Reviewer #2 (Public Review):

The mitotic spindle of eukaryotic cells is a microtubule-based assembly responsible for chromosome segregation during cell division. For a given cell type, the steady-state size and shape of this structure are remarkably consistent. How this morphologic consistency is achieved, particularly when one considers the complex interplay between dynamic microtubules, spatial and temporal regulation of microtubule nucleation, and the activities of several microtubule-based motor proteins, remains a fundamental unanswered question in cell biology. In this work by Richter et al., the authors use biochemical and biophysical perturbations to explore the feedback between mitotic spindle shape and the dynamics of one of its main structural elements, kinetochore fibers (k-fibers) - bundles of microtubules that extend from kinetochores to spindle poles. Overexpression of the p50 dynactin subunit in mammalian tissue culture cells (Ptk2) was used to inhibit the microtubule motor cytoplasmic dynein resulting in misshapen spindles with unfocused poles. Measurements of k-fiber lengths in control and unfocused conditions showed that although mean k-fiber length was not statistically different, the variation of length was significantly higher in unfocused spindles, suggesting that k-fiber length is set locally, occurring in the absence of focused poles. With a clever combination of live-cell imaging with photoablation and/or photobleaching of fluorescently-labeled k-fibers, the authors went on to explore the mechanistic bases of this length regulation. K-fiber regrowth following ablation occurred in both conditions, albeit more slowly in unfocused spindles. Paired ablation and localized photobleaching on the same k-fiber revealed that microtubule dynamics, specifically those at the plus-end, can be tuned at the level of individual k-fiber. Lastly, the authors show that chromosome segregation is severely impaired when cells with unfocused spindles are forced to enter mitosis. The work's biggest strength is the application of an innovative experimental approach to address thoughtful and well-articulated hypotheses and predictions. Conclusions stemming from the experiments are generally well-supported, though the experiments addressing the "tuning" of k-fiber dynamics could be bolstered by additional data points and perhaps better presented. The manuscript would also benefit from the inclusion of some investigation of spatial differences in the observed effects as well as the molecular and biophysical basis of the observed feedback between k-fiber length and focused poles.

Comments/Concerns/Questions:

1) In the discussion, the authors acknowledge that the changes in spindle morphology resulting from p50 overexpression are likely also causing changes in the well-characterized RanGTP/SAF gradients that radiate from chromosome surfaces. Why did the authors did not include an analysis of k-fiber length as a function of positioning within the spindle? The inclusion of this data would not require more experimentation and could be added as a plot showing K-fiber length versus distance from the geometric center of the spindle (defined by the intersection of the major and minor axes perhaps?).<br /> 2) The authors also acknowledge the established relationship between MT length and MT end dynamics, yet in their ablation studies, the average initial k-fiber length at ablation in control spindles was higher than that for k-fibers in unfocused spindles. It seems that this difference makes the interpretation of the data, particularly the conclusion that fiber growth rates differ due to the absence of focused poles, a bit tenuous. To address this, the authors should consider including plots of grow-back rates versus k-fiber length (again, this should not require additional experiments, just more analysis).<br /> 3) As presented, the data shown in Figure 4 is confusing and does not seem very compelling. The relationship between the kymographs and time series is unclear as is the relationship between the dashed lines in the kymographs and the triangles and the plots in the 4B time series and 4C, respectively. Furthermore, it's not always clear what the triangles are pointing to (e.g. in the unfocused condition time series). The authors might want to consider reworking this figure and providing more measurements of flux following ablation in both the control and unfocused conditions. Lastly, the authors should clarify what negative displacement means.

Reviewer #2 (Public Review):

To provide context into the HIV epidemic in Botswana over the latter half of the 20th century and the beginning of the 21st, the authors have analyzed micro census data to examine patterns of migration. They use this dataset to show how patterns between urban and rural areas have changed over several decades, and the demographic characteristics of migrants. The dataset used for this study is a very reliable source, and the insights in terms of migration patterns are interesting. The primary weakness of the analyses regards the link to HIV transmission: micro-census data only examine mobility that leads to individuals changing residence for longer periods of time, without accounting for shorter-term trips that may also lead to HIV transmission, such as seasonal migration or short trips. This is likely less of an issue with HIV than other diseases, however, due to its transmission often involving new sexual partners, which will generally be less likely to occur during short trips. Broadly, however, this is an interesting report on the migration patterns during a critical period for HIV transmission nationwide.

Reviewer #2 (Public Review):

This paper addresses the specific function of p38γ/p38δ isoforms in inflammation. This was achieved by developing a novel mouse model in which p38γ was replaced by a kinase-inactive mutant (D171A mutation in a p38δ knock out background (p38γ/δKIKO). The results demonstrate that the p38γ/p38δ MAPKs are required for regulating the expression of inflammatory mediators implicated in the innate immune response. The phosphorylation of the transcription factor MEF2D at Ser444 constitutes one potential mechanism by which p38γ/p38δ suppresses iNOS and IL-1β mRNA expression.

The strength of this paper resides in the novelty of the mouse model that permitted to assess the specific requirement of p38γ/p38δ isoforms independently of the loss of TPL2 expression caused by compound deletion of the p38γ/p38δ alleles. The finding that p38γ/p38δ MAPKs inhibit MEF2D activity by phosphorylation at Ser444 is also novel.

One weakness lies in the lack of consistency between the expression profiles performed by RNA-seq/qPCR/cytokine arrays to identify inflammatory mediators whose expression is dependent on p38γ/δ in the two in vivo models of septic shock (i.e. fungal infection and induced by LPS) and in LPS activated macrophages in vitro.

The other issue is that gene expression analyses are performed using bone marrow-derived macrophages (BMDM) (Figs. 3 and 5A), whereas the proteomic analysis employs peritoneal macrophages given that "p38γ and p38δ are expressed at much higher levels in these macrophages than in BMDM (p11)" (Fig. 4). Although the authors state on p11 "Additionally, the LPS-induced cytokine production in peritoneal macrophages was comparable to that of BMDM", only two cytokines were measured, i.e. IL1b and IFNg (SI Appendix Fig. S4B). This really emphasises the importance of verifying that i) MEF2D is indeed a substrate of p38δ in macrophages and ii) p38γ/δ-mediated phosphorylation of MEF2D at Ser444 negatively regulates the expression of iNOS and IL-1β transcripts in macrophages.

Finally, no experiment was performed to demonstrate that the lower fungal burden or increased survival rate following LPS-induced sepsis in p38γ/δKIKO mice (Fig. 1) is a consequence of impaired production of inflammatory mediators by p38γ/δKIKO macrophages. This important issue should be addressed.

Reviewer #2 (Public Review):

The significance of these findings is that the role of B cells in mediating cardiometabolic complications in PCOS is not completely understood. The approach taken by this research group is both innovative and translational. One of the clear strengths of this manuscript is that it combines basic research with clinical studies in PCOS women.

Reviewer #2 (Public Review):

This study explores the variability of cerebellar anatomy in the mammal. By capturing a set of anatomical measures in the cerebellum and including previously reported cerebral and cerebellar metrics in a set of 58 different mammalian species, this study depicts both consistency and heterogeneity in the co-occurrence of different brain features, with a focus on cerebellar structures such as folial wavelength or median depth of the molecular layer. This is very informative as the cerebellum is currently under-explored and the phylogenetic aspect of this work gives insights into evolutionary processes linked to the morphology of the cerebellum.

Strengths:

- The methods used to capture the different brain features are relevant, and include the reuse of previously reported metrics, which makes sense and valorises the previous work of other teams.<br /> - One interesting novel method to detect the depth of the molecular layer is implemented.<br /> - A generous amount of results are reported (including correlations, phylogenetic principal component analyses, ancestor character state estimation, and allometries), with visually effective figures to support them.<br /> - A remarkable effort has been made to make data and code available, which will be of great use to the community.

Weaknesses:

- The methods section does not address all the numerical methods used to make sense of the different brain metrics. In the results section, it sometimes makes it difficult for the reader to understand the reason for a sub-analysis and the interpretation of the numerical findings.<br /> - The originality of the article is not sufficiently brought forward:<br /> a) the novel method to detect the depth of the molecular layer is not contextualized in order to understand the shortcomings of previously-established methods. This prevents the reader from understanding its added value and hinders its potential re-use in further studies.<br /> b) The numerous results reported are not sufficiently addressed in the discussion for the reader to get a full grasp of their implications, hindering the clarity of the overall conclusion of the article.

Reviewer #2 (Public Review):

Root growth is driven by cell elongation, and its local control allows roots to navigate the complex soil environment. Cell growth is driven by the relaxation of the cell wall, a process requiring a drop in pH. Auxin is a key regulator of root development that inhibits root growth. Auxin effects on proton dynamics are complex, it can promote both acidification and alkalinization of the extracellular space through different signaling modules, some only recently uncovered. Serre et al. report on using a new dye to monitor extracellular pH in the region surrounding the Arabidopsis thaliana root. Their manuscript aims to clarify the relationships between pH around the root, proton flux, auxin, cell elongation, and root growth with this tool. They show a typical zonation of pH values along the root: a more acidic domain corresponding to the transit-amplifying compartment, followed by a more alkaline one at the transition and early elongation zones and a more acidic one in the late elongation/root hair zone. This zonation is in agreement with previous reports obtained by other methods. A particularly puzzling aspect is the origin of the more alkaline domain. Serre et al. present evidence supporting the involvement of the AUX1-AFB1-CNGC14 module for the emergence of this more alkaline domain and how it can contribute to the ability of the root to navigate its environment.

Serre et al. show that the more alkaline domain in the transition zone is not directly determined by the activity or localization of the AHA proton pumps but rather by the auxin influx carrier AUX1. They show that the components of the rapid auxin response pathway, in particular, the auxin co-receptor AFB1 and the calcium channel CNGC14, contribute to the emergence of this more alkaline domain. Finally, they show that mutants in these two genes, impaired in the rapid auxin response pathway, show less efficient navigation of the root tip.

The manuscript is clear and well-written. The logic is sound, and the conclusions are supported by the data.

The new dye appears as a promising tool for monitoring the pH in the rhizosphere with advantages over the previous ones. Yet, as pointed out by the authors in the discussion, it reports on pH at the organ scale in the region around the root, not in the apoplast or the cell wall, which can eventually complexify the elaboration of a mechanistic model joining auxin, proton efflux, cell wall properties, cell elongation, and root growth. Although several of the findings confirm previous reports, the manuscript brings novelty by demonstrating the involvement of the rapid auxin response. I am overall supportive of the manuscript. Yet, several points should be addressed:

- The presentation of the more acidic and alkaline domains could be easier to visualize.<br /> - The authors refer to acidic and alkaline domains but do not report on absolute pH values; they monitor the emission ratio of the dye. They justify why to use relative pH value in the discussion and refer there to internal controls that are not clearly defined. In my opinion, the wording should be more consistent across the text and figures and refer to *more* acidic and *more* alkaline domains rather than acidic (pH<7) and alkaline (pH>7) domains.<br /> - The data related to the unaltered distribution of AHA using antibody staining should be backed up.<br /> - The way the pH profile and the statistical analyses should be improved.<br /> - The authors should test the effect of extracellular auxin perception (tmk, abp) mutants on pH zonation.<br /> - Conclusion could be strengthened by moving several pieces of data currently in supplemental material to the main text.

Reviewer #2 (Public Review):

Mitchell and colleagues examined the contribution of a UV-sensitive cone photoreceptor to chromatic detection in Amphiprion ocellaris, a type of anemonefish. First, they used biophysical measurements to characterize the response properties of the retinal receptors, which come in four spectrally-distinct subtypes: UV, M1, M2, and L. They then used these spectral sensitivities to construct a 4-dimensional (tetrahedral) color space in which stimuli with known spectral power distributions can be represented according to the responses they elicit in the four cone types. A novel five-LED display was used to test the fish's ability to detect "chromatic" modulations in this color space against a background of random-intensity, "achromatic" distractors that produce roughly equal relative responses in the four cone types. A subset of stimuli, defined by their high positive UV contrast, were more readily detected than other colors that contained less UV information. A well-established model was used to link calculated receptor responses to behavioral thresholds. This framework also enabled statistical comparisons between models with varying number of cone types contributing to discrimination performance, allowing inferences to be drawn about the dimensionality of color vision in anemonefish.

The authors make a compelling case for how UV light in the anemonefish habitat is likely an important ecological source of information for guiding their behavior. The authors are to be commended for developing an elegant behavioral paradigm to assess visual performance and for incorporating a novel display device especially suited to addressing hypotheses about the role of UV light in color perception. While the data are suggestive of behavioral tetrachromacy in anemonefish, there are some aspects of the study that warrant additional consideration:

1) One challenge faced by many biological imaging systems is longitudinal chromatic aberration (LCA) - that is, the focal power of the system depends on wavelength. In general, focal power increases with decreasing wavelength, such that shorter wavelengths tend to focus in front of longer wavelengths. In the human eye, at least, this focal power changes nonlinearly with wavelength, with the steepest changes occurring in the shorter part of the visible spectrum (Atchison & Smith, 2005). In the fish eye, where the visible spectrum extends to even shorter wavelengths, it seems plausible that a considerable amount of LCA may exist, which could in turn cause UV-enriched stimuli to be more salient (relative to the distractor pixels) due to differences in perceived focus rather than due solely to differences in their respective spectral compositions. Such a mechanism has been proposed by Stubbs & Stubbs (2016) as a means for supporting "color vision" in monochromatic cephalopods (but see Gagnon et al. 2016). It would be worth discussing what is known about the dispersive properties of the crystalline lens in A. ocellaris (or similar species), and whether optical factors could produce sufficient cues in the retinal image that might explain aspects of the behavioral data presented in the current study.

2) The authors provide a quantitative description of anemonefish visual performance within the context of a well-developed receptor-based framework. However, it was less clear to me what inferences (if any) can be drawn from these data about the post-receptoral mechanisms that support tetrachromatic color vision in these organisms. Would specific cone-opponent processes account for instances where behavioral data diverged from predictions generated with the "receptor noise limited" model described in the text? The general reader may benefit from more discussion centered on what is known (or unknown) about the organization of cone-opponent processing in anemonefish and related species.

Reviewer #2 (Public Review):

This manuscript develops a new microfluidic platform to study how the chemotactic response of motile cells varies in relation to its strength. Typically, chemotaxis is assayed using one microfluidic channel at a time, which limits throughput when researchers want to how to resolve how chemotaxis varies with chemoeffector concentration/gradient strength. The authors have automated this process by designing a device that can logarithmically dilute a chemoaffector with a buffer "on chip", simultaneously generating five different chemical gradients in five different channels where the maximum concentration varies by five orders of magnitude (in addition to a control lacking a gradient).

Technically, this is a major feat, requiring the design of a two-layered device, the use of herringbone mixers, and the careful consideration of the hydraulic resistance of each section to ensure that flow splits at junctions in a defined way to achieve the desired dilutions. It is clear the authors had to overcome many challenges before obtaining the final design. The authors have achieved their intended aims and the results from the multiplexed device are consistent with that from lower throughput devices.

Strengths:

- The multiplexed device allows researchers to greatly increase their experimental throughput when mapping out how a microbe responds to chemicals at different concentrations. While such data might be useful in its own right, such a device might make it much easier to quantify how chemotaxis varies in a multidimensional parameter space using multiple runs of this device (e.g. in analyses of fold-change detection where both the background concentration and gradient strength are varied, or in analyses that compare how the sensitivity of a microbe's chemosensory system varies in response to different chemoaffectors). Currently, it is difficult to map out how multiple parameters affect chemotaxis by running only one microfluidic experiment at a time.

- The same exact cell culture can be used in simultaneous experiments. This could potentially dramatically reduce biological variability, as cells obtained from batch cultures often differ in their metabolic state and significant variability is often observed in cultures inoculated on different days. The reduction of such variability is expected to be particularly important for strains that are very difficult/slow to grow in the laboratory or when testing cells obtained directly from environmental/clinical samples.

Weaknesses:

- Given the complexity of the device, it appears difficult to validate that the concentrations within multiplexed are the ones that are expected. It is not clear whether these devices can be used directly "off the shelf" or whether each device would need to be calibrated individually with dye beforehand. In contrast, it is relatively straightforward to serially dilute chemoaffectors manually using pipettors and obtain accurate results. It is not clear whether the on-chip dilution is a distinct advantage or whether it might add additional uncertainty/complexity.

- It is not feasible to track swimming cells in six channels simultaneously, as one cannot automatically move the microscope stage from one channel to another rapidly enough (e.g. the data collected here have 8 seconds between subsequent frames). Thus, multiplexed devices are best suited to measuring independent snapshots of the distribution of track swimming cells, rather than resolving the cellular behaviours that generate chemotaxis. However, tracking the response of slower moving, surface attached cells (e.g. eukaryotes that use ameboid movement on surfaces or bacteria that chemotax using pili) might be feasible if the gradient is maintained with constant flow. This is not explored by the authors, but if feasible it would open up a completely new avenue. Surface-attached cells move ~1000 times slower than swimming cells and experiments last for ~10-15 hours. Thus, using these multiplexed devices with surface-attached cells might facilitate much larger time savings compared to swimming cell assays, which only last for several minutes.

Reviewer #2 (Public Review):

In the current manuscript, the authors select 24 surgically resected pancreatic cancer samples from patients who had a poor outcome (survival of less than one year) or better outcome (survival of at least 3 years). They use a Nanostring Geomx Digital Spatial profiler using a panel of 94 probes. The authors identify a proximal fibroblast population that expresses high levels of PDPN, while a distal fibroblast population expresses high levels of inflammatory genes such as IL6 and IL11, as well as complement genes. Using single-cell RNA sequencing, the authors are able to identify fibroblast populations reflecting those identified in the spatial data and identify other pathways that distinguish the two populations, and that define better or poorer outcomes (for instance, Hif signaling is associated with a poorer prognosis while markers of T cell activation are associated with better prognosis).

The manuscript addresses an important topic, namely whether fibroblasts, a heterogenous and relatively poorly understood cell population within the pancreatic cancer microenvironment, predict poor response. Further, the manuscript integrates spatial and single-cell data, in the quest to identify how the tissue composition of a tumor affects the overall prognosis. Some weaknesses are also noted and should be addressed. Most notably, the prognostic predictions are based on a relatively small number of samples. Further, as spatial transcriptomics is not a single cell-level technology, the authors could use co-immunofluorescence to validate their cell populations and specifically prove that the signatures correspond to genes expressed by fibroblasts, rather than infiltrating immune cells. Finally, the author shows that my-CAF-like fibroblasts correlate with worse prognosis, while inflammatory CAFs predict better prognosis: this finding should be discussed in the context of other CAF literature, some indicating that iCAFs are a negative prognostic predictor.

Reviewer #2 (Public Review):

The study makes a useful contribution by showing that the classical binary discrimination task cannot distinguish different sources of suboptimality (perceptual vs. categorical bias; observation noise vs. approximate inference) in contrast to another task that is more complex (cue combination task). The paper provides the computational framework to define and quantify those sources of suboptimality and report the results of a task in which those different sources are disentangled indeed, in both model fitting and qualitative features of the data.

Strengths:<br /> - A very timely question: How to characterize the sources of suboptimality in (human) perceptual decisions?<br /> - The text is very clear and although the content is technical, the main ideas are conveyed in simple terms and figures, and the detail of mathematical derivations is restricted to the methods section.<br /> - The design of the cue-combination task is very interesting because the posterior distributions over categories predict no difference between the central and matched conditions in the case of perfect inference, but a difference whenever not too many samples are used in approximate inference, making it possible to disentangle different sources of suboptimality in the task.<br /> - The results from the first experiment are followed up by another experiment that includes manipulation of the stimulus duration, which should change the accuracy of approximate inference (and perceptual noise). The results are compatible with those predictions.<br /> - Effects are characterized by model fitting and model comparison, but different models also make qualitatively different predictions, making it possible to adjudicate between models simply by looking at the data (shape of the psychometric curves in different conditions).

Weaknesses:<br /> - There is no parameter recovery analysis based on the generative model in the multi-modal task.<br /> - Several results are not conclusive in most subjects. They are clearly visible only in a few participants and the aggregated data. It is not clear whether this is specific to this dataset (and task design) or whether it is a general conclusion.<br /> - The dataset is reused from a previous study and includes 20 participants. A replication of the result in an independent group of participants would make the result much more robust.<br /> - A replication attempt could use a different task (the current results are based on multi-modal sound localization), which would make the conclusion even more convincing.

Reviewer #2 (Public Review):

The manuscript reports the triploid and haploid productions using an ecs1ecs2 mutant as the maternal donor, in addition to the evaluation of the sexual process observed in the mutant. The indicated data show exquisite quality. To improve the content, I recommend carefully reconsidering the descriptions because some of the insights would cause a stir in the controversy regarding EC1&2 functions in plant reproduction.

Strengths<br /> Triploid production by a combination of ecs1ecs2 mutant and HIPOD system has potential as a future plant breeding tool. Moreover, it's intriguing that both triploid and haploid productions were achieved using the same mutant as a maternal donor. I think authors can claim the value of their results more by adding descriptions about the usefulness of the aneuploid plants in plant breeding history.

The evidence of the persistent synergid nucleus (Figure 3A) is critical insight reported by this study. As Maruyama et al. (2013) reported by live cell imaging, synergid-endosperm fusion had occurred at the two endosperm nuclei stage. It would be valuable to claim the observed fact by citing Maruyama's previous observation.

Weakness<br /> As the authors suggested, the higher triploid frequency observed in ecs1ecs2 than WT was likely caused by the increased polyspermy. However, it also could be that reduction of normal seed number in ecs1ecs2 (whichever is due to failure of fertilization or embryo development arrest) accounts for the increased frequency of the triploid compared to WT.

The results in Figure 3C-E suggested the single fertilization for both egg and central cells at similar frequencies. This is an exciting result, but it is still possible that the fertilized egg or central cell degenerated after fertilization resulting in the disappearance of paternally inherited fluorescence. Evaluation of fertilization patterns at 7-10HAP in ecs1ecs2 mutant may provide more confident insight, although unfused sperm cell was evaluated at 1DAP (Figure 3-figure supplement 1B). The fertilization states can be distinguished depending on the HTR10RFP sperm nuclei morphology and their positions, as reported by Takahashi et al (2018).

Several recent studies have reported exciting insights on ECS1&2 functions; however, various results from different laboratories have raised controversy. Though, the commonly found feature is the repression of polytubey. For readers, it would be helpful to organize the explanation about which insights are concordant or different. In addition, a drawing that explains the time course in the process from pollination to seed development (up to 6DAP) based on WT would help to understand which point is evaluated in each data.

Reviewer #2 (Public Review):

The field of monoclonal antibody therapeutics for the treatment of clinical diseases is undergoing rapid growth in recent years and becoming a dominant force in the therapeutics market. In previous studies, Mone Zaidi's group has reported the development of a first-of-its-kind humanized FSH-blocking antibody, MS-Hu6, based on the established importance of FSH in bone loss, adiposity, and neurodegeneration. This study reports the creation of a unique formulation of highly concentrated MS-HU6 preparation and evaluates detailed physiochemical properties of formulated MS-Hu6 including viscosity, turbidity, and clarity. Furthermore, the structural integrity of the formulated MS-HU6 is confirmed through Circular Dichroism and Fourier Transform Infrared (FTIR). The manuscript is succinctly written, and the methods and results are well described. The authors' conclusions are largely supported by the experimental data. The methods described are highly relevant to the goal of future manufacturing of highly concentrated monoclonal antibody therapeutics for human trials, and, therefore, the study is significant.

