Reviewer #2 (Public Review):

This enzymological analysis of the DNA-repair protein PARP1 in the presence and absence of its recently discovered regulator, HPF1, is a welcome contribution to the field that provides new data as well as introducing a valuable conceptual framework (seeing PARP1 as simultaneously catalysing 4 different reactions) and novel assays. Some of its conclusions - e.g. regarding the importance of residues Glu284 and Asp283 within HPF1 - are an independent validation of some of those from a recently published study but here they are reached with partially orthogonal means and supported by additional data (e.g. precisely quantified stability, binding, and catalytic parameters). Moreover, the study offers new insights, with the most interesting observation pointing to the prevalence of NAD+ hydrolysis to free ADP-ribose by PARP1 in the presence of HPF1. The technical aspects of the study including the design, number of repeats, data presentation and analysis, and the level of detail provided in the method section are adequate.

Reviewer #2 (Public Review):

Anderson et al construct an epigenetic clock using samples from 245 individuals in the long-running Amboseli study of wild baboons. Their epigenetic clock tracks chronological age reasonably well, and also relates to other metrics of developmental tempo. Contrary to expectations from studies in humans and other species, deviations between epigenetic age and chronological age are unrelated to important predictors of life expectancy in this sample, including measures of early adversity and social integration. Instead, the key predictor of epigenetic aging is dominance rank: In males, more dominant animals show evidence for accelerated epigenetic aging using the epigenetic clock that they derive. In a longitudinal analysis the relationship between dominance and biological aging is shown to be at least partially transient and reversible, pointing to possible concurrent rather than cumulative or non-reversible effects. Although reproductive effort in the form of larger body size and muscularity are plausible factors linking dominance to epigenetic aging, the relationships documented here are shown to be largely independent of measures of body size and relative weight.

This study is important because the authors generate an epigenetic clock, a method increasingly important in research on human aging and life history, for use in this species of baboon. To achieve this, they use a long-running study in which the actual ages of animals are known. Their findings suggest that the aspect of biological aging indexed by this clock is distinct from other important influences on lifespan previously documented in this species, and specifically points to reproductive effort related to maintaining dominance as a key driver of this variation in males.

Reviewer #2 (Public Review):

The manuscript by Guo et al. focuses on the involvement of TRPM4 channel in the development of pressure overload-induced cardiac hypertrophy. They show that TRPM4 expression, in both mRNA and protein, was downregulated in response to left ventricular pressure overload in wild type mice. They demonstrate that a reduction in TRPM4 expression in cardiomyocytes reduces the hypertrophic response to pressure overload due to transverse aortic arch constriction. Furthermore, they show that activation of CaMKIIδ-HDAC4-MEF2A pathway is reduced in mice with cardiomyocyte-specific, conditional deletion of Trpm4. Originally, TRPM4 channel was well known for its association with cardiomyocyte action potential formation and arrhythmia, but this study is very interesting in that it clarified the association of TRPM4 channel with the mechanotransduction mechanism of ventricular pressure overload. Their work may lead to the development of treatment strategies for hypertensive heart disease.

Reviewer #2 (Public Review):

Alvarez et al. present a study of the heritability of functional properties of early visual cortex, as assessed by a population receptive field (pRF) analysis of retinotopic mapping data in monozygotic (MZ) versus dizygotic (DZ) twin pairs. The use of a MZ versus DZ twin design is a strength, as it permits estimates of heritability, and connects the retinotopic mapping and pRF literature to the literature examining heritability of a diverse range of cognitive functions.

I have only one point of concern that I feel the authors should address. It seems that the correlation analysis assumes that each vertex in the cortical surface model represents an independent observation, but an assumption of independence does not appear to be satisfied. FMRI responses in nearby vertices are expected to be highly inter-dependent, as a single fMRI voxel may be mapped onto many vertices. Spatial blurring intrinsic to the fMRI signal (i.e., point-spread function), as well as the spatial smoothing of pRF parameters that was performed, would be expected to exacerbate this issue.

Reviewer #2 (Public Review):

Understanding the mechanisms by which thermogenic brown adipocytes become activated in response to adrenergic signaling remains a high priority for the field of adipose tissue biology. The authors of this study investigate the importance of mitochondrial fusion protein optic atrophy 1 (OPA1) in brown adipocytes, which is highly regulated at the transcriptional and post-transcriptional level upon cold exposure and obesogenic conditions. Using a genetic loss of function mouse model, the authors demonstrate BAT specific knockout of OPA1 results in brown adipocyte mitochondrial dysfunction; however, knockout animals have improved thermoregulations due to the activation of compensatory mechanisms. Part of this compensatory mechanism involves the activation of an ATF4 mediated stress response leading to the induction of FGF21 from brown adipose tissue. These data highlight the presence of homeostatic mechanisms that can ensure thermoregulation in mammals.

Overall, the manuscript is very well-written and the data is nicely presented. The use of multiple genetic mouse models is elegant, rigorous, and yields convincing results. The authors acknowledge the strengths and limitations of the work in a nicely written discussion. This should be a valuable addition to the field, including those interested in mitochondrial biology, brown adipose tissue biology, and FGF21 function. There are minor issues that require attention and one important issue regarding the variability in FGF21 levels observed in the knockout model.

Reviewer #2 (Public Review):

This study explains the motivation behind considering a spatio-temporal model for modelling malaria transmission and achieves it by using two metrics - Plasmodium falciparum entomological inoculation rate (PfEIR) and Plasmodium falciparum prevalence rate (PfPR), as they believe the two metrics together provide a better picture of transmission. The study modeled the spatial distribution of PfEIR and PfPR for children (0.5-5yr) and women (15-49) in rural Malawi. To estimate PfEIR which is a product of Human biting Rate (HBR) and P.f. sporozite rate (PfSR), HBR and PfSR are modelled as Poisson mixed model with log link and Binomial mixed model with logit link, respectively.

The study then models the relationship between PfEIR and PfPR, where PfPR is modelled as a Binomial mixed model. Six different models were considered and compared for modelling the relationship between PfEIR and PfPR. Subsequently, the PfEIR and PfPR are then used for hotspot detection.It is satisfactory to note that separate models were used for different species of mosquitos, which eventually led to different set of covariates and random effects. We are also satisfied that the authors have provided the estimates of covariates, temporal trends, and spatial trends. The paper has a well-written discussion section.

The following issues warrant further attention and clarification.

1) It seems that a single model is fitted for all three focal regions. Please comment on why the authors believe that the parameter estimates should be common for the three regions (or is this a pragmatic decision)

2) In the model for PfSR, no spatial random effect was included (formula 2), despite mentioning the spatial heterogeneity throughout the manuscript. Some justification for not including the space term is needed.

3) In the six models for modelling the relationship between PfPR and PfEIR, do the results change when an overdispersion term (i.e. an independent Gaussian random effect) is included?

Reviewer #2 (Public Review):

This work evaluates the role for GAGA factor (GAF) as a pioneer factor during the zygotic genome activation (ZGA) of early Drosophila embryogenesis. GAF has previously been shown to regulate chromatin accessibility and higher order genome organization in a variety of biological contexts. However, it has historically been difficult to evaluate the role of GAF specifically during early embryogenesis through standard genetic approaches. This paper solves this problem by employing a combination of gene editing and targeted degradation strategies to specifically knock down GAF in early embryos. Through a combination of imaging and genomic approaches, this paper demonstrates a population of genomic loci that depend on GAF to gain chromatin accessibility and to be expressed during the maternal to zygotic transition. This work identifies an additional pioneer factor activity operating at ZGA and furthermore evaluates the potential interdependency of GAF and another pioneer, Zelda.

Reviewer #2 (Public Review):

Lundberg and colleagues provide a detailed set of data showing the utility of host-associated microbe PCR. By simultaneously amplifying microbial community and host DNA, hamPCR provides an opportunity to measure the microbial load of a sample. I was largely convinced about the robustness of this approach after seeing the many different optimization datasets that were presented in the paper. I also appreciated the various applications of hamPCR that were demonstrated and compared to other standard approaches (CFU counting and shotgun metagenomics, for example). As clearly illustrated in Figure 6f, hamPCR could dramatically improve our understanding of interactions within microbiomes as it helps remove issues of relative abundance data.

One challenge about the approach presented is that it cannot be quickly adapted to a new system. Unlike most primers for 'standard' microbial amplicon sequencing, considerable time will be required to determine which host gene to target, how to make that host gene size larger than the size of the microbial amplicon, etc. This may limit wide adoption of hamPCR in the field. I do appreciate the authors providing some details in the Supplement on how they developed hamPCR for the several different systems described in this paper. The helpful tips may make it easier for others to develop hamPCR for their own systems.

An issue that repeatedly came up is that at high and low ends of host:microbe ratios, inaccurate estimates can occur. For example, with high levels of microbial infection, the authors note that hamPCR has reduced accuracy. The authors propose three solutions to this problem (1. altering host:microbe amplicon ratio, 2. use a host gene with higher copy number, 3. and adjust concentrations of host primers), but only present data for #1 and 3. Do they have any data to show that #2 would actually work?

One instance of potential unreliable load that sticks out in the paper is in Figure 5b. The authors note that this is likely due to unreliable load calculation. Is this just one of 4 replicates? What are other potential reasons this would be an outlier and how can the authors rule this out? Did they repeat the hamPCR for this outlier to confirm the striking difference from the other three samples in the eds1-1 Hpa + Pto sample?

Could the DNA extraction method used cause biases in hamPCR for/against either the host or the microbiome? If two different labs study the same system (let's say bacterial communities growing on Arabidopsis leaves) but use different DNA extraction approaches, would we expect them to obtain different answers using hamPCR? Did the authors try several different DNA extraction methods to see if this is an issue? Or has another team of researchers considered this and addressed it in a separate paper? I would appreciate seeing either data to address this or a discussion paragraph that reasons through this.

One emerging theme in microbiome science is to have consistent methodologies that are used across studies/labs to allow direct comparisons of microbiome datasets. Standardization of approaches may make microbiome science more robust in the long-term. Given much of the nuance in developing hamPCR for different systems, my impression is that this method is best for comparing samples within a particular host-microbe system and not across systems. For example, it may be challenging to directly compare my bacterial load hamPCR data from Arabidopsis to another lab's if we used different Arabidopsis host genes or if we used different 16S gene regions. Can the authors unpack this a bit in a discussion paragraph? If it is widely adopted, is there a way to standardized hamPCR so that it can be consistently used and compared across datasets? Or should that not be the goal?

There appears to be considerable non-specific amplification or dimers in the gels presented throughout the manuscript. Could this non-specific amplification vary across host-microbe primer combinations? Would this impact quantification of host and microbial amplicons?

Reviewer #2:

This work combines an interesting experimental approach to measure temporal expansion/compression with EEG recordings. The authors find consistent evidence that a visual reference is judged as shorter/longer dependent on a previous adaptation. They report several EEG analyses suggesting the early visual activity is correlated with such temporal distortions.

Strengths:

The paper uses an interesting design to try to isolate temporal compression/expansion. The behavioral results are consistent and they show several different EEG analyses. The main result, of beta power being correlated with temporal processing, is consistent with previous reports.

Weaknesses:

1) The paper would strongly benefit from more details on some of the methodologies and results. In several moments, the authors show measures that are subtracted or normalized based on other conditions. Although these normalizations can sometimes help to illustrate effects, it also makes it harder to understand the data in a more general sense. For example, in their behavioral results, the authors present an Adaptation Effect to quantify temporal compression/expansion. It would also help if authors present the raw estimates of Points of Subjective Equality across all conditions (including the unadapted condition) so that the reader can have a better understanding of the effects. It would be even better if the average proportion of responses for each duration was shown so that readers can see differences in PSE, JND, and guess/lapse rates.

2) Further details about the EEG analysis would also help the readers. For example, it is not totally clear how the FFT analysis was performed. It would be important to add information about whether data was analyzed using moving windows, the size of the windows, whether there was an overlap between windows, whether there was a baseline correction and what was the baseline.

3) Several of the conclusions of the authors are based on linear mixed effect (LME) regressions in which the PSE or the behavioral effect is the dependent variable and an EEG measure is used as one of the fixed effects. However, in some of the analysis, it is not really clear how this was performed (for example, whether this was done at the single-trial or at the averaged data). Critically, it would help the reader if more output (both tables and graphs) were shown for these analyses so that what is being analyzed and concluded is made clearer.

Reviewer #2 (Public Review):

BonVision is a package to create virtual visual environments, as well as classic visual stimuli. Running on top of Bonsai-RX it tries and succeeds in removing the complexity of the above mentioned task and creating a framework that allows non-programmers the opportunity to create complex, closed loop experiments. Including enough speed to capture receptive fields while recording different brain areas.

At the time of the review, the paper benchmarks the system using 60Hz stimuli, which is more than sufficient for the species tested, but leaves an open question on whether it could be used for other animal models that have faster visual systems, such as flies, bees etc.

The authors do show in a nice way how the system works and give examples for interested readers to start their first workflows with it. Moreover, they compare it to other existing software, making sure that readers know exactly what "they are buying" so they can make an informed decision when starting with the package.

Being written to run on top of Bonsai-RX, BonVision directly benefits from the great community effort that exists in expanding Bonsai, such as its integration with DeepLabCut and Auto-pi-lot. Showing that developing open source tools and fostering a community is a great way to bring research forward in an additive and less competitive way.

Reviewer #2 (Public Review):

The paper presented by Boroumand et al. aims to delineate the impact of bone marrow resident adipocytes on the phenotype, development, and metabolism of murine monocyte subsets during diet-induced obesity and leanness. The paper provides an interesting analysis of the metabolic state and phenotype of mitochondria in murine monocytes during high-fat diet feeding. Furthermore, it provides some insight on the crosstalk between bone marrow resident adipocytes and different monocytes.

The paper will help to further delineate the response of monocytes during obesity, however, the impact the paper will have on the field of mononuclear phagocytes biology and our understanding of myelopoiesis during low-grade inflammation is limited.

Several claims should be more thoroughly addressed, such as the phenotypes of macrophages found within the adipose tissues and a more fine-grained analysis of the mononuclear phagocyte progenitors within the bone marrow. Furthermore, a central claim of the paper is that Ly6clow monocytes convert to Ly6chigh monocytes. If the authors would like to hold that claim it needs some experiments which are supportive of that hypothesis.

DOI: 10.7554/eLife.64267

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DOI: 10.7554/eLife.64267

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What is this?

DOI: 10.7554/eLife.64267

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What is this?

DOI: 10.7554/eLife.64267

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What is this?

DOI: 10.7554/eLife.64267

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What is this?

Reviewer #2 (Public Review):

The manuscript "Spatiotemporal dynamics of PIEZO1 localization controls keratinocyte migration during wound healing" by Holt and colleagues demonstrates that loss of function of PIEZO1 speeds up keratinocyte migration and wound closure, whereas enhancing PIEZO1 function, with a PIEZO1 gain-of-function mutant or by chemical means, slows down both processes. The topic of this manuscript is timely and relevant. The experimental design followed by the authors is straightforward and elegant and the vast majority of the conclusions are fully supported by their results. Overall, this manuscript provides solid evidence that normal (wild type) function of PIEZO1 slows down skin wound healing in vitro and in vivo.

Reviewer #2 (Public Review):

MprF is a lipid flippase involved in determining bacterial tolerance to cationic peptides of the innate immune system and to antibiotics such as daptomycin. Using Staphylococcus aureus as their model organism, the authors assessed the suitability of MprF as a target for anti-virulence treatments. For this purpose, a series of monoclonal antibodies directed against the extracellular loops of MprF were generated. The antibodies were tested for their ability to bind and inhibit the function of MprF, to sensitize S. aureus towards cationic peptides, and to promote phagocyte killing of S. aureus. Moreover, the antibodies were used to investigate the orientation of one specific loop of the MprF protein.

Strenghts:

The manuscript is well-written and the introduction provides a very good overview of the challenges associated with antibiotic resistance, anti-virulence strategies and the MprF protein. The Figures and the Figure legends are easy to follow. The described approach is innovative, and state of the art methods are used throughout the manuscript.

Weaknesses:

There is a discrepancy between the anti-virulence scope as indicated by the title and the introduction, and the actual content of the result section: here, the anti-virulence strategy is only preliminary addressed, and a lot of effort is instead put into determining the orientation of one specific loop of the MprF protein. This needs to be better aligned, and more compelling data are needed to support that MprF has potential for anti-virulence strategy. The conclusions of this paper are mostly well supported, however, additional controls are needed to fully support that the observed effects of the antibodies are mediated via specific binding to MprF.

Reviewer #2 (Public Review):

In this very extensive and somewhat lengthy manuscript Zewdu et al, characterize an oncogenic Braf-driven model of invasive mucinous lung adenocarcinoma. They show an effect of co-incident and sequential Nkx2-1 inactivation on cancer cells state and therapy responses. They show that BP and BPN tumors have distinct responses to RAF/MEK inhibition. Furthermore, they uncover potentially important cross talk between the MAPK and WNT pathways in invasive mucinous adenocarcinoma (IMA). Overall, this is an excellent manuscript that uncovers many interesting new aspects of IMA. The strengths of this manuscript include the sophisticated in vivo cancer models, detailed cellular analyses, and potential importance of these finds to therapy responses. Their claims are well supported by their data.

Reviewer #2 (Public Review):

Differentiation pathways for parasitic organisms are of considerable importance, as they are relevant to understanding transmission, mechanisms of host specificity as well as, in some cases, offering possible routes to control measures. The transition between mammalian host and insect vector for African trypanosomes has been widely addressed due to accessibility and tractability. However, one view has been dominant, despite, as the authors suggest, considerable counter evidence. The present work posits an alternate pathway, questioning the role of the so called stumpy stage. This is of considerable importance to the immediate field and possibly wider.

The major strengths here are in the use of a good model, and a high number of individual infections. The weaknesses include some assumptions with which I have issue, and given that this work is seeking to overturn a dogma, which also has assumptions, one needs to tread very carefully, to avoid falling into an unscholarly dispute. The major things are for me the assumption that PAD-1 cells are stumpy - almost anything seems to be able to activate PAD-1 and the lack of any quantitative data are concerning. This is difficult really and Matthews also says that PAD-1 does not equal stumpy and morphology is also important. Further, simple expression of EP procyclin is not sufficient for designation as pr cyclic, and the salivary gland cells are assumed metacyclic without demonstration of VSG expression for example. While I accept that these interpretations are reasonable, this is an assumption and in all three cases leads me to feel a little underwhelmed. Perhaps most concerning are the lack of statistical calculations as well as any attempt at further analysis beyond counting. The result is very much phenomenology and lacks any mechanistic insight.

