs). It is desirable to extend such methodology, for example to Arabidopsis where more than 1000 ecotypes from around the Northern hemisphere are available for study. These ecotypes may be locally adapted and are fully sequenced, so the system is set up for powerful exploration of GxE. But to do so, establishing consistent "in vitro" conditions that mimic ecologically relevant conditions like drought is essential.

The authors note that previous attempts to mimic drought response have shortcomings, many of which are revealed by 'omics type analysis. For example, three treatments thought to induce osmotic stress; the addition of PEG, mannitol, or NaCl, fail to elicit a transcriptional response that is comparable to that of bonafide drought. As an alternative, the authors suggest using a low water-agar assay, which in the things they measure, does a better job of mimicking osmotic stress responses. The major issues with this assay are, however, that it introduces another set of issues, for example, changing agar concentration can lead to mechanical effects, as illustrated nicely in the work of Olivier Hamant's group (e.g., https://elifesciences.org/articles/34460).

Reviewer #1 (Public Review):

In this study, the authors found that the chromatin remodeling complex mutant isw1Δ of the fungal pathogen Cryptococcus neoformans is resistant to multiple different antifungal drugs. The mutant, however, is fully virulent in a mouse model. By comparing transcript changes of the wild type and the mutant when treated with antifungal fluconazole, they found that many transporter genes are differentially expressed in the isw1Δ mutant. Consistently, they showed reduced expression of genes involved metabolism of another antifungal 5-FC and a lower level of cellular accumulation of 5-FC in the isw1Δ mutant, which likely contributes to its 5-FC resistance. They found that the Isw1 protein is degraded mostly through ubiquitination and identified K97 deacetylation as being critical for drug resistance/protein degradation. Then they mutated nine E3 ubiquitination ligase genes and identified Cdc4 to be responsible for Isw1 degradation. Lastly, they showed that Isw1 is low in some clinical isolates that are modestly resistant to antifungals. The evidence of the interplay between acetylation status and ubiquitination of Isw1 is strong. The finding that reduced Isw1 increases drug resistance also fits the growing interest in studying epigenetic regulation of drug resistance in fungal pathogens. One area that needs to be strengthened is the potential clinical relevance of Isw1 reduction in drug resistance.

Reviewer #1 (Public Review):

In this manuscript the authors overproduce two M. smegmatis DNA polymerases, DinB2 and DinB3, as a way to determine whether they may contribute to DNA damage tolerance and/or mutagenesis; the roles of these DNA polymerases in DNA damage tolerance and mutagenesis is currently unknown. The authors show that overproduced levels of DinB2, but not DinB3, impeded growth, and this inhibition was relieved by the disruption of DinB2 catalytic activity using the DinB-D109A mutation. They further demonstrate that the overproduction of DinB2 contributed to frameshift mutagenesis, while DinB3 did not. The contribution of overproduced levels of DinB2 to frameshift mutagenesis was studied in a careful and systematic way, convincingly showing that frameshifts correlated with DinB2 slipping while replicating homopolymeric nucleotide runs during dNTP and not rNTP incorporation. The authors also show that the metal cofactor (Mn vs Mg) contributes to the mutagenic behavior of DinB2. While this work is mostly compelling, the major concern is it fails to address the contribution of DinB2 and DinB3 to DNA damage tolerance and mutagenesis when they are expressed at normal levels from their respective chromosomal loci.

Reviewer #1 (Public Review):

Damon-Soubeyrand and colleagues use 3DISCO tissue clearing and light-sheet microscopy to provide a detailed atlas of the blood and lymphatic circulating networks of the mouse epididymis. While this manuscript does not address the function of these networks during the development or homeostasis of the epididymis, it is an outstanding example of a descriptive study that paves the way towards functional investigations of the role of epididymal vasculature in the post-testicular maturation of spermatozoa.

Strengths: The authors used a wide range of markers to carefully assess the differential patterns of epididymal blood and lymphatic vasculature, and elegantly describe each image in great detail. Where possible, the authors used appropriate quantitative methods to support their descriptive data, which are useful metrics for readers seeking to characterize vascular and lymphatic networks in disease models.

Weaknesses: In its current form, it is unclear which of the elements presented in the manuscript are novel discoveries about the blood and lymphatic networks of the epididymis, as the text lacks concise and precise statements about the major findings of the study. In addition, the authors frame this study of the vasculature as a way to understand the immune context of spermatozoa in the epididymis but do not integrate their data on blood and lymphatic networks with the immune system.

Reviewer #1 (Public Review):

This study aims at investigating temporal variation in patterns of germline mutation during the evolution of human populations. For this purpose, the authors analyzed polymorphism data from the 1000 Genomes project. They inferred the age of each derived variant using Relate, a newly developed method that reconstructs local genealogies based on phased haplotype sequences and estimates allele ages (Speidel et al. 2019).

Speidel et al. (2019) already had used their method to explore temporal variation in mutation patterns. Their analysis had confirmed the transient elevation in non-CpG C>T mutations in Europeans compared to African and Est Asians previously described by Harris (2015). However, Speidel et al. did not push their study very far, notably because of the difficulty of distinguishing the effects of changes in mutation patterns from those of GC-biased gene conversion (gBGC).

Here Gao et al. carefully accounted for gBGC to further explore variation in mutation patterns. As expected, they confirmed the previously described European-specific mutational shift. In addition, they identified two novel interpopulation differences in the mutation spectrum. This suggests that shifts in mutation spectra occur frequently, over a few thousands of generations. The reasons (environmental or genetic) for these recurrent shifts are not known, but the authors convincingly show that they cannot be explained by changes in the age of reproduction over time.

I found this manuscript very well written and very interesting. There is however an important point in their results that seems very puzzling. Indeed, the authors report that among mutations that are estimated to be old (>28800 generations), the ratio of T>C over T>G differs significantly in African samples compared to non-African samples (Fig. 2A). This difference is unexpected given that these old mutations largely predate the out-of-Africa migration (<3000 generations), and hence are a priori expected to be largely shared across populations. Curiously, this pattern is driven by variants for which the derived allele is observed in both Neanderthals and Denisovans (ND11 variants) (while ND01 and ND10 variants do not contribute to this pattern; Fig. 2D, SupFig 2.8). The authors hypothesize that the T>C/T>G ratio was higher in one or more populations in the remote past and those ancient groups contribute variable amounts of ancestry to contemporary populations. However, I do not understand how this model can account for the fact that ND10 and ND01 variants behave differently from ND11 variants (ND10 and ND01 variants are also expected to be emerged prior to the split of modern humans and archaic hominins).

It is possible that I misunderstood something, but in any case, there are several points in the methodology that have to be clarified. Notably, it is not clear to me if the reported pattern is driven by variants that are specific to the African samples, or if it is also observed among variants that are shared across populations. Furthermore, I suspect that polarization errors (notably at CpG sites) might be responsible for this pattern.

In summary, this manuscript reports very interesting observations, but several additional tests have to be done to check whether they are real or if they might result from methodological artefacts.

Reviewer #1 (Public Review):

Inter-cellular mitochondria transfer has been observed in many systems but the role or relevance of transferred mitochondria in recipient cells is poorly defined in contexts where recipient cells have intact functional networks. This manuscript directly addresses this important question and present a model in which transferred mitochondria act as signaling organelles to increase cancer cell proliferation.

The authors present compelling evidence that macrophages transfer mitochondria to cancer cells. Activated macrophages transfer mitochondria more effectively than non-activated macrophages, and this increased transfer is at least in part due to enhanced mitochondrial fragmentation in activated macrophages. Probing the significance of mitochondrial transfer, the authors find that transferred mitochondria remain distinct from endogenous mitochondrial networks and do not exhibit the polarization that traditionally characterizes functional mitochondria. The transferred mitochondria have features consistent with elevated oxidative stress and/or ROS production. A series of elegant imaging experiments demonstrate that mitochondrial transfer is associated with increased growth in daughter cells that inherit transferred mitochondria. Mechanistically, the authors propose that ROS produced by transferred mitochondria stimulate ERK signaling to induce a proliferation advantage.

Overall the work addresses an important question regarding the functional role of mitochondria transferred to cancer cells. The data largely support the model that transferred mitochondria are defective and induce proliferation in recipient cells. Some clarification on the effect timescales and the role role of ROS and ERK signaling in cell proliferation in cells that do not receive mitochondria is warranted. Overall this work provides an important new view for how mitochondrial transfer affects cell biology and provides a suite of tools and protocols for quantifying the impact of mitochondrial transfer on recipient cells.

Reviewer #1 (Public Review):

The present study investigates the anatomical connectivity between Mu opioid receptor (MOR) expressing neurons of the pontine respiratory group with down-stream targets of the respiratory network in the medulla oblongata. The study employs a variety of viral tracing approaches, optogenetic stimulation of pre-synapses of descending pontine projection neurons, and patch clamp electrophysiology. Overall the study is well conducted and the authors show that MOR expressing excitatory glutamatergic pontine neurons project to the medullary respiratory rhythm generator and adjacent ventral respiratory group. The study implies that opioids act on MOR-located somata and dendrites of the pontine and medullary respiratory groups. Importantly MOR are expressed on the pre-synapses of the descending pontine projections neurons. The authors, therefore, propose that opioids mediate respiratory depression via distinct pre- and post- synaptic mechanisms across inter-connected ponto-medullary respiratory neurons. The study advances our knowledge of network mechanisms that mediate opioid respiratory depression and may provide interesting frameworks for the development of therapies to counteract or prevent opioid respiratory depression. The study is of broad interest to the respiratory control research community, as well as medically relevant.

Reviewer #1 (Public Review):

Many previous studies have examined the regulation of hyphal growth in vitro, and have identified about 1,000 genes capable of influencing this process. However, a weakness is that most of these genes have weak effects and are not important in vivo. Therefore, it is very significant that this is the first large-scale study to examine the regulation of hyphal growth in vivo by analyzing a set of 156 transcription factor mutants in mice. A strength of these innovative studies is that mutant strains were injected into a mouse ear, which permitted the use of high-resolution microscopy to quantify the fraction of cells forming hyphae and the rate of hyphal elongation. Furthermore, wild-type cells were co-injected to serve as an internal control, which enhanced the rigor of these studies.

One major conclusion is that three core transcription factors were identified as being important in vivo (Rob1, Brg1, and Efg1) and two negative regulators (Tup1 and Efg1). Previously, many transcription factors were found to be important in vitro, so this is important for focusing future studies on the key regulators. Nanostring gene expression studies verified that these core factors regulate overlapping but distinct sets of genes in vitro and in vivo, which reinforces the importance of carrying out studies in vivo. Additional mutants were discovered to have minor defects in filamentous growth and were considered to be ancillary factors that act in concert with the core regulators.

Another innovative aspect of the manuscript is that they examined the rate of hyphal elongation in vivo. This is an understudied area both in vitro and in vivo. Transcription factors UME6, LYS14, and HMS1 were shown to regulate the elongation rate, which opens up new opportunities to study the mechanisms. Consistent with this, these transcription factors were shown to regulate a set of genes that is distinct from those regulated by transcription factors that control the initiation of hyphal growth.

Genetic approaches (complex haploinsufficiency) were used to examine the relationship between the core factors and the ancillary factor TEC1. Interestingly, these results revealed genetic interactions between TEC1 and the core factors EFG1 and BRG1, including their ability to regulate other transcription factors. This shows how these complex networks are functioning in vivo.

Another major advance was that the in vivo analysis of the two negative regulators of hyphal growth (Nrg1 and Tup1) revealed a new model for how they interact with the master transcriptional regulator Efg1. The results indicate that the major function of Efg1 in vivo is to mediate relief of Nrg1 repression. It was not needed to regulate the expression of hypha-induced genes.

Reviewer #1 (Public Review):

Taking advantage of a publicly available dataset, neuronal responses in both the visual and hippocampal areas to passive presentation of a movie are analyzed in this manuscript. Since the visual responses have been described in a number of previous studies (e.g., see Refs. 11-13), the value of this manuscript lies mostly on the hippocampal responses, especially in the context of how hippocampal neurons encode episodic memories. Previous human studies show that hippocampal neurons display selective responses to short (5 s) video clips (e.g. see Gelbard-Sagiv et al, Science 322: 96-101, 2008). The hippocampal responses in head-fixed mice to a longer (30 s) movie as studied in this manuscript could potentially offer important evidence that the rodent hippocampus encodes visual episodes.

The analysis strategy is mostly well designed and executed. A number of factors and controls, including baseline firing, locomotion, frame-to-frame visual content variation, are carefully considered. The inclusion of neuronal responses to scrambled movie frames in the analysis is a powerful method to reveal the modulation of a key element in episodic events, temporal continuity, on the hippocampal activity. The properties of movie fields are comprehensively characterized in the manuscript.

Although the hippocampal movie fields appear to be weaker than the visual ones (Fig. 2g, Ext. Fig. 6b), the existence of consistent hippocampal responses to movie frames is supported by the data shown. Interestingly, in my opinion, a strong piece of evidence for this is a "negative" result presented in Ext. Fig. 13c, which shows higher than chance-level correlations in hippocampal responses to same scrambled frames between even and odd trials (and higher than correlations with neighboring scrambled frames). The conclusion that hippocampal movie fields depend on continuous movie frames, rather than a pure visual response to visual contents in individual frames, is supported to some degree by their changed properties after the frame scrambling (Fig. 4). However, there are two potential issues that could complicate this main conclusion.

One issue is related to the effect of behavioral variation or brain state. First, although the authors show that the movie fields are still present during low-speed stationary periods, there is a large drop in the movie tuning score (Z), especially in the hippocampal areas, as shown in Ext. Fig. 3b (compared to Ext. Fig. 2d). This result suggests a potentially significant enhancement by active behavior.

Second, a general, hard-to-tackle concern is that neuronal responses could be greatly affected by changes in arousal or brain state (including drowsy or occasional brief slow-wave sleep state) in head-fixed animals without a task. Without the analysis of pupil size or local field potentials (LFPs), the arousal states during the experiment are difficult to know. Many example movie fields in the presented raw data (e.g., Fig. 1c, Ext. Fig. 4) are broad with low-quality tuning, which could be due to broad changes in brain states. This concern is especially important for hippocampal responses, since the hippocampus can enter an offline mode indicated by the occurrence of LFP sharp-wave ripples (SWRs) while animals simply stay immobile. It is believed that the ripple-associated hippocampal activity is driven mainly by internal processing, not a direct response to external input (e.g., Foster and Wilson, Nature 440: 680, 2006). The "actual" hippocampal movie fields during a true active hippocampal network state, after the removal of SWR time periods, could have different quantifications that impact the main conclusion in the manuscript.

Another issue is related to the relative contribution of direct visual response versus the response to temporal continuity in movie fields. First, the data in Ext. Fig. 8 show that rapid frame-to-frame changes in visual contents contribute largely to hippocampal movie fields (similarly to visual movie fields). Interestingly, the data show that movie-field responses are correlated across all brain areas including the hippocampal ones. This could be due to heightened behavioral arousal caused by the changing frames as mentioned above, or due to enhanced neuronal responses to visual transients, which supports a component of direct visual response in hippocampal movie fields. Second, the data in Ext. Fig. 13c show a significant correlation in hippocampal responses to same scrambled frames between even and odd trials, which also suggests a significant component of direct visual response.

Is there a significant component purely due to the temporal continuity of movie frames in hippocampal movie fields? To support that this is indeed the case, the authors have presented data that hippocampal movie fields largely disappear after movie frames are scrambled. However, this could be caused by the movie-field detection method (it is unclear whether single-frame field could be detected). Another concern in the analysis is that movie-fields are not analyzed on re-arranged neural responses to scrambled movie frames. The raw data in Fig. 4e seem quite convincing. Unfortunately, the quantifications of movie fields in this case are not compared to those with the original movie.

Cultural geography = human science, approach to the lives of people. Investigates relevance of culture to the global society today. * Contrast between mental and natural space * Space can be produced by society, but society creates itself within a cultural space

Consensus Public Review:

Ottenheimer et al., present an interesting study looking at the neural representation of value in mice performing a pavlovian association task. The task is repeated in the same animals using two odor sets, allowing a distinction between odor identity coding and value coding. The authors use state-of-the-art electrophysiological techniques to record thousands of neurons from 11 frontal cortical regions to conclude that 1) licking is represented more strongly in dorsal frontal regions, 2) odor cues are represented more strongly in ventral frontal regions, 3) cue values are evenly distributed across regions. They separately perform a calcium imaging study to track coding across days and conclude that the representation of task features increments with learning and remains stable thereafter.