Reviewer #2 (Public Review):

De Gieter et al.'s structural report follows a previous screening effort, which identified pLGIC from Alvinella pompejana as suitable for structural studies.<br /> In the present manuscript, the authors report several structures of one homopentamer named Alpo4. The manuscript is organized around a thoughtful, convincing, description of the common points shared by Alpo4 with the mammalian homologues of known structures, and of its distinctive features. The most striking differences are 1. the unexpected presence of a CHAPS detergent molecule bound to the orthosteric site; 2. the unique rotamer switch of a conserved tryptophan in the apo binding pocket, creating what the authors call a 'self-liganded' state; 3. a tightly closed hydrophobic gate with a ring of methionine residues within the M2 helices. 4. A reversed ECD twist associated with the binding of CHAPS

The principal strength of the manuscript is to extend the structural knowledge of the pLGIC family beyond the mammalian receptors to invertebrates, for which structural information has remained scarce. In particular, the binding of CHAPS to an 'extended' binding site is shown. That site does not only comprise the place where neurotransmitter usually binds but is prolonged by a hydrophobic patch underneath loop C and in contact with loop F/beta 8.

In the discussion, the authors suggest that the binding of CHAPS could be an inspiration to develop compounds, targeting for instance mammalian receptors, that would bind to both the orthosteric site and a potential groove underneath loop C (where the sterol moiety of CHAPS binds in Alpo4). A figure (SI4) shows a few homologues in surface representation, giving an idea of whether this groove is generally present in the family. Seeing this figure, I wondered if it would be relevant to compare several conformations of one or a few chosen homologues. Given that gating always impacts the quaternary assembly, is this groove more pronounced in say the inhibited state of a given homologue than in its agonist-bound state?<br /> A related thought was that some of the protein binders affecting pLGIC function (toxins, VHH) contact two subunits and wrap around/below loop C. Do these have binding sites that overlap with the groove?

Very interestingly, the binding of CHAPS stabilizes a conformation that differs from the apo one. It includes a twist of the ECDs but does not lead to a significant opening of the M2 bundle. The authors note that the direction of the twist is reversed to that often associated with the binding of ligands in homologues. This reversion is quite a feature, which deserves to be shown in a supplementary movie (e.g overlay of the Alpo apo>CHAPs transition with the nico>apo transition of a7). My mental framework was that in this family 1. inhibitors do not trigger much of a quaternary conformational change 2. agonists trigger changes always in the same direction (even if the amplitude and exact rotation vary from receptor to receptor). So it's interesting to see a compound (of unknown functional effect) triggering a reversed change.

The principal weakness of the manuscript lies in the absence of a known agonist for Alpo4, The authors do a good job at explaining what they tried and why (and they did perform quite an array of unsuccessful functional experiments), yet it remains frustrating to be unable to link the observed structures to some function.

Reviewer #2 (Public Review):

Geuzebroek and colleagues use computational modeling and EEG to investigate how people adjust continuous decision-making across different contexts. By neurally informing computational models of decision-making, they reject models in which in contexts with weaker sensory evidence a lower decision threshold or greater leak is applied, in favor of a model implementing a novel control mechanism, in which an adjustable sensory criterion determines which samples are considered evidence to be accumulated. This work was rigorously performed and in a compelling manner teases apart competing mechanisms to reveal a significant novel one.

The contributions of this work are at least two-fold: First, the work outlines a novel mechanism by which decision-makers adjust to different environments by taking expectations about sensory evidence into account. Second, they demonstrate how behavior alone can be insufficient to tease apart competing models and lead to misattribution of observed behavioral differences and how neural measures can help arbitrate between models and avoid misattribution.

This work is of great relevance for the decision-neuroscience community, calls for a re-examination of previous findings, and opens exciting new avenues for future research.

Reviewer #2 (Public Review):

Here, a simple model of cerebellar computation is used to study the dependence of task performance on input type: it is demonstrated that task performance and optimal representations are highly dependent on task and stimulus type. This challenges many standard models which use simple random stimuli and concludes that the granular layer is required to provide a sparse representation. This is a useful contribution to our understanding of cerebellar circuits, though, in common with many models of this type, the neural dynamics and circuit architecture are not very specific to the cerebellum, the model includes the feed-forward structure and the high dimension of the granule layer, but little else. This paper has the virtue of including tasks that are more realistic, but by the paper's own admission, the same model can be applied to the electrosensory lateral line lobe and it could, though it is not mentioned in the paper, be applied to the dentate gyrus and large pyramidal cells of CA3. The discussion does not include specific elements related to, for example, the dynamics of the Purkinje cells or the role of Golgi cells, and, in a way, the demonstration that the model can encompass different tasks and stimuli types is an indication of how abstract the model is. Nonetheless, it is useful and interesting to see a generalization of what has become a standard paradigm for discussing cerebellar function.

Reviewer #2 (Public Review):

The manuscript focuses on the cholinergic modulation of TRPM4 channels in the CA1 pyramidal neurons. The authors presented solid convincing evidence that TRPM4 but not TRPC channels are the Ca2+-activated nonselective cation channel in CA1 pyramidal neurons being modulated by activation of muscarinic receptors. Using bi-directional ramp protocol, the authors revealed that ACh modulation could lead to forward shifts in place field center of mass, whereas decreased ACh modulation could contribute to backward shifts. This represents a significant molecular/cellular finding that links neuromodulation of intrinsic properties to place field shifts, a phenomenon seen in vivo. The authors used a computational approach to model this CA1 neuron spiking to further reveal the mechanism.

To further improve the manuscript, I have the following suggestions/questions:<br /> 1. The triangular ramp stimulation (introduced by the same group; Upchurch et al., 2022) makes it possible to emulate the hill-shaped depolarization during place field firing. However, one concern is the time scale/duration of the ramp (2 sec) compared to the physiological pattern (100ms~200ms in the in vivo recording in freely moving rat, Epsztein et al., 2011). Using a longer ramp to generate more spikes for calculating the adaptation index is understandable. However, considering the Ca entry/accumulation during prolonged depolarization, repeating one set of experiments with a shorter ramp is crucial to verify the major findings.

2. Strictly speaking, the term "Ca2+-induced Ca2+ release (CICR)" is only used in ER Ca2+ release via ryanodine receptors (RyR) rather than IP3Rs. The author should be careful since it is used in the abstract (Line 36). In addition, pharmacology inhibition experiments should be incorporated to further dissect the role of RyR-induced CICR.

3. Applying strong buffering BAPTA not only removed the IP3R-TRPM nanodomain but also hindered Ca entry via VGCC. To validate the role of ER Ca2+ release in regulating TRPM, depletion of ER Ca2+ pool with SERCA inhibitor (e.g. thapsigargin) would be a more direct way to test the model (also make sure to add TRPC inhibitor to avoid the store-operated Ca2+ entry).

4. How does the TRPM current overcome the long-term inactivation of Nav? A channel state model should be added to the manuscript to make it easier to understand.

Reviewer #2 (Public Review):

The manuscript by Brunetti et al. represents an important contribution where SARS-CoV-2 infection of T-helper cells is implicated and found to be mediated by CD4. Interestingly and appealingly, the work progressed through a computationally driven hypothesis, by analysing the interaction partners of SARS-CoV-2 spike glycoprotein (as initially modelled through similar SARS-CoV-1), followed by experimental validations, and further computational and experimental insights on the mechanism of binding. I find most of the computational outcomes well validated, and the results and claims well supported by the performed experiments. There are a few points where the manuscript will benefit from dedicated discussion and additional simulation/exploratory plots to establish and validate the adopted methodology for analogous future usage in protein binding characterisations by others.

Major comments:

1) The bioinformatics selection method to arrive at CD4 as the main interaction partner is interesting, and the zoomed-in finding is well justified by the whole body of the experimentation as brought in the manuscript. However, it is interesting from a computational biology perspective that were we to remove GO database (too unvalidated), and "Cell surface" component of the Jensen database (considering its more dedicated "Plasma membrane" and "External side of plasma membrane" components considered in the work) out of the Venn diagram (Extended Data Fig. 3), then we would be left with more interaction partners shared between the remaining 3 databases. Interestingly, these additional partners would include CD8A and CD8B. However, the authors show that the interaction was experimentally noted to happen with CD4+ T cells but not with CD8+ ones. This warrants some discussion on why this might be the case. I wonder what would be the computational docking/MD results were you to attempt modelling an interaction between the spike glycoprotein and CD8? Should you not arrive at stable complexes with your MD workflow and 4 Angstrom cutoff for temperature-induced stability scrutinization, that would be extra validation and weight on the adopted computational scheme for the discovery.

2) Looking at the last complex in Figure 2, where the full-length sCov2 is recovered on top of the modelled fragment, one can see some additional interaction points or potential clashes with CD4 NTD. Were some of the models discarded on the ground of the orientation between CD4 NTD and sCov2 RBD being incompatible with the full-length sCov2 due to possible steric clashes?

3) The 4 Angstrom cutoff for the temperature gradient-based structural stability check sounds reasonable, but would be more justifiable if the authors would also present a histogram of all RMSDs (of final aberrations) for all the tried models and show how outlying the 4 Angstrom is in the whole distribution, additionally attributing a p-value on the selected cutoff.

Reviewer #2 (Public Review):

Diabetes mellitus is a worldwide public health menace, and the fracture healing is usually impaired in diabetic patients. Metformin is the first-line medicine for type-2 diabetes (T2D). However, its effects on bone in T2D patients remain unclear. To assess the impacts of metformin on fracture healing, the authors study the healing process after injuries caused by three different types of bone fractures in diabetic mouse models with or without metformin treatment. The authors studied three fracture models and looked at various aspects of the bone healing process and concluded that metformin rescues the delayed bone healing and remodeling in T2D mice. Moreover, the authors present novel information on the impact of metformin on the bone proliferation, bone formation, and cartilage formation in the bone marrow stromal cells (BMSCs) derived from T2D mice. Administration of metformin in T2D mice can rescue the impaired differentiation potential and lineage commitment of BMSCs both in vitro and in vivo, compromised by the hyperglycemic conditions. In addition, several key chondrocyte transcript factors such as SOX9 and PGC1α, are upregulated in callus tissue isolated at the fracture site of metformin-treated diabetic mice during the healing process after the fracture. In summary, the authors present convincing evidence that metformin facilitates bone healing, bone formation and chondrogenesis in diabetic mice. The prior literature has focused on the effects on mesenchymal stem cells (MSCs) and this paper's data is novel as it's using MKR models for studying Metformin 's role in bone formation under diabetes condition. The paper's conclusions and results are strong, but more attention needs to be paid to the introduction and description of the prior literature and understanding of the potential specific targets and signaling pathway of metformin in the MKR mouse model bone healing.

Reviewer #2 (Public Review):

Dhekne and colleagues present an unbiased genome-wide screen by systematic CRISPR-Cas9 gene knock-out in mouse NIH-3T3 fibroblasts to identify regulators of the LRRK2 pathway which is relevant for Parkinson's disease. The screen identified Rab12 as the most potent regulator of the LRRK2 activity. Phosphorylation of the well-established LRRK2 substrate Rab10 has been used as a read-out. To allow a large-scale screen, the authors established a flow cytometry-based assay using phospho-Rab10-specific antibodies. Subsequently, Rab12 has been confirmed as an upstream effector of LRRK2 acting in a similar way as Rab29. Using computational modelling by Alphafold in conjunction with Colabfold the authors could model the Rab12:LRRK2 complex and identify a third Rab binding site within the N-terminal Armadillo repeats which is distinct from the two sites, previously identified for Rab8a/Rab10 and Rab29. The predicted interaction epitope could be experimentally confirmed by systematic mutational analysis.

The experimental setting and the data presented are overall sound. It should however be considered that the selected cell model is most likely not covering the full set of LRRK2 pathway regulators as these are likely expressed in a tissue and cell-type-specific manner. It could therefore be interesting to also include more disease-relevant models, such as neuronal or immune cells. Nevertheless, Rab12 is an important effector, which is also expressed in cell types relevant to Parkinson's disease.<br /> To validate their computational model of the Rab12 binding epitope within the N-terminal Armadillo domain of LRRK2, the authors determined the binding affinity of Rab12 which is in the lower µM range and similar to the affinities of Rab10 and Rab29 to LRRK2. The authors conducted a mutational screen mutating surface exposed residues within the predicted Rab12 binding epitope in the N-terminus of LRRK2. The study could identify critical residues, which significantly contribute to the affinity of LRRK2 for Rab12. Corresponding alanine mutations could significantly reduce the enhanced LRRK2-mediated Rab10 phosphorylation observed upon Rab12 co-expression. The effect size is similar to the previously identified Rab29 effector. Furthermore, the authors could convincingly demonstrate that Rab12 and Rab29 bind to different LRRK2 epitopes.

Noteworthy, besides disrupting mutations targeting the predicted Rab12 binding epitope, the authors also found one mutation enhancing the cellular effect of Rab12 overexpression demonstrated by increased phospho-Rab10 levels. For a better evaluation of the presented computational model of the Rab12:LRRK2 complex, it would be interesting, if the authors could study the binding affinity of that mutant (F283A), as well.

Overall, the authors could convincingly demonstrate that Rab12, previously identified as LRRK2 substrate, acts upstream of LRRK2 similar to Rab29 but via a distinct binding site. The site located within the N-terminal Ankyrin domain has been predicted by a computational 3D model of the complex structure and experimentally validated. The interaction epitope might be an interesting target for the future development of allosteric modulators to treat LRRK2-mediated PD.

Reviewer #2 (Public Review):

In this manuscript, Castanera et al. investigated how transposable elements (TEs) altered gene expression in rice and how these changes were selected during the domestication of rice. Using GWAS, the authors found many TE polymorphisms in the proximity of genes to be correlated to distinct gene expression patterns between O. sativa ssp. japonica and O. sativa ssp. indica and between two different growing conditions (wet and drought). Thereby, the authors found some evidence of positive selection on some TE polymorphisms that could have contributed to the evolution of the different rice subspecies. These findings are underlined by some examples, which illustrate how changes in the expression of some specific genes could have been advantageous under different conditions. In this work, the authors manage to show that TEs should not be ignored when investigating the domestication of rise as they could have played an important role in contributing to the genetic diversity that was selected. However, this study stops short of identifying causations as the used method, GWAS, can only identify promising correlations. Nevertheless, this study contributes interesting insights into the role TEs played during the evolution of rice and will be of interest to a broader audience interested in the role TEs played during the evolution of plants in general.

Reviewer #2 (Public Review):

In this manuscript, Hoffmann et al. introduce a novel and innovative method to validate and study the mechanism of action of essential genes and novel putative drug targets. In the wake of many functional genomics approaches geared towards identifying novel drug targets or synthetic lethal interactions, there is a dire need for methods that allow scientists to ablate a gene of interest and study its immediate effect in culture or in xenograft models. In general, these genes are lethal, rendering conventional genetic tools such as CRISPR or RNAi inept.

The ARTi system is based on expression of a transgene with an artificial RNAi target site in the 3'-UTR as well as a TET-inducible miR-E-based shRNAi. Using this system, the authors convincingly show that they can target strong oncogenes such as EGFRdel19 or KRasG12 as well as synthetic lethal interactions (STAG1/2) in various human cancer cell lines in vivo and in vitro.

The system is very innovative, likely easy to be established and used by the scientific community and thus very meaningful.

Reviewer #2 (Public Review):

Fuijino et al provide interesting data describing the RNA-binding protein, FUS, for its ability to bind the RNA produced from the hexanucleotide repeat expansion of GGGGCC (G4C2). This binding correlates with reductions in RNA foci formation, the production of toxic dipeptides and concomitant reductions in toxic phenotypes seen in (G4C2)30+ expressing Drosophila. Both FUS and G4C2 repeats of >25 are associated with ALS/FTD spectrum disorders. Thus, these data are important for increasing our understanding of potential interactions between multiple disease genes.

Reviewer #2 (Public Review):

This important work presents an example of a contextual computation in a navigation task through a comparison of task driven RNNs and mouse neuronal data. Authors perform convincing state of the art analyses demonstrating compositional computation with valuable properties for shared and distinct readouts. This work will be of interest to those studying contextual computation and navigation in biological and artificial systems.

This work advances intuitions about recent remapping results. Authors trained RNNs to output spatial position and context given velocity and 1-bit flip-flops. Both of these tasks have been trained separately, but this is the first time to my knowledge that one network was trained to output both context and spatial position. This work is also somewhat similar to previous work where RNNs were trained to perform a contextual variation on the Ready-Set-Go with various input configurations (Remington et al. 2018). Additionally findings in the context of recent motor and brain machine interface tasks are consistent with these findings (Marino et al in prep). In all cases contextual input shifts neural dynamics linearly in state space. This shift results in a compositional organization where spatial position can be consistently decoded across contexts. This organization allows for generalization in new contexts. These findings in conjunction with the present study make a consistent argument that remapping events are the result of some input (contextual or otherwise) that moves the neural state along the remapping dimension.

The strength of this paper is that it tightly links theoretical insights with experimental data, demonstrating the value of running simulations in artificial systems for interpreting emergent properties of biological neuronal networks. For those familiar with RNNs and previous work in this area, these findings may not significantly advance intuitions beyond those developed in previous work. It's still valuable to see this implementation and satisfying demonstration of state of the art methods. The analysis of fixed points in these networks should provide a model for how to reverse engineer and mechanistically understand computation in RNNs.

I'm curious how the results might change or look the same if the network doesn't need to output context information. One prediction might be that the two rings would collapse resulting in completely overlapping maps in either context. I think this has interesting implications about the outputs of the biological system. What information should be maintained for potential readout and what information should be discarded? This is relevant for considering the number of maps in the network. Additionally, I could imagine the authors might reproduce their current findings in another interesting scenario: Train a network on the spatial navigation task without a context output. Fix the weights. Then provide a new contextual input for the network. I'm curious whether the geometric organization would be similar in this case. This would be an interesting scenario because it would show that any random input could translate the ring attractor that maintains spatial position information without degradation. It might not work, but it could be interesting to try!

I was curious and interested in the authors choice to not use activity or weight regularization in their networks. My expectation is that regularization might smooth the ring attractor to remove coding irrelevant fluctuations in neural activity. This might make Supplementary Figure 1 look more similar across model and biological remapping events (Line 74). I think this might also change the way authors describe potential complex and high dimensional remapping events described in Figure 2A.

Overall this is a nice demonstration of state-of-the-art methods to reverse engineer artificial systems to develop insights about biological systems. This work brings together concepts for various tasks and model organisms to provide a satisfying analysis of this remapping data.

Reviewer #2 (Public Review):

The ATPase protein machine cohesin shapes the genome by loop extrusion and holds sister chromatids together by topological entrapment. When executing these functions, cohesin is tightly regulated by multiple cofactors, such as Scc2/Nipbl, Pds5, Wapl, and Eco1/Esco1/2, and it undergoes dynamic conformational changes with ATP binding and hydrolysis. The mechanisms by which cohesin extrudes DNA loops and medicates siter-chromatid cohesion are still not understood. A major reason for the lack of understanding of cohesin dynamics and regulation is the failure to capture the structures of intact cohesin in different nucleotide-bound states and in complex with various regulators. So far only the ATP state cohesin bound to NIPBL and DNA have been experimentally determined.

In this manuscript, Nasmyth et al. made use of the powerful protein structure prediction tool, AlphaFold2 (AF), to predict the models of tens of cohesin subcomplexes from different species. The results provide important insight into how the Smc3-Scc1 DNA exiting gate is opened, how Pds5 and Wapl maintain the opened gate, how Pds5 and Scc3/SA recruit different cofactors, how Eco1 and Sororin antagonize Wapl, and how Scc2/Nipbl interacts with Scc3/SA. The models are for the most part consistent with published mutations in these proteins that affect cohesin's functions in vitro and in vivo and raise testable hypotheses of cohesin dynamics and regulation. This study also serves as an example of how to use AF to build models of protein complexes that involve the docking of flexible regions to globular domains.

Major points<br /> (1) As it stands, the manuscript is simply too long and not readable. The authors should streamline their presentations and remove excessive speculations and models of minor importance.

(2) AF has been accurate in predicting both the fold and sidechain conformations of globular domains. It is less accurate in predicting structural regions with conformational flexibility. Comparisons of predicted and determined structures of large protein complexes have shown considerable differences, particularly with respect to regions lacking tertiary fold. The authors should be more cautious in interpreting some of their models, particularly when the predicted models are inconsistent with determined structures and published biochemical data. For example, human WAPL-C in isolation does not interact with the SA-SCC1 complex while the N-terminal region of WAPL does.

(3) The predicted SA/Scc3-Pds5-Scc1-WaplC quaternary complex is fascinating. Can the authors provide some experimental evidence to support the formation of this quaternary complex or at least the formation of the SA/Scc3-Pds5-WaplC ternary complex? In vitro pulldown or gel filtration can be used to test their predictions.

Reviewer #2 (Public Review):

In this manuscript, González-Segarra et al. investigated how ISNs regulate sugar and water ingestion in Drosophila.

Strengths:

• In their previous paper, authors have shown that inhibiting neurotransmission in ISNs has opposite effects on sugar and water ingestion. In this new manuscript, they investigated the downstream neurons connected to ISNs.

• The authors first identified the effector molecules released by ISNs. Their RNAi screen found that, surprisingly, ISNs use ilp3 as a neuromodulator.

• Next, using light and electron microscopy, they investigated the downstream neural circuits ISNs connect with to regulate water or sugar ingestion. These analyses identified a new group of neurons named Bilateral T-shaped neurons (BiT) as the main output of ISNs, and several other peptidergic neurons as downstream effectors of ISNs. While BiT activity regulated both sugar and water ingestion, BiT downstream neurons, such as CCHa2R, only impacted water ingestion.

• These results suggested that ISNs might interact with distinct neural circuits to control sugar or water ingestion.

• The authors also investigated other ISN downstream neurons, such as ilp2 and CCAP, and revealed that their activity also contributes to ingestive behaviors in flies.

Areas for further development:

• Does BIT inhibit all of the IPCs or some of them? I think it is critical to indicate the ROIs used for each neuron in the methods. Which part of the neuron is used for imaging experiments? Dendrites, cell bodies, or synaptic terminals?

• The discussion section is not giving big picture explanation of how these neurons work together to regulate sugar and water ingestion. Silencing and activation experiments are good, but without showing the innate activity of these neural groups during ingestion, it is not clear what their functions are in terms of regulating fly behavior.

Reviewer #2 (Public Review):

This paper presents an extensive numerical study of microbial evolution using a model of fitness inspired by spin glass physics. It places special emphasis on elucidating the combined effects of microscopic epistasis, which dictates how the fitness effect of a mutation depends on the genetic background on which it occurs, and clonal interference, which describes the proliferation of and competition between multiple strains. Both microscopic epistasis and clonal interference have been observed in microbial evolution experiments, and are chief contributors to the complexity of evolutionary dynamics. Correlations between random mutations and nonlinearities associated with interactions between sub-populations consisting of competing strains make it extremely challenging to make quantitative theoretical predictions for evolutionary dynamics and associated observables such as the mean fitness. While the body of theoretical and computational research on modeling evolutionary dynamics is extensive, most theoretical efforts rely on making simplifications such as the strong selection weak mutation (SSWM) limit, which neglects clonal interference, or assumptions about the distribution of fitness effects that are not experimentally verifiable.

The authors have addressed this challenge by running a numerical microbial evolution experiment over realistic population sizes (~ 100 million cells) and timescales (~ 10,000 generations) using a spin glass model of fitness that considers pairwise interactions between mutations on distinct genetic loci. By independently tuning mutation rate as well as the strength of epistasis, the authors have shown that epistasis generically slows down the growth of fitness trajectories regardless of the amount of clonal interference. On the other hand, in the absence of epistasis, clonal interference speeds up the growth of fitness trajectories, but leaves the growth unchanged in the presence of epistasis. The authors quantitatively characterize these observations using asymptotic power law fits to the mean fitness trajectories. Further, the authors employ more simplified macroscopic models that are informed by their empirical findings, to reveal the mechanistic origins of the epistasis mediated slowing down of fitness growth. Specifically, they show that epistasis leads to a broadening of the distribution of fitness increments, leading to the fixation of a large number of mutations that confer small benefits. Effectively, this leads to an increase in the number of fixed mutations required to climb the fitness peak. This increased number of required beneficial mutations together with the decreasing availability of beneficial mutations at high fitness lead to the slowdown of fitness growth. The authors' data analysis is quite solid and their conclusions are well supported by quantitative macroscopic models. The paper can be strengthened further by conducting a deeper analysis of correlations between mutations, using tools for analyzing dynamical correlations developed in the spin glass literature.