Reviewer #2 (Public Review):

Analyzing EM data from the Drosophila larva, Hueckesfeld et al. investigate and describe the synaptic connectivity of sensory neurons and interneurons that provide input into the neuroendocrine system in fly larvae. The output of neuroendocrine neurons projecting to the ring gland is mostly non-synaptic and identified by receptor expression analysis. Using a modelling approach, they provide a more detailed analysis on newly discovered CO2-responsive cells and their downstream network and also other possible processing pathways from sensory to endocrine neurons. To test some of their model predictions, they analyze the response of predicted CO2-downstream neurons to CO2 exposure.

Strengths of the paper:

The authors did a great job in visualizing the complex connectivity between sensory inputs, interneurons, and endocrine neurons. Neuroendocrine neuron outputs, which are mostly non-synaptic, have been detected by identification of vesicle release regions. The authors went beyond the analysis of EM data and collected a lot of new data to confirm non-synaptic connectivity between neuroendocrine neurons and their downstream targets by performing antibody stainings and trans-tango experiments. This information will be highly valuable to the field.

Sensory inputs in the larvae have been attributed according to previous publications, but the authors also describe a new CO2 sensing function of tracheal TD neurons. Description of this new sensory function is also a valuable addition to the Drosophila field.

The authors used a modelling approach to describe and detect specific processing pathways, for example from a certain sensory modality, or to a specific endocrine neuron. This manuscript underlines that the use of a (simple) computational model framework to understand network motifs within an EM dataset is very powerful. Also, they can confirm that predicted CO2 downstream neurons indeed respond to CO2 in a certain way.

The authors discuss potential functional implications for faster and slower processing pathways (connections over interneurons or direct). Indeed there might be situations where the larva needs to respond in flexible ways that are however also easily reversable (fast pathways), but there might be also other situations where the larva needs to integrate more sensory evidence and which might induce non-reversible behaviors, such as pupation (slow pathways). I think this discussion suggests an interesting concept of the impact/cost of adaptive behavioral changes and the different timescales they can occur.

Weaknesses of the paper:

Data wise, this manuscript is a very descriptive study. The authors visualize the complex and diverse possible processing pathways; however, the function of the circuit remains unknown. To really understand the functional properties behind this complex architecture will require studies focused on single sensory modalities, single pathways and/or single peptidergic classes all in the context of a certain behavioral framework.

The authors try to provide a complete overview over the connectivity within the neuroendocrine system pathways. However, the authors should discuss that the connectivity data from the one EM dataset that they analyzed might be changing across individuals and development. Especially the vesicle release sites might be more variable across individual larvae than synaptic connections. Neuropeptide receptor expression might also change over development.

The authors investigate the TD CO2 sensing pathway in more detail. They show that the sensory neurons and the predicted downstream neurons respond to CO2. This shows that the neural connectivity might serve a functional purpose. There is however another type of sensory neurons that respond to CO2 in the larva (Gr21a receptor neurons- Faucher et al., 2006), which are required for an avoidance response to the stimulus. The authors should discuss and maybe analyze the EM data for possible circuit convergence between the two different CO2 sensory input neurons.

The authors discuss the CO2 response in the context of a stress response. However, the natural environment of larvae, rotten fruits, also emit CO2 as a by-product. Thus, sensing CO2 which converges together with information from Fructose/Glucose sensors might be used for finding or evaluating food sources.

DOI: 10.7554/eLife.58251

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What is this?

DOI: 10.1523/ENEURO.0022-20.2020

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What is this?

DOI: 10.7554/eLife.56725

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What is this?

DOI: 10.7554/eLife.60445

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What is this?

DOI: 10.1126/sciadv.aaz3173

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What is this?

DOI: 10.7554/eLife.55792

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What is this?

(A) Step-by-step process of each layer of the device to allow multiple functionalities and wearability. (B) Amplitude vs. Time graph of sensor worn on the wrist to measure motion when walking, running, jumping. (C) Thermal sensor can read forehead, abdomen, and hand temperature on skin when indoor and outdoor over time. (D) The acoustic sensor is placed on the neck to measure the amplitude (vibration) characteristics of the vocal chords to serve as a human-machine interface. (E) The electrocardiogram sensor measures heart activity while resting, after exercising for 13 seconds and then after 34 seconds. The heart rate resulted in 72, 96, and 114 per minute, respectively.

Reviewer #2 (Public Review):

KSR1 functions as a critical rheostat to fine-tune MAPK signalling, and identifying modes by which its over-expression promotes tumor progression is clinically important and potentially druggable. Ras is highly mutated in CRC and unfortunately inhibitors of Ras have been challenging to develop. However, small molecules which stabilize an inactive form of the KSR are actively being developed in an attempt to repress RAS signaling. Thus, this study, which seeks to identify how KSR1 promotes oncogenic mRNA translation, is potentially highly clinically relevant, as it may identify novel druggable targets.

In this manuscript the authors performed polysome profiling in colorectal cancer (CRC) cells and proposed that KSR1 and ERK regulate the translation of EPSTI1 mRNA. They go on to characterize the phenotypes associated with knock-down or knock-out of KSR1 in CRC, and show that their defects in invasion, anchorage-independent growth and switch to a less EMT-like phenotype are all EPSTI1-dependent.

The authors succeeded in providing ample in vitro data that KSR1 and EPSTI1 are potential therapeutic targets in CRC. However, the data demonstrating that KSR1 and ERK regulate EPSTI1 mRNA translation is tenuous. Although the authors state that "EPSTI1 is necessary and sufficient for EMT in CRC cells", the data presented are consistent with a more restrained conclusion of a partial-EMT and not EMT per se. Finally, without an in vivo model it is difficult to glean novel insight into the mechanism by which KSR1 and/or EPSTI1 control the invasive and metastatic behaviour of cells.

Reviewer #2 (Public Review):

Cell fate transitions (such as adenocarcinoma converting to small cell neuroendocrine fate) are an increasing phenomenon observed during therapeutic resistance in lung cancer, prostate cancer, and possibly other cancer types. It is important to understand these mechanisms if we ultimately seek to tailor treatment to a patient's disease and/or to control the pathways that lead to treatment resistance. However, the mechanisms that underly these cell fate changes are not well understood. It has been previously observed (Calbo et al, Cancer Cell, 2011) that activated mutant Kras (commonly associated with adenocarcinoma fate) can promote a non-neuroendocrine fate in SCLC, but the mechanisms are unknown.

Predominantly using three human small cell lung cancer (SCLC) cell lines, Inoue and colleagues use genetic and pharmacological approaches to focus on potential mechanisms by which Egfr/Kras/Mapk signaling can repress neuroendocrine fate. They make a number of interesting observations that extend our understanding of neuroendocrine cell fate regulation including:

1) Kras-induced NE suppression appears to depend mostly on ERK2, and not ERK1 or PI3K signaling.

2) Kras activation induces chromatin changes including increased H3K27Ac in 2/3 cell lines; increased H3K27Ac in response to HDAC inhibition is associated with NE suppression. Pharmacological inhibition of CBP/p300 (a HAT that promotes H3K27Ac) reduces H3K27Ac and restores NE suppression. Altogether, these findings are consistent with the notion that SCLC cannot tolerate high levels of H3K27Ac.

3) Kras induces the MSK/RSK pathway consistently in cell lines but appears to be functionally-relevant to NE fate only in H82 cells.

4) Kras activation induces chromatin occupancy at ERG and ETS family transcription factor (Etv1, 4, 5) binding sites in 2/3 cell lines, and induces ETV4 (2/3 lines) and ETV5 protein levels (3/3 lines). ETV1 and ETV5 overexpression are sufficient to inhibit NE fate markers in context-dependent manner. Ets family induction appears to occur in a CIC-independent manner.

In addition, some interesting negative data is presented, for example, SOX9 is induced upon Kras activation in 3/3 cell lines but it was not functionally relevant for NE suppression; Notch1, Notch2, and HES1 (known NE fate suppressors) are induced by Kras activation in a cell context-specific manner, but they did not appear functionally-relevant to NE suppression based on HES1 knockout and a pharmacological inhibitor of Notch signaling; Rb1 loss was not sufficient to promote NE fate in EGFR/p53 mutant cell lines, despite its known association with adeno-to-SCLC conversion. Overall, the conclusions in the manuscript are well justified. These findings will be of interest to those especially in lung and prostate cancer studying cell fate conversions in the context of EGFR and AR inhibitor resistance, respectively. These observations will be built upon by these fields.

Weaknesses:

1) One recurring issue in the manuscript is that the observations are often not consistent across the three cell lines and are context-specific effects, and the potential reasons could be explained better. The cell lines chosen unfortunately (but interestingly) represent some of the major cell states of SCLC. H2107 represents the ASCL1+ NE-high subset of SCLC (and has some MYCL). H82 and H524 represent the C-Myc (MYC)-high subset of SCLC, with H82 having a MYC amplification, and both representing the NEUROD1 subtype (which tend to be associated with more MYC). Assessment of NE score using a common approach in the field (Zhang et al, TLCR) shows that H82 cells are already considerably NE-low, with H524 as NE-intermediate/high, and H2017 as NE-high. So, this may be related to why H82 seemed to be the most permissive cell line to change NE fate in multiple assays.

In addition, H2107 and H524 appear to have EP300 mutations, which may contribute to their NE-high nature and contribute to the refractory response to A485 treatment based on the author's model. It's known that MYCL and MYC-driven cell lines differ in numerous aspects from transcriptional signatures, super enhancer usage, metabolic regulation, therapeutic response, etc. This information could be mentioned in the results and discussed when mentioned as a factor near line 540.

2) Related to Figure 4, the authors show that p300 pharmacological inhibition can restore NE fate in presence of Kras. Given that drugs can have off-target effects, it would be helpful to know if genetic knockdown/knockout of p300 phenocopies these effects. Given that CREBBP (CBP) or EP300 (p300) mutations are common in SCLC, it is also relevant whether any of these cell lines have CREBBP (CBP) or EP300 (p300) mutations. It appears H2107 and H524 may have EP300 mutations, and it would be good to know whether the authors have tried to restore EP300 function.

DOI: 10.1002/cne.24964

Resource: (ZFIN Cat# ZDB-ALT-090116-2,RRID:ZFIN_ZDB-ALT-090116-2)

Curator: @scibot

SciCrunch record: RRID:ZFIN_ZDB-ALT-090116-2

What is this?

AssayResult: 82

AssayResultAssertion: Normal

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AssayResult: 80

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AssayResult: 69

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AssayResult: 99

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AssayResult: 98

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AssayResult: 77

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AssayResult: 105

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AssayResult: 96

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AssayResult: 104

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AssayResult: 99

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AssayResult: 83

AssayResultAssertion: Normal

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AssayResult: 91

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AssayResult: 86

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AssayResult: 56

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AssayResult: 61

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AssayResult: 51

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AssayResult: 66

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AssayResult: 60

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AssayResult: 65

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AssayResult: 66

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AssayResult: 45

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AssayResult: 58

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AssayResult: 49

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AssayResult: 83

AssayResultAssertion: Normal

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AssayResult: 84

AssayResultAssertion: Normal

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AssayResult: 91

AssayResultAssertion: Normal

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AssayResult: 94

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AssayResult: 97

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AssayResult: 98

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AssayResult: 96

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AssayResult: 105

AssayResultAssertion: Normal

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AssayResult: 88

AssayResultAssertion: Normal

ControlType: Normal, wild type TP53 from control 3

Comment: See Table S2 for details

AssayResult: 95

AssayResultAssertion: Normal

ControlType: Normal, wild type TP53 from control 2

Comment: See Table S2 for details

AssayResult: 100

AssayResultAssertion: Normal

ControlType: Normal, wild type TP53 from control 1

Comment: See Table S2 for details

AssayResult: 7.1

AssayResultAssertion: Abnormal

Comment: See Table S2 for details

HGVS: NM000546.5:c.(?-202)(*1207?)del p.?

Comment: A CAID could not be generated for this deletion variant with uncertain breakpoints.

Reviewer #2 (Public Review):

The latest manuscript of Loring and coworkers solves a number of important problems of SARM1 structure and function at once, namely why the purified enzyme has little activity, what size is the active multimer, whether it produces cADPR on the way to ADPR, and how this enzyme may overcome autoinhibition by NAD+ in vivo. In work that is technically sound, the authors describe a phase transition that can be induced by macroviscogens and by citrate in which we are able to see cryoEM images of activated multimers and the induction of SARM1 activity in worms by citrate. Working with concentrated enzyme, the authors are further able to characterize SARM1 activity in detail and clearly show which cations are most inhibitory and that ADPR and not cADPR is the primary product of the reaction.

There is clearly a lot of regulation in the system with NAD+ inhibiting and NMN activating this enzyme and NMNAT, which controls conversion of NMN to NAD+ being localized to the outer Golgi membrane. Golgi and mitochondria are both moved along axons in processes that are totally dependent on cellular energetics. Given the broad contributions that are made by this work, I would not mind if the authors considered whether citrate, either from stressed mitochondria or from inhibition of the cytosolic enzyme ATP-citrate lyase, might be produced at high enough concentration to push SARM1 into the phase transition described herein.

Reviewer #2:

The authors address the vortex formation of bacteria in circular confinements with a particular focus on the difference of swarming vs. swimming (planktonic) motility of individuals. In the field of active matter, this critical distinction has rarely been studied so far but it is oftentimes ignored in modeling studies. Chen et al. show that qualitatively different patterns emerge for swarming and swimming bacteria. I do therefore believe that the work could have substantial influence on future studies devoted to bacterial pattern formation.

I have two main concerns detailed in the following.

1) A central finding of the present study is that the number of vortices/swirls as a function of the well diameter differs for swarming vs. swimming bacteria. The authors argue and show experimentally (Fig. 2) that the behavior is identical for small and large diameters. For intermediate values, however, they report that a single swirl is observed for swarming bacteria whereas swimming bacteria show multiple swirls.

The fact that the behavior is identical for large wells suggests that the bulk behavior is identical. This is also confirmed by Fig. 2E which shows that the spatial correlation function of the velocity is identical in large wells. To me, that suggests that the boundary conditions play a central role for understanding how the observed phenomenology emerges. [Indeed, it was shown in the past that the interaction of bacteria with boundaries crucially determines the formation of swirls in confinement (Lushi, Wieland & Goldstein PNAS 111 9733 (2014). The authors of this work assume reflecting boundary conditions, which -- to my knowledge -- contradicts the finding of Lushi et al.]. The authors, however, explain the difference of the observed patterns within their modeling study in a different way, namely by a different strength of the (anti-)alignment interactions. Changing the interaction at the level of individual cells will, however, change the bulk behavior too. Accordingly, the numerically observed bulk behavior (Fig. 5B ) is very different in both cases (at a qualitative level). It is difficult to judge the difference in detail because the correlation function was not calculated for the simulations.

In short:

The model (Fig. 5A) reproduces the experimental results partially (Fig. 2C), but the modeling analogue to Fig. 2E is missing. The line of arguments seems to me not to be entirely consistent.

2) Inferring the interactions of active particles from observations of the emergent patterns is a highly non-trivial task. In view of this I am not entirely convinced by the arguments put forward by the authors that "more substantial cell-cell cohesive interaction[s]" are the reason why the swirling patterns formed by swarming/swimming bacteria differ. In this context, I want to raise the attention of the authors to Ref. [Peruani, Deutsch & Bär: Phys. Rev. E 74 030904(R) 2006]. In this work, a clustering transition of self-propelled rods was described. "Rafts", referred to as clusters by Peruani et al., are observed as the aspect ratio of rods is increased. Notably, a kinetic transition towards clustering can emerge even in the absence of any attractive interactions. In short, the observation that cells move in parallel (polar clusters) next to each other does not allow to conclude that cohesive interactions are present. The movies S3 and S4 provided by the authors show that the particle shape of swarming and swimming particles is clearly different. In particular, the elongated swarming bacteria show pronounced clusters (Movie S3) whereas the shorter planktonic cells (Movie S4) do not. The difference in aspect ratio does indeed suggest that swarming and swimming bacteria differ in their alignment interaction. However, this contradicts the observation that spatial correlations in large wells are indistinguishable (see comment 1 and Fig. 2E). Side remark: in the main text, the authors argue that changes of the aspect ratio are not the reason for an increased alignment interaction, however, in the discussion section cell morphology changes (e.g. cell elongation and hyper-flagellation) are mentioned as an indicator that swarming is a different phenotype from swimming.

In summary, I believe that the connection of experimental observations and modeling are not entirely convincing.

Reviewer #2 (Public Review):

This manuscript establishes a novel rodent model for prenatal methadone exposure and characterizes various aspects of neurodevelopment in the offspring. Given the global opioid crisis and the rampant rise of drug use by pregnant mothers and incidence of neonatal abstinence/opioid withdrawal syndrome, there is a critical need to determine potential outcomes for children born with this condition. In their model, the investigators use mice that are already taking oxycodone and switched to methadone treatment prior to becoming pregnant, which is a major translational advantage compared to other models where opioid dosing does not start until sometime mid-gestation. The experimental design also included a wide variety of measurable endpoints, including physical development, sensorimotor behavioral tasks, vocalizations, brain imaging, circuit electrophysiology, and histology; this comprehensive approach allows for synthesis of the results that has traditionally been difficult to find in this field, given the vast differences in species, dosing paradigms, etc. Sex differences were also considered, which is especially important given what is known about varying rates of NOWS between males and females. The text is very well-written, including detailed descriptions of statistical analysis.

Despite overall enthusiasm for the study and its findings, there are some concerns regarding the brain volume analyses as well as potential stress confounds with the experimental design. The analysis of structural differences measured by volumetric MRI showed that there were no appreciable differences across grey matter structures with PME (Supp. Fig. 9). This was surprising, given that regional decreases in brain volume are a consistent finding with prenatal drug-exposed offspring (Yuan et al., 2014 [DOI 10.1038/jp.2014.111]; Sirnes et al., 2017 [DOI 10.1016/j.earlhumdev.2017.01.009]; Nygaard et al., 2018 [DOI 10.1016/j.ntt.2018.04.004]). Traditionally, these deficits tend to be more true for white matter than grey, though the authors do not indicate whether this was investigated.

The opioid dosing protocol required twice-daily subcutaneous injections for at least 3 weeks (possibly longer, but it was difficult to determine from the text when exactly the treatments were halted). The effects of maternal/prenatal stress, even in the vehicles, cannot be discounted. The authors rightly noted this caveat in the Discussion, but it remains a critical concern in this otherwise well-designed study.

