Reviewer #2 (Public Review):

This manuscript by Galdadas et. al. used a combination of equilibrium and non-equilibrium simulations to investigate the allosteric signaling propagation pathway in two class-A beta-lactamases, TEM-1 and KPC2, from allosteric ligand binding sites. The authors performed extensive analysis and comparison of the simulated protein allostery pathway with know mutations in the literature. The results are rigorously analyzed and neatly presented in all figures. The conclusions of this paper are mostly supported by previous mutational data, but a few aspects of simulation protocol and data analysis need to be validated or justified.

Line 293, by "comparing the Apo_NE and IB_EQ simulations at equivalent points in time" and perform subtraction "from the corresponding Ca atom from one system to another at 0.05, 0.5, 1, 3, 5ns". It is not clear to me why those time points were chosen? Have authors attempted at validating whether or not the signal from the ligand-binding site has had enough time to propagate across the allosteric signaling pathway? If one considers that the ligand is a spatially localized signal, it requires time to propagate. This is in contrast with the Kubo-Onsager paper cited by authors in which the molecule is responding to a global perturbation such as an external field. However, a local perturbation on one side of the protein will need time to propagate to the other side of the protein (30 angstroms away in this case). A simple and naive example is to map out all the bus stops on one's route. 800 simulations between the first and second stop will not be able to provide the locations of other stops. Since authors have used this "subtraction technique" on several other proteins, it would be nice to clarify how this approach works on mapping out signaling propagation perturbed by local ligand binding/unbinding and how to choose the time points for subtraction.

Another question is whether tracing the dynamics of Calpha alone is enough. As we have seen from the network analysis papers, Calpha sometimes missed some paths or could overemphasize others. The Center of the mass of residue has been proposed to be a better indicator of protein allostery. Authors may wish to clarify the particular choice of Calpah in this study.

In Figure5, the authors seem to use Pearson correlation to compute dynamic cross-correlation maps. Mutual information (M)I or linear MI have advantages over Pearson correlations, as has been discussed in the dynamical network analysis literature.

Reviewer #2 (Public Review):

In this manuscript, Dahlen et al. aimed to agnostically investigate the association between ABO and RhD blood group and disease occurrence for a large number of disease phenotypes using large-scale population-based Swedish healthcare registries. Using 2 large subject cohorts, they convincingly demonstrate that beyond the known associations between ABO, infectious diseases and thrombosis, there are other associations with very different diseases. This paper is purely epidemiological with no biological data to explain the observed associations. The clinical phenotypes are derived from hospital coding and probably lack precision, especially in terms of diagnostic certainty.

Reviewer #2 (Public Review):

eQTLs can vary between cell types. To capture this in an organism as complex as a mammal looks daunting and expensive if eQTLs have to be mapped a single cell type at a time. However, here the authors propose a 'one pot' method where whole animals are dissociated and the cell types deconvoluted based on a robust set of markers. Thus in a single experiment, eQTLS can be mapped in tens of cell types at once - here they identify 19 major cell types but in the case of the nervous system break it down with even more specificity, down to individual cells.

They test their method in C. elegans which is ideal for this - the lineage is invariant, there are extensive sets of cell type specific markers, and they can exploit their previously published method called ceX-QTL to generate massive pools of segregants using an elegant genetic trick.

Overall I was extremely impressed with the clarity of writing, the care of data analysis, and I honestly found that every analysis I was looking for had been done. They highlight some beautiful findings, most striking of which was the opposing regulation of nlp-21 in two neurons, a perfect example of the resolution this can achieve.

Reviewer #2 (Public Review):

In their manuscript "CEM500K - A large-scale heterogeneous unlabeled cellular electron microscopy image dataset for deep learning", the authors describe how they established and evaluated CEM500K, a new dataset and evaluation framework for unsupervised pre-training of 2D deep learning based pixel classification in electron microscopy (EM) images.

The authors argue that unsupervised pre-training on large and representative image datasets using contrastive learning and other methods has been demonstrated to benefit many deep learning applications. The most commonly used dataset for this purpose is the well established ImageNet dataset. ImageNet, however, is not representative for structural biases observed in EM of cells and biological tissues.

The authors demonstrate that their CEM500K dataset leads to improved downstream pixel classification results and reduced training time on a number of existing benchmark datasets a new combination thereof compared to no pre-training and pre-training with ImageNet.

The data is available on EMPIAR under a permissive CC0 license, the code on GitHub under a similarly permissive BSD 3 license.

This is an excellent manuscript. The authors established an incredibly useful dataset, and designed and conducted a strict and sound evaluation study. The paper is well written, easy to follow and overall well balanced in how it discusses technical details and the wider impact of this study.

Reviewer #2 (Public Review):

Landemard et al. compare the response properties of primary vs. non-primary auditory cortex in ferrets with respect to natural and model-matched sounds, using functional ultrasound imaging. They find that responses do not differentiate between natural and model-matched sounds across ferret auditory cortex; in contrast, by drawing on previously published data in humans where Norman-Haignere & McDermott (2018) showed that non-primary (but not primary) auditory cortex differentiates between natural and model-matched sounds, the authors suggest that this is a defining distinction between human and non-human auditory cortex. The analyses are conducted well and I appreciate the authors including a wealth of results, also split up for individual subjects and hemispheres in supplementary figures, which helps the reader get a better idea of the underlying data.

Overall, I think the authors have completed a very nice study and present interesting results that are applicable to the general neuroscience community. I think the manuscript could be improved by using different terminology ('sensitivity' as opposed to 'selectivity'), a larger subject pool (only 2 animals), and some more explanation with respect to data analysis choices.

Reviewer #2 (Public Review):

Hay et al. investigated the effect of optogenetic activation of MS cholinergic inputs on hippocampal spatial memory formation, which extended our current knowledge of the relationship between MS cholinergic neurons and hippocampal ripple oscillations.

The authors showed that optogenetic stimulation at the goal location during Y maze task impaired the formation of hippocampal dependent spatial memory. They also found that opto-stimulation at the goal location reduced the incidence of ripple oscillations, while having no effect on the power and frequency of theta and slow gamma oscillations.

Interestingly, the authors reported different results compared to previously published work by applying the analytical methods developed by Donoghue et al. (Donoghue et al., Nat Neurosci, 2020). They showed that optogenetic activation of MS cholinergic neurons during sleep not only reduced the incidence of hippocampal ripple oscillations, but also increased the power of both theta and slow gamma oscillations, which is contradict to both decreased or no change of theta and gamma power by previous reports (Vandecasteele et al., 2014, Ma et al., 2020). These results are valuable to the community of hippocampal oscillation studies.