Overall, these conclusions are interesting and mostly well supported by the data, although there are some doubts about their definition of value coding. One limitation is the lack of focus on population-level dynamics from the perspective of decoding, with the analysis focusing primarily on encoding analyses within individual neurons.

Some specific comments:

The authors use reduced-rank kernel regression to characterize the 5332 recorded neurons on a cell-by-cell basis in terms of their responses to cues, licks, and reward, with a cell characterized as encoding one of these parameters if it accounts for at least 2% of the observed variance. At least 50% of cells met this inclusion criterion in each recorded area. 2% feels like a lenient cutoff, and it is unclear how sensitive the results are to this cutoff, though the authors argue that this cutoff should still only allow a false positive rate of 0.02% (determined by randomly shuffling the onset time of each trial).

Having identified lick, reward, and cue cells, the authors next select the 24% of "cue-only" neurons and look for cells that specifically encode cue value. Because the animal's perception of stimulus value can't be measured directly, the authors created a linear model that predicts the amount of anticipatory licking in the interval between odor cue and reward presentations. The session-average-predicted lick rate by this model is used as an estimate of cue value and is used in the regression analysis that identified value cells. (Hence, the authors' definition of value is dependent on the average amount of anticipatory behavior ahead of a reward, which indicates that compared to the CS+, mice licked around 70% as much to the CS50 and 10% as much to the CS-.) The claim that this is an encoding of value is strengthened by the fact that cells show similar scaling of responses to two odor sets tested. Whereas the authors found more "lick" cells in motor regions and more "cue" cells in sensory regions, they find a consistent percentage of "value" cells (that is, cells found to be cue-only in the initial round of analysis that is subsequently found to encode anticipatory lick rate) across all 11 recorded regions, leading to their claim of a distributed code of value.

In subsequent sections, the authors expand their model of anticipatory-licking-as-value by incorporating trial and stimulus history terms into the model, allowing them to predict the anticipatory lick rate on individual trials within a session. They also use 2-photon imaging in PFC to demonstrate that neural coding of cue and lick are stable across three days of imaging, supported by two lines of evidence. First, they show that the correlation between cell responses on all periods except for the start of day 1 is more correlated with day 3 responses than expected by chance (although the correlation is still quite low, for example, 0.2 on day 2). Second, they show that cue identity is able to capture the highest unique fraction of variance (around 8%) in day 3 cue cells across three days of imaging, and similarly for lick behavior in lick cells and cue+lick in cue+lick cells. Nonetheless, their sample rasters for all imaged cells also indicate that representations are not perfectly stable, and it will be interesting to see what *does* change across the three days of imaging.

Importantly, the authors do not present evidence that value itself is stably encoded across days, despite the paper's title. The more conservative in its claims in the Discussion seems more appropriate: "these results demonstrate a lack of regional specialization in value coding and the stability of cue and lick [(not value)] codes in PFC."

Reviewer #1 (Public Review):

The authors use a model of neonatal E.coli pneumonia to study differences between early neonates ad juvenile animals. They observe increased monocyte derived macrophage recruitment in juveniles compared to neonates as well as an increase in IFNG related genes. The data are of potential interest but in its current form it is unclear how well the experiments were controlled for confounders, such as sex and CFU.

1. This paper conducted research to identify the window of susceptibility to pneumonia due to E. coli, a bacteria that most often causes pneumonia in the neonatal period. This is an understudied area and thus the research is significant.

2. The paper provides evidence of differences in immune response in neonatal mice vs juvenile mice. However, it is unclear if the data are controlled adequately for the bacterial burden in the lung, which would be a crucial control to control for epi-phenomena. Additionally, it is unclear if the molecules that regulate macrophage recruitment are defective in neonatal mice or if it is an issue of macrophage progenitor cells.

Reviewer #1 (Public Review):

Synapses are modulated by neural activity on a variety of timescales. Typical neural network models primarily consider long-lasting changes to synaptic strengths, applied while the network is learning, with synaptic strengths then being fixed after learning. However, shorter-term plasticity mechanisms are ubiquitous in the brain and have been shown to have significant computational and information-storage capabilities. Here the authors study these mechanisms in the context of the integration of information tasks. Their two primary contributions are to analyze these short-term mechanisms separately from recurrent connections to isolate the specific ways these might be useful and to apply ideas from population data analysis to dissect how their networks solve the tasks.

I thought this was a clear, well-written, and well-organized paper, tackling an important problem. I also found that the conclusions were adequately supported by the simulations and analyses shown. I particularly appreciated the careful analysis of how the different networks solved the task and found the distinction between hidden neurons reflecting accumulated evidence (attractor architecture) vs. reflecting inputs (MPN architecture) very interesting and potentially very useful for thinking about experimental observations. My comments are primarily about the connection to biology/biological interpretability as well as how this study relates to prior work.

1) I was confused about the nature of the short-term plasticity mechanism being modeled. In the Introduction, the contrast drawn is between synaptic rewiring and various plasticity mechanisms at existing synapses, including long-term potentiation/depression, and shorter-term facilitation and depression. And the synaptic modulation mechanism introduced is modeled on STDP (which is a natural fit for an associative/Hebbian rule, especially given that short-term plasticity mechanisms are more often non-Hebbian). On the other hand, in the network models the weights being altered by backpropagation are changes in strength (since the network layers are all-to-all), corresponding more closely to LTP/LTD. And in general, standard supervised artificial neural network training more closely resembles LTP/LTD than changing which neurons are connected to which (and even if there is rewiring, these networks primarily rely on persistent weight changes at existing synapses). Moreover, given the timescales of typical systems neuroscience tasks with input coming in on the 100s of ms timescale, the need for multiple repetitions to induce long-term plasticity, and the transient nature/short decay times of the synaptic modulations in the SM matrix, the SM matrix seems to be changing on a timescale faster than LTP/LTD and closer to STP mechanisms like facilitation/depression. So it was not clear to me what mechanism this was supposed to correspond to.

2) A number of studies have explored using short-term plasticity mechanisms to store information over time and have found that these mechanisms are useful for general information integration over time. While many of these are briefly cited, I think they need to be further discussed and the current work situated in the context of these prior studies. In particular, it was not clear to me when and how the authors' assumptions differed from those in previous studies, which specific conclusions were novel to this study, and which conclusions are true for this specific mechanism as opposed to being generally true when using STP mechanisms for integration tasks.

Reviewer #1 (Public Review):

This paper identifies an intracellular O-GlcNAc glycosylation of specific proteins in the control of bone formation and bone marrow adiposity. Compelling evidence is provided for the role of OGT-mediated O-GlcNAc glycosylation of RUNX2 in osteogenic differentiation versus OGT-mediated O-GlcNAc glycosylation of C/EBPβ in bone marrow adipogenesis.

Overall, the experiments have been done with great rigor, and sufficient details are provided for reproducibility. The authors developed a novel concept in the control of bone formation and bone marrow adiposity.

Reviewer #1 (Public Review):

The authors made some biologically reasonable approximations of the Pump and Leak model. e.g., assuming the alpha_0 parameter to be zero. These approximations significantly simplify the model and make the results much more intuitive, e.g., Eq. 4 in the main text. The authors proposed an interesting and simple model of amino acid production, which is argued to be the primary determinant of cell volume. Combined with the gene expression model proposed recently by Lin and Amir, their model can nicely explain the homeostasis of protein density. Furthermore, by considering the saturation of DNA and mRNA by RNA polymerase and ribosome, the authors extended Lin and Amir's model by introducing protein degradation, which I think is the key to explaining cytoplasm dilution. The authors also discussed other applications of their model, including mitotic swelling and nuclear scaling. Below are my major comments:

1. Eq. 2 is valid for stationary states where the cell volume is constant with time. However, many cells grow and divide, including yeast cells. I think the authors have implicitly neglected the effects of cell growth. The authors may want to mention this explicitly to avoid confusion.

2. It's unclear how the authors go from Eq. S.21 to Eq. 2, although the authors mentioned it is straightforward. I think the dilute solution assumption is used without explicit mention, at least in section A of the SI.

3. A slight deviation from equilibrium is implicitly assumed in Eq. S.22 I think since the flow is linearly proportional to the chemical potential difference. The authors may want to mention this explicitly since the linear assumption is not necessarily true for biological systems.

4. A more general gene expression model is recently proposed by some of the authors of Ref. 30, in which the saturation of DNA by RNAPs is due to a high free RNAP concentration near the promoter (Wang and Lin, Nature Communications, 2021). I think the exact saturation mechanism is not very important to the conclusions. Still, I think it's good to let readers be aware that there are biologically more realistic saturation mechanisms.

5. The success of the fitting in Figure 2E is intriguing but may not be a smoking gun evidence of the model's validity. All one needs is a protein number proportional to cell volume for tt**, as far as I understand. Alternative models incorporating the above features will be able to reproduce the fitting of Figure 2E as well, I think. For example, instead of adding protein degradation, one can alternatively assume that protein translation becomes much slower for t>t**, but amino acids are still produced at a constant rate. The time-dependences of amino acids and cell volume may not be important if one just wants to fit the data in Figure 2E since the cell volume dynamics are extracted from Figure 2B. The authors may want to discuss this point.

6. On line 752, the estimation of the average charge of proteins is unclear to me. How did the authors obtain z_p = 0.8?

Reviewer #1 (Public Review):

This work applies duplex sequencing to study point mutations in mice across tissues in young (4.5 months) and old mice (26 months). In this study, they identified 89,000 independent somatic mtDNA mutations representing the largest collection of somatic 'point' mtDNA mutation (not considering mtDNA deletions). They find that mtDNA mutations accumulate linearly with age in a clock-like manner but are not uniformly represented in all tissues. This indicates a likely constant 'clock-like' accumulation analogous to what is seen in the nuclear genome. This part of the paper is a comprehensive extension of work done by Arbeithuber et al., 2020. They also find variability between tissues of the ROS-linked (transversions) mutations. Similar to prior work by Kennedy and Loeb (2013 Plos Genetics) they conclude that ROS-linked mutations do not accumulate significantly with age. Lastly, the authors apply this knowledge and technique to interrogate whether mtDNA mutations are affected by two known treatments, elimipretide and nicotinamide mononucleotide, that have been shown to improve mitochondrial function and reverse apparent aging phenotypes. Here they demonstrate that these treatments reduced the low level of ROS accumulated mtDNA mutations seen in untreated tissues.

Comments:<br /> The paper states that they observed a combined total of 77,017 single-nucleotide variants (SNVs) and 12,031 insertion/deletions (In/Dels) across all tissue, age, and intervention groups. Collectively, these data represent the largest collection of somatic mtDNA mutations obtained in a single study to date. However, A study with more somatic mtDNA mutations by the LostArc method (PMID 32943091) revealed 35 million deletions (~ 470,000 unique spans) in skeletal muscle from 22 individuals with and 19 individuals without pathogenic variants in POLG. Thus, the authors should reword this part to say that this study represents the largest collections of mouse mtDNA point mutations detected, but not the largest amount of mutations (deletions exceed this number).

What is the theoretical limit of pt mutations in the mitochondrial genome, assuming only one pt mutation per genome? Doesn't 77000 detected independent pt mutations approach that limit? Can the authors estimate how many molecules contained two or more pt mutations? Did the analysis reveal any un-mutated regions implying an essential function? For example, on p.9 can the authors provide an explanation of why OriL and other G/C-rich regions were not uniformly covered as compared to the rest of the genome?

Given that mitochondrial disease usually doesn't present until >60% of the genomes are affected, the very low level of detected pt mutations observed in the mouse (and presumably similar to human) would mean that they are well below a physiological level. Thus, these low-level pt mutations are well tolerated. Can the authors estimate a theoretical age of the mouse (well beyond their life span) where over 50% of the genomes carry at least one pt mutation?

Also, the problem with this low level of pt mutations is that they are not physiological, the effect of the drug treatment causing a reduction in ROS-mediated transversions would not be expected to have a detectable effect on mitochondria. The improvement on mitochondrial seen by others is most likely independent of the mutations in the genome. There needs to be a cause and effect here and I don't see one.

There's no mention in this paper and methodology about how point mutations in nuclear-encoded mtDNA (NUMTs) are excluded from the reads and I'm worried that these errors are being read as rare errors in the mtDNA genome. While NUMTs have been documented for decades, a recent report in Science (PMID: 36198798) documents how frequently and fluidly NUMTs occur. Can the authors provide a clear explanation of how mutations in NUMTs are excluded?

Reviewer #1 (Public Review):

The manuscript by Hekselman et al presents analyses linking cell-types to monogenic disorders using over-expression of monogenic disease genes as the signal. The manuscript analyses data from 6 tissues (bone marrow, lung, muscle, spleen, tongue and trachea) together with ~1,000 rare diseases from OMIM (with ~2,000 associated genes) to identify cell-type of interest for specific disease of choice. The signal used by the approach is the relative expression of OMIM-genes in a particular cell type relative to the expression of the gene in the tissue of interest identifying cell-type-disease pairs that are then investigated through literature review and recapitulated using mouse expression. A potentially interesting finding is that disease genes manifesting in multiple tissues seem to hit same cell-types. Overall this important study combines multiple data analyses to quantify the connection between cell types and human disorders. However whereas some of the analyses are compelling, the statistical analyses are incomplete as they don't provide full treatment of type I error.

Reviewer #1 (Public Review):

This is a carefully performed and well-documented study to indicate that the FUS protein interacts with the GGGGCC repeat sequence in Drosophila fly models, and the mechanism appears to include modulating the repeat structure and mitigating RAN translation. They suggest FUS, as well as a number of other G-quadruplex binding RNA proteins, are RNA chaperones, meaning they can alter the structure of the expanded repeat sequence to modulate its biological activities.

Overall this is a nicely done study with nice quantitation. It remains somewhat unclear from the data and discussions in exactly what way the authors mean that FUS is an RNA chaperone: is FUS changing the structure of the repeat or does FUS binding prevent it from folding into alternative in vivo structure?

Reviewer #1 (Public Review):

This manuscript by Mahlandt, et al. presents a significant advance in the manipulation of endothelial barriers with spatiotemporal precision, and in the use of optogenetics to manipulate cell signaling in vascular biology more generally. The authors establish the role of Rho-family GTPases in controlling the cytoskeletal-plasma membrane interface as it relates to endothelial barrier integrity and function and adequately motivate the need for optogenetic tools for global and local signaling manipulation to study endothelial barriers.

Throughout the work, the optogenetic assays are conceptualized, described, and executed with exceptional attention to detail, particularly as it relates to potential confounding factors in data analysis and interpretation. Comparison across experimental setups in optogenetics is notoriously fraught, and the authors' control experiments and measurements to ensure equal light delivery and pathway activation levels across applications are very thorough. In demonstrating how these new opto-GEFs can be used to alter vascular barrier strength, the authors cleverly use fluorescent-labeled dextran polymers of different sizes and ECIS experiments to demonstrate the physiological relevance of BOEC monolayers to in vivo blood vessels. Of particular note, the resiliency of the system to multiple stimulation cycles and longer time course experiments is promising for use in vascular leakage studies.

Given that dozens of Rho GTPase-activating GEFs exist, an expanded rationale for the selection of p63, ITSN1, and TIAM1 in the form of discussion and literature citations would be helpful to motivate their selection as protein effectors in the engineered tools. Extensive tool engineering studies demonstrate the superiority of iLID over optogenetic eMags or rapamycin-based chemogenetic tools for these purposes. However, as the utility of iLID and eMags has been demonstrated for the manipulation of a variety of signaling pathways, the iSH-Akt demonstration does not seem necessary for these systems.

The demonstration of orthogonality in GTPase- and VE-cadherin-blocking antibody-mediated barrier function decreases and is compelling, even without full elucidation of the role of cell size or overlap in barrier strength. The discussion section presents a mature and thoughtful description of the limitations, remaining questions, and potential opportunities for the tools and technology developed in this work. Importantly, this manuscript demonstrates a commitment to scientific transparency in the ways in which the data are visualized, the methods descriptions, and the reagent and code sharing it presents, allowing others to utilize these tools to their full potential.