Reviewer #2:

In this study, the authors perform an impressive field phenotyping experiment on three grafted grapevines all with a common scion cultivar 'Chambourcin' alongside an ungrafted control to assess the associations between rootstock and leaf traits. The traits collected include ionomics, metabolomics, transcriptomics, leaf morphology and physiology. In addition, the authors collect these samples at three phenological stages to incorporate seasonal variation. The authors apply a combination of classification and machine learning methods to test whether features within each phenotypic measurement are predictive of genotype. In some cases, such as the ionomics data, certain ions are predictive of rootstock genotype but only at certain seasonal time points. The datasets presented here are extensive and will be of value to the horticulture field since grafting is such a common technique used in cultivating many crops. Considering the scale of this experiment, the manuscript is at times disconnected, in large part because each dataset is analyzed independently without any integration across phenotypes. The results presented do highlight more of an effect of phenology rather than rootstock on the phenotypes measured.

Major comments:

1) It would be very helpful to have a diagram with the layout in the field and the sampling strategy or a more detailed explanation. This would help to associate which phenotypic data was collected at the same time and on the same plants. For example, it would expand on what is mentioned on line 348 "row 8 sampled early in the day". It would help to know what time of day the samples in each row were collected. Additionally, how do the different irrigation treatments factor into the sampling? A better introduction of the experimental design is needed at the start of the results section along with a description of the genotypes and why they were selected.

2) I understand why running a PCA before the LDA can help reduce the dimensionality of the space to be able to invert the covariance matrix (if that was the motivation?) but is this because there were issues with running LDA alone? I wonder if you've lost important discriminating information between the classes by doing this. Was the LDA run on the datasets first prior to the PCA? This may uncover additional classification that was eliminated by the PCA.

3) For the Random Forest analysis, the authors might consider using k-fold cross validation rather than partitioning the dataset, this is especially beneficial when working with smaller datasets and might improve the predictions. Could all the importance scores be reported rather than just the couple mentioned in the text (line 296).

4) In reference to Figure 1B and C, it would be helpful to indicate on the plots which comparisons are significant based on their model tests. The full test results are presumably in the excel spreadsheet referred to in the reporting form although it was not found with the manuscript materials.

5) Throughout the text there is very little mention of the various grafted genotypes and what is known about the lines. The authors should consider introducing these genotypes and why they were selected for the grafting experiment. What is different among these lines? There is very little discussion of the comparisons between genotypes and what phenotypes are significantly different between the lines and what the implications are for the plant as a whole.

6) Line 287 refers to a post-hoc analysis of the ions, do the ions showing significant variation explained by rootstock and phenology match the ions identified in the ML as important classifiers?

7) For such a large metabolomic dataset, it is surprising that the authors do not present any identification of the metabolites highlighted. The identification of the metabolite features that were found to influence the rootstock main effect would be of interest and might reveal interesting biology. How did these metabolites differ between genotypes? On line 501 in the discussion there is mention of flavanols and stilbenes yet these weren't highlighted in the results section.

8) What is the reasoning for not simply applying a linear modeling approach such as limma on the gene expression data first instead of only applying it to the PCs in order to identify differentially expressed genes between the genotypes? If phenological stage is the strongest effect, what if you run the analysis within each stage to look specifically at the differential responses between grafted lines at each stage? The analysis of the gene expression data, similar to the metabolomics data, seems to be missing an opportunity to uncover underlying biological mechanisms contributing to any genotype effects of grafting, a stated goal of the study. What genes are differentially expressed and do they relate to the metabolomic or ionomic data?

9) In the methods, there are three irrigation treatments described yet this is not mentioned in the results section. While it seems as though rainfall mitigated much of the irrigation effect there does appear to be differences in water availability to the vines as described in the provided github page. Were various irrigation treatment sets sampled for all phenotypes? Or were the ionomics, metabolomics and transcriptome analysis done on the same irrigation treatments? If not, was this effect considered in the analysis? This is yet another variable that would greatly influence the response and should be considering when assessing the effects of grafting. Further detail about the sampling and conditions is needed to clarify.

10) In figure 1 there is information about leaf age. For the metabolomics a mature leaf was sampled, transcriptomics the youngest leaf, and physiology it is not specified. Could you clarify the leaves that were sampled and how they relate across phenotypes. This is an important point to mention given the differences observed for the ionomics data.

11) In reference to the vine physiology, were these all collected from the same irrigation treatment? Was the sampling of each genotype spread out over the 3h window to account for time of day variation? It would be helpful to have the significant comparisons indicated in the figure. What are the letters referring to on lines 402-403 with the p. values? This section would be greatly improved by additional clarity in the text.

12) Given the focus on grafting, the analysis presented in Figure 6 does not seem to contribute to this objective. Could this be expanded on to look within and across genotypes to see if different phenotypes covary and to compare the dimensions of variation across genotypes rather than combining them all together? This would complement the previous analyses and hopefully reveal the differences that were highlighted in the earlier sections.

13) The results section is very disjointed and the datasets are presented almost as completely separate studies. To improve clarity in the results section, the authors might consider expanding on the findings of the LDA and ML analysis for each phenotype and connecting them together.

Reviewer #2 (Public Review):

In this manuscript, the authors generated transgenic zebrafish reporter lines that allow observation of cytoplasmic lipid droplets in vivo. They knocked in GFP or RFP in the endogenous loci of perilipin 2 and 3, and showed that the reporter genes exhibited similar temporal and spatial expression in the intestine in response to acute high-fat feeding as the endogenous perilipin 2 and 3 transcripts. They also characterized the reporter gene expression in the liver, adipocytes, and around neuromasts. These tools open up new opportunities to study lipid droplets dynamics in live zebrafish that is not feasible in mouse models. Overall the manuscript is well written. The authors have discussed in details the strength and caveats of these reporters. The weakness is the descriptive nature of the study - many interesting observations but no mechanistic study. I have the additional comments:

1) It is curious that in plin2 and plin3 reporter fish, the fluorescent tags were inserted at the 5' and 3' of the open reading frame, respectively. The authors did not provide any explanation. Does the location where the fluorescent tag is inserted affect the expression of the reporter genes?

2) GFP and TagRFP-T are not fast folding fluorescent proteins and are very stable, which may not be the best options for studying the formation and degradation of lipid droplets. How the fluorescent tags affect the stability and clearance of the protein should be carefully characterized.

3) Was there any indel being introduced by TALENs in these knockin fish? Is there off target effects of the TALENs?

4) The authors also generated transgenic fish overexpressing human PLIN2 and PLIN3 fluorescent fusion proteins. Is the subcellular localization of these fusion proteins similar to the zebrafish knockin under nofed and fed conditions? In other words, do human PLIN2 and PLIN3 proteins behave similarly as the zebrafish orthologs?

Reviewer #2 (Public Review):

The authors eloquently showed that IL-33 was produced from stromal cells following Toxoplasma infection and that the absence of IL-33 signaling resulted in increased parasitemia. In agreement with this observation, they found that exogenous IL-33 reduced parasite load and increased the recruitment of inflammatory monocytes that are critical for resistance. The manuscript is well written and data presented here supports the major findings of this work.

Reviewer #2 (Public Review):

Overall this is a solid and technically sound manuscript, and I have only two relatively minor suggestions for improvement:

1) Tetramer versus dimer

The particles that were analyzed by cryoEM were composed of four THO-Sub2 protomers, yet the authors argue that a dimer is the functional unit. Why? The tetramer versus dimer organization needs to be better discussed, also in light of the observation that the human complex can also form a tetramer.

2) Sub2 activation mechanism

The authors should more carefully discuss how THO 'activates' Sub2 (and how the 'semi-open state' leads to activation) and indicate the RNA binding surface of Sub2 in their model.

Reviewer #2 (Public Review):

This manuscript will be of interest to a broad audience of immunologists especially those studying host-pathogen interactions, mucosal immunology, innate immunity and interferons. The study reveals a novel role for neutrophils in the regulation of pathological inflammation during viral infection of the genital mucosa. The main conclusions are well supported by a combination of precise technical approaches including neutrophil-specific gene targeting and antibody-mediated inhibition of selected pathways.

In this study by Lebratti, et al the authors examined the impact of neutrophil depletion on disease progression, inflammation and viral control during a genital infection with HSV-2. They find that removal of neutrophils prior to HSV-2 infection resulted in ameliorated disease as assessed by inflammatory score measurements. Importantly, they show that neutrophil depletion had no significant impact on viral burden nor did it affect the recruitment of other immune cells thus suggesting that the observed improvement on inflammation was a direct effect of neutrophils. The role of neutrophils in promoting inflammation appears to be specific to HSV-2 since the authors show that HSV-1 infection resulted in comparable numbers of neutrophils being recruited to the vagina yet HSV-1 infection was less inflammatory. This observation thus suggests that there might be functional differences in neutrophils in the context of HSV-2 versus HSV-1 infection that could underlie the distinct inflammatory outcomes observed in each infection. In ordered to uncover potential mechanisms by which neutrophils affect inflammation the authors examined the contributions of classical neutrophil effector functions such as NETosis (by studying neutrophil-specific PAD4 deficient mice), reactive oxygen species (using mice global defect in NADH oxidase function) and cytokine/phagocytosis (by studying neutrophil-specific STIM-1/STIM-2 deficient mice). The data shown convincingly ruled out a contribution by the neutrophil factors examined. The authors thus performed an unbiased single cell transcriptomic analysis of vaginal tissue during HSV-1 and HSV-2 infection in search for potentially novel factors that differentially regulate inflammation in these two infections. tSNE analysis of the data revealed the presence of three distinct clusters of neutrophils in vaginal tissue in mock infected mice, the same three clusters remained after HSV-1 infection but in response to HSV-2 only two of the clusters remained and showed a sustained interferon signature primarily driven by type I interferons (IFNs). In order to directly interrogate the impact of type I IFN on the regulation of inflammation the authors blocked type I IFN signaling (using anti IFNAR antibodies) at early or late times after infection and showed that late (day 4) IFN signaling was promoting inflammation while early (before infection) IFN was required for antiviral defense as expected. Importantly, the authors examined the impact of neutrophil-intrinsic IFN signaling on HSV-2 infection using neutrophil-specific IFNAR1 knockout mice (IFNAR1 CKO). The genetic ablation of IFNAR1 on neutrophils resulted in reduced inflammation in response to HSV-2 infection but no impact on viral titers; findings that are consistent with observations shown for neutrophil-depleted mice. The use of IFNAR1 CKO mice strongly support the importance of type I IFN signaling on neutrophils as direct regulators of neutrophil inflammatory activity in this model. Since type I IFNs induce the expression of multiple genes that could affect neutrophils and inflammation in various ways the authors set out to identify specific downstream effectors responsible for the observed inflammatory phenotype. This search lead them to IL-18 as possible mediator. They showed that IL-18 levels in the vagina during HSV-2 infection were reduced in neutrophil-depleted mice, in mice with "late" IFNAR blockade and in IFNAR1 CKO mice. Furthermore, they showed that antibody-mediated neutralization of IL-18 ameliorated the inflammatory response of HSV-2 infected mice albeit to a lesser extent that what was seen in IFNAR1 CKO. Altogether, the study presents intriguing data to support a new role for neutrophils as regulators of inflammation during viral infection via an IFN-IL-18 axis.

In aggregate, the data shown support the author's main conclusions, but some of the technical approaches need clarification and in some cases further validation that they are working as intended.

1) The use of anti-Ly6G antibodies (clone 1A8) to target neutrophil depletion in mice has been shown to be more specific than anti-Gr1 antibodies (which targets both monocytes and neutrophils) thus anti-Ly6G antibodies are a good technical choice for the study. Neutrophils are notoriously difficult to deplete efficiently in vivo due at least in part to their rapid regeneration in the bone marrow. In order to sustain depletion, previous reports indicate the need for daily injection of antibodies. In the current study the authors report the use of only one, intra-peritoneal injection (500 mg) of 1A8 antibodies and that this single treatment resulted in diminished neutrophil numbers in the vagina at day 5 after viral infection (Fig 1A). Data shown in figure 2B suggests that there are neutrophils present in the vagina of uninfected mice, that there is a significant increase in their numbers at day 2 and that their numbers remain fairly steady from days 2 to 5 after infection. In order to better understand the impact antibody-mediated depletion in this model the authors should have examined the kinetics of depletion from day 0 through 5 in the vaginal tissue after 1A8 injection as compared to the effect of antibodies in the periphery. These additional data sets would allow for a deeper understanding of neutrophil responses in the vagina as compared to what has been published in other models of infection at other mucosal sites.

2) The authors used antibody-mediated blockade as a means to interrogate the impact of type I IFNs and IL-18 in their model. The kinetics of IFNAR blockade were nicely explained and supported by data shown in supplementary figure 4. IFNAR blockade was done by intra-peritoneal delivery of antibodies at one day before infection or at day 4 after infection. When testing the role of IL-18 the authors delivered the blocking antibody intra-vaginally at 3 days post infection. The authors do not provide a rationale for changing delivery method and timing of antibody administration to target IL-18 relative to IFNAR signaling. Since the model presented argues for an upstream role for IFNAR as inducer of IL-18 it is unclear why the time point used to target IL-18 is before the time used for IFNAR.

3) An open question that remains is the potential mechanism by which IL-18 is acting as effector cytokine of epithelial damage. As acknowledged by the authors the rescue seen in IFNAR1 CKO mice (Fig 5C) is more dramatic that targeting IL-18 (Fig 6D). It is thus very likely that IFNAR signaling on neutrophils is affecting other pathways. It would have been greatly insightful to perform a single cell RNA seq experiment with IFNAR CKO mice as done for WT mice in Fig 3. Such an analysis might would have provided a more thorough understanding of neutrophil-mediated inflammatory pathways that operate outside of classical neutrophil functions.

4) The inflammatory score scale used is nicely described in the methods and it took into consideration external signs of vaginal inflammation by visual observation. It would have been helpful to mention whether the inflammation scoring was done by individuals blinded to the experimental groups.

5) The presence of distinct clusters of neutrophils in the scRNA-seq data analysis is a fascinating observation that might suggest more diversity in neutrophils than what is currently appreciated. In this study, the authors do not provide a list of the genes expressed in each cluster within the data shown in the paper. Although the entire data set is deposited and publicly available, having the gene lists within the paper would have been helpful to provide a deeper understanding of the current study.

equivalent in javascript:

{a, b, ...rest} = {a: 1, b: 2, c: 3, d: 4}

Not a bad replacement for that! I still find javascript's syntax a little more easily readable and natural, but given that we can't use the same syntax (probably because it would be incompatible with existing syntax rules that we can't break for compatibility reasons, unfortunately), this is a pretty good compromise/solution that they've come up with.

Reviewer #2 (Public Review):

This manuscript shows the functional relevance of mNatA catalytic subunit, mNAA10, in mammals' development. Moreover, authors have found a new NatA catalytic subunit in mice, mNAA12, that can compensate mNAA10 inactivation in mice. Interestingly, inactivation of mNAA10 in mice induces some developmental defects similar to those observed in Ogden syndrome (OS) patients including lethality in infants. This study provides several evidences and explains some of the defects observed in OS patients like supernumerary vertebrae and hydrocephaly supporting the relevance of hNAA10 mutations in the development of OS. Moreover, authors have observed in mice some developmental deficiencies not observed previously in OS patients, like supernumerary ribs, that after patient re-examination they have been observed in humans too. Curiously, the results presented in this article show that inactivation of mNatA catalytic subunit does not affects dramatically protein N-terminal acetylation, probably as consequence of mNAA12 paralog function as mNatA catalytic subunit when mNAA10 is not present. Interestingly, gene inactivation supports the biological significance of NAA10 as the main NatA catalytic subunit as mNAA12 inactivation is not associated with any clear phenotype. In spite of being one of the most frequent protein modifications protein N-terminal acetylation has not attracted proper attention, therefore this paper can draw more attention to this important protein modification.

Wibmer, C. K., Ayres, F., Hermanus, T., Madzivhandila, M., Kgagudi, P., Lambson, B. E., Vermeulen, M., Berg, K. van den, Rossouw, T., Boswell, M., Ueckermann, V., Meiring, S., Gottberg, A. von, Cohen, C., Morris, L., Bhiman, J. N., & Moore, P. L. (2021). SARS-CoV-2 501Y.V2 escapes neutralization by South African COVID-19 donor plasma. BioRxiv, 2021.01.18.427166. https://doi.org/10.1101/2021.01.18.427166

Reviewer #2 (Public Review):

The authors show that SUSD4 is expressed throughout the brain and is abundant in cerebellar dendrites and spines. Mice with deletion of SUSD4 have motor coordination and learning deficits, along with impaired LTD induction. The also attempt to show that GluA2 AMPA subunits are misregulated, but that is not as convincing. They find Nedd1, along with many other proteins in a proteomic screen for SUSD4 interactors, and try to explain the phenotypes through the regulation of GluA2 degradation by Nedd4 through SUSD4. These are potentially interesting findings, but very preliminary at this point. While the electrophysiology is good, the mechanistic studies are incomplete.

Major comments:

In Figure 1 localization images are shown using exogenous protein. Can the authors visualize endogenous protein?

It appears that SUSD4 is expressed in multiple brain regions, even at higher levels than the cerebellum. The authors should provide a good explanation for why deficits in the KO do not affect other functions, and seem to preferentially affect cerebellar functions.

Figure 4: immunofluorescence data are not very convincing.

Figure 5: The use of the word "could" is not supporting a strong conclusion. The authors should demonstrate whether SUSD4 DOES indeed regulate GluA2.

Overall, while the electrophysiology seems fine, the mechanistic studies are preliminary and speculative at this point.

Reviewer #2 (Public Review):

In this manuscript, Galbraith et al add to our understanding of COVID19 pathobiology by undertaking a cross-sectional survey of 73 hospitalized COVID19 patients with non-severe disease. They perform very broad multi-omics analysis, including plasma proteomics, cytokine profiling and mass cytometry. The authors propose that disease course can be classified by the titer of anti-CoV2 antibodies, which in turn is associated with distinct changes in circulating proteins, cytokines and immune subsets. Interesting correlations with complement and coagulation factors are noted. These findings suggest an alternative way to map disease progression in COVID19 and have implications for broader studies of COVID19 pathobiology. In particular, it will in interesting to extend this framework to analyze a broader spectrum of COVID19 patients, particularly those with poor outcome.

Reviewer #2 (Public Review):

"Feeding Experimentation Device version 3 (FED3): An open-source device for measuring food intake and operant behavior" describes the third iteration of an open-source automatic feeding device to be used with mice. I have no concerns about this paper and would recommend it as is. The authors have provided an incredible resource for the fields of feeding and reward-related behaviors, and provide all the details needed for assembly and use. Moreover, the data that they have collected using this device constitutes an advance, particularly the circadian rhythms of feeding, as well as the increase in operant responding during the light cycle. This device enables homecage measurement of feeding and training for motivational behavior, enabling most any laboratory to examine feeding behaviors in their experiments.