Reviewer #2 (Public Review):

In humans, extreme stresses, such as famine, can trigger multi-generational physiological responses through altered metabolism. In C. elegans, environmental stresses, such as heat shock, can similarly promote changes in gene expression and physiology. In addition, researchers observed more than two decades ago that dsRNA triggers can silence gene expression transgenerationally. This manuscript by Houri-Zeevi et al., entitled "Stress resets ancestral heritable small RNA responses", seeks to tie these two observations in C. elegans together mechanistically, showing that environmental stress (heat shock, high osmolarity, or starvation) can alter the small RNA populations in adults and their progeny, affecting their gene expression levels. The authors used a GFP reporter as a proxy for exo-siRNA levels in various experimental paradigms. P0 animals were fed dsRNA targeting the GFP transgene, and their F1 progeny were subjected to one of the environmental stresses. The GFP expression levels of P0, F2, and F2 adults were measured, showing that the stressed F1 and their F2 progeny have increased de-silencing of the GFP transgene compared to controls. The authors also performed small RNA sequencing on these populations, showing that a subset of small RNAs become "reset" or decreased after stress, while a different subset was increased. Additionally, the p38 MAPK pathway, SKN-1 TF, and MET-2 H3K4me1/2 HMT were shown to be required for the stress-dependent changes in transgene de-silencing. The manuscript is well-written and contains some very interesting and convincing results that should be of broad interest to the fields of stress biology and RNAi.

Reviewer #2:

In their paper "A graph-based algorithm called StormGraph for cluster analysis of diverse single-molecule localization microscopy data", Scurll et al. present a new algorithm to identify clusters in single-molecule localization microscopy (SMLM) data. They use graph-based clustering and show that StormGraph outperforms a selection of existing algorithms, both on simulated and experimental data. The improvement seems not huge, but is convincing, thus this work presents an important contribution to the field. Naturally, not all competing algorithms could be benchmarked in comparison to StormGraph, thus it is not clear if this algorithm is indeed among the best performing algorithms. This is especially true for the cross-correlation analysis. If the applicability of the software included with the manuscript was extended to more potential users, this could be a useful contribution to the field. The manuscript is well written, but quite long. The information content would not be jeopardized if part of the main text and some figures were to be moved to the supplementary information or methods section.

Reviewer #2:

This is a very interesting study, examining the properties of different types of neurons in the primate Frontal Eye Fields. It is commonly assumed that a serial processing of information takes place in the frontal lobe, from visual representation, to working memory maintenance, to motor output. However, some evidence to the contrary has also been reported, creating a debate in the field. The authors have characterized meticulously FEF neurons receiving V4 projections, by means of orthodromic stimulation. They report two main findings: that visual-input recipient neurons in FEF exhibit substantial motor activity and that working memory alters the efficacy of V4 input to FEF. The paper provides an important addition to our understanding of FEF processing. Although the first result is unambiguous, and goes against the traditional view of the FEF, the interpretation of the second is less straightforward and would need to be qualified further.

1) Orthodromic activation of FEF neurons via V4 stimulation increases the percentage of FEF events that lead to spikes and decreases their latency during working memory. Such an effect appears expectable if FEF neurons are at a higher level when a stimulus in their receptive field is held in memory compared to a stimulus out of their receptive field. Are the authors suggesting something special about working memory? Would the same outcome not be expected during fixation or smooth pursuit for FEF neurons that are activated by these states? It was not clear that the efficacy of transmission itself improves by working memory, just the likelihood that the spiking threshold would be reached.

2) It would strengthen the author's thesis to discuss the existing functional evidence (in addition to anatomical evidence) that motor FEF neurons receive visual input and can plan movements accordingly. See for example Costello et al. J. Neurosci 2013, 33(41):16394-408.

3) The authors match the receptive location of FEF and V4 neurons to maximize the chances of identifying monosynaptically connected neurons between the two areas. However, a negative finding of ia orthodromic activation does not entirely rule out that the FEF neuron under study receives V4 input, from another site. Some discussion is warranted on this point.

Reviewer #2:

This paper by Har-shai Yahav and Zion Golumbic investigates the coding of higher level linguistic information in task-irrelevant speech. The experiment uses a clever design, where the task-irrelevant speech is structured hierarchically so that the syllable, word, and sentence levels can be ascertained separately in the frequency domain. This is then contrasted with a scrambled condition. The to-be-attended speech is naturally uttered and the response is analyzed using the temporal response function. The authors report that the task-irrelevant speech is processed at the sentence level in the left fronto-temporal area and posterior parietal cortex, in a manner very different from the acoustical encoding of syllables. They also find that the to-be-attended speech responses are smaller when the distractor speech is not scrambled, and that this difference shows up in exactly the same fronto-temporal area--a very cool result.

This is a great paper. It is exceptionally well written from start to finish. The experimental design is clever, and the results were analyzed with great care and are clearly described.

The only issue I had with the results is that the possibility (or likelihood, in my estimation) that the subjects are occasionally letting their attention drift to the task-irrelevant speech rather than processing in parallel can't be rejected. To be fair, the authors include a nice discussion of this very issue and are careful with the language around task-relevance and attended/unattended stimuli. It is indeed tough to pull apart. The second paragraph on page 18 states "if attention shifts occur irregularly, the emergence of a phase-rate peak in the neural response would indicate that bits of 'glimpsed' information are integrated over a prolonged period of time." I agree with the math behind this, but I think it would only take occasional lapses lasting 2 or 3 seconds to get the observed results, and I don't consider that "prolonged." It is, however, much longer than a word, so nicely rejects the idea of single-word intrusions.

Reviewer #2 (Public Review):

Work in the nematode C. elegans has shown that these worms learn to avoid pathogens like Pseudomonas aeruginosa after consumption and infection over a period of 12 or more hours. Here, the authors confirm and expand upon earlier observations that - in contrast to P. aeruginosa - avoidance of Gram-positive pathogens such as E. faecalis, E. faecium and S. aureus occurs rapidly on a timescale as short as even several minutes. Consistent with this more rapid response, they present evidence that behavioral avoidance occurs via distinct molecular, neuronal and phenotypic mechanisms from those of P. aeruginosa.