Reviewer #1 (Public Review):

This is a well-conceived and well-executed investigation of how activation loop autophosphorylation and IN-box autophosphorylation synergistically activate AURKB/INCENP. An elegant chemical ligation strategy allowed construction of the intermediate phospho-forms so that the contributions of each phosphorylation event to structure, dynamics, and activity could be dissected. Autophosphorylation at both sites serves to rigidify both AURKB and the IN-box, and to coordinate opening, twisting, and activation loop movements. Consistent with previous findings, both sites are necessary for enzymatic activity; further, this work finds that activation loop autophosphorylation occurs slowly in cis while IN-box autophosphorylation occurs quickly in trans.

Due to abundant previous work in the field, many of the conclusions of this paper were expected. However, that does not diminish the quality of the work, and the addition of how kinase dynamics contribute to activation is important for AURKB and many other kinases. The experimental results are clear and interpreted appropriately, with good controls. The computational work is also clearly explained and directly tied to the function of the enzyme, making it highly complementary to the experimental findings and to previously published structures.

Some minor limitations of the study:

1. Of note when interpreting the HDX data, there is no coverage of the peptide containing the activation loop autophosphorylation site T248 (Fig S2A), and as mentioned in the Discussion, the time scale of HDX is not able to capture differences in exchange in very flexible regions like the activation loop.

2. Some data lack robust statistical analysis, which would make the findings more compelling.

3. One point that might be clarified is how the occupancy of T248 was confirmed to be either fully phosphorylated in the [AURKB/IN-box]IN-deltaC or fully dephosphorylated in the IN-box K846N/R827Q mutant. Especially because T248 autophosphorylation is found to occur in cis, it is unclear how incubating the [AURKB/IN-box]IN-deltaC with traces of wild-type [AURKB/IN-box]all-P would ensure that T248 is phosphorylated.

Reviewer #1 (Public Review):

This paper by Melo et al. is a technically elegant study investigating the important emerging hypothesis that the brainstem preBötzinger complex (preBötC) region - a critical nuclear structure where the rhythm of breathing in mammals originates - has segregated subgroups of output neurons that modulate specific behaviors coordinated with breathing, in this study the orofacial muscle activity. The preBötC has been under intense investigation for several decades but the subregional neuronal subtype composition and organization are not fully understood. Understanding this organization and how breathing modulates specific behaviors has many implications for normal brain function and pathophysiology.

Strengths of the paper include:<br /> 1) The authors use an effective combinatorial dual viral transgenic approach for Cre-dependent expression of the chloride channel (GtACR2) and labeling of neurons projecting to the facial motor nucleus controlling orofacial muscle activity, for optogenetic photoinhibition of these preBötC neurons in vivo.<br /> 2) The experimental results presented convincingly support the authors' conclusion that a subgroup of preBötC neurons provides inspiratory modulation of facial motoneurons that appear to be distinct from other output neurons that drive inspiratory activity to bulbospinal neurons and neurons projecting to autonomic nervous system circuits.<br /> 3) These results advance our understanding of preBötC circuit organization that coordinates and integrates breathing with different motor and physiological behaviors.

Weaknesses:<br /> There are a few technical issues related to the photoinhibition paradigm used and the patterns of neuronal transduction with the dual viral transgenic approach used that the authors need to clarify.

Reviewer #1 (Public Review):

This research tackles an important question in evolutionary biology that has long stood on theory, with little experimental evidence to support this big idea. This paper provides a large natural dataset on several morphometric factors that allow a robust testing of the "handicap principle". The strength in this dataset comes from extensive field observations not only on morphology, but also fecundity and pairing behavior. The manuscript could use a little tightening up in prose, but the statistics and results are well explained. As the discussion mostly focuses on shrimp, generalizable principles are somewhat unclear. Overall, the research is an important finding that could one day be incorporated into undergraduate textbooks.

DOI: 10.7554/eLife.85800

Resource: (ZFIN Cat# ZDB-ALT-090324-1,RRID:ZFIN_ZDB-ALT-090324-1)

Curator: @evieth

SciCrunch record: RRID:ZFIN_ZDB-ALT-090324-1

What is this?

Reviewer #1 (Public Review):

Kim et al carried out a genetic screening using Drosophila lines to identify genetic modifiers of ubiquilin 2 mutations associated with ALS/FTD. They generated Drosophila lines expressing wild-type or various mutations of ubiquilin 2 and used the rough eye phenotype as the primary screening criterion. They used the deficiency library in the screening and subsequently attempted to narrow down to single genes. They identified multiple suppressors and enhancers from the deficiency lines and carried out further studies on an endosomal gene rab5, an axon guidance gene unc5 and its co-receptor frazzled, and another axon guidance gene beat-1b. Critical findings were also confirmed in iPSC and induced motor neurons (iMNs), supporting the relevance of the findings in human neurons. The study is important as it provides compelling evidence linking axon guidance/synaptic maintenance to ubiquilin 2-mediated neurotoxicity.

With the above strengths and impact, there are several weaknesses. First, the heat shock effect in the drosophila lines was not understood in the study. Why did some lines show phenotypes only at 29C but not 22C? The study showed data that ubiquilin 2 expression was not impacted by 29C, then what caused the phenotypic differences? In addition, the method section did not describe clearly whether a temperature sensitive promoter was used in the flies. Second, the study showed data on male and female flies separately in some but not all experiments. In addition, the manuscript largely avoided discussing whether there was a sex difference in those experiments. Third, some data appear to be peripheral with no significant contribution to the main findings. Moreover, some data were introduced but were not explained. For instance, the RNA-Seq analysis (Fig 2) did not contribute much to the study. The rescue effect of UBA* (F594A mutant) in Fig 1-Supplemental 1B was interesting but was not elaborated or followed up. FUS flies in Fig 6-Supplement 2 were abrupted introduced with little discussion. Fourth, the main quadrupole (4xALS) mutation used in the study was not found in patients. The relevance of the findings needs to be thoroughly justified. Lastly, ALS and FTD are age-related neurodegenerative diseases, whereas the involvement of axon guidance genes in indicative of disruptions during the developmental stage. The manuscript did not discuss this potential caveat.

Overall, this study identified several potential genetic modifiers of ubiquilin 2 in the context of ALS/FTD. It represents a significant advancement of our understanding of ubiquilin 2-mediated ALS/FTD and related neurodegenerative diseases.

Reviewer #1 (Public Review):

The manuscript by Kschonsak et al. describes the rational structure-based design of novel hybrid inhibitors targeting human Nav1.7 channel. CryoEM structure of arylsulfonamide (GNE-3565) - VSD4 NaV1.7-NaVPas channel complex confirmed binding pose observed in x-ray structure GX-936 - VSD4 Nav1.7-NavAb channel. Remarkably, cryoEM structure of acylsulfonamide (GDC-0310) - VSD4 NaV1.7-NaVPas channel complex revealed a novel binding pocket between the S3 and S4 helices, with the S3 segment adopting a distinct conformation compared to the arylsulfonamide (GNE-3565) - VSD4 NaV1.7-NaVPas channel complex. Creatively, the authors designed a novel class of hybrid inhibitors that simultaneously occupy both the aryl- and acylsulfonamide binding pockets. This study underscores the power of structure-guided drug design to target transmembrane proteins and will be useful to develop safer and more effective therapeutics.

Reviewer #1 (Public Review):

In this manuscript, Chure and Cremer first provide a broad panorama of the different sector models for resource allocation in biosynthesis and how they provide an explanation of cellular growth physiology; then they formalise how optimal flux balance (flux parity) can reproduce many different physiological observables in a quantitative manner.

The first part of this study comprises a valuable synthesis of many literature results, which are here gathered together and clearly reformulated. The authors also assembled a rich and impressive collection of experimental published datasets in E. coli from several sources, which are then extensively compared with the outcomes of the models. In my view, these points are the main strengths of the manuscript.

The flux-parity regulation introduced in the second part emerges from the balance of metabolic and biosynthesis fluxes, which have to be mutually optimised in the authors' framework. Those ingredients are often found in the literature, and the reader has sometimes the impression that novelty is lacking. Although flux balance and optimisation are often assumed in modelling resource allocation, the authors have the merit of formalising the approach in a clearer way than was done before, making an extensive comparison with data.

Reviewer #1 (Public Review):

Pyrin domains (PYD) in inflammasome proteins oligomerize into filamentous assemblies and mediate inflammasome formation. Mammalian pyrin-only-proteins (POPs) exert inhibitory effects on inflammasome as they mimic the pyrin domains while lacking the effector domain. In this manuscript, Mazanek and colleagues combined computational prediction with cellular and in vitro experiments to investigate the mechanism and target specificity for three POPs, POP1, POP2, and POP3, in inflammasome activation.

The authors first modeled the structures of complex formed by POPs with inflammasomal PYDs, including ASCPYD, AIM2PYD, IFI16PYD, NLRP6PYD, and NLRP3PYD, then calculated their Rosetta interface energies(∆Gs). By comparing the ∆Gs of inflammasomal PYD(∆GPYD•PYD) with inflammasomal PYD/POPs complex (∆GPOP•PYD), they defined favorable and unfavorable interaction surfaces (∆∆G = ∆GPYD•PYD- ∆GPOP•PYD ). Their initial computational model indicates POP1 may have the strongest inhibitory effect on ASC, as it exhibits the most favorable interfaces. But the experiment results showed otherwise, with POP2 and POP3, which contain both favorable and unfavorable interfaces, exhibiting stronger inhibitory effects. They then revised the model and proposed the combination of favorable (recognition) and unfavorable interfaces (repulsion) is necessary for POPs to interfere with the assembly of inflammasome PYDs, which was further tested by other inflammasomal PYDs.

This is a timely study that enhanced our current understanding of inflammasome regulation by POPs, it is also interesting as it combined the newest computational prediction method with biological experimental validation. The explanations on 1.) sequence homology may not dictate the target specificity of POPs, and 2.) excess POPs are required to inhibit the polymerization of inflammasome assembly, are well supported; however, some questions about the target specificity need to be addressed/clarified:

1. The authors showed MBP tag affected the oligomerization of POPs, while the POPs used in Figures 2A, 3A, and 4A contain a GFP tag. It should be considered GFP may affect the property of POPs, such may change the inhibitory effect of POPs on ASC filament formation.

2. The authors take the reduction of PYD filamentation as an indication of inhibition, but it was not clear how they ruled out the possibility that POP1 co-assembles into ASCPYD filaments and inhibits inflammasome formation by repressing the recruitment of Caspase-1, as it lacks CARD the effector domain. Especially the model predicted comparable energy between POP1 and ASC, which could indicate POP1 co-assembled into ASC filament.

3. Further computational analysis should be performed to evaluate the interpretation of Rosetta interface energies. Could the "combination of favorable and unfavorable interfaces" theory apply to other PYD/PYD interactions and CARD/CARD interactions?

Reviewer #1 (Public Review):

In this manuscript, McQuate et al. use serial block face SEM to provide a high resolution, 3D analysis of mitochondrial structure in hair cells and surrounding supporting cells of the zebrafish lateral line. They first demonstrate that hair cells have a higher mitochondrial volume as compared to supporting cells, which likely reflects the high metabolic load of these sensory cells. Their deeper analysis of mitochondrial morphology in hair cells reveals that the base of the hair cell - near the presynapse is dominated by a large, networked mitochondrion, while the apex of the cell is dominated by many small mitochondria. By examining hair cells at different stages of development, the authors show that specialized features of hair cell mitochondria are gradually established over the course of development. Finally, by examining hair cells in mutants that lack mechanosensation or presynaptic calcium responses, McQuate et al. reveal that cellular activity contributes to the development of appropriate mitochondrial morphology and localization within hair cells. This dataset, which will be made publicly available, is an immense resource to the community and will facilitate the generation of novel hypotheses about hair cell mitochondrial function in health and disease.

Strengths:<br /> 1. The painstaking acquisition and analysis of hair cell EM data in a genetically tractable system that is easily accessible for in vivo functional experiments to address hypotheses that emerge from this work.<br /> 2. The use of multiple datasets and analysis methods to cross-validate results.<br /> 3. The thoughtful, careful analysis of the data highlights the richness of the dataset.<br /> 4. The use of both wild-type and mutant animals substantially adds to the manuscript, providing significantly more insight than wild-type data alone.

Weaknesses:<br /> 1. The manuscript could more strongly highlight the utility of this dataset and facilitate its future use by providing a summary table that lists each sample together with salient details.<br /> 2. The authors examine an opa-1 mutant with altered mitochondrial fission (which consequently has changes in mitochondrial morphology and organization) to suggest that aberrant mitochondrial architecture negatively impacts mitochondrial function. However, mitochondrial fusion is thought to be critical for mitochondrial health beyond just altered architecture. Because fusion has other roles, it is difficult to use this manipulation to conclude that it is simply disruptions in mitochondrial architecture that alters function.

3. Although the work of acquiring and reconstructing EM data is labor-intensive, ideally, multiple fish would be examined for each genotype. Readers should take into consideration that one of the mutant datasets is derived from just one animal.

Reviewer #1 (Public Review):

The article from Dumoux et al. shows the use of plasma-based focused ion beams for volume imaging on cryo-preserved samples. This exciting application can potentially increase the throughput and quality of the data acquired through serial FIB-SEM tomography on cryo-preserved and unstained biological samples. The article is well-written, and it is easy to follow. I like the structure and the experimental description, but I miss some points in the analyses, without which the conclusions are not adequately supported.

The authors state the following:<br /> "the application of serial FIB/SEM imaging of non-stained cryogenic biological samples is limited due to low contrast, curtaining, and charging artefacts. We address these challenges using a cryogenic plasma FIB/SEM (cryo-pFIB/SEM)".<br /> Reading the article, I do not find that the challenges are addressed; it appears that some of these are evaluated when the samples are prepared using plasma-based beams. To support the fact that charging, contrast, and curtaining are addressed, a comparison should be made with the current state of the art, or it is otherwise impossible to determine whether these systems bring any advantage.

Charging is an issue that is not described in detail, nor has it been adequately analysed. The effect of using plasma beams is independent of the presented algorithm for charging suppression, which is purely image processing based, although very interesting. Given that the focus of the work is on introducing the benefit of using plasma ion beams (from the title) and given that a great deal of data is presented on the effect of the multiple ion sources, one would expect to have comparable images acquired after the surfaces have been prepared with the different beams. This should also be compared against the current state-of-the-art (gallium) to provide a baseline for different beams' benefits. I realise that this requires access to another microscope and that this also imposes controls on the detector responses on each instrument to have a normalised analysis. Still, it also provides the opportunity to quantify the benefits of each instrumentation.

The curtaining scores. This is a good way to explain the problem, though a few aspects need to be validated. For example, curtains appear over time when milling, and it would be useful to understand how different sources behave over time in FIB/SEM tomography sessions. The score is currently done from individual windows milled, which gives a good indication of the performance. However, it would make sense to check that the behaviour remains identical in an imaging setting and with the moving milling windows (or lines). This will show the counteracting effect to the redeposition and etching effect reported when imaging with the E-beam the milled face.

No detail about the milling resolution has been reported. Since different currents and beams have different cross-sections, it is expected to affect the z-resolution achievable during an imaging session. It would be useful to have a description of the beam cross-sections at the various conditions used and how or whether these interfere with the preparation.

Contrast. No analysis of plasma FIBs' benefits on image contrast compared to the current state of the art has been provided. Measuring contrast is complex, especially when this value can change in response to the detector settings. Still, attempts can be made to quantify it through the FRC and through the analysis of the image MTF (amplitude and fall off), given that membranes are the only most prominent and visible features in cryoFIB/SEM images of biological samples.

Figure S4 points out that electrons that hit the sample at normal incidence give better signal/contrast or imaging quality than when the sample is imaged at a tilt. This fact is expected to significantly affect large areas as the collection efficiency will vary across the sample, particularly as regions get further away from the optimal location. The dynamic focusing option available on all SEM will compensate for the focal change but not the collection efficiency. Even though this is a fact, the authors show a loss of resolution, which is not explained by the tilt itself. In particular, the generation of secondary electrons is known to increase with the increased tilt, and to consider that the curtains (that are the prominent feature on the surface) are running along the tilt direction, it would be expected to see no contrast difference between the background and the edge of each curtain as the generation of secondary electrons will increase with tilt for both the edges and the background. Therefore, the contrast should be invariant, at least on the curtains.