Reviewer #2 (Public Review):

Although the major activation steps and general mechanistic underpinnings of SOCE have been reported in a flurry of literatures, they are largely descriptive and lack quantitative information. One topic of greatest interest to the CRAC channel field is the structural basis of CC1-CAD/SOAR-mediated STIM1 autoinhibition. Using single-molecule Förster resonance energy transfer (smFRET) and protein crosslinking approaches, Dorp et al provides a binding model for the CC1-CAD interaction. This model explains the role of CC1 in STIM1 activation, and delineates the activation process of STIM1 CT. It also clarifies the controversy on the two varying structures regarding the packing of the CAD/SOAR domain by favoring the X-ray structure over the NMR structure. The conclusions of this paper are mostly well supported by data. The only minor concern is to reconcile some of the conflicting results (regarding the relative positions of some residues used in the crosslinking study, as well as the CC1-alpha 1 helix), made between this study and a recent structural study, i.e., the NMR solution structure of CC1 reported by the Romanin/Muller's groups (PMID: 33106661). Overall, this study covers a timely topic to address a long-standing question in the ORAI-STIM signaling field, i.e., the structural basis of CC1-CAD association that keeps STIM1 largely quiescent in the resting condition. This work, regarded by this reviewer as a "tour-de-force" by meticulously scanning through many key residues within the multiple CC1/CAD helices, certainly warrants immediate publication.

Notable strengths:

1) smFRET is increasingly being used to determine distances, structures, and dynamics of biomolecules. Full length STIM1 and STIM1 C-terminus have been always difficult to obtain crystal structure due to its tendency for aggregation and the existence of large disordered regions. Herein, the authors selected smFRET as the major tool to overcome this hurdle and illuminated the CC1-CAD binding models to provide novel mechanistic insights into STIM1 auto-inhibition mediated by the intramolecular cis CC1-CAD association.

2) The efforts to extend crosslinking of ctSTIM1 to flSTIM1 are particularly commendable, moving one more step closer to the physiological scenario.

Minor weaknesses:

1) The authors proposed a CC1 model displaying "tandem connection of "CC1α1- CC1α2", that shows notable discrepancies with the recent CC1 NMR solution structure (PMID: 33106661). In the latter structure, the three helices are intertwined to form a bundle like structure. An in-depth discussion is certainly needed to clarify the difference. Some possibilities include: (i) Is this due to the artifact of the CC1 NMR structure (done in the presence of helix-stabilizing reagents)? (ii) is this due to the introduction of cysteine residues for the assays? (iii) is this due to absence of the CAD/SOAR part, or other regulatory components, in the solution structure? Repeating one or two key smFRET/crosslinking experiments in the presence of the similar buffer condition as in the NMR study would provide clues to these possibilities.

2) Another concern, very minor though, is regarding cysteine crosslinking flSTIM1 by 0.2 mM diamide. Will the addition of diamide cause undesired activation of STIM1 in the absence of cyclopiazonic acid?

Reviewer #2:

In this paper, Werkhoven and colleagues describe a large-scale effort, using Drosophila, to study variation in behavior among individuals with identical genotypes, and raised in very similar environmental conditions. This addresses the important and basic question of how much behavioral variability exists under such conditions, e.g. due to stochastic processes during development. By looking across many different behaviors, the authors are able also to investigate the nature of this variability. The key conclusion of the paper is that this intragenotypic variability is high dimensional, and cannot be explained by a small set of behavioral syndromes. They find that this observation is robust to the method they use to quantify behavior, and also holds to different degrees in data sets acquired from outbred flies, or files subjected to genetic perturbations of neural activity. Furthermore, they have generated a data set that allows correlation of behavioral biases in individual animals with transcriptomic data. Altogether, this is an impressive study that, beyond its important conclusions, opens up the possibilities for many further explorations in this area, and should be interesting to a broad audience. The experiments are well designed and overall the paper is very nicely written and clear to understand.

Reviewer #2:

Nguyen et al. developed a novel method of transcranial focused ultrasound stimulation and used it to stimulate anesthetized rats while performing extracellular recordings in the hippocampus. They find that the stimulation has different amplitude-dependent effects on putative inhibitory interneurons and excitatory principal cells. This finding is exciting because it suggests that transcranial ultrasound could be used to specifically reduce or increase firing rates in excitatory or inhibitory neurons in a particular part of the brain (resolution in the mm range). In principle, this could also be applied to humans. Simultaneously measured oscillations of the local field potential, particularly (but not exclusively) in the theta band (3-10 Hz) could also be manipulated in a bidirectional manner depending on the stimulation amplitude. Such cortical oscillations have been strongly linked to a wide range of functions including memory, and the potential to manipulate them in an anatomically precise manner is exciting and could even lead to new therapy approaches. Although it is not new that ultrasound can be used to modulate neuronal activity, this paper reaches a new level of precision by demonstrating that bidirectional effects can in principle be limited to one cell class or one frequency band. Thus, it could provide a great alternative to current methods that either provide much less precision (e.g. transcranial magnetic stimulation) or rely on more invasive methods (e.g. deep brain stimulation) or genetics (e.g. optogenetics).

The study is well-designed with stimulation at 3 different amplitudes applied in the same rat, whereby each 3-minute stimulation is compared to a 3-minute sham session where the transducer is 1 cm above the skull. Baseline sessions before each stimulation and sham session did not show any differences, showing no spillover-effects from the previous stimulus. Effects on brain temperature were also measured and shown to be negligible compared to normal variability.

Low intensity stimuli lead to a reduction in firing rates in putative interneurons and a reduction in theta oscillation power, whereas high intensity stimuli lead to an increase in firing rates in putative principal cells, with intermediate intensities having largely no effect.

In principle, these findings could provide novel insights into the mechanisms underlying ultrasound stimulation, but neither of the two discussed main modes of action (mechanical and thermal) appears consistent with the results. Thus, no model could be offered that might give some insight into the underlying mechanisms of ultrasound modulation of neuronal activity. This might be an issue for future work, and if the results were more robust perhaps this would not matter as much. However, the overall size of the effects appears to be too small to be of practical use as a reliable tool for manipulation of neural circuits. Although the authors show statistical significance, some details of the analysis are not fully clear and may need to be further corrected for multiple testing. It remains unclear if perhaps larger or different effects would be achieved when recording through the skin, without anesthesia, in different brain areas, in differently defined subclasses of neurons or with a different stimulation protocol (frequency, duration, amplitude). Thus, although the technique appears promising, more work is needed.

Reviewer #2 (Public Review):

Judd et al. systematically examine the input/output connectivity of discrete excitatory and inhibitory neuronal subpopulations in the cerebellar interposed anterior nucleus (IntA) using conditional AAV and rabies virus mapping strategies. The authors first define distinctions in the output connectivity of excitatory and inhibitory neurons in the IntA nucleus, and describe a surprisingly much wider projection pattern by inhibitory neurons than previously thought. They also characterize distinctions in projection pattern between identifiable subtypes of IntA inhibitory neurons as well as distinctions in morphology of their terminal fields. The authors next explore the input connectivity of excitatory and inhibitory neurons in the IntA nucleus and found that excitatory output neurons receive fewer, but more organized inputs than inhibitory output neurons, and that many output targets provide reciprocal connections with the CN.

In general, the output analysis is strong and there are only a few questions about interpretation of the distinctions of projections by different subtypes of IntA inhibitory neurons. For instance, the distribution of the initial targeting within the cerebellar nuclei, cerebellar cortex and outside the cerebellum was not analyzed in Ntsr1-Cre and Gad1-Cre similar to the analysis performed for the intersectional output analysis. Clarification on whether and how the distinctions in projections could be due to variability in the specificity of the initial targeting or recombination ability of the two mouse Cre-lines is needed to strengthen interpretation of the different projections patterns observed. As for the input analysis using rabies, there were two major issues identified.

First, the use of conditional GFP-labeled G protein and the use of rabies that is also GFP potentially confounds analysis of input cells.

Next, the low number of starter cells is a concern and the identity of starter cells outside the cerebellar nuclei in Ntsr1-Cre and Gad1-Cre is vague and needs to be clarified. This is important for interpretation of whether input structures observed project specifically into the CN or also into the cerebellar cortex, and whether distinctions observed in number of input structures may reflect amount of starter cells in each Cre line.

Reviewer #2 (Public Review):

This manuscript by Sando and Sudhof addresses whether GPCR activity of latrophilin2 and 3 is necessary for the role of these proteins in synapse formation. The key findings are:

— the generation and validation of mutants that lack transmembrane and intracellular domains (but are GPI-anchored instead), the lack only intracellular domains, or that contain all domains but lack GPCR-activity. All mutants work properly in cell aggregation assays and appear to be localized normally when overexpressed in wild type neurons. This also led to the development of an elegant PKA-phosphorylation reporter assay.

— in cultured latrohphilin 3 knockout neurons, latrophilin3 expression restores a decreased synapse density and mini-frequency, but the GPI-anchored, truncated or inactive versions do not restore these parameters.

— in vivo/hippocampal brain slices, latrophilin2 knockout impaired perforant path but not Schaffer collateral transmission onto CA1 neurons, and rescue required latrphilin2 GPCR activity. Conversely, Latrophilin3 knockout impaired Schaffer collateral but not perforant path transmission onto CA1 neurons, and rescue required latrophilin3 GPCR activity.

— finally, monosynaptic tracing confirmed that latrophilin3 knockout reduced inputs onto CA1 starter neurons, and rescue again required GPCR activity.

Altogether, the data are rigorously acquired, the paper is well written, and the finding that GPCR activity is necessary for latrophilins' role is both surprising and important. It is also elegant, as coupling cell-adhesion directly to signal transduction via a single molecule for synapse formation is a compelling way to drive synaptic assemblies. Naturally, the question arises how compartmentalized GPCR-signaling then instructs synapse formation, a topic that will undoubtedly require and attract more research. This is an exciting manuscript that will inspire new research on compartmentalized GPCR signaling at the synapse. Given the central importance of surface trafficking and localization within spines for the conclusions, better description of experimental procedures and quantification, and possibly additional data would clearly strengthen this point.

Reviewer #2:

Epilepsy is often an early sign observed in Alzheimer patients and there are several mechanisms that may contribute to this hyperexcitability. In this study, the authors focused on an important observation suggesting that intracellular Amyloid beta, a protein often found in plaques in the brain, is found early on inside neurons of the hippocampus, the learning and memory center of the brain. Interestingly, when unique early forms of Ab named oligomers were introduced inside neurons, the cells and surrounded circuits became hyperexcitable. This increased excitability was mediated mainly by the release of glutamate on AMPA glutamate receptors. Remarkedly, these excitatory effects were triggered by intracellular amyloid oligomers through a retrograde signal named nitrous oxide. This manuscript suggests that early stages of the disease may comprise significant increases in network excitability that may trigger a cascade of synaptic dysfunction and cognitive deficits such as memory loss.

Here are my comments to strengthen the manuscript. Overall this is a strong study with an interesting take on the role of intracellular amyloid and how it contributes to increased network excitability in AD.

There is an interest to determine the mechanisms responsible for the hyperexcitability often associated with familial and sporadic forms of Alzheimer's disease. Many have focused on possible reduction in inhibitory interneuron function as essential drivers of the increased excitability of the network. Although there exist a large number of investigations determining the effects of extracellular Ab on synaptic transmission, the intracellular effects of Ab and its contribution to disruptions of synaptic transmission remains less well understood. A couple of studies have shown that intracellular application of Ab (Ab42) induces decreases in long-term potentiation and basal synaptic transmission. In this study, the authors have investigated how intracellular Ab oligomers (iAbo) contribute to enhanced excitability in the CA1 region of the hippocampus. To do so, they have intracellularly applied human brain-derived and synthetic Ab oligomers through the patch-pipette in principal neurons recorded in vitro and in vivo.

In this study, the authors show that intracellular application of intracellular Ab oligomers increased the frequency and the amplitude of excitatory currents and spiking in ex vivo hippocampal slices. Effects that were mimicked by human oligomers. The intracellular amyloid mediated effects were through the amplification of AMPAergic spontaneous activity and currents, and, to a lesser extent, spontaneous GABAA mediated currents. Miniature frequency and amplitude of AMPA-mediated EPSCs were also increased and were sensitive to PKC blockers. Interestingly, since intracellular Ab increased the frequency of EPSCs, which is a presynaptic effect, a signaling molecule is likely to be released postsynaptically to modulate presynaptic terminals. The hypothesis that the retrograde signal NO was involved by determining the sensitivity of NOS inhibitor L-NAME. L-NAME reduced the increased iAbo mediated frequency of spontaneous post-synaptic excitatory currents in cultured neurons. The L-NAME compound was shown to reduce the iAbo -mediated No from both the recorded and neighboring neurons providing further evidence that intracellular Ab oligomers triggered NO release and increased glutamate release. Increases in the excitability of CA1 pyramidal cells were also observed in vivo by intracellular application of AB oligomer. Overall, this is a well written study that demonstrates a novel perspective of the effects of intracellular Ab oligomers on CA1 principal neurons and suggests possible mechanisms underlying hyperexcitability.

Novelty:

1) use intracellular oligomers, synthetics and humans

2) Showing that iAb oligo increased post and presynaptic AMPA-mediated EPSCs.

3) The presynaptic increases in EPSCs were mediated by NOS and NO, this could potentially spread widely across the network.

4) spontaneous IPSCs were also increased (through an undetermined mechanism).

5) the iAbo increase in excitation was also observed in vivo.

Questions:

Intracellular Ab produces both an increase in EPSCs and IPSCs. However, in Fig 3, the IPSCs, measures using a charge transfer quantification, did not show a significant change in response to iAbo, in contrast to EPSCs. This spontaneous inhibition here was measured as charge transfer which depends on the amount of charges in time. I wonder why this was not significant since this measurement should have picked up a possible increase in spontaneous IPSCs?

With regard to the inhibition, In the schematic on Fig. 10, I find this incomplete and slightly inaccurate since it shows one terminal releasing both glutamate and GABA with NO increasing both. While this is obviously an oversimplification, it's slightly inaccurate since NO was not directly shown to increase sIPSCs. Were NOS blockers able to disrupt the increase in sIPSCs? Moreover, there are many papers that have shown that PKC can also phosphorylate GABA receptors and increase their conductance. What could be the reason that this was not involved here? This needs to be discussed.

The experiments were done in cultured neurons, in slices and in vivo. It's not always easily discernible in what conditions the experiments were done when reading the manuscript, especially when looking at the figures and figure legends. This should be at least stated in the figure legends. To help the reader, the conditions in which the currents were recorded (GABA and or excitatory receptor blockers, other ion blockers could be indicated in the figure legends to ease the comprehension of how the experiments were done and what was measured). In relation to this, was the sIPSC iAbo-mediated increases also blocked by L-NAME?

In other studies, investigating intracellular application of Ab, such as the Ripoli et al., 2014 paper, showed that iAb produced significant reductions in EPSCs in their hippocampal neurons. What are the differences explaining this? This should be discussed. Similarly, Gulisano et al., 2019, showed that extracellular, but not intracellular oligo Ab had effects on excitability when it was applied extracellularly but not intracellularly. This should also be discussed.

In the introduction, it's mentioned that the nature of hyperexcitability is unknown. I agree that it's incompletely known, but what is known is that there is a large variety of possible causes. For example, changes in GABAergic interneuron function (see Hijazi et Al 2019) is well known to be a contributing factor. There are many studies that have shown possible contributing causes of hyperexcitability, therefore, something IS known, and this should be identified in the introduction.

How do these increases in synaptic transmission by applying pM concentrations of oligomers fit with the data showing that extracellular Ab oligomers of comparable concentrations decrease synaptic transmission through presynaptic reductions in glutamate release? This needs to be put into context and discussed.

Reviewer #2:

The authors are reporting a new approach termed ORACLE to develop locus-specific phage variants, which includes a recombination step, whose efficacy is improved by the overexpression of a dedicated recombinase, followed by an enrichment performed using CRISPR/Cas9. They applied this method to create a mutant library containing 1660 variants of the tip domain of the T7 tail fiber. Performance of each variant was determined by quantifying their abundance before and after selection on three E. coli strains compared to the WT phage. Their findings show that single amino acid changes in the tip of gp17 can have major consequences on phage performance on different hosts. Then they tested whether these variants would be less prone to select phage-resistant using an UTI strain. Finally, they searched for variants that would be more prone to infect one host than another and successfully tested their predictions.

The ORACLE approach is overall novel and has some advantages over existing methods, mainly for generation of mutation libraries of genes. Authors did a nice (even if very lengthy) job of showing how mutants have consequences to structure and function of the tail fiber gene and how that influences performance on different hosts, including combating host resistance.

The authors state that ORACLE overcomes three major hurdles that make it better than existing methods, one of which is "generalizability for virtually any phage", while denouncing other systems for being applicable for highly transformable hosts only. This is highly exaggerated since ORACLE requires transformation of two plasmids (helper and donor) including one with tunable gene expression, which is clearly not possible in many bacteria. Furthermore, the enrichment step requires a strain with a functional CRISPR/Cas9 system, which again is not so obvious in the bacterial world.

The authors disregard bias that can be generated at the "O" step if a variant reproduces better than the wt. They should also mention bias arising from non-viable or severely infection hampered variants, which is briefly mentioned later in the manuscript but should be mentioned earlier, would not pass the accumulation step.

The weakest paragraph is the one dealing with the UTI strain. I have the feeling that this paragraph could simply be deleted without changing the overall story. Approaching resistance, selection, and evolution would require more experiments than the very simplistic lysis curves. The authors did not even show adequately that cells growing after 5-10 hours are either genotypically or phenotypically resistant cells. A more appropriate qualification would be "insensitive" instead of resistant.

Reviewer #2 (Public Review):

In the present manuscript Radaszkiewicz et al. analyze the role of Ring Finger Protein 43 (RNF43) in inhibiting the noncanonical WNT5A pathway. The authors demonstrate that RNF43 can interact with proteins involved in the WNT5A pathway, including ROR1, ROR2, VANGL1 and VANGL2. Specifically, they propose that RNF43 induces: i) VANGL2 ubiquitination and proteasomal degradation and ii) clathrin-dependent internalization of the ROR1 receptor. Considering the role of the WNT5A pathway in melanoma metastasis and resistance to targeted therapy, the authors further explore the role of RNF43 in melanoma invasion and resistance to vemurafenib. The authors ultimately conclude that RNF43 can prevent invasion and resistance to targeted therapy by inhibiting the WNT5A pathway. The data supporting the interaction between RNF43 and proteins involved in the WNT5A pathway are pretty rigorous. However, the study would benefit from additional experiments in the context of RNF43's role in invasion and resistance to targeted therapy in melanoma. Overall, the techniques utilized in the manuscript are appropriate, however additional cell lines and in vivo studies are strongly recommended to strengthen the manuscript.

