Reviewer #2 (Public Review):

Summary:

In this manuscript, Liu et al. identified an important pathway regulating the nuclear translocation of the key transcriptional factor FOG1 during human hematopoiesis. The authors show that heat shock cognate B (HSCB) can interact with and promote the proteasomal degradation of TACC3, and this function is independent of its role in iron-sulfur cluster (ISC) biogenesis. TACC3 represses the activity of FOG1 by sequestering it in the cytoplasm. Therefore, HSCB can promote the nuclear translocation of FOG1 through down-regulating TACC3. The authors further show that the phosphorylation of HSCB by PI3K downstream of the EPO signaling pathway is important for its role in regulating the nuclear translocation of FOG1. The data are solid and the manuscript is overall well written. The findings of this manuscript provide new knowledge to the fields of hematopoiesis and cell biology.

Strengths:

This study uses a multi-pronged approach that combines techniques from a number of fields to convincingly demonstrate the pathway regulating the nuclear translocation of FOG1 during hematopoiesis.

Weaknesses:

This study only uses cell models. The significance of this work may be broadened by further studies using animal models.

Reviewer #2 (Public Review):

Summary:<br /> An analysis of images in the biology literature that are problematic for people with a color-vision deficiency (CVD) is presented, along with a machine learning-based model to identify such images and a web application that uses the model to flag problematic images. Their analysis reveals that about 13% of the images could be problematic for people with CVD and that the frequency of such images decreased over time. Their model yields 0.89 AUC score. It is proposed that their approach could help making biology literature accessible to diverse audiences.

Strengths:<br /> The manuscript focuses on an important yet mostly overlooked problem, and makes contributions both in expanding our understanding of the extent of the problem and in developing solutions to mitigate the problem. The paper is generally well-written and clearly organized. Their CVD simulation combines five different metrics. The dataset has been assessed by two researchers and is likely to be of high-quality. Machine learning algorithm used (convolutional neural network, CNN) is an appropriate choice for the problem. The evaluation of various hyperparameters for the CNN model is extensive.

Weaknesses:<br /> The focus seems to be on one type of CVD (deuteranopia) and it is unclear whether this would generalize to other types. The dataset consists of images from eLife articles. While this is a reasonable starting point, whether this can generalize to other biology/biomedical articles is not assessed. "Probably problematic" and "probably okay" classes are excluded from the analysis and classification, and the effect of this exclusion is not discussed. Machine learning aspects can be explained better, in a more standard way. The evaluation metrics used for validating the machine learning models seem lacking (e.g., precision, recall, F1 are not reported). The web application is not discussed in any depth.

Reviewer #3 (Public Review):

Summary:

Bernou et al. propose the existence of a distinct neuroblast population with increased regenerative and differentiation potential. Their claims are based on the analysis of a sorted population identified as LeX-EGFR+CD24low, which they refer to as "immature NeuroBlasts, iNB". This population is defined by transcriptomics features that have been assessed through bulk microarray studies of sorted cells and single cell RNA sequencing of the whole SVZ- lineage. Analysis of these data sets leads to the identification of these iNBs as cycling cells with a specific expression pattern of RNA splicing machinery components. On these grounds, they propose that RNA splicing plays a key role in neuronal differentiation. Although the authors bring an innovative point to the table, their claims are not fully supported by their results.

Strengths:

Interesting Hypothesis

Weaknesses:

The comparison of their microarray data to published single-cell RNA sequencing datasets (scRNAseq) highlights the cycling nature of the iNB population. Moreover, their own cell cycle analysis on their scRNAseq data attributes G2M/S-phase stages to clusters classified as iNBs, while clusters identified as TAPs are assigned to a restricted G1/S-phase stage. However, it would be expected that TAPs, as cycling progenitors, would go through all cell cycle stages and not just the beginning of it. Thus, authors should consider the possibility that their iNB population entails a major fraction of transit amplifying progenitors (TAP) and a couple neuroblasts, as described in numerous previous studies.

Authors regard the iNB population as neuroblasts due to the capacity of their sorted population to proliferate and differentiate into diverse neural cell types (neurons, oligodendrocytes and astrocytes) in vitro. It cannot be discarded that the sorted population (LeX-EGFR+CD24low) may not be pure and may be composed of a mixture of cells in different stages, including TAPs. Such a mixture of different cell types is unavoidable in sorted populations analyzed as bulk and is precisely one of the issues solved by single cell transcriptomics. Thus, the analysis of single cells resolves transition states at higher resolution and should be preferred over bulk analysis to prevent biases in analysis.

To align the authors' findings with the existing body of literature and earlier characterizations of the SVZ niche, it is advisable to combine their single-cell RNA sequencing data with datasets that have already been published. Such integration will enable precise understanding of the identity of their iNB cells.

On another note, the role of RNA splicing on neurogenesis lacks experimental validation. Unless manipulation of RNA splicing factors is conducted, the key role of this machinery in adult neurogenesis cannot be claimed.

Reviewer #2 (Public Review):

Summary:

The manuscript by Salinas-Pena et. al examines the distribution of a subgroup of histone H1 variants primarily with the use of high-resolution microscopy. The authors find that while some H1s have a universal distribution pattern, some display a preference for discrete regions within the nuclear landscape namely, the periphery, the center, or the nucleolus. They also show using that the various H1s within a cell did not colocalize significantly with each other, rather, they occupy discrete 'nanodomains' throughout the nucleus which is visualized as a punctate signal.<br /> The authors present evidence towards a long-standing question in the field regarding the spatial distribution of the different H1 variants. Since reliable, specific antibodies toward the variants were unavailable, this question was unable to elicit a definitive answer. This study uses more recently available antibodies against endogenous H1s to put together a systematic and comprehensive view of a group of H1 variant distribution inside a nucleus and ties it with previously generated genome wide data to demonstrate localization and some functional heterogeneity.

Strengths of the study.

(1) First systematic, high-resolution view of H1 variants providing a significant advance towards the long hypothesized functional differences between H1 variants.

(2) The use of endogenous antibodies allows the authors to bypass the need to use tagged proteins or overexpression strategies to study H1 distribution.

(3) The availability of genome wide H1 distribution data for the variants using the endogenous H1 antibodies to strengthen the presented visual data.

Weakness of the study.

One of the major reasons for slow progress in deciphering variant specific function has been the dearth of quality, specific, antibodies. This study is heavily dependent on the antibody function and its ability to accurately report on the distribution. The authors have cited previous validations of the antibodies used using H1 knockdown, immunoblotting and ChIP-seq. For the scope of this study, the controls are adequate.

Impact:

This study sets the stage for an exciting avenue of H1 study where variant-specific cellular functions can be explored which has otherwise been severely understudied.

Reviewer #2 (Public Review):

Yang et al. recorded the activity of D1- and D2-MSNs in the dorsal striatum and analyzed their firing activity in relation to single-limb gait in normal and 6-OHDA lesioned mice. The authors provided evidence that the striatal D1- and D2-MSNs were phase-locked to the walking gait cycles of individual limbs, and dopamine lesions led to enhanced phase-locking between D2-MSN activity and walking gait cycles.

Comments on revised version:

The authors addressed my largest concern, which questioned if D1 and D2 MSNs phase-locked to single limbs better than the global gait cycles.

As to my second major concern, which questioned the causal significance of single limb gait coding in D1 and D2 MSNs on gait control, they performed additional optogenetic experiments to establish evidence that D2 activity is causally relevant for gait pattern control. The additional experiments also closed the logic gap between dopamine lesion, D2 activity and gait control, supporting the hypothesis that dopamine affects gait control and global movement pattern via increasing D2 MSN activity.

Reviewer #2 (Public Review):

Summary:

Nonalcoholic fatty liver disease (NASH), recently renamed as metabolic dysfunction-associated steatohepatitis (MASH) is a leading cause of liver-related death. Farnesoid X receptor (FXR) is a promising drug target for treating NASH and several drugs targeting FXR is under clinical investigation for its efficacy in treating NASH. The authors intended to address whether FXR mediates its hepatic protective effects through regulation of lncRNAs, which would provide novel insights into the pharmacological targeting of FXR for NASH treatment. The authors went from an unbiased transcriptomics profiling to identify a novel enhancer-derived lncRNA FincoR enriched in the liver and showed that the knockdown of FincoR in a murine NASH model attenuated part of the effect of tropifexor, an FXR agonist, namely inflammation and fibrosis, but not steatosis. This study provides a framework how one can investigate the role of noncoding genes in pharmacological intervention targeting a known protein coding genes. Given that many disease-associated genetic variants are located in the non-coding regions, this study, together with others, may provide useful information for improved and individualized treatment for metabolic disorders.

Strengths:

The study leverages both transcriptional profile and epigenetic signatures to identify the top candidate eRNA for further study. The subsequent biochemical characterization of FincoR using FXR-KO mice combined with Gro-seq and Luciferase reporter assays convincingly demonstrates this eRNA as a FXR transcriptional targets sensitive to FXR agonists. The use of in vitro culture cells and the in vivo mouse model of NASH provide multi-level evaluation of the context-dependent importance of the FincoR downstream of FXR in regulation of functions related to liver dysfunction.

Weaknesses:

Future work to dissect the detailed mechanisms by which FincoR facilitates action of FXR and its agonists is warranted. A more direct approach to alter eRNA levels, e.g., overexpression of FincoR in the liver would provide important data to interpret its functional regulation.

Reviewer #4 (Public Review):

Summary:

In this work, the authors have used a mouse model of familial Amyotrophic lateral sclerosis (ALS) that carries a G93A mutation in the Sod1 gen to understand how the extraocular muscles (EOM) are preserved in ALS while other muscles undergo degeneration. Interestingly, the authors demonstrate that the integrity of neuromuscular junctions (NMJ) is affected by ALS in the limb and diaphragm muscles of G93A mice, while EOM is mostly preserved. The authors also further demonstrate that NaBu treatment partially restores the integrity of NMJ in the limb and diaphragm muscles of G93A mice. The results also indicate that chemokine Cxcl12 is expressed at higher levels in EOM myoblasts, and transduction with AAV encoding Cxcl12 improved the phenotypic characteristics of hindlimb-derived satellite cells.

Strengths:

The authors have used both in vivo and cell culture models. The findings have a translational potential.

Weaknesses:

The use of NaBu could be an issue as it has multiple effects and targets in ALS.

The sample size of animal experiments still needs to be improved.

The molecular mechanism of how Cxcl12 improved the phenotypic characteristics of hindlimb-derived satellite cells is still being determined.

Reviewer #2 (Public Review):

Summary:<br /> This is an interesting study that seeks to identify novel mosquito repellents that smell attractive to humans.

Strengths:<br /> The combination of standard machine learning methods with mosquito behavioral tests is a strength.

Weaknesses:<br /> The study would be strengthened by describing how other modern ML approaches (RF, decision trees) would classify and identify other potential repellents.

A comparison in the repellent activity between DEET and the top ten hits identified in this new study indicates little change in repellent activity (~3%), suggesting that DEET remains the gold standard. Without additional toxicity tests, the study is arguably incremental. The study's novelty should be better clarified.

The Methods in the repellency tests are sparse, and more information would be useful. Testing the top repellents at low doses (<<1%) and for long periods (2-12 h) would strengthen the manuscript. Without this information, the manuscript is lacking in depth.

Testing human subjects on their olfactory perceptions of the repellents would also increase the depth and utility of the manuscript. Without additional experiments, the authors' conclusions lack support and have limited impact on the state-of-the-art.

This manuscript is a mix of different approaches, which makes it lack cohesion. There is the ML method for classifying new repellents that smell good, but no testing of the repellents on human volunteers. The repellents are not tested at realistic concentrations and durations. And the calcium mobilization test is strange and makes little sense in the context of the other experiments and framing of the manuscript.

Reviewer #2 (Public Review):

Summary:

Using a combination of in vivo studies with testosterone-inhibited and aged mice with lower testosterone levels, as well as isolated mouse and human seminal vesicle epithelial cells, the authors show that testosterone induces an increase in glucose uptake. They find that testosterone induces differential gene expression with a focus on metabolic enzymes. Specifically, they identify increased expression of enzymes that regulate cholesterol and fatty acid synthesis, leading to increased production of 18:1 oleic acid.

Strength:

Oleic acid is secreted by seminal vesicle epithelial cells and taken up by sperm, inducing an increase in mitochondrial respiration. The difference in sperm motility and in vivo fertilization in the presence of 18:1 oleic acid and the absence of testosterone is small but significant, suggesting that the authors have identified one of the fertilization-supporting factors in seminal plasma.

Weaknesses:

Further studies are required to investigate the effect of other seminal vesicle components on sperm capacitation to support the author's conclusions. The author's experiments focused on potential testosterone-induced changes in the rate of seminal vesicle epithelial cell glycolysis and oxphos, however, provide conflicting results and a potential correlation with seminal vesicle epithelial cell proliferation should be confirmed by additional experiments.

Reviewer #3 (Public Review):

This paper considers a challenging motor control task - the critical stability task (CST) - that can be performed equally well by humans and macaque monkeys. This task is of considerable interest since it is rich enough to potentially yield important novel insights into the neural basis of behavior in more complex tasks that point-to-point reaching. Yet it is also simple enough to allow parallel investigation in humans and monkeys, and is also easily amenable to computational modeling. The paper makes a compelling argument for the importance of this type of parallel investigation and the suitability of the CST for doing so.

Behavior in monkeys and in human subjects suggests that behavior seems to include two qualitatively different kinds of behavior - in some cases, the cursor oscillates about the center of the screen, and in other cases, it drifts more slowly in one direction. The authors argue that these two behavioral regimes can be reliably induced by instructing human participants to either maintain the cursor in the center of the screen (position control objective), or keep the cursor still anywhere in the screen (velocity control objective) - as opposed to the usual 'instruction' to just not let the cursor leave the screen. A computational model based on optimal feedback control can reproduce the different behaviors under these two instructions.

Overall, this is a creative study that leverages experiments in humans and computational modeling to gain insight into the nature of individual differences in behavior across monkeys (and people). The authors convincingly demonstrate that they can infer the control objectives from participants who were instructed how to perform the task to emphasize either position or velocity control, based on the RMS cursor position and RMS cursor velocity. The authors show that, while other behavioral metrics do contain similar information about the control objective, RMS position and velocity are sufficient, and their approach classifies control objectives for simulated data with high accuracy (~95%).

The authors also convincingly show that the range of behaviors observed in the CST task cannot be explained as emerging from variations in effort cost, motor execution noise, or sensorimotor delays.

One significant issue, however relates to framing the range of possible control objectives as a simple dichotomy between 'position' and 'velocity' objectives. The authors do clearly state that this is a deliberate choice made in order to simplify their first attempts at solving this challenging problem. However, I do think that the paper at times gives a false impression that this dichotomous view of the control objectives was something that emerged from the data, rather than resulting from a choice to simplify the modeling/inference problem. For instance, line 115: "An optimal control model was used to simulate different control objectives, through which we identified two different control objectives in the experimental data of humans and monkeys."

In the no-instruction condition - which is the starting point and which the ultimate goal of the paper is to understand - there is a lot of variability in behavior across trials (even within an individual) and generally no clear correspondence to either the position or velocity objective. This variability is largely interpreted as the monkeys (and people) switching between control objectives on a trial-to-trial basis. If the behavior were truly a bimodal mixture of these two different behaviors, this might be a convincing interpretation. However, there are a lot of trials that fall in-between the patterns of behavior expected under the position and velocity control objectives. The authors do mention this issue in the discussion. However, it's not clearly examined whether these are simply fringe trials that are ambiguous (like some trials generated by the model are), or whether they reflect a substantial proportion of trials that require some other explanation (whether that is blended position/velocity control, or something else). The existence of these 'in-between' trials (which possibly amount to more than a third of all trials) makes the switching hypothesis a lot less plausible.

Overall, while I think the paper introduces a promising approach and overall helps to improve our understanding of the behavior in this task, I'm not fully convinced that the core issue of explaining the variability in behavior in the no-instruction condition (in monkeys especially) has been resolved. The main explanation put forward is that the monkeys are switching between control objectives on a trial-by-trial basis, but there is no real evidence in the data for this, and I don't think there is yet a good explanation of what is occurring in the 'in-between' trials that aren't explained well by velocity or position objectives.

Reviewer #3 (Public Review):

This study presents a valuable exploration of CD4+ T cell response in a fixed TCRβ chain FoxP3-GFP mouse model across stimuli and tissues through the analysis of their TCRα repertoires. This is an insightful paper for the community as it suggests several future directions of exploration.

The authors compare Treg and conventional CD4+ repertoires by looking at diversity measures and the relative overlap of shared clonotypes to characterize similarity across different tissues and antigen challenges. They find distinct yet convergent responses with occasional plasticity across subsets for some stimuli. The observed lack of a general behavior highlights the need for careful comparison of immune repertoires across cell subsets and tissues. Such comparisons are crucial in order to better understand the heterogeneity of the adaptive immune response. This mouse model demonstrates its utility for this task due to the reduced diversity of the TCRα repertoire and the ability to track a single chain.

The revised manuscript has significantly improved in terms of clarity of explanations and presentations of the results.

Reviewer #2 (Public Review):

This manuscript explores the mechanism underlying the accumulation of phytosphingosine (PHS) and its role in initiating vacuole fission. The study posits the involvement of membrane contact sites (MCSs) in two key stages of this process. Firstly, MCSs tethered by tricalbin between the endoplasmic reticulum (ER) and the plasma membrane (PM) or Golgi regulate the intracellular levels of PHS. Secondly, the amassed PHS triggers vacuole fission, most likely through the nuclear-vacuolar junction (NVJ). The authors propose that MCSs play a regulatory role in vacuole morphology via sphingolipid metabolism.

While some results in the manuscript are intriguing, certain broad conclusions occasionally surpass the available data. Despite the authors' efforts to enhance the manuscript, certain aspects remain unclear. It is still uncertain whether subtle changes in PHS levels could induce such effects on vacuolar fission. Additionally, it is regrettable that the lipid measurements are not comparable with previous studies by the authors. Future advancements in methods for determining intracellular lipid transport and levels are anticipated to shed light on the remaining uncertainties in this study.

Reviewer #2 (Public Review):

Summary:

The goal of this study was to examine the role of FNDC5 in the response of the murine skeleton to either lactation or a calcium-deficient diet. The authors find that female FNDC5 KO mice are somewhat protected from the bone loss and osteocyte lacunar enlargement caused by either lactation or a calcium-deficient diet. In contrast, male FNDC5 KO mice lose more bone and have a greater enlargement of osteocyte lacunae than their wild type controls. Based on these results, the authors conclude that in males irisin protects bone from calcium deficiency but that in females it promotes calcium removal from bone for lactation.

While some of the conclusions of this study are supported by the results, it is not clear that the modest effects of FNDC5 deletion have an impact on calcium homeostasis or milk production.

Specific comments.

(1) The authors sometimes refer to FNDC5 and other times to irisin when describing causes for a particular outcome. Because irisin was not measured in any of the experiments, the authors should not conclude that lack of irisin is responsible. Along these lines, is there any evidence that either lactation or a calcium-deficient diet increases production of irisin in mice?

(2) The results of the irisin-rescue experiment shown in figure 2G cannot be appropriately interpreted without normal diet controls. In addition, some evidence that the AAV8-irisin virus actually increased irisin levels in the mice would strengthen the conclusion.

(3) There is insufficient evidence to support the idea that the effect of FNDC5 on bone resorption and osteocytic osteolysis is important for the transfer of calcium from bone to milk. Previous studies by others have shown that bone resorption is not required to maintain milk or serum calcium when dietary calcium is sufficient but is critical if dietary calcium is low (Endo. 156:2762-73, 2015). To support the conclusions of the current study, it would be necessary to determine whether FNDC5 is required to maintain calcium levels when lactating mice lack sufficient dietary calcium.

(4) The amount of cortical bone loss due to lactation is very similar in both WT and FNDC5 KO mice. The results of the statistical analysis of the data presented in figure 1B are surprising given the very similar effect size of lactation. The key result from the 2-way ANOVA is whether there is an effect of genotype on the effect size of lactation (genotype-lactation interaction). The interaction terms were not provided. Similar concerns are noted for the results shown in figure 1G and H.

(5) It is not clear what justifies the term 'primed' or 'activated' for resorption. Is there evidence that a certain level of TRAP expression lowers the threshold for osteocytic osteolysis in response to a stimulus?

Reviewer #2 (Public Review):

In this manuscript, the authors propose a computational method based on deep convolutional neural networks (CNNs) to automatically detect cell divisions in two-dimensional fluorescence microscopy timelapse images. Three deep learning models are proposed to detect the timing of division, predict the division axis, and enhance cell boundary images to segment cells before and after division. Using this computational pipeline, the authors analyze the dynamics of cell divisions in the epithelium of the Drosophila pupal wing and find that a wound first induces a reduction in the frequency of division followed by a synchronised burst of cell divisions about 100 minutes after its induction.

Comments on revised version:

Regarding the Reviewer's 1 comment on the architecture details, I have now understood that the precise architecture (number/type of layers, activation functions, pooling operations, skip connections, upsampling choice...) might have remained relatively hidden to the authors themselves, as the U-net is built automatically by the fast.ai library from a given classical choice of encoder architecture (ResNet34 and ResNet101 here) to generate the decoder part and skip connections.

Regarding the Major point 1, I raised the question of the generalisation potential of the method. I do not think, for instance, that the optimal number of frames to use, nor the optimal choice of their time-shift with respect to the division time (t-n, t+m) (not systematically studied here) may be generic hyperparameters that can be directly transferred to another setting. This implies that the method proposed will necessarily require re-labeling, re-training and re-optimizing the hyperparameters which directly influence the network architecture for each new dataset imaged differently. This limits the generalisation of the method to other datasets, and this may be seen as in contrast to other tools developed in the field for other tasks such as cellpose for segmentation, which has proven a true potential for generalisation on various data modalities. I was hoping that the authors would try themselves testing the robustness of their method by re-imaging the same tissue with slightly different acquisition rate for instance, to give more weight to their work.

In this regard, and because the authors claimed to provide clear instructions on how to reuse their method or adapt it to a different context, I delved deeper into the code and, to my surprise, felt that we are far from the coding practice of what a well-documented and accessible tool should be.

To start with, one has to be relatively accustomed with Napari to understand how the plugin must be installed, as the only thing given is a pip install command (that could be typed in any terminal without installing the plugin for Napari, but has to be typed inside the Napari terminal, which is mentioned nowhere). Surprisingly, the plugin was not uploaded on Napari hub, nor on PyPI by the authors, so it is not searchable/findable directly, one has to go to the Github repository and install it manually. In that regard, no description was provided in the copy-pasted templated files associated to the napari hub, so exporting it to the hub would actually leave it undocumented.

Regarding now the python notebooks, one can fairly say that the "clear instructions" that were supposed to enlighten the code are really minimal. Only one notebook "trainingUNetCellDivision10.ipynb" has actually some comments, the other have (almost) none nor title to help the unskilled programmer delving into the script to guess what it should do. I doubt that a biologist who does not have a strong computational background will manage adapting the method to its own dataset (which seems to me unavoidable for the reasons mentioned above).

Finally regarding the data, none is shared publicly along with this manuscript/code, such that if one doesn't have a similar type of dataset - that must be first annotated in a similar manner - one cannot even test the networks/plugin for its own information. A common and necessary practice in the field - and possibly a longer lasting contribution of this work - could have been to provide the complete and annotated dataset that was used to train and test the artificial neural network. The basic reason is that a more performant, or more generalisable deep-learning model may be developed very soon after this one and for its performance to be fairly compared, it requires to be compared on the same dataset. Benchmarking and comparison of methods performance is at the core of computer vision and deep-learning.

Reviewer #2 (Public Review):

Summary

In this experiment, Voltage Sensitive Dye Imaging (VSDI) was used to measure neural activity in macaque primary visual cortex in monkeys trained to detect an oriented grating target that was presented either alone or against an oriented mask. Monkeys' ability to detect the target (indicated by a saccade to its location) was impaired by the mask, with the greatest impairment observed when the mask was matched in orientation to the target, as is also the case in human observers. VSDI signals were examined to test the hypothesis that the target-evoked response would be maximally suppressed by the mask when it matched the orientation of the target. In each recording session, fixation trials were used to map out the spatial response profile and orientation domains that would then be used to decode the responses on detection trials. VSDI signals were analyzed at two different scales: a coarse scale of the retinotopic response to the target and a finer scale of orientation domains within the stimulus-evoked response. Responses were recorded in three conditions: target alone, mask alone, and target presented with mask. Analyses were focused on the target evoked response in the presence of the mask, defined to be the difference in response evoked by the mask with target (target present) versus the mask alone (target absent). These were computed across five 50 msec bins (total, 250 msec, which was the duration of the mask (target present trials, 50% of trials) / mask + target (target present trials, 50% of trials). Analyses revealed that in an initial (transient) phase the target evoked response increased with similarity between target and mask orientation. As the authors note, this is surprising given that this was the condition where the mask maximally impaired detection of the target in behavior. Target evoked responses in a later ('sustained') phase fell off with orientation similarity, consistent with the behavioral effect. When analyzed at the coarser scale the target evoked response, integrated over the full 250 msec period showed a very modest dependence on mask orientation. The same pattern held when the data were analyzed on the finer orientation domain scale, with the effect of the mask in the transient phase running counter to the perceptual effect of the mask and the sustained response correlating the perceptual effect. The effect of the mask was more pronounced when analyzed at the scale.

Strengths

The work is on the whole very strong. The experiments are thoughtfully designed, the data collection methods are good, and the results are interesting. The separate analyses of data at a coarse scale that aggregates across orientation domains and a more local scale of orientation domains is a strength and it is reassuring that the effects at the more localized scale are more clearly related to behavior, as one would hope and expect. The results are strengthened by modeling work shown in Figure 8, which provides a sensible account of the population dynamics. The analyses of the relationship between VSDI data and behavior are well thought out and the apparent paradox of the anti-correlation between VSDI and behavior in the initial period of response, followed by a positive correlation in the sustained response period is intriguing.

Reviewer #2 (Public Review):

Summary:

This paper tackles the problem of understanding when the dynamics of neural population activity do and do not align with some target output, such as an arm movement. The authors develop a theoretical framework based on RNNs showing that an alignment of neural dynamics to output can be simply controlled by the magnitude of the read-out weight vector while the RNN is being trained. Small magnitude vectors result in aligned dynamics, where low-dimensional neural activity recapitulates the target; large magnitude vectors result in "oblique" dynamics, where encoding is spread across many dimensions. The paper further explores how the aligned and oblique regimes differ, in particular, that the oblique regime allows degenerate solutions for the same target output.

Strengths:

- A really interesting new idea that different dynamics of neural circuits can arise simply from the initial magnitude of the output weight vector: once written out (Eq 3) it becomes obvious, which I take as the mark of a genuinely insightful idea.

- The offered framework potentially unifies a collection of separate experimental results and ideas, largely from studies of the motor cortex in primates: the idea that much of the ongoing dynamics do not encode movement parameters; the existence of the "null space" of preparatory activity; and that ongoing dynamics of the motor cortex can rotate in the same direction even when the arm movement is rotating in opposite directions.

- The main text is well written, with a wide-ranging set of key results synthesised and illustrated well and concisely.

- The study shows that the occurrence of the aligned and oblique regimes generalises across a range of simulated behavioural tasks.

- A deep analytical investigation of when the regimes occur and how they evolve over training.

- The study shows where the oblique regime may be advantageous: allows multiple solutions to the same problem; and differs in sensitivity to perturbation and noise.

- An insightful corollary result that noise in training is needed to obtain the oblique regime.

- Tests whether the aligned and oblique regimes can be seen in neural recordings from primate cortex in a range of motor control tasks.

Weaknesses:

- The magnitude of the output weights is initially discussed as being fixed, and as far as I can tell all analytical results (sections 4.6-4.9) also assume this. But in all trained models that make up the bulk of the results (Figures 3-6) all three weight vectors/matrices (input, recurrent, and output) are trained by gradient descent. It would be good to see an explanation or results offered in the main text as to why the training always ends up in the same mapping (small->aligned; large->oblique) when it could, for example, optimise the output weights instead, which is the usual target (e.g. Sussillo & Abbott 2009 Neuron).