Looking at the images presented in the figure, they appear astigmatic and not properly focused when imaged at a tilt. As evidence of this claim, the cellular features do not measure the same, and the sharpness of the edge of the curtains is gone when tilted. This experience comes from improper astigmatism correction, which in turn, in scanning systems, leads to the impossibility of focusing. The tilt correction provides not only dynamic focusing but also corrects for the anisotropy in the sampling due to the tilt. If all imaging is set up correctly, the two images should show the imaged features with the exact sizes regardless of the resolution (which, in the presented case, is sufficient), and the sharpness of the curtain edges should be invariant regardless of the tilt, at least while or where in focus. Only at that point, the comparison will be fair.

Finally, the resolution measurements presented in the last supplementary figures have no impact or relation to the use of plasma FIB/SEM. It is an effect related to the imaging conditions used in the SEM regardless of the ion beam nature. The distribution of the resolution within images appears predominantly linked to local charging and the local sample composition (from fig8). Given the focus is aimed at introducing or presenting the use of the plasma-based beams the results should be presented in that optic in mind with a comparison between beams.

Reviewer #1 (Public Review):

Taliani et al. have studied the role of the lncRNA pCharme during cardiac development. pCharme knockout-mice present hyperplastic hearts and the authors attempt to decipher whether this cardiac phenotype result from a developmental alteration during heart formation. They showed that pCharme is specifically expressed in the heart from early stage of development at heart tube stage and persists until birth while its expression decreases after birth. The expression of pCharme in early cardiac progenitors is regulated by the transcription factor Tbx5. Several genes and signaling pathways are differentially affected in pCharme mutant hearts and most of them affected cell cycle activity and cardiac differentiation. pCharme is required to form chromatin aggregates including the MATR3 protein and this sounds important to regulate cardiac gene transcription.

One issue concerns the description of the cardiac phenotype in pCharme mutant embryos as immunofluorescent data are difficult to interpret. A deeper investigation of the level of compaction and hypotrabeculation is required to affirm that pCharme plays a role in the ventricular wall differentiation/maturation.

Another issue is that the cardiac phenotype in pCharme is not directly related to that observed in MATR3 mutants.

Reviewer #1 (Public Review):

Plasmodium falciparum must decide how much resources it will invest in within-host proliferation, or divert into gametocytogenesis to ensure onward transmission to the mosquito. The authors present here an interesting new perspective to this question using longitudinal data from a single study site over 18 years which covers three distinct transmission phases: pre-decline, decline and post-decline (which reflects a high malaria transmission setting declining to a low transmission setting). Laboratory studies in gametocyte commitment are having a renaissance in recent years, however in vivo studies have lagged behind. To address this knowledge gap, the authors have quantified the transcript levels in patient samples of ap2-g (a key player in gametocyte commitment), and PfSir2a (a gene hypothesised to sense various cellular processes via metabolic regulation). They found that transcripts of both genes increase (indicating increased investment in gametocyte production) as transmission declines. Using the Luminex platform, they were then able to link gene expression directly to key inflammatory markers within the patient, showing that as malaria transmission declines, host inflammatory response changes. Adding greater depth, using unbiased lipidomics the authors then went on to link identified inflammatory response phenotypes to specific lipid species. Excitingly, they link depleted levels of host lysophosphatidylcholine (LPC) with a defined immune response state and increased gametocyte commitment in the low transmission setting. Taken together, this gives strong in vivo support for LPC as a key modulator in both parasite development and host immune response, something that to date has been mostly characterised in vitro.

Reviewer #1 (Public Review):

In this manuscript, Wei & Robles et al seek to estimate the heritability contribution of Neanderthal Informative Markers (NIM) relative to SNPs that arose in modern humans (MH). This is a question that has received a fair amount of attention in recent studies, but persistent statistical limitations have made some prior results difficult to interpret. Of particular concern is the possibility that heritability (h^2) attributed to Neanderthal markers might be tagging linked variants that arose in modern humans, resulting in overestimation of h^2 due to Neanderthal variants. Neanderthal variants also tend to be rare, and estimating the contribution of rare alleles to h^2 is challenging. In some previous studies, rare alleles have been excluded from h^2 estimates.

Wei & Robles et al develop and assess a method that estimates both total heritability and per-SNP heritability of NIMs, allowing them to test whether NIM contributions to variation in human traits are similar or substantially different than modern human SNPs. They find an overall depletion of heritability across the traits that they studied, and found no traits with enrichment of heritability due to NIMs. They also developed a 'fine-mapping' procedure that aims to find potential causal alleles and report several potentially interesting associations with putatively functional variants.

Strengths of this study include rigorous assessment of the statistical methods employed with simulations and careful design of the statistical approaches to overcome previous limitations due to LD and frequency differences between MH and NIM variants. I found the manuscript interesting and I think it makes a solid contribution to the literature that addresses limitations of some earlier studies.

My main questions for the authors concern potential limitations of their simulation approach. In particular, they describe varying genetic architectures corresponding to the enrichment of effects among rare alleles or common alleles. I agree with the authors that it is important to assess the impact of (unknown) architecture on the inference, but the models employed here are ad hoc and unlikely to correspond to any mechanistic evolutionary model. It is unclear to me whether the contributions of rare and common alleles (and how these correspond with levels of LD) in real data will be close enough to these simulated schemes to ensure good performance of the inference.

In particular, the common allele model employed makes 90% of effect variants have frequencies above 5% -- I am not aware of any evolutionary model that would result in this outcome, which would suggest that more recent mutations are depleted for effects on traits (of course, it is true that common alleles explain much more h^2 under neutral models than rare alleles, but this is driven largely by the effect of frequency on h^2, not the proportion of alleles that are effect alleles). Likewise, the rare allele model has the opposite pattern, with 90% of effect alleles having frequencies under 5%. Since most alleles have frequencies under 5% anyway (~58% of MH SNPs and ~73% of NIM SNPs) this only modestly boosts the prevalence of low frequency effect alleles relative to their proportion. Some selection models suggest that rare alleles should have much bigger effects and a substantially higher likelihood of being effect alleles than common alleles. I'm not sure this situation is well-captured by the simulations performed. With LD and MAF annotations being applied in relatively wide quintile bins, do the authors think their inference procedure will do a good job of capturing such rare allele effects? This seems particularly important to me in the context of this paper, since the claim is that Neanderthal alleles are depleted for overall h^2, but Neanderthal alleles are also disproportionately rare, meaning they could suffer a bigger penalty. This concern could be easily addressed by including some simulations with additional architectures to those considered in the manuscript.

Reviewer #1 (Public Review):

Overall, this is a well-written and well-executed study that addresses the in vivo and in vitro functions of PCM1, a key component and regulator of centriolar satellites previously implicated in centrosome and ciliary biogenesis and function. The authors first generated mice lacking PCM1 and through careful phenotypic characterization, they demonstrate a tissue- and cell-type specific role for PCM1 in ciliogenesis in vivo, including a role in ciliogenesis in multiciliated ependymal cells but not airway epithelial cells. Consistently, Pcm1-/- mice were demonstrated to display perinatal lethality and select ciliopathy phenotypes such as hydrocephalus. Using high resolution immunofluorescence imaging and electron microscopy, the authors provide evidence that PCM1 promotes early stages of ciliogenesis, specifically removal of the CP110 capping protein from the distal end of (mother) centrioles. They go on to investigate this in more detail using cultured mouse embryonic fibroblasts (MEFs) and RPE1 cells lacking PCM1. Intriguingly, they find that PCM1 is required for ciliogenesis in RPE1 cells but not in MEFs, even though CP110 levels at the mother centriole are elevated in both cell types when PCM1 is depleted. The authors propose that PCM1 promotes ciliogenesis in select cell types by "wicking away" CP110 from the mother centriole at the onset of ciliogenesis, and provide some additional evidence (e.g. co-immunoprecipitation and live cell imaging analysis) to support this model. The manuscript represents a significant amount of high-quality work, and most of the claims are justified by the data. However, the manuscript would be strengthened by addressing the following points:

1) Based on their results, including the observation that CP110 and CEP97 centrosomal levels are increased in PCM1-/- cells, the authors propose that PCM1 promotes ciliogenesis by mediating removal/"wicking away" of CEP97 and CP110 from the mother centriole at the onset of ciliogenesis (Figure 9). Although this model could explain the authors' observations, alternative models should be considered. For example, an equally plausible mechanism is that PCM1 promotes centrosome/mother centriole recruitment of an E3 ligase that (negatively) regulates CP110. Indeed, the authors show in Fig. 4 that MEFs lacking PCM1 display reduced centrosome levels of the E3 ligase MIB1. This raises the question if MIB1 is also reduced at the centrosome in RPE1 cells lacking PMC1, and whether other E3 ligases known to promote CP110 removal/degradation are also decreased at the mother centriole of PCM1-/- cells. This includes EDD1/UBR5, which was previously implicated in CP110 removal from the mother centriole of RPE1 cells (Hossain et al. 2017; Goncalves et al., 2021), and which may be linked to centriolar satellites via CSPP-L (Shearer et al. 2018). Other relevant CP110 regulators to check include LUBAC and PRPF8, which may act in parallel with UBR5 to mediate CP110 removal from the mother centriole (Shen et al., 2021). The authors should at least discuss the possibility that PCM1 might affect the centrosome localization of these known CP110 regulators, if not address it experimentally. Finally, to confirm that reduced ciliogenesis in PCM1-/- cells is indeed due to increased levels of CP110 at the mother centriole, the authors could (partially) deplete CP110 from PCM1-/- RPE1 cells to investigate if this rescues the ciliogenesis phenotype of the mutant cells, e.g. as done recently by Goncalves et al. for CEP78-/- cells.

2) Figure 5 supplement 1A, B; lines 232-242; 430-439: the authors report that Talpid3 localization at the centrosome in PCM1 mutant cells is equivalent to that of controls. However, when looking at Figure 5 supplement 1B it seems that Talpid3 levels at the centrosome may be slightly elevated at the centrosome in the mutant cells although the change is not statistically significant. I suggest the authors specifically state this in the text, given that previous work by Wang et al. (2016) indicated that PCM1 does have an effect on centrosomal Talpid3 levels. A change in Talpid3 centrosomal level could be very small, requiring larger sample size to reach statistical significance, and different experimental conditions (fixation, permeabilization, antibody dilution etc.) could also influence the results and explain the discrepancy between the authors' observations and those of Wang et al. (2016).

3) Figure 5 supplement 1C, D: given that the authors´ results are in contrast to those of Wang et al. (2016), they should measure the actual fluorescence intensity of Centrobin at the mother centriole rather than just counting number of Centrobin foci, as they have done for e.g. CP110.

4) The observed requirement for PCM1 in promoting ciliogenesis in RPE1 cells and not MEFs is puzzling, given that the authors still observed increased CP110 levels at the mother centriole in the Pcm1-/- MEFs. In the discussion (lines 464-473), the authors suggest that CP110 removal from the mother centriole may be more important for ciliogenesis in cells using the "extracellular" pathway of ciliogenesis compared to cells forming cilia via the "intracellular" pathway. However, mouse fibroblasts and RPE1 cells were shown to both form cilia via the "intracellular" pathway (e.g. see Ganga et al. 2021) thus this explanation seems insufficient to explain the observed differences between RPE1 cells and MEFs lacking PCM1. It would be helpful if the authors could comment on this.

Reviewer #1 (Public Review):

In the present manuscript, the authors investigated circuits mechanisms that underlie habituation of visually evoked escape behaviors in larval zebrafish. For eliciting escape behaviors, the authors used dark looming stimuli. Larvae habituate to repeated stimulation with dark looming stimuli. The authors decomposed a dark looming stimulus into two independent components: one that is characterized by an overall spatial expansion, and the other that represents an overall dimming within the whole visual field. The authors found that pre-exposure to just the dimming component habituates responsiveness to dark looming in a comparable fashion than repeated exposure to the full dark looming. They investigated neural mechanisms that account for this using two photon calcium imaging experiments. Based on the results, the authors propose a circuits model where a subset of inhibitory DS (dimming sensitive) neurons are incrementally potentiated by repetitive stimulation and where these neurons serve to locally depress the looming selective relay pathway.

There are two caveats in the present study. First, there exists another independent habituation pathway as habituation also occurs for spatial expansion stimuli that do not accompany dimming (checkerboard stimuli). This manuscript does not investigate neural mechanisms of this habituation pathway at all. Second, the authors performed no experiment that supports the validity of the model (i.e., no ablation experiment). These two caveats reduce the impact of the manuscript. Nevertheless, I think the manuscript is worth publishing, as the model the authors propose is interesting. The model generates a series of predictions about behavior, neural response properties and synaptic connectivity, which, I hope, will be tested in future experiments.

Reviewer #1 (Public Review):

The study by Scinicariello et al. set out to identify novel factors that controlled TTP stability and identified HUWE1 by CRISPR screening in macrophages. HUWE1 phosphorylated TTP on residues distinct from those phosphorylated by MAPKs and regulated TTP protein stability. Overall, the biochemical and cellular signaling experiments were thoughtfully designed and well executed, leading to the discovery of HUWE1 as a TTP regulator.

Reviewer #1 (Public Review):

Understanding the evolution of nitrogenases is a very important problem in the field of evolutionary biogeochemistry. Ancestral sequence reconstruction at least in theory could offer insights into how this planet alerting activity evolved from ancestors that did not reduce nitrogen. But the very many components of the nitrogenase enzyme system make this a very challenging question to answer.

This paper now demonstrates the first empirical resurrection of functional ancestral nitrogenases both in vivo and in vitro. The nodes that are resurrected are very shallow in the nitrogenase tree and do not help answer how these proteins evolved. The authors' reasoning for choosing these nodes is that they are likely compatible with the metal cluster assembly machinery of their chosen host organism, A. vinelandii. The reader is left to wonder if deeper, more interesting nodes were tried but didn't yield any activity. As the paper stands, it proves that relatively shallow nitrogenase ancestors can be resurrected, but these nodes do not yet teach us anything very fundamental about how these enzymes evolved.

Technically, this work was no doubt challenging. Genome engineering in A vinelandii is very difficult and time-consuming. This organism was chosen because it is an obligate aerobe, which makes it easier to handle than the many anaerobic bacteria and archaea that harbor nitrogenases. It does make one wonder if this choice of organism is wise: the authors themselves note that it probably has a set of specialized proteins that allow the nitrogenase to be assembled and function in the presence of oxygen. This may limit A. vinelandii's potential future ancestral reconstructions deeper in the tree, which according to the authors' reasoning probably requires different assembly machinery.

The ancestral sequence reconstruction is done in two different ways: Two out of three reconstructions are carried out with what appears to be an incorrect algorithm implemented in older versions of RaxML. This algorithm is not a full marginal reconstruction, because it only considers the descendants of the node of interest for the reconstruction. The full algorithm (implemented e.g. in PAML and the newest versions of RaxML) considers all tips for a marginal reconstruction. The fact that this was called a marginal ancestral sequence reconstruction in RaxML's manual is unfortunate - as far as I understand it is in fact just the internal labelling of nodes produced by the pruning algorithm, which is not equivalent to a marginal reconstruction. In this specific case, it is unlikely that this has led to any fundamental issues with the reconstructions (as all are functional nitrogenases, which is to be expected in this part of the tree). For the shallower of the two nodes, the authors in fact verify that they get the same experimental results if they use PAML's full implementation of a marginal reconstruction (which yields a somewhat different sequence for this node). It would have been helpful to point this RaxML-related issue out in the methods, so as to prevent others from using this incorrect implementation of the ASR algorithm.

One other slightly confusing aspect of the paper is that it contains two different maximum likelihood trees, which were apparently inferred using the same dataset, model, and version of RaxML. It is unclear why they have different topologies. This probably indicates a lack of convergence. Again, this does not cast any doubt on the uncontroversial findings of this paper that shallow nodes within the nitrogenases are also nitrogenases.