Reviewer #2 (Public Review):

P2X2 activation depends on both ATP binding and voltage. However, the voltage sensor of P2X2 is not elucidated. This manuscript describes the study of voltage dependent conformational changes of P2X2 using voltage clamp fluorometry of the fluorescent unnatural amino acid Anap that substituted P2X2 amino acid residues. 96 positions in different structural domains were scanned by substituting with Anap, and voltage dependent fluorescence signals were detected only at two positions, A337 and I341 in the TM2 domain. A fast and linear voltage dependence of fluorescence suggested that the membrane voltage converged at and around these two positions. With a mutation K308R that was supposed to enhance voltage dependent conformational changes, Anap at the A337 position showed a time and voltage dependent fluorescence. The authors concluded that this result indicated a voltage dependent conformational change. Structure guided mutations suggested that F44 in TM1 might move to interact with A337 in response to voltage. In this study the fluorescence signals were small, but the authors made a great effort and managed to obtain the data that are convincing. The experiments were well designed and the manuscript is clearly reasoned. Considering that among all the positions that were tested only at the two positions in the TM2 segment Anap showed voltage dependent fluorescence, and that the F44 mutations abolished voltage dependence of the P2X2 currents, the conclusion that voltage converges at the A337/I341/F44 and induces a conformational change seems to be well supported.

Reviewer #2 (Public Review):

The goal of this work is to advance knowledge of the neural bases of color perception. Color vision has been a model system for understanding how what we see arises from the coordinated action of neurons; detailed behavioral measurements revealed color vision's dependence upon three types of photoreceptors (trichromacy) and three second stage retinal circuits that compute sums and differences of the cone signals (color opponency). The processing of color at later, cortical stages has remained poorly understood however, and studies of human cortex have been hampered by methodologies that abandoned the detailed approach. Typical past work simply compared neural responses in two conditions, the presentation of colorful (formally, chromatic) vs grayscale (luminance) images. The present work returns to the older tradition that proved so successful.

The project's specific goals were to measure functional MRI responses in human cortex to a large range of colors, and equally importantly, capture the pattern responses with a quantitative model that can be used to predict response to many additional colors with just a few parameters. The reported work achieved these goals, establishing both a comprehensive data set and a modeling framework that together will provide a strong basis for future investigations. I would not hesitate to query the data further or to use the QCM model the paper provides to characterize other data sets.

The strengths of the work include its methodological rigor, which gives high confidence that the goals were achieved. Specifically:

1) The visual presentation equipment was uniquely sophisticated, allowing it to correct for possible confounds due to differences in photoreceptor responses across the retina.

2) The testing of the model was quite rigorous, aided by distinct replications of the experiment planned prior to data collection.

3) The fMRI methods were also state of the art.

The work was well-situated within the literature, comparing its findings to past results. The limitations and assumptions of the present work were also clearly stated, and conclusions were not overstated.

Weaknesses of the current draft are relatively minor, however, I believe:

1) The data could be presented in a way to make them more comparable to prior fMRI work, e.g. by using percent change units in more places, comparing the R^2 of model fits reported here to those reported in other papers, and explaining and exploring how the spatially uniform stimuli, used here but not in other fMRI studies, limited responses in visual areas beyond V1.

2) Comparison between the two models, the GLM and QCM is not quite complete.

3) The present results are not discussed in context with past results using EEG, and Brouwer and Heeger's model of fMRI responses to color.

4) Implications of the basic pattern of response for the cortical neurons producing the data are discussed less than they could be.

Reviewer #2 (Public Review):

The manuscript by Myers et al provides new insight into the mechanism of transient muscle in myotonia congenita, a question that has escaped understanding since its first description over >40 years ago. The authors use a complementary set of approaches (including measurements of in situ muscle force production, membrane voltage and ion currents) to determine the membrane conductances that underlie transient weakness in muscle from both genetic (Clc1-/- adr mice) and pharmacologic (9-AC-treated WT mice) models of myotonia congenita. The authors utilize a combination of a non-conducting Cav1.1 mouse and treatment with ranolazine to dissect the relative contribution of Cav1.1 and persistent Nav1.4 conductances, respectively, to sustained plateau membrane depolarizations observed following myotonic runs, which are proposed to underlie the transient weakness observed following myotonic runs.

Reviewer #2 (Public Review):

Levi-Ferber and colleagues showed in their previous paper that ER stress regulates germline transdifferentiation in a way that is IRE-1 dependent, but XBP-1 independent. An open question at that time was how IRE-1 activation could mediate this signaling. The authors present several experiments in this manuscript that support the idea that neuronal Ire-1 can cell non-autonomously control germline differentiation through regulation of the neuropeptide FLP-6. Mechanistically, the authors characterize that FLP-6 is a target of IRE-1 RIDD activity. This is the first demonstration of RIDD in C. elegans, an important finding given that no RIDD targets have yet been identified in this organism. Using a wide range of mutants, the authors were also able to identify a neuronal circuit that can control the germline ectopic differentiation (GED) phenotype, involving the sensory neuron ASE, the interneuron AIY, and the motor neuron HSN. The data presented in the manuscript are sound, the mapping of a pivotal three-neuron circuit is impressive, and the findings are likely to be of high interest to a broad readership. However, some more evidence is required to support some of the conclusions made, in particular the characterization of flp-6 as a substrate for RIDD.

Reviewer #2 (Public Review):

This is an interesting paper showing that prolonging the integrated stress response provides protection to oligodendrocytes in the presence of an inflammatory cytokine. For their experiments, the authors use the cuprizone model in transgenic mice overexpressing IFNg in an inducible manner in combination with a genetic and pharmacological approach to enhance the integrated stress response. The experiments are well conducted and the results clearly presented in the text. The Popko lab has previously demonstrated in a series of papers the importance of the integrated stress response for oligodendrocyte function. The novel aspect of this work is that targeting the integrated stress response requires a neuroinflammtory environment for the protective effects to occur.

It is important to improve the introduction. As written it is not clear what was known before and how this paper goes beyond the existing literature.

The rational for combining for combining BZA and Seph needs to be explained.

The figures and legends could be improved according to the following suggestions:

The evidence that Sephin1 promotes remyelination in the EAE model shown in Figure 1 is only based on differences in g-ratio with the overall number of myelinated axons being unchanged. It is difficult to make conclusion based on these results. It is difficult to obtain accurate g-ratios in lesions. Maybe the authors could extend the analysis by performing histology and counting the number of oligodendrocytes.

Figure 2 contains only a scheme. Figure 2 should be combined with Figure 3. In addition, a scheme showing the time line of the cuprizone treatment and recovery from the treatment would be helpful. I assume W0 is at the time of treatment, W5 after 5 weeks of cuprizone and W8 represents 5 weeks of cuprizone and 3 weeks of recovery. If yes, it is not clear why the ASPA cell count shown it not reduced between W0 and W5. The numbers seem to be similar for W0, W5 and W8 in the absence of IFNg. In addition, the comparison shown in Figure 3 are incomplete. W0 is only shown without IFNg but not with. Does IFNg affect ASPA number in the absence of cuprizone?

Panel B and C in Figure 5 could be combined to be able to compare the analyses and to evaluate the recovery of cell number by Seph at W8. The number of mice per group is borderline (only 3 mice).

Same issue as above: Panel B and C in Figure 6 should be combined and a multiple comparison should be performed between W0, W5 and W8.

The rational for combining BZA and Seph as shown in Figure 8 should be explained in the text. The figure and legends should be improved to clarify at which time point the analyses were performed. The panel number stated in the legends do not match with what is shown in the figure. I assume the analyses were done at W8. Only g-ratios change, whereas the number of ASPA cells and amount of myelinated axons are not affected by the combined treatment. The interpretation of this result is not easy, and the emphasis of this result should be removed from the abstract.

Reviewer #2:

Ziółkowska et al. investigate synaptic processes in the dorsal hippocampal CA1(dCA1) region with the goal of testing the role of postsynaptic density protein 95 (PSD-95) dynamics in contextual fear extinction. They conclude that 1) extinction increases synaptic dCA1 PSD-95 levels and induces remodeling of dendritic spines, 2) extinction-related PSD-95 changes are mediated by phosphorylation of PSD-95 at serine 73, and 3) phosphorylation of PSD-95 at serine 73 as well as dCA1 activity are required to "update a partially extinguished fear memory". The experiments provide new insight and address a timely and important issue. The major strengths of the paper lie in the use of a wide range of complementary technical approaches, and the significance of addressing specific molecular mediators of fear attenuation. However, some of the analysis is based on inadequately justified or inappropriate measures (e.g. that do not directly assay the phenomenon under investigation), and there are concerns about independent effects of viral overexpression in this system as well as the relevance of the behavioral analysis. The conclusions from the paper, if true, would appear to support a very intricate model involving PSD95 phosphorylation and synaptic accumulation after extinction, but because of weaknesses in the underlying evidence, these mechanisms and their relationship to extinction memory were not persuasively demonstrated. Following are some specific concerns:

1) The mean intensity of PSD95 labeling per spine appears to be affected in some hippocampal layers (Fig. 1), but this might be attributable in some cases to elimination of spines that have relatively lower PSD-95, rather than a change in PSD-95 levels, per se.

2) The quantification of overexpressed PSD-95 in Fig. 2 makes unclear what specifically has been measured. The methods suggest that % area is defined as the total area of mCherry labeling divided by the total image area. This is not a direct measure of PSD-95 levels, rather than morphological or protein localization changes. Furthermore, the localization of overexpressed PSD-95 (Fig. 2) is clearly very different from that of endogenous PSD-95 (Fig. 1) in that it accumulates throughout the dendrites. This makes it unclear what a "puncta" represents, or whether the analysis implies anything about synaptic function.

3) The authors argue that S73 phosphorylation is required for synapse elimination during extinction, but Fig. S2 (which is not referenced or discussed in the manuscript) and Fig. 3 indicate that the effect of S73A overexpression is to dramatically reduce spine density in both behavioral groups. It is therefore not clear whether the manipulation interacted with extinction to prevent spine removal, or simply occluded such an effect because spine density was already at an artificial floor prior to any behavioral training. Overexpression of the wildtype construct also reduced spine density to a similar degree. Furthermore, the S73A mutant protein dramatically increased PSD area (Fig. 3d), which apparently contradicts the notion that phosphorylation of this site is required for synaptic accumulation, when applying the same logic used elsewhere in the paper. These are serious confounding issues because the central claim of the paper is that S73 phosphorylation mediates PSD95 synaptic accumulation and synaptic strengthening.

4) The authors suggest that successive days of extinction represent a distinct process called updating of a partly extinguished memory, which they seem to imply has different molecular requirements. There appears to be no basis in the literature for this idea.

5) The analysis of extinction relies on measurement of within-session decreases in freezing. However, within-session extinction has been shown to be neither sufficient nor essential for between-session extinction. It is not even clear that within-session extinction is really even extinction at all, rather than, for example, habituation. It is essential to examine the retention of decreased freezing across days in order to establish that the formation of long-term memory is involved.

6) Finally, numerous comparisons are made between animals that received FC, with no further manipulation, and extinguished animals. This design leaves open the possibility that any differences are attributable not to an extinction process but instead to context exposure independent of fear regulation. A behavioral control in which animals receive context exposures, but no shocks, would be very useful.

Reviewer #2 (Public Review):

Parker et al attempted to show that the FPA protein functions to regulate the widespread premature transcription termination of the Arabidopsis NLR genes. Using in vivo interaction proteomic-mass spectrometry, FPA was shown to co-purified with the mRNA 3' end processing machinery. Metagene analysis was used to show that FPA co-localized with Pol II phosphorylated at Ser2 of the CTD heptad repeat at the 3' end of Arabidopsis genes. Using a combination of Illumina RNA-Seq, Helicos, and nanopore DRS technologies, FPA was found to affect RNA processing by promoting poly(A) site choice, and hence controls the processing of NLR transcripts whereas such process is independent of IBM1.

Overall, it is a potentially important research. The data is rich and could be useful. However, the biological stories described are not thoroughly supported by the data presented, especially when the authors tried to touch on several aspects without some important validations and strong connections among different parts. Some special comments are provided below:

1) The title of this manuscript is "The expression of Arabidopsis NLR immune response genes is modulated by premature transcription termination and this has implications for understanding NLR evolutionary dynamics". Therefore, the readers will expect some functional connections between the FPA and the novel NLR isoforms due to premature transcription termination. However, the transcript levels of plant NLR genes are under strict regulation (e.g. Mol. Plant Pathol. 19:1267). Since the functions of NLR genes are related to effector-triggered immunity, it is more important to study the function of FPA on premature transcription termination when the plants are challenged with pathogens. In this manuscript, most transcript analyses are based on samples under normal growth conditions. It is therefore a weak link between the genomic studies and the functional aspects. For instance, it is more important to identify unique NLR isoforms produced upon pathogen challenges that are regulated by FPA. The authors will need to provide some of these data to fill this gap.

2) Since the function of FPA is to regulate NLR immune response genes, we should expect a change in plant defense phenotype in FPA loss-of-function mutants. Could the authors provide more information on this? On the contrary, in line 728 of this manuscript, the authors found that at least for some pathogens, "loss of FPA function does not reduce plant resistance". It is not consistent with the hypothesis that FPA is important to regulate NLR immune response genes.

3) Furthermore, the authors mentioned in lines 729-731 "Greater variability in pathogen susceptibility was observed in the fpa-8 mutant and was not restored by complementation with pFPA::FPA, possibly indicating background EMS mutations affecting susceptibility." Does it mean that fpa-8 contains other mutations? Will these additional mutations complicate the results of the RNA processing? Could the authors outcross the fpa-8 mutation to a clean background?

4) In line 318, the authors found 285 and 293 APA events in the fpa-8 mutant and the 35S::FPA:YFP construct respectively, but only 59 loci (line 347) exhibited opposite APA events (about one fifth). The low overlapping frequency suggests that some results could be false positive.

5) In line 732-736: "In contrast, 35S::FPA:YFP plants exhibited a similar level of sporulation to the pathogen-sensitive Ksk-1 accession (median 3 sporangiophores per plant). This suggests that the premature exonic termination of RPP7 caused by FPA has a functional consequence for Arabidopsis immunity against Hpa-Hiks1." It is contradictory to the statement in line 728 that "loss of FPA function does not reduce plant resistance". Is it possible that overexpression of FPA:YFP had generated an artificial condition that is not related to the natural function of FPA?

6) The fpa-8 mutant has a delayed flower phenotype (Plant Cell 13:1427). Could the 35S::FPA:YFP fusion protein construct reverse this phenotype and the plant defense response phenotype? It is important to interpret the data when the 35S::FPA:YFP construct was used to represent the overexpression of FPA.

7) Under the subheading "FPA co-purifies with the mRNA 3' end processing machinery". The results were based on in vivo interaction proteomics-mass spectrometry. MS prompts to false positives and will need proper controls and validations. Have the authors added the control of 35S:YFP instead of just the untransformed Col-0? At least for the putative interacting partners in Figure 1A, could the authors perform validations of some important targets, using techniques such as reverse co-IP, or to show direct protein-protein interaction between FPA to a few of the important targets by in vitro pull-down, BiFC, or FRET, etc.

8) In Fig. 3, the data show that the last exon of the FPA gene is missing in the FPA transcripts generated from the 35S::FPA:YFP construct. Will the missing of this exon affect the function of the transcript and the encoded protein?

9) The function of FPA is still ambiguous. There was a quantitative shift toward the selection of distal poly(A) sites in the loss-of-function fpa-8 mutant and a strong shift to proximal poly(A) site selection when FPA is overexpressed (35S::FPA:YFP) in some cases (Fig. 3, Fig. 5, Fig. 8). But the situation could be kind of reversed in other cases (Fig. 6). What is the mechanism behind it?

10) Under the subheading: "The impact of FPA on NLR gene regulation is independent of its role in controlling IBM1 expression". IBM1 is a common target of FPA and IBM2. Indeed, FPA and IBM2 share several common targets (Plant Physiol. 180:392). It may be more meaningful to compare the impact of FPA and IBM2 on NLR gene instead.

11) In lines 423-425, the authors described "Consistent with previous reports, the level of mRNA m6A in the hypomorphic vir-1 allele was reduced to approximately 10% of wild-type levels (Parker et al., 2020b; Ruzicka et al., 2017) (Figure 4 - supplement 3)." This data could not be found.

12) In line 426: "However, we did not detect any differences in the m6A level between genotypes with altered FPA activity." Which data is this statement referring to?

Reviewer #2 (Public Review):

In this manuscript, Anchimiuk et al reported that B. subtillis SMC can collide with each other, and that the collision is modulated by several factors including the number, strength, distribution of parS sites, the residence time of SMC on DNA, the translocation rate, and the cellular abundance of SMC. The authors suggested that these parameters are fine-tuned in the wild-type B. subtillis to minimize SMC collision. In my opinion, the finding is interesting, the experimental setup is creative, and the experiments were beautifully executed. Arguably, these experiments can only be performed in B. subtilis since parAB- and the insertion of another parS site at the mid-arm are not detrimental to cell viability (in Caulobacter crescentus, insertion of another parS mid-arm affects chromosome segregation, hence cell viability severely). Furthermore, the rare set of arm-modified SMCs from the Gruber lab also gives this manuscript a unique mechanistic angle. Given the available data, the conclusion of the manuscript is safe. I especially appreciate that the authors did not bias towards the model of SMC traversing each other by Z-loop formation.

Reviewer #2 (Public Review):

The authors present a compelling study that aims to resolve the extent to which synaptic responses mediated by metabotropic GABA receptors (i.e. GABA-B receptors) summate. The authors address this question by evaluating the synaptic responses evoked by GABA released from cortical (L1) neurogliaform cells (NGFCs), an inhibitory neuron subtype associated with volume neurotransmission, onto Layer 2/3 pyramidal neurons. While response summation mediated by ionotropic receptors is well-described, metabotropic receptor response summation is not, thereby making the authors' exploration of the phenomenon novel and impactful. By carrying out a series of elegant and challenging experiments that are coupled with computational analyses, the authors conclude that summation of synaptic GABA-B responses is linear, unlike the sublinear summation observed with ionotropic, GABA-A receptor-mediated responses.

The study is generally straightforward, even if the presentation is often dense. Three primary issues worth considering include:

1) The rather strong conclusion that GABA-B responses linearly summate, despite evidence to the contrary presented in Figure 5C.

2) Additional analyses of data presented in Figure 3 to support the contention that NGFCs co-activate.

3) How the MCell model informs the mechanisms contributing to linear response summation.

These and other issues are described further below. Despite these comments, this reviewer is generally enthusiastic about the study. Through a set of very challenging experiments and sophisticated modeling approaches, the authors provide important observations on both (1) NGFC-PC interactions, and (2) GABA-B receptor mediated synaptic response dynamics.