The first major finding that the authors describe is that behavioral avoidance of E. faecalis occurs as a consequence of rapid intestinal distension and not through immune responses or other pathways. They show that anterior intestinal distension occurs rapidly - as early as 1 hr, which is a striking finding and is consistent with rapid behavioral effects. They show that neither E. faecalis bacterial RNA, nor bacterial virulence are necessary for behavioral avoidance and that immune response genes are induced only after distension. These data are consistent with a model in which intestinal distension underlies behavioral avoidance, but this assertion could be strengthened by showing that bloating is necessary for behavioral avoidance, that it occurs prior to observable behavioral avoidance, and by more definitively ruling out a role for immune responses.

Next, the authors show that behavioral avoidance in laboratory conditions requires intact neuropeptide signaling via the npr-1 receptor and this is because worms tend to avoid high oxygen conditions outside of bacterial lawns that typically exists in the lab. At lower oxygen concentrations, npr-1 is dispensable for avoidance. This is consistent with previous work implicating this neuropeptide pathway in lawn avoidance and is convincingly demonstrated.

The second major finding presented in this manuscript is that rapid behavioral avoidance of Gram-positive bacteria occurs via a learning process involving both gustatory and olfactory neurons. This suggests that worms may rapidly learn to avoid the taste and smell of these bacteria. They show that lawn avoidance of E. faecalis occurs in minutes and coincides with changes in lawn leaving and re-entry rates. They identify sensory neurons involved in lawn avoidance through genetic ablation and cell-specific rescue of signal transduction in the ASE, AWC and AWB neurons. A role for ASE in avoidance is specific to E. faecalis and is a new finding. The authors also show that after a 4hr training exposure to E. faecalis, worms switch from their naïve preference for E. faecalis odors to preferring E. coli odors. This switch in olfactory preference appears to require the AWC and AWB neurons, but not the ASE neurons. While the authors show a clear change in olfactory preference with these data, it is currently unclear whether this reflects associative learning as opposed to non-associative olfactory plasticity resulting from, for example, intestinal distension. Previous work from this group showed that longer-term bloating from bacteria could induce avoidance of different bacteria, arguing against a strictly associative learning role for previously described bloating phenotypes. It is also not currently clear from the authors' data whether ASE plays a role in training-dependent changes in food preference, how this training process relates to the timecourse of intestinal distension, and what role nutrient status might play here.

Lastly, the authors present the intriguing hypothesis that TRPM family channels may sense bloating either directly or indirectly to mediate this colonization-dependent aversive behavior. Mutations in TRPM channels gon-2 and gtl-2 block lawn aversion that occurs after intestinal distension elicited by E. faecalis colonization or through interference with the defecation motor program. The authors convincingly show that these channels, which are expressed in the intestine but also play known roles in the germline, do not act via the germline in this context. The hypothesis that these channels act in the intestine to sense bloating is an exciting and particularly important one; however, both of these channels are known to be expressed in multiple tissues, and there is no data demonstrating a sensory function for these receptors in the intestine as opposed to other roles.

Reviewer #2 (Public Review):

This manuscript addresses how myeloid cells are rapidly regenerated during periods of consumptive stress, such as that what occurs during infection. The authors defined a novel migration pattern activated upon inflammation wherein bone marrow-derived myeloid progenitors rapidly seed lymph nodes to produce dendritic cells. Using an in vivo model (injection of LPS) they demonstrated systemic inflammation was necessary for triggering this migratory pathway. A key observation was that prior to detection in the blood, myeloid progenitors were detected in the lymphatics, including the thoracic duct and lymph nodes. Using a combination of imaging strategies, in vitro assays, and transplantation assays the specific myeloid differentiation of these progenitors was revealed: progenitors in lymphatics did not have stem cell function but maintained potential to generate dendritic cells. Using adoptive transfer experiments they determined that labeled progenitors did not home to the bone marrow after LPS. Moreover, prior to their detection in the lymph nodes, these progenitors were found in close proximity to lymphatic endothelial cells in the bone, as determined with intra vital imaging of Lyve-1-GFP mice. They also observed the existence of Lyve-1+ vessels in the bone of LPS treated mice, rarely observed in controls. Therefore, it was concluded that myeloid progenitors are released from the bone marrow and enter the lymphatics very rapidly upon LPS challenge via a network of lymphatic vessels in the bone.

To determine mechanisms that were required for this migratory pathway, they first focused on the signaling molecule TRAF6, a key signaling protein downstream of TLR signaling. Using Mx1-Cre inducible TRAF6 deficiency they observed reduce mobilization of progenitors and found a cell-autonomous defect in migration towards LPS-stimulated cells in vitro. These chemotactic assays were used to identify the specific role of myeloid cells in driving migration of progenitors. The authors ruled out the role of NF-kB signaling via over-expressing the degradation-resistant mutant of IkBa, but revealed that protein-trafficking was necessary for progenitor mobilization. Analysis of chemokines and potential factors that could drive this trafficking pattern identified the chemokine CCL19 and its receptor CCR7 in migration. In vivo targeting of this pathway via antibody blockade experiments demonstrated that CCL19 and CCR7 were required for the myeloid progenitor mobilization, and, furthermore, that the mature myeloid (CD11b+CD11c+) cells in the LNs were sources of CCL19.

The main strengths of this manuscript include: (1) the intriguing and novel observation of lymphatic migration early during inflammation; (2) the various techniques used to address the questions, including imaging and flow cyotmetric analysis, as well as functional assays; and (3) the thorough mechanistic model they have built through their investigation of signaling molecules and the chemokine-receptor interactions necessary for dendritic cell replenishment. Using the Lyve-1 mouse, they were able to identify vessels in the bone, suggesting a specific route for migration. They were also able to determine that the Lin- progenitors were in close proximity to these vessels upon LPS challenge and differentiated into dendritic cells. The ability of myeloid cells to rapidly release preformed CCL19 was also dependent on TRAF6, thus suggesting that mature cells in the lymph nodes initiate recruitment of CCR7+ myeloid progenitors, highlighting a novel circuitry of regeneration.