- It is unclear what it means for neural activity to be "aligned" for target outputs that are not continuous time-series, such as the 1D or 2D oscillations used to illustrate most points here. Two of the modelled tasks have binary outputs; one has a 3-element binary vector.

- It is unclear what criteria are used to assign the analysed neural data to the oblique or aligned regimes of dynamics.

Reviewer #2 (Public Review):

Summary:

This paper poses the interesting and important question of whether plasticity rules are mathematically degenerate, which would mean that multiple plasticity rules can give rise to the same changes in neural activity. They claim that the answer is "yes," which would have major implications for many researchers studying the biological mechanisms of learning and memory. Unfortunately, I found the evidence for the claim to be weak and confusing, and I don't think that readers can currently infer much beyond the results of the specific numerical experiments reported in the paper.

Strengths:

I love the premise of the paper. I agree with the authors that neuroscientists often under-emphasize the range of possible models that are consistent with empirical findings and/or theoretical demands. I like their proposal that the field is shifting its thinking towards characterizing the space of plasticity rules. I do not doubt the accuracy of most reported numerical results, just their meaning and interpretation. I therefore think that readers can safely use most of the the numerical results to revise their thinking about plasticity mechanisms and draw their own conclusions.

Weaknesses:

Unfortunately, I found many aspects of the paper to be problematic. As a result, I did not find the overarching conclusions drawn by the authors to be convincing.

First, the authors aren't consistent in how they mathematically define and conceptually interpret the "degeneracy" of plasticity mechanisms. In practice, they say that two plasticity mechanisms are "degenerate" if they can't build a neural network to distinguish between a set of neural trajectories generated by them. Their interpretation extrapolates far beyond this, and they seem to conclude that such plasticity rules are in principle indistinguishable. I think that this conclusion is wrong. Plasticity rules are simply mathematical functions that specify how the magnitude of a synaptic weight changes due to other factors, here presynaptic activity (x), postsynaptic activity (y), and the current value of the weight (w). Centuries-old mathematics proves that very broad classes of functions can be parameterized in a variety of non-degenerate ways (e.g., by their Taylor series or Fourier series). It seems unlikely to me that biology has developed plasticity rules that fall outside this broad class. Moreover, the paper's numerical results are all for Oja's plasticity rule, which is a third-order polynomial function of x, y, and w. That polynomial functions cannot be represented by any other Taylor series is a textbook result from calculus. One might wonder if this unique parameterization is somehow lost when many synapses combine to produce neural activity, but the neuron model used in this work is linear, so the function that specifies how the postsynaptic activity changes is simply a fourth-order polynomial in 3N+1 variables (i.e., the presynaptic activities of N neurons prior to the plasticity event, the weights of N synapses prior to the plasticity event, the postsynaptic activity prior to the plasticity event, the presynaptic activities of N neurons after the plasticity event). The same fundamental results from calculus apply to the weight trajectories and the activity trajectories, and a non-degenerate plasticity rule could in principle be inferred from either. What the authors instead show is that their simulated datasets, chosen parameterizations for the plasticity rule, and fitting procedures fail to reveal a non-degenerate representation of the plasticity rule. To what extent this failure is due to the nature of the simulated datasets (e.g., their limited size), the chosen parameterization (e.g., an overparameterized multi-layer perceptron), and their fitting procedure (e.g., their generative adversarial network framework) is unclear. I suspect that all three aspects contribute.

Second, I am concerned by the authors' decision to use a generative adversarial network (GAN) to fit the plasticity rule. Practically speaking, the quality of the fits shown in the figures seems unimpressive to me, and I am left wondering if the authors could have gotten better fits with other fitting routines. For example, other authors fit plasticity rules through gradient descent learning, and these authors claimed to accurately recover Oja's rule and other plasticity rules (Mehta et al., "Model-based inference of synaptic plasticity rules," bioRxiv, 2023). Whether this difference is one of author interpretation or method accuracy is not currently clear. The authors do include some panels in Figure 3A and Figure 8 that explore more standard gradient descent learning, but their networks don't seem to be well-trained. Theoretically speaking, Eqn. (7) in Section 4.4 indicates that the authors only try to match p(\vec y) between the data and generator network, rather than p(\vec x, \vec y). If this equation is an accurate representation of the authors' method, then the claimed "degeneracy" of the learning rule may simply mean that many different joint distributions for \vec x and \vec y can produce the same marginal distribution for \vec y. This is true, but then the "degeneracy" reported in the paper is due to hidden presynaptic variables. I don't think that most readers would expect that learning rules could be inferred by measuring postsynaptic activity alone.

Third, it's important for readers to note that the 2-dimensional dynamical systems representations shown in figures like Figures 2E are incomplete. Learning rules are N-dimensional nonlinear dynamical systems. The learning rule of any individual synapse depends only on the current presynaptic activity, the current postsynaptic activity, and the current weight magnitude, and slices through this function are shown in figures like Figure 2D. However, the postsynaptic activity is itself a dynamical variable that depends on all N synaptic weights. It's therefore unclear how one is supposed to interpret figures like Figure 2E, because the change in y is not a function of y and any single w. My best guess is that figures like Figure 2E are generated for the case of a single presynaptic neuron, but the degeneracies observed in this reduced system need not match those found when fitting the larger network.

Reviewer #2 (Public Review):

Knudstrup et al set out to probe prediction errors in the mouse visual cortex. They use a variant of an oddball paradigm and test how repeated passive exposure to a specific sequence of visual stimuli affects oddball responses in layer 2/3 neurons. Unfortunately, there are problems with the experimental design which make it difficult to interpret the results in light of the question the authors want to address. The conceptual framing, choice of block design structure, and not tracking the same cells over days, are just some of the reasons that make this work difficult to interpret. Specific comments are as follows:

(1) There appears to be some confusion regarding the conceptual framing of predictive coding. Assuming the mouse learns to expect the sequence ABCD, then ABBD does not probe just for negative prediction errors, and ACBD is not just for positive prediction errors. With ABBD, there is a combination of a negative prediction error for the missing C in the 3rd position, and a positive prediction error for B in the 3rd. Likewise, with ACBD, there is a negative prediction error for the missing B at 2nd and missing C at 3rd, and a positive prediction error for the C in 2nd and B in 3rd. Thus, the authors' experimental design does not have the power to isolate either negative or positive prediction errors. Moreover, looking at the raw data in Figure 2C, this does not look like an "omission" response to C, but more like a stronger response to a longer B. The pitch of the paper as investigating prediction error responses is probably not warranted - we see no way to align the authors' results with this interpretation.

(2) Related to the interpretation of the findings, just because something can be described as a prediction error does not mean it is computed in (or even is relevant to) the visual cortex. To the best of our knowledge, it is still unclear where in the visual stream the responses described here are computed. It is possible that this type of computation happens before the signals reach the visual cortex, similar to mechanisms predicting moving stimuli already in the retina (https://pubmed.ncbi.nlm.nih.gov/10192333/). This would also be consistent with the authors' finding (in previous work) that single-cell recordings in V1 exhibit weaker sequence violation responses than the author's earlier work using LFP recordings.

(3) Recording from the same neurons over the course of this paradigm is well within the technical standards of the field, and there is no reason not to do this. Given that the authors chose to record from different neurons, it is difficult to distinguish representational drift from drift in the population of neurons recorded.

(4) The block paradigm to test for prediction errors appears ill-chosen. Why not interleave oddball stimuli randomly in a sequence of normal stimuli? The concern is related to the question of how many repetitions it takes to learn a sequence. Can the mice not learn ACBD over 100x repetitions? The authors should definitely look at early vs. late responses in the oddball block. Also, the first few presentations after the block transition might be potentially interesting. The authors' analysis in the paper already strongly suggests that the mice learn rather rapidly. The authors conclude: "we expected ABCD would be more-or-less indistinguishable from ABBD and ACBD since A occurs first in each sequence and always preceded by a long (800 ms) gray period. This was not the case. Most often, the decoder correctly identified which sequence stimulus A came from." This would suggest that whatever learning/drift could happen within one block did indeed happen and responses to different sequences are harder to interpret.

(5) Throughout the manuscript, many of the claims are not statistically tested, and where they are the tests do not appear to be hierarchical (https://pubmed.ncbi.nlm.nih.gov/24671065/), even though the data are likely nested.

(6) The manuscript would greatly benefit from thorough proofreading (not just in regard to figure references).

(7) With a sequence of stimuli that are 250ms in length each, the use of GCaMP6s appears like a very poor choice.

(8) The data shown are unnecessarily selective. E.g. it would probably be interesting to see how the average population response evolves with days. The relevant question for most prediction error interpretations would be whether there are subpopulations of neurons that selectively respond to any of the oddballs. E.g. while the authors state they "did" not identify a separate population of omission-responsive neurons, they provide no evidence for this. However, it is unclear whether the block structure of the experiments allows the authors to analyze this.

Reviewer #2 (Public Review):

Summary:

In their manuscript, Ardaya et al have addressed the impact of ischemia-induced gliogenesis from the adult SVZ and their effect on the remodeling of the extracellular matrix (ECM) in the glial scar. They use Thbs4, a marker previously identified to be expressed in astrocytes of the SVZ, to understand its role in ischemia-induced gliogenesis. First, the authors show that Thbs4 is expressed in the SVZ and that its expression levels increase upon ischemia. Next, they claim that ischemia induces the generation of newborn astrocyte from SVZ neural stem cells (NSCs), which migrate toward the ischemic regions to accumulate at the glial scar. Thbs4-expressing astrocytes are recruited to the lesion by Hyaluronan where they modulate ECM homeostasis.

Strengths:

The findings of these studies are in principle interesting and the experiments are in principle good.

Weaknesses:

The manuscript suffers from an evident lack of clarity and precision in regard to their findings and their interpretation.

Reviewer #3 (Public Review):

The authors collected BALF samples from lung cancer patients newly diagnosed with PCP, DI-ILD or ICI-ILD. CyTOF was performed on these samples, using two different panels (T-cell and B-cell/myeloid cell panels). Results were collected, cleaned-up, manually gated and pre-processed prior to visualisation with manifold learning approaches t-SNE (in the form of viSNE) or UMAP, and analysed by CITRUS (hierarchical clustering followed by feature selection and regression) for population identification - all using Cytobank implementation - in an attempt to identify possible biomarkers for these disease states. By comparing cell abundances from CITRUS results and qualitative inspection of a small number of marker expressions, the authors claimed to have identified an expansion of CD16+ T-cell population in PCP cases and an increase in CD57+ CD8+ T-cells, FCRL5+ B-cells and CCR2+ CCR5+ CD14+ monocytes in ICI-ILD cases.

By the authors' own admission, there is an absence of healthy donor samples and, perhaps as a result of retrospective experimental design and practical clinical reasons, also an absence of pre-treatment samples. The entire analysis effectively compares three yet-established disease states with no common baseline - what really constitutes a "biomarker" in such cases? These are very limited comparisons among three, and only these three, states.

By including a new scRNA-Seq analysis using a publicly available dataset, the authors addressed this fundamental problem. Though a more thorough and numerical analysis would be appreciated for a deeper and more impactful analysis, this is adequate for the intended objectives of the study.

Reviewer #2 (Public Review):

Summary:

Peptidoglycan remodeling, particularly that carried out by enzymes known as amidases, is essential for the later stages of cell division including cell separation. In E. coli, amidases are generally activated by the periplasmic proteins EnvC (AmiA and AmiB) and NlpD (AmiC). The ABC family member, FtsEX, in turn, has been implicated as a modulator of amidase activity through interactions with EnvC. Specifically how FtsEX regulates EnvC activity in the context of cell division remains unclear.

Strengths:

Li et al. make two primary contributions to the study of FtsEX. The first, the finding that ATP binding stabilizes FtsEX in vitro, enables the second, structural resolution of full-length FtsEX both alone (Figure 2) and in combination with EnvC (Figure 3). Leveraging these findings, the authors demonstrate that EnvC binding stimulates FtsEX-mediated ATP hydrolysis approximately two-fold. The authors present structural data suggesting EnvC binding leads to a conformational change in the complex. Biochemical reconstitution experiments (Figure 5) provide compelling support for this idea.

Weaknesses:<br /> The potential impact of the study is curtailed by the lack of experiments testing the biochemical or physiological relevance of the model which is derived almost entirely from structural data.

Altogether the data support a model in which interaction with EnvC, results in a conformational change stimulating ATP hydrolysis by FtsEX and EnvC-mediated activation of the amidases, AmiA and AmiB. However, the study is limited in both approach and scope. The importance of interactions revealed in the structures to the function of FtsEX and its role in EnvC activation are not tested. Adding biochemical and/or in vivo experiments to fill in this gap would allow the authors to test the veracity of the model and increase the appeal of the study beyond the small number of researchers specifically interested in FtsEX.

Reviewer #2 (Public Review):

Summary:

Li et al. investigated the mechanism of action of an important herbicide, caprylic acid (CAP). The authors used untargeted metabolomics to find out differently expressed metabolites (DEM). It led to the identification of metabolites involved in amino acid metabolism, carbon fixation, carbon, glyoxylate, and dicarboxylate metabolism. Using previously published proteomics data and the newly conducted metabolomics data, the authors identified a serine hydroxymethyl transferase in Conyza canadensis (CcSHMT1) to be a likely candidate for CAP inhibition.

The authors conducted a series of in vitro and in vivo tests to elucidate the effect of CAP on SHMT1 inhibition. Plants overexpressing SHMT1 were used to analyze the effect of SHMT1 expression, activity, and inhibition, among others. Purified SHMT1 was used to elucidate enzyme kinetics in the presence or absence of inhibitors. CRISPR-based editing was a powerful method of investigating the effect of SHMT1 mutants on CAP application and complements the overexpression and in vitro studies. Finally, computational docking of CAP on SHMT1 was conducted to identify key interacting residues. The results are overall consistent with one another and present a unified framework for CAP activity as an herbicide. Unexpected variations in SHMT1 expression and activity levels upon CAP treatment suggest complex biological compensatory mechanisms in response to SHMT1 deficiency. Further studies are needed to understand the effect of these perturbations that will be required to successfully develop and deploy CAP-resistant crops for widespread use in agriculture. In conclusion, the authors did a commendable job of elucidating SHMT1 as a biologically relevant target for CAP.

Strengths:

- Combines computational docking, enzyme kinetics using purified proteins, and several different model plant species and two different methods of testing (overexpression and base editing) to establish plant response and survival.

- Sound experimental designs and the presence of controls validate the results and provide additional confidence in the authors' conclusions.

Weaknesses:

- Relied too heavily on the study of plants overexpressing SHMT1, which do not have native gene regulation, and this might limit the generalizability of their conclusions.

-The authors did not leverage computational docking analysis to validate or seek corroboration of the performance of plant alleles obtained from the base editing experiments.

Reviewer #2 (Public Review):

Summary:

This article develops CRISPR-based gene drives designed to spread in viral populations. By targeting the gene drives to neutral loci, or at least loci where the presence of a gene drive is tolerated. This type of gene drive is designed to work by recognising the cognate target sequence of the CRISPR-Cas nuclease on a wild type virus genome, cutting it and then invoking the homology-directed DNA repair machinery to copy itself into the repaired genome, thereby increasing its frequency in the population. Two types of CRISPR nuclease are tested in this setup: Cas9 and Cas12. There have been a large number of studies describing Cas9- based gene drives, but very few using other Cas nucleases, such as Cas12 reported here. Other nucleases have different targeting ranges and different features of cleavage that may make them more attractive for several reasons, including propensity to generate mutations that may be undesirable for certain applications. For this reason the work reported here is an important step.

There are advantages to this system, in terms of its throughput and speed of testing, which could generate insights into the dynamics of gene drive mutation and repair events. However, its suitability as a proxy for probability of selection of resistant mutations in gene drives designed to work in higher organisms is overstated since this is in large part determined by the force of selection acting on those mutations in the genomes of those target organisms.

Strengths:

Overall I found the experiments to be well planned and executed, with sound rationale and logic. The paper is well structured and well written. The evidence for CRISP-HDR in placing transgenes in specific parts of the viral genome is solid. The experiments to measure frequency of gene drive genotypes invading in the context of convertible WT target sites, and non-convertible target sites, are largely well designed. The authors go further and show in subsequent experiments that there are converted genotypes that contain combinations of linked alleles that should only segregate together in the event of conversion to the gene drive allele (assuming this signal is not conflated by two separate genotypes covering each other). The description of the different types and rates of accumulation of mutations according to Cas architecture is valuable.

Figures are very clear and informative (but could be improved with clearer labelling of genotypes).

The paper is well referenced and captures the literature well.

Weaknesses:

It is not immediately clear to me how you can determine, in your experimental setup, that the three alleles (gD+, GFP+ and gE-) are on the same genome/haplotype rather than split across two or more genomes that infect a cell. Presumably this is because you make a clonal population that started from a dilution that ensure there was at most one genome to start the infection?

Some more discussion of the results, and some surprising observations therein, is warranted. For example: in the invasion experiments, which are generally well described, it is curious that when nearly all the WT target sites are depleted there should still be a further disappearance of the original gene drive allele to the expense of the new converted drive alelle - once WT target sites are exhausted (e.g. V10 in Fig 3B), there are no more opportunities to convert, one would expect ration of green:yellow to stay the same (assuming equal fitness between genotypes)? In fact, the yellow genotype, having both gene drive and Us8 deletion, is expected to be less fit, is it not? So this result is surprising, yet not discussed.

It is not clear why general levels of mutation increase across the whole amplicon, regardless of proximity to target site? e.g by Passage 7 in the Cas12 lines , Fig3D and 3E). Not discussed. This may be due to the fact that their ratio to WT target sequences is inflated due to the presence of the non-mapped sequences but again, the origin of the not mapped sequences is itself not explained.

Gene drives could theoretically increase their frequency by 'destroying' or disabling other genotypes, for example if Cas-induced cleavage removed the cut genome, rather than converting it. Presumably this is what motivated the authors to try and get a concrete signal of converted genotypes rather than just increase in frequency of the original gene drive genotype. This possibility is never discussed.

Line 140 re: the use of refractory target sites to show that gene drive genomes do not increase in frequency when there is no opportunity for genomes to convert; I like this control but it should be noted that there is the possibility, albeit unlikely, that general UL-3/4 deletions compete better than WT generally, and that has not been tested here.

In some places, the description of genotypes rather than arbitrary, non-informative strain names would really help.

It is not obvious to me either where the 'unmapped reads' come from - it is stated that "gene drive viruses took over and interefrered with PCR, causing many unmapped NGS reads". I am not sure what is meant here, and besides, this doesn't explain why reads would be unmapped. If the gene drive allele were too large to be amplified then it should not contribute to sequences in the amplicon.

Re: HSV1 viruses being multiploid - for people, like me, whose virology is not very good, some more explanation would be useful - are you proposing that this happens on 'loose' viral genomes circulating within nucleus or cytoplasm of host cell, or within virions? Can there be more than one genome per virion?

The suggestion that slow reproduction in insects (where many types of gene drive are proposed for control of pest populations) is a barrier to testing at scale is only true to an extent - rue to an extent but there are screens for resistance that are higher throughput and do not need selection experiments over time, but rather in a single generation (e.g KaramiNejadRanjbar et al PNAS 2018; Hammond et al PLoS Genetics 2021) and, for the reasons stated above, selection on an insect genome cannot be replicated in this HSV system.

In the intro, much is made of utility in viral engineering for therapeutic approaches but there is never any detail of this in the discussion other than vague contemplations on utility in 'studying horizontal gene transfer' and 'prevention and treatment of diseases'.<br /> I have other suggestions for improving clarity of text around experimental design but I have confined these to 'Recommendations for Authors'

Reviewer #3 (Public Review):

Summary:

Khaitova et al. report the formation of micronuclei during Arabidopsis meiosis under elevated temperature. Micronuclei form when chromosomes are not correctly collected to the cellular poles in dividing cells. This happens when whole chromosomes or fragments are not properly attached to the kinetochore microtubules. The incidence of micronuclei formation is shown to increase at elevated temperature in wild type and more so in the weak centromere histone mutant cenH3-4. The number micronuclei formation at high temperature in the recombination mutant spo11 is like that in wild type, indicating that the increased sensitivity of cenh3-4 is not related to the putative role of cenh3 in recombination. The abundance of CENH3-GFP at the centromere declines with higher temperature and correlates with a decline in spindle assembly checkpoint factor BMF1-GFP at the centromeres. The reduction in CENH3-GFP under heat is observed in meiocytes whereas CENH3-GFP abundance increases in the tapetum, suggesting there is a differential regulation of centromere loading in these two cell types. These observations are in line with previous reports on haploidization mutants and their hypersensitivity to heat stress.

Strength:

The paper shows that the kinetochore function during meiosis is sensitive to high temperature and this leads to inequivalent chromosome segregation during meiosis and reduced fertility.

Weakness:

The increased sensitivity to high temperature stress of the hypomorphic mutant cenh3-4 mutant not only reduces fertility but also growth, which is not accompanied with the formation of micronuclei as in meiosis. The impact on mitosis therefore seems to be different from that in meiosis.

Reviewer #2 (Public Review):

The authors present an image-analysis pipeline for mother-machine data, i.e., for time-lapses of single bacterial cells growing for many generations in one-dimensional microfluidic channels. The pipeline is available as a plugin of the python-based image-analysis platform Napari. The tool comes with two different previously published methods to segment cells (classical image transformation and thresholding as well as UNet-based analysis), which compare qualitatively and quantitatively well with the results of widely accessible tools developed by others (BACNET, DelTA, Omnipose). The tool comes with a graphical user interface and example scripts, which should make it valuable for other mother-machine users, even if this has not been demonstrated yet.

The authors also add a practical overview of how to prepare and conduct mother-machine experiments, citing their previous work, referring to detailed instructions on their github page, and giving more advice on how to load cells using centrifugation.

Finally, the authors emphasize that machine-learning methods for image segmentation reproduce average quantities of training datasets, such as the length at birth or division. Therefore, differences in training can propagate to differences in measured average quantities. This result is not surprising but good to remember before interpreting absolute measurements of cell shape.

Reviewer #2 (Public Review):

Summary:<br /> The manuscript titled 'Proteolytic cleavage and inactivation of the TRMT1 tRNA modification enzyme by SARS-CoV-2 main protease' from K. Zhang et al., demonstrates that several RNA modifications are downregulated during SARS-CoV-2 infection including the widespread m2,2G methylation, which potentially contributes to changes in host translation. To understand the molecular basis behind this global hypomodification of RNA during infection, the authors focused on the human methyltransferase TRMT1 that catalyzes the m2,2G modification. They reveal that TRMT1 not only interacts with the main SARS-CoV-2 protease (Nsp5) in human cells but is also cleaved by Nsp5. To establish if TRMT1 cleavage by Nsp5 contributes to the reduction in m2,2G levels, the authors show compelling evidence that the TRMT1 fragments are incapable of methylating the RNA substrates due to loss of RNA binding by the catalytic domain. They further determine that expression of full-length TRMT1 is required for optimal SARS-CoV-2 replication in 293T cells. Nevertheless, the cleavage of TRMT1 was dispensable for SARS-CoV-2 replication hinting at the possibility that TRMT1 could be an off-target or fortuitous substrate of Nsp5. Overall, this study will be of interest to virologist and biologists studying the role of RNA modification and RNA modifying enzyme in viral infection.

Strengths:<br /> • The authors use state-of-the-art mass spectrometry approach to quantify RNA modifications in human cells infected with SARS-CoV-2.<br /> • The authors go to great lengths to demonstrate that SARS-CoV-2 main protease, Nsp5, interacts and cleaves TRMT1 in cells and perform important controls when needed. They use a series of overexpression with strategically placed tags on both TRMT1 and Nsp5 to strengthen their observations.<br /> • The use of an inactive Nsp5 mutant (C145A) strongly supports the claim of the authors that Nsp5 is solely responsible for TRMT1 cleavage in cells.<br /> • Although the direct cleavage was not experimentally determined, the authors convincingly show that TRMT1 Q530N is not cleaved by Nsp5 suggesting that the predicted cleavage site at this position is most likely the bona fide region processed by Nsp5 in cells.<br /> • To understand the impact of TRMT1 cleavage on its RNA methylation activity, the authors rigorously test four protein constructs for their capacity not only to bind RNA but also to introduce the m2,2G modification. They demonstrate that the fragments resulting from TRMT1 cleavage are inactive and cannot methylate RNA. They further establish that the C-terminal region of TRMT1 (containing a zinc-finger domain) is the main binding site for RNA.<br /> • While 293T cells are unlikely an ideal model system to study SARS-CoV-2 infection, the authors use two cell lines and well-designed rescue experiments to uncover that TRMT1 is required for optimal SARS-CoV-2 replication.

Weaknesses:<br /> • Immunoblotting is extensively used to probe for TRMT1 degradation by Nsp5 in this study. Regretfully, the polyclonal antibody used by the authors shows strong non-specific binding to other epitopes. This complicates the data interpretation and quantification since the cleaved TRMT1 band migrates very closely to a main non-specific band detected by the antibody (for instance Fig 3A). While this reviewer is concerned about the cross-contamination during quantification of the N-TRMT1, the loss of this faint cleaved band with the TRMT1 Q530N mutant is reassuring. Nevertheless, the poor behavior of this antibody for TRMT1 detection was already reported and the authors should have taken better precautions or designed a different strategy to circumvent the limitation of this antibody by relying on additional tags.<br /> • While 293T cells are convenient to use, it is not a well-suited model system to study SARS-CoV-2 infection and replication. Therefore, some of the conclusions from this study might not apply to better suited cell systems such as Vero E6 cells or might not be observed in patient infected cells.<br /> • The reduction of bulk TRMT1 levels is minor during infection of MRC5 cells with SARS-CoV-2 (Fig 1). This does not seem to agree with the more dramatic reduction in m2,2G modification levels. Cellular Localization experiments of TRMT1 would help clarify this. While TRMT1 is found in the cytoplasm and nucleus, it is possible that TRMT1 is more dramatically degraded in the cytoplasm due to easier access by Nsp5.<br /> • In fig 6, the authors show that TRMT1 is required for optimal SARS-CoV-2 replication. This can be rescued by expressing TRMT1 (fig 7). Nevertheless, it is unknown if the methylation activity of TRMT1 is required. The authors could have expressed an inactive TRMT1 mutant (by disrupting the SAM binding site) to establish if the RNA modification by TRMT1 is important for SARS-CoV-2 replication or if it is the protein backbone that might contribute to other processes.<br /> • Fig 7, the authors used the Q530N variant to rescue SARS-CoV-2 replication in TRMT1 KO cells. This is an important experiment and unexpectedly reveals that TRMT1 cleavage by Nsp5 is not required for viral replication. To strengthen the claim of the authors that TRMT1 is required to promote viral replication and that its cleavage inhibits RNA methylation, the authors could express the TRMT1 N-terminal construct in the TRMT1 KO cells to assess if viral replication is restored or not to similar levels as WT TRMT1. This will further validate the potential biological importance of TRMT1 cleavage by Nsp5.<br /> • Fig 7, shows that the TRMT1 Q530N variant rescues SARS-CoV-2 replication to greater levels then WT TRMT1. The authors should discuss this in greater detail and its possible implications with their proposed statement. For instance, are m2,2G levels higher in Q530N compared to WT? Does Q530N co-elute with Nsp5 or is the interaction disrupted in cells?

Reviewer #2 (Public Review):

Summary:

Cells cultured in high glucose tend to repress mitochondrial biogenesis and activity, a prevailing phenotype type called Crabree effect that observed in different cell types and cancer. Many signaling pathways have been put forward to explain this effect. Vengayil et al proposed a new mechanism involved in Ubp3/Ubp10 and phosphate that controls the glucose repression of mitochondria. The central hypothesis is that ∆ubp3 shift the glycolysis to trehalose synthesis, therefore lead to the increase of Pi availability in the cytosol, then mitochondrial received more Pi and therefore the glucose repression is reduced.

Strengths:

The strength is that the authors used an array of different assays to test their hypothesis. Most assays were well designed and controlled.

Weaknesses:

I think the main conclusions are not strongly supported by the current dataset. Here are my comments on authors' response and model.

(1) The authors addressed some of my concerns related to ∆ubp3. But based on the results they observed and discussed, the ∆ubp3 redirect some glycolytic flux to gluconeogenesis while the 0.1% glucose in WT does not. Similarly, the shift of glycolysis to trehalose synthesis is also not relevant to the WT cells cultured in low glucose situation. This should be discussed in the manuscript to make sure readers are not misled to think ∆ubp3 mimic low glucose. It is likely that ∆ubp3 induce proteostasis stress, which is known to activate respiration and trehalose synthesis.