One of the highlights of this paper is the author's astute choice of model, which strikes an impressive balance between complexity, flexibility, and numerical accessibility. In particular, the authors were able to achieve results over realistic population sizes and timescales largely because of the amenability of the model to the implementation of an efficient simulation algorithm. At the same time, the strength of epistasis and clonal interference can be tuned in a facile manner, enabling the authors to map out a phase diagram spanning these two axes. One could argue that the numerical scheme employed here would only work for a specific class of models, and is therefore not generalizable to all models of evolutionary dynamics. While this is likely true, the model is capable of recapitulating several complex aspects of microbial evolution, and is therefore not unduly restrictive.

Spin glass physics has already provided significant insights into a wide range of topics in the life sciences including protein folding, neuroscience, ecology and evolution. The present work carries this approach forward, with immediate implications for microbial evolution, and potential implications in related areas of research such as microbial ecology. In addition to the theoretical value of spin glass physics, the high performance algorithm developed in this work lays the foundation for formulating data driven approaches aimed at understanding evolutionary dynamics. In the future, there is considerable scope for utilizing data generated by such models to train machine learning algorithms for quantifying parameters associated with epistasis, clonal interference, and the distribution of fitness effects in laboratory experiments.

I didn't realize how hard it was to retain information in the earlier times before the internet became a thing. This made me appreciate how much easier our generation has to gather information. I attached a picture of how the stock photo industry is growing and people are no longer traveling all over trying to get in contact with people from different places for information. In 2020 the stock photography market value was at 3.3 billion dollars.

Reviewer #2 (Public Review):

Ibar and colleagues address the role of the spectrin cytoskeleton in the regulation of tissue growth and Hippo signaling in an attempt to elucidate the underlying molecular mechanism(s) and reconcile existing data. Previous reports in the field have suggested three distinct mechanisms by which the Spectrin cytoskeleton regulates Hippo signaling and this is, at least in part, due to the fact that different groups have mainly focused on different spectrins (alpha, beta, or beta-heavy) in previous reports.

The authors start their investigation by trying to reconcile their previous data on the role of Ajuba in the regulation of Hippo signaling via mechanotransduction and previous observations suggesting that Spectrins affect Hippo signaling independently of any effect on myosin levels or Ajuba localization. Contrary to previous reports, the authors reveal that, indeed, depletion of alpha- and beta-heavy-spectrin leads to an increase in myosin levels at the apical membrane. Moreover, the authors also reveal that the depletion of spectrins leads to an increase in Ajuba levels.

Reviewer #2 (Public Review):

This manuscript reports on the use of Optogenetics to influence endothelial barrier integrity by light. Light-induced membrane recruitment of GTPase GEFs is known to stimulate GTPases and modulate cell shape, and here this principle is used to modulate endothelial barrier function. It shows that Rac and CDc42 activating constructs enhance barrier function and do this even when a major junctional adhesion molecule, VE-cadherin, is blocked. Activation of Rac and Cdc42 enhanced lamellipodia formation and cellular overlaps, which could be the basis for the increase in barrier integrity.

The authors aimed at developing a light driven technique with which endothelial barrier integrity can be modulated on the basis of activating certain GTPases. They succeeded in using optogenetic tools that recruit GEF exchange domains to membranes upon light induction in endothelial cell monolayers. Similar tools were in principle known before to modulate cell shape/morphology upon light induction, but were used here for the first time as regulators of endothelial barrier integrity. In this way it was shown that the activation of Cdc42 and Rac can increase barrier integrity even if VE-cadherin, a major adhesion molecule of endothelial junctions, is blocked. Although it was shown before that stimulation of S1P1 receptor or of Tie-2 can enhance endothelial barrier integrity in dependence of Cdc42 or Rac1 and can do this independent of VE-cadherin, the current study shows this with tools directly targeting these GTPases.

Furthermore, this study presents very valuable tools. The immediate and repeatable responses of barrier integrity changes upon light-on and light-off switches are fascinating and impressive. It will be interesting to use these tools in the future in the context of analyzing other mechanisms which also affect endothelial barrier function and modulate the formation of endothelial adherens junctions.

Reviewer #2 (Public Review):

This paper addresses an important computational problem in learning and memory. Why do related memory representations sometimes become more similar to each other (integration) and sometimes more distinct (differentiation)? Classic supervised learning models predict that shared associations should cause memories to integrate, but these models have recently been challenged by empirical data showing that shared associations can sometimes cause differentiation. The authors have previously proposed that unsupervised learning may account for these unintuitive data. Here, they follow up on this idea by actually implementing an unsupervised neural network model that updates the connections between memories based on the amount of coactivity between them. The goal of the authors' paper is to assess whether such a model can account for recent empirical data at odds with supervised learning accounts. For each empirical finding they wish to explain, the authors built a neural network model with a very simple architecture (two inputs layers, one hidden layer, and one output layer) and with prewired stimulus representations and associations. On each trial, a stimulus is presented to the model, and inhibitory oscillations allow competing memories to pop up. Pre-specified u-shaped learning rules are used to update the weights in the model, such that low coactivity leaves model connections unchanged, moderate coactivity weakens connections, and high coactivity strengthens connections. In each of the three models, the authors manipulate stimulus similarity (following Chanales et al), shared vs distinct associations (following Favila et al), or learning strength (a stand in for blocked versus interleaved learning schedule; following Schlichting et al) and evaluate how the model representations evolve over trials.

As a proof of principle, the authors succeed in demonstrating that unsupervised learning with a simple u-shaped rule can produce qualitative results in line with the empirical reports. For instance, they show that pairing two stimuli with a common associate (as in Favila et al) can lead to *differentiation* of the model representations. Demonstrating these effects isn't trivial and a formal modeling framework for doing so is a valuable contribution. Overall, the authors do a good job of both formally describing their model and giving readers a high level sense of how their critical model components work, though there are some places where the robustness of the model to different parameter choices is unclear. In some cases, the authors are very clear about this (e.g. the fast learning rate required to observe differentiation). However, in other instances, the paper would be strengthened by a clearer reporting of the critical parameter ranges. For instance, it's clear from the manipulation of oscillation strength in the model of Schlichting et al that this parameter can dramatically change the direction of the results. The authors do report the oscillation strength parameter values that they used in the other two models, but it is not clear how sensitive these models are to small changes in this value. Similarly, it's not clear whether the 2/6 hidden layer overlap (only explicitly manipulated in the model of Chanales et al) is required for the other two models to work. Finally, though the u-shaped learning rule is essential to this framework, the paper does little formal investigation of this learning rule. It seems obvious that allowing the u-shape to collapse too much toward a horizontal line would reduce the model's ability to account for empirical results, but there may be other more interesting features of the learning rule parameterization that are essential for the model to function properly.

There are a few other points that may limit the model's ability to clearly map onto or make predictions about empirical data. The model(s) seems very keen to integrate and do so more completely than the available empirical data suggest. For instance, there is a complete collapse of representations in half of the simulations in the Chanales et al model and the blocked simulation in the Schlichting et al model also seems to produce nearly complete integration. Even if the Chanales et al paper had observed some modest behavioral attraction effects, this model would seem to over-predict integration. The author's somewhat implicitly acknowledge this when they discuss the difficulty of producing differentiation ("Practical Advice for Getting the Model to Show Differentiation") and not of producing integration, but don't address it head on. Second, the authors choice of strongly prewiring associations in the Chanales and Favila models makes it difficult to think about how their model maps onto experimental contexts where competition is presumably occurring while associations are only weakly learned. In the Chanales et al paper, for example, the object-face associations are not well learned in initial rounds of the color memory test. While the authors do justify their modeling choice and their reasons have merit, the manipulation of AX association strength in the Schlichting et al model also makes it clear that the association strength has a substantial effect on the model output. Given the effect of this manipulation, more clarity around this assumption for the other two models is needed.

Overall, this is strong and clearly described work that is likely to have a positive impact on computational and empirical work in learning and memory. While the authors have written about some of the ideas discussed in this paper previously, a fully implemented and openly available model is a clear advance that will benefit the field. It is not easy to translate a high-level description of a learning rule into a model that actually runs and behaves as expected. The fact that the authors have made all their code available makes it likely that other researchers will extend the model in numerous interesting ways, many of which the authors have discussed and highlighted in their paper.

Reviewer #2 (Public Review):

This study highlights the importance of including not only spatio-termporal scales to biodiversity assessments, but also to include some of the possible drivers of biodiversity loss and to study their joint contribution as environmental stressors.

Introduction - Well written and placed within the current trends of unprecedented biodiversity loss, with an emphasis on freshwater ecosystems. The authors identify three important points as to why biodiversity action plans have failed. Namely, community changes occur over large spatio-temporal scales and monitoring programs capture a fraction of these long-term dynamics (e.g. few decades) which although good at capturing trends in biodiversity change, they often fail at identifying the drivers of these changes. Additionally, most of these rely on manual sorting of samples, overlooking cryptic diversity, or state-of-the-art techniques such as sedimentary DNA (sedaDNA) which allow studying decade-long dynamics, usually focus on specific taxonomic groups unable to represent community-level changes. Secondly, the authors identify that biodiversity is threatened by multiple factors and are rarely studied in tandem. Finally, the authors stress the need for high-throughput approaches to study biodiversity changes since historically, most conservation efforts rely on highly specialized skills for biodiversity monitoring, and even well-studied species have relatively short time series data. The authors identify a model freshwater lake (Lake Ring, Denmark) - suitable due to its well-documented history over the last 100 years - to present a comprehensive framework using metabarcoding, chemical analysis and climatic records for identifying past and current impacts on this ecosystem arising from multiple abiotic environmental stressors.

Results - They are brief and should expand some more. Particularly, there are no results regarding metabarcoding data (number of reads, filtering etc.). These details are important to know the quality of the data which represents the bulk of the analyses. Even the supplementary material gives little information on the metabarcoding results (e.g. number of ASVs - whether every ASV of each family were pooled etc.). The drivers of biodiversity change section could be restructured and include main text tables showing the families positively or negatively correlated with the different variables (akin to table S2 but simplified).

Discussion<br /> The discussion is well written, identifying first some of the possible caveats of this study, particularly regarding the classification of metabarcoding data, its biases and the possible DNA degradation of ancient sediment DNA. The authors discuss how their results fit to general trends showing how agricultural runoff and temperature drive changes in freshwater functional biodiversity primarily due to their synergistic effects on bioavailability, adsorption, etc. The authors highlight the advantage of using a system-level approach rather than focusing on taxa-specific studies due to their indicator status. Similarly, the authors justify the importance of studying community composition as far back as possible since it reveals unexpected patterns of ecosystem resilience. Lake Ring, despite its partially recovered status, has not returned to its semi-pristine levels of biodiversity and community assemblage. Additionally, including enzyme activity allows to assess the functional diversity of the studied environment, although reference databases of these pathways are still lacking. Finally, the authors discuss the implications of their findings under a conservation and land management framework suggesting that by combining these different approaches, drivers of biodiversity stressors can be derived with high accuracy allowing for better-informed mitigation and conservation efforts.

Reviewer #2 (Public Review):

Chen et. al investigated the effects of natural tannins, proanthocyanidins, and punicalagin, against infection by the SARS-CoV-2 virus and its variants. The authors found that these two compounds affect different parts of the SARS-CoV-2 viral infection mechanisms, namely that punicalagin may act ACE2-spike protein interaction and repress Main protease activity, whereas tannic acid and OPC inhibits TMPRSS2 activity. Additionally, the authors show that these tannic compounds can act upon multiple variants of the virus, which suggests a pan-inhibitory effect on SARS-CoV-2 viruses. The studies performed herein present a novel alternative to inhibiting viral infection by SARS-CoV-2 which may be of interest to patients with concerns about reinfection.

The conclusions of this paper are mostly well supported by data, but some aspects of the data analysis need to be clarified and extended.

1) All compounds should be tested in vivo to test not only safety but efficacy and whether these compounds elicit any acute liver toxicity when administered in proposed doses.

2) Efficacy in vaccinated patients would be of great interest, especially since many reinfections occur in the vaccinated population (especially by variants such as Delta).

Reviewer #2 (Public Review):

The molecular mechanisms by which monoaminergic antidepressants exert their therapeutic effects are unknown. An emerging hypothesis in this regard is that these antidepressants work by modulating the glutamatergic system, yet the precise links remain unclear. In this manuscript, Lin et al. describe one such link. First, they observe that the small nucleolar RNA (snoRNA), SNORD90 is consistently elevated following antidepressant treatments in peripheral blood samples, in postmortem brain samples of individuals that received antidepressant treatments, mouse models of depression, and in induced neurons treated with antidepressants in culture. To test whether the elevation of SNORD90 could be significant for antidepressive-like behaviors, the authors perform bilateral injections of viral vectors carrying either SNORD90 or scrambled controls into the mouse cg1/2 and show that overexpression of SNORD90 reduces anxiety and depressive-like behaviors. Using in-silico analysis of base complementarity, the authors predict that the growth factor, neuregulin 3 (NRG3), could be a potential target of SNORD90, and they then validate this prediction by directly showing that SNORD90 overexpression results in the reduction of NRG3 in human neural progenitor cells, whereas knockdown of SNORD90 upregulates NRG3. The authors then show that the binding of SNORD90 to NRG3 pre-mRNA and mature mRNA results in their methylation and subsequent decay. Finally, they show that SNORD90 overexpression in the mouse anterior cingulate cortex is sufficient to increase the levels of glutamatergic neurotransmission.

Overall, the experiments described in the manuscript are well executed and their conclusions are fairly drawn. The observations that SNORD90 overexpression is sufficient to reduce anxiety and depression-like behaviors are indeed exciting, as are the links between SNORD90, and m6A methylation of NRG3, and glutamatergic neurotransmission. There are a few weaknesses in the data and the text, but these should be addressable by the authors.

Reviewer #2 (Public Review):

This work is significant as it provides insights into the global transcriptomic changes of Borrelia burgdorferi during tick feeding. The manuscript also provides methodological advances for the study of the transcriptome of Borrelia burgdorferi in the tick host.

This manuscript documents the study of the transcriptome of Borrelia burgdorferi at 1, 2, 3 and 4 days post-feeding in nymphs of Ixodes scapularis. The authors use antibody-based pull-downs to separate bacteria from tick and mouse cells to perform an enrichment. The data presented support that the transcriptome of B. burgdorferi changes over time in the tick. This work is important as, until now, only limited information on specific genes had been collected. The methodological advances described in this study are valuable for the field.

Reviewer #2 (Public Review):

This paper explores the mechanisms by which cells in tissues use the extracellular matrix (ECM) to reinforce and establish connections. This is a mechanistic and quantitative paper that uses imaging and genetics to establish that the Type IV collagen, DDR-2/collagen receptor discoidin domain receptor 2, signaling through Ras to strengthen an adhesion between two cell types in C. elegans. This connection needs to be strong and robust to withstand the pressure of the numerous eggs that pass through the uterus. The major strengths of this paper are in crisply designed and clear genetic experiments, beautiful imaging, and well supported conclusions. I find very few weaknesses, although, perhaps the evidence that DDR-2 promotes utse-seam linkage through regulation of MMPs could be stronger. This work is impactful because it shows how cells in vivo make and strengthen a connection between tissues through ECM interactions involving collaboration between discoidin and integrin.

Reviewer #2 (Public Review):

Numerous neurodegenerative diseases are thought to be driven by the aggregation of proteins into insoluble filaments known as "amyloids". Despite decades of research, the mechanism by which proteins convert from the soluble to insoluble state is poorly understood. In particular, the initial nucleation step is has proven especially elusive to both experiments and simulation. This is because the critical nucleus is thermodynamically unstable, and therefore, occurs too infrequently to directly observe. Furthermore, after nucleation much faster processes like growth and secondary nucleation dominate the kinetics, which makes it difficult to isolate the effects of the initial nucleation event. In this work Kandola et al. attempt to surmount these obstacles using individual yeast cells as microscopic reaction vessels. The large number of cells, and their small size, provides the statistics to separate the cells into pre- and post-nucleation populations, allowing them to obtain nucleation rates under physiological conditions. By systematically introducing mutations into the amyloid-forming polyglutamine core of huntingtin protein, they deduce the probable structure of the amyloid nucleus. This work shows that, despite the complexity of the cellular environment, the seemingly random effects of mutations can be understood with a relatively simple physical model. Furthermore, their model shows how amyloid nucleation and growth differ in significant ways, which provides testable hypotheses for probing how different steps in the aggregation pathway may lead to neurotoxicity.

In this study Kandola et al. probe the nucleation barrier by observing a bimodal distribution of cells that contain aggregates; the cells containing aggregates have had a stochastic fluctuation allowing the proteins to surmount the barrier, while those without aggregates have yet to have a fluctuation of suitable size. The authors confirm this interpretation with the selective manipulation of the PIN gene, which provides an amyloid template that allows the system to skip the nucleation event.

In simple systems lacking internal degrees of freedom (i.e., colloids or rigid molecules) the nucleation barrier comes from a significant entropic cost that comes from bringing molecules together. In large aggregates this entropic cost is balanced by attractive interactions between the particles, but small clusters are unable to form the extensive network of stabilizing contacts present in the larger aggregates. Therefore, the initial steps in nucleation incur an entropic cost without compensating attractive interactions (this imbalance can be described as a surface tension). When internal degrees of freedom are present, such as the conformational states of a polypeptide chain, there is an additional contribution to the barrier coming from the loss of conformational entropy required to the adopt aggregation-prone state(s). In such systems the clustering and conformational processes do not necessarily coincide, and a major challenge studying nucleation is to separate out these two contributions to the free energy barrier. Surprisingly, Kandola et al. find that the critical nucleus occurs within a single molecule. This means that the largest contribution to the barrier comes from the conformational entropy cost of adopting the beta-sheet state. Once this state is attained, additional molecules can be recruited with a much lower free energy barrier.

There are several caveats that come with this result. First, the height of the nucleation barrier(s) comes from the relative strength of the entropic costs compared to the binding affinities. This balance determines how large a nascent nucleus must grow before it can form interactions comparable to a mature aggregate. In amyloid nuclei the first three beta strands form immature contacts consisting of either side chain or backbone contacts, whereas the fourth strand is the first that is able to form both kinds of contacts (as in a mature fibril). This study used relatively long polypeptides of 60 amino acids. This is greater than the 20-40 amino acids found in amyloid-forming molecules like ABeta or IAPP. As a result, Kandola et al.'s molecules are able to fold enough times to create four beta strands and generate mature contacts intramolecularly. The authors make the plausible claim that these intramolecular folds explain the well-known length threshold (L~35) observed in polyQ diseases. The intramolecular folds reduce the importance of clustering multiple molecules together and increase the importance of the conformational states. Similarly, manipulating the sequence or molecular concentrations will be expected to manipulate the relative magnitude of the binding affinities and the clustering entropy, which will shift the relative heights of the entropic barriers.

The authors make an important point that the structure of the nucleus does not necessarily resemble that of the mature fibril. They find that the critical nucleus has a serpentine structure that is required by the need to form four beta strands to get the first mature contacts. However, this structure comes at a cost because residues in the hairpins cannot form strong backbone or zipper interactions. Mature fibrils offer a beta sheet template that allows incoming molecules to form mature contacts immediately. Thus, it is expected that the role of the serpentine nucleus is to template a more extended beta sheet structure that is found in mature fibrils.

A second caveat of this work is the striking homogeneity of the nucleus structure they describe. This homogeneity is likely to be somewhat illusory. Homopolymers, like polyglutamine, have a discrete translational symmetry, which implies that the hairpins needed to form multiple beta sheets can occur at many places along the sequence. The asparagine residues introduced by the authors place limitations on where the hairpins can occur, and should be expected to increase structural homogeneity. Furthermore, the authors demonstrate that polyglutamine chains close to the minimum length of ~35 will have strict limitations on where the folds must occur in order to attain the required four beta strands.

A novel result of this work is the observation of multiple concentration regimes in the nucleation rate. Specifically, they report a plateau-like regime at intermediate regimes in which the nucleation rate is insensitive to protein concentration. The authors attribute this effect to the "self-poisoning" phenomenon observed in growth of some crystals. This is a valid comparison because the homogeneity observed in NMR and crystallography structures of mature fibrils resemble a one-dimensional crystal. Furthermore, the typical elongation rate of amyloid fibrils (on the order of one molecule per second) is many orders of magnitude slower than the molecular collision rate (by factors of 10^6 or more), implying that the search for the beta-sheet state is very slow. This slow conformational search implies the presence of deep kinetic traps that would be prone to poisoning phenomena. However, the observation of poisoning in nucleation during nucleation is striking, particularly in consideration of the expected disorder and concentration sensitivity of the nucleus. Kandola et al.'s structural model of an ordered, intramolecular nucleus explains why the internal states responsible for poisoning are relevant in nucleation.

To achieve these results the authors used a novel approach involving a systematic series of simple sequences. This is significant because, while individual experiments showed seemingly random behavior, the randomness resolved into clear trends with the systematic approach. These trends provided clues to build a model and guide further experiments.

Reviewer #2 (Public Review):

This is an interesting paper from a reputable group in the field of islet physiology. The authors have provided the results from extensive studies, which will contribute to the knowledge of islet dysfunction and diabetes pathophysiology. One major critique is that the authors studied "the human orthologues of the correlated mouse proteins that are proximal to the glycemia-associated SNPs in human GWAS". This implies two assumptions - (1) human and mouse proteins do not differ in terms of islet physiology and calcium signaling; (2) the proteins proximal to the SNPs are the causal factors for functional differences, though the SNPs could affect protein/gene function distant from the SNPs.

Reviewer #2 (Public Review):

This work explored the biological functions of a small family of RNA-binding proteins that was previously studied in animals, but was uncharacterized in plants. Combinatorial T-DNA insertional mutants disrupting the expression of the four Mushashi-like (MSIL) genes in Arabidopsis revealed that only the msil2 msil4 double mutant visibly alters plant development. The msil2/4 plants produced stems that could not stand upright. Transgene complementation, site-directed mutagenesis of MSIL4 conserved RNA-binding motifs, and in vitro RNA binding assays support the conclusion that the loss of MSIL2 and MISL4 function is responsible for the observed morphological defects. MSIL2/4 interact with proteins associated with mRNA 3'UTR binding and translational regulation.

The authors present compelling biochemical evidence that Mushashi-like2 (MSIL2) and MSIL4 jointly regulate secondary cell wall biosynthesis in the Arabidopsis stem. Quantitative analyses of proteins and transcripts in msil2/4 stems uncovered upregulation of several xylan-related enzymes (despite WT-like RNA levels). Consistent with MALDI-TOF data for released xylan oligosaccharides, the authors propose a model in which MSIL2/4 negatively regulate the translation of GXM (glucuronoxylan methyltransferase), a presumed rate-limiting step. The molecular links between overmethylated xylans and the observed stem defects (which include subtle reductions in lignin and increases beta-glucan polymer distribution) warrants further investigation in future studies. Similarly, as the authors point out, it is intriguing that the loss of the broadly expressed MSIL2/4 genes only significantly affects specific cell types in the stem.

Reviewer #2 (Public Review):

This manuscript by Daly et al., probes the emerging paradigm of GPCR signaling from endosomes using the V2R as a model system with an emphasis on Gq/11 and β-arrestins. The study employs cellular imaging, enzyme complementation assays and energy transfer-based sensors to probe the potential formation of GPCR-G-protein-β-arrestin megaplexes. While the study is certainly very interesting, it appears to be very preliminary at many levels, and clearly requires further development in order to make robust conclusions.