This study is very comprehensive, though there are several questions remaining: (1) the conclusion regarding the physiological role of this early response in survival is not well supported by the data; (2) the link with observations in humans is not robust; (3) a number of questions regarding progenitor survival and proliferation remain. First, studies revealing enhanced mortality when CCR7 is blocked or when CCL19 production is lacking may be due to impacts on a variety of other cell types, most notably T regulatory cells. The reason these mice die faster was not carefully investigated and is unclear. While the authors conclude it is due to reduced anti-inflammatory dendritic cells, they provide very little data to support this. Second, data presented in the manuscript highlighting the presence of side population cells in human lymph nodes under specific conditions is consistent with the observations in the mouse model. However, the authors do not investigate functional potential in detail and do not account for abundance of mature cells in these lymph nodes (particularly the lymphoma patients, that may result in decreased frequency of HSPCs). Finally, though the findings are very interesting and the studies are robust, one potential concern is that TRAF6 is downstream of a variety of innate signaling pathways and, in general, the dysfunction of myeloid cells may be profound and beyond the conclusion of directing migration, as TRAF6-dependent proliferation may also contribute to the observations made in vivo.

Overall, this is a compelling story and reveals a novel migratory pathway that may operate in a variety of settings to replenish immune cells to maintain homeostasis, and how this trafficking is impacted in different immune/inflammatory and diseased states warrants more investigation.

Reviewer #2 (Public Review):

The manuscript "Adult Stem Cell-derived Complete Lung Organoid Models Emulate Lung Disease in COVID-19" by Das and colleagues introduces a new model system of airway epithelium derived from adult lung organoids (ALO) to be utilised for the study of COVID-19-related processes. In this manuscript two main novelties are claimed: the development of a new model system which represents both proximal as distal airway epithelium and a computationally acquired gene signature that identifies SARS-CoV-2-infected individuals. While interesting data are presented, the novelty claim is questionable and the data is not always convincing.

Strengths:

Multiple model systems have been developed for COVID-19. The lack of a complete ex vivo system is still hampering quick development of efficient therapies. The authors in this manuscript describe a new model system which allows for both proximal and distal airway infectious studies. While their claim is not completely novel, the method used can be used in other studies for the discovery of potential new therapies against COVID-19. Moreover, their computational analyses shows the promise of bioinformatics in discovering important features in COVID-19 diseased patients which might elucidate new therapeutic targets.

Weaknesses:

Although the paper does have strengths in principle, the weaknesses of the paper are that these strengths are not directly demonstrated and their model system is not completely novel. That is, insufficient analyses are performed to fully support the key claims in the manuscript by the data presented. In particular:

The characterisation of the adult lung organoids and their monolayers is insufficient and sometimes incorrect. Their claims are based on contradicting data which includes cell composition in the culture system. Therefore, the claim of a novel model system seems invalid and rushed. Moreover, the characterisation of a new gene signature is based on this model system which has been infected with SARS-CoV-2. The infection however is hard to interpret and therefore claims are hard to validate.

Reviewer #2 (Public Review):

The recent discovery of CTP as a co-factor for the ParB protein family has prompted the field to revisit all the experimental data and models on ParABS-mediated chromosome/plasmid segregation from the past 35 years. Some recent research has been performed to investigate ParB-CTP interaction and the roles of CTP on ParB spreading/sliding. However, the important roles of CTP on ParB-ParA interaction have not been investigated so far. This manuscript from Taylor et al is the first to investigate this important area, thus this work is timely and is very welcomed. I note that Mizuuchi et al proposed the ground-breaking "diffusion-ratchet" model of plasmid/chromosome segregation, and the latest findings in his manuscript here have very important implications for this model. The work here has been done rigorously; I have read it with much interest.

Neocolonialist strategy is the idea of accommodating new realities as to retain dominance

MYTH 2: Origin Narratives

• Puritan covenant with God to take the land

  • Reinforced by Columbus Myth

    • "Columbia," represented by lady, is found everywhere throughout the USA, in names and idea

  • Reinforced by the "Doctrine of Discovery"

    • European nations acquired titles to lands they discovered and Indigenous inhabitants lost natural right to land after Europeans claimed it.
    • Law of Nations required the subjugation of all people who diverge from European-derived norms of right conduct

  • Reinforced by Academia: Threatened by civil rights

    • Called for "balance," against "moralizing," and pro "culturally relative approach." "There were good and bad people on both sides."

      • "MULTICULTURALISM" is used to support the origin story. "We all got along from the beginning and now we are all a big happy nation"

Origin Story myth is perpetuated by idea of multiculturalism

MULTICULTURALISM: US history revision that emphasizes the "contributions" of ethnic groups to the United States, while obscuring the fact that these groups were instead PLUNDERED of their natural resources - it was not a consensual giving process.

Danger of accepting origin myth 2: put asides responsibility for continued harm done by past - puts aside option of reparations, restitution, and reordering of society.

(Why the Origin Myth is currently harmful)

Academics attempt to justify settler colonialism and origin MYTH, with idea that there was dialogue between settler and indigenous, when in reality it was one-sided robbery and murder.

Reviewer #2 (Public Review):

The authors have been able to carry out a well-planned countrywide sero-survey in a cohort of 10,427 employees of their organization with 23 laboratories spread over 17 states and 2 union territories. The reported sero-positivity of 10.14% among persons mainly from cities and towns, helps understand the spread of the pandemic across the country and corelates well with the point prevalence of active infections in the various states of India during the same period. It helps understand the role of asymptomatic cases in increasing sero-positivity as 2/3 of the personnel could not remember any symptoms or illness.

Strengths:

1) The strength of this study is a large pan India cohort with all demographic details captured, which can be easily followed up. The sero-positivity datasets corelate well with the national Covid cases data in the states of India as reported in the public domain during the same time frame. The time period of Aug Sept after the mass migration of labourers from cities to rural India was possibly responsible for a quick spread of the infection and this study is able to capture the same effectively.

2) The study has also correlated the antibodies to Nuclear Capsid Antigen with the Neutralizing antibody levels and the correlation is good. However, this needs to be followed up to interpret humoral stability especially with the interesting observation of declining Antibodies to nuclear capsid antigen at six months but levels of neutralizing antibodies being stable after an initial drop at three months.

3) The study demonstrates an inverse correlation between the changes in test positivity rate and sero-positivity suggesting reduced transmission with increasing sero-positivity. The sero-positivity was higher in densely populated areas suggesting faster transmission.

Weakness:

1) The extrapolation of the study results to the country may not be completely acceptable with the basic difference from the country's urban rural divide and a largely agricultural economy. The female gender is underrepresented in the study cohort, and no children have been included.