(2) Pi flux: it is known that vacuole can compensate the reduction of Pi in the cytosol. The paper they cited in the response, especially the Van Heerden et al., 2014 showed that the pulse addition of glucose caused transient Pi reduction and then it came back to normal level after 10min or so. If the authors mean the transient change of glycolysis and respiration, they should point that out clearly in the abstract and introduction. If the authors are trying to put out a general model, then the model must be reconsidered.

The cytosol has ~50mM Pi (van Eunen et al., 2010 FEBSJ), while only 1-2mM of glycolysis metabolites, not sure why partial reduction of several glycolysis enzymes will cause significant changes in cytosolic Pi level and make Pi the limiting factor for mitochondrial respiration. In response to this comment, the authors explained the metabolic flux that the rapid, continuous glycolysis will drain the Pi pool even each glycolytic metabolite is only 1-2mM. However, the metabolic flux both consume and release Pi, that's why there is such measurement of overall free Pi concentration amid the active metabolism. One possibility is that the observed cytosolic Pi level changes was caused by the measurement fluctuation, as they showed in "Reviewer response image 3".

Importantly, the authors measured Pi inside mito for ethanol and glucose, but not the cytosolic Pi, which is the key hypothesis in their model. The model here is that the glycolysis competes with mito for free cytosolic Pi, so it needs to inhibit glycolysis to free up cytosolic Pi for mitochondrial import to increase respiration. I don't see measurement of cytosolic Pi upon different conditions, only the total Pi or mito Pi. The fact is that in Fig.3C they saw WT+Pi in the medium increase total free Pi more than the ∆ubc3, while WT decrease mito Pi compared to WT control and ∆ubc3 and therefore decrease basal OCR upon Pi supplement. A simple math of Pitotal = Pi cyto + Pi mito tells us that if WT has more Pitotal (Fig.3C) but less Pi mito (fig.5 supp 1C), then it has higher Pi cyto. This is contradictory to what the authors tried to rationalize. Furthermore, as I pointed out previously, the isolated mitochondria can import more Pi when supplemented, so if there is indeed higher Picyto, then the mito in WT should import more Pi. So, to address these contradictory points, the authors must measure Pi in the cytosol, which is a critical experiment not done for their model. For example, they hypothesized that adding 2-DG, or ∆ubp3, suppress glycolysis and thus increase the supply of cytosolic Pi for mito to import, but no cytosolic Pi was measured (need absolute value, not the relative fold changes). It is also important to specific how the experiments are done, was the measurement done shortly after adding 2-DG. Given that the cells response to glucose changes/pulses differently in transient vs stable state, the authors are encouraged to specify that.

The most likely model to me is that, which is also the consensus in the field, is that no matter 2-DG or ∆ubp3, the cells re-wiring metabolism in both cytosol and mitochondria, and it is the total network shift that cause the mitochondrial respiration increase, which requires the increase of mito import of Pi, ADP, O2, and substrates, but not caused/controlled by the Pi that singled out by the authors in their model.

(3) The explanation that cytosolic pH reduction upon glucose depletion/2DG is a mistake. There are a lot of data in the literature showing the opposite. If the authors do think this is true, then need to show the data. Again, it is important to distinguish transient vs stable state for pH changes.

Reviewer #2 (Public Review):

The question the authors pose is very simple, and yet very important. Does the fact that many genes compete for Pol II to be transcribed explain why so many trans-eQTL contribute to the heritability of complex traits? That is, if a gene uses up a proportion of Pol II, does that in turn affect the transcriptional output of other genes relevant or even irrelevant for the trait in a way that their effect will be captured in a genome-wide association study? If yes, then the large number of genetic effects associated with variation in complex traits can be explained but such trans-propagating effects on transcriptional output of many genes.

This is a very timely question given that we still don't understand how, mechanistically, so many genes can be involved in complex traits variation. Their approach to this question is very simple and it is framed in classic enzyme-substrate equations. The authors show that the trans-propagating effect is too small to explain the ~70% of heritability of complex traits that is associated with trans-effects. Their conclusion relies on the comparison of the order of magnitude of a) the quantifiable transcriptional effects due to Pol II competition, and b) the observed percentage of variance explained by trans effects (data coming from Liu et al 2019, from the same lab).

The results shown in this manuscript rule out that competition for limiting resources in the cell (not restricted to Pol II, but applicable to any other cellular resource like ribosomes, etc) could explain heritability of complex traits.

Reviewer #2 (Public Review):

Summary:

The paper by Kuhn and colleagues follows upon a 2022 eLife paper in which they identified residues in CD4 constrained by evolutionary purifying selection in placental mammals, and then performed functional analyses of these conserved sequences. They showed that sequences distinct from the CXC "clamp" involved in recruitment of Lck have critical roles in TCR signaling, and these include a glycine-rich motif in the transmembrane (TM) domain and the cys-containing juxtamembrane (JM) motif that undergoes palmitylation, both of which promote TCR signaling, and a cytoplasmic domain helical motif, also involved in Lck binding, that constrains signaling. Mutations in the transmembrane and juxtamembrane sequences led to reduced proximal signaling and IL-2 production in a hybridoma's response to antigen presentation, despite retention of abundant CD4 association with Lck in the detergent-soluble membrane fraction, presumably mislocalized outside of lipid rafts and distal to the TCR. A major conclusion of that study was that CD4 sequences required for Lck association, including the CXC "clasp" motif, are not as consequential for CD4 co-receptor function in TCR signaling as the conserved TM and JM motifs. However, the experiments did not determine whether the functions of the TM and JM motifs are dependent on the Lck-binding properties of CD4 - the mutations in those motifs could result in free Lck redistributing to associate with CD4 in signaling-incompetent membrane domains or could function independently of CD4-Lck association. The current study addresses this specific question.

Using the same model system as in the earlier eLife paper (the entire methods section is a citation to the earlier paper), the authors show that truncation of the Lck-binding intracellular domain resulted in a moderate reduction in IL-2 response, as previously shown, but there was no apparent effect on proximal phosphorylation events (CD3z, Lck, ZAP70, PLCg1). They then evaluated a series of TM and JM motif mutations in the context of the truncated Lck-nonbinding molecule and showed that these had substantially impaired co-receptor function in the IL-2 assay and reduced proximal signaling. The proximal signaling could be observed at high ligand density even with a MHC non-binding mutation in CD4, although there was still impaired IL-2 production. This result additionally illustrates that phosphorylation of the proximal signaling molecules is not sufficient to activate IL-2 expression in the context of antigen presentation.

Strengths:

The strength of the paper is the further clear demonstration that the classical model of CD4 co-receptor function (MHCII-binding CD4 bringing Lck to the TCR complex, for phosphorylation of the CD3 chain ITAMs and of the ZAP70 kinase) is not sufficient to explain TCR activation. The data, combined with the earlier eLife paper, further implicate the gly-rich TM sequence and the palmitylation targets in the JM region as having critical roles in productive co-receptor-dependent TCR activation.

Weaknesses:

The major weakness of the paper is the lack of mechanistic insight into how the TM and JM motifs function. The new results are largely incremental in light of the earlier paper from this group as well as other literature, cited by the authors, that implicates "free" Lck, not associated with co-receptors, as having the major role in TCR activation. It is clear that the two motifs are important for CD4 function at low pMHCII ligand density. The proposal that they modulate interactions of TCR complex with cholesterol or other membrane lipids is an interesting one, and it would be worth further exploring by employing approaches that alter membrane lipid composition. The JM sequence presumably dictates localization within the membrane, by way of palmitylation, which may be critical to regulate avidity of the TCR:CD4 complex for pMHCII or TCR complex allosteric effects that influence the activation threshold. Experiments that explore the basis of the mutant phenotype could substantially enhance the impact of this study.

Additional comments:

- Is the "IL-2 sensitivity" measurement for the T1-TP (3C) meaningful (Table 3)? It is showing only a moderate reduction compared to T1 control, while TP (2C) or just the 3C palmitylation mutations essentially eliminate response.

- It is unclear how the pairs of control and mutant cells connected by lines in the figures are related. They are presumably cells from distinct biological experiments, with technical replicates for each, but are they paired because they were derived at the same time with different constructs? This should be explained in this paper, not in a reference.

Reviewer #2 (Public Review):

The authors have successfully addressed all of the concerns I had about the original version.

Reviewer #2 (Public Review):

Summary:

In this study, researchers aimed to understand how a transmitted/founder (T/F) HIV virus escapes host immune pressure during early infection. They focused on the V1V2 domain of the HIV-1 envelope protein, a key determinant of virus escape. The study involved four participants from the RV217 Early Capture HIV Cohort (ECHO) project, which allowed tracking HIV infection from just days after infection.

The study identified a significant H173Y escape mutation in the V2 domain of a T/F virus from one participant. This mutation, located in the relatively conserved "C" β-strand, was linked to viral escape against host immune pressure. The study further investigated the epitope specificity of antibodies in the participant's plasma, revealing that the H173Y mutation played a crucial role in epitope switching during virus escape. Monoclonal antibodies from the RV144 vaccine trial, CH58, and CH59, showed reduced binding to the V1V2-Y173 escape variant. Additionally, the study examined antibody-dependent cellular cytotoxicity (ADCC) responses and found resistance to killing in the Y173 mutants. The H173Y mutation was identified as the key variant selected against the host's immune pressure directed at the V2 domain.

The researchers hypothesized that the H173Y mutation caused a structural/conformational change in the C β-strand epitope, leading to viral escape. This was supported by molecular dynamics simulations and structural modeling analyses. They then designed combinatorial V2 immunogen libraries based on natural HIV-1 sequence diversity, aiming to broaden antibody responses. Mouse immunizations with these libraries demonstrated enhanced recognition of diverse Env antigens, suggesting a potential strategy for developing a more effective HIV vaccine.

In summary, the study provides insights into the early evolution of HIV-1 during infection, highlighting the importance of the V1V2 domain and identifying key escape mutations. The findings suggest a novel approach for designing HIV vaccine candidates that consider the diversity of escape mutations to induce broader and more effective immune responses.

Strengths:

The article presents several strengths:

(1) The experimental design is well-structured, involving multiple stages from phylogenetic analyses to mouse model testing, providing a comprehensive approach to studying virus escape mutations.

(2) The study utilizes a unique dataset from the RV217 Early Capture HIV Cohort (ECHO) project, allowing for the tracking of HIV infection from the very early stages in the absence of antiretroviral therapy. This provides valuable insights into the evolution of the virus.

(3) The use of advanced techniques such as phylogenetic analyses, nanoscaffold technology, controlled mutagenesis, and monoclonal antibody evaluations demonstrates the application of cutting-edge methodologies in the study.

(4) The research goes beyond genetic analysis and provides an in-depth characterization of the escape mutation's impact, including structural analyses through Molecular Dynamics simulations, antibody responses, and functional implications for virus survival.

(5) The study provides insights into the immune responses triggered by the escape mutation, including the specificity of antibodies and their ability to recognize diverse HIV-1 Env antigens.

(7) The exploration of combinatorial immunogen libraries is a strength, as it offers a novel approach to broaden antibody responses, providing a potential avenue for future vaccine design.

(8) The research is highly relevant to vaccine development, as it sheds light on the dynamics of HIV escape mutations and their interaction with the host immune system. This information is crucial for designing effective vaccines that can preemptively interfere with viral acquisition.

(9) The study integrates findings from virology, immunology, structural biology, and bioinformatics, showcasing an interdisciplinary approach that enhances the depth and breadth of the research.

(10) The article is well-written, with a clear presentation of methods, results, and implications, making it accessible to both specialists and a broader scientific audience.

Reviewer #2 (Public Review):

Summary:

In this study, Wang and colleagues study the potential probiotic effects of Bacillus velezensis. Bacillus species have the potential benefit of serving as probiotics due to their ability to form endospores and synthesize secondary metabolites. B. velezensis has been shown to have probiotic effects in plants and animals but data for human use are scarce, particularly with respect to salmonella-induced colitis. In this work, the authors identify a strain of B. velezensis and test it for its ability to control colitis in mice.

Key findings:

(1) The authors sequence an isolate for B. velezensis - HBXN2020 and describe its genome (roughly 4 mb, 46% GC-content etc).

(2) The authors next describe the growth of this strain in broth culture and survival under acid and temperature stress. The susceptibility of HBXN2020 was tested against various antibiotics and against various pathogenic bacteria. In the case of the latter, the authors set out to determine if HBXN2020 could directly inhibit the growth of pathogenic bacteria. Convincing data, indicating that this is indeed the case, are presented.

(3) To determine the safety profile of BHXN2020 (for possible use as a probiotic), the authors infected the strain in mice and monitored weight, together with cytokine profiles. Infected mice displayed no significant weight loss and expression of inflammatory cytokines remained unchanged. Blood cell profiles of infected mice were consistent with that of uninfected mice. No significant differences in tissues, including the colon were observed.

(4) Next, the authors tested the ability of HBXN2020 to inhibit the growth of Salmonella typhimurium (STm) and demonstrate that HBXN2020 inhibits STm in a dose-dependent manner. Following this, the authors infect mice with STm to induce colitis and measure the ability of HBXN2020 to control colitis. The first outcome measure was a reduction in STm in faeces. Consistent with this, HBXN2020 reduced STm loads in the ileum, cecum, and colon. Colon length was also affected by HBXN2020 treatment. In addition, treatment with HBXN2020 reduced the appearance of colon pathological features associated with colitis, together with a reduction in inflammatory cytokines.

(5) After noting the beneficial (and anti-inflammatory effects) of HBXN2020, the authors set out to investigate the effects on microbiota during treatment. Using a variety of algorithms, the authors demonstrate that upon HXBN2020 treatment, microbiota composition is restored to levels akin to that seen in healthy mice.

(6) Finally, the authors assessed the effect of using HBXN2020 as prophylactic treatment for colitis by first treating mice with the spores and then infecting them with STm. Their data indicate that treatment with HBXN2020 reduced colitis. A similar beneficial impact was seen with the gut microbiota.

Strengths:

(1) Good use of in vitro and animal models to demonstrate a beneficial probiotic effect.

(2) Most observations are supported using multiple approaches.

(3) The mouse experiments are very convincing.

Weaknesses:

(1) Whilst a beneficial effect is observed, there is no investigation of the mechanism that underpins this.

(2) The mouse experiments would have benefited from the use of standard anti-inflammatory therapies to control colitis. That way the authors could compare their approach of using bacillus spores with the current gold standard for treatment.

Reviewer #2 (Public Review):

Summary:

The overall goal of Eleni et al. is to determine if the suppression of LH pulses during lactation is mediated by prolactin signaling at kisspeptin neurons. To address this, the authors used GCaMP fiber photometry and serial blood sampling to reveal that in vivo episodic arcuate kisspeptin neuron activity and LH pulses are suppressed throughout pregnancy and lactation. The authors further utilized knockout models to demonstrate that the loss of prolactin receptor signaling at kisspeptin cells prevents the suppression of kisspeptin function and results in early reestablishment of fertility during lactation. The work demonstrates exemplary design and technique, and the outcomes of these experiments are sophistically discussed.

Strengths:

This manuscript demonstrates exemplary skill with powerful techniques and reveals a key role for arcuate kisspeptin neurons in maintaining lactation-induced infertility in mice. In a difficult feat, the authors used fiber photometry to map the activity of arcuate kisspeptin cells into lactation and weaning without disrupting parturition, lactation, or maternal behavior. The authors used a knockout approach to identify if prolactin inhibition of fertility is mediated by direct signaling at arcuate kisspeptin cells. Although the model does not perfectly eliminate prolactin receptor expression in all kisspeptin neurons, results from the achieved knockdown support the conclusion that prolactin signaling at kisspeptin neurons is required to maintain lactational infertility. The methods were advanced and appropriate for the aims, the studies were rigorously conducted, and the conclusions were thoughtfully discussed. Overall, the aims of this study were achieved.

Reviewer #2 (Public Review):

Summary:

* To verify the function of PT-associated protein CYLC1, the authors generated a Cylc1-KO mouse model and revealed that loss of cylicin-1 leads to severe male subfertility as a result of sperm head deformities and acrosome detachment.

* Then they also identified a CYLC1 variant by WES analysis from 19 infertile males with sperm head deformities.

* To prove the pathogenicity of the identified mutation site, they further generated Cylc1-mutant mice that carried a single amino acid change equivalent to the variant in human CYLC1. The Cylc1-mutant mice also exhibited male subfertility with detached acrosomes of sperm cells.

Strengths:

* The phenotypes observed in the Cylc1-KO mice provide strong evidence for the function of CYLC1 as a PT-associated protein in spermatogenesis and male infertility.

* Further mechanistic studies indicate that loss of cylicin-1 in mice may disrupt the connections between the inner acrosomal membrane and acroplaxome, leading to detached acrosomes of sperm cells.

Weaknesses:

* The authors identified a missense mutation (c.1377G>T/p. K459N) from 19 infertile males with sperm head deformities. The information for the variant in Table 1 is insufficient to determine the pathogenicity and reliability of the mutation site. More information should be added, including all individuals in gnomAD, East Asians in gnomAD, 1000 Genomes Project for allele frequency in the human population; MutationTaster, M-CAP, FATHMM, and more other tools for function prediction. Then, the expression of CYLC1 in the spermatozoa from men with CYLC1 mutation should be explored by qPCR, Western blot, or IF staining analyses.

* Although 19 infertile males were found carrying the same missense mutation (c.1377G>T/p. K459N), their phenotypes are somewhat different. For example, sperm concentrations for individuals AAX765, BBA344, and 3086 are extremely low but this is not observed in other infertile males. Then, progressive motility for individuals AAT812, 3165, 3172, 3203, and 3209 are extremely low but this is also not observed in other infertile males. It is worth considering why different phenotypes are observed in probands carrying the same mutation.

Reviewer #2 (Public Review):

Summary:

Nishi et al, investigate the well-known and previously described phenomenon of age-associated myeloid-biased hematopoiesis. Using a previously established HoxB5mCherry mouse model, they used HoxB5+ and HoxB5- HSCs to discriminate cells with long-term (LT-HSCs) and short-term (ST-HSCs) reconstitution potential and compared these populations to immunophenotypically defined 'bulk HSCs' that consists of a mixture of LT-HSC and ST-HSCs. They then isolated these HSC populations from young and aged mice to test their function and myeloid bias in non-competitive and competitive transplants into young and aged recipients. Based on quantification of hematopoietic cell frequencies in the bone marrow, peripheral blood, and in some experiments the spleen and thymus, the authors argue against the currently held belief that myeloid-biased HSCs expand with age.

While aspects of their work are fascinating and might have merit, several issues weaken the overall strength of the arguments and interpretation. Multiple experiments were done with a very low number of recipient mice, showed very large standard deviations, and had no statistically detectable difference between experimental groups. While the authors conclude that these experimental groups are not different, the displayed results seem too variable to conclude anything with certainty. The sensitivity of the performed experiments (e.g. Figure 3; Figure 6C, D) is too low to detect even reasonably strong differences between experimental groups and is thus inadequate to support the author's claims. This weakness of the study is not acknowledged in the text and is also not discussed. To support their conclusions the authors need to provide higher n-numbers and provide a detailed power analysis of the transplants in the methods section.

As the authors attempt to challenge the current model of the age-associated expansion of myeloid-biased HSCs (which has been observed and reproduced by many different groups), ideally additional strong evidence in the form of single-cell transplants is provided.

It is also unclear why the authors believe that the observed reduction of ST-HSCs relative to LT-HSCs explains the myeloid-biased phenotype observed in the peripheral blood. This point seems counterintuitive and requires further explanation.

Based on my understanding of the presented data, the authors argue that myeloid-biased HSCs do not exist, as<br /> a) they detect no difference between young/aged HSCs after transplant (mind low n-numbers and large std!); b) myeloid progenitors downstream of HSCs only show minor or no changes in frequency and c) aged LT-HSCs do not outperform young LT-HSC in myeloid output LT-HScs in competitive transplants (mind low n-numbers and large std!).

However, given the low n-numbers and high variance of the results, the argument seems weak and the presented data does not support the claims sufficiently. That the number of downstream progenitors does not change could be explained by other mechanisms, for instance, the frequently reported differentiation short-cuts of HSCs and/or changes in the microenvironment.

Strengths:

The authors present an interesting observation and offer an alternative explanation of the origins of aged-associated myeloid-biased hematopoiesis. Their data regarding the role of the microenvironment in the spleen and thymus appears to be convincing.

Weaknesses:

"Then, we found that the myeloid lineage proportions from young and aged LT-HSCs were nearly comparable during the observation period after transplantation (Figure 3, B and C)."<br /> Given the large standard deviation and low n-numbers, the power of the analysis to detect differences between experimental groups is very low. Experimental groups with too large standard deviations (as displayed here) are difficult to interpret and might be inconclusive. The absence of clearly detectable differences between young and aged transplanted HSCs could thus simply be a false-negative result. The shown experimental results hence do not provide strong evidence for the author's interpretation of the data. The authors should add additional transplants and include a detailed power analysis to be able to detect differences between experimental groups with reasonable sensitivity.

Line 293: "Based on these findings, we concluded that myeloid-biased hematopoiesis observed following transplantation of aged HSCs was caused by a relative decrease in ST-HSC in the bulk-HSC compartment in aged mice rather than the selective expansion of myeloid-biased HSC clones."<br /> Couldn't that also be explained by an increase in myeloid-biased HSCs, as repeatedly reported and seen in the expansion of CD150+ HSCs? It is not intuitively clear why a reduction of ST-HSCs clones would lead to a myeloid bias. The author should try to explain more clearly where they believe the increased number of myeloid cells comes from. What is the source of myeloid cells if the authors believe they are not derived from the expanded population of myeloid-biased HSCs?

Reviewer #2 (Public Review):

This work introduces PLMGraph-Inter, a new deep learning approach for predicting inter-protein contacts, which is crucial for understanding protein-protein interactions. Despite advancements in this field, especially driven by AlphaFold, prediction accuracy and efficiency in terms of computational cost still remains an area for improvement. PLMGraph-Inter utilizes invariant geometric graphs to integrate the features from multiple protein language models into the structural information of each subunit. When compared against other inter-protein contact prediction methods, PLMGraph-Inter shows better performance which indicates that utilizing both sequence embeddings and structural embeddings is important to achieve high-accuracy predictions with relatively smaller computational costs for the model training.

Reviewer #2 (Public Review):

Summary:

The authors developed a bioinformatic pipeline to aid the screening and identification of inhibitory receptors suitable as drug targets. The challenge lies in the large search space and lack of tools for assessing the likelihood of their inhibitory function. To make progress, the authors used a consensus protein membrane topology and sequence motif prediction tool (TOPCOS) combined with both a statistical measure assessing their likelihood function and a machine learning protein structural prediction model (AlphaFold) to greatly cut down the search space. After obtaining a manageable set of 398 high-confidence known and putative inhibitory receptors through this pipeline, the authors then mapped these receptors to different functional categories across different cell types based on their expression both in the resting and activated state. Additionally, by using publicly available pan-cancer scRNA-seq for tumor-infiltrating T-cell data, they showed that these receptors are expressed across various cellular subsets.

Strengths:

The authors presented sound arguments motivating the need to efficiently screen inhibitory receptors and to identify those that are functional. Key components of the algorithm were presented along with solid justification for why they addressed challenges faced by existing approaches. To name a few:

• TOPCON algorithm was elected to optimize the prediction of membrane topology.<br /> • A statistical measure was used to remove potential false positives.<br /> • AlphaFold is used to filter out putative receptors that are low confidence (and likely intrinsically disordered).

To examine receptors screened through this pipeline through a functional lens, the authors proposed to look at their expression of various immune cell subsets to assign functional categories. This is a reasonable and appropriate first step for interpreting and understanding how potential drug targets are differentially expressed in some disease contexts.

Weaknesses:

The paper has strength in the pipeline they presented, but the weakness, in my opinion, lies in the lack of concrete demonstration on how this pipeline can be used to at least "rediscover" known targets in a disease-specific manner. For example, the result that both known and putative immune inhibitory receptors are expressed across a wide variety of tumor-infiltrating T-cell subsets is reassuring, but this would have been more informative and illustrative if the authors could demonstrate using a disease with known targets, as opposed to a pan-cancer context. Additionally, a discussion that contrasts the known and putative receptors in the context above would help readers better identify use cases suitable for their research using this pipeline. Particularly,<br /> • For known receptors, does the pipeline and the expression analysis above rediscover the known target in the disease of interest?<br /> • For putative receptors, what do the functional category mapping and the differential expression across various tumor-infiltrating T-cell subsets imply on a potential therapeutic target?

Reviewer #2 (Public Review):

This is an interesting study in which the authors show that a thermal injury leads to extensive sensory axon damage and impaired regrowth compared to a mechanical transection injury. This correlates with increased keratinocyte migration. That migration is inhibited by CK666 drug treatment and isotonic medium. Both restrict ROS signalling to the wound edge. In addition, the isotonic medium also rescues the regrowth of sensory axons and recovery of sensory function. The findings may have implications for understanding non-optimal re-innervation of burn wounds in mammals.

The interpretation of results is generally cautious and controls are robust.

Here are some suggestions for additional discussion:<br /> The study compares burn injury which produces a diffuse injury to a mechanical cut injury which produces focal damage. It would help the reader to give a definition of wound edge in the burn situation. Is the thermally injured tissue completely dead and is resorbed or do axons have to grow into damaged tissue? The two-cut model suggests the latter. Also giving timescales would help, e.g. when do axons grow in relation to keratinocyte movement? An introductory cartoon might help.

Could treatment with CK666 or isotonic solution influence sensory axons directly, or through other non-keratinocyte cell types, such as immune cells?

Reviewer #2 (Public Review):

This study uses an AI-based image analysis approach to classify different cell types in cultures of different densities. The authors could demonstrate the superiority of the CNN strategy used with nucleocentric cell profiling approach for a variety of cell types classification.

The paper is very clear and well-written. I just have a couple of minor suggestions and clarifications needed for the reader.

The entire prediction model is based on image analysis. Could the authors discuss the minimal spatial resolution of images required to allow a good prediction? Along the same line, it would be interesting to the reader to know which metrics related to image quality (e.g. signal to noise ratio) allow a good accuracy of the prediction.

The authors show that nucleocentric-based cell feature extraction is superior to feeding the CNN-based model for cell type prediction. Could they discuss what is the optimal size and shape of this ROI to ensure a good prediction? What if, for example, you increase or decrease the size of the ROI by a certain number of pixels?

It would be interesting for the reader to know the number of ROI used to feed each model and know the minimal amount of data necessary to reach a high level of accuracy in the predictions.

From Figure 1 to Figure 4 the author shows that CNN based approach is efficient in distinguishing 1321N1 vs SH-SY5Y cell lines. The last two figures are dedicated to showing 2 different applications of the techniques: identification of different stages of neuronal differentiation (Figure 5) and different cell types (neurons, microglia, and astrocytes) in Figure 6.

It would be interesting, for these 2 two cases as well, to assess the superiority of the CNN-based approach compared to the more classical Random Forest classification. This would reinforce the universal value of the method proposed.

Reviewer #2 (Public Review):

Summary:

This very interesting study originated from a serendipitous observation that the deletion of the disordered N-terminal tail of human SUMO1 enhances its binding to its interaction partners. This suggested that the N terminus of SUMO1 might be an intrinsic competitive inhibitor of SUMO-interacting motif (SIM) binding to SUMO1. Subsequent experiments support this mechanism, showing that in humans it is specific to SUMO1 and does not extend to SUMO2 or SUMO3 (except, perhaps, when the N terminus of SUMO2 becomes phosphorylated, as the authors intriguingly suggest - and partially demonstrate). The auto-inhibition of SUMO1 via its N-terminal tail apparently explains the lower binding of SUMO1 compared to SUMO2 to some SIMs and lower SIM-dependent SUMOylation of some substrates with SUMO1 compared to SUMO2, thus adding an important element to the puzzle of SUMO paralogue preference. In line with this explanation, N-terminally truncated SUMO1 was equally efficient to SUMO2 in the studied cases. The inhibitory role of SUMO1's N terminus appears conserved in other species including S. cerevisiae and C. elegans, both of which contain only one SUMO. The study also elucidates the molecular mechanism by which the disordered N-terminal region of SUMO1 can exert this auto-inhibitory effect. This appears to depend on the transient, very highly dynamic physical interaction between the N terminus and the surroundings of the SIM-binding groove based mostly on electrostatic interactions between acidic residues in the N terminus and basic residues around the groove.