1. The use of mini-G-proteins in these experiments is a major concern as these are highly engineered and may not represent the true features of G-proteins. While these have been used as a readout in other publications, their use in demonstrating megaplex formation is sub-optimal, and native, full-length G-proteins should be used.<br /> 2. The interpretation of complementation (NanoLuc) or proximity (BRET) as evidence of signaling not appropriate, especially when overexpression system and engineered constructs are being used.<br /> 3. After the original work from the same corresponding authors on megaplex formation, the major challenge in the field is to demonstrate the existence and relevance of megaplex formation at endogenous levels of components, and the current study focuses solely on showing the proximity of Gq and β-arrestins.<br /> 4. The study lacks a coherent approach, and the assays are often shifted back and forth between the two β-arrestin isoforms (1 and 2), for example, confocal vs. complementation etc.<br /> 5. In every assay, only the G-proteins and β-arrestins are monitored without a direct assessment of the presence of receptor, and absent that data, it is difficult to justify calling these entities megaplexes.

In conclusion, the authors should consider expanding on this work further to make the points more convincingly to make the work solid and impactful. The two corresponding authors are among the leaders in the field having demonstrated the existence of megaplexes, and building on the work in a systematic fashion should certainly move the paradigm forward. As the work presented in the current manuscript is already pre-printed, the authors should take this opportunity to present a completer and more comprehensive story to the field.

Reviewer #2 (Public Review):

Bernou et al use a FACS-based method to sort different cells along the neurogenesis trajectory. They identify cells that are LeX+EGFR+CD24+ which they call i-NBs. The authors suggest these cells proliferate performing neurosphere assays, and that they can make all NSC-derived differentiated cell types through transplantation into mice. They performed microarrays on the different cell subtypes, which led them to their interest in RNA splicing proteins. They additionally performed single-cell analyses to try to identify the cluster of i-NBs compared to other cell types. Further, they performed an irradiation experiment to initiate quiescence exit and depletion of the dividing cell types to create a directionality in the progression through cell types. Comparison with other published sequencing datasets of the same cell type revealed that the i-NBs were most similar to Mitotic TAPs. The authors use their single cell sequencing data to observe expression changes of the RNA splicing factors in different clusters. They also suggest that the i-NB population is heterogeneous in their DCX mRNA levels, with a high group and a low group that have different characteristics. They erroneously use a DCX-Cre-ERT2 line to identify GFP+ or GFP- cells to transplant, and find no GFP+ cells at the end of 5 weeks after transplantation, and draw the conclusion that the high DCX cells don't have the same NSC potential. The authors propose they have identified a new cell type, and that there should be a rewrite of the SVZ neurogenesis cascade to include this population.

Summary of response<br /> This manuscript postulates the identification of a new cell type in the adult neurogenesis cascade. However, all of the author's analyses point to this population of sorted cells being the late mitotic TAPs on their way to becoming neuroblasts. This would suggest that these cells are in the trajectory between TAPs and NBs, so a pivot point, but not a unique cell type in its own. In their sequencing analyses, cell cycle becomes the defining factor of the clustering. Indeed, their cell type as compared to other datasets suggests this population is a mitotic TAP, which is supported by their own transcriptome data (Fig S2) showing that i-NBs are just further in mitosis than the TAPs.

Reviewer #2 (Public Review):

This paper tried to assess the link between genetic and environmental factors on psychotic-like experiences, and the potential mediation through cognitive ability. This study was based on data from the ABCD cohort, including 6,602 children aged 9-10y. The authors report a mediating effect, suggesting that cognitive ability is a key mediating pathway in the link between several genetic and environmental (risk and protective) factors on psychotic-like experiences.

While these findings could be potentially significant, a range of methodological unclarities and ambiguities make it difficult to assess the strength of evidence provided.

Strengths of the methods:

The authors use a wide range of validated (genetic, self- and parent-reported, as well as cognitive) measures in a large dataset with a 2-year follow-up period. The statistical methods have the potential to address key limitations of previous research.

Weaknesses of the methods:

The rationale for the study is not completely clear. Cognitive ability is probably a more likely mediator of traits related to negative symptoms in schizophrenia, rather than positive symptoms (e.g., psychosis, psychotic-like symptom). The suggestion that cognitive ability might lead to psychotic-like symptoms in the general population needs further justification.

Terms are used inconsistently throughout (e.g., cognitive development, cognitive capacity, cognitive intelligence, intelligence, educational attainment...). It is overall not clear what construct exactly the authors investigated.

Not the largest or most recent GWASes were used to generate PGSes.<br /> It is not fully clear how neighbourhood SES was coded (higher or lower values = risk?). The rationale, strengths, and assumptions of the applied methods are not fully clear. It is also not clear how/if variables were combined into latent factors or summed (weighted by what). It is not always clear when genetic and when self-reported ethnicity was used. Some statements might be overly optimistic (e.g., providing unbiased estimates, free even of unmeasured confounding; use of representative data).

It appears that citations and references are not always used correctly.

Strengths of the results:

The authors included a comprehensive array of analyses.

Weaknesses of the results:

Many results, which are presented in the supplemental materials, are not referenced in the main text and are so comprehensive that it can be difficult to match tables to results. Some of the methodological questions make it challenging to assess the strength of the evidence provided in the results.

Appraisal:

The authors suggest that their findings provide evidence for policy reforms (e.g., targeting residential environment, family SES, parenting, and schooling). While this is probably correct, a range of methodological unclarities and ambiguities make it difficult to assess whether the current study provides evidence for that claim.

Impact:

The immediate impact is limited given the short follow-up period (2y), possibly concerns for selection bias and attrition in the data, and some methodological concerns.

Reviewer #2 (Public Review):

The manuscript by Petroccione et al., examines the modulatory role of the neuronal glutamate transporter EAAC1 on glutamatergic and GABAergic synaptic strength at D1- and D2-containing medium spiny neurons within the dorsolateral striatum. They find that pharmacological and genetic disruption of EAAC1 function increases glutamatergic synaptic strength specifically at D1-MSNs. They show that this is due to a structural change in release sites, not release probability. They also show that EAAC1 is critical in maintaining lateral inhibition specifically between D1-MSNs. Taken together, the authors conclude that EAAC1 functions to constrain D1-MSN excitation. Using a computational modeling technique, they posit that EAAC1's modulatory role at glutamatergic and GABAergic inputs onto D1-MSNs ultimately manifests as a reduction of gain of the input-output firing relationship and increases the offset. They go on to show that EAAC1 deletion leads to enhanced switching behavior in a probabilistic operant task. They speculate that this is due to a dysregulated E/I balance at D1-MSNs in the DLS.

Overall, this is a very interesting study focused on an understudied glutamate transporter. Generally, the study is done in a very thorough and methodical manner and the manuscript is well written.

Major Comments/Concerns:<br /> 1. Regional/Local manipulations in behavior study: The manuscript would be greatly improved if they provided data linking the ex vivo electrophysiological findings within the DLS with the behavior. Although they are using a DLS-dependent task, they are nonetheless, using a constitutive EAAC1 KO mouse. Thus, they cannot make a strong conclusion that the behavioral deficits are due to the EAAC1 dysfunction in the DLS (despite the strong expression levels in the DLS).

2. Statistics used in the study: There are some missing details regarding the precise stats using for the different comparisons. I am particularly concerned that the electrophysiology studies that were a priori designed as a 2-factor analysis did not have 2-way ANOVAs performed, but rather a series of t-tests. For example, in Figure 3b, the two factors are 1) cell type and 2) genotype. Was a 2-way ANOVA performed? It is hard for me to tell from the text.

Moderate Concerns:<br /> 3. Control mice: I am moderately concerned that littermates were not used for controls for the EAAC1 KO, but rather C57Bl/6NJ presumably ordered from a vendor. It has been shown that issues like transit and rearing conditions can have long term affects on behavior. Were the control mice reared in house? How long was the acclimation time before use?

4. OCD framework: I generally find the OCD framework unnecessary, particularly in the introduction. Compulsive behaviors are not restricted to OCD. Indeed, the link between the behavioral observations and OCD phenotype seems a bit tenuous. In addition, studying the mechanisms of behavioral flexibility in and of itself is interesting. I don't think such a strong link needs to be made to OCD throughout the entirety of the paper. The authors should consider tempering this language or restricting it to the discussion and end of the abstract.

Reviewer #2 (Public Review):

This paper explores the mechanisms by which cells in tissues use the extracellular matrix (ECM) to reinforce and establish connections. This is a mechanistic and quantitative paper that uses imaging and genetics to establish that the Type IV collagen, DDR-2/collagen receptor discoidin domain receptor 2, signaling through Ras to strengthen an adhesion between two cell types in C. elegans. This connection needs to be strong and robust to withstand the pressure of the numerous eggs that pass through the uterus. The major strengths of this paper are in crisply designed and clear genetic experiments, beautiful imaging, and well supported conclusions. I find very few weaknesses, although, perhaps the evidence that DDR-2 promotes utse-seam linkage through regulation of MMPs could be stronger. This work is impactful because it shows how cells in vivo make and strengthen a connection between tissues through ECM interactions involving collaboration between discoidin and integrin.

Reviewer #2 (Public Review):

Harris et al. have described the cryo-EM structure of PI3K p110gamma in a complex with a nanobody that inhibits the enzyme. This provided the first structure of full-length of PI3Kgamma in the absence of a regulatory subunit. This nanobody is a potent allosteric inhibitor of the enzyme, and might provide a starting point for developing allosteric, isotype-specific inhibitors of the enzyme. One distinct effect of the nanobody is to greatly decrease the dynamics of the enzyme as shown by HDX-MS, which is consistent with a growing body of observations suggesting that for the whole PI3K superfamily, enzyme activators increase enzyme dynamics.

The most remarkable outcome of the study is that upon observing the site of nanobody binding, the authors searched the literature and found that there was a previous report of a PKCbeta phosphorylation of PI3Kgamma in the helical domain that is near the nanobody binding site. This led the authors to re-examine the consequence of the phosphorylation armed with better structural models and the tools to study the effects of this phosphorylation on enzyme dynamics. They found that the site of phosphorylation is buried in the helical domain, suggesting that a large conformational change would have to take place to enable the phosphorylation. HDX-MS showed that phosphorylation at three sites clustered in the helical domain generate a distinctly different conformation with rapid deuterium exchange. This suggests that the phosphorylation locks the enzyme in a more dynamic state. Their enzyme kinetics show that the phosphorylated, dynamic enzyme is activated.

While this phosphorylation was reported before, the authors have provided a mechanism for why this activates the enzyme, and they have shown why binders that stabilise the helical domain (such as binding to the p101 regulatory subunit and the nanobody) prevent the phosphorylation. It is this insight into the dynamics of the PI3Kgamma that will likely be the long-lasting influence of the work.

The paper is well written and the methods are clear.

Reviewer #2 (Public Review):

In recent years, the role of the ECM in synaptic organization has been increasingly studied, leading to a better appreciation of how proteins that comprise the ECM influence synaptic structure and function. How the ECM affects neuronal structure and axonal biology is less well understood, however. Guss and colleagues begin to remedy this by assessing the role of Perlecan in the maintenance of NMJ terminals in the fly. They demonstrate a role for Perlecan in synaptic NMJ stability - loss of Perlecan results in a drastic increase in synaptic retractions. These retractions occur as a result of multiple non-cell-autonomous sources of Perlecan, as neither one tissue RNAi induces phenotypes nor does neuronal cDNA rescue a mutant. They advocate that multiple cellular mechanisms, including Wallerian degeneration and Wnt signaling, are not involved and demonstrate cytoskeletal and functional deficits. They also show that entire nerve bundles degenerate in a coordinated manner, likely due to the disruption of the neural lamella.

This is a strong and thorough genetic analysis of the role of Perlecan in neuronal stability and axonal retraction. The conclusions are largely valid, and the controls and experiments reasonable to answer the stated questions. I have some requests for additional experiments to bolster the existing conclusions.

Reviewer #2 (Public Review):

This manuscript describes an interesting study assessing the impact of acute stress on neural activity and helping behavior in young, healthy men. Strengths of the study include a combination of neuroimaging and psychoneuroendocrine measures, as well as computational modeling of prosocial behavior. Weaknesses include complex, difficult to understand 3-way interactions that the sample size may not be large enough to reliably test. Nonetheless, the study and results provide useful information for researchers seeking to better understand the influence of stress on the neural bases of complex behavior.

The stressor was effective at eliciting physiological and psychological stress responses as shown in Figure 2.

Higher perceived stress in more selfish participants (lower social value orientation (SVO) angle) was associated with lower prosocial responding (Figure 4). How can we reconcile this finding with the finding (presented on page 15) that those with a more prosocial SVO showed a significant decline in dACC activation to subjective value at increasing levels of perceived stress? This seems contrary to the behavioral response.

A larger issue with the study is that the power analysis presented on page 23 is based on a 2 (between: stress v. control) by 2 (within: self v. other) design. Most of the reported findings come from analyses of 3-way interactions. How can the readers have confidence in the reliability of results from 3-way interaction analyses, which were not powered to detect such effects?

Reviewer #2 (Public Review):

This paper describes the results of a set of complementary and convergent experiments aimed at describing roles for the non-selective cation channels NALCN and TRPC6 in mediating subthreshold inward depolarizing currents and action potential generation in VTA DA neurons under normal physiological conditions. That said, some datasets are underpowered, and general flaws in statistical reporting make assessment difficult. There is also a lack of clarity at various points throughout the manuscript, as well as overinterpretation of the data generated in these experiments. Specific comments follow:

1. These results do not show that TRPC6 mediates stress effects on depression-like behavior. As stated by the authors in the first sentence of the final paragraph, "downregulation of TRPC6 proteins was correlated with reduced firing activity of the VTA DA neurons, the depression-like behaviors, and that knocking down of TRPC6 in the VTA DA neurons confer the mice with depression behaviors." Therefore, the results show associations between TRPC6 downregulation and stress effects on behavior, occlusion of the effects of one by the other on some outcome measures, and cell manipulation effects that resemble stress effects. There is no experiment that shows reversal of stress effects with cell/circuit-specific TRPC6 manipulations. Please adjust the title, abstract and interpretation accordingly.<br /> 2. Statistical tests and results are unclear throughout. For all analyses, please report specific tests used, factors/groups, test statistic and p-value for all data analyses reported. In some cases, the chosen test is not appropriate. For example, in Figure 6E, it is not clear how an experiment with 2 factors (stress and drug) can be analyzed with a 1-way RM ANOVA. The potential impact of inappropriate statistical tests on results makes it difficult to assess the accuracy of data interpretation.<br /> 3. Why were only male mice used? Please justify and discuss in the manuscript. Also, change the title to reflect this.<br /> 4. Number of recorded cells is very low in Figure 1. Where in VTA did recordings occur? Given the heterogeneity in this brain region, this n may be insufficient. Additional information (e.g., location within VTA, criteria used to identify neurons) should be included. Report the number of mice (i.e., n = 6 cells from X mice) in all figures.<br /> 5. Authors refer to VTA DA neurons as those that are DAT+ in line 276, although TH expression is considered the standard of DAergic identity, and studies (e.g., Lammel et al, 2008) have shown that a subset of VTA DA neurons have low levels of DAT expression. Authors should reword/clarify that these are DAT-expressing VTA DA neurons.<br /> 6. Neuronal subtype proportions should be quantified and reported (Fig. 1Aii).<br /> 7. In addition to reporting projection specificity of neurons expressing specific channels, it would be ideal to report these data according to spatial location in VTA.<br /> 8. The authors state that there are a small number of Glut neurons in VTA, then they state that a "significant proportion" of VTA neurons are glutamatergic.<br /> 9. It is an overstatement that VTA DA neurons are the key determinant of abnormal behaviors in affective disorders.

Reviewer #2 (Public Review):

In this study, Li et al. examined the involvement of astrocyte-like glia in responding to traumatic brain injury in Drosophila. Using a previously-established method that induces high-impact, whole-body trauma to flies (HIT device), the authors observed increased blood-brain-barrier permeabilization, neuronal cell death, and hypertrophy of astrocyte-like cells in the fly brain following injury. The authors provide compelling evidence implicating a signaling pathway involving the PDGF/VEGF-like Pvr receptor tyrosine kinase, the AP-1 transcription factor, and the matrix metalloprotease Mmp1 in the astrocytic cell response to TBI. The authors' data was generally high-quality data and combined multiple experimental approaches (microscopy, RNA sequencing, and transgenic), increasing the rigor of the study. The identification of injury-induced gene expression changes in astrocytic cells helps increase our limited understanding of roles this glial subtype plays in the adult fly brain. Limitations of the study include a reliance on RNAi-mediated gene silencing without validation via genetic mutants and a limited examination of how astrocyte-like and ensheathing glia could interact following TBI, especially given that several genes identified in this study are known to mediate ensheathing glial responses to axotomy. The conclusions are generally well-supported by the presented data, however some further clarification of quantitative methods and analyses will help to strengthen the findings:

1. The significance and quantification method for the astrocytic cell body sizes in Fig. 2C, D and appearance of GFP+ accumulations in Fig. 2F should be better defined - how were cell bodies and GFP+ puncta identified relative to other astrocytic cell structures, are they homogeneous in size/intensity in different brain regions following injury, and what could the GFP+ puncta represent?<br /> 2. The relative contributions of astrocyte-like and ensheathing glia in the brain's response to TBI is unclear. RNA sequencing identified Mmp1 and Draper as genes upregulated following TBI, however, these genes have previously been implicated in ensheathing glial (and not astrocytic) responses to acute nerve injury. The authors provide convincing evidence that their transcriptomic data is devoid of neuronal genes, but what about the possibility of ensheathing glial contaminants? Figures 2I-Q suggest that the majority of Mmp1 protein co-localizes with ensheathing rather than astrocytic glial membranes following TBI. Does knockdown of Pvr, Jra, or kay in ensheathing glia affect Mmp1 upregulation following injury? A closer examination of how these two glial subtypes contribute to and interact-and what proportion of Mmp1 is cell autonomous to astrocytes-during injury responses would be valuable.<br /> 3. The authors rely on RNAi and overexpression methods to manipulate expression of candidate genes in Figures 4, 5, and 7. In most cases, only a single RNAi line is used to reduce expression of a candidate gene, increasing the possibilities of off-target effects or insufficient gene knockdown. These data could be strengthened by using multiple RNAi lines as well as mutants to validate findings for Pvr, Jra, and kay knockdown in Figures 4 and 5, and perhaps confirmation of knockdown efficiency, particularly in Fig. 7.<br /> 4. Single channel images should be included in Fig. 1L and M to help strengthen the conclusion that Dcp-1+ puncta are elav+ and repo-.<br /> 5. Sample sizes and a description of power analysis should be included in figure legends/methods. Based on the graphs, some sample sizes appear low (e.g., Fig. 1H+K and 2D+Q).

2

Reviewer #2 (Public Review):

Jackson et al present a study focused on the role of TLR7 in emergency myelopoiesis following infection or injury. The investigators observe that TLR7 stimulation to the skin with the TLR7 agonist R848 causes an increase in circulating monocytes. This effect appears to require stimulation at an epithelial surface as it occurred with skin or intestinal administration but not intraperitoneal or intravenous administration. They demonstrate TLR7 specificity using TLR7-/- mice and the requirement for TLR7 expression in hematopoietic cells, likely myeloid cells. To determine if other TLR ligands can stimulate myelopoiesis, they compared skin administration with other TLR ligands (LPS, Poly I:C, CpG) or a general pro-inflammatory stimuli (TPA). None of these resulted in increased myelopoiesis, further highlighting TLR7 specificity. They confirm that this TLR7-mediated myelopoiesis occurs in the bone marrow as opposed to extramedullary sites (i.e. spleen) and differentiation occurs through the HSPC-MDP-cMoP pathway as opposed to a GMP-mediated differentiation. In addition to myelopoiesis, they demonstrate that R848 facilitates the transition of Ly6c high monocytes to Ly6c-low monocytes and tissue macrophages and this effect requires the Ly6c high monocytes. Furthermore, these effects occur independently of Ccr2 and Cx3cr1, known monocyte chemoattractant receptors. Finally, they identified that R848 administration enhances anti-viral responses. In mice topically treated with R848, they then exposed these mice to RSV and/or influenza. They observed that the R848 treated mice had reduced viral responses (defined by a decrease in weight loss and reduced viral replication). Overall, the data support that TLR7 administration to epithelial surfaces drives an increase in circulating monocytes, and this required TLR7 expression in myeloid cells. This is an interesting study that has implications for our understanding of how immune signals at peripheral sites drive the expansion of monocytes required to respond to infections and/or inflammation.

The conclusions are largely supported by the data, and several aspects of TLR7-mediated myelopoiesis are explored. However, there are some limitations to the data that need to be considered and reduce the generalizability of the conclusions made by the authors.

1. Data convincingly demonstrates that skin administration of the TLR7 agonist R848 causes an increase in circulating monocytes, particularly Ly6c low monocytes. In addition, this requires TLR7 expression and specifically TLR7 expression on myeloid cells. However, this raises an important question that is not answered by the present investigations. Specifically, the connection between local TLR7 administration requiring myeloid cells and how this directly leads to emergency myelopoiesis. Presumably, there is some factor released from local myeloid cells that then stimulates the bone marrow response and then a response that leads to the differentiation of Ly6c high monocytes to Ly6c low monocytes and infiltrating tissue macrophages. It is not clear if this is one factor or several factors. Presumably, this would be a circulating factor, though this is also not clear from the data. This appears to be a critical piece to tie in the connection between local TLR7 and emergency myelopoiesis. Furthermore, it is not clear how the dermal administration of R848 impacts the skin and if this is a critical feature of the response. Presumably, it generates local inflammation as evidenced by the data in 3C showing the proportions of monocytes and neutrophils. However, the impact on skin structure/function is not clear nor is there a definition of how this changes over the time course of the treatments.

2. The requirement for TLR7 stimulation on the skin is convincing. However, it is not clear how generalizable it is to all epithelial surfaces. The authors administer R848 in the drinking water and this causes myelopoiesis. However, the data supporting this as a direct effect of intestinal epithelial exposure is not explicitly demonstrated. The data using IP injections would seem to suggest that this is not a generic "epithelial surface response". IP injections are an administration to the peritoneum, an epithelial surface. The lack of an IP injection response would seem to argue that TLR7 responses are only to specific epithelial surfaces. This limits the generalizability of the observation. Alternatively, differences could be attributed to differences in TLR7 doses required at the distinct epithelial barrier sites. Further exploration of the specific epithelial interface requirements would provide better insight into the specific mechanism of how TLR7 stimulation works.

3. The authors demonstrate that dermal TLR7 and not other TLR ligands cause the increase in monocytes. Though the data is convincing for TLR7, the lack of a response with the other TLR ligands requires additional experiments to clarify if this is really TLR7-specific. Specifically, dose ranging experiments are needed to clarify if a lack of effect is simply due to differences in the sensitivity of TLR ligands to dermal exposure as opposed to being a TLR7 only effect.

4. The evidence of increased Ly6C low monocytes following dermal TLR7 in CCR2 null mice is intriguing. This suggests that TLR7-mediated emergency myelopoiesis is occurring independently of CCR2. However, this data is confusing as the authors also report that Ly6C low monocytes are generated from a Ly6C high monocyte intermediate. The data in Figure 6A supports that CCR2 null mice have baseline monocytopenia (a known feature of these mice) and then fail to generate Ly6C high monocytes following R848 exposure. Then how does this lead to an increase in Ly6C low monocytes if there are no Ly6C high monocytes as shown in the third panel of 6A? This is not clarified but critical to making this argument. There are also missing vehicle controls that would be important to interpreting these provocative results.

5. Data is lacking for direct TLR7 effects on the lung. These would appear to be important, given the focus on RSV and influenza responses in the study. As presented, the TLR7 protection from respiratory viral responses is via dermal TLR7 exposure followed by respiratory viral infection. This is unlikely to be clinically relevant, raising the significance of this model to human respiratory viral infection. An improved experimental design would incorporate respiratory TLR7 stimulation followed by pathogen exposure. In addition, given the focus on monocytes and macrophages, elucidating the impact on monocytes and lung macrophages, prior to and following infection would better define the connection between TLR7 exposure at epithelial barrier sites, emergency myelopoiesis, and respiratory viral infection.