2) The observations regarding corelates of sero-positivity such as diet smoking etc would need specifically designed adequately powered studies to confirm the same. The sample size for the three and six months follow up to conclude stability of the humoral immunity, is small and requires further follow-up of the cohort. The role of migration of labour helping the spread of the pandemic simultaneously to all parts of the country though attractive may not explain lower rates in states like UP and Bihar where maximum migrants moved to.

3) A large chunk of seropositive data set has been removed representing the big cities of Delhi and Bengaluru while correlating Test Positivity Rate citing duration as the reason. However, these cities also had different testing strategies and health infrastructure and hence are important.

4) Test positivity rate depends on testing strategy and type of test used; whether RTPCR or the Rapid Antigen Test and the ratio of the two tests was different in different parts of the country.

Overall a good study where the authors have been able to effectively show a relatively high sero-positivity than reported infections possibly due to asymptomatic cases. It will be able to provide insight into immune memory in COVID 19 as they continue with follow-up quantitative sero-assay for the cohort

Reviewer #2 (Public Review):

Hesse et al. implemented a murine model of cardiac ischemia to study two populations, epicardial stromal cells (EpiSC) and activated cardiac stromal cells (aCSC). Furthermore, uninjured cardiac stromal cells were used as a control. An isolation method for EpiSC was used by applying a gentle shear force to the cardiac surface. The authors show heterogeneity in the Epi-SC populations. Certain markers were confirmed by in-situ hybridization. Furthermore, molecular programs within these subsets were explored. A comparison between EpiSC and aCSCs cells (and EpiSC and uninjured CSCs cells) was performed, which showed differences in expression of multiple genes namely HOX, HIF1 and cardiogenic factor genes. A WT1 population was marked by tdTomato, confirming the localization of expression to a cell population. There are however specific weaknesses. First, a major concern is regarding clarity of the experimental conditions and sample purity. Data is not robustly presented showing differences across conditions, namely between uninjured CSCs and activated CSCs. Furthermore, the purity of isolating EpiSC was not explored, along with the anticipated overlap of cells between aCSC and EpiSC. Specifically, the in-situ findings do not clarify the subject of purity. For example, EpiSC-3 (Pcsk6) is a large population in the scRNA-seq shown in Fig 1; however, this gene is also expressed in the myocardium. There is an attempt to perform EpiSC and aCSC comparison analysis in Figure 3; however without clarity the expected overlap, these data are hard to interpret. Furthermore, cluster-based approaches for comparing population fractions can be problematic due to the inherent stochasticity of sampling. Lastly, there is no actual lineage tracing over time, but rather marking of WT1 cells with tdTomato. The RNA velocity analysis is not particularly robust with the number of expressed genes driving these results, rather than the author's conclusion of developmental potential.

Reviewer #2 (Public Review):

In their study, Lutes et al examine the fate of thymocytes expressing T cell receptors (TCR) with distinct strengths of self-reactivity, tracking them from the pre-selection double positive (DP) stage until they become mature single positive (SP) CD8+ T cells. Their data suggest that self-reactivity is an important variable in the time it takes to complete positive selection, and they propose that it thus accounts for differences in timescales among distinct TCR-bearing thymocytes to reach maturity. They make use of three MHC-I restricted T cell receptor transgenics, TG6, F5, and OT1, and follow their thymic development using in vitro and in vivo approaches, combining measures at the individual cell-level (calcium flux and migratory behaviour) with population-level positive selection outcomes in neonates and adults. By RNA-sequencing of the 3 TCR transgenics during thymic development, Lutes et al make the additional observation that cells with low self-reactivity have greater expression of ion channel genes, which also vary through stages of thymic maturation, raising the possibility that ion channels may play a role in TCR signal strength tuning.

This is a well-written manuscript that describes a set of elegant experiments. However, in some instances there are concerns with how analyses are done (especially in the summaries of individual cell data in Fig 2 and 3), how the data is interpreted, and the conclusions from the RNA-seq with regard to the ion channel gene patterns are overstated given the absence of any functional data on their role in T cell TCR tuning. As such the abstract is currently not an accurate reflection of the study, and the discussion also focuses disproportionately on the data in the final figure, which forms the most speculative part of this paper.

(1) As the authors themselves point out (discussion), one of the strengths of this study is the tracking of individual cells, their migratory behaviour and calcium flux frequency and duration over time. However, the single-cell experiments presented (Figure 2 and 3) do not make use of the availability of single-cell read-outs, but focus instead on averaging across populations. For instance, Figure 3a,b provides only 2 sets of examples, but there is no summary of the data providing a comparison between the two transgenics across all events imaged. In Figure 3c, the question that is being asked, which is to test for between-transgenic differences is ultimately not the question that is being answered: the comparison that is made is between signaling and non-signaling events within transgenics. However, this latter question is less interesting as it was already shown previously that thymocytes pause in their motion during Ca flux events (as do mature T cells). Moreover, the average speed of tracks is probably not the best measure here in reading out self-reactivity differences between TCR transgenic groups.

(2) The authors conclude from their data that the self-reactivity of thymocytes correlates with the time to complete positive selection. However the definition of what this includes is blurry. It could be that while an individual cell takes the same amount of time to complete positive selection (ie, the duration from the upregulation of CD69 until transition to the SP stage is the same), but the initial 'search' phase for sufficient signaling events differs (eg. because of lower availability of selecting ligands for TG6 than for OT1), in which case at the population level positive selection would appear to take longer. Given that from Fig 2/3 it appears that both the frequency of events and their duration differ along the self-reactivity spectrum, this needs to be clarified. Moreover, whether the positive selection rate and positive selection efficiency can be considered independently is not explained. It appears that the F5 transgenic in particular has very low positive selection efficiency (substantially lower %CD69+ and of %CXCR4-CCR7+ cells than the OT1 and TG6) and how this relates to the duration of positive selection, or is a function of ligand availability is unclear.

(3) While the question of time to appearance of SP thymocytes of distinct self-reactivities during neonatal development presented (Figure 5) is interesting, it is difficult to understand the stark contrast in time-scales seen here compared with their in vitro thymic slice (Figure 4) and in vivo EdU-labelling data (Figure 6), where differences in positive selection time was estimated to be ~1-2 days between TCR transgenics of high versus low affinity. This would suggest that there may be other important changes in the development of neonates to adults not being considered, such as the availability of the selecting self-antigens.