Strengths:

A key strength of this study is the interplay of different techniques, including biochemical experiments, NMR, molecular dynamics simulations, and, at the end, in vivo experiments. The experiments performed with these different techniques inform each other in a productive way and strengthen each others' conclusions. A further strength is the detailed and clear text, which patiently introduces, describes, and discusses the study. Finally, in terms of the message, the study has a clear, mechanistic message of fundamental importance for various aspects of the SUMO field, and also more generally for protein biochemists interested in the functional importance of intrinsically disordered regions.

Weaknesses:

Some of the authors' conclusions are similar to those from a recent study by Lussier-Price et al. (NAR, 2022), the two studies likely representing independent inquiries into a similar topic. I don't see it as a weakness by itself (on the contrary), but it seems like a lost opportunity not to discuss at more length the congruence between these two studies in the discussion (Lussier-Price is only very briefly cited). Another point that can be raised concerns the wording of conclusions from molecular dynamics. The use of molecular dynamics simulations in this study has been rigorous and fruitful - indeed, it can be a model for such studies. Nonetheless, parameters derived from molecular dynamics simulations, including kon and koff values, could be more clearly described as coming from simulations and not experiments. Lastly, some of the conclusions - such as enhanced binding to SIM-containing proteins upon N-terminal deletion - could be additionally addressed with a biophysical technique (e.g. ITC) that is more quantitative than gel-based pull-down assays - but I don't think it is a must.

Reviewer #2 (Public Review):

Summary:

Mismatches occur as a result of DNA polymerase errors, chemical modification of nucleotides, during homologous recombination between near-identical partners, as well as during gene editing on chromosomal DNA. Under some circumstances, such mismatches may be incorporated into nucleosomes but their impact on nucleosome structure and stability is not known. The authors use the well-defined 601 nucleosome positioning sequence to assemble nucleosomes with histones on perfectly matched dsDNA as well as on ds DNA with defined mismatches at three nucleosomal positions. They use the R18, R39, and R56 positions situated in the middle of the outer turn, at the junction between the outer turn and inner turn, and in the middle of the inner turn, respectively. Most experiments are carried out with CC mismatches and Xenopus histones. Unwrapping of the outer DNA turn is monitored by single-molecule FRET in which the Cy3 donor is incorporated on the 68th nucleotide from the 5'-end of the top strand and the Cy5 acceptor is attached to the 7th nucleotide from the 5' end of the bottom strand. Force is applied to the nucleosomal DNA as FRET is monitored to assess nucleosome unwrapping. The results show that a CC mismatch enhances nucleosome mechanical stability. Interestingly, yeast and Xenopus histones show different behaviors in this assay. The authors use FRET to measure the cyclization of the dsDNA substrates to test the hypothesis that mismatches enhance the flexibility of the 601 dsDNA fragment and find that CC, CA, CT, TT, and AA mismatches decrease looping time, whereas GA, GG, and GT mismatches had little to no effect. These effects correlate with the results from DNA buckling assays reported by Euler's group (NAR 41, 2013) using the same mismatches as an orthogonal way to measure DNA kinking. The authors discuss that substitution rates are higher towards the middle of the nucleosome, suggesting that mismatches/DNA damage at this position are less accessible for repair, consistent with the nucleosome stability results.

Strengths:

The single-molecule data show clear and consistent effects of mismatches on nucleosome stability and DNA persistence length.

Weaknesses:

It is unclear in the looping assay how the cyclization rate relates to the reporting looping time. The biological significance and implications such as the effect on mismatch repair or nucleosome remodelers remain untested. It is unclear whether the mutational pattern reflects the behavior of the different mismatches. Such a correlation could strengthen the argument that the observed effects are relevant for mutagenesis.

Reviewer #2 (Public Review):

Summary:

This study presents a significant finding that enhances our understanding of spermatogenesis. TMC7 belongs to a family of transmembrane channel-like proteins (TMC1-8), primarily known for their role in the ear. Mutations to TMC1/2 are linked to deafness in humans and mice and were originally characterized as auditory mechanosensitive ion channels. However, the function of the other TMC family members remains poorly characterized. In this study, the authors begin to elucidate the function of TMC7 in acrosome biogenesis during spermatogenesis. Through analysis of transcriptomics datasets, they identify TMC7 as a transmembrane channel-like protein with elevated transcript levels in round spermatids in both mouse and human testis. They then generate Tmc7-/- mice and find that male mice exhibit smaller testes and complete infertility. Examination of different developmental stages reveals spermatogenesis defects, including reduced sperm count, elongated spermatids, and large vacuoles. Additionally, abnormal acrosome morphology is observed beginning at the early-stage Golgi phase, indicating TMC7's involvement in proacrosomal vesicle trafficking and fusion. They observed localization of TMC7 in the cis-Golgi and suggest that its presence is required for maintaining Golgi integrity, with Tmc7-/- leading to reduced intracellular Ca2+, elevated pH, and increased ROS levels, likely resulting in spermatid apoptosis. Overall, the work delineates a new function of TMC7 in spermatogenesis and the authors suggest that its ion channel activity is likely important for Golgi homeostasis. This work is of significant interest to the community and is of high quality.

Strengths:

The biggest strength of the paper is the phenotypic characterization of the TMC7-/- mouse model, which has clear acrosome biogenesis/spermatogenesis defects. This is the main claim of the paper and it is supported by the data that are presented.

Weaknesses:

The claim is that TMC7 functions as an ion channel. It is reasonable to assume this given what has been previously published on the more well-characterized TMCs (TMC1/2), but the data supporting this is preliminary here, and more needs to be done to solidify this hypothesis. The authors are careful in their interpretation and present this merely as a hypothesis supporting this idea.

Reviewer #2 (Public Review):

Summary:

In this study, the authors study intraflagellar transport (IFT) in cilia of diverse organs in zebrafish. They elucidate that IFT88-GFP (an IFT-B core complex protein) can substitute for endogenous IFT88 in promoting ciliogenesis and use it as a reporter to visualize IFT dynamics in living zebrafish embryos. They observe striking differences in cilia lengths and velocity of IFT trains in different cilia types, with smaller cilia lengths correlating with lower IFT speed. They generate several mutants and show that disrupting the function of different kinesin-2 motors and BBSome or altering post-translational modifications of tubulin does not have a significant impact on IFT velocity. They however observe that when the amount of IFT88 is reduced it impacts the cilia length, IFT velocity as well as the number and size of IFT trains. They also show that the IFT train size is slightly smaller in one of the organs with shorter cilia (spinal cord). Based on their observations they propose that IFT velocity determines cilia length and go one step further to propose that IFT velocity is regulated by the size of IFT trains.

Strengths:

The main highlight of this study is the direct visualization of IFT dynamics in multiple organs of a living complex multi-cellular organism, zebrafish. The quality of the imaging is really good. Further, the authors have developed phenomenal resources to study IFT in zebrafish which would allow us to explore several mechanisms involved in IFT regulation in future studies. They make some interesting findings in mutants with disrupted function of kinesin-2, BBSome, and tubulin modifying enzymes which are interesting to compare with cilia studies in other model organisms. Also, their observation of a possible link between cilia length and IFT speed is potentially fascinating.

Weaknesses:

The manuscript as it stands, has several issues.

(1) The study does not provide a qualitative description of cilia organization in different cell types, the cilia length variation within the same organ, and IFT dynamics. The methodology is also described minimally and must be detailed with more care such that similar studies can be done in other laboratories.

(2) They provide remarkable new observations for all the mutants. However, discussion regarding what the findings imply and how these observations align (or contradict) with what has been observed in cilia studies in other organisms is incomprehensive.

(3) The analysis of IFT velocities, the main parameter they compare between experiments, is not described at all. The IFT velocities appear variable in several kymographs (and movies) and are visually difficult to see in shorter cilia. It is unclear how they make sure that the velocity readout is robust. Perhaps, a more automated approach is necessary to obtain more precise velocity estimates.

(4) They claim that IFT speeds are determined by the size of IFT trains, based on their observations in samples with a reduced amount of IFT88. If this was indeed the case, the velocity of a brighter IFT train (larger train) would be higher than the velocity of a dimmer IFT train (smaller train) within the same cilia. This is not apparent from the movies and such a correlation should be verified to make their claim stronger.

(5) They make an even larger claim that the cilia length (and IFT velocity) in different organs is different due to differences in the sizes of IFT trains. This is based on a marginal difference they observe between the cilia of crista and the spinal cord in immunofluorescence experiments (Figure 5C). Inferring that this minor difference is key to the striking difference in cilia length and IFT velocity is incorrect in my opinion.

Impact:

Overall, I think this work develops an exciting new multicellular model organism to study IFT mechanisms. Zebrafish is a vertebrate where we can perform genetic modifications with relative ease. This could be an ideal model to study not just the role of IFT in connection with ciliary function but also ciliopathies. Further, from an evolutionary perspective, it is fascinating to compare IFT mechanisms in zebrafish with unicellular protists like Chlamydomonas, simple multicellular organisms like C elegans, and primary mammalian cell cultures. Having said that, the underlying storyline of this study is flawed in my opinion and I would recommend the authors to report the striking findings and methodology in more detail while significantly toning down their proposed hypothesis on ciliary length regulation. Given the technological advancements made in this study, I think it is fine if it is a descriptive manuscript and doesn't necessarily need a breakthrough hypothesis based on preliminary evidence.

Reviewer #2 (Public Review):

Summary:

ECM components are prominent constituents of the pericellular environment of CNS cells and form complex and dynamic interactomes in the pericellular spaces. Based on bioinformatic analysis, more than 300 genes have been attributed to the so-called matrisome, many of which are detectable in the CNS. Yet, not much is known about their functions while increasing evidence suggests important contributions to developmental processes, neural plasticity, and inhibition of regeneration in the CNS. In this respect, the present work offers new insights and adds interesting aspects to the facets of ECM contributions to neural development. This is even more relevant in view of the fact that neurocan has recently been identified as a potential risk gene for neuropsychiatric diseases. Because ECM components occur in the interstitial space and are linked in interactomes their study is very difficult. A strength of the manuscript is that the authors used several approaches to shed light on ECM function, including proteome studies, the generation of knockout mouse lines, and the analysis of in vivo labeled neural progenitors. This multi-perspective approach permitted to reveal hitherto unknown properties of the ECM and highlighted its importance for the overall organization of the CNS.

Strengths:

Systematic analysis of the ternary complex between neurone, TNC, and hyaluronic acid; establishment of KO mouse lines to study the function of the complex, use of in utero electroporation to investigate the impact on neuronal migration.

Reviewer #2 (Public Review):

Sztangierska et al. have investigated the impact of the nucleotide exchange (NEF) factor Hsp110 on the Hsp70-dependent dissolution of amorphous aggregates in the presence of representative members of two classes of J-domain protein.

The authors find that the nucleotide exchange factor of the Hsp110 family, sse1, stimulates the disaggregation activity of yeast Hsp70, ssa1, in particular in the presence of the J-domain protein sis1. Linking chaperone-substrate interactions as determined by biolayer interferometry (BLI) to activity assays, they show that sse1 facilitates the loading of more ssa1 onto the aggregate substrate and propose that this is due to active remodeling of the protein aggregate which exposes more chaperone binding sites and thus facilitates reactivation. This study highlights two important facets of Hsp70 biology: different Hsp70 functions rely on the functional cooperation of specific co-chaperone combinations and the stoichiometry of the different players of the Hsp70 system is an important parameter in tuning Hsp70 chaperone activity.

Strengths:

The manuscript presents a systematic analysis of the functional cooperation of sse1 with a class B J-domain protein sis1 in the disaggregation of two different model aggregate substrates, allowing the authors to draw more general conclusions about Hsp70 disaggregation activity.

The authors can pinpoint the role of sse1 to the initial remodeling of aggregates, rather than the later stages of refolding, highlighting the functional specificity of Hsp70 co-chaperones.

They demonstrate the competitive nature of binding to ssa1 between sse1 and sis1 which can explain the poisoning of Hsp70 chaperone activities observed at high NEF concentrations.

Weaknesses:

Experimental data concerning the class A JDPs should be interpreted with caution. These experiments show very small reactivation activities for luciferase in the range of 0-1% without the addition of Hsp104 and 0-15% with the addition of Hsp104. Moreover, since the assay is based on the recovery of luciferase activity, it conflates two chaperone activities, namely disaggregation and refolding. It is possible that the small degree of reactivation observed for the class A JDP reflects a minor subpopulation of the aggregated species that is particularly easy to disaggregate/refold and may thus not be representative of bulk behaviour.

While structural requirements have been identified that allow sse1, in cooperation with sis1, to facilitate the loading of Hsp70 on the amorphous aggregate substrate, how this is achieved on a mechanistic level remains an open question.

Reviewer #2 (Public Review):

Summary:

This paper describes some experiments addressing 3' exonuclease and 3' trimming activity of bacterial exonuclease III. The quantitative activity is in fact very low, despite claims to the contrary. The work is of low interest with regard to biology, but possibly of use for methods development. Thus the paper seems better suited to a methods forum.

Strengths:

Technical approaches.

Weaknesses:

The purity of the recombinant proteins is critical, but no information on that is provided. The minimum would be silver-stained SDS-PAGE gels, with some samples overloaded in order to detect contaminants.

Lines 74-76: What is the evidence that BER in E. coli generates multinucleotide repair patches in vivo? In principle, there is no need for the nick to be widened to a gap, as DNA Pol I acts efficiently from a nick. And what would control the extent of the 3' excision?

Figure 1: The substrates all report only the first phosphodiester cleavage near the 3' end, which is quite a limitation. Do the reported values reflect only the single phosphodiester cleavage? Including the several other nucleotides likely inflates that activity value. And how much is a unit of activity in terms of actual protein concentration? Without that, it's hard to compare the observed activities to the many published studies. As best I know, Exo III was already known to remove a single-nucleotide 3'-overhang, albeit more slowly than the digestion of a duplex, but not zero! We need to be able to calculate an actual specific activity: pmol/min per µg of protein.

Figures 2 & 3: These address the possible issue of 1-nt excision noted above. However, the question of efficiency is still not addressed in the absence of a more quantitative approach, not just "units" from the supplier's label. Moreover, it is quite common that commercial enzyme preparations contain a lot of inactive material.

Figure 4D: This gets to the quantitative point. In this panel, we see that around 0.5 pmol/min of product is produced by 0.025 µmol = 25,000 pmol of the enzyme. That is certainly not very efficient, compared to the digestion of dsDNA or cleavage of an abasic site. It's hard to see that as significant.

Line 459 and elsewhere: as noted above, the activity is not "highly efficient". I would say that it is not efficient at all.

Reviewer #2 (Public Review):

Chen, Dixit et al. report on the first structure of a bivalent interaction between a natural interaction partner of Pin1: the C-terminal tail of PKC phosphorylated at two sites. The biggest strength of the paper is the impressive amount of NMR-based structural data that is sound and clearly reported. The authors strive to propose a novel non-catalytic mechanistic role for Pin1 that is supported by cell culture models and somewhat by the interaction assays, however, in my eyes, they fell short in proving their mechanistic hypothesis. Nevertheless, the potential ways Pin1 may modulate PKC's activity is nicely discussed.

Reviewer #2 (Public Review):

Strengths

(1) The statements made in the paper are precise, separating observations from inferences, with claims that are well supported by empirical evidence. Releasing the underlying code repository further bolsters the credibility and reproducibility. I especially appreciate the detailed discussion of limitations and future work.

(2) The main claims with respect to the two convolutional architectures are well supported by thorough analyses. The analyses are well-chosen and overall include good controls, such as changes in the training diet. Going beyond "passive" empirical tests, the paper makes use of the fully accessible nature of computational models and includes more "causal" insertion and deletion tests that support the necessity and sufficiency of local object features.

(3) Based on modeling results, the paper makes a testable prediction: that mirror-symmetric viewpoint tuning is not specific to faces and can also be observed in other bilaterally symmetric objects such as cars and chairs. To test this experimentally in primates (and potentially other model architectures), the stimulus set is available online.

Weaknesses

My main concern with this paper is in its choice of the two model architectures AlexNet and VGG. In an earlier study, Yildirim et al. (2020) found an inverse graphics network "EIG" to better correspond to neural and behavioral data for face processing than VGG. All claims in the paper thus relate to a weaker model of the biological effects since this work does not analyze the EIG model. Since EIG follows an analysis-by-synthesis approach rather than standard classification training, it is unclear whether the claims in this paper generalize to this other model architecture. It is also unclear if the claims will hold for: 1) transformer architectures, 2) the HMAX architecture by Leibo et al. (2017) which has also been proposed as a computational explanation for mirror-symmetric tuning, and, as the authors note in the Discussion, 3) deeper architectures such as ResNet-50 which tend to better align to neural and behavioral data in general. These architectures include different computational motifs such as skip connections and a much smaller proportion of fully-connected layers which are a major focus of this work.

Overall, I thus view the paper's claims as limited to AlexNet- and VGG-like architectures, both of which fall behind state-of-the-art in their alignment to primates in general and also specifically for mirror-symmetric viewpoint tuning.

Minor weaknesses

(1) Figure 1A: since the relevance to primate brains is a major motivator of this work, the results from actual neural recordings should be shown and not just schematics. For instance, the mirror symmetry in AL is not as clean as the illustration (compare with Fig. 3 in Yildirim et al. 2020), and in the paper's current form, this is not easily accessible to the reader.

(2) Figure 4 / L832-845: The claims for the effect of training on mirror-symmetric viewpoint tuning are with respect to the training data only, but there are other differences between the models such as the number of epochs (250 for CIFAR-10 training, 200 for all other datasets), the learning rate (2.5 * 10^-4 for CIFAR-10, 10^-4 for all others), the batch size (128 vs 64), etc. I do not expect these choices to make a major difference for your claims, but it would be much cleaner to keep everything but the training dataset consistent. Especially the different test accuracies worry me a bit (from 81% to 92%, and they appear different from the accuracy numbers in figure S4 e.g. for CIFAR-10 and asymSVHN), at the very least those should be comparable.

(3) L681-685: The general statement made in the paper that "deeper models lose their advantage as models of cortical representations" is not supported by the cited limited comparison on a single dataset. There are many potential confounds here with respect to prior work, e.g. the recording modality (fMRI vs electrodes), the stimulus set (62 images vs thousands), the models that were tested (9 vs hundreds), etc.

Reviewer #2 (Public Review):

Here, the authors tried to identify the genes and biological pathways underlying iron overload and its associated pathologies in mice. Several wet lab experiments and measurements alongside many bioinformatic analyses like GWAS, RNA-seq data analysis (DEG), eQTL analysis, TWAS, and gene-set enrichment analysis have been performed. The study design is good enough and the author tried to validate the results. The data have been submitted (Accession #: GSE230674) but are not public yet.

(1) The main issue of this manuscript is its length. It's too long, especially the result section. It's hard for readers to follow the paper. Moreover, you added results about other minerals, mostly copper, which seems too much (considering the fact that this study is about iron). The text doesn't have the required Integrity and focus. You should decide where you want to put the focus of this manuscript and I strongly recommend shortening the manuscript, try to be short and sweet as much as you can.<br /> (2) Also, the "Methods" section is long, some parts are over-detailed (mostly wet lab procedures) and some parts are not detailed enough. It seems the "Statistical analyses" part doesn't have extra information. I recommend removing the first paragraph and moving some of the information from the second paragraph to the right place in the Method section.<br /> (3) Some part of your discussion section, is retelling the results. Please discuss your results and compare them with previous findings.<br /> (4) Add detail about your GWAS model. As you had repeated samples from each strain, it's good to mention how you considered this. Also, show how you determined the significance threshold.<br /> (5) The abstract could be better. It also doesn't have a conclusion.<br /> (6) Page 8, lines 4-7: Please remove these lines or move them to the Method section. The last paragraph of the introduction should clearly explain the goal of the study.<br /> (7) Page 68, line 13: Explain the abbreviation (RINe) before use. Also, most probably it is RIN (RNA Integrity Number).<br /> (8) The heritability estimates seem high and the 1% difference between broad- and narrow-sense heritability means there is almost no dominant and epistatic genetic variance between alleles affecting the studied trait (which is hard to accept). I recommend considering a within-group (strain) variance (common environmental effect) component in the model to absorb this source of variation in this component, so the genetic variance and consequently the heritability estimates would be more accurate. You also can consider this source of variance in your GWAS model.

Reviewer #2 (Public Review):

Summary of what the authors were trying to achieve:<br /> The authors thought they studied membrane potential dynamics in E.coli biofilms. They thought so because they were unaware that the dye they used to report that membrane potential in E.coli, has been previously shown not to report it. Because of this, the interpretation of the authors' results is not accurate.

Major strengths and weaknesses of the methods and results:<br /> The strength of this work is that all the data is presented clearly, and accurately, as far as I can tell.

The major critical weakness of this paper is the use of ThT dye as a membrane potential dye in E.coli. The work is unaware of a publication from 2020 https://www.sciencedirect.com/science/article/pii/S0006349519308793 that demonstrates that ThT is not a membrane potential dye in E. coli. Therefore I think the results of this paper are misinterpreted. The same publication I reference above presents a protocol on how to carefully calibrate any candidate membrane potential dye in any given condition.

I now go over each results section in the manuscript.

Result section 1: Blue light triggers electrical spiking in single E. coli cells

I do not think the title of the result section is correct for the following reasons. The above-referenced work demonstrates the loading profile one should expect from a Nernstian dye (Figure 1). It also demonstrates that ThT does not show that profile and explains why is this so. ThT only permeates the membrane under light exposure (Figure 5). This finding is consistent with blue light peroxidising the membrane (see also following work Figure 4 https://www.sciencedirect.com/science/article/pii/S0006349519303923 on light-induced damage to the electrochemical gradient of protons-I am sure there are more references for this).

Please note that the loading profile (only observed under light) in the current manuscript in Figure 1B as well as in the video S1 is identical to that in Figure 3 from the above-referenced paper (i.e. https://www.sciencedirect.com/science/article/pii/S0006349519308793), and corresponding videos S3 and S4. This kind of profile is exactly what one would expect theoretically if the light is simultaneously lowering the membrane potential as the ThT is equilibrating, see Figure S12 of that previous work. There, it is also demonstrated by the means of monitoring the speed of bacterial flagellar motor that the electrochemical gradient of protons is being lowered by the light. The authors state that applying the blue light for different time periods and over different time scales did not change the peak profile. This is expected if the light is lowering the electrochemical gradient of protons. But, in Figure S1, it is clear that it affected the timing of the peak, which is again expected, because the light affects the timing of the decay, and thus of the decay profile of the electrochemical gradient of protons (Figure 4 https://www.sciencedirect.com/science/article/pii/S0006349519303923).

If find Figure S1D interesting. There authors load TMRM, which is a membrane voltage dye that has been used extensively (as far as I am aware this is the first reference for that and it has not been cited https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1914430/). As visible from the last TMRM reference I give, TMRM will only load the cells in Potassium Phosphate buffer with NaCl (and often we used EDTA to permeabilise the membrane). It is not fully clear (to me) whether here TMRM was prepared in rich media (it explicitly says so for ThT in Methods but not for TMRM), but it seems so. If this is the case, it likely also loads because of the damage to the membrane done with light, and therefore I am not surprised that the profiles are similar.

The authors then use CCCP. First, a small correction, as the authors state that it quenches membrane potential. CCCP is a protonophore (https://pubmed.ncbi.nlm.nih.gov/4962086/), so it collapses electrochemical gradient of protons. This means that it is possible, and this will depend on the type of pumps present in the cell, that CCCP collapses electrochemical gradient of protons, but the membrane potential is equal and opposite in sign to the DeltapH. So using CCCP does not automatically mean membrane potential will collapse (e.g. in some mammalian cells it does not need to be the case, but in E.coli it is https://www.biorxiv.org/content/10.1101/2021.11.19.469321v2). CCCP has also been recently found to be a substrate for TolC (https://journals.asm.org/doi/10.1128/mbio.00676-21), but at the concentrations the authors are using CCCP (100uM) that should not affect the results. However, the authors then state because they observed, in Figure S1E, a fast efflux of ions in all cells and no spiking dynamics this confirms that observed dynamics are membrane potential related. I do not agree that it does. First, Figure S1E, does not appear to show transients, instead, it is visible that after 50min treatment with 100uM CCCP, ThT dye shows no dynamics. The action of a Nernstian dye is defined. It is not sufficient that a charged molecule is affected in some way by electrical potential, this needs to be in a very specific way to be a Nernstian dye. Part of the profile of ThT loading observed in https://www.sciencedirect.com/science/article/pii/S0006349519308793 is membrane potential related, but not in a way that is characteristic of Nernstian dye.

Result section 2: Membrane potential dynamics depend on the intercellular distance

In this chapter, the authors report that the time to reach the first intensity peak during ThT loading is different when cells are in microclusters. They interpret this as electrical signaling in clusters because the peak is reached faster in microclusters (as opposed to slower because intuitively in these clusters cells could be shielded from light). However, shielding is one possibility. The other is that the membrane has changed in composition and/or the effective light power the cells can tolerate (with mechanisms to handle light-induced damage, some of which authors mention later in the paper) is lower. Given that these cells were left in a microfluidic chamber for 2h hours to attach in growth media according to Methods, there is sufficient time for that to happen. In Figure S12 C and D of that same paper from my group (https://ars.els-cdn.com/content/image/1-s2.0-S0006349519308793-mmc6.pdf) one can see the effects of peak intensity and timing of the peak on the permeability of the membrane. Therefore I do not think the distance is the explanation for what authors observe.

Result section 3: Emergence of synchronized global wavefronts in E. coli biofilms

In this section, the authors exposed a mature biofilm to blue light. They observe that the intensity peak is reached faster in the cells in the middle. They interpret this as the ion-channel-mediated wavefronts moved from the center of the biofilm. As above, cells in the middle can have different membrane permeability to those at the periphery, and probably even more importantly, there is no light profile shown anywhere in SI/Methods. I could be wrong, but the SI3 A profile is consistent with a potential Gaussian beam profile visible in the field of view. In Methods, I find the light source for the blue light and the type of microscope but no comments on how 'flat' the illumination is across their field of view. This is critical to assess what they are observing in this result section. I do find it interesting that the ThT intensity collapsed from the edges of the biofilms. In the publication I mentioned https://www.sciencedirect.com/science/article/pii/S0006349519308793#app2, the collapse of fluorescence was not understood (other than it is not membrane potential related). It was observed in Figure 5A, C, and F, that at the point of peak, electrochemical gradient of protons is already collapsed, and that at the point of peak cell expands and cytoplasmic content leaks out. This means that this part of the ThT curve is not membrane potential related. The authors see that after the first peak collapsed there is a period of time where ThT does not stain the cells and then it starts again. If after the first peak the cellular content leaks, as we have observed, then staining that occurs much later could be simply staining of cytoplasmic positively charged content, and the timing of that depends on the dynamics of cytoplasmic content leakage (we observed this to be happening over 2h in individual cells). ThT is also a non-specific amyloid dye, and in starving E. coli cells formation of protein clusters has been observed (https://pubmed.ncbi.nlm.nih.gov/30472191/), so such cytoplasmic staining seems possible.

Finally, I note that authors observe biofilms of different shapes and sizes and state that they observe similar intensity profiles, which could mean that my comment on 'flatness' of the field of view above is not a concern. However, the scale bar in Figure 2A is not legible, so I can't compare it to the variation of sizes of the biofilms in Figure 2C (67 to 280um). Based on this, I think that the illumination profile is still a concern.

Result section 4: Voltage-gated Kch potassium channels mediate ion-channel electrical oscillations in E. coli

First I note at this point, given that I disagree that the data presented thus 'suggest that E. coli biofilms use electrical signaling to coordinate long-range responses to light stress' as the authors state, it gets harder to comment on the rest of the results.