The differences between the sublinear, ionotropic responses and the linear, metabotropic responses are small. Understandably, these experiments are difficult – indeed, a real tour de force – from which the authors are attempting to derive meaningful observations. Therefore, asking for more triple recordings seems unreasonable. That said, the authors may want to consider showing all control and gabazine recordings corresponding to these experiments in a supplemental figure. Also, why are sublinear GABA-B responses observed when driven by three or more action potentials (Figure 5C)? It is not clear why the authors do not address this observation considering that it seems inconsistent with the study's overall message. Finally, the final readout – GIRK channel activation – in the MCell model appears to summate (mostly) linearly across the first four action potentials. Is this true and, if so, is the result inconsistent with Figure 5C?

Presumably, the motivation for Figure 3 is that it provides physiological context for when NGFCs might be coactive, thereby providing the context for when downstream, PC responses might summate. This is a nice, technically impressive addition to the study. However, it seems that a relevant quantification/evaluation is missing from the figure. That is, the authors nicely show that hind limb stimulation evokes responses in the majority of NGFCs. But how many of these neurons are co-active, and what are their spatial relationships? Figure 3D appears to begin to address this point, but it is not clear if this plot comes from a single animal, or multiple? Also, it seems that such a plot would be most relevant for the study if it only showed alpha-actin 2-positive cells. In short, can one conclude that nearby, presumptive NGFCs co-activate, and is this conclusion derived from multiple animals?

The inclusion of the diffusion-based model (MCell) is commendable and enhances the study. Also, the description of GABA-B receptor/GIRK channel activation is highly quantitative, a strength of the study. However, a general summary/synthesis of the observations would be helpful. Moreover, relating the simulation results back to the original motivation for generating the MCell model would be very helpful (i.e. the authors asked whether "linear summation was potentially a result of the locally constrained GABAB receptor - GIRK channel interaction when several presynaptic inputs converge"). Do the model results answer this question? It seems as if performing "experiments" on the model wherein local constraints are manipulated would begin to address this question. Why not use the model to provide some data – albeit theoretical – that begins to address their question?

In sum, the authors present an important study that synthesizes many experimental (in vitro and in vivo) and computational approaches. Moreover, the authors address the important question of how synaptic responses mediated by metabotropic receptors summate. Additional insights are gleaned from the function of neurogliaform cells. Altogether, the authors should be congratulated for a sophisticated and important study.

Reviewer #2 (Public Review):

In "Evolution of cytokine production capacity in ancient and modern European populations", Dominguez-Andrés et al. collect a large amount of trait association data from various studies on immune-mediated disorders and cytokine production, and use this data to create polygenic scores in ancient genomes. They then use the scores to attempt to test whether the Neolithic transition was characterized by strong changes in the adaptive response to pathogens. The impact of pathogens in human prehistory and the evolutionary response to them is an intriguing line of inquiry that is now beginning to be approachable with the rapidly increasing availability of ancient genomes.

While the study shows a commendable collection of association data, great expertise in immune biology and an interesting study question, the manuscript suffers from severe statistical issues, which makes me doubt the validity and robustness of their conclusions. I list my concerns below, in rough order of how important I believe they are to the claims of the paper:

— In addition to the magnitude of an effect away from the null, P-values are a function of the amount of data one has to fit a model or test a hypothesis. In this case, the authors have vastly more data after the Neolithic Revolution than before, and so have much higher power to reject the null hypothesis of "no relationship to time" after the revolution than before. One can see this in the plots the authors provided, which show vastly more data after the Neolithic, and consequently a greater ability to fit a significant linear model (in any direction) afterwards as well.

— The authors argue that Figure S2 makes their results robust to sample size differences, but showing a consistency in direction before and after downsampling in the post-neolithic samples is not enough, because:

1) you still lack power to detect changes in direction before the Neolithic.

2) even for the post-Neolithic, the relationship may be in the same direction but no longer significant after downsampling. How much the significance of the linear model fit is affected by the downsampling is not shown.

— The authors chose to test "relationship between PRS with time" before and after the Neolithic as a way to demonstrate that "the advent of the Neolithic was a turning point for immune-mediated traits in Europeans". A more appropriate way to test this would be creating a model that incorporates both sets of scores together, accounts for both sample size and genetic drift in the change of polygenic scores, and shows a significant shift occurs particularly in the Neolithic, rather in any other time period, instead of choosing the Neolithic as an "a priori" partition of the data. My guess is that one could have partitioned the data into pre- and post-Mesolithic and gotten similar results, largely due to imbalances in data availability.

— The authors only talk about partitions before and after the Neolithic, but plots are colored by multiple other periods. Why is the pre- and post-Neolithic the only transition that is mentioned?

— Extrapolating polygenic scores to the distant past is especially problematic given recent findings about the poor portability of scores across populations (Martin et al. 2017, 2019) and the sensitivity of tests of polygenic adaptation to the choice of GWAS reference used to derive effect size estimates (Berg et al. 2019, Sohail et al. 2019). In addition to being more heavily under-represented, paleolithic hunter-gatherers are the most differentiated populations in the time series relative to the GWAS reference data, and so presumably they are also the genomes for which PGS estimates built using such a reference would have higher error (see, e.g. Rosenberg et al. 2019). Some analyses showing how believable these scores are is warranted (perhaps by comparing to phenotypes in distant present-day populations with equivalent amounts of differentiation to the GWAS panel).

— In multiple parts of the paper, the authors mention "adaptation" as equivalent to the patterns they claim to have found, but alternative hypotheses like genetic drift are not tested (see e.g. Guo et al. 2018 for a review of methods that could be used for this).

— 250 kb window is too short a physical distance for ensuring associated loci that are included in the score are not in LD, and much shorter than standard approaches for building polygenic scores in a population genomic context (e.g. see Berg et al. 2019, Berisa et al. 2016). Is this a robust correction for LD?

— If one substitutes dosage with the average genotyped dosage for a variant from the entire dataset, then one is biasing towards the partitions of the dataset that are over-represented, in this case, post-Neolithic samples.

— It seems from Figure 2, that some scores are indeed very sensitive to the choice of P-value cutoff (e.g., Malaria, Tuberculosis) and to the amount of missing data (e.g. HIV). This should be highlighted in the main text.

— Some of the score distributions look a bit strange, like the Tuberculosis ones in Figure 2, which appear concentrated into particular values. Could this be because some of the scores are made with very few component SNPs?

Reviewer #2 (Public Review):

The authors examined the role of Rab25 during cell division within a developing epithelia. Strikingly, they found that the RabGTPase, Rab25, localized to mitotic structures such as centrosomes and cytokinetic midbodies in dividing cells of the developing zebrafish embryo. They went on to create maternal-zygotic Rab25a and Rab25b mutant embryos where they clearly demonstrate that apical cytokinetic bridges fail to undergo abscission leading to anisotropic cell morphologies that likely contribute to a delayed epiboly.

The major strengths of this study is the clear cell biology defects found in a developing embryo that lead to downstream developmental defects (delayed epiboly). The rab25 localization is beautiful. The examination of the viscoelastic properties is also compelling. The main improvements would be to expand upon the spatio-temporal localization of Rab25a and Rab25b during cell division at different stages of epiboly, present Rab11 localization patterns in the Rab25 mutant embryos, and clearly demonstrate that changes in viscoelasticity are also in their multinucleated cells that occur in Rab25 mutant conditions. These additions will help the authors support their conclusions that Rab25 localization/regulation of endomembranes (potentially recycling endosomes) regulates abscission and subsequently the viscoelastic properties of the developing tissue.

This study has identified novel roles for Rab25 in cytokinesis/abscission and opens the doors for examining it in regulating mitotic centrosome function. It is paradigm shifting in that it creates a new way to think about Rab25 and potentially its relationship with Rab11 and recycling endosomes during division in the early embryo.

Reviewer #2 (Public Review):

This manuscript set out to address several outstanding questions concerning the impact of 'eusocial' behaviour in mammals, here represented by the experimental model of the Damaraland mole-rat, on skeletal remodelling. Specifically, the transition to breeding status (queen) for some individuals in the colony is accompanied by changes that support high fecundity. The authors investigate the extent to which changes are localised in the skeleton and the underlying regulatory changes that are associated with these morphological features. The paper is well-written, the experiments have been planned thoughtfully and described carefully, and the panel figures convey information without over-crowding. Overall, I thoroughly enjoyed reading this manuscript, which represents as a multi-pronged approach to advancing understanding of the unusual biology and phenotype of queen mole rats.

Reviewer #2 (Public Review):

The authors investigated how alternative polyadenylation (APA) is modulated in yeast using appropriate transcriptomic methodologies.

The authors found that mutants for mRNA 3' end formation factors and cordycepin treatment alter alternative polyadenylation in the same manner, generating transcripts with longer 3'UTRs, due to a switch to distal polyadenylation sites (PAS). Most mutants analyzed cause a PAS switch, in particular mutants for RNA14, PCF11, YSH1, FIP1, NAB4 and PAP1. They also found that MPA and a rpb1 mutant, with a slower transcription elongation rate, reverts the cordycepin effect of distal PAS selection. This implies that in yeast, as in higher organisms, APA is modulated by RNAPII elongation. There is nucleosome depletion in the 3' end of convergent genes that undergo cordycepin-driven APA alterations, which is a new finding.

On the basis of their data, the authors propose a kinetic model for APA in yeast that is regulated by the concentration of core mRNA 3' end factors and nucleotide levels, which in turn modulates RNAPII elongation. This integrative model has been already described in higher organisms, but not in yeast, and overall this study covers an impressive body of work that makes an important contribution to the field.

1) The authors show that cordycepin have the same effect in APA as most of the 3' end factors mutants used, but there is a lack of integration between the two sets of PAS-seq data. The cordycepin APA effect may be due to decreased expression of mRNA 3' end factors but this hypothesis was not fully explored. Treating those mRNA 3' end mutants with cordycepin could shed some light on this.

2) A new role for SEN1 in APA for a subset of protein coding was observed. The SEN1 mechanism could be clarified if the authors show that SEN1 is within the subset of convergent genes analyzed, and also if SEN1 expression changes upon cordycepin treatment.

Reviewer #2 (Public Review):

This study induced tactile percepts through microstimulation via two multi-electrode arrays implanted over a quadriplegic's primary somatosensory hand region. The report focuses on manipulation of the stimulation frequency of microstimulation, though further manipulations were tested and are briefly reported.

For different stimulation sites, the perceived intensity was highest at different stimulation frequencies. This result contradicted the expectation that higher stimulation frequency would be related to higher perceived intensity. This expectation derived from previous work in non-human primates that showed lower detection thresholds for higher-frequency stimulation. The authors show that the same result is obtained in their human patient, suggesting that differences exist between near- and supra-threshold perceived stimuli and that, accordingly, generalizing from non-human primate work has its traps.

The authors grouped stimulation sites according to optimal stimulation frequency into low, intermediate, and high frequency preferring sites. These three classes were spatially clustered, and related to different patterns of reported perceptual qualities (such as vibration, pressure etc).

The paper's results are important for practical developments of sensory feedback in brain-machine interfaces. Understanding the perceptual result of brain stimulation requires reports by human participants, as underlined by the differences uncovered here between near- and supra-threshold stimulation. They furthermore reveal new aspects of the cortical organisation of primary somatosensory cortex.

The conclusion of clustered patches sensitive to specific frequencies is tentative. As an inherent limitation of intracranial recordings, the total number of stimulation sites is small, and some electrodes did not produce significant results, further reducing the number of analysable sites. Therefore, it is possible that stimulation doesn't truly fall into three distinct clusters (even if such clustering is statistically supported with the current data set), but are actually continuous or divide into a larger number of classes. Notably, this critique does not invalidate the main finding that different patches of cortex show specific frequency preferences.

Reviewer #2 (Public Review):

Hallast et al. have performed an extensive genetic analysis of a cohort of men with idiopathic infertility, for whom many have accompanying phenotypic data in the form of andrological parameters. The complex genetic architecture of repeating sequences on the Y chromosome gives rise to recurrent AZFc deletions that affect male infertility. However, while partial deletions of AZFc are reasonably frequent, they have less clear phenotypic effects. gr/gr and b2/b3 deletions seem to be a risk factor for spermatogenic impairment in some populations but are fixed in others. Hallast et al. focus on these partial AZFc deletions in a reference cohort and a cohort with idiopathic infertility from the same geographic population, characterising further structural and sequence variation, Y-chromosomal haplogroups, and gene dosage.

While the gr/gr deletion is present in the reference group of individuals with normal andrological parameters, Hallast et al. show that this deletion is enriched among patients, with 2.2-fold increased susceptibility to infertility. As observed in other European populations, the prevalence of b2/b3 deletion was similar in the reference group and the patient group, suggesting that it is not a spermatogenic impairment risk factor for this Estonian population either.

A quarter of Estonian gr/gr deletion carriers belonged to the Y chromosomal haplogroup R1a1-M458. Within this Y haplogroup, an inversion has occurred that promotes subsequent deletion, likely causing severe spermatogenic failure in the majority of carriers as this complex rearrangement is enriched 8.6-fold in individuals with severe spermatogenic impairment.

Some major strengths of this paper are the size of the groups recruited (1,190 patients and 1,134 reference individuals) from a single national population, the extensive accompanying andrological data, and the genomic characterisation of many individuals to elucidate the relationship between specific structural variants and effects on fertility.

The discovery of the fixed inversion infertility risk factor on a specific Y haplogroup is a useful contribution that could aid genetic counselling efforts through carrier identification and risk mitigation.

However, I am seeking clarity on multiple testing correction for microdeletion association with specific andrological parameters. Besides this, the main conclusions of this paper are supported by other data presented.

Reviewer #2 (Public Review):

Aly et al. investigated anticipatory signals in the cortex by analysing data in which participants repeatedly watched the same movie clip. The authors identified events using an HMM-based data-driven event segmentation method and examined how the timing of events shifted between the initial and repeated presentation of the same video clip. A number of brain regions were identified in which event timings were shifter earlier in time due to repeated viewing. The main findings is that more anterior brain regions showed more anticipation than posterior brain regions. The reported findings are very interesting, the approach the authors used is innovative and the main conclusions are supported by the results and analyses. However, many cortical regions did not show any anticipatory effects and it is not clear why that is. In part, this may be due to a number of suboptimal aspects in the analysis approach. In addition, the analyses of behavioural annotations are open to multiple interpretations.

Methods and Results:

1) The paper shows that across multiple regions in the cortex, there is significant evidence for anticipation of events with repeated viewing. However, there are also many areas that do not show evidence for anticipation. It is not clear whether this is due to a lack of anticipation in those areas, or due to noise in the data or low power in the analyses. There are two factors that may be causing this issue. First, the data that were used are not optimal, given the short movie clip and relatively low number of participants. Second, there are a number of important issues with the analyses that may have introduced noise in the observed neural event boundaries (see points 2-4 below).

2) Across all searchlights, the number of estimated events was fixed to be the same as the number of annotated events. However, in previous work, Baldassano and colleagues (2017) showed that there are marked differences between regions in the timescales of event segmentation across the cortex. Therefore, it may be that in regions such as visual cortex, that tends to have very short events, the current approach identifies a mixture of neural activity patterns as one 'event'. This will add a lot of noise to the analysis and decrease the ability of the method to identify anticipatory event timings, particularly for regions lower in the cortical hierarchy that show many more events than tend to be observed in behavioural annotations.

3) If I understand correctly, the HMM event segmentation model was applied to data from voxels within a searchlight that were averaged across participants. Regular normalization methods typically do not lead to good alignment at the level of single-voxels (Feilong et al., 2018, Neuroimage). Therefore, averaging the data without first hyperaligning them may lead to noise due to functional alignment issues within searchlights.

4) In the analyses the five repeated viewings of the clips were averaged into a single dataset. However, it is likely that participants' ability to predict the upcoming information still increased after the first viewing. That is especially true for perceptual details that may not have been memorised after watching the clip once, but will be memorised after watching it five times. It is not clear why the authors choose to average viewings 2-6 rather than analyse only viewing 6, or perhaps even more interesting, look at how predictive signals varied with the number of viewings. I would expect that especially for early sensory regions, predictive signals increase with repeated viewing.

5) In the analyses of the alignment between the behavioural and neural event boundaries, the authors show the difference in correlation between the initial and repeated viewing without taking the estimated amount of anticipation into account. I wonder why the authors decided on this approach, rather than estimating the delay between the neural and behavioural event boundaries. The finding that is currently reported, i.e. a lower correlation between neural and annotated events in the repeated viewing condition, does not necessarily indicate anticipation. It could also suggest that with repeated viewing, participants' neural events are less reflective of the annotated events. Indeed the results in figure 5 suggest that the correlations are earlier but also lower for the repeated viewing condition.

6) To do the comparison between neural and annotated event boundaries, the authors refit the HMM model to clusters of significant voxels in the main analysis. I wonder why this was done rather than using the original searchlights. By grouping larger clusters of voxels, which cover many searchlights with potentially distinct boundary locations, the authors may be introducing noise into the analyses.

Discussion:

7) To motivate their use of the HMM model, the authors state that: "This model assumes that the neural response to a structured narrative stimulus consists of a sequence of distinct, stable activity patterns that correspond to event structure in the narrative." If neural events are indeed reflective of the narrative event structure, what does it mean if these neural events shift in time? How does this affect the interpretation the association between neural events and narrative events?

Reviewer #2 (Public Review):

This manuscript provides convincing evidence that the ICEBs1 conjugative element confers a fitness advantage on the model bacterium B. subtilis during biofilm formation and sporulation. This effect is frequency dependent and is effected in large measure via an element gene, named devI, by an unknown mechanism that probably decreases the concentration of Spo0A-P. The data are well presented and successfully make the case for a fitness advantage conferred by the mobile element during biofilm formation and sporulation. It is likely that a mechanistic exploration of DevI will follow and will provide another facet to the regulation of Spo0A, a gift that keeps on giving.

Delaying sporulation in a mixed culture confers an advantage for the delayers. This has been convincingly shown. But I wonder about the effects in a clonal population of cells carrying ICEBs1 in competition with a null population. I appreciate that the delay in sporulation is transient, as pointed out in lines 404-407. But a delay of a few hours may be critical in this type of competition between populations as resources become limiting. This is presumably why sporulation is so exquisitely regulated on so many levels and in response to many external an internal signals. If so, ICEBs1 would have a deleterious effect and the element might be in danger of extinction. I suppose that an analogous discussion could be considered for biofilm formation.

Reviewer #2 (Public Review):

This study builds on a previously published paper from this group showing that KLF10 is under circadian control, and it in turn affects the oscillation of a set of metabolic genes in the liver. While the previous study utilized a systemic Klf10 KO mouse model, here, Ruberto et al. generated a conditional hepatocyte-specific Klf10 KO mouse model (Klf10Δhep).