Reviewer #2 (Public Review):

The manuscript by Becker and coworkers describes a target-binding myristoyl switch in the calcium-binding EF hand protein CHP3 using one of its targets, the NHE1. The work uses a suite of biophysical methods including SEC, nanoDSF, fluorescence, and native MS, to address conformations, ligand binding (Ca2+, Mg2+, NHE1), and liposome association, pinpointing a conformation switch which they term a target-dependent myristoyl switch. The strength of the manuscript is a convincing mapping of the different conformations and the conclusion that target binding, and not Ca2+ binding is necessary to expel the lipid from the protein, and that this jointly enhances membrane binding. It would have been even stronger if additional structural data had been included to address the properties of the different states and hence support if there indeed are changes in dynamics and flexibility.

Reviewer #2 (Public Review):

The authors present a thorough and comprehensive analysis of 13000 Typhi genomes sampled globally over the last 21 years. The paper is an important example of how to perform meta-analysis of large numbers of published genomes while keeping credit equitable and including all original investigators as authors. This should be commended and maintained by the genomics community as the correct protocol when performing meta-analyses of this kind.

The study presents important findings on the emergence, maintenance and dynamics of AMR in different Typhi lineage backgrounds globally. This is extremely important for surveillance and appropriate adjustments to empirical therapy guidelines.

The study was also able to deduce new findings on the emergence of XDR Typhi in Pakistan and to date the first case to much earlier than previously thought. This is a good demonstration of why collating and re-analysing data in this fashion can be so valuable.

The authors present interesting evidence that settings where MDR is chromosomally integrated has remained at high prevalence whereas it seems to be declining in settings where MDR is plasmid-borne. I found Figure S11 particularly interesting. As noted by the authors, this is consistent with the hypothesis that the IncHI1 MDR plasmid is associated with a fitness cost that is removed when the MDR transposon becomes chromosomally-integrated.

This study also represents a good demonstration of why patient travel information can be such a useful metadata field for genomic studies and the potential for its use in helping to survey areas where no genomic studies have taken place yet. Other studies (e.g. https://www.medrxiv.org/content/10.1101/2022.08.23.22279111v1) have used this information from UKHSA to similarly represent the phylogeography of a different serovar of Salmonella and have found that data collected in this way can provide broader global coverage and more uniform sampling than what is currently available on NCBI. This data should be encouraged to be shared and this study goes a long way in proving its general utility for surveillance studies in public health.

Reviewer #2 (Public Review):

Embryonic development requires differential gene expression, which is regulated by enhancer elements. Regulatory proteins bind to these DNA elements to regulate close-by promoters. Key insights into the molecular mechanisms of enhancer function have been gained by studying fly segmentation, where a hierarchical cascade of gene regulation subdivides the embryo into ever smaller units. However, segmentation in other insects and arthropods as well as in vertebrates relies on a much more dynamic process where repetitive gene expression patterns appear to migrate across tissues similar to waves. Only recently, models have been proposed that make predictions on the underlying gene regulatory networks (GRN) and the properties of the respective enhancers. Specifically, the previously suggested model of the authors, the enhancer switching model, predicted that each gene expression wave should actually be regulated by two GRNS - one based on a "dynamic enhancer", which directs the early wave-like pattern and the other involving a "static enhancer", which directs the more stable expression defining the segment anlagen at the end of each cycle. However, these predicted enhancer types have not been described so far. In flies, where the respective methodology would be available, the segmentation does not show prominent wave-like patterns. In beetles, where pronounced wave-like patterns have been described, the respective methodology has been missing.

With this work, the authors establish a genomic resource and a transgenic line in the red flour beetle in order to establish it as a model system to tackle questions on enhancers driving dynamic expressions during development. First, they determine the open chromatin at early embryonic stages thereby generating a valuable resource for enhancer detection. They did so by dissecting the embryos of two temporal stages into three parts (head, middle part, and growth zone) and then determined open chromatin via ATAC-seq. This setup allowed for a comparison across tissues and stages to identify dynamically regulated chromatin. Indeed, Mau et al. find that dynamic chromatin regulation is a good criterion to enrich for active enhancers.

Second, they established an enhancer reporter system, which allows for visualization of de novo transcription by both in situ hybridization and in vivo. This MS2 system has for the first time been implemented in this beetle and the authors convincingly show its functionality. Indeed, the expression dynamics can be very nicely visualized in vivo at blastoderm stages.

Combing these two resources, they predicted enhancers based on the criterion of dynamic chromatin regulation (from their ATAC-seq resource) and tested them using their novel MS2 system. Out of 9 tested enhancers located close to the gap genes hunchback and Krüppel and the pair-rule gene runt, 4 drove expression. Combining these data with previously published beetle enhancers, they show that DNA regions with differential accessibility were indeed enriched in active enhancers (appr. 60%), providing a good selection criterion.

Finally, they characterize two of the newly identified enhancers that reflect wave-like expression patterns in fixed embryos and in vivo by using the MS2 system to test predictions of the enhancer switching model. The results are compared with an elaboration of their previously suggested enhancer-switching model, which predicts different patterns for static vs. dynamic enhancers. Indeed, they think that the runB enhancer fits the predictions of a static enhancer.

The authors have generated a genomic resource that will be of very high value to the community in the future. The fact that they dissected the embryos for that purpose makes it even more precise and valuable. Likewise, the transgenic system that allows for testing enhancer activity in vivo will be very valuable for the highly active research field dealing with the prediction and analyses of enhancers.

The analysis of the identified enhancers provides partial confirmation for their model. As the authors state in the discussion, finding at least one pair of such enhancers for one gene would be a great test of their hypothesis - finding pairs of static and dynamic enhancers in several genes would be strong support. Unfortunately, they found only one of the two enhancer types in runt and one in hunchback, respectively. Hence, the prediction of the model remains to be tested in the future.

The authors provide a lot of high-quality data visualized nicely in the figures. The text would profit from some re-formulation, re-structuring, and shortening.

Open questions:<br /> What happens with the runB enhancer at later stages of embryogenesis? With what kind of dynamics do the anterior-most stripes fade and does that agree with the model? Do they show the same dynamics throughout segmentation? I think later stages need to be shown because the prediction from the model would be that the dynamics are repeated with each wave. I am not so sure about the prediction for ageing stripes - yet it would have been interesting to see the model prediction and the activity of the static enhancer.<br /> I understand that the mRNA of the reporter gene yellow is more stable than the runt mRNA. This might interfere with the possibility to test your prediction for static enhancers: The criterion is that the stripes should increase in strength as the wave migrates towards the anterior. You show this for runB - but given that yellow has a more stable transcript - could this lead to a "false positive" increase in intensity with the slower migration and accumulation of transcripts? I would feel more comfortable with the statement that this is a static enhancer if you could exclude that the signal is blurred by an artifact based on different mRNA stability. What about re-running the simulation (with the parameters that have shown to well reflect endogenous runt mRNA levels) but increasing the parameter for the stability of the mRNA? Are static and dynamic enhancers still distinguishable? The claim of having found a static enhancer rests on this increase in signal, hence, other explanations need to be excluded carefully.<br /> What about the head domain of the runB enhancer (e.g. Fig. 6A lowest row): This seems to be different from endogenous expression in your work and in Choe et al. Is that aspect different from endogenous expression and can this be reconciled with your model?<br /> The claim of similar dynamics of expression visualized by in situ and MS2 in vivo relies on comparing Fig. 6C with 6A. To compare these two panels, I would need to know to what stage in A the embryo in C should be compared. Actually, the stripe in 6C appears more crisp than the stripes in 6A.<br /> Were the enhancer dynamics tested in vivo at later stages as well? I would appreciate a clear statement on what stages can be visualized and where the technical boundaries are because this will influence any considerations by others using this system.<br /> How do the reported accessibility dynamics of runA enhancer correlate with the activity of the reporter: E.g. is the enhancer open in the middle body region but closed at the posterior part of the embryo? Or is it closed at the anterior - and if so: why is there a signal of the reporter in the head?<br /> You show that chromatin accessibility dynamics help in identifying active enhancers. Is this idea new or is it based on previous experience with Drosophila (e.g. PMID: 29539636 or works cited in https://doi.org/10.1002/bies.201900188)? Or in what respect is this novel?

Reviewer #2 (Public Review):

Gfeller et al. performed an experiment to test the mechanism underlying plant-soil feedback-induced effects on crop yield using two common rotation partners, corn and wheat, that are grown in sequence with one another in agricultural fields across years. The authors use a benzoxazinoid-deficient corn genotype to show that, compared to soil conditioned in year one by a wild-type (normal) corn variety, wheat growth, and yield decreased in year two. As part of this experiment, the authors also showed that benzoxazinoids exuded from corn roots are persistent over time (i.e., they can be detected in the soil long after corn was harvested), resulting in changes to the structure of bacterial and fungal communities, and reduce insect feeding damage to wheat. These effects were replicated across three different wheat cultivars. Weed pressure (benzoxazinoids have previously been shown to be allelopathic towards other plants) and wheat quality were unaffected in the experiment.

Strengths:

The authors use a large-scale field experiment to test their hypothesis. This is a very important aspect of the study. Most plant-soil feedback studies are conducted using potted plants or, at best, small-plot trials. This experiment was performed using large field plots, which is essential for making reliable inferences about crop rotations and yields in agriculture.

The study does a nice job of testing the underlying chemical mechanisms of how plant-soil feedbacks operate. Many studies have shown that conditioning the soil with one plant species affects the performance of a second plant species sharing that soil, but in virtually all cases we don't know, and can only speculate, what mechanisms are causing this effect.

The data reported are impressive for a few reasons. First, the authors make it a point to measure a wide variety of variables, making the findings particularly robust. I was impressed with the breadth of phenotyping considered by the authors. For example, their plant growth measurements were highly detailed, going from early-season crop performance (e.g., seedling emergence, chlorophyll, height, biomass, water content) to late-season yield effects (e.g., tiller density per plant and unit area, kernel weight) and even considering crop quality (e.g., protein, dough stability), which is usually ignored and assumed to not differ. This was clearly a ton of work! As part of this, they comprehensively measured variables related to plants, insects, weeds, soil microbes, etc., making this highly interdisciplinary work. And a second factor related to the data - the treatment effects were very consistent and impressive in magnitude. While not all variables were significantly affected, the ones that showed effects were consistent and not trivial (i.e., they were biologically significant).

Weaknesses:

While corn and wheat are common rotation partners around the world, it still seems like wheat was an odd choice for this experiment. The main reason I say this is that, as the authors point out, both plants produce benzoxazinoids. This makes it difficult to ascertain the effects of corn-derived benzoxazinoids since wheat is also exuding these compounds from its roots. A non-benzoxazinoid crop like soybean seems like it would've been a better choice since you wouldn't have the confounding effect of both the conditioning and feedback plants producing the same secondary metabolites. On the other hand, the fact that wheat produces benzoxazinoid could be a factor driving its yield response (i.e., crops that don't produce benzoxazinoids may show allelopathic-negative reactions).

The authors show that experimentally eliminating benzoxazinoids has a negative effect on the subsequent crop. While this is interesting from a mechanistic standpoint, it's less compelling than if the reverse was true. In other words, the authors simply show why corn is a good rotation crop for wheat, which has been known for a very long time, even if the mechanism was unclear. The authors argue that this could open the door for breeding that targets benzoxazinoids, which may very well be true; however, the outcomes would be more interesting if the study was showing that existing practices result in low yields and they were paving a path for how to ameliorate this.

In the end, it remains unclear whether the effect is driven by a direct effect from benzoxazinoids on wheat or an indirect effect caused by changes in soil microbes. The authors do a good job of speculating on the likelihood of these two mechanisms in the Discussion; however, they can't say with certainty. They would have to use sterilized soil as a separate treatment to differentiate these mechanisms.

Reviewer #2 (Public Review):

This is an interesting manuscript with a worthwhile approach to receptor mechanisms. The paper contains an impressive amount of new data. These single molecule concentration response curves have been compiled with care and the authors deserve great credit for obtaining these data. I judge the main result to be that there are different values of the recently-proposed agonist-related quantity "efficiency". These values are clustered into 5 quite closely spaced groups. The authors propose that these groups are the same whether considering mutations in the binding site or different agonists.

It was unclear to me in several places, what new data and what old data are included in each figure. Therefore readers may have difficulty judging the claimed advance. This difficulty is not helped by the discussion, which includes some previous findings as "results".

A further weakness is that it is unclear how general or how specific these concepts are. The authors assert that they are, by definition, completely universal. However, we do not have reference to previous work or current data on any other receptor than the muscle nicotinic. I could not square the concept that "every receptor works like this" with the evident lack of desire to demonstrate this for any other receptor.

On one hand, if the framework can be extended, this can be a very important concept, and in some sense, could be the missing link to understanding concentration response curves. On the other, if it proves not to be general, or not to be generally applicable because of circumstances.

Reviewer #2 (Public Review):

In this study, Song and colleagues applied a Hidden Markov Model to whole-brain fMRI data from the unique SONG dataset and a grad-CPT task, and in doing so observed robust transitions between low-dimensional states that they then attributed to specific psychological features extracted from the different tasks.

The methods used appeared to be sound and robust to parameter choices. Whenever choices were made regarding specific parameters, the authors demonstrated that their approach was robust to different values, and also replicated their main findings on a separate dataset.

I was mildly concerned that similarities in some of the algorithms used may have rendered some of the inter-measure results as somewhat inevitable (a hypothesis that could be tested using appropriate null models).

This work is quite integrative, linking together a number of previous studies into a framework that allows for interesting follow-up questions.

Overall, I found the work to be robust, interesting, and integrative, with a wide-ranging citation list and exciting implications for future work.

Reviewer #2 (Public Review):

This study addresses the molecular mechanism by which the FruC transcription factor regulates neurogenesis in Drosophila. The authors combine genetics and genomics to profile FruC genomic binding along with that of trithorax-like (Trl) and Su(H) and several histone modifications including H3K27me3. They propose that Fru acts to fine-tune the expression of Notch effector genes and they show that this regulation does not involve changes in H3K27ac, nor H3K4me3, but rather that FruC-regulated gene expression is correlated with changes in H3K27me3 levels at Fru target genes. While the study is well conducted and combines state-of-the-art techniques, there are several aspects that could be improved. The authors propose that Fru fine-tunes the expression of Notch effector genes, but they do not directly measure gene expression in any of the genetic backgrounds. It is important to do this to have some type of precise measure of transcriptional changes (what does 'fine tune' really mean), as the authors' model is based on subtle changes in H3K27me3. It would be important to quantify and correlate both processes more precisely. Similarly, the authors claim that Fru promotes 'low levels' of H3K27me3 at its bound loci throughout the genome, but they do not describe the criteria that define 'low levels' versus high levels of HK27me3.

In the authors' model, FruC likely functions together with PRC2 to regulate gene expression, and local low-level enrichment of repressive histone marks act to fine-tune gene expression. However, in the absence of experiments directly addressing the molecular mechanisms by which Fru regulates transcription, it would be more accurate to claim that changes in H3K27me3 correlate with altered gene expression.

Reviewer #2 (Public Review):

Where this study is interesting is that the authors do a meta-analysis of studies in which metabolic rate was experimentally manipulated and both this rate and glucocorticoid levels were simultaneously measured. Unsurprisingly, there are relatively few such studies and many are from the lab of Michael Romero. While the results of the analysis are compelling, they are not surprising. That said, this work is important.

It is worth noting that in this analysis, the majority of the studies, if not all, are dealing with variation in baseline levels of glucocorticoids. That means the hormone is mostly acting metabolically at these lower levels and not as a stress response hormone as it does when levels are much higher. This difference is probably due to differences in receptors being activated. This could be discussed.

Reviewer #2 (Public Review):

Lalun and co-authors investigate the signalling outputs triggered by the perception of IDA, a plant peptide regulating organs abscission. The authors observed that IDA perception leads to a transient influx of Ca2+, to the production of reactive oxygen species in the apoplast, and to an increase accumulation of transcripts which are also responsive to an immunogenic epitope of bacterial flagellin, flg22. The authors show that IDA is transcriptionally upregulated in response to several biotic and abiotic stimuli. Finally, based on the similarities in the molecular responses triggered by IDA and elicitors (such as flg22) the authors proposed that IDA has a dual function in modulating abscission and immunity. The manuscript is rather descriptive and provide little information regarding IDA signalling per se. A potential functional link between IDA signalling and immune signalling remains speculative.

Reviewer #2 (Public Review):

The central theme of the manuscript is to report on the structure of SBPase - an enzyme central to the photosynthetic Calvin-Benson-Bassham cycle. The authors claim that the structure is first of its kind from a chlorophyte Chlamydomonas reinhardtii, a model unicellular green microalga. The authors use a number of methods like protein expression, purification, enzymatic assays, SAXS, molecular dynamics simulations and xray crystallography to resolve a 3.09 A crystal structure of the oxidized and partially reduced state. The results are supported by the claims made in the manuscript. One of the main weakness of the work is the lack of wider discussion presented in the manuscript. While the structure is the first from a chlorophyte, it is not unique. Several structures of SBPase are available. As the manuscript currently reads, the wider context of SBPase structures available and comparisons between them is missing from the manuscript. Another important point is that the reported structure of crSBPase is 0.453A away from the alphafold model. Though fleetingly mentioned in the methods section, it should be discussed to place it in the wider context.

Reviewer #2 (Public Review):

Microsporidia has a special invasion mechanism, which the polar tube (PT) ejects from mature spores at ultra-fast speeds, to penetrate the host and transfer the cargo to host. This work generated models for the physical basis of polar tube firing and cargo transport through the polar tube. They also use a combination of experiments and theory to elucidate possible biophysical mechanisms of microsporidia. Moreover, their approach also provided the potential applications of such biophysical approaches to other cellular architecture.

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

According to the model 5 (E-OE-PTPV-ExP) in P42 Fig. 6, is the posterior vacuole connected with the polar tube? If yes, how does the nucleus unconnected with the posterior vacuole enter the polar tube? In Fig. 6, would the posterior vacuole become two parts after spore germination? One part is transported via the polar tube, and the other is still in the spore. I recommend this process requires more experiments to prove.

Reviewer #2 (Public Review):

The manuscript "Novel axonemal protein ZMYND12 interacts with TTC29 and DNAH1, and is required for male fertility and flagellum function" by Dacheux et al. interestingly reported homozygous deleterious variants of ZMYND12 in four unrelated men with asthenoteratozoospermia. Based on the immunofluorescence assays in human sperm cells, it was shown that ZMYND12 deficiency altered the localization of DNAH1, DNALI1, WDR66 and TTC29 (four of the known key proteins involved in sperm flagellar formation). Trypanosoma brucei and mouse models were further employed for mechanistic studies, which revealed that ZMYND12 is part of the same axonemal complex as TTC29 and DNAH1. Their findings are solid, and this manuscript will be very informative for clinicians and basic researchers in the field of human infertility.

Reviewer #2 (Public Review):

This study presents valuable findings including the use of an improved method of Raman spectroscopy to measure accumulation of microplastics in ovarian follicular fluid obtained from cows and women and demonstration that experimental direct exposure of bovine eggs to biologically relevant levels of polystyrene, a microplastic found in both cows and women's follicular fluid, negatively influenced ova maturation status and the abundance of proteins involved in oxidative stress, DNA damage, apoptosis, and oocyte maturation. The evidence supporting the claims of the authors is solid but inclusion of human population from which the follicular fluid was obtained (e.g., demographics, reason for assisted reproduction), and details about quality control for proteome profiling experiments (i.e., peptide count cut-off for significant proteins) would have strengthened the study. The work will be of interest to exposure scientists, reproductive toxicologists, regulatory scientists, and reproductive health clinicians.

Reviewer #2 (Public Review):

Yanagihara and colleagues investigated the immune cell composition of bronchoalveolar lavage fluid (BALF) samples in a cohort of patients with malignancy undergoing chemotherapy and with with lung adverse reactions including Pneumocystis jirovecii pneumonia (PCP) and immune-checkpoint inhibitors (ICIs) or cytotoxic drug induced interstitial lung diseases (ILDs). Using mass cytometry, their aim was to characterize the cellular and molecular changes in BAL to improve our understanding of their pathogenesis and identify potential biomarkers and therapeutic targets. In this regard, the authors identify a correlation between CD16 expression in T cells and the severity of PCP and an increased infiltration of CD57+ CD8+ T cells expressing immune checkpoints and FCLR5+ B cells in ICI-ILD patients.

The conclusions of this paper are mostly well supported by data, but some aspects of the data analysis need to be clarified and extended.

1) The authors should elaborate on why different set of markers were selected for each analysis step. E.g., Different set of markers were used for UMAP, CITRUS and viSNE in the T cell and myeloid analysis.

2) The authors should state if a normality test for the distribution of the data was performed. If not, non-parametric tests should be used.

3) The authors should explore the correlation between CD16 intensity and the CTCAE grade in T cell subsets such as EMRA CD8 T cells, effector memory CD4, etc as identified in Figure 1B.

4) The authors could use CITRUS to better assess the B cell compartment.

Reviewer #2 (Public Review):

The authors developed 11 key measures of clinical activity in primary care and measured changes in the frequency of these measures throughout the first 1.5 years of the COVID-19 pandemic. The biggest strength of the study is the data source, which comprises records from 99% of general practices in England. The biggest limitation lies in the analysis of the data: The authors used only descriptive statistics for the investigation of time trends and have not accounted for long-term time trends (only one "control year" was considered). Still, owing to the large study size, the time trends observed are convincing. The work is of high significance to the field because the OpenSAFELY platform will enable the continuous and real-time monitoring of primary care activity.

Reviewer #2 (Public Review):

In this computational study, Delamare et al identify slow neuronal excitability as one mechanism underlying representational drift in recurrent neuronal networks and that the drift is informative about the temporal structure of the memory and when it has been formed. The manuscript is very well written and addresses a timely as well as important topic in current neuroscience namely the mechanisms that may underlie representational drift.

The study is based on an all-to-all recurrent neuronal network with synapses following Hebbian plasticity rules. On the first day, a cue-related representation is formed in that network and on the next 3 days it is recalled spontaneously or due to a memory-related cue. One major observation is that representational drift emerges day-by-day based on intrinsic excitability with the most excitable cells showing highest probability to replace previously active members of the assembly. By using a day-decoder, the authors state that they can infer the order at which the reactivation of cell assemblies happened but only if the excitability state was not too high. By applying a read-out neuron, the authors observed that this cell can track the drifting ensemble which is based on changes of the synaptic weights across time. The only few questions which emerged and could be addressed either theoretically or in the discussion are as follows:

1. Would the similar results be obtained if not all-to-all recurrent connections would have been molded but more realistic connectivity profiles such as estimated for CA1 and CA3?<br /> 2. How does the number of excited cells that could potentially contribute to an engram influence the representational drift and the decoding quality?<br /> 3. How does the rate of the drift influence the quality of readout from the readout-out neuron?

Reviewer #2 (Public Review):

In this manuscript, the authors describe the role of cibarial mechanosensory neurons in fly ingestion. They demonstrate that pumping of the cibarium is subtly disrupted in mutants for piezo, TMC, and nomp-C. Evidence is presented that these three genes are co-expressed in a set of cibarial mechanosensory neurons named md-C. Silencing of md-C neurons results in disrupted cibarial emptying, while activation promotes faster pumping and/or difficulty filling. GRASP and chemogenetic activation of the md-C neurons is used to argue that they may be directly connected to motor neurons that control cibarial emptying.

The manuscript makes several convincing and useful contributions. First, identifying the md-C neurons and demonstrating their essential role for cibarium emptying provides reagents for further studying this circuit and also demonstrates the important of mechanosensation in driving pumping rhythms in the pharynx. Second, the suggestion that these mechanosensory neurons are directly connected to motor neurons controlling pumping stands in contrast to other sensory circuits identified in fly feeding and is an interesting idea that can be more rigorously tested in the future.