Reviewer #1 (Public Review):

Because of the importance of brain and cognitive traits in human evolution, brain morphology and neural phenotypes have been the subject of considerable attention. However, work on the molecular basis of brain evolution has tended to focus on only a handful of species (i.e., human, chimp, rhesus macaque, mouse), whereas work that adopts a phylogenetic comparative approach (e.g., to identify the ecological correlates of brain evolution) has not been concerned with molecular mechanism. In this study, Kliesmete, Wange, and colleagues attempt to bridge this gap by studying protein and cis-regulatory element evolution for the gene TRNP1, across up to 45 mammals. They provide evidence that TRNP1 protein evolution rates and its ability to drive neural stem cell proliferation are correlated with brain size and/or cortical folding in mammals, and that activity of one TRNP1 cis-regulatory element may also predict cortical folding.

There is a lot to like about this manuscript. Its broad evolutionary scope represents an important advance over the narrower comparisons that dominate the literature on the genetics of primate brain evolution. The integration of molecular evolution with experimental tests for function is also a strength. For example, showing that TRNP1 from five different mammals drives differences in neural stem cell proliferation, which in turn correlate with brain size and cortical folding, is a very nice result. At the same time, the paper is a good reminder of the difficulty of conclusively linking macroevolutionary patterns of trait evolution to molecular function. While TRNP1 is a moderate outlier in the correlation between rate of protein evolution and brain morphology compared to 125 other genes, this result is likely sensitive to how the comparison set is chosen; additionally, it's not clear that a correlation with evolutionary rate is what should be expected. Further, while the authors show that changes in TRNP1 sequence have functional consequences, they cannot show that these changes are directly responsible for size or folding differences, or that positive selection on TRNP1 is because of selection on brain morphology (high bars to clear). Nevertheless, their findings contribute strong evidence that TRNP1 is an interesting candidate gene for studying brain evolution. They also provide a model for how functional follow-up can enrich sequence-based comparative analysis.

Reviewer #1 (Public Review):

The study's primary motivating goal of understanding how nutrigenomic signaling works in different contexts. The authors propose that OGT- a sugar-sensing enzyme- connects sugar levels to chromatin accessibility. Specifically, the authors hypothesize that the OGT/Plc-PRC axis in sweet taste neurons interprets the sugar levels and alters chromatin accessibility in sugar-activated neurons. However, the detailed model presented by authors on OGT/PRC/Pcl Rolled in regulating nutrigenomic signaling relies on pharmacological treatments and overexpression of transgenes to derive genetic interactions and pathways; these approaches provide speculative rather than convincing evidence. Secondly, evidence is absent to show that PRC occupancy remains the same in other neurons (non-sweet taste neurons) under varied sugar levels or OGT manipulations. Hence, the claim that OGT-mediated access to chromatin via PRC-Plc is a key regulatory arm of nutrigenomic signaling needs further substantiation.

Reviewer #1 (Public Review):

In this study, the authors introduced a new mathematical model of coarsening of protein ensembles between chromosome axes and nucleoplasm to explain the random distribution of the complexes including Hei10 in a chromosome synapsis-defective, zyp1a/zyp1b double mutant. Although the modeling of the new regulatory mechanism of the crossover (CO) control during meiosis (nucleoplasmic coarsening model and/or trans-interference), which seems to be validated by the super-resolution imaging results, is intriguing, it incrementally contributes to our understanding of the molecular mechanism of CO control during "wild-type" meiosis, since the new model only explains the distribution of COs only in the synapsis-defective mutant (little implication of CO patterning in wild-type).

Reviewer #1 (Public Review):

In this work, the authors investigate a means of cell communication through physical connections they call membrane tubules (similar or identical to the previously reported nanotubes, which they reference extensively). They show that Cas9 transfer between cells is facilitated by these structures rather than exosomes. A novel contribution is that this transfer is dependent on the pair of particular cell types and that the protein syncytin is required to establish a complete syncytial connection, which they show are open ended using electron microscopy.

The data is convincing because of the multiple readouts for transfer and the ultrastructural verification of the connection. The results support their conclusions. The implications are obvious, since it represents an avenue of cellular communication and modifications. It would be exciting if they could show this occurring in vivo, such as in tissue. The implication of this would be that neighboring cells in a tissue could be entrained over time through transfer of material.

Reviewer #1 (Public Review):

The authors conducted a case-control study in the NHANES database and found that women who tested positive for HPV infection had lower bone mineral density (BMD) measures at the spine and at the hip. A major strength is the novelty of the association that they are reporting. Major weaknesses include not controlling for covariates that might account for the association between HPV and osteoporosis; unclear definition of the hip (described as "leg") BMD; and unclear methodology used for the propensity score matching and correlations. These weaknesses mean that it is unclear whether the authors' results support their conclusions. The impact of the work on the field and the utility of the methods and data to the community is therefore limited.

Reviewer #1 (Public Review):

This study investigated how changes in spatial stimulus statistics affect neuronal tuning properties in the barn owl, a well-studied model organism of spatial processing and sound localization. The authors utilized the fact that the owls' facial ruff significantly affects the reliability of binaural cues at specific frequencies. To this end, they compared the tuning to frequency and interaural time differences (ITD) of midbrain (ICX) neurons in adult owls with intact or removed ruff and juvenile owls (with undeveloped ruff). They find that frequency preference is lowered at frontally tuned neurons in the absence of intact /fully developed facial ruff, in accordance with the notion that ITD reliability is lowered for higher frequencies by the lack of ruff. Likewise, they find that ITD tuning width is increased in juvenile and ruff-removed owls, providing further indications for a lowered frequency preference (because ITD tuning width is correlated with wavelength). While the authors cannot provide causal evidence that ITD reliability is the driver for these experience-dependent changes, the data is very consistent with this interpretation. Thus, the conclusions are mostly well supported and will add interesting aspects to our understanding of spatial (ITD) coding and the role of stimulus statistics in general. Nonetheless, a few questions should be clarified that would strengthen the conclusions in my opinion:

1) It would be helpful to include some sort of comparison in Fig. 4, e.g. the regressions shown in Fig 3, to indicate to what extent the ICCl data corresponds to the "control range" of frequency tuning.<br /> 2) A central hypothesis of the study is that the frequency preference of the high-frequency neurons is lower in ruff-removed owls because of the lowered reliability caused by a lack of the ruff. Yet, while lower, the frequency range of many neurons in juvenile and ruff-removed owls seems sufficiently high to be still responsive at 7-8 kHz. I think it would be important to know to what extent neurons are still ITD sensitive at the "unreliable high frequencies" even if the CFs are lower since the "optimization" according to reliability depends not on the best frequency of each neuron per se, but whether neurons are less ITD sensitive at the higher, less reliable frequencies.<br /> 3) It would be interesting to have an estimate of the time scale of experience dependency that induces tuning changes. Do the authors have any data on this question? I appreciate the authors' notion that the quantifications in Fig 7 might indicate that juvenile owls are already "beginning to be shaped by ITD reliability" (line 323 in Discussion). How many days after hearing onset would this correspond to? Does this mean that a few days will already induce changes?

Joint Public Review

Although several biochemical pathways have been proposed for doxorubicin-induced cardiotoxicity, the exact causal mechanisms remain elusive. Enhanced knowledge of these mechanisms would allow the identification of new therapeutic targets to prevent doxorubicin cardiac adverse effects and thus, extend its use in cancer treatment. Mazevet et al. investigated the role of the exchange protein directly activated by cAMP (EPAC) in doxorubicin-induced cardiotoxicity. The authors found that doxorubicin elicited an increase in EPAC1 isoform expression and activity in neonatal cardiac myocytes and that EPAC1 genetic and pharmacological inhibition successfully reduced doxorubicin-induced DNA damage, mitochondrial dysfunction, and apoptotic cell death. These findings were confirmed in in vivo studies using EPAC1 KO mice, which did not show the deteriorated cardiac function observed in WT mice after doxorubicin treatment. Moreover, the authors showed that doxorubicin-induced cytotoxicity in two cancer cell lines was not altered or even potentiated by pharmacological EPAC1 inhibition. Overall the results of this paper suggest that EPAC1 inhibition is a novel strategy to alleviate doxorubicin-induced cardiotoxicity.

Reviewer #1 (Public Review):

This manuscript presents a fascinating "connectome" dataset of the Octopus vulgaris vertical lobe (VL), a brain region involved in learning and memory with a unique structure. It presents the cell types and connectivity of several major classes of cells in this region. One of the most notable findings is that the most numerous neurons, the SAMs, receive only one synaptic input, while another much less numerous class, the CAMs, receive many. Both of these feed onto an output layer of neurons named LNs. This organization is strikingly different from many other associative learning areas in other species.

Overall, the paper presents an interesting and important collection of anatomical results that will be of interest to those working on this system, as well as (at least at first glance) related systems like the insect mushroom body or mammalian cerebellum. The authors do a good job of highlighting the key properties of this system and contrasting them to other systems. My detailed suggestions are largely about the presentation, but I do have some conceptual comments.

This paper raises an interesting question about learning signals. The most intriguing property of this system is the one-to-one convergence, plasticity, and apparently linear input/output function of the SFL-to-SAM relay. These properties suggest that, unlike structures like the insect mushroom body or mammalian cerebellum, in which the intermediate layer is thought to increase the dimensionality of the representation, the SAMs should be thought of more like the weights of a linear readout of the SFL inputs by the LNs. What learning signal guarantees appropriate weight changes? In a few places (the section on "associativity" and the section on AFs), it is suggested that SAMs can themselves, through coordinated local activity, cause LTP, which the authors call "self LTP-induction." But what is the purpose of such plasticity? It doesn't seem like it would permit, for example, LTP which associates a pattern of SFL activity with the appropriate LNs for the correct vs. the incorrect action. Presumably, appropriately routed information from the NMs and AFs sends the appropriate learning signals to the right places. Does the pattern of innervation of NMs and AFs reveal how these signals are distributed across association modules? Does this lead to a prediction for the logic of the organization of the association modules?

One challenge for a reader who is not an expert on the VL is that the manuscript in its present form lacks discussion about the impact (or hypothesized impact) of the VL on behavior. There is a reference to a role for LNs suppressing attack behavior, but a more comprehensive picture of what the readout layer of this system is likely controlling would be helpful.

The authors do a thorough job of characterizing the "fan-out" architecture from SFL axons to SAMs and CAMs. A few key numbers remain to characterize the "fan-in" architecture of LNs. There appears to be a 400:1 convergence from AMs to LNs. Is it possible to estimate the approximate number of presynaptic inputs per LN? The text around Figure 7 states a median of 162 sites per 100μm dendrite length. One could combine this with an estimate of the total dendritic length for one of these cells from previously available data to estimate the number of inputs per LN. This would help determine the degree of overlap of different association modules in Figure 11, which would be interesting from a computational perspective.

This is an exciting and intriguing set of results that contributes significantly to our knowledge about the brain regions that control learning and memory.

Reviewer #1 (Public Review):

Insect chemosensory receptors function as ligand-gated ion channels, while vertebrate and nematode chemoreceptors are G-protein coupled receptors. This difference led to multiple questions. One was whether there are vertebrate homologs of insect chemosensory receptors or receptor-like proteins. This manuscript of Benton and Himmel titled "Structural screens identify candidate human homologs of insect chemoreceptors and cryptic Drosophila gustatory receptor-like proteins" addressed this key question. First, it showed consistent results using the new tool for protein structure prediction, AlphaFold2, and confirmed the previously identified OR, GR, GRL, and DUF proteins in the 7TMIC superfamily as structural homologs of Orco. Then the authors identified human/vertebrate homologs: PHTF, but the function of this protein is not clear. Finally, they further expanded drosophilid-specific GRL proteins. It is great to see new members of the 7TMIC superfamily!

Reviewer #1 (Public Review):

The authors sequence some of the oldest maize macroremains found to date, from lowland Peru. They find evidence that these specimens were already domesticated forms. They also find a lack of introgression from wild maize populations. Finally, they find evidence the Par_N16 sample already carried alleles for lowland adaptation.<br /> Overall I think this is an interesting topic, the study is well-written and executed for the most part.

I have a variety of comments, most important of which revolve around methodological clarity. I will give those comments first.

The authors should say in the Results section how "alleles previously reported to be adaptive to highlands and lowlands, specifically in Mesoamerica or South America" were identified in Takuno et al. 2015. What method was used? I see this partly comes in the Discussion eventually, but it would help to have it in the Results with more detail. The answer to this question would help a skeptical reader decide the appropriateness of the resource, given that many selection scans have been performed on maize genomes, the choice would ideally not be arbitrary.

How were the covered putative adaptive SNPs distributed in the genome? Were any clustered and linked? The random sampled SNPs should be similarly distributed to give an appropriate null.

How is genetic similarity calculated? It should be briefly described in the Results.

It would help for the authors to state why they focus on Par_N16, I did not see this in my reading. Presumably, the analyses done are because of the higher quality data, but it would also help to mention why Par_N16 was sequenced in an additional run.

In the sections on phylogenetic analysis, introgression, and D statistics, the authors could do a better job specifically indicating how the results support their conclusions.

Reviewer #1 (Public Review):

The authors investigated state-dependent changes in evoked brain activity, using electrical stimulation combined with multisite neural activity across wakefulness and anesthesia. The approach is novel, and the results are compelling. The study benefits from an in-depth sophisticated analysis of neural signals. The effects of behavioral state on brain responses to stimulation are generally convincing.

It is possible that the authors' use of "an average reference montage that removed signals common to all EEG electrodes" could also remove useful components of the signal, which are common across EEG electrodes, especially during deep anesthesia. For example, it is possible (in fact from my experience I would be surprised if it is not the case) that under isoflurane anesthesia, electrical stimulation induces a generalized slow wave or a burst of activity across the brain. Subtracting the average signal will simply remove that from all channels. This does not only result in signals under anesthesia being affected more by the referencing procedure than during waking but also will have different effects on different channels, e.g. depending on how strong the response is in a specific channel.

Reviewer #1 (Public Review):

This manuscript studies the representation by gender and name origin of authors from Nature and Springer Nature articles in Nature News. The representation of author identities is an important step towards equality in science, and the authors found that women are underrepresented in news quotes and mentions with respect to the proportion of women authors.

Strengths:

The research is rigorously conducted. It presents relevant questions and compelling answers. The documentation of the data and methods is thoroughly done, and the authors provide the code and data for reproduction.

Weaknesses:

The article is not so clearly structured, which makes it hard to follow. A better framing, contextualization, and conceptualization of their analysis would help the readers to better understand the results. There are some unclear definitions and wrong wording of key concepts.

Reviewer #1 (Public Review):

Ibar and colleagues investigate the function of spectrin in Drosophila wing imaginal discs and its effect on the Hippo pathway and myosin activity. The authors find that both βH-Spec and its canonical binding partner α-Spec reduce junctional localization of the protein Jub and thereby restrict Jub's inhibitory effect on Hippo signaling resulting in activation of the Hippo effector Yorkie regulating tissue shape and organ size. From genetic epistasis analysis and analysis of protein localization, the authors conclude that βH-Spec and α-Spec act independently in this regulation. The major point of this study is that the apical localization of βH-Spec and myosin is mutually exclusive and that the proteins antagonize each other's activity in wing discs. In vitro co-sedimentation assays and in silico structural modeling suggest that this antagonization is due to a competition of βH-Spec and myosin for F-actin binding.

The study's strengths are the genetic perturbation that is the basis for the epistasis analysis which includes specific knockdowns of the genes of interest as well as an elegant CRISPR-based overexpression system with great tissue specificity. The choice of the model for such an in-depth analysis of pathway dependencies in a well-characterized tissue makes it possible to identify and characterize quantitative differences between closely entangled and mutually dependent components. The method of quantifying protein localization and abundance is common for multiple figures which makes it easy to assess differences across experiments.

A weakness in the methodology is the link to tissue tension and conclusions about tissue mechanics. Methods that directly affect tissue tension and a more thorough and systematic application of laser ablation experiments would be needed to profoundly investigate mechanosensation and consequential effects on tissue tension by the various genetic perturbations. While the in-silico analysis of competing for F-actin binding sites for βH-Spec and myosin appears logical and supports the authors' claims, no point mutation or truncations were used to test these results in vivo. In its current structure the manuscript's strength, the genetic perturbations, is compromised by missing clear assessments of knockdown efficiencies early in the manuscript and other controls such as the actual effect on myosin by ROCK overactivation.