In this result section the authors look at the effect of Kch, a putative voltage-gated potassium channel, on ThT profile in E. coli cells. And they see a difference. It is worth noting that in the publication https://www.sciencedirect.com/science/article/pii/S0006349519308793 it is found that ThT is also likely a substrate for TolC (Figure 4), but that scenario could not be distinguished from the one where TolC mutant has a different membrane permeability (and there is a publication that suggests the latter is happening https://onlinelibrary.wiley.com/doi/10.1111/j.1365-2958.2010.07245.x). Given this, it is also possible that Kch deletion affects the membrane permeability. I do note that in video S4 I seem to see more of, what appear to be, plasmolysed cells. The authors do not see the ThT intensity with this mutant that appears long after the initial peak has disappeared, as they see in WT. It is not clear how long they waited for this, as from Figure S3C it could simply be that the dynamics of this is a lot slower, e.g. Kch deletion changes membrane permeability.

The authors themselves state that the evidence for Kch being a voltage-gated channel is indirect (line 54). I do not think there is a need to claim function from a ThT profile of E. coli mutants (nor do I believe it's good practice), given how accurate single-channel recordings are currently. To know the exact dependency on the membrane potential, ion channel recordings on this protein are needed first.

Result section 5: Blue light influences ion-channel mediated membrane potential events in E. coli

In this chapter the authors vary the light intensity and stain the cells with PI (this dye gets into the cells when the membrane becomes very permeable), and the extracellular environment with K+ dye (I have not yet worked carefully with this dye). They find that different amounts of light influence ThT dynamics. This is in line with previous literature (both papers I have been mentioning: Figure 4 https://www.sciencedirect.com/science/article/pii/S0006349519303923 and https://ars.els-cdn.com/content/image/1-s2.0-S0006349519308793-mmc6.pdf especially SI12), but does not add anything new. I think the results presented here can be explained with previously published theory and do not indicate that the ion-channel mediated membrane potential dynamics is a light stress relief process.

Result section 6: Development of a Hodgkin-Huxley model for the observed membrane potential dynamics

This results section starts with the authors stating: 'our data provide evidence that E. coli manages light stress through well-controlled modulation of its membrane potential dynamics'. As stated above, I think they are instead observing the process of ThT loading while the light is damaging the membrane and thus simultaneously collapsing the electrochemical gradient of protons. As stated above, this has been modelled before. And then, they observe a ThT staining that is independent from membrane potential.

I will briefly comment on the Hodgkin Huxley (HH) based model. First, I think there is no evidence for two channels with different activation profiles as authors propose. But also, the HH model has been developed for neurons. There, the leakage and the pumping fluxes are both described by a constant representing conductivity, times the difference between the membrane potential and Nernst potential for the given ion. The conductivity in the model is given as gK*n^4 for potassium, gNa*m^3*h sodium, and gL for leakage, where gK, gNa and gL were measured experimentally for neurons. And, n, m, and h are variables that describe the experimentally observed voltage-gated mechanism of neuronal sodium and potassium channels. (Please see Hodgkin AL, Huxley AF. 1952. Currents carried by sodium and potassium ions through the membrane of the giant axon of Loligo. J. Physiol. 116:449-72 and Hodgkin AL, Huxley AF. 1952. A quantitative description of membrane current and its application to conduction and excitation in nerve. J. Physiol. 117:500-44).

Thus, in applying the model to describe bacterial electrophysiology one should ensure near equilibrium requirement holds (so that (V-VQ) etc terms in authors' equation Figure 5 B hold), and potassium and other channels in a given bacterium have similar gating properties to those found in neurons. I am not aware of such measurements in any bacteria, and therefore think the pump leak model of the electrophysiology of bacteria needs to start with fluxes that are more general (for example Keener JP, Sneyd J. 2009. Mathematical physiology: I: Cellular physiology. New York: Springer or https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0000144)

Result section 7: Mechanosensitive ion channels (MS) are vital for the first hyperpolarization event in E. coli.

The results that Mcs channels affect the profile of ThT dye are interesting. It is again possible that the membrane permeability of these mutants has changed and therefore the dynamics have changed, so this needs to be checked first. I also note that our results show that the peak of ThT coincides with cell expansion. For this to be understood a model is needed that also takes into account the link between maintenance of electrochemical gradients of ions in the cell and osmotic pressure.

A side note is that the authors state that the Msc responds to stress-related voltage changes. I think this is an overstatement. Mscs respond to predominantly membrane tension and are mostly nonspecific (see how their action recovers cellular volume in this publication https://www.pnas.org/doi/full/10.1073/pnas.1522185113). Authors cite references 35-39 to support this statement. These publications still state that these channels are predominantly membrane tension-gated. Some of the references state that the presence of external ions is important for tension-related gating but sometimes they gate spontaneously in the presence of certain ions. Other publications cited don't really look at gating with respect to ions (39 is on clustering). This is why I think the statement is somewhat misleading.

Result section 8: Anomalous ion-channel-mediated wavefronts propagate light stress signals in 3D E. coli biofilms.

I am not commenting on this result section, as it would only be applicable if ThT was membrane potential dye in E. coli.

Aims achieved/results support their conclusions:

The authors clearly present their data. I am convinced that they have accurately presented everything they observed. However, I think their interpretation of the data and conclusions is inaccurate in line with the discussion I provided above.

Likely impact of the work on the field, and the utility of the methods and data to the community:

Any other comments:

I note, that while this work studies E. coli, it references papers in other bacteria using ThT. For example, in lines 35-36 authors state that bacteria (Bacillus subtilis in this case) in biofilms have been recently found to modulate membrane potential citing the relevant literature from 2015. It is worth noting that the most recent paper https://journals.asm.org/doi/10.1128/mbio.02220-23 found that ThT binds to one or more proteins in the spore coat, suggesting that it does not act as a membrane potential in Bacillus spores. It is possible that it still reports membrane potential in Bacillus cells and the recent results are strictly spore-specific, but these should be kept in mind when using ThT with Bacillus.

Reviewer #2 (Public Review):

The study builds on the work of the Pan group and others which has described the existence of core Hippo pathway proteins in Capsaspora and, more recently, described a role for a Yorkie/YAP homologue in regulation of cell shape and actin, as opposed to proliferation. For this recent study, they developed genetic techniques to mutate genes in Capsaspora, and this technology has been leveraged again in this study. Using loss of function genetic approaches, the authors find that loss of either of the two major kinases in the Hippo pathway core kinase cassette (Warts and Hippo) impact Capsaspora morphology and the actin cytoskeleton. This is phenocopied by overexpression of Capsaspora Yorkie/YAP. In addition, Capsaspora Yorkie/YAP accumulates in the nucleus of organisms lacking Warts or Hippo, as it does in metazoans. While these experiments are not overly surprising, they still provide important verification that core Hippo signaling events are conserved in Capsaspora.

Subsequently, they show that Capsaspora lacking Warts or Hippo do not overproliferate, which contrasts with many studies in metazoans (flies, mice, fish), particularly in epithelial tissues where loss of Warts or Hippo often causes overproliferation. Rather, the authors show that Capsaspora Warts and Hippo regulate cell morphology and actomyosin-dependent contractile behaviour. They speculate from these findings that Hippo signalling could regulate the density of Capsaspora when they grow in aggregates and draw parallels to the known role of the Hippo pathway in contact inhibition of mammalian cells grown in culture.

Together with their 2022 paper, this study paints an emerging picture that the ancestral function of the Hippo pathway is to regulate the actin cytoskeleton, not proliferation, which is a significant finding. This also suggests that the ability to control proliferation was something that the Hippo pathway was re-purposed to do at some stage during the evolution of metazoans. These findings are important for the Hippo field, and our understanding of cellular signalling and evolution more broadly.

In future studies, further biochemical and genetic experiments would allow the authors to more conclusively prove that core features of Hippo signalling are conserved in Capsaspora - e.g., that Capsaspora Hippo/MST activates Warts/LATS by phosphorylation and Warts/LATS represses Yorkie/YAP by phosphorylation hey serine residues. Some of these experiments are challenging or not yet possible due to technical limitations. Higher resolution imaging approaches such as electron microscopy would likely give further mechanistic insights into how Hpo, Wts and Yki modulate actomyosin contractility in Capsaspora. Recent advances in mass spectrometry of the phospho-proteome should provide a valuable way to explore Hippo signalling in Capsaspora. The benefit of this approach is it has the potential to give information on all Hippo pathway proteins and could be used to probe signalling events under different culture conditions (e.g., aggregate, non-aggregate).

Reviewer #2 (Public Review):

Summary:

The present article describes a series of experiments examining how a gradual reduction in unconditional stimulus intensity facilitates fear reduction and reduces relapse (spontaneous recovery and reinstatement) relative to a standard extinction procedure. The experiments provide compelling, if somewhat inconsistent, evidence of this effect and couch the results in a scholarly discussion surrounding how mechanisms of prediction error contribute to this effect.

Strengths:

The experiments are theoretically motivated and hypothesis-driven, well-designed, and appropriately conducted and analyzed. The results are clear and appropriately contextualized into the broader relevant literature. Further, the results are compelling and ask fundamental questions regarding how to persistently weaken fear behavior, which has both strong theoretical and real-world implications. I found the 'scrambled' experiment especially important in determining the mechanism through which this reduction in shock intensity persistently weakens fear behavior.

Weaknesses:

Overall, I found very few weaknesses in this paper. I think some might view the somewhat inconsistent effects on relapse between experiments to be a substantial weakness, I appreciate the authors directly confronting this and using it as an opportunity to aggregate data to look at general trends. Further, while Experiment 1 only used males, this was corrected in the rest of the experiments and therefore is not a substantial concern.

Reviewer #2 (Public Review):

Summary:

The manuscript entitled "Decoupling of the Onset of Anharmonicity between a Protein and Its Surface Water around 200 K" by Zheng et al. presents a neutron scattering study trying to elucidate if at the dynamical transition temperature water and protein motions are coupled. The origin of the dynamical transition temperature has been highly debated for decades, specifically its relation to hydration.

Strengths:

The study is rather well conducted, with a lot of effort to acquire the perdeuterated proteins, and some results are interesting.

Weaknesses:

The present work could certainly contribute some arguments, but I have the feeling that not all known facts are properly discussed.

The points the authors should carefully discuss are the following:

(1) Daniel et al. (10.1016/S0006-3495(98)77694-5) have shown that enzymes can be functional below the dynamical transition temperature which is at odds with some of the claims of the authors.

(2) It is not as easy to say that protonated proteins in D2O reflect protein dynamics while perdeuterated proteins in H2O reflect water dynamics. A recent study by Nidriche et al. (PRX LIFE 2, 013005 (2024)) reveals that H <-> D exchange is much faster than usually assumed and has important consequences for such studies.

(3) A publication by Jasnin et al. (10.1039/b923878f) on heparin sulfate shows a resolution effect.

(4) The authors should discuss the impact of the chosen q-range on their findings (see Phys. Chem. Chem. Phys., 2012, 14, 4927-4934, where the authors see a huge effect !).

(5) The authors underline that the dynamical transition is intrinsic to the protein. However, Cupane et al. (ref 12) have shown that it can also be found in a mixture of amino acids without any protein backbone.

(6) The authors say that they find similar dependences from MSD. They should explain that the MSD is inversely proportional to the summed intensities squared.

(7) A decoupling between water dynamics and membrane dynamics has already been discussed by K. Wood, G. Zaccai et al.

(8) The fact that transition temperature in lipid membranes is higher when the membrane is dry is also well known (A.V. Popova, D.K. Hincha, BMC Biophys. 4, 11 (2011)).

(9) The authors should mention the slope (K/min) they used for DSC and discuss the impact of it on the results.

(10) In the introduction, the authors should present the different explanations forwarded for the dynamical transition.

Reviewer #2 (Public Review):

Summary:

The authors have performed a detailed analysis of the complex transcriptional status of numerous cell types present in wounded tissue, including keratinocytes, fibroblasts, macrophages, neutrophils, and endothelial cells. The comparison between infected and uninfected wounds is interesting and the analysis suggests possible explanations for why infected wounds are delayed in their healing response.

Strengths:

The paper presents a thorough and detailed analysis of the scRNAseq data. The paper is clearly written and the conclusions drawn from the analysis are appropriately cautious. The results provide an important foundation for future work on the healing of infected and uninfected wounds.

Weaknesses:

The analysis is purely descriptive and no attempt is made to validate whether any of the factors identified are playing functional roles in wound healing. The experimental setup is analyzing a single time point and does not include a comparison to unwounded skin.

Reviewer #2 (Public Review):

Summary:

The paper entitled "Plural molecular and cellular mechanisms of pore domain KCNQ2 encephalopathy" by Abreo et al. is a complex and integrated paper that is well-written with a focus on a single gene variant that causes a severe developmental encephalopathy. The paper collates clinical outcomes from 4 individuals and investigates a variant causing KCNQ2-DEE using a wide range of experimental techniques including structural biology, in vitro electrophysiology, generation of genetically modified animal models, immunofluorescence, and brain slice recordings. The overall results provide a plausible explanation of the pathophysiology of the G265W variant and provide important findings to the KCNQ2-DEE field as well as beginning to separate the understanding between seizures and encephalopathies.

Strengths:

(1) The authors describe in detail how the structural biology of the channel with a mutation changes the movement of the protein and adds insights into how one variant can change the function of the M-current. The proposed model linking this change to pathogenic consequences should help pave the way for additional studies to further support this type of approach.

(2) The multiple co-expression ratio experiments drill down to the complex nature of the assembly of channels in over-expression systems and help to move toward an understanding of heterozygosity. It might have been interesting if TEA was tested as a blocker to better understand the assembly of the transfected subunits or possibly use vectors to force desired configurations.

(3) The immunofluorescent approach to understanding re-distribution is another component of understanding the function of this critical current. The demonstration that Q2 and Q3 are diminished at the AIS is an important finding and a strength to the totality of the data presented in the paper.

(4) Brain slice work is an important component of studying genetically modified animals as it brings in the systems approach, and helps to explain seizure generation and EEG recordings. The finding that G265W/+ neurons were more sensitive to current injections is a critical component of the paper.

(5) The strength of this body of work is how the authors integrated different scientific approaches to knitting together a compelling set of experiments to better explain how a single variant, and likely extrapolation to other variants, can cause a severe neonatal developmental encephalopathy with a poor clinical outcome.

Weaknesses:

(1) Minor comment: Under the clinical history it is unclear whether the mother was on Leviracetam for suspected in-utero seizures or if Leviracetam was given to individual 1. The latter seems more likely, and if so this should be reworded.

(2) As described in the clinical history of patient 1, treatment with ezogabine was encouraging with rapid onset by a parental global impression with difficulty in weaning off the drug. When studying the genetically modified mice, it would have been beneficial to the paper to talk about any ezogabine effects on the genetically modified mice.

(3) It is a bit surprising that CA1 pyramidal neurons from the heterozygous G256W mice have no difference in resting membrane potential. The discussion section might explore this in a bit more detail.

(4) It was mentioned in the paper about a direct comparison between SLFNE and G256W. However, in the slice recordings, there was no comparison. Having these data comparing SLFNE to G256W would have been a more fulsome story and would have added to the concept around susceptibility to action potential firing.

Reviewer #2 (Public Review):

Summary:

Dr Lenz and colleagues report on their in vitro studies comparing gene transcription and epigenetic modifications in Plasmodium falciparum NF54 parasites selected or not selected for adhesion of the infected erythrocytes (IEs) to the placental IE adhesion receptor chondroitin sulfate A (CSA).

The authors report that selection led to preferential transcription of var2csa, the gene that encodes the VAR2CSA-type PfEMP1 well-established as the PfEMP1 mediating IE adhesion to CSA. They confirm that transcriptional activation of var2csa is associated with distinct depletion of H3K9me3 marks and that transcriptional activation is linked to repositioning of var2csa.

Strengths:

The study confirms previously reported features of gene transcription and epigenetic modifications in Plasmodium falciparum.

Weaknesses:

No major new finding is reported.

Reviewer #2 (Public Review):

Summary:

This paper reports a role for a substantial number of RNA binding proteins (RBPs), in particular hnRNPs, in the function of ASAR "genes". ASARs are (very) long, non-coding RNAs (lncRNAs) that control allelic expression imbalance (e.g.: mono-allelic expression) and replication timing of their resident chromosomes. These relatively novel "genes" have recently been identified on all human autosomes and are of broad significance given their critical importance for basic chromosomal functions and stability. However, the mechanism(s) of ASAR function remain unclear. ASARs exhibit some functional relatedness to Xist RNA, including persistent association of the expressed RNA with its resident chromosome, and similarities in the composition of RNA sequences associated with ASARs, in particular Line1 RNAs. Recent findings that certain hnRNPs control the chromosome territory retention of Cot1-bearing RNAs (which includes Line1) led the authors to test the hypothesis that hnRNPs might regulate ASARs.

Specific new findings in this paper:

-Analysis of eCLIP (RNA-protein interaction) ENCODE data shows numerous interactions of the ASAR6-141 RNA with RBPs, including hnRNPs (e.g.: HNRNPU) that have been implicated in the retention of RNAs within local chromosome territories.

-Most of these interactions can be mapped to a 7kb region of the 185kb ASAR6-141 RNA.

-Deletion of this 7kb region is sufficient to induce the DMC/DRT phenotype associated with deletion of the entire ASAR region.

-Ectopic integration into mouse autosomes of the 7kb region is sufficient to cause DMC/DRT of the targeted autosome, and a similar effect upon ectopic integration into inactive X. This raises the question about integration into the active X, which was not mentioned. Is integration into the active X observed? Is it possible that integration might alter Xist expression confounding this interpretation?

-Knockdown of RBPs that bind the 7kb region causes dissociation of ASAR6-141 RNA from its chromosome territory, and, remarkably, dissociation of Xist RNA from inactive X, and mis-colocalization of the ASAR6-141 and Xist RNAs. Depletion of these RBPs causes DMC/DRT on all autosomes.

Strengths:

These are compelling results suggesting shared mechanism(s) in the regulation of ASARs and Xist RNAs by RBPs that bind Cot1 sequences in these lncRNAs. The identification of these RBPs as shared effectors of ASARs and Xist that are required for RNA territory localization mechanistically links previously independent phenomena.

The data are convincing and support the conclusions. The replication timing method is low resolution and is only a relative measure but seems adequate for the task at hand. The FISH experiments are convincing. The quality of the images is impressive.

Links to other subfields like X-inactivation and RNA association with chromosome territories provide novel context and protein players, new phenotypes to examine.

Weaknesses:

The exact effects of knockdown experiments are unclear and may be indirect, which is acknowledged.

The mechanism is not much clearer than before.

Reviewer #3 (Public Review):

Syntactic parsing is a highly dynamic process: When an incoming word is inconsistent with the presumed syntactic structure, the brain has to reanalyze the sentence and construct an alternative syntactic structure. Since syntactic parsing is a hidden process, it is challenging to describe the syntactic structure a listener internally constructs at each time moment. Here, the authors overcome this problem by (1) asking listeners to complete a sentence at some break point to probe the syntactic structure mentally constructed at the break point, and (2) using a DNN model to extract the most likely structure a listener may extract at a time moment.

After obtaining incremental syntactic features using a DNN model, i.e., BERT, the authors analyze how these syntactic features are represented in the brain using MEG. The advantage of the approach is that BERT can potentially integrate syntactic and semantic knowledge and is a computational model, instead of a static theoretical construct, that may more precisely reflect incremental sentence processing in the human brain. The results indeed confirm the similarity between MEG activity and measures from the BERT model.

Reviewer #2 (Public Review):

A number of previous reports have demonstrated that theta synchrony between the hippocampus (HPC) and prefrontal cortex (PFC) is associated with correct performance on spatial working memory tasks. The main goal of the current study is to examine this relationship by initiating trials either randomly (as has typically been done in previous studies) or during periods of high or low PFC-HPC coherence. To this end, they develop a 'brain-machine interface' (BMI) that provides real-time estimates of PFC-HPC theta coherence, which are then used to control trial onset using an automated figure-eight maze. Their main finding is that choice accuracy is significantly higher on trials initiated when theta coherence is high whereas performance on low coherence trials does not differ from randomly initiated control trials. They also observe a similar result using a non-working memory task in the same maze.

Overall the main experiments (Figures 1-4) are well designed and the BMI approach is convincingly validated. There are also appropriate controls and analyses to rule out behavioral confounds and the results are clearly presented. Although the BMI can not establish a causal relationship between PFC-HPC coherence and behavior, it is helpful for examining how extremes in the distribution of brain states are associated with behavioral performance, something that might be more difficult to examine if trials are initiated randomly. As such, the BMI is an interesting approach for studying the neuronal basis of behavior that could be applied in other fields of neuroscience.

In addition to the behavioral results, the authors also examine what neuronal mechanisms might support enhanced PFC-HPC synchrony (Figures 5-6). Here, they examine the potential contribution of the ventromedial thalamus (VMT) but the results are inconclusive. In particular, the results of optogenetic stimulation of the VMT (Figure 6) show that it both increases and decreases PFC-HPC theta synchrony, depending on the exact frequency range examined. These results are also somewhat preliminary as they come from only 2 animals.

Reviewer #2 (Public Review):

The manuscript details an investigation aimed at developing a protocol to render centimeter-scale formalin-fixed paraffin-embedded specimens optically transparent and suitable for deep immunolabeling. The authors evaluate various detergents and conditions for epitope retrieval such as acidic or basic buffers combined with high temperatures in entire mouse brains that had been paraffin-embedded for months. They use various protein targets to test active immunolabeling and light-sheet microscopy registration of such preparations to validate their protocol. The final procedure, called MOCAT pipeline, briefly involves 1% Tween 20 in citrate buffer, heated in a pressure cooker at 121 {degree sign}C for 10 minutes. The authors also note that part of the delipidation is achieved by the regular procedure.

Major Strengths<br /> - The simplicity and ease of implementation of the proposed procedure using common laboratory reagents distinguish it favorably from more complex methods.

- Direct comparisons with existing protocols and exploration of alternative conditions enhance the robustness and practicality of the methodology.

Major Weaknesses

- The assertion that MOCAT can be rapidly applied in hospital pathology departments seems overstated due to the limited availability of light-sheet microscopes outside research labs. In the first rebuttal letter, authors explain the limitations of other microscopes more readily available in hospitals. This explanation relies on your own investigations and practical experience on the matter, so including them in some part of the manuscript would be beneficial.

- Refractive index matching is a critical point in the protocol, the one providing final transparency. Authors utilized the commercial solutions NFC1 and NFC2 (Nebulem, Taiwan) with a known refractive index, but for which its composition is non-disclosable. My knowledge on the organic chemistry around refractive index matching is limited, but if users don't really know what is going on in this final step, the whole protocol would rely on a single world-wide provider and troubleshooting would be fishing. I suggest that you try to validate the approach with solutions of known composition, or at least provide the solutions sold by other providers.

Final considerations<br /> The evidence presented supports the effectiveness of the proposed method in rendering thick FFPE samples transparent and facilitating repeated rounds of immunolabeling.

The developed procedure holds promise for advancing tissue and 3D-specific determination of proteins of interest in various settings, including hospitals, basic research, and clinical labs, particularly benefiting neuroscience research.

The methodological findings suggest that MOCAT could have broader applications beyond FFPE samples, differentiating it from other tissue-clearing approaches in that the equipment and chemicals needed are broadly accessible.

Reviewer #3 (Public Review):

Summary:

Harada and colleagues describe an interesting set of experiments characterizing the relationship between dopamine cell activity in ventral tegmental area (VTA) and orexin neuron activity in lateral hypothalamus (LH). All experiments are conducted in the context of an opto-Pavlovian learning task, in which a cue predicts optogenetic stimulation of VTA dopamine neurons. With training, cues that predict DA stimulation come to elicit dopamine release in LH (a similar effect is seen in accumbens). After training, omission trials (cue followed by no laser) result in a dip (inhibition) of dopamine release in LH, characteristic of reward prediction error observed in striatum. Across cue training, the activity pattern of orexin neurons in LH mirrors that of LH DA levels. However, unlike the DA signal, orexin neurons do not exhibit a decrease in activity in omission trials. Systemic blockade of D2 but not D1 receptors blocked DA release in LH following VTA DA cell stimulation.

Strengths:

Although much work has been dedicated to examining projections from orexin cells to VTA, less has been done to characterize reciprocal projections and their function. In this way, this paper is a very important addition to the literature. The experiments are technically sound (with some limitations, below) and utilize sophisticated approaches, the manuscript is nicely written, and the conclusions are mostly reasonable based on the data collected.

Weaknesses:

I believe the impact of the paper could be enhanced by considering and/or addressing the following:

Major<br /> • I encourage the authors to discuss in the Introduction previous work on DA regulation of orexin neurons. In particular, the authors cite, but do not describe in any detail, the very relevant Linehan paper (2019; Am J Physiol Regul) which shows that DA differentially alters excitatory/inhibitory input onto orexin neurons and that these actions are reversed by D1 vs D2 receptor antagonists. Another paper (Bubser, 2005, EJN) showed that dopamine agonists increase activity of orexin neurons and that these effects are blocked by D1/D2 antagonists. The current findings should be discussed in the context of these (and any other relevant) papers in the Discussion, too.

The revised manuscript addresses these concerns.

• In the Discussion, the authors provide 2 (plausible) explanations for why they did not observe a dip in calcium signal of orexin neurons during omission trials. Is it not possible that these cells do not encode for this type of RPE?

The revised manuscript addresses these concerns.

• Related to the above - I am curious about the authors' thoughts on why there is such redundancy in the system. i.e. why is dopamine doing the same thing in NAC and LH in the context of cue-reward learning?

The revised manuscript addresses these concerns.

• The data, as they stand, are largely correlative and do not indicate that DA recruitment of orexin neurons is necessary for learning to occur. It would be compelling if blocking the orexin cell recruitment affected some behavioral outcome of learning. Similarly - does raclopride treatment across training prevent learning?

I maintain that experiments testing the causality of these effects on learning/behavior would enhance the impact of the paper. However, I recognize that this would require substantial additional experimentation and the data here are interesting regardless.

• Only single doses of SCH23390 and raclopride were used. How were these selected? It would be nice to use more of a dose range to show that 1) and effect of D1R blockade was not missed, and 2) that the reduction in orexin signal with raclopride was dose-dependent.

Additional information on dose selection has been included - thank you. Again, these data might be more impactful if the effects of antagonists were found to be dose-dependent.

• Fig 1C, could the effect the authors observed due to movement? Relatedly, what was the behavior like when the cue was on? Did mice orient/approach the cue? Also, when does the learning about the cue occur? Does it take all 10 days of learning or does this learning/cue-induced increase in dopamine signaling occur in less than 10 days?

These have been addressed in the revised manuscript

• Also related to above, could the observed dopamine signal be a result of just the laser turning on? It would seem important to include mice with a control sensor.

The authors note that a control channel was recorded. I agree this is useful, but my concern is that the illumination of laser itself might startle the animal (promote movement), resulting in dopamine release. Showing this does not occur with the same laser in chr2-lacking vta neurons would help resolve this issue.

• Fig 1E, the effect seems to be driven by one mouse which looks like it could be a statistical outlier. Inclusion of additional animals would make these data more compelling.

I would still argue that these data could be strengthened by the addition of more mice. I note that the graph depicting individual data points has been removed from the revised manuscript - i would recommend re-including this figure.

• For Fig 1C, 3D, 3F, and 4D, could the authors please show the traces for the entire length of laser onset? It would be helpful to see both the rise and the fall of dopamine signals.<br /> • Fig 2C, could the authors comment on how they compared the AUC to baseline? Was this comparison against zero? Because of natural hills and troughs during signals prior to cue (which may not equate to a zero), comparing the omission-induced dip to a zero may not be appropriate. A better baseline might be using the signals prior to the cue.<br /> • Could the authors comment on how they came up with the 4-5.3s window to observe the AUC in Fig 3H?

These have all been addressed.

Minor<br /> • When discussing the understudied role of dopamine in brain regions other than the striatum in the Introduction, it might be helpful to cite this article: https://elifesciences.org/articles/81980 where the authors characterize dopamine in the bed nucleus of stria terminalis in associative behaviors and reward prediction error.<br /> • In Discussion, it might be better to refrain from describing the results as 'measuring dopamine release' in the LH. Since there was no direct detection of dopamine release, rather dopamine binding to the dLight receptors, referring to the detection as dopamine signaling/binding/transients is a better alternative.<br /> • In Discussion, without measuring tonic dopamine release, it is difficult to say that there was a tonic dopamine release in the LH prior to negative RPE. In addition, I wouldn't describe the negative RPE as silencing of dopamine neurons projecting to the LH since this was not directly measured and it is hard to say for sure if the dip in dopamine is caused by silencing of the neurons. There certainly seems to be a reduction in extrasynaptic dopamine signaling in LH, however what occurs upstream is unknown.<br /> • Typo at multiple places: 'Tekey's multiple comparison test'.