At the same time, there are several shortcomings that limit the scope of the paper and the confidence in some claims. These include:

a) the MN-LexA lines used for GRASP experiments are not characterized in any other way to demonstrate specificity. These were generated for this study using Phack methods, and their expression should be shown to be specific for MN11 and MN12 in order to interpret the GRASP experiments.

b) There is also insufficient detail for the P2X2 experiment to evaluate its results. Is this an in vivo or ex vivo prep? Is ATP added to the brain, or ingested? If it is ingested, how is ATP coming into contact with md-C neuron if it is not a chemosensory neuron and therefore not exposed to the contents of the cibarium?

c) In Figure 3C, the authors claim that ablating the labellum will remove the optogenetic stimulation of the md-L neuron (mechanosensory neuron of the labellum), but this manipulation would presumably leave an intact md-L axon that would still be capable of being optogenetically activated by Chrimson.

d) Average GCaMP traces are not shown for md-C during ingestion, and therefore it is impossible to gauge the dynamics of md-C neuron activation during swallowing. Seeing activation with a similar frequency to pumping would support the suggested role for these neurons, although GCaMP6s may be too slow for these purposes.

e) The negative result in Figure 4K that is meant to rule out taste stimulation of md-C is not useful without a positive control for pharyngeal taste neuron activation in this same preparation.

In addition to the experimental limitations described above, the manuscript could be organized in a way that is easier to read (for example, not jumping back and forth in figure order).

Reviewer #2 (Public Review):

Although the trans-synaptic tracing method mediated by the rabies virus (RV) has been widely utilized to infer input connectivity across the brain to a genetically defined population in mice, the analysis of labeled pre-synaptic neurons in terms of cell-type has been primarily reliant on classical low-throughput histochemical techniques. In this study, the authors made a significant advance toward high-throughput transcriptomic (TC) cell typing by both dissociated single-cell RNAseq and the spatial TC method known as BARseq to decode a vast array of molecularly-labeled ("barcoded") RV vector library. First, they demonstrated that a barcoded-RV vector can be employed as a simple retrograde tracer akin to AAVretro. Second, they provided a theoretical classification of neural networks at the single-cell resolution that can be attained through barcoded-RV and concluded that the identification of the vast majority (ideally 100%) of starter cells (the origin of RV-based trans-synaptic tracing) is essential for the inference of single-cell resolution neural connectivity. Taking this into consideration, the authors opted for the BARseq-based spatial TC that could, in principle, capture all the starter cells. Finally, they demonstrated the proof-of-concept in the somatosensory cortex, including infrared connectivity from 381 putative pre-synaptic partners to 31 uniquely barcoded-starter cells, as well as many insightful estimations of input convergence at the cell-type resolution in vivo. While the manuscript encompasses significant technical and theoretical advances, it may be challenging for the general readers of eLife to comprehend. The following comments are offered to enhance the manuscript's clarity and readability.

Major points:<br /> 1. I find it difficult to comprehend the rationale behind labeling inhibitory neurons in the VISp through long-distance retrograde labeling from the VISal or Thalamus (Fig. 2F, I and Fig. S3) since long-distance projectors in the cortex are nearly 100% excitatory neurons. It is also unclear why such a large number of inhibitory neurons was labeled at a long distance through RV vector injections into the RSP/SC or VISal (Fig. 3K). Furthermore, a significant number of inhibitory starter cells in the somatosensory cortex was generated based on their projection to the striatum (Fig. 5H), which is unexpected given our current understanding of the cortico-striatum projections.

2. It is unclear as to why the authors did not perform an analysis of the barcodes in Fig. 2. Given that the primary objective of this manuscript is to evaluate the effectiveness of multiplexing barcoded technology in RV vectors, I would strongly recommend that the authors provide a detailed description of the barcode data here, including any technical difficulties or limitations encountered, which will be of great value in the future design of RV-barcode technologies. In case the barcode data are not included in Fig. 2, I would suggest that the authors consider excluding Fig. 2 and Fig. S1-S3 in their entirety from the manuscript to enhance its readability for general readers.

3. Regarding the trans-synaptic tracing utilizing a barcoded RV vector in conjunction with BARseq decoding (Fig. 5), which is the core of this manuscript, I have a few specific questions/comments. First, the rationale behind defining cells with only two rolonies counts of rabies glycoprotein (RG) as starter cells is unclear. Why did the authors not analyze the sample based on the colocalization of GFP (from the AAV) and mCherry (from the RV) proteins, which is a conventional method to define starter cells? If this approach is technically difficult, the authors could provide an independent histochemical assessment of the detection stringency of GFP positive cells based on two or more colonies of RG. Second, it is difficult to interpret the proportion of the 2,914 barcoded cells that were linked to barcoded starter cells (single-source, double-labeled, or connected-source) and those that remained orphan (no-source or lost-source). A simple table or bar graph representation would be helpful. The abundance of the no-source network (resulting from Cre-independent initial infection of the RV vector) can be estimated in independent negative control experiments that omit either Cre injection or AAV-RG injection. The latter, if combined with BARseq decoding, can provide an experimental prediction of the frequency of double-labeled events since connected-source networks are not labeled in the absence of RG. Third, I would appreciate more quantitative data on the putative single-source network (Fig. 5I and S6) in terms of the distribution of pre- and post-synaptic TC cell types. The majority of labeling appeared to occur locally, with only two thalamic neurons observed in sample 25311842 (Fig. S6). How many instances of long-distance labeling (for example, > 500 microns away from the injection site) were observed in total? Is this low efficiency of long-distance labeling expected based on the utilized combinations of AAVs and RV vectors? A simple independent RV tracing solely detecting mCherry would be useful for evaluating the labeling efficiency of the method. I have experienced similar "less jump" RV tracing when RV particles were prepared in a single step, as this study did, rather than multiple rounds of amplification in traditional protocols, such as Osakada F et al Nat Protocol 2013.

Reviewer #2 (Public Review):

In this valuable manuscript Li & Jin record from the substantial nigra and dorsal striatum to identify subpopulations of neurons with activity that reflects different dynamics during action selection, and then use optogenetics in transgenic mice to selectively inhibit or excite D1- and D2- expressing spiny projection neurons in the striatum, demonstrating a causal role for each in action selection in an opposing manner. They argue that their findings cannot be explained by current models and propose a new 'triple control' model instead, with one direct and two indirect pathways. These findings will be of broad interest to neuroscientists, but lacks some direct evidence for the proposal of the new model.

Overall there are many strengths to this manuscript including the fact that the empirical data in this manuscript is thorough and the experiments are well-designed. The model is well thought through, but I do have some remaining questions and issues with it.

Weaknesses:<br /> 1. The nature of 'action selection' as described in this manuscript is a bit ambiguous and implies a level of cognition or choice which I'm not sure is there. It's not integral to the understanding of the paper really, but I would have liked to know whether the actions are under goal-directed/habitual or even Pavlovian control. This is not really possible to differentiate with this task as there are a number of Pavlovian cues (e.g. lever retraction interval, house light offset) that could be used to guide behavior.<br /> 2. In a similar manner, the part of the striatum that is being targeted (e.g. Figures 4E,I, and N) is dorsal, but is central with regards to the mediolateral extent. We know that the function of different striatal compartments is highly heterogeneous with regards to action selection (e.g. PMID: 16045504, 16153716, 11312310) so it would have been nice to have some data showing how specific these findings are to this particular part of dorsal striatum.<br /> 3. I'm not sure how I feel about the diagrams in Figure 4S. In particular, the co-activation model is shown with D2-SPNs represented as a + sign (which is described as "having a facilitatory effect to selection" in the caption), but the co-activation model still suggests that D2-SPNs are largely inhibitory - just of competing actions rather than directly inhibiting actions. Moreover, I am not sure about these diagrams because they appear to show that D2-SPNs far outnumbers D1-SPNs and we know that this isn't the case. I realize the diagrams are not proportionate, but it still looks a bit misrepresented to me.<br /> 4. There are a number of grammatical and syntax errors that made the manuscript difficult to understand in places.<br /> 5. I wondered if the authors had read PMID: 32001651 and 33215609 which propose a quite different interpretation of direct/indirect pathway neurons in striatum in action selection. I wonder if the authors considered how their findings might fit within this framework.<br /> 6. There is no direct evidence of two indirect pathways, although perhaps this is beyond the scope of the current manuscript and is a prediction for future studies to test.

Reviewer #2 (Public Review):

In this study, the authors design a study to examine how place cell representations in the hippocampus change when the rules of a navigational task change. In one group of animals (group 1), the rules change in the same environment as the initial task was performed, and in the second group of animals (group 2), the task with the new rules is presented in a different environment, and then the animals are returned to the first environment with the original rule. (Briefly, on a cross maze, animals first learned to turn right, then the task rule changed to require turning east, and then the rule changed back to turning right). Broadly, using one photon calcium imaging with head mounted mini microscopes, the authors show that, at both the single cell and population level, more remapping occurs in group 1 animals in the initial environment than in group 2 animals.

This work is bolstered by the unique and rigorous way in which the authors track cells across days, in which they compare the rotation angles of crossed-registered groups of cells-I will definitely be using this in the future! The work also benefits from the extensive analysis of both temporal and spatial correlations of cellular activity. However, there are several shortcomings of the behavioral setup and learning conditions that need to be addressed in order to fully support the conclusions of the authors:

First, group 1 animals spend significantly more time in maze 1 than group 2 animals, since group two animals were switched to a different maze when the rule was changed. It is thus difficult to make direct comparison between the two groups, particularly in the last phase of experimentation when although both groups are in the first environment with the task rule, group 1 has experienced maze one for 6 days while group 2 has only experienced in for 3 days. It is therefore potentially difficult to disentangle differences caused by task changes versus length of environmental exposure.

Secondly, and similarly, during the task period, group 1 animals only have exposure to one environment while group 2 animals have exposure to 2 environments. Ideally, group 1 animals would also be exposed to environment 2, to rule out any potential effects of experiencing a novel environment may have on place cell representations, otherwise this cannot be disentangled from the effect of a task rule change.

Third, two concerns about how the animals are trained: First, if I am interpreting the methods correctly, both Group 1 and Group 2 animals are trained so turn-right is on one maze and turn east is on another way. As such, both groups thus have an "original understanding" that different rules are associated with different mazes. This seems potentially confounding given that it is consistent with the future training of Group 2 but not Group 1 mice. Additionally confounding is the fact that, because of the pretraining, group 1 mice have actually experienced the task in 3 different environments; I am unclear if and how this might be expected to affect results. Additionally, it is methodically unclear why pre-training occurs in a different environment than testing does, and what the criterion is for switching the animals from pre-training to training.

It would additionally be useful to discuss the results of this study in the context of spatial and non-spatial tasks. The authors, usefully, spend a significant portion of the paper comparing their results to results seen during fear extinction. It might be worth contextualizing the differences in how fear conditioning has a contextual "background" (i.e., the animals are conditioned to the context) while in their experiment the entire task is based entirely on navigation.

Overall, this is an interesting manuscript that attempts to address how contextual representations change as task parameters change. While the paper contains thorough statistical analysis but could benefit from more discussion of behavior in the context of learning as well as more rigorous behavioral controls. This work will be of interest to researchers studying hippocampus, navigation, and learning.

Reviewer #2 (Public Review):

Respiratory chain complexes assemble in higher-ordered structures termed supercomplexes or respirasomes. The functional significance of these assemblies is currently investigated, there are two main hypothesis tested, namely that supercomplexes provide kinetic advantages or structural stability. Here, the authors use the fruitfly to reveal that, while the respiratoy chain in the organism normally does not form higher-order assemblies, it does so under conditions when their assembly is impaired. Because the rather moderate increase in supercomplex formation does not change oxygen consumption stimulated by CI or CII substrate, the authors conclude that supercomplex formation has more a structural than a functional role. The main strength of this work is that the technical quality of the experiments is high and that the authors induced defects in respiratory chain assembly through sets of well-controlled genetic models. The obtained data are mostly descriptive using standard approaches and are very well executed. The authors claim that their experiments allow to conclude that the role of supercomplex formation is restricted to a structural role and, hence, exclude a function directly related to electron transport efficiency. However, while the authors can show convincingly that supercomplexes form in the mutants, but not in the wild type, their main claim is not well supported by data and both the structural mechanism of supercompelx formation and their significance remain unknown. While the supercomplex formation observed only in mitochondrial mutants per se is interesting, it would be good to great to define structural aspects of supercomplex formation and their potential impact on the stability of the respiratory chain complexes in these mutants.

Reviewer #2 (Public Review):

There is increasing evidence that viruses manipulate vectors and hosts to facilitate transmission. For arthropods, saliva plays an essential role for successful feeding on a host and consequently for arthropod-borne viruses that are transmitted during arthropod feeding on new hosts. This is so because saliva constitutes the interaction interface between arthropod and host and contains many enzymes and effectors that allow feeding on a compatible host by neutralizing host defenses. Therefore, it is not surprising that viruses change saliva composition or use saliva proteins to provoke altered vector-host interactions that are favorable for virus transmission. However, detailed mechanistic analyses are scarce. Here, Zhao and coworkers study transmission of rice stripe virus (RSV) by the planthopper Laodelphax striatellus. RSV infects plants as well as the vector, accumulates in salivary glands and is injected together with saliva into a new host during vector feeding.

The authors present evidence that a saliva-contained enzyme - carbonic anhydrase (CA) - might facilitate virus infection of rice by interfering with callose deposition, a plant defense response. In vitro pull-down experiments, yeast two hybrid assay and binding affinity assays show convincingly interaction between CA and a plant thaumatin-like protein (TLP) that degrades callose. Similar experiments show that CA and TLP interact with the RSV nuclear capsid protein NT to form a complex. Formation of the CA-TLP complex increases TLP activity by roughly 30% and integration of NT increases TLP activity further. This correlates with lower callose content in RSV-infected plants and higher virus titer. Further, silencing CA in vectors decreases virus titers in infected plants. Interestingly, aphid CA was found to play a role in plant infection with two non-persistent non-circulative viruses, turnip mosaic virus and cucumber mosaic virus (Guo et al. 2023 doi.org/10.1073/pnas.2222040120), but the proposed mode of action is entirely different.

While this is an interesting work, there are, in my opinion, some weak points. The microinjection experiments result in much lower virus accumulation in rice than infection by vector inoculation, so their interpretation is difficult. Also, the effect of injected recombinant CA protein might fade over time because of degradation or dilution. The authors claim that enzymatic activity of CA is not required for its proviral activity. However, this is difficult to assess because all CA mutants used for the corresponding experiments possess residual activity. It remains also unclear whether viral infection deregulates CA expression in planthoppers and TLP expression in plants. However, increased CA and TLP levels could alone contribute to reduced callose deposition.

Reviewer #2 (Public Review):

This manuscript discusses the posttranscriptional regulation of flagella synthesis in Escherichia coli. The bacterial flagellum is a complex structure that consists of three major domains, and its synthesis is an energy-intensive process that requires extensive use of ribosomes. The flagellar regulon encompasses more than 50 genes, and the genes are activated in a sequential manner to ensure that flagellar components are made in the order in which they are needed. Transcription of the genes is regulated by various factors in response to environmental signals. However, little is known about the posttranscriptional regulation of flagella synthesis. The manuscript describes four UTR-derived sRNAs (UhpU, MotR, FliX, and FlgO) that are controlled by the flagella sigma factor σ28 (fliA) in Escherichia coli. The sRNAs have varied effects on flagellin protein levels, flagella number, and cell motility, and they regulate different aspects of flagella synthesis.<br /> UhpU corresponds to the 3´ UTR of uhpT.

UhpU is transcribed from its own promoter inside the coding sequence of uhpT.

MotR originates from the 5´ UTR of motA. The promoter for motR is within the flhC CDS and is also the promoter of the downstream motAB-cheAW operon.

FliX originates from the 3´ UTR of fliC. Probably processed from parental mRNA.

FlgO originates from the 3´ UTR of flgL. Probably processed from parental mRNA.

This is a very interesting study that shows how sRNA-mediated regulation can create a complex network regulating flagella synthesis. The information is new and gives a fresh outlook at cellular mechanisms of flagellar synthesis. The presented work could benefit from additional experiments to confirm the effect of endogenous sRNAs expressed at natural level.

Reviewer #2 (Public Review):

In this study, the authors describe a pipeline to sequence expressed var genes from RNA sequencing that improves on a previous one that they had developed. Importantly, they use this approach to determine how var gene expression changes with short-term culture. Their finding of shifts in the expression of particular var genes is compelling and casts some doubt on the comparability of gene expression in short-term culture versus var expression at the time of participant sampling. The authors appear to overstate the novelty of their pipeline, which should be better situated within the context of existing pipelines described in the literature.

Other studies have relied on short-term culture to understand var gene expression in clinical malaria studies. This study indicates the need for caution in over-interpreting findings from these studies.

The novel method of var gene assembly described by the authors needs to be appropriately situated within the context of previous studies. They neglect to mention several recent studies that present transcript-level novel assembly of var genes from clinical samples. It is important for them to situate their work within this context and compare and contrast it accordingly. A table comparing all existing methods in terms of pros and cons would be helpful to evaluate their method.

Reviewer #2 (Public Review):

In this work Ushio et al. combine environmental DNA metabarcoding with novel statistical approaches to demonstrate how fish communities respond to changing sea temperatures over a seasonal cycle. These findings are important due to the need for new techniques that can better measure community stability under climate change. The eDNA metabarcoding dataset of 550 water samples over two years is, I feel, of sufficient scale to provide power to detect fine-scale ecological interactions, the experiments are well controlled, and the statistical analysis is thorough.

The major strengths of the manuscript are: (1) the magnitude of the dataset, which provides densely replicated sampling that can overcome some of the noise associated with eDNA metabarcoding data and scale up the number of data points to make unique inferences; (2) the novel method of transforming the metabarcode reads using endogenous qPCR "spike-in" data from a common reference species to obtain estimates of DNA concentration across other species; and (3) the statistical analysis of time-series and network data and translating it into interaction strengths between species provides a cross-disciplinary dimension to the work.

I feel like this kind of study showcases the power of eDNA metabarcoding to answer some really interesting questions that were previously unobtainable due to the complexities and cost of such an exercise. Notwithstanding the problems associated with PCR primer bias and PCR stochasticity, the qPCR "spike-in" method is easy to implement and will likely become a standardised technique in the field. Further studies will examine and improve on it.

Overall I found the manuscript to be clear and easy to follow for the most part. I did not identify any serious weaknesses or concerns with the study, although I am not able to comment on the more complex statistical procedures such as the "unified information-theoretic causality" method devised by the authors. The section on limitations of the study is important and acknowledges some issues with interpretation that need to be explained. The methods, while brief in parts, are clear. The code used to generate the results has been made available via a GitHub repository. The figures are clear and attractive.

Reviewer #2 (Public Review):

This paper explores the possibility of integrating diverse and multiple DNA fragments in the genome taking advantage of plasmids in arrays, and CRISPR. Since the efficiency of integration in the genome is low, they, as others in the field, use selection markers to identify successful events of integration. The use of these selection markers is common and diverse, but they use a couple of distinct strategies of selection to:

- Introduce bar codes in the genome of individuals at one specific genomic site (gene for Hygromycin resistance with bar code in an intron with homology arms to complete a functional gene);

- Introduce promoters at two specific genomic landing pads downstream of fluorescent reporters.

The strengths of the study are the clever design of the selection markers, which enrich the collection of this type of markers. While the work is not methodologically novel - it adds to other recent studies, e.g. from Nonet, Mouridi et al., and Malaiwong et al, that use the integration of single and multiple/diverse DNA sequences in the C. elegans genome - it provides a protocol for doing so and tool to make it practical. A limited number of experiments using the method are presented here, and the real test of this method will be its use to address biological questions.

Reviewer #2 (Public Review):

This study reports a novel role of thalamic activity in the late components of a cortical event related potential (ERP). To show this association, the authors used high-density EEG together with multiple deep electrophysiological recordings combined with electrical stimulation of superficial and deep cortical layers. Stimulation of deep layers elicits a late ERP component that is closely related to bursts of thalamic activity during quiet wakefulness. This relationship is quite noticeable when deep layers of the cortex are stimulated, and it does depend on arousal state, being maximal during quiet wakefulness, diminished during active wakefulness, and absent during anesthesia.

The study is very well performed, with a high number of subjects and appropriate methodology. Performing simultaneous recording of EEG and several neuropixels probes together with cortical microstimulation is no small feat considering the size of the mouse head and the fact that mice are freely behaving in many of the experiments. It is also noticeable how the authors use a seemingly outdated technique (electrical microstimulation) to produce compelling and significant research. The conclusions regarding the thalamic contributions to the ERP components are strongly supported by the data.

The spatiotemporal complexity is almost a side point compared to what seems to me the most important point of the paper: showing the contribution of thalamic activity to some components of the cortical ERP. Scalp ERP's have long been regarded as purely cortical phenomena, just like most of EEG, and this study shows convincing evidence to the contrary.

The data presented seemingly contradicts the results presented in Histed et al. (2009), who asserts that cortical microstimulation only affects passing fibers near the tip of the electrodes, and results in distant, sparse, and somewhat random neural activation. In this study, it is clear that the maximum effect happens near the electrodes, decays with distance, and it is not sparse at all, suggesting that not only passing fibers are activated but that also neuronal elements might be activated by antidromic propagation from the axonal hillock. This appears to offer proof that microstimulation might be much more effective than it was thought after the publication of Histed 2009, as the uber-successful use of DBS to treat Parkinson disease has also shown.

Reviewer #2 (Public Review):

The authors should be commended for developing a high throughput platform for the formation and study of human cardiac tissues, and for discussing its potential, advantages and limitations. The study is addressing some of the key needs in the use of engineered cardiac tissues for pharmacological studies: ease of use, reproducible preparation of tissues, and high throughput.

There are also some areas where the manuscript should be improved. The design of the platform and the experimental design should be described in more detail.

It would be of interest to comprehensively document the progression of tissue formation. To this end, it would be helpful to show the changes in tissue structure through a series of images that would correspond to the progression of contractile properties shown in Figure 3.

The very interesting tissue morphology (separation into the two regions) that was observed in this study is inviting more discussion.

Finally, the reader would benefit from more specific comparisons of the contractile function of cardiac tissues measured in this study with data reported for other cardiac tissue models.

Reviewer #2 (Public Review):

The authors provide compelling data to demonstrate that the Notch-related transcription factor RBP-J can influence the number of circulating and recruited monocytes. The authors first delete the Rbpj gene in the myeloid lineage (Lyz2) and show that, as a proportion, only Ly6Clo monocytes are increased in the blood. The authors then attempted to identify why these cells were increased but ruled out proliferation or reduced apoptosis. Next, they investigated the gene signature of Rbpj null monocytes using RNA-sequencing and identified elevated Ccr2 as a defining feature. Crossing the Rbpj null mice to Ccr2 null mice showed reduced numbers of Ly6Clo monocytes compared with Rbpj null alone. Finally, the authors identify that an increased burden of blood Ly6Clo monocytes is correlated with increased lung recruitment and expansion of lung interstitial macrophages.

The main conclusion of the authors, that there is a 'cell intrinsic requirement of RBP-J for controlling blood Ly6CloCCR2hi monocytes' is strongly supported by the data. However, other claims and aspects of the study require clarification and further analysis of the data generated.

Strengths<br /> The paper is well written and structured logically. The major strength of this study is the multiple technically challenging methods used to reinforce the main finding (e.g. parabiosis, adoptive transfer). The finding reinforces the fact that we still know little about how immune cell subsets are maintained in situ, and this study opens the way for novel future work. Importantly, the authors have generated an RNA-sequencing dataset that will prove invaluable for identifying the mechanism - they have promised public access to this data via GEO.