The authors find that the absence of hepatocyte KLF10 alters the circadian oscillation of a number of metabolic genes. In response to sugar consumption, Klf10Dhep mice demonstrate exacerbated adverse effects as well as significantly increased hepatic expression of many glycolysis, gluconeogenesis, and lipogenesis related genes. They conclude that Klf10 normally acts as a "transcriptional brake" to protect animals against the effects of high sugar consumption and show via ChIP-seq that KLF10 is present at a wide range of metabolic genes, particularly at those involved in acetyl-CoA metabolism. The findings are interesting, particularly in the context of the burgeoning burden of metabolic disease and its relation to high sugar consumption, and are supported by the experimental findings.

Reviewer #2 (Public Review):

This manuscript by Nicholas Strash et al. compares the effects of several potential mitogens on cell cycle of the two most used in vitro models of cardiomyocytes (CMs): neonatal rat ventricular myocytes (NRVMs) and human induced pluripotent stem cell (hiPSC)-derived CMs. In addition, they use a 3D model of NRVMs as a model that represents more mature, non-proliferating CMs. The work is interesting for researchers working in the field of cardiac regeneration and provides the first direct comparison of several potential mitogens. The inclusion of several in vitro models to account for potential species differences strengthens the data. The results support previously published findings and the main conclusions are supported by the data presented.

The authors used a 3D model, cardiobundles made from NRVMs, as a more mature CM model. However, these cardiobundles still had a considerable number of CMs in active cell cycle in basal conditions. Whether this reflects true proliferation or the postnatal multinucleation process of rat cardiomyocytes, is unclear. Furthermore, post-mitotic human CMs were not studied. These can be obtained from hiPSC-CMs by prolonged culture or using metabolic stimuli as shown by Mills et al. 2017 (PNAS).

The authors demonstrate that the known mitogenic pathway for CMs, Erbb2-mediated signalling, promotes cell cycle activation in 2D cultures or NRVMS and hiPSC-CMs as well as in 3D cardiobundles. Although cell cycle activity was clearly induced, no actual proof of cytokinesis has been presented. For the cardiobundle work, it remains unclear if the increase in cross-sectional size of cardiobundles induced by Erbb2 signalling is due to increased number of CMs or increased size of CMs. Both the physiological ligand of Erbb3, Neuregulin-1, and the downstream ERK pathway are known to induce CM hypertrophy (see for example Zurek et al. 2020 Circulation; Bueno and Molkentin 2002 Circ Res).

The data analysis and statistics raise some concerns, which require clarification. First, the N numbers are really big and according to the Table 1 it is unclear if they all indeed represent independent samples. For example, one field in a monolayer (Table 1, definition of n in Figures 1J, 1P, 4C, 4E, 4G) should not be considered to represent n=1, if several images were analysed from the same sample and/or if several technical replicates (samples prepared from the same cell isolation or differentiation and treated similarly) were analysed. Only samples from separate differentiations or cell isolations should be considered as representatives of n and the results from technical replicates should be averaged to form the n=1 data. Second, the selection of statistical tests is a concern. It is unclear if the data were analysed for equal variances before selecting the test (parametric vs. non-parametric). It is also unclear why the authors carried out multiple t tests instead of using ANOVA or its variations, which are generally considered more suitable for multiple comparisons.

Reviewer #2 (Public Review):

Rosello et al. present very compelling evidence that Cytosine base editors can be used to introduce G:C to A:T base conversions with high efficiency in zebrafish. Furthermore, they describe engineering and validation of a base editor targeting the NAA PAM sequence. Finally, they have developed a potential novel model of the Noonan syndrome. The manuscript represents an important and much needed advance in precision genome editing in the zebrafish model system.

Reviewer #2 (Public Review):

The manuscript "Archaeal chromatin 'slinkies' are inherently dynamic complexes with deflected DNA wrapping pathways" by Bowerman and colleagues describes a study of archaeasome dynamics combining molecular simulations, cryo-EM, and sedimentation velocity analytical ultracentrifugation. How chromatin evolved is a fundamental question in biology, marking a striking departure from the bacterial nucleoid. Indeed, ever since the first description of archaeal nucleosomes and histones HmfA/B (Sandman and Reeves mid-80s) from thermophilic archaea, this question has fascinated and puzzled the field.

Recent work from the Luger lab figured out the organization of these archaeal chromatin fibers as a continuous loop structure. Here, the authors extend this question further. MD analyses show that Arc90 has two preferred states (closed and flexible ends), but the same 5T5K structure on 120 or 180 bp of DNA prefer a single state (closed). Sedimentation velocity analytical ultracentrifugation showed that Arc207 sediments slower than the H3 mononucleosome, implying that that Arc207 has a shape with higher anisotropy, resulting in excessive drag compared to a mononucleosome. Subsequently, high-resolution cryoEM showed that at least two distinct classes for Arc207 exist, where one class represents a 5-mer and another class represent a 7-mer. The latter has a unique shape in that the 7-mer forms an L-shape (or open clam) with a 3-mer hinging on a 4-mer.

Overall, these data provide exciting structural insights into how archaeal chromatin is folded up at its basic unit level, which the authors describe as most fittingly as a "slinkie". Because so little is known about how nucleosomes evolved during the transition from archaea to eukaryotes, we found this interdisciplinary report well written and with compelling data, that will be of interest to the chromosome biology field at large. We suggest a minor revision in which a few technical points are addressed.

Considerations:

1) The cryoEM data showed two main groups of particles: 5-mer protecting 150 bp and a 7-mer protecting either 90bp or 120bp. A few times in the manuscript (both in the results and discussion section) the authors mention a 30-bp MNase digestion ladder is observed. The Mnase data should be included, as this provides evidence that the structures observed by cryoEM indeed represent physiological structures, especially if strong discrete bands are observed at 90, 120, and 150 bp.

2) The two main classes found by cryoEM give the impression that adding dimers results in altered structures. The 7-mer shows an angled structure, which is interpreted as an open structure. The 5-mer shows a more uniform structure, which is interpreted as a closed structure. The former structure protects the full length of DNA on which HTkA histones were reconstituted, whereas the latter might be an incomplete reconstitution or a partially disassembled structure. It also raises the question if the length of the DNA is a limiting factor. What if HTkA was reconstituted on 170 bp or 307 bp instead? Would this in turn only permit the formation of the 5-mer on the 170 bp construct and two 5-mers on the 307 bp construct? The authors should consider addressing this point because the reconstitution might be constrained by the length of the DNA construct used. Indeed, a related topic might be AT content- what does archaeal DNA look like from the perspective of DNA sequence for chromatin (Jon Widom's group had a ChIPSeq paper on this a few years ago, just after his untimely passing).

3) In the discussion the authors cite that in one archaeal species the Mg2+ concentration is ~120 mM, more than a magnitude greater than that tested in Figure 5. What happens to reconstituted archaeasomes at higher Mg+? This is relevant because in vivo, archaea are thought to have 10x the concentration of Mg+ (amongst other ions) relative to us humble eukaryotes who would probably die of kidney failure at those ionic concentrations. Indeed at high ionic conditions, eukaryotic chromatin can be made to precipitate out of solution (for e.g. 10mM Mg+, 3M NaCl). An AUC assay with higher Mg2+ concentrations seems a doable and physiologically relevant addition to the ms that would strengthen it. It is relevant to consider that in vivo structure in these halophilic and thermophilic organisms might be dependent on the concentration of various salts and temperature, it would be nice to read the authors' thoughts on this issue.

Reviewer #2 (Public Review):

Bifulco et al. performed a large-scale in silico study to test whether the spatial fibrosis distribution measured via LGE-MRI in 45 patient with embolic stroke of undetermined source (ESUS) as compared to the distribution in 45 atrial fibrillation (AFib) patients without stroke leads to differences in reentrant arrhythmia inducibility of dynamics.

1) This study comprises a high number of simulations and is one of the computational electrophysiology studies that covers the most anatomical and structural variability on the atrial level. In their comprehensive analysis, Bifulco et al. answered their question and found no pronounced differences in arrhythmia inducibility and dynamics between ESUS and AFib models. It would be interesting to learn how the spatial fibrosis distributions compare in terms of the previously suggested features density and entropy (Zahid et al.). This might also influence the statements in L170/L207.

2) The authors chose to exclude patients with stroke from the AFib group, the reasons for this choice are not entirely clear. The same holds for the fact that the ESUS models included AFib-induced electrophysiological remodeling even though these patients have not been diagnosed with AFib (by definition).

3) An acknowledged limitation of the study is the assumption of fixed conduction velocity and action potential duration/effective refractory period. Bifulco et al. base this assumption on previous studies by the group (e.g. L312), which, however, concluded that reentrant driver locations and inducibility are sensitive to changes of action potential and conduction velocity (Deng et al.). For conduction velocity, wider ranges have been reported since the publication of the supporting reference (35) in 1994, e.g. Verma et al.; Roney et al.

4) The number of pacing sites is rather low for a comprehensive in silico arrhythmia inducibility test but likely a good balance of coverage and computational feasibility considering that the primary goal of this research was to check whether the two groups of models show differences when undergoing the same (but not necessarily exhaustive) protocol.

5) The discussion does a good job in putting the results into context. Two interesting observations that deserve more attention are that i) the Inducibility Score was always higher for AFib vs. ESUS (Figure 6A, no statistical test performed). However, this did not translate to a difference in silico arrhythmia burden (inducibility). ii) Reentrant drivers were about twice as likely to localize to the left pulmonary veins than the right pulmonary veins in the AFib models (Figure 6D).

6) The study succeeded in answering the question it posed in the sense that no marked difference was found between the ESUS and AFib models. This leads to the question what the stroke-inducing mechanism is in the ESUS patients. A hypothesis for future work could be that the fibrotic infiltrations in the ESUS patients reduce the hemodynamic efficacy of the left atrium and render clot formation (e.g. in the atrial appendage) more likely in this way.

7) The negative finding in this study (no difference between groups) does not naturally allow us to draw clinical implications for diagnosis or stratification. Additional ways to put the hypothesis proposed by the authors (fewer arrhythmogenic triggers in the ESUS patients) to test could be to consider readouts/surrogate measures of the autonomic nervous system.

Reviewer #2 (Public Review):

The authors used several approaches to define a discrete population of Langerhans cell-like (LC-like) dendritic cells (DC) in the dermis of mice. By flow cytometry, these cells expressed langerin/CD207 and EpCAM/CD236 and were found in the CD103-CD11b- fraction of dermal cells. It was also shown that LC-like cells, rather than LC, are the main contributors to CD103- langerin+ cells in the lymph node. By single cell RNAseq of dermal cells they clustered with Langerhans cells but lacked expression of DC-SIGN/CD209. Fate-mapping with Kitmercremer/Rosa26loxPSTOPloxPeYFP showed a similar origin to other dermal DC, with no yolk sac signal as observed in LC. Bone marrow chimeras, however, indicated a much slower turnover compared with other dermal DC. Independence from LC and other DCSIGN+ DC was also demonstrated by unchanged kinetics during continuous ablation of DCSIGN+ fractions in a DTR model.

The results explain the expression of langerin on two fractions of dermal DC observed several years ago (CD103+ and CD103-). The demonstration that epidermal LC do not contribute to LN populations in the steady state is completely unexpected and raises an important question of the in vivo function of the LC-like subset. LC-like cells appear to be related to LC but a more in-depth analysis of their gene expression differences compared with LC would be interesting. Also, their potential relationships with other DC (cDC1 or cDC2), was not defined. Langerin expression by human cDC2 is well described and possibly correlated with these observations in mice. Finally, the turnover of LC-like cells was slow, yet they contributed as many langerin+ cells to the resting lymph node as cDC1, which turnover quickly. Proliferation in situ might explain this observation but there were no data on this.

Reviewer #2:

In this study, entitled “APEX-Gold: A genetically-encoded particulate marker for robust 3D electron microscopy” Rae et al. describe a method to improve the visualization of the staining for genetically encoded probes that they described in previous studies (namely APEX2 constructs).

These techniques are very powerful as they increase the sensitivity for detecting low level of expression of the tagged proteins (e.g. compared to GFP tagged proteins). The novelty in this study is that the reaction product (DAB precipitates) is revealed by the nucleation of silver/gold precipitates. Such enhancement has been used extensively in the past for pre-embedding immuno-peroxidase techniques, but has never been combined with the use of APEX2. This has one major advantage: that the contrast of the positive staining can stand out from the contrast of the surrounding ultrastructure, making the sample preparation more adapted to 3D EM techniques, especially volume SEM where contrast is a bottleneck. Moreover, the sensitivity of the technique is shown to be compatible with the detection of endogenous levels of expression.

The technique is very well detailed and elegantly illustrated by convincing applications on cultured cell systems. The apparent simplicity of use, together with a growing interest in the community for the APEX2 based techniques (also in correlative imaging), significantly raises the potential for it to become a standard in the field, and should thus be shared with the community.

Reviewer #2:

Miningou and Blackwell in their manuscript titled "Temporal pattern and synergy influence activity of ERK signaling pathways during L-LTP induction" explored the contributions of upstream pathways to ERK activation during LTP. The authors expanded on their previously published LTP model to assess the influence on ERK activation of each of the upstream pathways originating from cAMP or Ca+2 activated with differing temporal patterns. This manuscript's aim is quite germane, since 1) ERK plays such a central role in learning and memory and its cellular proxy LTP; 2) the Ca+2/cAMP/PKA system is highly complex and nonlinear, with multiple feedback loops. The resulting manuscript has the potential to be impactful. The approach of using a stochastic reaction-diffusion model is state of the art and appropriate for the modeling of these subcellular events in spines. And the modeling insights are very intriguing as the authors predict that ERK activation by cAMP/PKA or Ca+2 pathways differ in their linearity, these pathways can synergize during LTP and this may involve a novel feedforward loop containing synGAP. The authors do a marvelous job placing their findings within the huge body of LTP literature.

There are, however, a couple of points that I feel should be addressed:

1) There needs to be additional technical detail on how the original models were expanded. The model presented here was developed by merging Jȩdrzejewska-Szmek et al., 2017 and Jain and Bhalla, 2014 models. These models were developed based on experimental data and validated with independent experimental datasets in a rigorous manner. It is not clear how the combining these two models, and the additional molecules and reactions added have affected the dynamics of ERK activation, and how comparable they are to the original experimental data used for model development in the previous modeling efforts. It is not clear if the model was reparameterized.

2) Beyond the ERK activation traces, it would be useful for clarity sake to also include the simulated traces for the activation of the upstream molecules (PKA, RAS, RAP, etc). Given how additional changes have been made additional information should be provided to ensure that the contribution of each pathway is accurately represented.

Test out GPT-2

Reviewer #2:

In this work, Hofmann and colleagues conduct a study investigating the relationship between EEG alpha and subjective arousal in naturalistic (as opposed to controlled experimental) settings. Participants completed an immersive virtual reality experience while EEG was recorded, and continuously rated their subjective arousal while a video of the experience was replayed. Three different decoding methods were evaluated (Source Power Comodulation, Common Spatial Patterns, and Long Short-Term Memory Recurrent Neural Networks), each of which demonstrated above chance levels of performance, substantiating a link between lower levels of parietal/occipital alpha and subjective arousal. This work is notable because the roller-coaster simulation is a well-controlled, yet dynamic manipulation of arousal, and in its comparison of multiple decoding approaches (that can model the dynamics of affective responses). Indeed, this is an interesting proof of concept that shows it is possible to decode affective experience from brain activity measured during immersive virtual reality.

Major concerns:

The authors advocate that naturalistic experiments are needed to study emotional arousal, because "static" manipulations are not well-suited to capture the continuity and dynamics of arousal. This point is well-taken, but no comparisons were made between static and dynamic methods. Thus, although the work succeeds in showing it is possible to use machine learning to decode the subjective experience of arousal during virtual reality, it is not clear what new insights naturalistic manipulations and the machine learning approaches employed have to offer.

The methods used to assess model performance are also a concern. Decoding models were evaluated separately for each subject using 10-fold cross-validation, and inference on performance was made using group-level statistics. Because time-series data are being decoded, if standard cross-validation was performed the results could be overly optimistic. Additionally, hyperparameters were selected to maximize model performance which can also lead to biased estimates. This is particularly problematic because overall decoding performance is not very high.

Reviewer #2:

This paper proposes a novel and relevant evolutionary model that explains many aspects of replication origin statistics in a family of yeast species. It is a step forward in our understanding of the evolutionary pressures that affect the distribution of replication origins in Eukaryotes. I recommend the authors address the following issue:

1) Many of the conclusions of the paper are based on the claim that the extending the model by adding an efficiency bias to the origin death rate makes the model fit the data better; in particular, they say in line 213 that "the observed huge divergence in efficiency between lost origins and their neighbors is absent in the model simulations." This is reinforced in line 243, and in other parts of the text. But inspecting Fig 3, the two models (with and without a death rate bias) yield almost identical box-plots; if anything, the box-plots for the lost/nearest fractions of the pure double-stall aversion model seem visually to match the data marginally better. So why do the authors claim that the model with death rate bias is a much better fit? This is far from clear by just inspecting the data. I see no "huge difference" in the plots. There is a difference, but it is far from huge - the differences in the mean are much smaller than the size of the boxes. It seems to me unjustified to use this to choose one model over another. One way to ascertain this is to do rigorous statistical tests to determine if the differences in the means of the simulated and observed data are statistically significant; for example, a t-test.

Reviewer #2:

The conclusion was quite surprising from their anatomical differences and connectivity to the cortex, however, implies different mechanisms underlie for species specific circuit organization.

The manuscript is well-organized and well-written with strong figures. I have only a few comments/suggestions to further improve the overall quality of this manuscript.

I understand obtaining human and NHP tissue is difficult and hard to perform numbers of ISH. Therefore, there is a database that provides additional information on gene expression in NHP LGN (https://gene-atlas.brainminds.riken.jp/). From this database, it is possible to obtain parvocellular specific and magnocellular specific gene expression by fine structure search, which may be worth comparing with the results in the current paper. Many researchers have realized that marmoset is one of the good animal models to understand human brain function and dysfunction, therefore, it is worth including marmoset for comparative analysis for community interest.

Reviewer #2:

The paper investigates the temporal signatures of single-neuron activity (the autocorrelation timescale and latency) in two frontal areas, MCC and LPFC. These signatures differ between the two areas and cell classes, and form an anatomical gradient in MCC. Moreover, the intrinsic timescales of single neurons correspond with their coding of behaviorally relevant information on different timescales. The authors develop a detailed biophysical network model which suggests that after-hyperpolarization potassium and inhibitory GABA-B conductances may underpin the potential biophysical mechanism that explains diverse temporal signatures observed in the data. The results appear exciting, as the proposed relationship between the intrinsic timescales, coding of behavioral timescales, and anatomical properties (e.g., the amount of local inhibition) in the two frontal areas is novel. The use of the biophysically detailed model is creative and interesting. However, there are serious methodological concerns undermining the key conclusions of this study, which need to be addressed before the results can be credited.