These have been addressed.

Reviewer #2 (Public Review):

In this study, authors identified TOR, HOG and CWI signaling network genes as modulators of the development, aflatoxin biosynthesis and pathogenicity of A. flavus by gene deletions combined with phenotypic observation. They also analyzed the specific regulatory process and proposed that the TOR signaling pathway interacts with other signaling pathways (MAPK, CWI, calcineurin-CrzA pathway) to regulate the responses to various environmental stresses. Notably, they found that FKBP3 is involved in sclerotia and aflatoxin biosynthesis and rapamycin resistance in A. flavus, especially that the conserved site K19 of FKBP3 plays a key role in regulating aflatoxin biosynthesis. In general, the study involved a heavy workload and the findings are potentially interesting and important for understanding or controlling the aflatoxin biosynthesis. However, the findings have not been deeply explored and the conclusions mostly are based on parallel phenotypic observations.

Reviewer #2 (Public Review):

The following submission titled "Xanthomonas citri subsp. citri type III effector PthA4 directs the dynamical expression of a putative citrus carbohydrate-binding gene for canker formation" by Chen et al. provides evidence that PthA4 binds to PCs9g12620 to downregulate expression potentially for citrus canker disease development. They tackle a relevant, complicated problem about the timing and regulation of an S gene expression and its relationship to disease development. Most often research stops at an S gene that is upregulated. This study aims to define the complexity of TAL effector family proteins beyond their standard activation role. Cs9g12620 encodes a putative carbohydrate-binding protein, and downregulation of this occurs via PthA4-CsLOB1 direct interaction. Silencing of Cs9g12620 leads to reduced virulence of X. citri, highlighting its importance as an S gene target from PthA4-mediated CsLOB1 induction. The authors also hypothesize that PthA4 represses the expression of Cs9g12620, and it seems to depend not on DNA binding by PthA4 but rather CsLOB1 interaction. This provides an interesting mode of action for a TAL effector, which typically is described as a transcription factor. An overall curiosity is that TAL effectors like PthA4 induce gene expression for virulence activity, but the authors do not probe this question with artificial TAL effectors or PthA4 variants to define the domains required for this activity. These tools, which are widely used in TAL effector research, could help determine what domain is responsible for this repression and if it is unique to PthA4 or a general TAL phenomenon. Work is further needed to also demonstrate the repressive role of PthA4 over time because it is not explicitly clear that the time-related suppression is directly attributed to the PthA4-CsLOB1 interactions.

(1) The authors show that both WT but not WT expressing AvrXa7 induce Cs9g12620 and CsLOB1. They performed an adjacent supportive experiment comparing a Tn5-disrupted pthA4 to WT and saw a similar induction. Do the authors have a southern blot or genome sequence to show this is the true mutation? Have the authors complemented the Tn5 strain with pthA4 and an artificial TAL effector?

(2) Figure 2 and "The expression of Cs9g12620 depends on pthA4 during Xcc infection" section: Overall I cannot determine the biological importance as written in the text about examining an ortholog of Cs9g12620 that is not expressed. The title of Figure 2 is: "Cs9g12620 and Cs9g12650 show different profiles of expression owing to the genetic variation in promoter." What is the biological importance of showing that there is promoter variation when the RNA-seq pointed to this target? This is unclear. Now, an interesting experiment would be to create an artificial TAL that activates the expression of Cs9g12650, which was, yes, not expressed in Nicotiana, but this wasn't examined in citrus and could be with an artificial TAL effector. Moreover, if this is about how something is not expressed, this seems out of place in the story before we arrive at the repression aspect of the narrative. Is the lack of expression a typical state of this gene family and do TAL effectors induce this for virulence? Is it also possible that RT alone isn't sensitive enough to detect relevant Cs9g12650 expression? Could the authors rather build on their RNAseq data or maybe use qPCR, a more sensitive approach, to see if this gene is expressed. Overall, this seems like a non-issue still because it isn't clear why this is important to support their narrative. Finally "2 μg of total RNA extracted" seems to be an extremely high input for RT. In summary here, it would be nice to see the hypothesis they tested and how it supports their overall aim because this is unclear.

(3) Figure 3C: The authors should include a 35S::GUS + 35S::pthA4 control. This control is missing to show that the suppression is not due to overexpressing the two proteins simultaneously.

(4) Figure 3E&G are just the same but rotated. Please include a separate replicate as this would be more beneficial to examine. With this and concerns on some of the reporting, the raw data and images should be included as supplemental for each replicate and detailed as if they are a regular figure.

(5) Figure 3G: What is low and high? There are quantifiable values (e.g. RLU) here that correspond to the intensity of the figure legend. There should be a water/buffer infiltrated control.

(6) Figure 3F: The Y1H data demonstrate that PCs9g12620 is bound by PthA4. The second panel for the gel mobility shift is however lacking a complementary treatment with PCs9g12620 WT. These gel mobility shift assays are always relative to something, and there is no comparison here unfortunately to other treatments. An example to follow as a model for formatting and experimental design could include as seen in Figure 5 by Duan et al. MPP (DOI:10.1111/mpp.12667). These should be performed as a single experiment not separated by panel D. A GST-Tag only should always be an additional control.

(7) Figure 4: CsLOB1 activates Cs9g12620. Figure 4C: A reasonable control would be to include 35S::GUS and 35S::PthA4.

(8) Figure 5F: The purpose of this experiment to show the multiplication over time and increase is not clear. It would be expected to see an increase in growth over time during susceptibility; so why was this documented?

(9) Figure 5: Cs9g12620 expression decreases along with expansion and pectin esterase expression. How do we know that this is not a general downregulation of gene expression more broadly due to cell death or tissue deformation at 10 dpi? To test if this is also PthA4-specific, an experiment needed would be to test a specific pthA4 mutant rather than the TAL effectorless strain, which is already pretty weak a pathogen and does not trigger expression of any tested genes to wild-type levels to see if this is a general trend or specific to PthA4 activity. Finally, why are the color bars switched for time points 5 & 10 dpi for the effectorless strain? This is the finding that led them to suggest the repression. According to the rest of the figure, the gray and black are typically 5 and 10 dpi, respectively, but they seem to be switched to fit the narrative.

(10) Figure 6 nicely documents the interaction between PthA4 and CsLOB1, but why did the authors not take the additional step to define what domains are required for PthA4 interaction? This is an important curiosity of what mediates this interaction. Was it the repeats or C- or N-terminus? Is this general to TAL effectors or precise to PthA4? This seems like the crux of the story especially since there is a TAL effector binding cited in the promoter.

(11) Figure 7: RNAi-mediated silencing of Cs9g12620 demonstrates that this gene is a susceptibility target for X. citri as seen by colonization (E). First, the symptoms are not quite clear in A, and the morphological changes are unclear. Are there additional images for these to showcase the difference reproducibly? They hypothesize that there is complexity in Cs9g12620 expression during infection as proposed in Figure 8. It seems pretty important to perturb this in the opposite direction with artificial TAL effectors that either target a) Cs9g12620 for induction and b) CsLOB1 in a 049E background. One would hypothesize that this would not allow for the CsLOB1 interaction because they demonstrate this is PthA4-specific and therefore Cs9g12620 expression would not decrease while CsLOB1 is induced.

(12) Figure 8: It is unclear if this is an appropriate model. The impact of CsLOB1-PthA4 interaction is depicted as a late phenomenon based on Cs9g12620 expression. However, it is not clear from their data that the CsLOB1-PthA4 interaction does not happen at the early stages of infection. This is not defined by their experiments proposed. As mentioned above, an overall concern is that the authors do not test variants of PthA4 or domains that could examine specifically what permits this suppression. Is this a general TAL effector structure-mediated phenomenon or is it something unique about PthA4 in this family? Does it require both DNA binding and interaction with CsLOB1?

Reviewer #2 (Public Review):

Summary:

Wang and collaborators have evaluated the impact of inflammation on bone loss induced by Doxorubicin, which is commonly used in chemotherapy to treat various cancers. In mice, they show that a single injection of Doxorubicin induces systemic inflammation, leukopenia, and a significant bone loss associated with increased bone-resorbing osteoclast numbers. In vitro, the authors show that Doxorubicin activates the AIM2 and NLRP3 inflammasomes in macrophages and neutrophils. Importantly, they show that the full knockouts (germline deletions) of AIM2 (Aim2-/-) and NLRP3 (Nlrp3-/-) and Caspase 1 (Casp1-/-) limit (but do not completely abolish) bone loss induced 4 weeks after a single injection of Doxorubicin in mice. From these results, they conclude that Doxorubicin activates inflammasomes to cause inflammation-associated bone loss.

Strength:

This manuscript provides functional experiments demonstrating that NRLP3 and/or AIM2 loss-of-functions (and thus the systemic impairment of the inflammatory response) prevent bone-loss induced by Doxorubicin in mice.

Weaknesses:

Numerous studies have reported that Doxorubicin induces systemic inflammation and activates the inflammasome in myeloid cells and various other cell types. It is also known that systemic inflammation and Doxorubicin treatment lead to bone loss. Hence, the key conclusions drawn from this work have been known already or were very much expected. Therefore, the novelty appears somewhat limited. One important limitation is the lack of experiments that could determine which cell lineages are involved in bone loss induced by Doxorubicin in vivo, while the tools to do so exist. The characterization of the bone phenotype is incomplete, and unfortunately does not tell us whether the inflammasome is activated in some of the cell lineages present in bones in vivo. Another limitation is that the relative importance of the inflammasomes compared to cell senescence and autophagy, which are also induced by Doxorubicin, has not been evaluated. Hence the main molecular mechanisms responsible for bone loss induced by Doxorubicin in vivo remains unknown. Lastly, it would have been interesting, on a more clinical point of view, to compare the few relevant treatments that could limit the deleterious effect of Doxorubicin on bone loss while preserving the toxicity on tumor cells.

Reviewer #2 (Public Review):

Diaphorina citri is the primary vector of Candidatus Liberibacter asiaticus (CLas), but the mechanism of how D. citri maintains a balance between lipid metabolism and increased fecundity after infection with CLas remains unknown. In their study, Li et al. presented convincing methodology and data to demonstrate that CLas exploits AKH/AKHR-miR-34-JH signaling to enhance D. citri lipid metabolism and fecundity, while simultaneously promoting CLas replication. These findings are both novel and valuable, not only have theoretical implications for expanding our understanding of the interaction between insect vectors and pathogenic microorganisms but also provide new targets for controlling D. citri and HLB in practical implications. The conclusions of this paper are mostly well supported by data, but some aspects of phrasing and data analysis need to be further clarified and extended.

Key Considerations:

There are specific instances where additional information would enhance comprehension of the results and their interpretation.

There seem to be two inconsistencies related to some results depicted in Figures 1, 2, 3 and 5.

Firstly, Figure 1 shows the effect on CLas infection (CLas+) compared to the control (CLas-), where results show an increase of TAG, Glycogen, lipid droplet size, oviposition period, and fecundity. In Figures 2, 3, and 5, the authors establish the involvement of the genes DcAKH, DcAKHR, and miR34 in this process, by showing that by preventing the function of these three factors the effects of CLas+ are lost. However, while Figure 1 shows the increase of TAG and lipid droplet size in CLas+, Figures 2, 3, and 5 do not show a significant elevation in TAG when comparing CLas- and CLas+.

Secondly, in addition to the absence of statistical difference in TAG and lipid droplet size observed in Figure 1, Figures 2, 3, and 5 show an increase in TAG and lipid droplet size after dsDcAKH (Figure 2), dsDcAKHR (Figure 3) and agomiR34 (Figure 5) treatments. Considering that AKH, AKHR, and miR34 are important factors to CLas-induce increase in TAG and lipid droplet size, one might expect a reduction in TAG and lipid droplet size when CLas+ insects are silenced for these factors, contrary to the observed results.

Reviewer #2 (Public Review):

Summary:

In their work, the authors study local mechanics in an invaginating epithelial tissue. The mostly computational work relies on the Cellular Potts model. The main result shows that an increased apical "contractility" is not sufficient to properly drive apical constriction and subsequent tissue invagination. The authors propose an alternative model, where they consider an alternative driver, namely the "apical surface elasticity".

Strengths:

It is surprising that despite the fact that apical constriction and tissue invagination are probably most studied processes in tissue morphogenesis, the underlying physical mechanisms are still not entirely understood. This work supports this notion by showing that simply increasing apical tension is perhaps not sufficient to locally constrict and invaginate a tissue.

Weaknesses:<br /> The findings and claims in the manuscript are only partially supported. With the computational methodology for studying tissue mechanics being so well developed in the field, the authors could probably have done a more thorough job of supporting the main findings of their work.

Reviewer #2 (Public Review):

Zirin, Jusiak, and Lopes et al presented an efficient pipeline for making LexA-GAD and QF2 drivers. The tools can be combined with a large collection of existing GAL4 drivers for a dual genetic control of two cell populations. This is essential when studying inter-organ communications since most of the current genetic drivers are biased toward the expression of the central nervous system. In this manuscript, the authors described the methodology for efficiently generating T2A-LexA-GAD and T2A-QF2 knock-ins by CRISPR, targeting a number of genes with known tissue-specific expression patterns. The authors then validated and compared the expression of double as well as single drivers and found the tissue-specific expression results were largely consistent as expected. Finally, a collection of plasmids for LexA-GAD and QF,2 as well as the corresponding LexAop and QUAS plasmids were generated to facilitate the expansion of these tool kits. In general, this study will be of considerable interest to the fly community and the resources can be readily generalized to make drivers for other genes. I believe this toolkit will have a significant, immediate impact on the fly community.

DOI: 10.1242/dev.202251

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

Curator: @evieth

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

What is this?

Reviewer #2 (Public Review):

Summary:

The authors of the study use a technically well-thought-out approach to dissect the question of how far TRPV1 and TRPM2 are involved in the perception of warm temperatures in mice. They supplement the experimental data with a drift-diffusion model. They find that TRPM2 is required to trigger the preference for 31{degree sign}C over warmer temperatures while TRPV1 increases the fidelity of afferent temperature information. A lack of either channel leads to a depletion of warm-sensing neurons and in the case of TRPV1 to a deficit in rapid responses to temperature changes. The study demonstrates that mouse phenotyping can only produce trustworthy results if the tools used to test them measure what we believe they are measuring.

Strengths:

The authors tackle a central question in physiology to which we have not yet found sufficient answers. They take a pragmatic approach by putting existing experimental methods to the test and refining them significantly.

Weaknesses:

It is difficult to find weaknesses. Not only the experimental methods but also the data analysis have been refined meticulously. There is no doubt that the authors achieved their aims and that the results support their conclusions.

There will certainly be some lasting impact on the future use of DRG cultures with respect to (I) the incubation periods, (II) how these data need to be analyzed, and (III) the numbers of neurons to be looked at.

As for the CPT assay, the future will have to show if mouse phenotyping results are more accurate with this technique. I'm more fond of full thermal gradient environments. However, behavioural phenotyping is still one of the most difficult fields in somatosensory research.

Reviewer #3 (Public Review):

Summary:<br /> The authors report a scene-selective areas in the posterior intraparietal gyrus (PIGS). This area lies outside the classical three scene-selective regions (PPA/TPA, RSC/MPA, TOS/OPA), and is selective for ego motion.

Strengths:<br /> The authors firmly establish the location and selectivity of the new area through a series of well-crafted controlled experiments. They show that the area can be missed with too much smoothing, thus providing a case for why it has not been previously described. They show that it appears in much the same location in different subjects, with different magnetic field strengths, and with different stimulus sets. Finally, they show that it is selective for ego motion - defined as series of sequential photographs of an egocentric trajectory along a path. They further clarify that the area is not generically motion selective by showing that it does not respond to biological motion without an egomotion component to it. All statistics are standard and sound; the evidence presented is strong.

Weaknesses:<br /> There are a few weaknesses in this work. If pressed, I might say that the stimuli depicting ego motion do not, strictly speaking, depict motion, but only apparent motion between 2s apart photographs. However, this choice was made to equate frame rates and motion contrast between the 'ego motion' and a control condition, which is a useful and valid approach to the problem.

This is a very strong paper.

Reviewer #3 (Public Review):

Summary:

Non enzymatic replication of RNA or a similar polymer is likely to be important for the origin of life. The authors present a model of how a functional catalytic sequence could emerge from a mixture of sequences undergoing non-enzymatic replication.

Strengths:

Interesting model describing details of the proposed replication mechanism.

Weaknesses:

The idea of the virtual circular genome proposed in [37] is included in the discussion section together with the problem of sequence scrambling faced by this mechanism that was raised in [38]. Sequence scrambling arises in models that assume cycles of melting and reannealing, in which case only part of a template is copied in one cycle. Scrambling is due to the many alternative ways in which pairs of sequences can reanneal. Many of these alternatives are incorrect and this leads to the disappearance of the original sequence. This problem exists even in the limit where there is zero mutational error rate. Thus, it is a separate problem from the usual error threshold problem. Scrambling would not occur if there was complete copying of a template from one end to the other.

The authors seem to believe that their model avoids the scrambling problem to some extent. If I understand correctly, this is because the functional activity is located in a short sequence region. I can imagine that if the length of a strand that is synthesized in a single melting/annealing cycle is long enough to cover the complete functional region, then sometimes the complete functional sequence can be copied in one cycle. The authors give an estimate of a scrambling-free length. I am not sure how this is determined. I think that the problem of how to encode functional sequences in RNA strands undergoing non-enzymatic replication is still not fully resolved.

Reviewer #2 (Public Review):

Summary:

Keratin 17 is a highly stress-inducible keratin that has been implicated in various human disorders. For example, higher K17 expression was shown to be associated with poor survival in several cancers including pancreatic carcinoma. To follow up on these observations, Kawalerski et al. assessed the relevance of K17 and its phosphorylation on this deadly tumor. In particular, they identified novel K17 phosphorylation sites and demonstrated that they affect K17 solubility as well as its nuclear localization. They also studied their significance in vivo.

Strengths:

The overall structure is very logical, the manuscript is well-written.

Weaknesses:

Unfortunately, the key experiment, i.e. the assessment of growth of cancer cell lines with different phospho-variants of K17, turned largely negative.

Reviewer #2 (Public Review):

Summary:

The authors wish to apply established psychophysical methods to the study of numbers. Specifically, they wish to test the hypothesis - supported by their previous work - that human sensorimotor processes are tuned to specific number ranges. In a novel set of tasks, they ask participants to tap a button N times (either fast or slow), where N varies between 8 and 32 across trials. As I understood it, they then computed the Weber fraction (WF) for each participant for each number and correlated those values across participants and numbers. They find stronger correlations for nearby numbers than for distant numbers and interpret this as evidence of sensorimotor tuning functions. Two other analyses - cluster analyses and principal component analyses (PCA) - suggest that participants' performance relied on at least 2 mechanisms, one for encoding low numbers of taps (around 10) and another encoding larger numbers (around 27).

Strengths:

Individual differences can be a rich source of scientific insight and I applaud the authors for taking them seriously.

Weaknesses:

Implications of intercorrelation. The experiment "is based on the idea that interindividual variability conveys information that can reveal common sensory processes (Peterzell & Kennedy, 2016)" but I struggle to understand the logic of this technique. The authors explain it most clearly when they write "Regions of high intercorrelation between neighbouring stimuli intensity can be interpreted to imply that sets of stimuli are processed by the same (shared) underlying channel. This channel, while responding relatively more to its preferred stimulus, will also be activated by neighbouring stimuli that although slightly different from the preferred intensity, are nevertheless included in the same response distribution." Why does high intercorrelation imply a shared channel and why should it be calculated across participants? Shouldn't performance on any set of tasks (that vary in difficulty) correlate across participants? Why in principle should people have distinct channels for processing similar stimuli and how could such a system improve (rather than impede) discrimination abilities? What pattern of intercorrelation would disconfirm the existence of tuning mechanisms? And perhaps most fundamentally: What is a channel and why do they matter?

Different channels? I had trouble understanding much of the analyses, and this may account for at least some of my confusion. That said, as I understand it, the results are meant to provide "evidence that tuned mechanisms exist in the human brain, with at least two different tunings" because of the results of the clustering analysis and PCA. But as the authors acknowledge, "PCA aims to summarize the dataset with the minimal number of components (channels). We can therefore not exclude the possible existence of more than two (perhaps not fully independent) channels." I would go a step further and say this technique does not provide more evidence for the existence of 2 channels as for the existence of 4, 8 or 24 channels, the upper bound for a task testing 24 different numbers. If we can conclude that people may have one channel per number, what does "channel" mean?

Several other questions arise when thinking through this technique, which left me skeptical of its utility. If people did have two channels (at least in this range), why would they be so broad? Why would they be centered so near the ends of the tested range? Can such effects be explained by binning on the part of the participants, who might have categorized each number (knowingly or not) as either "small" or "large"? Or by the kind of data-binning or distributions (i.e. Gaussian) used in the analyses? Or by the physical limits and affordances of the effector participants used (i.e. their finger)? Moreover, if people had sensorimotor channels tuned to different numbers, wouldn't this cause discontinuities in their own WF? Why look at correlations across individuals rather than correlations or discontinuities within individuals? Whereas the experiment tested numbers 8-32, numbers are infinite - How could a small number of channels cover an infinite set? Or even the set 8-10,000? What would the existence of multiple such channels mean for our understanding of numerical cognition? There may be good answers to these questions, but they are not clear to this reader.

Theories of numerical cognition. An expansive literature on numerical cognition suggests that many animals, human children, and adults across cultures have two systems for representing numerosity without counting - one that can represent the exact cardinality of sets smaller than about 4 and another that represents the approximate number of larger sets. Recent accounts suggest that what appears to be two systems can be explained by a single system of numerical approximation with limited information capacity (see Cheyette & Piantadosi, 2020). The current paper would benefit from better relating its findings to this long lineage of theories and findings in numerical approximation across cultures, ages, and species.

Specific to numbers? The authors argue that their effects are "number selective" but they do not provide compelling evidence for this selectivity. In principle, their main findings could be explained by the duration of tapping rather than the number of taps. They argue this is unlikely for two reasons. The first reason is that the overall pattern of results was unchanged across the fast and slow tapping conditions, but differences in duration were confounded with numerosity in both conditions, so the comparison is uninformative. The second reason is that temporal reproduction was less precise in their control condition than numerical reproduction, but this logic is unclear: Participants could still use duration (or some combination of speed and duration) as a helpful cue to numerosity, even if their duration reproductions were imperfect.

If the authors wish to test the role of duration, they might consider applying the same analytical techniques they use for number to their duration data. Perhaps participants show similar evidence for duration-selective channels, in the absence of number, as they do for other non-numerical domains (like spatial frequency).

Reviewer #2 (Public Review):

Summary:

The work of Volotsky et al presented here shows that adult archerfish are able to adjust their shooting in response to their own visual feedback, taking consistent alterations of their shot, here by an air flow, into account. The evidence provided points to an internal mechanism of shooting adaptation that is independent of external cues, such as wind. The authors provide evidence for this by forcing the fish to shoot from 2 different orientations to the external alteration of their shots (the airflow). This paper thus provides behavioral evidence of an internal correction mechanism, that underlies adaptive motor control of this behavior. It does not provide direct evidence of refractory index-associated shoot adjustance.

Strengths:

The authors have used a high number of trials and strong statistical analysis to analyze their behavioral data. They used an elegant experimental design in which they force the fish to shoot from directions chosen by the authors, which elegantly reduced shooting variability.

Weaknesses:

A large portion of fish did not make it to the final test (as is often the case in behavioral studies) which raises the question whether all individuals are able to solve the task.

Reviewer #2 (Public Review):

Summary:

The goal of this project was to test the hypothesis that a common neuroanatomic substrate in the left inferior parietal lobule (area PF) underlies reasoning about the physical properties of actions and objects. Four functional MRI (fMRI) experiments were created to test this hypothesis. Group contrast maps were then obtained for each task, and overlap among the tasks was computed at the voxel level. The principal finding is that the left PF exhibited differentially greater BOLD response in tasks requiring participants to reason about the physical properties of actions and objects (referred to as technical reasoning). In contrast, there was no differential BOLD response in the left PF when participants engaged in fMRI variant of the Raven's progressive matrices to assess fluid cognition.

Strengths:

This is a well-written manuscript that builds from extensive prior work from this group mapping the brain areas and cognitive mechanisms underlying object manipulation, technical reasoning, and problem-solving. Major strengths of this manuscript include the use of control conditions to demonstrate there are differentially greater BOLD responses in area PF over and above the baseline condition of each task. Another strength is the demonstration that area PF is not responsive in tasks assessing fluid cognition - e.g., it may just be that PF responds to a greater extent in a harder condition relative to an easy condition of a task. The analysis of data from Task 3 rules out this alternative interpretation. The methods and analysis are sufficiently written for others to replicate the study, and the materials and code for data analysis are publicly available.

Weaknesses:

The first weakness is that the conclusions of the manuscript rely on there being overlap among group-level contrast maps presented in Figure 2. The problem with this conclusion is that different participants engaged in different tasks. Never is an analysis performed to demonstrate that the PF region identified in e.g., participant 1 in Task 2 is the same PF region identified in Participant 1 in Task 4.

A second weakness is that there is a variance in accuracy between tasks that are not addressed. It is clear from the plots in the supplemental materials that some participants score below chance (~ 50%). This means that half (or more) of the fMRI trials of some participants are incorrect. The methods section does not mention how inaccurate trials were handled. Moreover, if 50% is chance, it suggests that some participants did not understand task instructions and were systematically selecting the incorrect item.

A third weakness is related to the fluid cognition task. In the fMRI task developed here, the participant must press a left or right button to select between 2 rows of 3 stimuli while only one of the 3 stimuli is the correct target. This means that within a 10-second window, the participant must identify the pattern in the 3x3 grid and then separately discriminate among 6 possible shapes to find the matching stimulus. This is a hard task that is qualitatively different from the other tasks in terms of the content being manipulated and the time constraints.

In sum, this is an interesting study that tests a neuro-cognitive model whereby the left PF forms a key node in a network of brain regions supporting technical reasoning for tool and non-tool-based tasks. Localizing area PF at the level of single participants and managing variance in accuracy is critically important before testing the proposed hypotheses.

Reviewer #1 (Public Review):

Summary:

The manuscript by Hoogstraaten et al. investigates the effect of constitutive Rabphilin 3A (RPH3A) ko on the exocytosis of dense core vesicles (DCV) in cultured mouse hippocampal neurons. Using mCherry- or pHluorin-tagged NPY expression and EGFP- or mCherry tagged RPHA3, the authors first analyse the colocalization of DCVs and RPH3A. Using FRAP, the authors next analyse the mobility of DCVs and RAB3A in neurites. The authors go on to determine the number of exocytotic events of DCVs in response to high-frequency electrical stimulation and find that RPH3A ko increases the number of exocytotic events by a factor 2-3, but not the fraction of released DCVs in a given cell (8x 50Hz stim). In contrast, the release fraction is also increased in RBP3A KOs when doubling the stimulation number (16x 50Hz). They further observe that RPH3A ko increases dendrite and axon length and the overall number of ChgrB-positive DCVs. However, the overall number of DCVs and dendritic length in ko cells directly correlate, indicating that the number of vesicles per dendritic length remains unaffected in the RPH3A KOs. Lentiviral co-expression of tetanus toxin (TeNT) showed a non-significant trend to reduce axon and dendrite length in RPH3a KOs. Finally, the authors use co-expression of RAB3A and SNAP25 constructs to show that RAB3A but not SNAP25 interaction is required to allow the exocytosis-enhancing effect in RPH3A KOs.

While the authors' methodology is sound, the microscopy results are performed well and analyzed appropriately, but their results in larger parts do not sufficiently support their conclusions. Moreover, the experiments are not always described in sufficient detail (e.g. FRAP; DCV counts vs. neurite length) to fully understand their claims.

Overall, I thus feel that the manuscript does not provide a sufficient advance in knowledge.