Weaknesses - The main weakness of the study, is that although the main result is solidly supported, as written it is mostly descriptive in nature. For instance, there is no given mechanism by which RBP-J increases Ly6Clo monocytes. The authors conclude this is dependent on CCR2, however CCR2 deletion has a global effect on monocyte numbers and importantly in this study, it does not remove the Ly6Clo bias of cell proportions, if anything it seems to enhance the difference between the ly6C low and high populations in Rbpj null mice (figure 5C). This oversight in data interpretation likely occurred because this experiment is missing a potentially important control (Lyz2cre/cre Ccr2RFP/RFP or RBP-J variations). In general, there seemed to be a focus on the Ly6C low cells, where the mechanism may be more identifiable in their precursors - likely the Ly6C high monocytes.

Other specific weaknesses were identified:<br /> 1) The confirmation of knockout in supplemental figure 1A shows only a two third knockdown when this should be almost totally gone. Perhaps poor primer design, cell sorting error or low Cre penetrance is to blame, but this is below the standard one would expect from a knockout.<br /> 2) Many figures (e.g. 1A) only show proportional data (%) when the addition of cell numbers would also be informative<br /> 3) Many figures only have an n of 1 or 2 (e.g. 2B, 2C)<br /> 4) Sometimes strong statements were based on the lack of statistical significance, when more n number could have changed the interpretation (e.g. 2G, 3E)<br /> 5) There is incomplete analysis (e.g. Network analysis) and interpretation of RNA-sequencing results (figure 4), the difference between the genotypes in both monocyte subsets would provide a more complete picture and potentially reveal mechanisms<br /> 6) The experiments in Figures 5 and 7 are missing a control (Lyz2cre/cre Ccr2RFP/RFP or the Rbpj+/+ versions) and may have been misinterpreted. For example if the control (RBP-J WT, CCR2 KO) was used then it would almost certainly show falling Ly6C low numbers compared to RBP-J WT CCR2 WT, but RBP-J KO CCR2 KO would still have more Ly6c low monocytes than RBP-J WT, CCR2 KO - meaning that the RBP-J function is independent of CCR2. I.e. Ly6c low numbers are mostly dependent on CCR2 but this is irrespective of RBP-J.<br /> 7) Figure 6 was difficult to interpret because of the lack of shown gating strategy. This reviewer assumes that alveolar macrophages were gated out of analysis<br /> 8) The statements around Figure 7 are not completely supported by the evidence, i) a significant proportion of CD16.2+ cells were CCR2 independent and therefore potentially not all recently derived from monocytes, and ii) there is nothing to suggest that the source was not Ly6C high monocytes that differentiated - the manuscript in general seems to miss the point that the source of the Ly6C low cells is almost certainly the Ly6C high monocytes - which further emphasises the importance of both cells in the sequencing analysis<br /> 9) The authors did not refer to or cite a similar 2020 study that also investigated myeloid deletion of Rbpj (Qin et al. 2020 - https://doi.org/10.1096/fj.201903086RR). Qin et al identified that Ly6Clo alveolar macrophages were decreased in this model - it is intriguing to synthesise these two studies and hypothesise that the ly6c low monocytes steal the lung niche, but this was not discussed

Reviewer #2 (Public Review):

Zheng et al. have investigated the effects of PTPMT1 Knock-out on cellular metabolic flexibility. Using several types of appropriate tissue-specific mouse models, the authors have generated data that are both reasonable and broadly significant. While the central mechanism driving the metabolic fuel preference and flexibility remains elusive as the author mentioned in the main text, the finding that the absence of PTPMT1 inhibits glucose (pyruvate) utilization and promotes FAO, resulting in cellular stress and damage, particularly in skeletal and cardiac muscle cells, is intriguing and has practical implications for further research. However, some quantitative data are lacking and certain explanations may be misleading, warranting revisions.

Reviewer #2 (Public Review):

In this manuscript, Mizukami et al. investigate the differences in coronary vasculature morphology across several diverse species to investigate the transition of extrinsic coronary arteries existing on the outflow track in non-amniotes to arteries presenting on the ventricle surface itself in amniotes. They use various visualization techniques, including resin-filling, tissue staining, and fluorescence microscopy to compare the gross morphology and orifice locations of the aortic subepicardial vessels (ASVs) between several amniotes and non-amniotes. Intriguingly, the authors show that the embryonic amniotes rely on a similar ASV structure to adult non-amniotes, but this primitive structure is lost during development in favor of the formation of true coronary arteries on the ventricle surface. While these data intend to show that the difference in coronary artery structure exists between amniotes and non-amniotes, the authors only investigated mice and quail as amniote representatives. Without the inclusion of an ectothermic reptile species as an additional amniote representative, it is entirely possible that the difference in coronary artery structure may instead exist across the endotherm-ectotherm axis as opposed to amniotes and non-amniotes. Despite these concerns, Mizukami et al. show intriguing evolutionary differences between coronary artery structure that draw parallels to changes observed during amniote development.

Reviewer #2 (Public Review):

In this work, the authors extend a mathematical model that they previously developed. Their original paper (Niehaus..Momeni, Nature Comm., 2019) models species interactions using mediators (i.e. metabolites) that species produce and that can affect other species' growth rates. Here, they extend the original model, which was well-mixed, to study communities in space. To do this, here they assume that species grow on a 1D grid, that species can possibly overlap in the same grid spot, and that species and mediators can diffuse in space. They find that spatial structure promotes the coexistence of species when interactions are more facilitating than inhibiting, and when species dispersal is low. Both of these features separately allow for species to self-organize in a way that allows them to be closer in space to partners that facilitate their growth. Properties of the metabolic interactions, such as the amount of metabolites produced and consumed, consumption and production rates, and metabolite diffusion also have effects on species coexistence.

Strengths: The authors extend their previously published model (Niehaus..Momeni, Nature Comm., 2019) to study the role of space in maintaining species diversity. The authors have the goal of modeling realistic bacterial communities; they in fact claim that the model's motivation is to "capture situations in which microbes can disperse inside a matrix", such as the mucosal layer of the digestive or intestinal tract, yogurt or cheese. To do this, the authors add relevant spatial aspects to their previous well-mixed model: species grow on a grid (even though 1D), where they can possibly overlap in the same grid spot, and species and mediators can diffuse in space. The advantage of the model they develop here is that it is simple enough for it to be used to explore general features of systems for which the assumptions of the model are justified. The authors perform a thorough investigation of the effect of spatial structure on the diversity that is maintained in the system. Their investigation includes the role of different types of interactions (facilitation and inhibition), species dispersal, and a range of properties of the metabolic interactions (number of mediators consumed and produced, consumption and production rates, mediator diffusion). Every scenario is compared to the well-mixed scenario to highlight the role of space.

Weaknesses: We are not convinced about some assumptions the authors make when extending their model from well-mixed (Niehaus..Momeni, Nature Comm., 2019) to spatial (this manuscript). The authors want to model a spatially structured system, with a framework that resembles the metacommunity framework, to which they add specific biophysical processes, such as the diffusion of metabolites. However, when adding these specific biophysical processes, the authors use parameters that seem to be unrealistic. One example is the packing of cells: 10^9, which implies a ratio between cells and the environment of 1:1000 volume-wise. Another example is the diffusion of molecules, which is 10 times slower than stated in the literature. With these parameters, the authors aim at describing physical processes in their model, but overall the parameters seem to be far from real values. Thus we suggest either changing these parameters to realistic values, discussing why the chosen parameters are meaningful or reframing the model as an heuristic model.

Overall, we think that the contribution of the paper is to extend a previously published work (Niehaus..Momeni, Nature Comm., 2019) to model spatial communities. It is thus fundamental that the assumptions made by the authors to model the spatial dynamics are well justified. Several physical parameters are chosen to values that do not represent realistic values for spatially structured communities. The authors should discuss if the results hold also for more realistic values.

Reviewer #2 (Public Review):

This paper uses single-cell RNA sequencing to assess the B cell response in a mouse model of autoimmunity. The authors find that the B cell response is transcriptionally similar to the response induced by protein immunization. They further determine that the memory B cell response is composed of transcriptionally distinct subsets that may have distinct spatial distributions.

A major strength of this manuscript is the author's use of an elegant model of autoimmunity in which self-reactive B cells can escape negative selection to become activated and participate in the germinal center response. This system allows the author's a system to study the development of B cells in an autoimmune setting without restricting the repertoire of those cells though the use of BCR transgenes. This single-cell data generated in this study is also likely to be useful to individuals interested in understanding the differences in the B cell response between autoimmune and protein immunization settings.

One weakness of this study is that its main findings do not seem to represent a major conceptual advancement. There are already many published single-cell RNA-seq data sets that show that heterogeneity exists within B cell subsets. Therefore, the author's data primarily extends these findings to indicate that heterogeneity also exists in their model of autoimmunity.

Another major weakness of this study is that the authors only analyze about 13K cells in their single cell RNA-seq experiment with only 3.3K coming from the immunized mice. This low number of cells likely prevents the authors from identifying differences between specific B cell subsets between the two disease settings because there are likely very few cells in many of the clusters in the immunized group.

Finally, the author's data in which they seek to validate their use of Fcrl5 and CD23 to identify memory B cell subsets is not convincing. The flow cytometry gating used to distinguish the memory B cell subsets seem somewhat arbitrary with there not being a clear separation between the four populations shown using the author's gating strategy. This strategy also causes many CD23+ cells to not be analyzed in Fig. 6G.

The imaging data is also not clear as it is not apparent whether the S1pr2-expressing cells indicated by the authors express Fcrl5 since Fcrl5 does not encircle the indicated cell. The authors also do not quantify their images. While the authors do see a difference between the populations following in vivo labeling, it is not clear why the CD45+ population among the Fcrl5+ cells have a higher staining intensity than the Cd23+ cells. It is expected that cells that are exposed to circulation would have a similar staining intensity. Therefore, it is possible that there may be a technical issue with this data. Finally, it is not clear whether the results in figure 6 were repeated with several of the plots only having three mice per group limiting the conclusions that can be drawn from this data.

Reviewer #2 (Public Review):

In this paper, Bond et al. build on previous behavioral modelling of a reversal-learning task. They replicate some features of human behavior with a spiking neural network model of cortical basal ganglia thalamic circuits, and they link some of these same behavioral patterns to corresponding areas with BOLD fMRI. I applaud the authors for sharing this work as a preprint, and for publicly sharing the data and code.

While the spiking neural network model offers a helpful tool to complement behavior and neuroimaging, it is not very clear which predictions are specific to this model (and thus dissociate it from, or go beyond, previous work). Thus, the main strength of this work (combining behavior, brain, and in silico experiments) is not fully fleshed out and could be stronger in the conclusions we can draw from them.

It would be helpful to know more about which features of the spiking NN model are crucial in precisely replicating the behavioral patterns of interest (and to be more precise in which behaviors are replicated from previous work with the same task, vs. which ones are newly acquired because the task has changed - or the spiking CBGT model has afforded new predictions for behavior). Throughout, I am wondering if the authors can compare their results to a reasonable 'null model' which can then be falsified (e.g. Palminteri et al. 2017, TICS); this would give more intuition about what it is about this new CBGT model that helps us predict behavior.

The same question about model comparison holds for the behavior: beyond relying on DIC score differences, what features of behavior can and cannot be explained by the family of DDMs?

Reviewer #2 (Public Review):

Modi and colleagues describe a multivariate framework to analyze local field potentials, which is specifically applied to CA1 data in this work. Multivariate approaches are welcome in the field and the effort of the authors should be appreciated. However, I found the analyses presented here are too superficial and do not seem to bring new insights into hippocampal dynamics. Further, some surrogate methods used are not necessarily controlling for confounding variables. These concerns are further detailed below.

1. The authors in reality do not analyze oscillations themselves in this manuscript but only the power of signals filtered at determined frequency bands. This is particularly misleading when the authors talk about "spindles". Spindles are classically defined as a thalamico-cortical phenomenon, not recorded from hippocampus LFPs. Thus, the fact that you filter the signal in the same frequency range matching cortical spindles does not mean you are analyzing spindles. The terminology, therefore, is misleading. I would recommend the authors to change spindles to "beta", which at least has been reported in the hippocampus, although in very particular behavioral circumstances. However, one must note that the presence of power in such bands does not guarantee one is recording from these oscillations. For example, the "fast gamma" band might be related to what is defined as fast gamma nested in theta, but it might also be related to ripples in sleep recordings. The increase of "spindle" power in sleep here is probably related to 1/f components arising from the large irregular activity of slow wave sleep local field potentials. The authors should avoid these conceptual confusions in the manuscript, or show that these band power time courses are in fact matching the oscillations they refer to (for example, their spindle band is in fact reflecting increased spindle occurrence).

2. The shuffling procedure to control for the occupancy difference between awake and sleep does not seem to be sufficient. From what I understand, this shuffling is not controlling for the autocorrelation of each band which would be the main source of bias to be accounted for in this instance. Thus, time shifts for each band would be more appropriate. Further, the controls for trial durations should be created using consecutive windows. If you randomly sample sleep bins from distant time points you are not effectively controlling for the difference in duration between trial types. Finally, it is not clear from the text if the UMAP is recomputed for each duration-matched control. This would be a rigorous control as it would remove the potential bias arising from the unbalance between awake and sleep data points, which could bias the subspace to be more detailed for the LFP sleep features. It is very likely the results will hold after these controls, given it is not surprising that sleep is a more diverse state than awake, but it would be good practice to have more rigorous controls to formalize these conclusions.

3. Lots of the observations made from the state space approach presented in this manuscript lack any physiological interpretation. For example, Figure 4F suggests a shift in the state space from Sleep1 to Sleep2. The authors comment there is a change in density but they do not make an effort to explain what the change means in terms of brain dynamics. It seems that the spectral patterns are shifting away from the Delta X Spindle region (concluding this by looking at Fig4B) which could be potentially interesting if analyzed in depth. What is the state space revealing about the brain here? It would be important to interpret the changes revealed by this method otherwise what are we learning about the brain from these analyses? This is similar to the results presented in Figure 5, which are merely descriptions of what is seen in the correlation matrix space. It seems potentially interesting that non-REM seems to be split into two clusters in the UMAP space. What does it mean for REM that delta band power in pyramidal and lm layers is anti-correlated to the power within the mid to fast gamma range? What do the transition probabilities shown in Figures 6B and C suggest about hippocampal functioning? The authors just state there are "changes" but they don't characterize these systematically in terms of biology. Overall, the abstract multivariate representation of the neural data shown here could potentially reveal novel dynamics across the awake-sleep cycle, but in the current form of this manuscript, the observations never leave the abstract level.

Reviewer #2 (Public Review):

In the present study, Briana M. Bohannon et al. expand on the study of the effect of Polyunsaturated fatty acids (PUFAS) on Iks (KV7.1 + KCNE1), a delayed rectifier potassium channel of critical relevance in cardiac physiology. PUFAs are amphipathic molecules that activate IKs channels by interacting with positively charged residues on the voltage sensor domain and in the channel's pore. The authors aim to characterize the molecular mechanisms behind the Iks activation by PUFA analogs that contains a tyrosine head group instead of the carboxyl or sulfonyl group present in other PUFAs.

The authors present a well-written manuscript with clear data and well-presented figures. The authors describe the effects of various tyrosine-PUFA analogs and unveil the mechanistic nature of their interactions with the channel. The focus is the N -(alpha-linolenoyl) Tyrosine (NALT), a potent activator by shifting the channel G-V by more than 50mV facilitating the opening of the channel, although the authors tested other tyrosine-PUFA analogs. Remarkably, the hydroxyl group in the tyrosine head is essential to shift the voltage-dependence of activation due to an H-bond with a threonine from the S3-S4 linker that helps coordinate the PUFA together with an electrostatic interaction with arginine in the S4. Furthermore, to test whether the aromatic ring from the tyrosine had a role in the interaction, the authors took a fascinating and exciting approach by modifying it and making the ring more electronegative by adding negatively charged atoms. Interestingly, they discovered that an electronegative-modified aromatic PUFA could increase the channel's conductance, an effect mediated by a specific interaction with a Lysine at the top of the S6 helix.

Although the question addressed in the manuscript is fascinating due to the possible use of these tyrosine-PUFA analogs as IKs modulators, the presented work is very mechanistic and specialized. While the effect of tyrosine-PUFA analogs is robust, the authors could improve the story by highlighting their interest in them and discussing whether they have potential therapeutic uses.

Due to the relevance of IKs currents in cardiac physiology and Long QT syndrome, the discovery and characterization of activators are highly relevant. The present manuscript presents a group of potent IKs channel activators that have the potential to impact the cardiac physiology field dramatically if they can perform under pathophysiological conditions or in the presence of disease-causing mutations.

Reviewer #2 (Public Review):

In this manuscript, the authors used an original empirical design to test if somatic mutation rates are different depending on the plant growth rates. They detected somatic mutations along the growth axes of four trees - two individuals per species for two dipterocarp tree species growing at different rates. They found here that plant somatic mutations are accumulated are a relatively constant rate per year in the two species, suggesting that somatic mutation rates correlate with time rather than with growth, i.e. the number of cell divisions. The authors then suggest that this result is consistent with a low relative contribution of DNA replication errors (referred to as α in the manuscript) to the somatic mutation rates as compared to the other sources of mutations (β). Given that plants - in particular, trees - are generally assumed to deviate from the August Weismann's theory (a part of the somatic variation is expected to be transmitted to the next generation), this work could be of interest for a large readership interested by mutation rates as a whole, since it has implications also for heritable mutation rates too. In addition, even if this is not discussed, the putatively low contribution of DNA replication errors could help to understand the apparent paradox associated to trees. Indeed, trees exhibit clear signatures of lower molecular evolution (Lanfear et al. 2013), therefore suggesting lower mutation rates per unit of time. Trees could partly keep somatic mutations under control thanks to a long-term evolution towards low α values, resulting in low α/β ratios as compared to short-lived species. I therefore consider that the paper tackles a fundamental albeit complex question in the field.

Overall, I consider that the authors should clearly indicate the weakness of the studies. For instance, because of the bioinformatic tools used, they have reasonably detected a small part of the somatic mutations, those that have reached a high allele frequency in tissues. Mutation counts are known to be highly dependent on the experimental design and the methods used. Consequently, (i) this should be explicit and (ii) a particular effort should be made to demonstrate that the observed differences in mutation counts are robust to the potential experimental biases. This is important since, empirically, we know how mutation counts can vary depending on the experimental designs. For instance, a difference of an order of magnitude has been observed between the two papers focusing on oaks (Schmid-Siegert et al. 2017 and Plomion et al. 2018) and this difference is now known to be due to the differences in the experimental designs, in particular the sequencing effort (Schmitt et al. 2022).

Having said that, my overall opinion is that (i) the authors have worked on an interesting design and generated unique data, (ii) the results are probably robust to some biases and therefore strong enough (but see my comments regarding possible improvements), (iii) the interpretations are reasonable and (iv) the discussion regarding the source of somatic mutations is valuable.

Reviewer #2 (Public Review):

Harnessing macrophages to attack cancer is an immunotherapy strategy that has been steadily gaining interest. Whether macrophages alone can be powerful enough to permanently eliminate a tumor is a high-priority question. In addition, the factors making different tumors more vulnerable to macrophage attack have not been completely defined. In this paper, the authors find that chromosomal instability (CIN) in cancer cells improves the effect of macrophage targeted immunotherapies. They demonstrate that CIN tumors secrete factors that polarize macrophages to a more tumoricidal fate through several methods. The most compelling experiment is transferring conditioned media from MSP1 inhibited and control cancer cells, then using RNAseq to demonstrate that the MSP1-inhibited conditioned media causes a shift towards a more tumoricidal macrophage phenotype. In mice with MSP1 inhibited (CIN) B16 melanoma tumors, a combination of CD47 knockdown and anti-Tyrp1 IgG is sufficient for long term survival in nearly all mice. This combination is a striking improvement from conditions without CIN.

Like any interesting paper, this study leaves several unanswered questions. First, how do CIN tumors repolarize macrophages? The authors demonstrate that conditioned media is sufficient for this repolarization, implicating secreted factors, but the specific mechanism is unclear. In addition, the connection between the broad, vaccination-like IgG response and CIN is not completely delineated. The authors demonstrate that mice who successfully clear CIN tumors have a broad anti-tumor IgG response. This broad IgG response has previously been demonstrated for tumors that do not have CIN. It is not clear if CIN specifically enhances the anti-tumor IgG response or if the broad IgG response is similar to other tumors. Finally, CIN is always induced with MSP1 inhibition. To specifically attribute this phenotype to CIN it would be most compelling to demonstrate that tumors with CIN unrelated to MSP1 inhibition are also able to repolarize macrophages.<br /> Overall, this is a thought-provoking study that will be of broad interest to many different fields including cancer biology, immunology and cell biology.

Reviewer #2 (Public Review):

This article examines the ability of dietary supplementation with indole-3-actetate (I3A) to attenuate western diet-induced fatty liver disease. The experiments are appropriately described, and convincing data are provided that I3A can attenuates fat accumulation in the liver. Several possible mechanisms of action were explored and one likely mechanism, an alteration in AMPK signaling pathway was observed, and is likely involved in the observed phenotype. However, I3A has already been shown to yield similar data in a high fat diet mouse model system (PMID: 31484323), although the I3A was administered through IP injection, not in the drinking water. In both studies the effects seen may well be due to activation of PPAR-alpha. Another study (PMID: 19469536) gave acetic acid in the drinking water and obtained data similar to this manuscript, supporting that the effect seen in this study may not be specific to I3A. These references should be included and discussed. Overall, the data and experimental approach taken support the stated conclusions.

Reviewer #2 (Public Review):

Pinos et al present five atherosclerosis studies in mice to investigate the impact of dietary supplementation with b-carotene on plaque remodeling during resolution. The authors use either LDLR-ko mice or WT mice injected with ASO-LDLR to establish diet-induced hyperlipidemia and promote atherogenesis during 16 weeks, and then they promote resolution by switching the mice for 3 weeks to a regular chow, either deficient or supplemented with b-carotene. Supplementation was successful, as measured by hepatic accumulation of retinyl esters. As expected, chow diet led to reduced hyperlipidemia, and plaque remodeling (both reduced CD68+ macs and increased collagen contents) without actual changes in plaque size. But, b-carotene supplementation resulted in further increased collagen contents and, importantly, a large increase in plaque regulatory T-cells (TREG). This accumulation of TREG is specific to the plaque, as it was not observed in blood or spleen. The authors propose that the anti-inflammatory properties of these TREG explain the atheroprotective effect of b-carotene, and found that treatment with anti-CD25 antibodies (to induce systemic depletion of TREG) prevents b-carotene-stimulated increase in plaque collagen and TREG.

An obvious strength is the use of two different mouse models of atherogenesis, as well as genetic and interventional approaches. The analyses of aortic root plaque size and contents are rigorous and included both male and female mice (although the data was not segregated by sex). Unfortunately, the authors did not provide data on lesions in en face preparations of the whole aorta.

Overall, the conclusion that dietary supplementation with b-carotene may be atheroprotective via induction of TREG is reasonably supported by the evidence presented. Other conclusions put forth by the authors (e.g., that vitamin A production favors TREG production or that BCO1 deficiency reduces plasma cholesterol), however, will need further experimental evidence to be substantiated.

The authors claim that b-carotene reduces blood cholesterol, but data shown herein show no differences in plasma lipids between mice fed b-carotene-deficient and -supplemented diets (Figs. 1B, 2A, and S3A). Also, the authors present no experimental data to support the idea that BCO1 activity favors plaque TREG expansion (e.g., no TREG data in Fig 3 using Bco1-ko mice).

As the authors show, the treatment with anti-CD25 resulted in only partial suppression of TREG levels. Because CD25 is also expressed in some subpopulation of effector T-cells, this could potentially cloud the interpretation of the results. Data in Fig 4H showing loss of b-carotene-stimulated increase in numbers of FoxP3+GFP+ cells in the plaque should be taken cautiously, as they come from a small number of mice. Perhaps an orthogonal approach using FoxP3-DTR mice could have produced a more robust loss of TREG and further confirmation that the loss of plaque remodeling is indeed due to loss of TREG.