(4) The conclusion that "ion channel activity may be an important component of T cell tuning during both early and late stages of T cell development" is not supported by any data provided. The authors have shown an interesting association between levels of expression of ion channels, their self-affinity and the thymus selection stage. However, some functional data on their expression playing a role in either the strength of TCR signaling or progression through the thymus (for instance using thymic slices and the level of CD69 expression over time), would be needed to make this assertion. Moreover, from how the data is presented it is difficult to follow the conclusion that a 'preselection signature' is retained by the low but not the high self-reactivity thymocytes.

Reviewer #2 (Public Review):

Using budding yeast, the authors have generated transcriptome and proteome data for a series of experimental conditions, augmented with measurement of some amino acid abundances. These data are subjected to a number of correlation and enrichment analyses. Based on those, the authors put forward a verbal "model of information flow, material flow and global control of material abundance".

The main message of this paper was not sufficiently clear because at different places of the manuscript the authors highlight different aspects: Based on the title it seems that the "distinct regulation" is the key aspect. Notably, however, this point has only a minor role in the manuscript itself. In the abstract, it seems that the key aspect is a "framework", although after having read the paper it was not clear what the authors mean with the term. Later in the manuscript the authors also use the term "coarse-graining approach", but it was not clear whether this is the same as the "framework". Beyond, throughout the manuscript, the authors make the point that global physiological parameters (such as growth rate) determine gene and protein expression level. Even though this point is important and often overlooked, it has been made before in several papers, which the authors also cite. Thus, this aspect mostly provides confirmation of previous work. Finally, at the end of the introduction, where the authors refer to "our findings... ", it is unclear to which findings they particularly refer to.

The manuscript could be clearer in certain specific aspects:

1) The paper uses lots of terms that are not well defined: For instance, it is not explained well what the authors mean by "metabolic parameters". I know metabolite concentrations, and metabolic fluxes, but I don't know what metabolic parameters are. It is also not explained well what is meant with "global control mechanisms" and what is meant by "augment".

2) Similarly, this lack of clarity also exists when the authors step from a particular analysis (i.e. a correlation) to a conclusion statement. The hard evidence supporting particular statements is not sufficiently explained.

DOI: 10.1016/j.celrep.2020.108039

Resource: (ZFIN Cat# ZDB-ALT-001220-2,RRID:ZFIN_ZDB-ALT-001220-2)

Curator: @scibot

SciCrunch record: RRID:ZFIN_ZDB-ALT-001220-2

What is this?

Reviewer #2 (Public Review):

The research community has been frustrated by difficulties in using AAVs to obtain robust experimental access to neurons co-expressing Cre and Flp recombinase (often called the intersectional approach). In many cases, the approach is sufficiently inefficient as to not be usable. This is in part due to difficulties in designing AAVs that will efficiently express protein-encoded tools in a Cre-ON/Flp-ON fashion, and in part due to the relative inefficiency of Flp recombinase. This present study presents a new intersectional approach for solving this problem. The approach involves co-injecting two AAVs into sites in the brain where Cre/Flp-co-expressing neurons reside - in this case, neurons in the ventromedial nucleus of the hypothalamus (VMH) which co-expresses VGLUT2 (Slc17a6)-Flp and Leptin receptor (Lepr)-Cre. One of the AAVs, in a Flp-dependent fashion, expresses the tTA transcriptional activator, while the other AAV, in a tTA and Cre-dependent fashion, expresses the protein-encoded tool. This new system produced robust expression in neurons co-expressing Flp and Cre in the VMH which previously could not be accomplished using existing intersectional AAVs. The authors also demonstrate a Flp-ON/Cre-OFF version of this approach. Finally, by using these tools the authors show, as was suspected based on prior work, that the Lepr/Vglut2-coexpressing VMH neurons increase brown fat thermogenesis and energy expenditure when stimulated. The results presented very strongly support the effectiveness of this new approach. The only weakness of this study is that, at this point in time, the universality of this approach for all Cre/Flp-co-expressing neurons is unknown. Its effectiveness was only evaluated in VMH neurons. While it is expected that this approach will work for most or all Cre/Flp-co-expressing neurons, there is anecdotal evidence of this or that AAV approach not working in this or that neuron.

Reviewer #2 (Public Review):

• The aim of this paper was to demonstrate whether FLIM-based imaging of optical redox ratio can be used to monitor metabolic states of immune cells in vivo during the course of inflammatory responses.

• The study is rigorous and well-presented and the findings are interesting and novel. The main strength is in the in vivo data, where the authors used a variety of inflammatory challenges and perturbations and were able to detect previously unreported trends in metabolic states of macrophages.

• The authors have demonstrated the potential of the technique to be used in vivo. Their initial findings are intriguing and can be followed up by more mechanistic studies.

• The work is timely, because of growing interest in the role of metabolism in immune cell signalling and functions. Relevant microscopy-based assays in vivo are limited, so this innovation is important and can form the basis of further technology developments.

Reviewer #2 (Public Review):

Here are three notable examples (among a long list of new discoveries). (1) The authors provided a comprehensive description of the antennal lobe local interneuron (LN) network for the first time, providing a "final" counts of neuronal number and type of LNs as well as the preference for the input and output partners of each LN type. (2) They introduced "layer" as a quantitative parameter to describe how many synapses away on average a particular neuron or neuron type is from the sensory world. A few interesting new discoveries from this analysis include that on average, multi-glomerular antennal lobe projection neurons (PNs) are further away from the sensory world than uniglomerular PNs; inhibitory lateral horn neurons are closer to the sensory world than excitatory lateral horn neurons. (3) By leveraging previous analyses they performed on another EM volume (FAFB) and comparing n = 3 (bilateral FAFB, unilateral hemibrain) samples, they analyzed stereotypy and variability of neurons and connections, something rarely done in serial EM reconstruction studies but is very important.

Overall, the text is clearly written, figures well illustrated, and quantitative analysis expertly performed. I have no doubt that this work will have long-lasting values for anyone who study the fly olfactory system, and for the connectomics field in general.

Reviewer #2 (Public Review):

Open source software for data rendering in neuroanatomy is either too specific to be generically useful (for example, designed for only one specific brain atlas, or brain atlases of a single species), or too general, and thus not integrated with atlases or other relevant software. Additionally, despite the growing popularity of the Python programming language in science, 3D rendering tools in Python are still very limited. Claudi et al have sought to narrow both of these gaps with brainrender. Biologists can use their software to display co-registered data on any atlas available through their AtlasAPI, explore the data in 3D, and create publication quality screenshots and animations.