Strengths:

- The authors' methodology is sound, and the microscopy results are performed well and analyzed appropriately.<br /> - Figure 2: The exocytosis imaging is elegant and potentially very insightful. The effect in the RPH3A KOs is convincing.<br /> - Figure 4: the logic of this experiment is elegant. It shows that the increased number of DCV fusion events in RPH3A KOs is related to the interaction of RPH3A with RAB3A but not with SNAP25.

Weaknesses:<br /> - The results in larger parts do not sufficiently support the conclusions.<br /> - The experiments are not always described in sufficient detail (e.g. FRAP; DCV counts vs. neurite length) to fully understand their claims.<br /> - Not of sufficient advance in knowledge for this journal<br /> - The significance of differences in control experiments WT vs. KO) varies between experiments shown in different figures.<br /> - Axons and dendrites were not analyzed separately in Figures 1 and 2.<br /> - The colocalization study in Figure 1 would require super-resolution microscopy.

Reviewer #2 (Public Review):

MotorNet aims to provide a unified interface where the trained RNN controller exists within the same TensorFlow environment as the end effectors being controlled. This architecture provides a much simpler interface for the researcher to develop and iterate through computational hypotheses. In addition, the authors have built a set of biomechanically realistic end effectors (e.g., a 2 joint arm model with realistic muscles) within TensorFlow that are fully differentiable.

MotorNet will prove a highly useful starting point for researchers interested in exploring the challenges of controlling movement with realistic muscle and joint dynamics. The architecture features a conveniently modular design and the inclusion of simpler arm models provides an approachable learning curve. Other state-of-the-art simulation engines offer realistic models of muscles and multi-joint arms and afford more complex object manipulation and contact dynamics than MotorNet. However, MotorNet's approach allows for direct optimization of the controller network via gradient descent rather than reinforcement learning, which is a compromise currently required when other simulation engines (as these engines' code cannot be differentiated through).

The paper has been reorganized to provide clearer signposts to guide the reader. Importantly, the software has been rewritten atop PyTorch which is increasingly popular in ML and computational neuroscience research.

One paragraph in the discussion regarding a "spinal cord" module is a bit perplexing. Quite sensibly, the software architecture partitions motor control into the plant or effector (the physical body being moved) and the controller (a model of the brain and spinal cord). Of course, the authors certainly appreciate this, though a reader from outside of neuro might not realize that control of movement is distributed throughout the central nervous system, spanning a network of spinal, subcortical (cerebellum, basal ganglia, thalamus, brainstem), and cortical brain regions. Casting the spinal cord as a pre-filter within the effector module would seem to belie its complex and dynamic role in these distributed neural circuits. This is particularly noticeable when contrasted with the subsequent paragraph on "Modular polices" (which is excellent). In my view, the spinal cord would be better treated as a module of this policy section rather than as part of the effector. I understand the nuance here, and suspect I'd see eye to eye with the authors for the most part. The choice of controller vs. plant depends on perspective (one could call the arm itself part of the controller, and treat the environment / manipulated object as the plant; similarly, one could treat the brain as controlling the cord rather than the body). However, I fear that someone lacking the appropriate neurophysiological/anatomical context might read the "Spinal Compartment" paragraph, think that it would be fine to introduce a simple filter module as the spinal cord, and then start referring to the MotorNet policy network as a model of motor cortex per se.

Reviewer #2 (Public Review):

Summary:

This study reports the levels of expression of selected genes implicated in Wnt signaling in trabecular bone from femur heads obtained after surgery from post-menopausal women with (15 women) or without (21 women) type 2 diabetes. They find higher expression levels of SOST and WNT5A, and lower expression levels of LEF-1 and WNT10B in tissues from subjects with T2D, correlating with glycemia and advanced glycation products. No significant differences in bone density were observed. Overall, this is a cross-sectional, observational study measuring a limited set of genes found to vary with glycemia in postmenopausal women undergoing hip surgery.

Strengths:

The study demonstrates the feasibility of measuring gene expression in post-surgical trabecular bon samples and finds differences associated with glycemia despite a relatively small number of subjects. It can form the basis for further research on the causes and consequences of changes in elements of the WNT signaling pathway in bone biology and disease.

Weaknesses:

The small number of targeted genes does not provide a comprehensive view of the transcriptional landscape within which the effects are observed. The gene expression changes are not associated with cellular or physiological properties of the tissue, raising questions about the biological significance of the observations.

Reviewer #2 (Public Review):

Summary:

The manuscript presents a valuable investigation into the use of Fisher Kernels for extracting representations from temporal models of brain activity, with the aim of improving regression and classification applications. The authors provide solid evidence through extensive benchmarks and simulations that demonstrate the potential of Fisher Kernels to enhance the accuracy and robustness of regression and classification performance in the context of functional magnetic resonance imaging (fMRI) data. This is an important achievement for the neuroimaging community interested in predictive modeling from brain dynamics and, in particular, state-space models.

Strengths:

(1) The study's main contribution is the innovative application of Fisher Kernels to temporal brain activity models, which represents a valuable advancement in the field of human cognitive neuroimaging.

(2) The evidence presented is solid, supported by extensive benchmarks that showcase the method's effectiveness in various scenarios.

(3) Model inspection and simulations provide important insights into the nature of the signal picked up by the method, highlighting the importance of state rather than transition probabilities.

(4) The documentation and description of the methods are solid including sufficient mathematical details and availability of source code, ensuring that the study can be replicated and extended by other researchers.

Weaknesses:

(1) The generalizability of the findings is currently limited to the young and healthy population represented in the Human Connectome Project (HCP) dataset. The potential of the method for other populations and modalities remains to be investigated.

(2) The possibility of positivity bias in the HMM, due to the use of a population model before cross-validation, needs to be addressed to confirm the robustness of the results.

(3) The statistical significance testing might be compromised by incorrect assumptions about the independence between cross-validation distributions, which warrants further examination or clearer documentation.

(4) The inclusion of the R^2 score, sensitive to scale, would provide a more comprehensive understanding of the method's performance, as the Pearson correlation coefficient alone is not standard in machine learning and may not be sufficient (even if it is common practice in applied machine learning studies in human neuroimaging).

(5) The process for hyperparameter tuning is not clearly documented in the methods section, both for kernel methods and the elastic net.

(6) For the time-averaged benchmarks, a comparison with kernel methods using metrics defined on the Riemannian SPD manifold, such as employing the Frobenius norm of the logarithm map within a Gaussian kernel, would strengthen the analysis, cf. Jayasumana (https://arxiv.org/abs/1412.4172) Table 1, log-euclidean metric.

(7) A more nuanced and explicit discussion of the limitations, including the reliance on HCP data, lack of clinical focus, and the context of tasks for which performance is expected to be on the low end (e.g. cognitive scores), is crucial for framing the findings within the appropriate context.

(8) While further benchmarks could enhance the study, the authors should provide a critical appraisal of the current findings and outline directions for future research, considering the scope and budget constraints of the work.

Reviewer #2 (Public Review):

Summary:<br /> This is a carefully done study containing interesting results.

Strengths:<br /> These findings have significant implications for periodontal care and highlight the potential for systemic immunomodulation management on periodontitis, which is of interest to readers in the fields of periodontology, immunology, and epidemiology.

Many students don’t know how to use AI correctly.

Reviewer #2 (Public Review):

Traditional thinking has been that cortical oligodendrocyte progenitor cells (OPCs) arise in the development of the brain from the medial ganglionic eminence (MGE), lateral/caudal ganglionic eminence (LGE/CGE), and cortical radial glial cells (RGCs). Indeed a landmark study demonstrated some time ago that cortical OPCs are generated in three waves, starting with a ventral wave derived from the medial ganglionic eminence (MGE) or the anterior entopeduncular area (AEP) at embryonic day E12.5 (Nkx2.1+ lineage), followed by a second wave of cortical OLs derived from the lateral/caudal ganglionic eminences (LGE/CGE) at E15.5 (Gsx2+/Nkx2.1- lineage), and then a final wave occurring at P0, when OPCs originate from cortical glial progenitor cells (Emx1+ lineage). However, the authors challenge the idea in this paper that cortical progenitors are produced from the LGE. They have found previously that cortical glial progenitor cells were also found to express Gsx2, suggesting this may not have been the best marker for LGE-derived OPCs. They have used fate mapping experiments and lineage analyses to suggest that cortical OPCs do not derive from the LGE.

Strengths:<br /> (1) The data is high quality and very well presented, and experiments are thoughtful and elegant to address the questions being raised.

(2) The authors use two elegant approaches to lineage trace LGE derived cells, namely fate mapping of LGE-derived OPCs by combining IUE (intrauterine electroporation) with a Cre recombinase-dependent IS reporter, and Lineage tracing of LGE-derived OPCs by combining IUE with the PiggyBac transposon system. Both approaches show convincingly that labelled LGE-derived cells that enter the cortex do not express OPC markers, but that those co-labelling with oligodendrocyte markers remain in the striatum.

(3) The authors then use further approaches to confirm their findings. Firstly they lineage trace Emx1-Cre; Nkx2.1-Cre; H2B-GFP mice. Emx1-Cre is expressed in cortical RGCs and Nkx2.1-Cre is specifically expressed in MGE/AEP RGCs. They find that close to 98% of OPCs in the cortex co-label with GFP at later times, suggesting the contribution of OPCs from LGE is minimal.

(4) They use one further approach to strengthen the findings yet further. They cross Nkx2.1-Cre mice with Olig2 F/+ mice to eliminate Olig2 expression in the SVZ/VZ of the MGE/AEP (Figures 4A-B). The generation of MGE/AEP-derived OPCs is inhibited in these Olig2-NCKO conditional mice. They find that the number of cortical progenitors at E16.5 is reduced 10-fold in these mice, suggesting that LGE contribution to cortical OPCs is minimal.

Weaknesses:<br /> (1) The authors use IUE in experiments mentioned in point 2 of 'Strengths' above (Figures 1 and 2) and claim that the reporter was delivered specifically into LGE VZ at E13.5 using this IUE. It would be nice to see some sort of time course of delivery after IUE to show the reporter is limited to LGE VZ at early times post-IUE.

(2) In the experiments mentioned in point 3 of 'Strengths' (Figure 3), statistical analysis showed that only approximately 2% of OPCs were GFP-negative cells. This 2% could possibly be derived from the LGE/CGE so does not totally rule out that LGE contributes some cortical OPCs.

(3) In the experiments mentioned in point 4 of 'Strengths' (Figure 4), they do still find cortical OPCs at E16.5 in the Olig2-NCKO conditional mice. It is unclear whether this is due to the recombination efficiency of the CRE enzyme not being 100%, or whether there is some LGE contribution to the cortical OPCs.

Impact of Study:<br /> The authors show elegantly and convincingly that the contribution of the LGE to the pool of cortical OPCs is minimal. The title should perhaps be that the LGE contribution is minimal rather than no contribution at all, as they are not able to rule out some small contribution from the LGE. These findings challenge the traditional belief that the LGE contributes to the pool of cortical OPCs. The authors do show that the LGE does produce OPCs, but that they tend to remain in the striatum rather than migrate into the cortex. It is interesting to wonder why their migration patterns may be different from the MGE-derived OPCs which migrate to the cortex. The functional significance of these different sources of OPCs for adult cortex in homeostatic or disease states remains unclear though.

Reviewer #2 (Public Review):

The authors employed the Mendelian Randomization method to analyze the association between type 2 diabetes (T2D) and fracture using the UK Biobank data. They found that "genetically predicted T2D was associated with higher BMD and lower risk of fracture". Additionally, they identified 10 loci that were associated with both T2D and fracture risk, with the SNP rs4580892 showing the highest signal. While the negative relationship between T2D and fracture has been previously observed, the discovery of these 10 loci adds an intriguing dimension to the findings, although the clinical implications remain uncertain.

Many thanks for your response which has clarified my understanding of your paper. And, thank you for the additional analyses. I still find the paper challenging to understand due to two different analyses that yielded conflicting results: (a) in the observational analysis, the authors found that type 2 diabetes was associated with both higher BMD and a higher risk of fracture (ie a paradox); but (b) in the Mendelian randomization analysis, 'genetically predicted type 2 diabetes' was associated with greater BMD and a lower risk of fracture. I consider that your conclusion is not consistent with the data you presented.

Reviewer #2 (Public Review):

Summary:

In this study, the authors systematically explore the mechanism(s) of impaired postnatal lung development with relevance to BPD (bronchopulmonary dysplasia) in two murine models of 'alveolar simplification', namely hyperoxia and epithelial loss of TGFb signaling. The work presented here is of great importance, given the limited treatment options for a clinical entity frequently encountered in newborns with high morbidity and mortality that is still poorly understood, and the unclear role of TGFb signaling, its signaling levels, and its cellular effects during secondary alveolar septum formation, a lung structure generating event heavily impacted by BPD. The authors show that hyperoxia and epithelial TGFb signaling loss have similar detrimental effects on lung structure and mechanical properties (emphysema-like phenotype) and are associated with significantly decreased numbers of PDGFRa-expressing cells, the major cell pool responsible for generation of postnatal myofibroblasts. They then use a single-cell transcriptomic approach combined with pathway enrichment analysis for both models to elucidate common factors that affect alveologenesis. Using cell communication analysis (NicheNet) between epithelial and myofibroblasts they confirm increased projected TGFb-TGFbR interactions and decreased projected interactions for PDGFA-PDGFRA, and other key pathways, such as SHH and WNT. Based on these results they go on to uncover in a sequela of experiments that surprisingly, increased TGFb appears reactive to postnatal lung injury and rather protective/homeostatic in nature, and the authors establish the requirement for alpha V integrins, but not the subtype alphaVbeta6, a known activator of TGFb signaling and implied in adult lung fibrosis. The authors then go beyond the TGFb axis evaluation to show that mere inhibition of proliferation by conditional KO of Ect2 in Pdgfra lineage results in alveolar simplification, pointing out the pivotal role of PDGFRa-expressing myofibroblasts for normal postnatal lung development.

Strengths:

(1) The approach including both pharmacologic and mechanistically-relevant transgenic interventions both of which produced consistent results provides robustness of the results presented here.

(2) Further adding to this robustness is the use of moderate levels of hyperoxia at 75% FiO2, which is less extreme than 100% FiO2 frequently used by others in the field, and therefore favors the null hypothesis.

(3) The prudent use of advanced single-cell analysis tools, such as NicheNet to establish cell interactions through the pathways they tested and the validation of their scRNA-seq results by analysis of two external datasets. Delineation of the complexity of signals between different cell types during normal and perturbed lung development, such as attempted successfully in this study, will yield further insights into the underlying mechanism(s).

(4) The combined readout of lung morphometric (MLI) and lung physiologic parameters generates a clinically meaningful readout of lung structure and function.

(5) The systematic evaluation of TGFb signaling better determines the role in normal and postnatally-injured lungs.

Weaknesses:

(1) While the study convincingly establishes the effect of lung injury on the proliferation of PDGFRa-expressing cells, differentiation is equally important. Characterization of PDGFRa expressing cells and tracking the changes in the injury models in the scRNA analysis, a key feature of this study, would benefit from expansion in this regard. PDGFRa lineage gives rise to several key fibroblast populations, including myofibroblasts, lipofibroblasts, and matrix-type fibroblasts (Collagen13a1, Collagen14a1). Lipofibroblasts constitute a significant fraction of PDGFRa+ cells, and expand in response to hyperoxic injury, as shown by others. Collagen13a1-expressing fibroblasts expand significantly under both conditions (Figure 3), and appear to contain a significant number of PDGFRa-expressing cells (Suppl Fig.1). Effects of the applied injuries on known differentiation markers for these populations should be documented. Another important aspect would be to evaluate whether the protective/homeostatic effect of TGFb signaling is supporting the differentiation of myofibroblasts. Postnatal Gli1 lineage gains expression of PDGFRa and differentiation markers, such as Acta2 (SMA) and Eln (Tropoelastin). Loss of PDGFRa expression was shown to alter Elastin and TGFb pathway-related genes. TGFb signaling is tightly linked to the ECM via LTBPs, Fibrillins, and Fibulins. An additional analysis in the aforementioned regard has great potential to more specifically identify the cell type(s) affected by the loss of TGFb signaling and allow analysis of their specific transcriptomic changes in response and underlying mechanism(s) to postnatal injury.

(2) Of the three major lung abnormalities encountered in BPD, the authors focus on alveolarization impairment in great detail, to a very limited extent on inflammation, and not on vascularization impairment. However, this would be important not only to better capture the established pathohistologic abnormalities of BPD, but also it is needed since the authors alter TGFb signaling, and inflammatory and vascular phenotypes with developmental loss of TGFb signaling and its activators have been described. Since the authors make the point about the absence of inflammation in their BPD model, it will be important to show the evidence.

(3) Conceptually it would be important that in the discussion the authors reconcile their findings in the experimental BPD models in light of human BPD and the potential implications it might have on new ways to target key pathways and cell types for treatment. This allows the scientific community to formulate the next set of questions in a disease-relevant manner.

Reviewer #2 (Public Review):

Severe leptospirosis in humans and some mammals often meet death in the endpoint. In this article, authors explored the role of the gut microbiota in severe leptospirosis. They found that Leptospira infection promoted a dysbiotic gut microbiota with an expansion of Proteobacteria and LPS neutralization therapy synergized with antileptospiral therapy significantly improved the survival rates in severe leptospirosis. This study is well-organized and has potentially important clinical implications not only for severe leptospirosis but also for other gut-damaged infections.

Reviewer #2 (Public Review):

Summary:<br /> The authors comprehensively assess differences in the TCRB and TCRA repertoires in the fetal and adult mouse thymus by deep sequencing of sorted cell populations. For TCRB and TCRA they observed biased gene segment usage and less diversity in fetal thymocytes. The TCRB repertoire was less evenly distributed and displayed more evidence of clonal expansions and repertoire sharing among individuals in fetal thymocytes. In both fetal and adult thymocytes they show skewing of V segment (CDR1-2) repertoires in CD4 and CD8 as compared to DP thymocytes, which they attribute to MHC-I vs MHC-II restriction during positive selection. However the authors assess these effects to be weaker in fetal thymocytes, suggesting weaker MHC-restriction. They conclude that in multiple respects fetal repertoires are distinct from and more innate-like than adult.

Strengths:<br /> The analyses of the F18.5 and adult thymic repertoires are comprehensive with respect to the cell populations analyzed and the diversity of approaches used to characterize the repertoires. Because repertoires were analyzed in pre- and post-selection thymocyte subsets, the data offer the potential to assess repertoire selection at different developmental stages. The analysis of repertoire selection in fetal thymocytes may be unique.

Weaknesses:<br /> (1) Problematic experimental design and some lack of familiarity with prior work have resulted in highly problematic interpretations of the data, particularly for TCRA repertoire development.<br /> The authors note fetal but not adult thymocytes to be biased towards usage of 3' V segments and 5'J segments. It should be noted that these basic observations were made 20 years ago using PCR approaches (Pasqual et al., J.Exp.Med. 196:1163 (2002)), and even earlier by others. The authors also note that in fetal thymus this bias persists after positive selection, and it can be reproduced in adults during recovery from hydrocortisone treatment. The authors conclude that there are fewer rounds of sequential TCRA rearrangements in the fetal thymus, perhaps due to less time spent in the DP compartment in fetus versus adult. However, the repertoire difference noted by the authors does not require such an explanation. What the authors are analyzing in the fetus is the leading edge of a synchronous wave of TCRA rearrangements, whereas what they are analyzing in adults is the unsynchronized steady state distribution. It is certainly true, as has been shown previously, that the earliest TCRA rearrangements use 3' TRAV and 5'TRAJ segments. But analysis of adult thymocytes has shown that the progression from use of 3' TRAV and 5' TRAJ to use of 5' TRAV and 3' TRAJ takes several days (Carico et al., Cell Rep. 19:2157 (2017)). The same kinetics, imposed on fetal development, would put development of a more complete TCRA repertoire at or shortly after birth. In fact, Pasqual showed exactly this type of progression from F18 through D1 after birth, and could reproduce the progression by placing F16 thymic lobes in FTOC. It is not appropriate to compare a single snapshot of a synchronized process in early fetal thymocytes to the unsynchronized steady state situation in adults. In fact, the authors' own data support this contention, because when they synchronize adult thymocytes by using hydroxycortisone, they can replicate the fetal distribution. Along these lines, the fact that positive selection of fetal thymocytes using 3' TRAV and 5' TRAJ segments occurs within 2 days of thymocyte entry into the DP compartment does not mean that DP development in the fetus is intrinsically rapid and restricted to 2 days. It simply means that thymocytes bearing an early rearranging TCR can be positively selected shortly after TCR expression. The expectation would be that those DP thymocytes that had not undergone early positive selection using a 3' TRAV and a 5' TRAJ would remain longer in the DP compartment and continue the progression of TCRA rearrangements, with the potential for selection several days later using more 5'TRAV and 3'TRAJ.<br /> (2) The authors note 3' V and 5'J biases for TCRB in fetal thymocytes. The previously outlined concerns about interpreting TCRA repertoire development do not directly apply here. But it would be appropriate to note that by deep sequencing, Sethna (PNAS 114:2253 (2017)) identified skewed usage of some of the same TRBV gene segments in fetal versus adult. It should also be noted that Sethna did not detect significantly skewed usage of TRBJ segments. Regardless, one might question whether the skewed usage of TRBJ segments detected here should be characterized as relating to chromosomal location. There are two logical ways one can think about chromosomal location of TRBJ segments - one being TRBJ1 cluster vs TRBJ2 cluster, the other being 5' to 3' within each cluster. The variation reported here does not obviously fit either pattern. Is there a statistically significant difference in aggregate use of the two clusters? There is certainly no clear pattern of use 5' to 3' across each cluster.<br /> (3) The authors show that biases in TCRA and TCRB V and J gene usage between fetal and adult thymocytes are mostly conserved between pre- and post-selection thymocytes (Fig 2). In striking contrast, TCRA and TCRB combinatorial repertoires show strong biases pre-selection that are largely erased in post-selection thymocytes (Fig 3). This apparent discrepancy is not addressed, but interpretation is challenging.<br /> (4) The observation that there is a higher proportion of nonproductive TCRB rearrangements in fetal thymus compared to adult is challenging to interpret, given that the results are based upon RNA sequencing so are unlikely to reflect the ratio in genomic DNA due to processes like NMD.<br /> (5) An intriguing and paradoxical finding is that fetal DP, CD4 and CD8 thymocytes all display greater sharing of TCRB CDR3 sequences among individuals than do adults (Fig 5DE), whereas DP and CD8 thymocytes are shown to display greater CDR3 amino acid triplet motif sharing in adults (with a similar trend in CD4). The authors attribute high amino acid triplet sharing to the result of selection of recurrent motifs by contact with pMHC during positive selection. But this interpretation seems highly problematic because the difference between fetal and adult thymocytes is dramatic even in unfractionated DP thymocytes, the vast majority of which have not yet undergone positive selection. How then to explain the differences in CDR3 sharing visualized by the different approaches?<br /> (6) The authors conclude that there is less MHC restriction in fetal thymocytes, based on measures of repertoire divergence from DP to CD4 and CD8 populations (Fig. 6). But the authors point to no evidence of this in analysis of TRBV usage, either by PC or heatmap analyses (A,B,D). The argument seems to rest on PC analysis of TRAV usage (Fig S6), despite the fact that dramatic differences in the SP4 and SP8 repertoires are readily apparent in the fetal thymocyte heatmaps. The data do not appear to be robust enough to provide strong support for the authors' conclusion.

Reviewer #2 (Public Review):

Summary:

In this manuscript, the authors have meticulously constructed a comprehensive atlas delineating hematopoietic stem/progenitor cell (HSPC) and immune-cell types within the zebrafish kidney, employing single-cell transcriptome profiling analysis. Notably, these cell populations exhibited distinctive responses to viral infection. Intriguingly, the investigation revealed that HSPCs manifest positive reactivities to viral infection, indicating the effective induction of trained immunity in select HSPCs. Furthermore, the study unveiled the capacity for the generation of antigen-stimulated adaptive immunity within the kidney, suggesting a role for the zebrafish kidney as a secondary lymphoid organ. This research elucidates the distinctive features of the fish immune system and underscores the multifaceted biology of the kidney in ancient vertebrates.

Strengths:

This study, encompassing 13 figures along with supplementary material, distinguishes itself as one of the most comprehensive investigations on this subject to date.

Reviewer #2 (Public Review):

In this report Yu et al. try to demonstrate how O-GlcNAcylation of ribosomal proteins in the mushroom body (MB) is required for protein synthesis and olfactory learning. The authors develop a new method combining the O-GlcNAc binding activity of an OGlcNAcase (OGN) and TurboID for efficient isolation. This novel method is a useful tool for the identification of O-GlcNAc modified proteins and closely interacting partners. Transgenic expression of this binder allows the authors to perform a profiling that can be time and tissue/region/cell specific. This novel tool is thoroughly tested to show it works in cultured cells, whole Drosophila and in a tissue specific manner expressing it pan-neuronally or specific regions of the brain.

The authors had previously shown that reduced O-GlcNAcylation through transgenic expression of a highly active OGN affected olfactory learning. In this work the same approach is used to reduce O-GlcNAcylation in different brain regions to show that specific reduction in the adult MB reduced olfactory learning performance. As control OGN expression in the ellipsoid body has no effect on olfactory learning. Optic and antennal lobes could not be tested as OGN expression affected olfactory acuity. The most critical part of this finding is time specific expression of OGN in the adult in a tissue specific manner given the developmental defects it induces with earlier expression. The MB has a widely reported role in associative learning, therefore this finding while not unexpected it is satisfying.

Yu et al. use their TurboID-OGA to identify O-GlcNAcylated proteomes in different brain regions. The authors focus on the MB given its role in associative learning and the effect of reduced O-GlcNAcylation in this region. Among other substrates several ribosomal proteins are found to be specifically O-GlcNAcylated to a greater extent in the MB compared to other brain regions.

To demonstrate the role of MB O-GlcNAcylated ribosomes in protein synthesis an ex vivo OPP fluorescent assay is used in brains of flies expressing OGN or a mutant form lacking its catalytic and binding activities. The experiment shows reduced protein synthesis in the MB. In addition, the authors can increase protein synthesis inducing ribosomal biogenesis through the expression of dMyc. Flies expressing of dMyc and OGN together do not present the learning deficits of flies carrying only OGN. Protein synthesis in MB has been previously reported to be required for associative learning (for example Wu et al.2017 or Lin et al. 2022) and the present results bring further support. A link between ribosomal O-GlcNAcylation and protein synthesis could be a really interesting finding but, unfortunately the experiments presented in this work are still too preliminary.

The experiments presented just focus on ribosomal proteins while these are just some of the O-GlcNAcylation substrates in the MB. While a correlation between ribosomal modification and protein synthesis is shown, a demonstration is not provided. Many other mechanisms and O-GlcNAcylation of other substrates could account for the same observations. For example, O-GlcNAcylation has been reported to have a role in protein synthesis affecting different translation initiation factors (Li et al 2018, Shu et al 2022). In vitro experiments where specific O-GlcNAcylation ribosomal components could be targeted are required. In addition, O-GlcNAcylation is also known to modify ribosomal-associated mRNAs. Experiments where specific mutations preventing O-GlcNAcylation in ribosomes could demonstrate a direct link of such ribosomal modifications in olfactory learning.

Reviewer #2 (Public Review):

In the manuscript entitled, "Convergent Epigenetic Evolution Drives Relapse in Acute Myeloid Leukemia", Majeti and colleagues describe patterns of chromatin accessibility alterations at relapse in AML. Through an analysis of publicly available datasets as well as their samples, they show that a subset of AML cases show significant changes in chromatin accessibility despite showing little to no change in clonal composition. Evaluation of predicted changes in gene expression based on chromatin accessibility identifies common differentially expressed pathways at relapse and indicates that blasts are more immature at relapse. Using mitochondrial single-cell ATAC-seq, the authors identify "mitoclones' and observe that mitochondrially-defined clones exhibit more similar chromatin accessibility at relapse relative to diagnosis. Based on these data, the authors conclude that epigenetic evolution is a feature of relapsed AML and that convergent epigenetic evolution can occur following induction chemotherapy.