The flow of experiments is logical and in general, the author's conclusions are supported by the presented data. The findings are very well embedded into the context of relevant literature and both confronting and confirming literature are discussed.

The study shows how components of the cytoskeleton are directly involved in the regulation of the mechanosensitive Hippo pathway in vivo and thus ultimately regulate organ size supporting previous data in other contexts. The molecular mechanism regulating myosin activity by out-competing it for F-actin binding has been observed for small actin-binding proteins such as cofilin but is a new mode for such a big, membrane-associated actin-binding protein. This may inspire future experiments in different morphogenetic contexts for the investigation of similar mechanisms. For example, the antagonistic activity of βH-Spec and myosin in this tissue context might help explain phenomena in other systems such as spectrin-dependent ratcheting of apical constriction during mesoderm invagination (as the authors discuss). Against the classical view, the work shows that βH-Spec can act independently of α-Spec. Together the results will be of interest to the cell biology community with a focus on the cytoskeleton and mechanotransduction.

Reviewer #1 (Public Review):

Weber et al. collect locus coeruleus (LC) tissue blocks from 5 neurotypical European men, dissect the dorsal pons around the LC and prepare 2-3 tissue sections from each donor on a slide for 10X spatial transcriptomics. From three of these donors, they also prepared an additional section for 10x single nucleus sequencing. Overall, the results validate well-known marker genes for the LC (e.g. DBH, TH, SLC6A2), and generate a useful resource that lists genes which are enriched in LC neurons in humans, with either of these two techniques. A comparison with publicly available mouse and rat datasets identifies genes that show reliable LC-enrichment across species. Their analyses also support recent rodent studies that have identified subgroups of interneurons in the region surrounding the LC, which show enrichment for different neuropeptides. In addition, the authors claim that some LC neurons co-express cholinergic markers, and that a population of serotonin (5-HT) neurons is located within or near the LC. These last two claims must be taken with great caution, as several technological limitations restrict the interpretation of these results. Overall, there is limited integration between the spatial and single-nucleus sequencing, thus the data does not yet provide a conclusive list of bona fide LC-specific genes. The authors transparently present limitations of their work in the discussion, but some points discussed below warrant further attention.

Specific comments:

1) snRNAseq:

a. Major concerns with the snRNAseq dataset are A) the low recovery rate of putative LC-neurons in the snRNAseq dataset, B) the fact that the LC neuron cluster is contaminated with mitochondrial RNA, and C) that a large fraction of the nuclei cannot be assigned to a clear cell type (presumably due to contamination or damaged nuclei). The authors chose to enrich for neurons using NeuN antibody staining and FACS. But it is difficult to assess the efficacy of this enrichment without images of the nuclear suspension obtained before FACS, and of the FACS results. As this field is in its infancy, more detail on preliminary experiments would help the reader to understand why the authors processed the tissue the way they did. It would be nice to know whether omitting the FACS procedure might in fact result in higher relative recovery of LC-neurons, or if the authors tried this and discovered other technical issues that prompted them to use FACS.

b. It is unclear what percentage of cells that make up each cluster.

c. The number of subjects used in each analysis was not always clear. Only 3 subjects were used for snRNAseq, and one of them only yielded 4 LC-nuclei. This means the results are essentially based on n=2. The authors report these numbers in the corresponding section, but the first sentence of the results section (and Figure 1C specifically!) create the impression that n=5 for all analyses. Even for spatial transcriptomics, if I understood it correctly, 1 sample had to be excluded (n=4).

2) Spatial transcriptomics:

a. It is not clear to me what the spatial transcriptomics provides beyond what can be shown with snRNAseq, nor how these two sets of results compare to each other. It would be more intuitive to start the story with snRNAseq and then try to provide spatial detail using spatial transcriptomics. The LC is not a homogeneous structure but can be divided into ensembles based on projection specificity. Spatial transcriptomics could - in theory - offer much-needed insights into the spatial variation of mRNA profiles across different ensembles, or as a first step across the spatial (rostral/caudal, ventral/dorsal) extent of the LC. The current analyses, however, cannot address this issue, as the orientation of the LC cannot be deduced from the slices analyzed.

b. Unfortunately, spatial transcriptomics itself is plagued by sampling variability to a point where the RNAscope analyses the authors performed prove more powerful in addressing direct questions about gene expression patterns. Given that the authors compare their results to published datasets from rodent studies, it is surprising that a direct comparison of genes identified with spatial transcriptomics vs snRNAseq is lacking (unless this reviewer missed this comparison). Supplementary Figure 17 seems to be a first step in that direction, but this is not a gene-by-gene comparison of which analysis identifies which LC-enriched genes. Such an analysis should not compare numbers of enriched genes using artificial cutoffs for significance/fold-change, but rather use correlations to get a feeling for which genes appear to be enriched in the LC using both methods. This would result in one list of genes that can serve as a reference point for future work.

c. Maybe the spatial transcriptomics could be useful to look at the peri-LC region, which has generated some excitement in rodent work recently, but remains largely unexplored in humans.

3) The comparison of snRNAseq data to published literature is laudable. Although the authors mention considerable methodological differences between the chosen rodent work and their own analyses, this needs to be further explained. The mouse dataset uses TRAPseq, which looks at translating mRNAs associated with ribosomes, very different from the nuclear RNA pool analyzed in the current work. The rat dataset used single-cell LC laser microdissection followed by microarray analyses, leading to major technical differences in terms of tissue processing and downstream analyses. The authors mention and reference a recent 10x mouse LC dataset (Luskin et al, 2022), however they only pick some neuropeptides from this study for their analysis of interneuron subtypes (Figure S13). Although this is a very interesting part of the manuscript, a more in-depth analysis of these two datasets would be very useful. It would likely allow for a better comparison between mouse and human, given that the technical approach is more similar (albeit without FACS), and Luskin et al have indicated that they are willing to share their data.

4) Statements in the manuscript about the unexpected identification of a 5-HT (serotonin) cell-cluster seem somewhat contradictory. Figure S14 suggests that 5-HT markers are expressed in the LC-regions just as much as anywhere else, but the RNAscope image in Figure S15 suggests spatial separation between these two populations. And Figure S17 again suggests almost perfect overlap between the LC and 5HT clusters. Maybe I misunderstood, in which case the authors should better clarify/explain these results.

Reviewer #1 (Public Review):

The authors sought to assess how not only RNA but also protein changes across the developmental time course of cortical organoid development. The methods used included reporter lines to label progenitor and neuronal populations, RNA-sequencing, protein quantification using mass spectrometry, and analysis of these results. The primary findings included the identification of RNA sequences that impact translation, the most significant of which was a 5'-TOP cassette that is mediated by mTOR.

Strengths of the paper include strong experimental design, replicates, and images to show the quality of the organoids used in the studies. Additionally, the analysis of elements regulating translation was strong, and the polysome experiments exploring an impact when TSC is deleted were interesting.

Potential limitations include technical challenges related to the specificity of the reporters, ambiguity about the impact of normalization on the actual protein/RNA data, and potential over-interpretation of the TSC result to encompass all of the mTOR signalings.

The paper validates already observed and documented results in translational regulation whereby RNA does not fully predict protein levels. The impact of the specific examples upon functional significance in cortical development is currently unclear but this work could set the stage for additional future impactful work.

Rainbow money

Reviewer #1 (Public Review):

This paper presents the results of two fragment screens of PTP1B using room-temperature (RT) crystallography, and compares these results with a previously published fragment screen of PTP1b using cryo-temperature crystallography. The RT screen identified fewer fragment hits and lower occupancy compared to the cryo screen, consistent with prior publications on other proteins. The authors attempted to identify additional hits by applying two additional layers of data processing, which resulted in a doubling in the number of possible hits in one of the screens. Because I am not an expert in panDDA modeling, however, I am unable to evaluate the reproducibility and potential potency of these fragment hits as protein binders or their potential use as starting points for follow-up chemistry.

The fragment library used in this study was larger than those used in previously published RT crystallography experiments. Among the cryo hits that bound in RT, most fragments bound in the same manner as they did in cryo, while some bound in altered orientations or conformations, and two bound at different locations in RT compared to cryo. This level of variability is not surprising. However, one fragment was observed to bind covalently to lysines in RT, even though it showed no density in the cryo crystallization attempt. It is unclear from the provided information whether this fragment decayed during storage or if the higher temperatures accelerated the covalent chemistry. The authors also observed temperature-dependent changes in the solvation shell, and modifications to the protein structure upon fragment binding, including a distal modification.

The current version of the paper is somewhat repetitive in its presentation of the results and could be clearer in its presentation of the variations and comparisons of the two different protocols. It would be helpful to have a more concise summary of the differences between the two protocols in the current paper, as well as a discussion of how they compare to the protocol used in the previously published cryo-temperature fragment screen.

While I appreciate the speculative nature of the discussion at the end of the paper, the evidence presented by the authors does not instil confidence that these results will correspond to meaningful binders that could be used to train future machine learning models. However, depending on the intended use, it may be acceptable to train ML models to predict expected densities under typical experimental conditions.

Reviewer #1 (Public Review):

Autoantibodies to nuclear proteins are commonly associated with autoimmune conditions. Since their discovery, several reports have suggested that T-follicular regulatory cells (Tfr) Tfr cells have the capacity to preferentially suppress autoimmune antibody responses. Tfr have a TCR repertoire strongly skewed to self-antigens and in this report Ke et al. probe the idea that Tfr directly recognize nuclear proteins and inhibit nuclear protein specific B-cells. They find that vaccination of mice with an ongoing GC reaction to a foreign antigen using nuclear proteins causes expansion of Tfr and a Tfr dependent inhibition of the germinal center. Overall, this is a well written paper that significantly advances the idea that Tfr can control autoreactive B-cells in a selective manner. Most experiments are convincing. Some of the novel methods regarding the use of nuclear proteins during sequential vaccinations in mice or Tfr-B-cell doublet formation will be of interest to members of the same fields.

A primary weakness of the paper is that despite detailed analysis of cells involved in antibody production, there is very little analysis of the antibodies themselves. Particularly when Tfr deficient mice are used in figure 5 analysis of both anti-SA and anti-NucPr antibodies between the Tfr cKO and other groups would significantly advance the findings.

Reviewer #1 (Public Review):

The authors have succeeded in demonstrating that they can further extend the methodology and value of Mendelian randomization by combining their two recently developed novel approaches to Mendelian randomization studies (1) Lifecourse MR which relates the genetic instruments to the outcome, eg obesity, at different stages of life eg childhood and adulthood and (2) Tissue partitioned MR to determine if the genetic instruments have different effects on different tissues such as the brain and adipose tissue. They have successfully combined these two to investigate the influence of adiposity on circulating leptin to demonstrate the value/proof of concept of these techniques in extending the use of MR.

This is a very clearly presented and well-conducted work showing both new methodology and clear-cut results on the impact of adiposity at age 10 and in middle life and the weight gain in between on leptin levels and that the effect is mediated via the brain. They show that childhood obesity has a direct effect on leptin levels at age 10 years and an indirect effect on adult leptin along a causal pathway involving adulthood body size. They also show that BMI exerts its effect on leptin levels at both life stages via brain-tissue-mediated pathways.

Major strengths are the well-characterized data sets used and in particular, having a comprehensive data set for children and the successful use of a new approach to address a complex issue. There are no major weaknesses

The authors have achieved their two aims - the use of the new methodology and its application to the specific issue to demonstrate how it works ie proof of concept. Their results support their conclusions.

The main advance here is a demonstration of a new further enhanced approach to Mendelian randomization. This is likely to end up being used by other researchers to address complex questions.

Reviewer #1 (Public Review):

This work endeavours to delineate the relationship between IL-7R+ and IL-7R- ILC1 in the liver. They elegantly utilize a PLZF reporting system to identify the progenitor/product relationship between ILC subsets and show that ILC1s emerge separately from NK cells and LTi cells.

Furthermore, ILC1 are enriched in the liver. Extending this work in Rora-deficient mice, they demonstrate that over time, these cells are poorly replaced in the liver, and that IL-7R+ cells did not convert into IL-7R- cells at steady-state. Fetal liver IL-7R+ ILC1s were shown to partially contribute to mature ILC1s. Interestingly, they show that there were localization changes between ILC1 precursors and mature ILC1s in the liver. They then analysed the factors that might underpin these different localizations by examining IL-15 which is highly produced by macrophages and endothelial cells. They identify that hepatocyte-derived IL-15 supports the development of 7R− ILC1s in the parenchyma to maintain adult 7R− ILC1s within the sinusoids. Finally, the authors addressed the discrepancy in understanding of cytotoxicity expressed by ILC1s and identify that constitutive expression of mTOR was necessary to effect this function, thereby providing a mechanistic explanation for variable cytotoxicity observed in other studies. Overall, this study advances our knowledge of how ILC1 are generated and maintained in the liver, and how they acquire their effector functions.

Reviewer #1 (Public Review):

In this manuscript, Scagliotti and colleagues investigate the role of Dlk1 in regulating pituitary size in multiple mouse models with different Dlk1 gene dosages in order to understand the mechanisms of organ size control. They find that overexpression of Dlk1 leads to pituitary overgrowth and loss of Dlk1 causes undergrowth. Authors find two compartments of Dlk1 expression in the pituitary, in the marginal zone stem cell compartment and the parenchymal differentiated cell compartment, and by combing genetic mouse models show that a specific interaction of Dlk1 expression in both regions is necessary to affect pituitary organ size. They present to suggest that Dlk1 may repress Wnt signaling during development to control a shift from progenitor proliferation to differentiation. The data are meticulous, high quality, and clear.

I have some questions about the interpretation of their data regarding the mechanism of Dlk1 regulation of pituitary organ size, as I believe there could be potential alternative explanations for their observations:

I was wondering about the cause of the enlargement of the pituitary gland in Fig 1E, and whether it is caused by an increased number of cells (hyperplasia), an increased cell size (hypertrophy), or both. Line 104 states it is hyperplasia, and that cell size was not affected in WT-TG ('not shown', line 121). However, line 444 says the TG is hypertrophic. It would be good if the authors could elaborate on this and show or state how cell size was determined. Figs 5/6 show that WT-Tg proliferation is generally similar to WT, which suggests the increased size is not hyperplasia. It would be good to know whether this is correct. Some previous studies have shown that in pregnancy, lactotroph hypertrophy can be responsible for pituitary enlargement without hyperplasia (Castrique 2010, Hodson 2012).

Related to the organ size question above, I had a question about the cell number and proportions in Fig 1D/E/F, which shows the maintenance of endocrine cell proportions and an increase in the volume of ~30% in WT-Tg. For the cell proportions to be maintained, I thought the increase in volume per cell type (Fig 1G) would therefore have to also increase proportionally in every cell type, while 1G appears to show an increase in GH (sig) and PRL/TSH cells (ns). It would be good if the authors could discuss this briefly.

This study is impactful and will be of interest to several research communities, including those interested in pituitary development and function, organ size control, and gene imprinting mechanisms.

Reviewer #1 (Public Review):

The authors of this study sought to test whether the optogenetic induction of context-related freezing behavior could be enhanced by synchronizing light pulses to the ongoing hippocampal theta rhythm. Theta is a hippocampus-wide oscillation that strongly modulates almost every cell in this structure, which suggests that causal interventions locked to theta could have a more pronounced impact than open-loop ones. Indeed, the authors found that activating engram-associated dentate gyrus (DG) neurons at the trough of theta resulted in an increase in freezing relative to baseline when averaging across all stimulation epochs. In contrast, open-loop stimulation and peak-locked stimulation had weaker effects. Analysis of local field potentials showed that only the theta-locked stimulation facilitated coupling between theta and mid-gamma, indicating that this manipulation likely enhances the flow of activity from DG to CA1 via CA3 (as opposed to promoting transmission from entorhinal cortex to CA1). Previous results from mice, rats, and humans support the hypothesis that memory encoding and recall occur at distinct phases of theta. This work further strengthens the case for phase-specific segregation of memory-related functions and opens up a path toward more precise clinical interventions that take advantage of intrinsic theta rhythm.

Strengths:

This study recognizes that, when artificially reactivating a context-specific memory, the brain's internal context matters. In contrast to previous attempts at optogenetically inducing recall, this work adds an additional layer of precision by synchronizing the light stimulus to the ongoing theta rhythm. This approach is more challenging, because, in addition to viral expression and bilateral optical fibers, it also requires a recording electrode and real-time signal processing. The results indicate that this additional effort is worth it, as it results in a more effective intervention.