The authors should be commended for the level of modularity they have achieved in the design of their software. Brainrender depends on atlasAPI (Claudi et al, 2020), which means that compatibility for new atlases can be added in that package and brainrender will support them automatically. Similarly, by supporting standard data storage formats across the board, brainrender lets users import data registered with brainreg (Tyson et al, 2020), but does not depend on brainreg for its functionality.

Like all software, brainrender still has limitations. For example, it's unclear from the paper exactly what input and output formats are supported, particularly from the GUI. Additionally, at publication, using the software still requires a Python installation, with all the complexity that currently entails. However, thanks to the rich and growing scientific Python ecosystem, including application packaging tools, I am confident that the authors, perhaps in collaboration with some readers, will be able to address these issues as the software matures.

Reviewer #2 (Public Review):

A summary of what the authors were trying to achieve. This interesting and data-rich paper reports the results of several detailed experiments on the pollination biology of the dioceus plant Silene latfolia. The authors uses multiple accessions from several European (native range) and North American (introduced range) populations of S. latifolia to generate an experimental common garden. After one generation of within-population crosses, each cross included either two (half-)siblings or two unrelated individuals, they compared the effects of one-generation of inbreeding on multiple plant traits (height, floral size, floral scent, floral color), controlling for population origin. Thereby, they set out to test the hypothesis that inbreeding reduces plant attractiveness. Furthermore, they ask if the effect is more pronounced in female than male plants, which may be predicted from sexual selection and sex-chromosome-specific expression, and if the effect of inbreeding larger in native European populations than in North American populations, that may have already undergone genetic purging during the bottleneck that inbreeding reduces plant attractiveness. Finally, the authors evaluate to what extent the inbreeding-related trait changes affect floral attractiveness (measured as visitation rates) in field-based bioassays.

An account of the major strengths and weaknesses of the methods and results. The major strength of this paper is the ambitious and meticulous experimental setup and implementation that allows comparisons of the effect of multiple predictors (i.e. inbreeding treatment, plant origin, plant sex) on the intraspecific variation of floral traits. Previous work has shown direct effects of plant inbreeding on floral traits, but no previous study has taken this wholesale approach in a system where the pollination ecology is well known. In particular, very few studies, if any, has tested the effects of inbreeding on floral scent or color traits. Moreover, I particularly appreciate that the authors go the extra mile and evaluate the biological importance of the inbreeding-induced trait variation in a field bioassay. I also very much appreciate that the authors have taken into account the biological context by using a relevant vision model in the color analyses and by focusing on EAD-active compounds in the floral scent analyses.

The results are very interesting and shows that the effects of inbreeding on trait variation is both origin- and sex-dependent, but that the strongest effects were not always consistent with the hypothesis that North American plants would have undergone genetic purging during a bottleneck that would make these plants less susceptible to inbreeding effects. The authors made a large collection effort, securing seeds from eight populations from each continent, but then only used population origin and seed family origin as random factors in the models, when testing the overall effect of inbreeding on floral traits. It would have been very interesting with an analysis that partition the variance both in the actual traits under study and in the response to inbreeding to determine whether to what extent there is variation among populations within continents. Not the least, because it is increasingly clear that the ecological outcome of species interactions (mutualistic/antagonistic) in nursery pollination systems often vary among populations (cf. Thompson 2005, The geographic mosaic of coevolution), and some results suggest that this is the case also in Hadena-Silene interactions (e.g. Kephardt et al. 2006, New Phytologist). Furthermore, some plants involved in nursery pollination systems both show evidence of distinct canalization across populations of floral traits of importance for the interaction (e.g. Svensson et al. 2005), whereas others show unexpected and fine-grained variation in floral traits among populations (e.g. Suinyuy et al. 2015, Proceedings B, Thompson et al. 2017 Am. Nat., Friberg et al. 2019, PNAS). Hence, it is possible that the local population history and local variation in the interactions between the plants and their pollinators may be more important predictors for explaining variation in floral trait responses to inbreeding, than the larger-scale continental analyses. Not the least, because North American S. latifolia probably has multiple origins, with subsequent opportunity for admixture in secondary contact.

I see no major weaknesses in the study, and but in my detailed response, I have made a few questions and suggestions about the floral scent analyses. In short, the authors have used a technique that is not the standard method used for making quantitative floral scent analyses, and I am curious about how it was made sure that the results obtained from the static headspace sampling using PDMS adsorbents could be used as a quantitative measure. I would suggest the authors to validate the use of this method more thoroughly in the manuscript, and have detailed this comment in my response to the authors.

Also, and this may seem like a nit-picky comment, I am not convinced that the best way to describe the traits under study is "plant attractiveness", because in the experimental bioassays, most of the traits under study that are affected by the inbreeding treatment, did not result in a reduced pollinator visitation. Most (or all) of these traits may also be involved in other plant functions and important for other interactions, so I suggest potentially using a term like "floral traits" or "(putative) signalling traits".

An appraisal of whether the authors achieved their aims, and whether the results support their conclusions: By and large, the authors achieved the aims of this study, and drew conclusions based in these results. One interesting aspect of this work that I think could be discussed a bit deeper is the lack of congruence between the effects of inbreeding on floral traits and the variation in visitation pattern in the bioassay. In fact, the only large effect of inbreeding on a floral trait that may play a role as an explanatory factor is the reduction of emission of lilac aldehyde A in inbred female S. latifolia from North America, which correspond to a reduced visitation rate in this group in the pollinator visitation bioassay. I have made some specific suggestions in my comments to the authors.

A discussion of the likely impact of the work on the field, and the utility of the methods and data to the community: I think that one important aspect of this work that may broaden the impact of this study further is the link between these experiment, and our expectations from the evolution of selfing. Selfing plant species most often conform to the selfing syndrome, presenting smaller, less scented flowers than outcrossing relatives. Traditionally, the selfing syndrome is explained by natural selection against individuals that invest energy into floral signalling, when attracting pollinators is no longer crucial for reproduction. Some studies (for example Andersson, 2012, Am. J. Bot), however, have shown that only one, or a few, generations of inbreeding may reduce floral size as much as quite strong selection for reduced signalling. Here, at least for some populations and sexes, similar results are obtained in this paper regarding several traits (including floral scent), and one way to put this paper in context is by discussing the results in the light of these previous papers.

Any additional context that would help readers interpret or understand the significance of the work: I would like to reiterate here the potential to utilize the population sampling to make additional conclusions about the geography of trait variation and its importance for the phenotypic response to inbreeding.