Major Concerns:

1) One of the key findings is the correspondence between the intrinsic timescales of single neurons and their coding of information on different behavioral timescales (Fig. 4). However, the method for estimating the intrinsic timescales has serious problems which can undermine the finding.

1.1 The authors developed a new method for estimating autocorrelograms from spike data but the details of this method are not specified. It is stated that the method computes the distribution of inter-spike-intervals (ISIs) up to order 100, which was "normalized", but how it was normalized is not described. The correct normalization is crucial, as it converts the counts of spike coincidences (ISI distribution) into autocorrelogram (where the coincidence counts expected by chance are subtracted) and can produce artifacts if not performed correctly.

1.2 The new method, described as superior to the previous method by Murray et al, 2014, appears to have access to more spikes than the Murray's method (Fig. 2). Where is this additional data coming from? While Murray's method was applied to the pre-cue period, the time epoch used for the analysis with the new method is not stated clearly. It seems that the new method was applied to the data through the entire trial duration and across all trials, hence more spikes were available. If so, then changes in firing rates related to behavioral events contribute to the autocorrelation, if not appropriately removed. For example, the Murray's method subtracts trail-averaged activity (PSTH) from spike-counts, similar to shuffle-correction methods. If a similar correction was not part of the new method, then changes in firing rates due to coding of task variables will appear in the autocorrelogram and estimated timescales. This is a serious confound for interpretation of the results in Fig. 4. For example, if the firing rate of a neuron varies slowly coding for the gauge size across trials, this will appear as a slow timescale if the autocorrelogram was not corrected to remove these rate changes. In this case, the timescale and GLM are just different metrics for the same rate changes, and the correspondence between them is expected. Before results in Fig. 4 can be interpreted, details of the method need to be provided to make sure that the method measures intrinsic timescales, and not timescales of rate changes triggered by the task events. This is an important concern also because recent work showed that there is no correlation between task dependent and intrinsic timescales of single neurons, including in cingulate cortex and PFC (Spitmaan et al., PNAS, 2020).

2) The balanced network model with a variety of biophysical currents is interesting and it is impressive that the model reproduces the autocorrelation signatures in the data. However, we need to better understand the network mechanism by which the model operates.

2.1 The classical balanced network (without biophysical currents such as after-hyperpolarization potassium) generates asynchronous activity without temporal correlations (Renart et al., Science, 2010). The balanced networks with slow adaptation currents can generate persistent Up and Down states that produce correlations on slow timescales (Jercog et al., eLife, 2017). Since slow after-polarization potassium current was identified as a key ingredient, is the mechanism in the model similar to the one generating Up and Down states, or is it different? Although the biophysical ingredients necessary to match the data were identified, the network mechanism has not been studied. Describing this network mechanism and presenting the model in the context of existing literature is necessary, otherwise the results are difficult to interpret for the reader.

2.2 Does the model operate in a physiologically relevant regime where the firing rates, Fano factor etc. are similar to the data? It is hard to judge from Fig. 5b and needs to be quantified.

2.3 The latency of autocorrelation is an interesting feature in the data. Since the model replicates this feature (which is not intuitive), it is important to know what mechanism in the model generates autocorrelation latency.

3) HMM analysis is used to demonstrate metastability in the model and data, but there are some technical concerns that can undermine these conclusions.

3.1 HMM with 4 states was fitted to the data and model. The ability to fit a four-state HMM to the data does not prove the existence of metastable states. HMM assumes a constant firing rate in each "state", and any deviation from this assumption is modeled as state transitions. For example, if some neurons gradually increase/decrease their firing rates over time, then HMM would generate a sequence of states with progressively higher/lower firing rates to capture this ramping activity. In addition, metastability implies exponential distributions of state durations, which was not verified. No model selection was performed to determine the necessary number of states. Therefore, the claims of metastable dynamics are not supported by the presented analysis.

3.2 HMM was fit to a continuous segment of data lasting 600s, and the data was pooled across different recording sessions. However, different sessions have potentially different trial sequences due to the flexibility of the task. How were different trial types matched across the sessions? If trial-types were not matched/aligned in time, then the states inferred by the HMM may trivially reflect a concatenation of different trial types in different sessions. For example, the same time point can correspond to the gauge onset in one session and to the work trial in another session, and vice versa at a different time. If some neurons respond to the gauge and others to the work, then the HMM would need different states to capture firing patterns arising solely from concatenating the neural responses in this way. This confound needs to be addressed before the results can be interpreted.

Reviewer #2:

A state of the art imaging of the dynamics of astroglial glutamate transporters that certainly add novel perspective into this, quite important and hot field. Experiments and clean and convincing, the data obtained fully support conclusions.

Comments:

1) The authors mention the importance of efficient glutamate uptake in the development of neuropathological conditions, but do not discuss this in regards to their results. Such a discussion would seem relevant.

2) Authors conclude that the membrane turnover pathway should be a particularly important GLT-1 resupply mechanism near excitatory synapses as some earlier studies have found the lowest lateral membrane mobility of GLT-1 there. In this context, it would be of interest to have some quantitative tips as to the relationship between the level of excitatory activity and the occupancy of local GLT-1.

3) There is a recent work implicating the C-terminus in the surface assembly of GLT-1 (Peacy et al Mol Pharm 2020), which seems relevant to the present findings. Please discuss further.

4) Functional activity of glutamate transporters is linked to (and is being regulated by) astroglial Na signalling; any suggestions how proposed turnover cycle may affect cytosolic Na+ dynamics

5) The Fig. 5A imaging data seem to nicely provide both surface and cytosol labelling of the same cell. Perhaps the authors could thus assess the distribution of surface-to-volume ratios across live astroglia: to my knowledge, such data has not yet been available.

6) Figure 1B: Please provide further detail regarding fast-exchange solution application, its physical arrangement, etc.

7) Figure 2, whole-cell photobleaching: Please expand on what is 'tornado' mode scanning and how it has been applied.

8) Figure 3, dSTORM data: Please provide further details regarding the numbers of sampled ROIs and/or individual molecules / distances analysed.

Reviewer #2:

This manuscript addresses the question of whether duplication of tumor suppressors occurred coincidently with the enlarged body size and reduced cancer risk evolved independently in Afrotherians. Using the human genome as reference, the authors systematically searched for gene duplications in 13 publicly available Afrotherian genomes, including 9 extant and 4 extinct species. The authors also reconstructed the ancestral body sizes, cancer risks and gene duplication events across the Afrotherian phylogeny. These data showed that both increased body sizes and reduced cancer risks are gradually evolved. Reactome pathway enrichment analysis for gene duplicates showed unexpectedly that gene duplicates in both lineages with or without major increases in body size/lifespan/decreases in cancer risk are enriched in many cancer related pathways. However, the authors found that 157 genes duplicated in Proboscidean stem-lineage, in which extremely large species evolved, were uniquely enriched in 12 cancer pathways. These genes might facilitate further body enlargement and cancer resistance evolution in Proboscidean. Most interestingly, the authors found that several genes both upstream and downstream of a famous tumor suppressor TP53 have also been duplicated, either before or after initial TP53 duplication. These genes are involved in transcriptional regulation of TP53 and may have facilitated re-functionalization of TP53 retroduplicates. Overall, this is an important and interesting study that can help us understand the evolution of body size, lifespan and cancer risk in mammals more deeply.

Major comments:

1) In general, the evolutionary fate of gene duplication includes: 1) Conservation of gene function; 2) Neofunctionalization; 3) Pseudogenization; 4) Subfunctionalization (doi:10.1016/S01695347(03)00033-8). To execute the function of tumor suppression, as this study focused on, gene duplicates were supposed to be functionally conserved or subfunctionalized. Gene duplicates that have been neofunctionalized or pseudogenized will not be helpful (also mentioned by authors in the Caveats section). Therefore, it might be more convincing to investigate the functional status of each gene duplicate, especially those in Fig 4C/D. In many cases, however, a related function, rather than an entirely new function, evolves by neofunctionalization after gene duplication, and also that to check new functions for a batch of genes is not realistic, the authors could simply check the coding sequences to ensure these genes duplicates are not pseudogenes and are functional. This is necessary because in Fig 4D, many genes have only 2 copies expressed. If one of them is a young pseudogene, it could be stochastically expressed and will encode a dysfunctional protein.

2) In Results section 3, the cancer pathway frequency data of many nodes seems not consistent with data shown in Table 2. For example, Line 293-296: "55.8% (29/52) of the pathways that were enriched in the Tethytherian stem-lineage..., 27.8% (20/72) of the pathways that were enriched in the Proboscidean stem-lineage...were related to tumor suppression", the cancer pathway percentages shown in Table 2 for these 2 nodes are 63.4% and 38.81%, respectively. While the frequency data in Table 2 are consistent with Supplementary Data File S3: "Atlantogenata_Reactome_ORA.xlsx". It is possible that the frequency data shown in the main text are specific to pathways of tumor suppression, rather than cancer related pathways. If this is the case, more detailed data should be shown somewhere else.

3) The titles of Results section 3 and section 4 are highly similar and actually the data in section 4 seems to be used to further solidify the conclusion of section 3. Therefore, is it possible to merge them into one single section?

Reviewer #2:

When animals are given a choice between drug and nondrug reinforcers, they will most often choose the nondrug alternative even when presented with highly reinforcing drugs of abuse. This is difficult to reconcile with known behavior in humans and for modeling aspects of addiction that are critical to the disorder, such as choosing to use drugs above all other reinforcers. Recent work by this same group has reported that responding for nondrug reinforcer is, surprisingly, insensitive to devaluation. This suggests that the choice for the nondrug reinforcer is under habitual, rather than the presumed goal-directed, control and may explain why animals most often choose the nondrug reinforcer over drug reinforcers. Moreover, because there is no devaluation procedure for determining whether drug choice is habitual or goal directed, it's not known if choice for drug is also habitual or remains goal-directed.

The manuscript by Vandaele et al., therefore, sought to develop a procedure for determining whether behavior of rats making choices between saccharin and cocaine reinforcers was habitual or goal-directed based on reaction times (RT). Based on previous theories, the authors argue that goal-directed behavior should have slower RTs on choice trials versus sampling trials (e.g., because animals are deliberating between the alternatives) whereas habitual behavior should have similar RTs across both sampling and choice trials. The authors also present a third possibility in which options are evaluated sequentially, rather than simultaneously, resulting in RTs being longer in the sampling versus choice trials. The authors report that rats with minimal training and who are presumed to be goal-directed have slower RTs in choice trials compared to sample trials whereas rats that have had extensive training have similar RTs in the choice and sampling phases. These findings are consistent with their hypotheses. Moreover, they demonstrate that in the small subset of rats that prefer cocaine over saccharin, RTs in the sampling trials are longer than that in the choice trial suggesting that cocaine preferring rats are not evaluating each of the options. These data are the first to evaluate habitual responding for a drug reinforcer and suggest that comparing latencies across different task phases could be used to measure habitual and goal-directed behaviors.

Reviewer #2:

This manuscript by Mathsyaraja et al. studies the oncogenic loss of the Max-gene-associated (MGA) protein due to deletion or mutation in cell-lines, in mice and in human cancers (cell-lines and tumors). The authors knocked out MGA by aerosol-delivered, CRISPR-CAS expressing lentiviruses that simultaneously Cre-activated a Lox-stop Kras oncogene. The loss of MGA accelerated proliferation and oncogenesis, and shortened survival. Oncogenesis was further enhanced by enforced TP53 deletion in these lung tumors. RNA-seq and ChIP-seq of MGA+ or - cell-lines demonstrated the up and downregulation of various gene classes (thousands of genes) according to function and regulation including of PRC1.6 targets, meiosis regulators, TGF-beta signaling pathway components, EMT regulators, anti-tumor immunity, as well as of MYC, E2F, etc. Different cell lines exhibited both overlapping and distinct target sets. MGA knockout cells were more migratory and invasive and displayed actin-protrusions in accord with this behavior. They show that a Domain of Unknown Function in the mid-region of MGA engages PRC1.6 and is required to depress proliferation. The DUF is also required to limit actin-protrusions. Human colon organoids were studied since MGA mutations and deletions are also apparent in colon cancer. Again, shared and distinct targets of MGA action were inferred.

The authors make a strong case that MGA is an important tumor suppressor that operates through PRC1.6 for some of its actions.

Reviewer #2:

1) The authors hint towards the involvement of c-di-GMP signaling via the YcgR protein. This hypothesis can be tested by knocking down the ycgr gene and repeating the assay, but this has not been done or reported. Addition of these data to the manuscript would make the paper significantly stronger.

2) Do other chemoreceptors (Tar, Tsr, Tap) also act in the same way with their respective ligands? It would be useful to know if this effect is specific to Trg or if it is also found in the other chemoreceptors.

3) In figure 3C, what is the reason that the GFP intensity and the speed do not have the same range? In other words, why is the slope not equal to 1? Since there is 1:1 correspondence between the number of MotB and the number of GFP, shouldn't the slope be 1?

4) The authors do not cite or discuss the recent literature on load-dependent stator remodeling (e.g. PMIDs: 29183968, 31142644). It would be helpful to have a more in-depth discussion on how the observed stator unit recruitment relates to stator remodeling in response to load.

Reviewer #2:

This impressive manuscript describes a comprehensive, multifaceted analysis of the morphological and molecular changes that accompany photosynthetic establishment during seedling de-etiolation. Morphological data, focusing in particular on the photosynthetic thylakoid membranes, are derived using transmission electron microscopy (TEM), serial block face scanning electron microscopy (SBF-SEM), and confocal microscopy, while quantitative molecular data on the abundances of proteins and lipids are derived using mass spectrometry and western blotting. The various data are acquired over a time course between 0 h and 96 h post illumination, and with a high level of temporal resolution. The data allow the authors to develop a mathematical model for the expansion of the surface area of thylakoids (reaching 500-times the surface area of the cotyledon leaf), which matches well with experimental observations from the SBF-SEM analysis for earlier, but not later, stages of de-etiolation. Moreover, the data point to a two-phase organization of the de-etiolation process, with the first phase ("Structure Establishment") characterized by thylakoid assembly and photosynthetic establishment, and the second phase ("Chloroplast Proliferation") characterized by chloroplast division and cell expansion.

The data are of a high standard, and the depth and breadth of analysis in a single, unified study is unprecedented. While it is arguable that there are few major, completely novel insights reported here (indeed, in the Discussion, the authors very helpfully point out how many of the parameters they have measured are consistent with data reported elsewhere by others), this should not detract from the overall value of the study; a major and unique strength here is that all of the data have been acquired together and so are directly comparable. I have no doubt that this dataset will be extremely interesting to many researchers, and prove to be an invaluable resource for the plant science community. Consequently, I am sure that it will attract many citations.

I have a few specific comments that I would like the authors to consider carefully, as follows.

1) Figure 3. The 3D reconstructions are undoubtedly useful for deriving quantitative data, as they enable the derivation of thylakoid surface area data to verify the mathematical model. However, it is very difficult to see anything clearly in the images shown in the Figure. I wonder if the authors can make the images clearer, and then also point to and describe some of the key features. The videos do help a bit, but even these are not that clear.

2) Page 9, second paragraph. It is here that the "two phases" model is first proposed. I really could not see a clear basis for proposing this model here, using the data that had been presented thus far. As I see it (and based on the way the two phases are described in the Discussion), one can't really propose this model until after the chloroplast number and cell size data have been presented.

Moreover, the description of the second phase here ("and a second phase...") seems a bit inconsistent with the statement in the paragraph above that thylakoid surface area increases dramatically between T4 and T24, and much less between T24 and T96.

3) Figure 6, and the related supplementary figure. Loading controls are missing here, and should be added. Also, it is stated that a number of proteins (PsbA, PsbD, PsbO, Lhcb2) are "detectable" at T0 (line 348, page 11). To me, they look UNdetectable.

4) Dividing chloroplasts. On page 13, line 412-413, it is stated that the volume of dividing chloroplasts was measured, and we are referred to Figures 8E and 4B in support of this statement. However, it is not explained how this was done. More clear and specific explanation is needed. Was it the case that the authors sought out and measured dumbbell-shaped organelles, and quantified those? If so, images are needed to illustrate this point. And, I don't see anything relevant in Fig. 4B - this callout apparently belongs in the following sentence. The statement that the average size of dividing chloroplasts was higher than that of all chloroplasts (lines 413-414) is not really surprising if the authors were measuring organelles just on the point of becoming two organelles.

5) Page 13, beginning of modelling section. The motivation for this section needs to be better introduced. When I first read it, I could not understand why the authors wished to again "determine the thylakoid membrane surface area", as this had already been discussed earlier in the manuscript.

Also related to the modelling: Did the authors take into account the existence of appressed membranes when calculating the surface area exposed to the stroma (lines 431-432). And, assuming it is clearly established that there is a 1:1 relationship between these proteins and the relevant complexes (lines 441-443), perhaps this should be stated and the relevant literature cited.

Reviewer #2:

The study investigates key components of the entorhinal circuits through which signals from the hippocampus are relayed to the neocortex. The question addressed is important but the stated claim that layer 5b (L5b) to layer 5a (L5a) connections mediate hippocampal-cortical outputs in LEC but not MEC appears to be an over-interpretation of the data. First, the experiments do not test hippocampal to L5a connections, but instead look at L5b to L5a connections. Second, the data provide evidence that there are L5b to L5a projections in LEC and MEC, which contradicts the claim made in the title. These projections do appear denser in LEC under the experimental conditions used, but possible technical explanations for the difference are not carefully addressed. If these technical concerns were addressed, and the conclusions modified appropriately, then I think this study could be very important for the field and would complement well recent work from several labs that collectively suggests that information processing in deep layers of MEC is more complex than has been appreciated (e.g. Sürmeli et al. 2015, Ohara et al. 2018, Wozny et al. 2018, Rozov et al. 2020). Major Concerns:

1) An impressive component of the study is the introduction of a new mouse line that labels neurons in layer 5b of MEC and LEC. However, in each area the line appears to label only a subset (30-50%) of the principal cell population. It's unclear whether the unlabelled neurons have similar connectivity to the labelled neurons. If the unlabelled neurons are a distinct subpopulation then it's difficult to see how the experiments presented could support the conclusion that L5b does not project to L5a; perhaps there is a projection mediated by the unlabelled neurons? I don't think the authors need to include experiments to investigate the unlabelled population, but given that the labelling is incomplete they should be more cautious about generalising from data obtained with the line.