Reviewer #2 (Public Review):

Manuscript entitled "Uremic toxin indoxyl sulfate (IS) induces trained immunity via the AhR-dependent arachidonic acid pathway in ESRD" presented some interesting findings. The manuscript strengths included use of H3K4me3-CHIP-Seq, AhR antagonist, IS treated cell RNA-Seq, ALOX5 inhibitor, MTA inhibitor to determine the roles of IS-AhR in trained immunity related to ESRD inflammation and trained immunity.

Reviewer #2 (Public Review):

The paper describes the various types of immune cells interacting with SARS-CoV-2 spike protein and undergoing pathological changes upon different routes of administration into mice mainly in the absence of human ACE-2. Multiple murine cell types in the lungs, the cremaster muscle and surrounding tissues, and the liver were studied. The spike interactions with various cells from the human peripheral blood ex vivo and in cultures were also examined. This study focused on hACE-2-independent effects of the spike protein in vivo in mice and in vitro on human leukocytes and touched upon the potential involvement of sialic-acid-binding lectins (Siglec) as non-hACE-2 receptors for spike. Hence, a multitude of aspects about spike-cell interactions was studied, although each was covered without significant depths and the key findings are difficult to parse through. Many inconsistencies are not explained and the critical experimental parameters and controls are missing. Ultimately, the main message of the study is buried among supporting vs confounding data.

Reviewer #2 (Public Review):

The manuscript by Sebastian-Perez describes determinants of heterochromatin domain formation (chromocenters) at the 2-cell stage of mouse embryonic development. They implement an inducible system for transition from ESC to 2C-like cells (referred to as 2C+) together with proteomic approaches to identify temporal changes in associated proteins. The conversion of ESCs to 2C+ is accompanied by dissolution of chromocenter domains marked by HP1b and H3K9me3, which reform upon transition back to the 2C-like state. The innovation in this study is the incorporation of proteomic analysis to identify chromatin-associated proteins, which revealed SMARCAD1 and TOPBP1 as key regulators of chromocenter formation.

In the model system used, doxycycline induction of DUX leads to activation of EGFP reporter regulated by the MERVL-LTR in 2C+ cells that can be sorted for further analysis. A doxycycline-inducible luciferase cell line is used as a control and does not activate the MERVL-LTR GFP reporter. The authors do see groups of proteins anticipated for each developmental stage that suggest the overall strategy is effective.

The major strengths of the paper involve the proteomic screen and initial validation. From there, however, the focus on TOPBP1 and SMARCAD1 is not well justified. In addition, how data is presented in the results section does not follow a logical flow. Overall, my suggestion is that these structural issues need to be resolved before engaging in comprehensive review of the submission. This may be best achieved by separating the proteomic/morphological analyses from the characterization of TOPBP1 and SMARCAD1.

Reviewer #2 (Public Review):

The authors introduce "HAMA", a new automated pipeline for architectural analysis of the bacterial cell wall. Using MS/MS fragmentation and a computational pipeline, they validate the approach using well-characterized model organisms and then apply the platform to elucidate the PG architecture of several members of the human gut microbiota. They discover differences in the length of peptide crossbridges between two species of the genus Bifidobacterium and then show that these species also differ in cell envelope stiffness, resulting in the conclusion that crossbridge length determines stiffness.

The pipeline is solid and revealing the poorly characterized PG architecture of the human gut microbiota is worthwhile and significant. However, it is unclear if or how their pipeline is superior to other existing techniques - PG architecture analysis is routinely done by many other labs; the only difference here seems to be that the authors chose gut microbes to interrogate.

I do not agree with their conclusions about the correlation between crossbridge length and cell envelope stiffness. These experiments are done on two different species of bacteria and their experimental setup therefore does not allow them to isolate crossbridge length as the only differential property that can influence stiffness. These two species likely also differ in other ways that could modulate stiffness, e.g. turgor pressure, overall PG architecture (not just crossbridge length), membrane properties, teichoic acid composition etc.

Reviewer #2 (Public Review):

In this paper, the authors utilize optogenetic stimulation and imaging techniques with fluorescent reporters for pH and membrane voltage to examine the extent of intracellular acidification produced by different ion-conducting opsins. The commonly used opsin CheRiff is found to conduct enough protons to alter intracellular pH in soma and dendrites of targeted neurons and in monolayers of HEK293T cells, whereas opsins ChR2-3M and PsCatCh2.0 are shown to produce negligible changes in intracellular pH as their photocurrents are mostly carried by metal cations. The conclusion that ChR2-3M and PsCatCh2.0 are more suited than proton conducting opsins for optogenetic applications is well supported by the data.

Reviewer #2 (Public Review):

Sadanandan et al describe their studies in mice of HDAC and Polycomb function in the context of vascular endothelial cell (EC) gene expression relevant to the blood-brain barrier, (BBB). This topic is of interest because the BBB gene expression program represents an interesting and important vascular diversification mechanism. From an applied point of view, modifying this program could have therapeutic benefits in situations where BBB function is compromised.

The study involves comparing the transcriptomes of cultured CNS ECs at E13 and adult stages and then perturbing EC gene expression pharmacologically in cell culture (with HDAC and Polycomb inhibitors) and genetically in vivo by EC-specific conditional KO of HDAC2 and Polycomb component EZH2.

This reviewer has several critiques of the study.

First, based on published data, the effect of culturing CNS ECs is likely to have profound effects on their differentiation, especially as related to their CNS-specific phenotypes. Related to this, the authors do not state how long the cells were cultured.

Second, the use of qPCR assays for quantifying ChIP and transcript levels is inferior to ChIPseq and RNAseq. Whole genome methods, such as ChIPseq, permit a level of quality assessment that is not possible with qPCR methods. The authors should use whole genome NextGen sequencing approaches, show the alignment of reads to the genome from replicate experiments, and quantitatively analyze the technical quality of the data.

Third, the observation that pharmacologic inhibitor experiments and conditional KO experiments targeting HDAC2 and the Polycomb complex perturb EC gene expression or BBB integrity, respectively, is not particularly surprising as these proteins have broad roles in epigenetic regulation in a wide variety of cell types.

Reviewer #2 (Public Review):

In the study by Hreich et al, the potency of P2RX7 positive modulator HEI3090, developed by the authors, for the treatment of Idiopathic pulmonary fibrosis (IPF) was investigated. Recently, the authors have shown that HEI3090 can protect against lung cancer by stimulating dendritic cell P2RX7, resulting in IL-18 production that stimulates IFN-γ production by T and NK cells (DOI: 10.1038/s41467-021-20912-2). Interestingly, HEI3090 increases IL-18 levels only in the presence of high eATP. Since the treatment options for IPF are limited, new therapeutic strategies and targets are needed. The authors first show that P2RX7/IL-18/IFNG axis is downregulated in patients with IPF. Next, they used a bleomycin-induced lung fibrosis mouse model to show that the use of a positive modulator of P2RX7 leads to the activation of the P2RX7/IL-18 axis in immune cells that limits lung fibrosis onset or progression. Mechanistically, treatment with HEI3090 enhanced IL-18-dependent IFN-γ production by lung T cells leading to a decreased production of IL-17 and TGFβ, major drivers of IPF. The major novelty is the use of the small molecule HEI3090 to stimulate the immune system to limit lung fibrosis progression by targeting the P2RX7, which could be potentially combined with current therapies available. However, there is the lack of information on the reproducibility of data, especially for the data presented in Figures 3 and 4, and related supplementary figures, as well as the lack of support data for experiments that emphasize the role of P2RX7 expressed on immune cells (e.g. frequency of transferred cells compared to endogenous cells).

intervallically more diverse. The most common method of generating an open voicing is to drop certain notes from a close-position chord down an octave. In a “drop 2” voicing, the second note, counting from the top note, is dropped down an octave. “Drop 2” refers to voicings above the bass in which the bass note is not counted as one of the voices being “dropped.” Each chord in Figure 4.15 includes three “drop 2” voicings because the three notes above the bass can be rotated three times.

see figure 4.15 on p 47

https://docdrop.org/pdf/Miller---Unknown---Modal-Jazz-Composition-and-Harmony.-Vol.2-ggtxc_ocr.pdf/?src=ocr

Modal Jazz Composition and Harmony Vol. 2 Miller, R 1996

Reviewer #2 (Public Review):

This study describes the development of a robotic system that allows investigators to track the movements of Drosophila larvae for extremely long time durations. Prior studies were limited by the fact that tracking of larval movements needed to be stopped whenever the animal reached the edge of a behavioral arena. This new study overcomes this limitation with a robot arm that gently picks up the larvae when they reach the edge of the arena and then gently releases them again so that tracking can be resumed. The very long periods of data acquisition are performed with a video camera that provides a low-resolution 64x64 pixel representation of the larvae. Nevertheless, the authors are able to extract postural information from the animals using a sophisticated machine vision based neural network. The authors use this system to continuously track the behaviors of individual larvae for six hours in the presence or absence of a thermal gradient. They argue that high inter-animal variability in a navigation index occurs in the presence of a thermal gradient but not in its absence. The intra-animal mean navigation also appears to be bimodal, apparently switching between "non-navigating" and "strongly navigating" states (not the authors' words). Interestingly, when only the population means are investigated a single mode is indicated with an overall weak navigation index. This comparison very nicely illustrates the power of this method to reveal richness in the data that leads to insights that cannot be observed with short-term measurements. Another impressive feature of the robotic system design is that it is capable of delivering small droplets of food to individual larvae. This allowed the authors to track a single larva for a remarkable 30 hours in which it is seen to crawl for more than 48 meters. Overall, the robotic system presented here will allow the researchers to investigate behaviors of larvae in long-term experiments in ways that were previously unimaginable.

Reviewer #2 (Public Review):

This is an interesting manuscript in which the authors demonstrate the power of serial section reconstruction at the EM level of a volume within the anterior ventral cochlear nucleus (aVCN) containing bushy cells and their large afferent synapses - the endbulbs of Held. Integration of this information with compartmental modelling of the neuronal excitability is then used to make observations about the form and function of these neurons and their synaptic inputs. While this is technically impressive (in regards to both the structure and modelling) there are significant weaknesses because this integration makes massive assumptions and lacks a means of validation; for example, by checking that the results of the structural modelling recapitulate the single-cell physiology of the neuron(s) under study. This would require the integration of in vivo recorded data, which would not be possible (unless combined with a third high throughput method such as calcium imaging) and is well beyond the present study. The authors need to be more open about the limitations of their observations and their interpretations and focus on the key conclusions that they can glean from this impressive data set. The manuscript would be considerably improved by re-writing to focus the science on the most important results and provide clear declarations of limitations in interpretation.

Reviewer #2 (Public Review):

Maturation of inhibitory synapses requires multiple vital biological steps including, i) translocation of cargos containing GABAARs and scaffolds (e.g. gephyrin) through microtubules (MTs), ii) exocytosis of inhibitory synapse proteins from cargo followed by the incorporation to the plasma membrane for lateral diffusion, and iii) incorporation of proteins to inhibitory synaptic sites where gephyrin and GABAARs are associated with actin. A number of studies have elucidated the molecular mechanisms for GABAARs and gephyrin translocation in each step. However, the molecular mechanisms underlying the transition between steps, particularly from exocytosis to lateral diffusion of inhibitory proteins, still need to be elucidated. This manuscript successfully characterizes three stages of inhibitory synapses during maturation, cluster1: an initial stage that receptors are being brought in and out by the MT system; cluster2: lateral diffusion stage; cluster 3: matured postsynapses anchored by gephyrin and actin, by quantifying the abundance of MAP2 or Actin in inhibitory synapse labeled by gephyrin. Importantly, the authors' findings suggest that TEN2, a trans-synaptic adhesion molecule that has two EB1 binding motifs, plays an important role in the transition from clusters 1 to 2, and inhibitory synapse maturation. The imaging results are impressive and compelling, these data will provide new insights into the mechanisms of protein transport during synapse development. However, the present study contains several loose ends preventing convincing conclusions. Most importantly, (1) it remains more TEN2 domain characterization on inhibitory synapse maturation, (2) further validation of the HA knock-in TEN2 mouse model is required, and (3) it requires additional physiology data that complement the authors' findings.

Reviewer #2 (Public Review):

In this manuscript, the authors have proposed that the suppression of hepatic GPR110, known as a tumorigenic gene, could improve non-alcoholic fatty liver disease (NALFD). With AAV-mediated GPR110 overexpression or a GalNAc-siGPR110 experiment, they have suggested that GPR110 could increase hepatic lipids through SCD1.

Major comments<br /> 1. Although the authors claimed that GPR110 could enhance SCD1-mediated hepatic de novo lipogenesis, the level of GPR110 expression was decreased in obese mice (Figure 1E-F). However, it has been reported that the levels of de novo lipogenic genes, including SCD1, are upregulated in HFD-fed mice (PMID: 18249166, PMID: 31676768). Thus, they should show the levels of hepatic lipids and lipogenic gene expression, including SCD-1, in liver tissues from NCD vs. HFD-fed mice, which will provide insights between GPR110 level and hepatic lipogenic activity.

2. In Figure 2, the authors have characterized metabolic phenotypes of hepatic GPR110 overexpression upon HFD, exhibiting significant phenotypes (including GTT, ITT, HOMA-IR, serum lipids, and hepatic lipid level). However, it is likely that these phenotypes could stem from increased body weight gain. Since they cannot explain how hepatic GPR110 overexpression could increase body weight, it is hard to conclude that the increased hepatic lipid level would be a direct consequence of GPR110 overexpression. Also, given the increased fat mass in GPR110 overexpressed mice, they should test whether GPR110 overexpression would affect adipose tissue. Along the same line, they have to carefully investigate the reason of increased body weight gain in GPR110 overexpressed mice (ex., food intake, and energy expenditure).

3. GPR110 enhances hepatic lipogenesis via SCD1 expression (Figures 5 and 6). To verify whether GPR110 would specifically regulates SCD1 transcript, they have to provide the expression levels of other lipogenic genes, including Srebf1, Chrebp, Acaca, and Fasn. Also, measurement of de novo lipogenic activity using primary hepatocyte with GPR110 overexpression or knockdown would be valuable to affirm the authors' proposed model.

4. In Figure 6, the author should provide the molecular mechanisms how GPR110 signaling could enhance SCD-1 transcription.

5. Figure 9C shows the increased level of GPR110 with NAFLD severity. They should test whether the levels of hepatic GPR110 and SCD-1 might be elevated in a severe NAFLD mouse model. If it is the case, it would be better to show the beneficial effects of GPR110 suppression against NAFLD progression using a severe NAFLD (ex., NASH) mouse model.

Reviewer #2 (Public Review):

This manuscript describes a study of a novel role of FAM76B in regulation of NF-kB-mediated inflammation, specially in neuroinflammation both in animal model and human brain disease. This study was logically designed and laid out and data from gene knockdown and knockout cell line and animals strongly support the note that FAM76B is involved in the neuroinflammatory diseases. This notion was further confirmed in patients with brain inflammatory diseases. Importantly, the authors further dissected the cellular molecular action of FAM76B in regulation of NF-kB pathway through binding to the hnRNPA2B1. However, it is still unclear how the FAM76B regulates/or affects the cytoplasmic translocation of hnRNPA2B1 in brain cells after a variety of inflammatory stimuli or injuries. Nonetheless, this study greatly enhances our understanding of the mechanisms of the brain inflammation and inflammation related brain degeneration.

Reviewer #2 (Public Review):

While aging is known to cause cerebral blood flow deficits, some studies suggested that exercise could reverse - at least partially - these deficits. In this study, the authors used technically-challenging techniques and approaches to test the hypothesis that 5 months of voluntary exercise reverses impairments in cerebrovascular function and cognition. Overall, I find the evidence for a favorable impact of exercise on microvascular perfusion and oxygenation convincing. The impact of exercise was most evident in the white matter and deep cortical tissues, which I believe to be a major finding of the study. The methods are very well-detailed and easy to follow. It is not clear, however, why the authors chose to study only one sex (female mice). This is an important consideration given that age-dependent hormonal changes could play a role in the findings. There are a few instances where it is unclear whether the number of vessels or animals were used for statistical analyses. It'd be very useful for the reader to understand why whisker stimulation led to a reduction in detected light intensity that reflects hyperemia as previously published by the authors (Sencan et al., 2022 JCBFM).

Reviewer #2 (Public Review):

In this manuscript by Huang et al. the authors explore the genetic underpinnings that may cause human oocyte meiotic arrest. The meiotic arrest of oocytes can cause female infertility leading patients to seek treatment at IVF clinics to assist in having genetically related babies. However, because oocytes fail to develop to MII, oocytes from these patients cannot be fertilized, leaving no current options for genetically related babies for patients with this pathology. Huang et al identified 50 IVF patients with this phenotype, and after the whole exome sequence, 3 patients had mutations in a spindle assembly checkpoint regulator, Mad1bp1. This study describes these mutations in detail, shows how these mutations affect Mad1bp1 expression, evaluates gross function in mouse oocytes, and explores therapeutic treatment in human oocytes. Overall, this is an important translational study that adds to the growing body of literature that genetic mutations impact oocyte quality and fertility.

In its current form, I find that the strengths exist in the analysis of the patients' genomes and pedigree information. This is unique data and is important for the field. The expression in oocytes, structure modeling, and conservation in evolution, while not essential for this study, add interesting information for the reader to consider. I sometimes find these distracting in manuscripts, but appreciate them here in this context. The conclusion using human oocytes to propose possible treatment takes the study to completion and is not an easy approach to carry out.

I do find some weaknesses that weaken the conclusions. The conclusion described is that the SAC is not satisfied in oocytes from these patients. The authors attempt to show this by analysis of mouse oocytes using polar body extrusion and its timing as an assay. There could be many reasons contributing to arrest, therefore a singular assay is not ideal to justify the conclusions. While I do suspect the authors are correct, an intact SAC should be shown at the molecular level to fully justify this conclusion. There are many assays routinely performed in mouse oocytes that the authors can consider (check papers by authors from Wassmann, FitzHarris, and Schindler labs for example).

Reviewer #2 (Public Review):

In this work the authors use a simple biophysical model to predict evolutionary trajectories of resistance to pyrimethamine - inhibitor of PfDHFR from P. falciparum and PvDHFR from P. vivax - pathogens causing malaria which presents a worldwide health concern. The authors use a simple fitness model that posits that selection coefficient -relative change in fitness between WT and mutant strains is determined by the fraction of unbound (to antibiotic inhibitor) DHFR. The population genetics simulations use the Kimura formula which is applicable to low mutation high selection regime where populations are monoclonal. The authors use computational tool Rosetta Flex ddG to assess binding of the antibiotic ligand to WT and mutant protein and compare their predicted evolutionary trajectories with lab evolution and data on naturally evolved variants worldwide and find semi-quantitative agreement, albeit sith significant variation in detail.

The paper is of potential interest as it presents one of the first (but not the first) attempts to compare evolutionary dynamics based on biophysics inspired fitness model with laboratory evolution and natural data for very important problem of emergence and fixation of antibiotic resistant alleles. As such it can be a useful starting point for more detailed and biophysical realistic models of evolution of resistance against anti-DHFR drugs.

Reviewer #2 (Public Review):

Microfluidics-assisted live-cell imaging is often the method of choice to gain insight into the growth behavior of single cells, in particular unicellular organisms with simple shapes. While growth rate measurements of symmetrically dividing and rod-shape organisms such as E.coli or fission yeast are simplified by their geometry, measurements of the common model organism budding yeast are more complicated due to growth in three dimensions and asymmetric 'budding'. As a consequence, analysis of live-cell imaging experiments typically still requires time-consuming manual work, in particular, to correct automated segmentation and tracking, assign mother-bud pairs, and determine the time point of cell division. In the present manuscript, Pietsch et al. aim to address this important issue by developing deep-learning-based analysis software named BABY for the automated extraction of growth rate measurements performed with microfluidic traps that are designed to keep mother cells, but quickly lose newborn daughters.

To achieve this, Pietsch et al. introduce several innovative approaches. 1.) In contrast to previous deep-learning segmentation tools they allow 3D data (z-stacks) as inputs and allow for overlapping segmentation masks. 2.) By introducing 3 different object categories based on their size, they can take more specified approaches for each category and for the segmentation of overlapping objects 3.) By using cell edges and bud necks as additional predicted channels, they facilitate downstream post-processing of segmentation masks and mother-bud pairing, respectively. 4.) By using machine learning to predict tracking and mother-bud pairs from multiple features, they develop a novel approach to automate these steps. Using their automated analysis pipeline, the authors then study the growth behavior in different mutants and propose a novel mechanism in which growing buds are regulated by a combination of a 'sizer' and a 'timer' mechanism.

This manuscript introduces exciting steps towards a fully automated analysis of bright-field microscopy data of growing yeast cells, which makes this manuscript an important contribution to the field. However, in part the quantitative reporting on the actual performance is not sufficient. For example, what is the actual overall success-rate in predicting mother-bud pairs? How accurately can cell cycle durations be predicted? This lack of information makes it hard to evaluate how appropriate using fully automated BABY actual is. In addition, the experiments supporting the major biological insight, i.e. the sizer-timer transition for bud growth are rather limited, and further experiments would be needed to strengthen this conclusion.

Reviewer #2 (Public Review):

The manuscript addresses the important question of how EVs are targeted to their recipient cells once they are produced and released.

The present manuscript contains 4 messages:<br /> First, it shows that the transmembrane protein Sas gets incorporated into EVs and that this protein binds to its receptor Ptp10D on target cells, thus targeting the EVs. Second, the manuscript shows that the Sas cytoplasmic domain ICD binds to dARC1 protein (and perhaps darc1 RNAs), which are incorporated into EVs where they form capsids, before being targeted to recipient cells. dARC1 is important for neuron development in flies! Interestingly the motif in the Sas ICD is conserved in mammalian APP that also binds ARC1, suggesting a conserved mechanism of targeting EVs in mammalian neural development. Third, exposure of target cells (ex vivo wing discs) to EVs positive to FL Sas leads to its increased targetting when the target cells also expressed Ptp10D and Numb, which are acting as Sas receptors in a synergetic manner. Fourth, dARC1 ORF expression in the EV-producing cells (SG) leads to the increased expression of dARC1 protein and mRNAs in the recipient cells in vivo (Trachea). Many techniques are used, including IEM, fly genetics, S2 cells, and Ips. It is broad, and well executed, and the questions are interesting.

However, the manuscript should be strengthened. It is a lot of data and techniques but because there are so many messages in the paper, each needs more substances and controls.

1: Use of more extensive fly genetics using specific Ptp10D LOF in wing discs and trachea (to show the converse of the GOF).<br /> Does Ptp10D acts as the MAIN receptor to FL Sas? Numb LOF, a combination of LOF and GOF?<br /> does Ptp10D GOF compensate for Numb and vice versa?

2: What is the specificity for FL Sas? The expression of short Sas should not lead to its incorporation in EVs and their overnight addition should not lead to the same effect (Figure 3). This should be better investigated as short Sas is a good control for FL Sas.

3: A better quantitative analysis should be provided. For instance, there is no quantitative data for Figure 5.

4: All experiments are done with flies. There is no data on mammalian neurons in culture. This is missing. Exposure of neurons with SAS-positive EVs (or APP)

5: Are the capsid reconstitution with purified dARC1 and 2 performed in the presence of darc1 rRNA? Any RNA (figure 2).

6: The dAC1 increased expression in the target cells upon dARC1 increased production in SG(Figure 5) becomes an important part of the paper (and the model) but is not investigated!<br /> How does it work? Does the delivery of darc1 mRNAs packaged in capsids simply lead to more dARC1 translation? Is it proportional?<br /> OR is there also stimulation of darc1 transcription? Is there also an increase in the mRNA level (I cannot see the SG control of 5o (sage>+) supporting the authors' claim on line 562!).

7: Most (all) experiments are performed with overexpression of FL Sas or ICD. Does endogenous Sas bind endogenous Ptp10D and dARC1? ICDs? Also full-length APP?