Reviewer #2 (Public Review):

The authors showed that the TNX treatment is able to reduces the liver steatosis. But, a lot of results are contradictory. Fer example, the PPAR-gamma is well known insulin sensitizing and the authors did not show the effect of the ntagonism on PPAR-gamma in insulin and glucose homeostasis. Moreover, more analyzis about the adipose tissue are mandatory, since the inhibition of PPAR-gamma might induce the pro-inflammatory status. Thus, to publish in this outstanding journal it is necessary additional experiments to proof that the PPAr-gamma is the main pathway of beneficial effects of TXN.

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

Resource: (ZFIN Cat# ZDB-ALT-130816-2,RRID:ZFIN_ZDB-ALT-130816-2)

Curator: @scibot

SciCrunch record: RRID:ZFIN_ZDB-ALT-130816-2

What is this?

DOI: 10.7554/eLife.64267

Resource: (ZFIN Cat# ZDB-ALT-110721-2,RRID:ZFIN_ZDB-ALT-110721-2)

Curator: @scibot

SciCrunch record: RRID:ZFIN_ZDB-ALT-110721-2

What is this?

DOI: 10.7554/eLife.64267

Resource: (ZFIN Cat# ZDB-ALT-081027-2,RRID:ZFIN_ZDB-ALT-081027-2)

Curator: @scibot

SciCrunch record: RRID:ZFIN_ZDB-ALT-081027-2

What is this?

DOI: 10.7554/eLife.64267

Resource: (ZFIN Cat# ZDB-ALT-130314-2,RRID:ZFIN_ZDB-ALT-130314-2)

Curator: @scibot

SciCrunch record: RRID:ZFIN_ZDB-ALT-130314-2

What is this?

DOI: 10.7554/eLife.64267

Resource: (ZFIN Cat# ZDB-ALT-120103-2,RRID:ZFIN_ZDB-ALT-120103-2)

Curator: @scibot

SciCrunch record: RRID:ZFIN_ZDB-ALT-120103-2

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

Resource: (ZFIN Cat# ZDB-ALT-060919-2,RRID:ZFIN_ZDB-ALT-060919-2)

Curator: @scibot

SciCrunch record: RRID:ZFIN_ZDB-ALT-060919-2

What is this?

DOI: 10.1523/ENEURO.0022-20.2020

Resource: (ZFIN Cat# ZDB-GENO-180220-2,RRID:ZFIN_ZDB-GENO-180220-2)

Curator: @scibot

SciCrunch record: RRID:ZFIN_ZDB-GENO-180220-2

What is this?

DOI: 10.7554/eLife.56725

Resource: (ZFIN Cat# ZDB-GENO-070810-2,RRID:ZFIN_ZDB-GENO-070810-2)

Curator: @scibot

SciCrunch record: RRID:ZFIN_ZDB-GENO-070810-2

What is this?

DOI: 10.7554/eLife.60445

Resource: (ZFIN Cat# ZDB-GENO-170316-2,RRID:ZFIN_ZDB-GENO-170316-2)

Curator: @scibot

SciCrunch record: RRID:ZFIN_ZDB-GENO-170316-2

What is this?

DOI: 10.1126/sciadv.aaz3173

Resource: (ZFIN Cat# ZDB-ALT-100505-2,RRID:ZFIN_ZDB-ALT-100505-2)

Curator: @scibot

SciCrunch record: RRID:ZFIN_ZDB-ALT-100505-2

What is this?

DOI: 10.7554/eLife.55792

Resource: (ZFIN Cat# ZDB-ALT-150922-2,RRID:ZFIN_ZDB-ALT-150922-2)

Curator: @scibot

SciCrunch record: RRID:ZFIN_ZDB-ALT-150922-2

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

Comment: See Table S2 for details

AssayResult: 80

AssayResultAssertion: Normal

Comment: See Table S2 for details

AssayResult: 69

AssayResultAssertion: Normal

Comment: See Table S2 for details

AssayResult: 99

AssayResultAssertion: Normal

Comment: See Table S2 for details

AssayResult: 98

AssayResultAssertion: Normal

Comment: See Table S2 for details

AssayResult: 77

AssayResultAssertion: Normal

Comment: See Table S2 for details

AssayResult: 105

AssayResultAssertion: Normal

Comment: See Table S2 for details

AssayResult: 96

AssayResultAssertion: Normal

Comment: See Table S2 for details

AssayResult: 104

AssayResultAssertion: Normal

Comment: See Table S2 for details

AssayResult: 99

AssayResultAssertion: Normal

Comment: See Table S2 for details

AssayResult: 83

AssayResultAssertion: Normal

Comment: See Table S2 for details

AssayResult: 91

AssayResultAssertion: Normal

Comment: See Table S2 for details

AssayResult: 86

AssayResultAssertion: Normal

Comment: See Table S2 for details

AssayResult: 56

AssayResultAssertion: Normal

Comment: See Table S2 for details

AssayResult: 61

AssayResultAssertion: Normal

Comment: See Table S2 for details

AssayResult: 51

AssayResultAssertion: Normal

Comment: See Table S2 for details

AssayResult: 66

AssayResultAssertion: Normal

Comment: See Table S2 for details

AssayResult: 60

AssayResultAssertion: Normal

Comment: See Table S2 for details

AssayResult: 65

AssayResultAssertion: Normal

Comment: See Table S2 for details

AssayResult: 66

AssayResultAssertion: Normal

Comment: See Table S2 for details

AssayResult: 45

AssayResultAssertion: Normal

Comment: See Table S2 for details

AssayResult: 58

AssayResultAssertion: Normal

Comment: See Table S2 for details

AssayResult: 49

AssayResultAssertion: Normal

Comment: See Table S2 for details

AssayResult: 83

AssayResultAssertion: Normal

Comment: See Table S2 for details

AssayResult: 84

AssayResultAssertion: Normal

Comment: See Table S2 for details

AssayResult: 91

AssayResultAssertion: Normal

Comment: See Table S2 for details

AssayResult: 94

AssayResultAssertion: Normal

Comment: See Table S2 for details

AssayResult: 97

AssayResultAssertion: Normal

Comment: See Table S2 for details

AssayResult: 98

AssayResultAssertion: Normal

Comment: See Table S2 for details

AssayResult: 96

AssayResultAssertion: Normal

Comment: See Table S2 for details

AssayResult: 105

AssayResultAssertion: Normal

Comment: See Table S2 for details

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.