The findings on theta-gamma cross-frequency coupling suggest a possible mechanism underlying the observed behavioral effects: trough stimulation enhances DG to CA1 interactions via CA3. LFP recordings showed that stimulation increases the coupling between theta and mid-gamma (though not in all mice), and the percentage of freezing during reactivation is correlated with the gamma modulation index.

Weaknesses:

Given the precision of the intervention being performed, one might expect to see a stronger behavioral impact. Instead, the overall effect is subtle, and quite variable across mice. Looking at individual data points, the biggest overall increase in freezing actually occurred in 2 mice during the 6 Hz stimulation condition. Furthermore, trough stimulation decreased freezing in 3 mice This is not a weakness in itself; rather, the weakness lies in the lack of an attempt to make sense of this variability. There are a number of factors that could explain these differences, such as viral expression levels, electrode/fiber placement, and behavior during baseline. There is of course a risk of over-interpreting results from a few mice, but there is also a chance that the results will appear more consistent after accounting for these additional sources of variation.

While trough-locked optogenetic stimulation significantly increases freezing, the effects are much weaker than placing the mouse in the actual fear-conditioned context (average time freezing of 15% vs. 50%). The discussion would benefit from additional treatment of ways to further increase the specificity and effectiveness of artificial memory reactivation.

Using an open-source platform (RTXI) for real-time signal processing is commendable; however, more work could be done to make it easier to adopt these methods and make them compatible with other tools. The RTXI plugin used for closed-loop stimulation should be fully documented and publicly available, to allow others to replicate these results.

Reviewer #1 (Public Review):

In this article, Prassad and colleagues describe a new mechanism involved in the elimination of misspecified/mislocated cells in the wing imaginal disc. This study follows a previous study from the same group (Bilmeier et al. Curr Biol 2016) which showed that a large panel of genetic backgrounds changing locally cell fate can trigger aberrant sorting of the misspecified cells triggered by the increased of contractility at clone interfaces. This process was suggested to directly participate to clone elimination below a certain clone size. However, the mechanism involved in apoptosis induction was not really studied per se. Here, they use similar genetic backgrounds and showed that JNK activation occurs specifically at the interface of the misspecified clones on both side (inside and outside the clone) hence leading to a local increase of cell death both in the WT and misspecified cells. This local activation of cell death participates to clone elimination, although the authors also delineate an alternative mechanism of death induction in the center of the clone that may correlate with the local buckling and the deformation. Importantly, this mechanism seems quite specific of these misspecified backgrounds and is unrelated to other more classical cell competition scenarios which trigger the elimination of Minute mutant (affecting ribosomes) or based on differential levels of Myc.<br /> The model proposed is interesting and clearly delineate a distinctive feature of this quality control mechanism which triggers local JNK activation. It is based on solid genetic evidences and use a large panel of genetic backgrounds and careful quantifications. The demonstration is overall very convincing. Moreover, these results provide a novel perspective for the field of cell competition and quality control mechanism which has been dominated by the concept of absolute fitness, which is not at all required in this context (where both WT or altered cells can be eliminated provided they are in minority in the tissue).

Admittedly, the unicity and novelty is bit tuned down by former studies showing similar patterns of JNK activity upon local distortion of morphogens (so called morphogenetic apoptosis, Adachi-Yamada and O'Connor Dev Biol 2002), or the pattern of JNK activation observed near polarity mutant clones (Ohsawa et al, Dev Cell 2011) suggesting that this bilateral JNK activation might not be completely unique to these contexts. But non of these studies characterised such large range of genetic backgrounds and this study clearly provide new mechanistic insights.

It is important to note that at this stage, it is not clear whether there is any link between the sorting behaviour and the activation of JNK (they could be both activated by unknown upstream factors), while the terminology "interfacial contractility" used to define this type of clone elimination may convey the idea that this is the most upstream factor in the process. Also further quantifications may be required to see to which extend JNK activation is indeed restricted to cell directly contacting clone border and also to support the final proposed model suggesting that the number of contact could influence the levels of JNK (actually alternative models could also explain why smaller clones get eliminated). Finally, while the JNK levels clearly influence death in the clone, further experiments may be required to test how the line of JNK activation in WT cells contribute to their death and their elimination similar to mispecified cells, specially in the context where the majority of tissue is covered by mispecified clones.

Joint Public Review:

Hepatitis E virus (HEV) causes over 20 million infections per year. The open reading frame 1 (ORF1) is responsible for genome replication, however very little is known about the structure and functions of several of the components. The author use a diverse a diverse number of techniques (molecular virology, structure prediction using AlphaFold, site directed mutagenesis and biochemistry) to probe ORF1 activity. The work is thorough, well prepared, and discusses the strength and weakness of the structural information. Interestingly, AlphaFold prediction of the papain-like cysteine protease domain did not identify a classic papain-like fold. Lastly, the authors demonstrate the necessity of six conserved cysteines within the putative PCP domain.

The presence and necessity of proteolysis for genome replication or cleavage of other host factors still remains an uncharacterized problem, which is beyond the scope of this manuscript. My only concern relates to the presence of a zinc ion in ORF1.<br /> The authors use extensive triplet alanine scanning to test for virus replication capacity and in some cases see gains above WT (Figure 3). Do these patterns match natural variation observed in comparisons of HEV sequences un any way?

Overall, the study presents an intriguing hypothesis for HEV ORF1 function not involving protease processing as assumed by early bioinformatic analysis. The alternate hypothesis of metal ion coordination is supported by increasingly sophisticated structural modeling tools and related experiments. However, a lack of direct evidence leaves, as the authors note, alternate hypotheses such as disulfide bond coordination or protease functions that occur intramolecularly within ORF1.

The study will likely have an impact on the field, especially if evidence builds in the future directly supporting the mechanism proposed. HEV is an impactful pathogenic virus that is relatively underappreciated. In addition to a major revision in HEV biology, the idea that many proteins initially annotated with canonical functions might instead have different mechanisms is also of high interest beyond the field of virology.

Reviewer #1 (Public Review):

Of course, many of the most important aspects of feeding happen post-ingestion. As digested food moves through the intestines specialized epithelial cells (called Enterochromaffin Cells or EECs) sense and respond to the constituent chemicals. The function of EECs initiates physiological responses to facilitate nutrient absorption, protect from toxins and encourage proper waste removal. EECs are sparse and heterogenous and release a variety of transmitters and diffusible signaling molecules that signal to peripheral neurons and the brain. Their collective activity slows or speeds gut transit and promotes feelings of satiety or malaise. The current work by Liberles and colleagues seeks to provide deeper insight into the function of EECs. They build on previous work by further categorizing these cells by their unique gene expression signatures. The work utilizes single-cell transcriptomic analyses and intersectional approaches to define and genetically manipulate subsets of EECs. A key aspect of the study is behavioral assays used to investigate how direct stimulation of EEC subtypes influences key aspects of feeding, specifically gut transit, ingestion, and food preference.

The work has several strengths. A new mouse line (Villin-flp) is developed and used intersectionally with Cre mouse lines to manipulate different subsets of epithelial cells. The authors characterize these compound mouse strains and how the labeled cells map onto transcriptomic class. These data are reasonably comprehensive and show the exclusion of marker expression from the central nervous system, important controls. The chemogenetic activation strategy is an elegant way to probe the consequences of EEC stimulation by Gq coupled GPCR signalling. The gut transit experiments show clear effects.

The weakness is it remains unclear whether stimulation of the DREADD receptor outside the intestinal EECs really has consequences (e.g. in the tongue), the behaviors tested are somewhat limited, the responses to CNO administration variable between animals, and the effect sizes are small.

Overall, this is an interesting study and provides useful tools for the field.

Reviewer #1 (Public Review):

To explore possible functions of SA proteins in the absence of cohesin, authors use conditional AII-dependent proteins SA1 and SA2, after whose degradation they observe the phenotypes just indicated. 3D analysis shows that SA proteins cluster at specific regions. In addition, it is shown that SA proteins not only interact with CTCF after RAD21 degradation but with other F/YXF-motif containing proteins such as CHD6, MCM3 or HRNPUL2 as determined by ChIP. Mass spectrometry of proteins co-immunoprecipitated with SA1 reveals 136 interactor proteins that include a number of chromatin remodeling factors, transcription factors and RNA binding proteins including factors involved in RNA processing and modification, ribosome biogenesis and translation. After these results, authors perform CLIP to show that SA1 protein binds RNA in the absence of cohesin. Different analysis using RNH, mainly IF and IP and the S9.6 antibody, are used to conclude that SA1 binds to R-loop regions. The authors conclude that SA proteins are loaded to chromatin via NIPBL/mMAu complex at RNA:DNA hybrid regions. Further analyses suggest that SA proteins stabilize RNA via interaction with other RNA-binding proteins, some of which have been shown by other authors to be enriched at R loop-containing regions, a property that localizes to exon 32 in SA2. The manuscript provides a large amount of work that has been put together in a large collaboration to bring new roles for SA in RNA metabolism, even though this is not investigated.

Reviewer #1 (Public Review):

This manuscript by Koropouli et al. is a much-needed study that provides novel mechanistic insight of how signaling receptors can be targeted to distinct subcellular domains or membrane locations that, in part, confer their functional specificity. It is well-established that members of the class 3 secreted semaphorins guidance cues can bind to the receptors the neuropilins (Nrp1 and Nrp2) to elicit numerous cellular processes important for circuit assembly. Previously, it was demonstrated that Sema3F signaling with Nrp2 and its co-receptor Plexin-A3 is required for the removal of excess excitatory synaptic spines on the apical dendrite of layer V cortical neurons, while the closely related member Sema3A signaling with Nrp1/Plexin-A4 promotes the elaboration of the basal dendritic arbor on the same neuron. The question is then how do the two different signaling pathways convey such precise and opposite cellular function of eliminating spines and promoting dendritic elaboration in distinct subcellular compartments of the same neuron? While some hints were provided that the Nrp2 receptor is localized to the apical dendrite and Nrp1 is distributed widely along all dendrites on the same cortical neuron in vitro, this has not been shown in vivo and the mechanism of such targeted subcellular localization is not known. In the current study, the authors used biochemical, cellular, and molecular assays in combination with mouse genetics and live-cell imaging to demonstrate that the post-translational modification of S-palmitoylation dictates the proper subcellular localization and trafficking of Nrp2, but not Nrp1, and is required for Sema3F-dependent pruning of spines on the apical dendrites of layer V cortical neurons. The following are the strength and novel findings of this study.

1. This study confirms previous findings and adds new information by mapping the specific locations of the cysteine amino acid residues to the transmembrane/juxtamembrane region of neuropilin receptors for palmitoylation, which confers the subcellular localization specificity for Nrp2 but not Nrp1, in cortical neurons and non-neuronal cells.<br /> 2. The study also found that select cysteine residues on Nrp2 are palmitoylated by the palmitoyltransferase DHHC15, and palmitoylation of these sites are required for the homo-oligomerization of the Nrp2 receptor but not for the association with the co-receptor Plexin-A3.<br /> 3. The authors demonstrated that Sema3F signaling itself seems to enhance the level of Nrp2 palmitoylation in some sort of positive feedback loop. It would be interesting for future experiments to determine how Sema3F signaling promotes this palmitoylation.

Although most of the key claims are supported by data presented in the paper, clarification of the following concerns would further strengthen the overall conclusion of the study.

1. While some of the qualitative micrograph images are very convincingly in illustrating the drastic difference in Nrp2 versus Nrp1 expression patterns/cell-surface localization, such as Fig. 1A and 1D, many of the quantitative analyses have a low n number and/or low sample size, with only 2 replicate experiments or only 2 brains/animals per genotype analyzed. To increase the rigor of this study, the authors should add a few more replicates to the experiments with low n numbers.<br /> 2. The substitutions of C878, C885, and C887 to serines caused an ~80%, ~50%, and ~60% reduction, respectively, in Nrp2 palmitoylation compared to WT neuroblastoma-2a cells (as show in Fig. 2D and 2E). However, when mutating all three of these cysteine sites (the TCS plasmid), there is only ~80% total reduction in Nrp2 palmitoylation (Fig. 2F and 2G), just about equal to the C878S substitution alone. One would expect that the reduction in palmitoylation to be more severe with the TCS plasmid, but might this be due to the low n number in quantifications shown in Fig. 2E and 2G. It would add substantially to support the specificity of these cysteine residues' function if the single C878 was demonstrated to be required for either subcellular localization of Nrp2 leading to the rescue of the dendritic spine phenotype in Nrp2-/- primary neurons or in an in utero experiment.

Reviewer #1 (Public Review):

The manuscript by Curtis et al. reports the interaction between CaMKII and alpha-actinin-2. The authors found that the interaction was elevated after NMDA receptor activation in dendritic spines. In addition, this study reveals NMDA receptor binding to CaMKII facilitates alpha-actinin-2 access to the CaMKII regulatory segment, indicating that the NMDA receptor is involved in this interaction. The authors identified the EF1-4 motifs mediated this interaction, and overexpression of this motif inhibited structural LTP. Moreover, biochemical measurements of affinities from various combination of protein fragments including autoinhibited CaMKII 1-315, regulatory segments of CaMKII, and the EF-hand motif reveals that autoinhibited CaMKII has limited access to alpha-actinin-2. The authors also solved the structure of the interaction, supporting their finding in neurons at the molecular level. The authors claim that the interaction between CaMKII and alpha-actinin-2 is essential for structural LTP through cooperative action by the NMDA receptor and actin cytoskeleton.

Overall, the experiments are well-designed and the results are largely convincing and well-interpreted. But some aspects of the experiments need to be clarified.

1. Time resolution of the interaction analysis appears to be poor, as calcium elevation in a dendritic spine would be at milli-second order. What is the time window to interact alpha-actinin-2 with CaMKII during NMDA receptor activation or LTP?<br /> 2. The authors analyzed the binding of CaMKII and alpha-actinin-2 with partial fragments. It remains to be unknown whether CaMKII can form a protein complex with GluN2B and alpha-actinin-2 in a single CaMKII protomer.<br /> 3. Besides synaptic localization, the effect of the interaction on the enzymatic activity of CaMKII is not known.<br /> 4. Although the authors quantify the effect of the EF-hand disruptor by measuring numbers of the dendritic spine by its shape, the specificity of the EF-hand disruptor needs to be clarified.

Reviewer #1 (Public Review):

The study provides mechanistic insight into molecular events occurring at the onset of differentiation mediated by the kinase PASK. Specifically, the work focuses on the multiple steps that converge on post-translational modifications of PASK and its translocation to the nucleus during myogenesis. The authors present evidence that glutamine-fueled, CPB/EP300-mediated acetylation of PASK is required for its nuclear translocation. This allows (nuclear) PASK to interact with Wdr5 and consequently disrupt its association with the anaphase-promoting complex/cyclosome and inhibit Pax7 transcription, marking the onset of muscle differentiation. The conclusions are supported by an analysis of the effects of glutamine modulation on differentiation and maintenance of stemness in primary muscle stem cells; PASK localization in myoblasts and primary muscle stem cells as well as detailed biochemistry with modified forms of PASK to interrogate molecular interactions. C2C12 myoblast cells and primary muscle stem cells are cellular systems employed in the study with observations confirmed in cells derived from mice with genetic ablation of PASK. The study provides molecular detail on events linking glutamine metabolism to the transcriptional control of lineage differentiation, through the regulation of PASK. The analysis of these events in other systems would be of value to understanding their broader applicability.

Reviewer #1 (Public Review):

The authors of this study used SMART-seq to study differentiating B cells. Then they performed extensive in silico analyses to validate that a subset of the cells mimicked human antibody-secreting cells. For example, they compared gene expression profile of each cluster in B cell developmental trajectory (Figs 1, 2), investigated gene enrichment in ASC-like cluster (Fig 3), adopted independent dataset (Fig 3), and compared gene expression signatures of their cells to those of GC ASCs (Fig 4). Overall, the results from these analyses are convincing and valuable, but still do not seem to be a big leap from their Unger 2021 paper and therefore making this study preliminary.

The methodology that they established should be described more clearly so that it can be shared with the research community. For example, they say cells how many donors were recruited for this experiment? are there differences in efficiency in B cell differentiation by individual?

Also, it would be important to assay for antibodies in the culture media. How would you suggest to improve the culture system to be used to model diseases?

At the beginning the largest contributing factor for cell culstering was cell cycle. But B cell differentiation may also influence to cell cycle regulation. Rather than normalize its effect, can authors analyze effect of cell cycle in B cell differentiation? For example, identify sub-clusters shown in supple Fig 1g.

Reviewer #1 (Public Review):

Doostani et al. present work in which they use fMRI to explore the role of normalization in V1, LO, PFs, EBA, and PPA. The goal of the manuscript is to provide experimental evidence of divisive normalization of neural responses in the human brain. The manuscript is well written and clear in its intentions; however, it is not comprehensive and limited in its interpretation. The manuscript is limited to two simple figures that support its concussions. There is no report of behavior, so there is no way to know whether participants followed instructions. This is important as the study focuses on object-based attention and the analysis depends on the task manipulation. The manuscript does not show any clear progression towards the conclusions and this makes it difficult to assess its scientific quality and the claims that it makes.

Strengths:<br /> The intentions of the paper are clear and the design of the experiment itself is simple to follow. The paper presents some evidence for normalization in V1, LO, PFs, EBA, and PPA. The presented study has laid the foundation for a piece of work that could have importance for the field once it is fleshed out.

Weakness:<br /> The paper claims that it provides compelling evidence for normalization in the human brain. Very broadly, the presented data support this conclusion; for the most part, the normalization model is better than the weighted sum model and a weighted average model. However, the paper is limited in how it works its way up to this conclusion. There is no interpretation of how the data should look based on expectations, just how it does look, and how/why the normalization model is most similar to the data. The paper shows a bias in focusing on visualization of the 'best' data/areas that support the conclusions whereas the data that are not as clear are minimized, yet the conclusions seem to lump all the areas in together and any nuanced differences are not recognized. It is surprising that the manuscript does not present illustrative examples of BOLD series from voxel responses across conditions given that it is stated that that it is modeling responses to single voxels; these responses need to be provided for the readers to get some sense of data quality. There are also issues regarding the statistics; the statistics in the paper are not explicitly stated, and from what information is provided (multiple t-tests?), they seem to be incorrect. Last, but not least, there is no report of behavior, so it is not possible to assess the success of the attentional manipulation.

Reviewer #1 (Public Review):

The article by Mann et al. describes a knockin (KI) mouse model of mitofusin 2- related lipodystrophy, in mice carrying MFN2 R707W. The mice recapitulate some but not all aspects of the human phenotype, as summarized in Table 2. The phenotypic characterization is extensive and is generally well done. There was an adipose-specific alteration of mitochondrial morphology, accompanied by activation of the integrated stress response and reduced adipokine secretion. These findings are consistent with the human phenotype. The alteration in fat distribution that is present in humans with this mutation was not observed, and the mice did not have the insulin resistance seen in humans. The transcriptome analyses revealed a reduced epithelial-mesenchymal transition (EMT) in the KI mice, suggesting possible involvement of TGF-beta related pathways. There was also upregulation of the mTorc signaling pathway, suggesting that a possible therapeutic approach in humans may involve the mTORC1 inhibitor sirolimus. The reason for the largely adipose -specific effect of the mutation remains unexplained. As well, the hypothesis that changes in EMT pathways reflect altered activity of TGF-beta pathways must remain somewhat speculative at this point. Notwithstanding these weaknesses, the manuscript provides an important advance in understanding this lipodystrophy (and potentially other lipodystrophies), and the model that has been generated will enable further studies to further characterize the pathophysiology.

Reviewer #1 (Public Review):

In this study, Barthe et al. developed an approach to selectively activate beta-adrenergic receptors in the sarcolemma of ventricular myocytes. The approach involved the linking of a 5Kd PEG chain to the beat agonist isoprenaline. This prevents the agonist from entering transverse tubules. Using this approach, the authors find that activation of beta-adrenergic receptors in the surface sarcolemma of ventricular myocytes leads to lower cytosolic cAMP levels but longer-lasting effects on EC coupling than when TT receptors were activated.

Strengths of the study:<br /> 1) The PEG-ISO, size exclusion approach is very interesting and useful.<br /> 2) The observation that activation of beta-adrenergic receptors in the surface sarcolemma of ventricular myocytes leads to lower cytosolic cAMP levels, but longer-lasting effects on EC coupling than when TT receptors were activated is interesting.<br /> 3) The observation that beta-adrenergic receptors in the TT lead to stronger nuclear activation of nuclear cAMP/PKA signaling is interesting.

Weaknesses of the study:<br /> 1) There seems to be a paucity of mechanistic insights into the study.<br /> 2) It is unclear what would be the ideal control for these experiments. Would the addition of the PEG chain, by itself, alter the binding of and activation of beta-adrenergic receptors regardless of their location?<br /> 3) The novelty of the findings is unclear, as other studies have suggested differential effects of beta-adrenergic receptors in membrane compartments.

Impact on the field:<br /> 1) PEG-ISO may become a useful strategy to selectively activate surface sarcolemmal beta-adrenergic receptors.

Reviewer #1 (Public Review):

In this manuscript, Braet et al provide a rigorous analysis of SARS-CoV-2 spike protein dynamics using hydrogen/deuterium exchange mass spectrometry. Their findings reveal an interesting increase in the dynamics of the N-terminal domain that progressed with the emergence of new variants. In addition, the authors also observe an increase in the stabilization of the spike trimeric core, which they identify originates from the early D614G mutation.

Overall this is a timely and interesting exploration of spike protein dynamics, which have so far remained largely unexplored in the literature.<br /> What I find a bit missing in this manuscript is a link between how the identified changes in protein dynamics lead to increased viral fitness. While there are some possibilities listed in the discussion, I think these should be elaborated upon further. In addition, it should also be discussed how understanding the changes in the spike protein dynamics could have implications for the development of small molecule inhibitors for the virus.

Reviewer #1 (Public Review):

This is an exceptional paper that investigates a 208.6 kb region of the Burkholderia thailandensis chromosome that had previously been thought to excise itself and form extrachromosomal circles. Through a series of elegant experiments , the authors conclusively show that (i) the 208.6 kb region in fact forms tandem duplications, (ii) the region can switch between duplicated and non-duplicated forms via RecA-mediated homologous recombination, and (iii) duplication provides a selective advantage in biofilms. The data are of uniformly high quality and the conclusions are fully supported by the data. The significance of the work is high because it identifies a novel form of phase variation in bacteria that represents a bet-hedging strategy to facilitate growth in diverse environments.

Reviewer #1 (Public Review):

This work aims to understand whether MSCs support the resistance in tumor cells upon CAR T cell treatment and whether the expression of STC1 in MSCs contributes to those changes. Overall, the in vivo data is interesting. However, the mechanistic understandings are correlated and based on many assumptions. Furthermore, the differences in Treg changes presented in Figure 2 are not convincing. It is also not clear the underlying mechanisms by which the presence of MSCs leads to these changes.

Major points:

1. How STC1 controls changes in MSCs' ability for hampering CAR T cell-mediated anti-tumor responses is unclear.

2. Is ROS important? It is not tested directly.

3. The changes in CD8 and Treg are not convincing. Moreover, it is not tested how these changes can be elicited by the presence of MSCs.

Reviewer #1 (Public Review):

This theoretical (computational modelling) study explores a mechanism that may underlie beta (13-30Hz) oscillations in the primate motor cortex. The authors conjecture that traveling beta oscillation bursts emerge following dephasing of intracortical dynamics by extracortical inputs. This is a well written and illustrated manuscript that addressed issues that are both of fundamental and translational importance. Unfortunately, existing work in the field is not well considered and related to the present work. The rationale of the model network follows closely the description in Sherman et al (2016). The relation (difference/advance) to this published and available model needs to be explicitly made clear. Does the Sherman model lack emerging physiological features that the new proposed model exhibits? The authors may also note the stability analysis in: Yaqian Chenet et al., "Emergence of Beta Oscillations of a Resonance Model for Parkinson's Disease", Neural Plasticity, vol. 2020, https://doi.org/10.1155/2020/8824760

The model-based analysis of the traveling nature of the beta frequency bursts appears to be the most original component of the manuscript. Unfortunately, this is also the least worked out component. The phase velocity analysis is limited by the small number (10 x 10) of modeled (and experimentally recorded) sites and this needs to be acknowledged. How much of the phase velocities are due to unsynchronized random fluctuations? At least an analysis of shuffled LFPs needs to be performed. How were border effects treated in the model and which are they? Is there a relationship between the localizations of the non-global external input and the starting sites of the traveling waves?

In summary, this work could benefit from a widening of its scope to eventually inspire new experimental research questions. While the model is constructed well, there is insufficient evidence to conclude that the presented model advances over another published model (e.g. Sherman et al., 2016).

Reviewer #1 (Public Review):

Luckey et al. used a sophisticated, multimodal approach to test the hypothesis that engaging LC-hippocampal pathways promote behavioral tagging processes in humans. To activate this mechanism in a causal manner, they apply transcutaneous electrical stimulation of the greater occipital nerve (NITESGON), a relatively novel and non-invasive technique for stimulating brainstem pathways linked to arousal-related neuromodulation. To test the behavioral tagging hypothesis, they use a variety of indirect methods, including pharmacology, EEG, fMRI, saliva assays, and eye-tracking to measure LC-related activity, hippocampal activity/connectivity, and potential dopamine states/release. At the behavioral level, they demonstrate that NITESGON stimulation during or after learning benefits long-term but not immediate associative memory. These long-term memory improvements were related to increased gamma power in the MTL. In another set of experiments, they show that NITESGON during associative learning promotes associative learning on a subsequent unrelated (object-location) or highly overlapping (paired word associates) task. Consistent with prior VNS and other NITESGON studies, they show robust evidence that this intervention leads to significant increases in salivary alpha-amylase, a putative marker of central noradrenergic activity. This increase in sAA was also correlated with long-term associative memory across several experiments using paired word associates. Using fMRI, they demonstrate resting-state increases in local hippocampal, LC, and VTA low-frequency fluctuations as well as increased rs-FC between the LC and hippocampus during and after stimulation. Finally, they show that NISTESGON does not enhance long-term associative memory in individuals taking a dopamine antagonist medication, implicating a potential dopamine mechanism in these stimulation-induced memory effects.

This paper is impressive in scope and takes advantage of both causal and indirect methods to cross-validate their results. Behavioral tagging is a relatively nascent area of research in humans, and this paper provides compelling evidence for the role of noradrenergic activity (whether related to behavioral tagging or more general arousal-related consolidation processes) in facilitating memory encoding and consolidation. Beyond basic science research, these findings also have important clinical implications. In recent years, there has been intense interest in studying the LC's role in promoting healthy cognitive function and its involvement in AD-related neuropathology. The LC is one of the earliest sites of tau pathology and thereby represents an important target for clinical intervention in early AD. The current study advances our understanding of a non-invasive technique that may be used to bolster learning in both healthy populations and potentially in older individuals with AD.

The key claims of the manuscript are generally well supported by the data. However, while the large number of studies is a significant virtue of this paper, it is also - at times - a potential weakness. There are many measures and pieces to this puzzle to assemble. While the multimodal approach is admirable and rigorous, the fit between some of these pieces is sometimes overstated. The correlational nature of the data helps cross-validate some of the predictions about the LC mechanisms involved in behavioral tagging. But the most compelling test of this hypothesis would be to link the LC/hipp/VTA fMRI data - arguably the most direct outcome measure in this study - to long-term memory performance and the other neurophysiological measures (e.g., sAA, blink rate, etc.). Many of the results are compelling but they are often observed in parallel studies. Thus, interpreting them as engaging a common mechanism is tenuous. This important shortcoming notwithstanding, there is still a strong replication in other findings (e.g., sAA-memory correlations) across experiments that lend support to some of the hypotheses.

A related issue is that the reliability of these indirect measures of noradrenergic signaling and dopaminergic receptors, including salivary alpha-amylase and spontaneous eyeblink rate, is oversold. While this stimulation technique elicits parallel increases in many of the neurophysiological and behavioral measures, these patterns might not reflect the engagement of a shared underlying mechanism. It's an especially big stretch to interpret the eyeblink effects as relating to LC-DA, which cannot be verified using the current methods. In addition, the spatial resolution of the neuroimaging data is poorly suited for testing predictions about such a small brain structure. This represents a potential weakness of the paper, as the large smoothing kernel in the fMRI data may capture the contributions of other brainstem nuclei and regions activated by NITESGON. It is also worth noting that many of the individual differences findings are confounded by group clustering effects. That is, the between-group effects belie whether the same linear relationships exist in the sham and stimulation groups individually. This necessitates additional correlation analyses within groups to verify that stimulation doesn't decorrelate the relationship between physiological measures and performance.

While the behavioral tagging predictions are intriguing and supported by some findings in the literature, they may not be entirely appropriate for this study. In short, I'm not fully convinced these data satisfy all assumptions of BT (see Dunsmoor et al., 2022 for an overview). Behavioral tagging is thought to be a process that stabilizes weak learning. While it's very difficult to operationalize the "strength" of a memory representation, I'm not sure if the current paired-associates paradigm yields weak learning. Participants have multiple opportunities to learn the memoranda, which casts some doubt as to whether these are weak memory representations. This possibility is supported by the generally high memory performance (~80% on average) during the immediate test and even accurate recall after 7 days.

Behavioral tagging also does not make any explicit predictions about interference effects. Much of this theory centers upon the idea that arousing learning events lead to memory enhancements/benefits; but it does not speak directly as to whether these events confer protection from memory interference (and there was no baseline condition in Dunsmoor et al., 2015 to test any predictions regarding reduced retroactive interference for CS+ stimuli, for example). I find the protective effects of stimulation in Experiment 4 very interesting, and they speak to the importance of this technique as a memory intervention. However, I think this is an example of the authors relying too heavily on a behavioral tagging framework when these could simply reflect arousal-related (Nielson et al., 1996; 2014) and/or noradrenergic-related (e.g., McGaugh, 2013) consolidation benefits more broadly. In summary, I think it would strengthen the paper to walk back claims related to behavioral tagging specifically and address the possibility of alternative (but related) mechanisms.

To summarize, the results of this study are very interesting and the project is very ambitious. There is much therapeutic potential for NITESGON to improve memory and this study represents an important advance towards achieving that goal. The work would primarily be improved by not relying on too many assumptions or inferences, and being more agnostic with respect to certain mechanisms (e.g., whether this is behavioral tagging or general consolidation mechanisms).

Reviewer #1 (Public Review):

Ras is the first discovered oncogene and KRAS is the most frequently mutated isoform. Recent studies led to the development of mutation specific inhibitors, especially against the KRASG12C mutant. However, unfortunately the patients treated with Adagrasib or others develop resistance due to further gain of function mutations and amplification of KRASG12C allele apart from mutations in the downstream signaling components. One of the oldest approaches to target Rho GTPases like RAS is to compete with the nucleotide binding of RAS and it has for a long time remained difficult owing to the picomolar affinity for GTD/GDP. Gray and colleagues in 2014 tried to overcome these issues by employing GDP derivatives that can undergo covalent reaction with disease specific mutations but Muller etal reported in their previous work (Sci.reports 2017) that the issue with these derivatives was with the loss of reversible affinities for beta modified derivatives for RAS of atleast 10000 fold compared to GDP and GTP. Here the authors present novel GDP derivatives different from Gray and colleagues and demonstrate that they could lock KRASG13C covalently, another important mutant of KRAS in an inactive form with a multiple set of biochemical, structural and cellular assays.

However, the issue is a lack of evidence to demonstrate "target engagement" in cells and these derivatives need to be developed further as they cannot pass through cell membranes. The complete covalent modification of the compound is achieved at very high pH. Also its not clear if addition of edaGDP would disrupt KRASG13C and effector interaction directly.

Finally, a culture that rewards big personal accomplishments over smaller social ones threatens to create a cohort of narcissists

Stadium Mentioned

Mention of a wall and council chamber

Another Description of a piller

Town Hall and Metroum Descriptions

Further Description of the Temple of Hera

Temple of Hera Described.

Alter of Artemis mentioned next to Leonidaeum

Leonidaeum is described.

Alters near the stadium.