The strengths of this study are its novelty in AML and its rigorous use of single-cell ATAC-seq and mitochondrial single-cell ATAC-seq to identify chromatin accessibility patterns in AML blasts at diagnosis and relapse, including in clonally related blasts determined by mitochondrial DNA sequencing. That epigenetic changes contribute to relapse and therapy resistance, or that blasts at relapse are less differentiated are not new ideas, but these studies rigorously demonstrate these concepts in AML patient samples. These insights are important since they have the potential to identify novel targets that can be targeted in combination with induction chemotherapy.

While these findings advance our understanding of potential mechanisms or disease relapse/therapy resistance in AML, some of the conclusions are less supported due to the lack of more information on clonally unstable cases. Given that 60-70% of AML cases are not clonally stable following chemotherapy, this raises questions regarding the broad applicability of the authors' proposed model. Indeed, it remains unclear why only a subset of AML cases shows stable clonal patterns.

Reviewer #2 (Public Review):

Summary:

The authors are interested in large-scale cell flow during gastrulation and in particular in the polonaise movement. This movement corresponds to a bilateral vortex-like counter-rotating cell flow and transport the mesendodermal cells allowing ingression of cells through the primitive streak and ultimately the formation of the mesoderm and endoderm. The authors specifically wanted to investigate the coupling of the polonaise movement and primitive streak to understand whether the polonaise movement is a consequence of the formation of the primitive streak or the other way around. They propose a model where the primitive streak elongation is not required for the cell flow but rather for its maintenance and that robust cell flow is not required for primitive streak extension.

Strengths:

Overall, the manuscript is well written with clear experimental designs. The authors have used live imaging and cell flow analysis in different conditions, where either the formation of the primitive streak or the cell flow was perturbed.<br /> Their live imaging and PIV-based analyses convincingly support their conclusions that primitive streak deformation or mitotic arrest do not impact the initiation of the polonaise movement but rather the location or maintenance of these rotations. They additionally showed that disruption of the polonaise movement in the authentic primitive streak by elegant addition of an ectopic primitive streak does not impact the original primitive streak elongation.

Weaknesses:

- Since myosin cables have been shown to be instrumental for the polonaise movement, it would be interesting to better investigate how the manipulations by the delta-DEP-GFP construct, or Vg1/Cos affect the myosin cables (as shown in preliminary form for the aphidicolin-treated embryos).

Thank you for indicating that this will be a focus of future studies.

Reviewer #2 (Public Review):

As a report of the first structure of VMAT2, indeed the first structure of any vesicular monoamine transporter, this manuscript represents an important milestone in the field of neurotransmitter transport. VMAT2 belongs to a large family (the major facilitator superfamily, MFS) containing transporters from all living species. There is a wealth of information relating to the way that MFS transporters bind substrates, undergo conformational changes to transport them across the membrane and couple these events to the transmembrane movement of ions. VMAT2 couples the movement of protons out of synaptic vesicles to the vesicular uptake of biogenic amines (serotonin, dopamine and norepinephrine) from the cytoplasm. The new structure presented in this manuscript can be expected to contribute to an understanding of this proton/amine antiport process.

The structure contains a molecule of the inhibitor TBZ bound in a central cavity, with no access to either luminal or cytoplasmic compartments. The authors carefully analyze which residues interact with bound TBZ and measure TBZ binding to VMAT2 mutated at some of those residues. These measurements allow well-reasoned conclusions about the differences in inhibitor selectivity between VMAT1 and VMAT2 and differences in affinity between TBZ derivatives.

The structure also reveals polar networks within the protein and hydrophobic residues in positions that may allow them to open and close pathways between the central binding site and the cytoplasm or the vesicle lumen. The authors propose involvement of these networks and hydrophobic residues in coupling of transport to proton translocation and conformational changes.

Reviewer #3 (Public Review):

Neuronal migration is one of the key processes for appropriate neuronal development. Defects in neuronal migration are associated with different brain disorders often accompanied by intellectual disabilities. Therefore, the study of the mechanisms involved in neuronal migration helps to understand the pathogenesis of some brain malformations and psychiatric disorders.

FMRP is an RNA-binding protein implicated in RNA metabolism regulation and mRNA local translation. FMRP loss of function causes fragile X syndrome (FXS), the most common form of inherited intellectual disability. Previous studies have shown the role of FMRP in the multipolar to bipolar transition during the radial migration in the cortex and its possible relation with periventricular heterotopia and altered synaptic communication in humans with FXS. However, the role of FMRP in neuronal tangential migration is largely unknown. In this manuscript, the authors aim to decipher the role of FMRP in the tangential migration of neuroblasts along the rostral migratory stream (RMS) in the postnatal brain. By extensive live-imaging analysis of migrating neuroblasts along the RMS, they demonstrate the requirement of FMRP for neuroblast migration and centrosomal movement. These migratory defects are cell-autonomous and mediated by the microtubule-associated protein Map1b.

Overall, the manuscript highlights the importance of FMRP in neuronal tangential migration. They performed an analysis of different aspects of migration such as nucleokinesis and cytokinesis in migrating neuroblasts from live-imaging videos. The authors have reinforced the results that associate defects in microtubule organization in Fmrp1 KO neurons and this rescue with the microtubule-associated protein Map1b. Overall, results concerning the role of Fmr1 in the tangential migration of neuroblasts are solid and convincing.

However, the work is still quite incomplete. My main concern is still what are the functional consequences of delay in neuroblast migration in the integration and function of OB interneurons and this relation with FXS pathophysiology. An anatomical examination of the RMS in the Fmr1KO mice is still missing.

Reviewer #2 (Public Review):

Summary:<br /> In this paper, Oláh and colleagues introduce new research data on the cellular and biophysical elements involved in transmission within the pyramidal circuits of the human neocortex. They gathered a comprehensive set of patch-clamp recordings from human and rat pyramidal neurons to compare how the temporal aspect of neuronal processing is maintained in the larger human neocortex. A broad range of experimental, theoretical, and computational methods are used, including two-photon guided dual whole-cell recordings, electron microscopy, and computational simulations of reconstructed neurons.

Recordings from synaptically connected pyramidal neurons revealed longer intercellular path lengths within the human neocortex. Further, by using dual whole-cell recordings from soma-dendrite and soma-axon locations, they found that short latencies from soma to soma can be partly attributed to an increased propagation speed for synaptic potentials, but not for the propagation of action potentials along the axon.

Next, in a series of extensive computational modeling studies focusing on the synaptic potentials, the authors observe that the short-latency within large human pyramidal neural circuits may have a passive origin. For a wide array of local synaptic input sites, the authors show that the conductance load of the dendrites, electrically coupled to a large diameter apical dendrite, affects the cable properties. The result is a relatively faster propagation of EPSPs in the human neuron.

The manuscript is well-written and the physiological experiments and biophysical arguments are very well explained. I appreciated the in-depth theoretical steps for the simulations. That passive cable properties of the dendrites are causing a higher velocity in human dendrites is interesting but there is a disconnect between the experimental findings and the model simulations. Based on the present data the contribution of active membrane properties cannot be dismissed and deserves further experiments.

Strengths:<br /> The authors present state-of-the-art 2P-guided dual whole-cell recordings in human neurons. In combination with detailed reconstructions, these approaches represent the next steps in unravelling the information processing in human circuits.

The computational modeling based on cable theory and experimentally constrained simulations provides an excellent integrated view of the passive membrane properties.

Weaknesses:<br /> There are smaller and larger issues with the statistical analyses of the experimental data which muddles the interim conclusions.

That the cable properties alone are the main explanation for speeding the electrical signaling in human pyramidal neurons appears inconsistent with the experimental data.

Some of the electrophysiological experiments require further control experiments to make robust conclusions.

Reviewer #2 (Public Review):

Summary:

PKA is a major signaling protein that has been long studied and is vital for synaptic plasticity. Here, the authors examine the mechanism of PKA activity and specifically focus on addressing the question of PKA dissociation as a major mode of its activation in dendritic spines. This would potentially allow us to determine the precise mechanisms of PKA activation and address how it maintains spatial and temporal signaling specificity.

Strengths:

The results convincingly show that PKA activity is governed by the subcellular localization in dendrites and spines and is mediated via subunit dissociation. The authors make use of organotypic hippocampal slice cultures, where they use pharmacology, glutamate uncaging, and electrophysiological recordings.

Overall, the experiments and data presented are well executed. The experiments all show that at least in the case of synaptic activity, the distribution of PKA-C to dendritic spines is necessary and sufficient for PKA-mediated functional and structural plasticity.

The authors were able to persuasively support their claim that PKA subunit dissociation is necessary for its function and localization in dendritic spines. This conclusion is important to better understand the mechanisms of PKA activity and its role in synaptic plasticity.

Weaknesses:

While the experiments are indeed convincing and well executed, the data presented is similar to previously published work from the Zhong lab (Tillo et al., 2017, Zhong et al 2009). This reduces the novelty of the findings in terms of re-distribution of PKA subunits, which was already established. A few alternative approaches for addressing this question: targeting localization of endogenous PKA, addressing its synaptic distribution, or even impairing within intact neuronal circuits, would highly strengthen their findings. This would allow us to further substantiate the synaptic localization and re-distribution mechanism of PKA as a critical regulator of synaptic structure, function, and plasticity.

Reviewer #2 (Public Review):

Summary:<br /> Zhixin and collaborators have investigated if the molecular pathways present in glia play a role in the proliferation, maintenance, and differentiation of Neural Stem Cells. In this case, Drosophila Neuroblasts are used as models. The authors find that neuronal iron metabolism modulated by glial ferritin is an essential element for Neuroblast proliferation and differentiation. They show that loss of glial ferritin is sufficient to impact on the number of neuroblasts. Remarkably, the authors have identified that ferritin produced in the glia is secreted to be used as an iron source by the neurons. Therefore iron defects in glia have serious consequences in neuroblasts and likely vice versa. Interestingly, preventing iron absorption in the intestine is sufficient to reduce NB number. Furthermore, they have identified Zip13 as another regulator of the process. The evidence presented strongly indicates that loss of neuroblasts is due to premature differentiation rather than cell death.

Strengths:<br /> - Comprenhensive analysis of the impact of glial iron metabolism in neuroblast behaviour by genetic and drug-based approaches as well as using a second model (mouse) for some validations.<br /> - Using cutting-edge methods such as RNAseq as well as very elegant and clean approaches such as RNAi-resistant lines or temperature-sensitive tools<br /> - Goes beyond the state of the art highlighting iron as a key element in neuroblast formation as well as as a target in tumor treatments.

Weaknesses:<br /> Although the manuscripts have clear strengths, there are also some strong weaknesses that need to be addressed.<br /> - Some literature is missing<br /> - In general, the authors succeeded but in some cases, the authors´ claims are not fully supported by the evidence presented and additional experiments are critical to discriminate among different hypotheses.<br /> - Moreover, some potential flaws might be present in the analysis of cell death and mitochondrial iron.

Reviewer #2 (Public Review):

Summary:

In this work Ibtisam and Kisselev explore the role of DDI2 in the proteasome function recovery after a clinically relevant pulse dosing using different proteasome inhibitors and their corresponding PK properties. The authors report that despite lack of NRF1 activation by DDI2 there was no difference in recovery from pulsed proteasome inhibition observed in DDI2 KO cells as compared to WT controls suggesting DDI2 is not required for recovery in this system. They further show that transcription of the proteasome subunits is initiated only after partial recovery of proteasome activity is already observed suggesting that non-transcriptional mechanisms might be also involved. The authors further show that translation inhibition blocked the recovery from proteasome inhibitors.

Strengths:

Overall, it is very important and informative to use a pulse treatment type approach (mimicking the PK properties of the drugs) to explore the biology of PIs as used in this study. The authors also provide convincing data that DDI2 is not required for proteasome activity recovery post-PI pulse treatment in the systems they explored.

Weaknesses:

The authors show that the recovery of one specific catalytic activity of the proteasome post-PI treatment is transcription independent. However, in this work they do not explore the other catalytic activities of the proteasome, the protein levels of the individual subunits and most importantly the level of the different assembled proteasome complexes and how they change over time. Without this data the proposed mechanism is still speculative, in particular the conclusion on the role of translation, and ignores other findings in the field that suggest that alternative mechanisms (such as proteasome complex assembly regulation for instance) might be just as plausible.

Reviewer #2 (Public Review):

In this study Weinberger et al. investigated cardiac macrophage subsets after ischemia/reperfusion (I/R) injury in mice. The authors studied a ∆FIRE mouse model (deletion of a regulatory element in the Csf1r locus), in which only tissue resident macrophages might be ablated. The authors showed a reduction of resident macrophages in ∆FIRE mice and characterized its macrophages populations via scRNAseq at baseline conditions and after I/R injury. 2 days after I/R protocol ∆FIRE mice showed an enhanced pro inflammatory phenotype in the RNAseq data and differential effects on echocardiographic function 6 and 30 days after I/R injury. Via flow cytometry and histology the authors confirmed existing evidence of increased bone marrow-derived macrophage infiltration to the heart, specifically to the ischemic myocardium. Macrophage population in ∆FIRE mice after I/R injury were only changed in the remote zone. Further RNAseq data on resident or recruited macrophages showed transcriptional differences between both cell types in terms of homeostasis-related genes and inflammation. Depleting all macrophage using a Csf1r inhibitor resulted in a reduced cardiac function and increased fibrosis.

Strengths:

(1) The authors utilized robust methodology encompassing state of the art immunological methods, different genetic mouse models and transcriptomics.<br /> (2) The topic of this work is important given the emerging role of tissue resident macrophages in cardiac homeostasis and disease.

Comments on revised version:

The authors have responded to all questions. I have no further comments and congratulate the authors on their work.

Reviewer #2 (Public Review):

Summary:

The gut microbiome contributes to variation in the efficacy of immune checkpoint blockade in cancer therapy; however, the mechanisms responsible remain unclear. Klupt et al. build upon prior data implicating the secreted peptidoglycan hydrolase SagA produced by Enterococcus faecium in immunotherapy, leveraging novel strains with sagA deleted and complemented. They find that sagA is non-essential, but sagA deletion leads to a marked growth defect due to impaired cell division. Furthermore, sagA is necessary for the immunogenic and anti-tumor effects of E. faecium. Together, this study utilizes compelling methods to provide fundamental new insights into E. faecium biology and host interactions, and a proof-of-concept for identifying the bacterial effectors of immunotherapy response.

Strengths:

Klupt et al. provide a well-written manuscript with clear and compelling main and supplemental figures. The methods used are state-of-the-art, including various imaging modalities, bacterial genetics, mass spectrometry, sequencing, flow cytometry, and mouse models of immunotherapy response. Overall, the data supports the conclusions, which are a valuable addition to the literature.

Weaknesses:

Only minor revision recommendations were noted.

Reviewer #2 (Public Review):

Summary:<br /> This work describes a statistical framework that combines functional linear mixed modeling with joint 95% confidence intervals, which improves statistical power and provides less conservative statistical inferences than in previous studies. As recently reviewed by Simpson et al. (2023), linear regression analysis has been used extensively to analyze time series signals from a wide range of neuroscience recording techniques, with recent studies applying them to photometry data. The novelty of this study lies in 1) the introduction of joint 95% confidence intervals for statistical testing of functional mixed models with nested random-effects, and 2) providing an open-source R package implementing this framework. This study also highlights how summary statistics as opposed to trial-by-trial analysis can obscure or even change the direction of statistical results by reanalyzing two other studies.

Strengths:<br /> The open-source package in R using a similar syntax as the lme4 package for the implementation of this framework on photometry data enhances the accessibility, and usage by other researchers. Moreover, the decreased fitting time of the model in comparison with a similar package on simulated data, has the potential to be more easily adopted.

The reanalysis of two studies using summary statistics on photometry data (Jeong et al., 2022; Coddington et al., 2023) highlights how trial-by-trial analysis at each time-point on the trial can reveal information obscured by averaging across trials. Furthermore, this work also exemplifies how session and subject variability can lead to opposite conclusions when not considered.

Weaknesses:<br /> Although this work has reanalyzed previous work that used summary statistics, it does not compare with other studies that use trial-by-trial photometry data across time-points in a trial.

As described by the authors, fitting pointwise linear mixed models and performing t-test and Benjamini-Hochberg correction as performed in Lee et al. (2019) has some caveats. Using joint confidence intervals has the potential to improve statistical robustness, however, this is not directly shown with temporal data in this work. Furthermore, it is unclear how FLMM differs from the pointwise linear mixed modeling used in this work.

In this work, FLMM usages included only one or two covariates. However, in complex behavioral experiments, where variables are correlated, more than two may be needed (see Simpson et al. (2023), Engelhard et al. (2019); Blanco-Pozo et al. (2024)). It is not clear from this work, how feasible computationally would be to fit such complex models, which would also include more complex random effects.

Reviewer #2 (Public Review):

Summary:<br /> The authors provide an open-source graphic user interface (GUI) called Heron, implemented in Python, that is designed to help experimentalists to<br /> (1) design experimental pipelines and implement them in a way that is closely aligned with their mental schemata of the experiments,<br /> (2) execute and control the experimental pipelines with numerous interconnected hardware and software on a network.

The former is achieved by representing an experimental pipeline using a Knowledge Graph and visually representing this graph in the GUI. The latter is accomplished by using an actor model to govern the interaction among interconnected nodes through messaging, implemented using ZeroMQ. The nodes themselves execute user-supplied code in, but not limited to, Python.

Using three showcases of behavioral experiments on rats, the authors highlighted three benefits of their software design:<br /> (1) the knowledge graph serves as a self-documentation of the logic of the experiment, enhancing the readability and reproducibility of the experiment,<br /> (2) the experiment can be executed in a distributed fashion across multiple machines that each has a different operating system or computing environment, such that the experiment can take advantage of hardware that sometimes can only work on a specific computer/OS, a commonly seen issue nowadays,<br /> (3) the users supply their own Python code for node execution that is supposed to be more friendly to those who do not have a strong programming background.

Strengths:<br /> (1) The software is light-weight and open-source, provides a clean and easy-to-use GUI,<br /> (2) The software answers the need of experimentalists, particularly in the field of behavioral science, to deal with the diversity of hardware that becomes restricted to run on dedicated systems.<br /> (3) The software has a solid design that seems to be functionally reliable and useful under many conditions, demonstrated by a number of sophisticated experimental setups.<br /> (4) The software is well documented. The authors pay special attention to documenting the usage of the software and setting up experiments using this software.

Weaknesses:<br /> (1) While the software implementation is solid and has proven effective in designing the experiment showcased in the paper, the novelty of the design is not made clear in the manuscript. Conceptually, both the use of graphs and visual experimental flow design have been key features in many widely used softwares as suggested in the background section of the manuscript. In particular, contrary to the authors' claim that only pre-defined elements can be used in Simulink or LabView, Simulink introduced MATLAB Function Block back in 2011, and Python code can be used in LabView since 2018. Such customization of nodes is akin to what the authors presented.

(2) The authors claim that the knowledge graph can be considered as a self-documentation of an experiment. I found it to be true to some extent. Conceptually it's a welcoming feature and the fact that the same visualization of the knowledge graph can be used to run and control experiments is highly desirable (but see point 1 about novelty). However, I found it largely inadequate for a person to understand an experiment from the knowledge graph as visualized in the GUI alone. While the information flow is clear, and it seems easier to navigate a codebase for an experiment using this method, the design of the GUI does not make it a one-stop place to understand the experiment. Take the Knowledge Graph in Supplementary Figure 2B as an example, it is associated with the first showcase in the result section highlighting this self-documentation capability. I can see what the basic flow is through the disjoint graph where 1) one needs to press a key to start a trial, and 2) camera frames are saved into an avi file presumably using FFMPEG. Unfortunately, it is not clear what the parameters are and what each block is trying to accomplish without the explanation from the authors in the main text. Neither is it clear about what the experiment protocol is without the help of Supplementary Figure 2A.

In my opinion, text/figures are still key to documenting an experiment, including its goals and protocols, but the authors could take advantage of the fact that they are designing a GUI where this information, with properly designed API, could be easily displayed, perhaps through user interaction. For example, in Local Network -> Edit IPs/ports in the GUI configuration, there is a good tooltip displaying additional information for the "password" entry. The GUI for the knowledge graph nodes can very well utilize these tooltips to show additional information about the meaning of the parameters, what a node does, etc, if the API also enforces users to provide this information in the form of, e.g., Python docstrings in their node template. Similarly, this can be applied to edges to make it clear what messages/data are communicated between the nodes. This could greatly enhance the representation of the experiment from the Knowledge graph.

(3) The design of Heron was primarily with behavioral experiments in mind, in which highly accurate timing is not a strong requirement. Experiments in some other areas that this software is also hoping to expand to, for example, electrophysiology, may need very strong synchronization between apparatus, for example, the record timing and stimulus delivery should be synced. The communication mechanism implemented in Heron is asynchronous, as I understand it, and the code for each node is executed once upon receiving an event at one or more of its inputs. The paper, however, does not include a discussion, or example, about how Heron could be used to address issues that could arise in this type of communication. There is also a lack of information about, for example, how nodes handle inputs when their ability to execute their work function cannot keep up with the frequency of input events. Does the publication/subscription handle the queue intrinsically? Will it create problems in real-time experiments that make multiple nodes run out of sync? The reader could benefit from a discussion about this if they already exist, and if not, the software could benefit from implementing additional mechanisms such that it can meet the requirements from more types of experiments.

(4) The authors mentioned in "Heron GUI's multiple uses" that the GUI can be used as an experimental control panel where the user can update the parameters of the different Nodes on the fly. This is a very useful feature, but it was not demonstrated in the three showcases. A demonstration could greatly help to support this claim.

(5) The API for node scripts can benefit from having a better structure as well as having additional utilities to help users navigate the requirements, and provide more guidance to users in creating new nodes. A more standard practice in the field is to create three abstract Python classes, Source, Sink, and Transform that dictate the requirements for initialisation, work_function, and on_end_of_life, and provide additional utility methods to help users connect between their code and the communication mechanism. They can be properly docstringed, along with templates. In this way, the com and worker scripts can be merged into a single unified API. A simple example that can cause confusion in the worker script is the "worker_object", which is passed into the initialise function. It is unclear what this object this variable should be, and what attributes are available without looking into the source code. As the software is also targeting those who are less experienced in programming, setting up more guidance in the API can be really helpful. In addition, the self-documentation aspect of the GUI can also benefit from a better structured API as discussed in point 2 above.

(6) The authors should provide more pre-defined elements. Even though the ability for users to run arbitrary code is the main feature, the initial adoption of a codebase by a community, in which many members are not so experienced with programming, is the ability for them to use off-the-shelf components as much as possible. I believe the software could benefit from a suite of commonly used Nodes.

(7) It is not clear to me if there is any capability or utilities for testing individual nodes without invoking a full system execution. This would be critical when designing new experiments and testing out each component.

Reviewer #2 (Public Review):

In the manuscript by Salmani et al., the authors explore the transcriptomic characterization of dopamine neurons in order to explore which neurons are particularly vulnerable to 6-OHDA-induced toxicity. To do this they perform single nucleus RNA sequencing of a large number of cells in the mouse midbrain in control animals and those exposed to 6-OHDA. This manuscript provides a detailed atlas of the transcriptome of various types of ventral midbrain cells - though the focus here is on dopaminergic cells, the data can be mined by other groups interested in other cell types as well. The results in terms of cell type classification are largely consistent with previous studies, though a more nuanced picture of cellular subtypes is portrayed here, a unique advantage of the large dataset obtained. The major advance here is exploring the transcriptional profile in the ventral midbrain of animals treated with 6-OHDA, highlighting potential candidate genes that may influence vulnerability. This approach could be generalizable to investigate how various experiences and insults alter unique cell subtypes in the midbrain, providing valuable information about how these stimuli impact DA cell biology and which cells may be the most strongly affected.

Comments on the revised version

The authors addressed most of my concerns about the depth of analysis and implemented further analyses of the data. However I still think that the manuscript would be strengthened with an acknowledgement and deeper integration with the concepts from recent papers in the field, as mentioned by Reviewer 1. There is a rich amount of biology that can be gleaned from understanding the anatomical topology of the VTA and how that relates to gene expression patterns, both at a basal state and following 6-OHDA injection. For example, I made the point about medially-located DA cells in the VTA being the DA that co-express vGluT2. The work would provide more value to the field if more effort was made in the introduction and discussion to briefly mention the recent key papers in the field and how their work relates to our knowledge of the VTA and adjacent SNc in terms of cell-type identity, spatial location, and co-expression of various genes e.g., DAT and vGluT2.

Reviewer #2 (Public Review):

Summary:

This study addresses an intriguing and little-studied population of large excitatory cells that lie in the stratum radiatum, outside the classical cell body layers in the hippocampus. Interestingly, the authors show that these "giant excitatory neurons in stratum radiatum" strongly drive both bistratified and basket interneurons. Activating a single giant cell could induce action potential firing in postsynaptic interneurons, which in turn inhibit their postsynaptic pyramidal cell targets. They appear to receive excitatory input from CA3 but not the entorhinal cortex; at a local level, they are not strongly interconnected with CA1 pyramidal cells, and receive inhibitory input from bistratified but not basket cells.

The lack of perisomatic input from basket cells is unique in comparison with the vast majority of excitatory cells in the hippocampus. It is however not surprising, given the fact that the giant excitatory neurons studied in this paper are defined by their position in a particular hippocampal layer (stratum radiatum), and the axons of inhibitory basket cells are largely restricted to another layer (stratum pyramidale). Nonetheless, the fact that this study draws attention to this unique property, and also provides data to support it, is valuable. As the authors also point out, given the importance of such perisomatic input for rhythmogenesis in the hippocampus, the lack of such input may leave these cells free to operate outside of the dominant rhythm.

In combination with the strong drive onto interneurons, which strongly control the activity of pyramidal cells, the giant excitatory cells in the stratum radiatum appear to be in a unique position to influence the hippocampal circuit. Although clearly such an alternative pathway provides the potential for more diverse functions within the hippocampal circuit, and the connectivity shown in this study will likely be of interest to anyone interested in hippocampal function, the authors do not show a concrete function for this pathway.

Strengths:

Overall, the main value of this study is to demonstrate that this small population of oft-neglected cells could have an unexpectedly large impact on hippocampal function via a uniquely strong excitatory output onto two types of interneurons. Whereas activating a "classical" pyramidal cell produces only subthreshold activity in postsynaptic interneurons, meaning that several pyramidal cells have to be co-active to drive their postsynaptic targets to fire, here the authors show that a single giant excitatory neuron in the stratum radiatum can directly drive at least a subset of its postsynaptic targets to fire.

The authors also show the effect of this output both on the membrane potential of CA1 pyramidal cells and on the extracellular field potential as measured with silicon probes. The fact that the authors identified a relatively large number of these sparse giant excitatory cells in the stratum radiatum and performed paired recordings from them is itself a strength of this study.

Another strength is the fact that the authors also investigate the inputs to these giant excitatory cells. The method of paired patch-clamp recordings in rat brain slices enables in principle to record connectivity in both directions, by stimulating one and checking for a response in the other. Recording the interconnectivity of giant excitatory cells with bistratified, basket, and pyramidal cells, as well as the connectivity between pyramidal cells and the two types of interneurons, allows insightful comparisons between "classic" CA1 pyramidal cells and the displaced giant excitatory cells. Although the lack of connectivity between the latter two cell types that the authors report is not so surprising (given the generally very low connectivity between excitatory cells in CA1), it is nonetheless important data. To also check non-local inputs the authors used optogenetics, whereby a Camk2a promoter likely limited cells expressing channelrhodopsin to mostly excitatory cells.

Weaknesses:

The main weakness of this study is perhaps the lack of a clear function for the described circuitry. Although the authors do speculate on this, it remains to be demonstrated what the role of these cells and their connections with the identified interneuron types might be for hippocampal function.

For the first experimental result, it's not fully clear from the evidence the authors present, that indeed the injections were limited to CA3 (for Figure 1c) and to EC (for Figure 1d). This is important since in theory the CA3 injection could also include e.g. CA2 or CA1 itself, which is not that unlikely given the relatively large injected volume of 1ul per side (bilateral). Similarly for the EC injection, it appears the injection may be 2 ul per side (the methods are a bit ambiguous, unfortunately), and this could lead to infection in e.g. Subiculum. Given that these potential mistargeted areas may also project to CA1, this could obviously change the conclusions one can draw from the optogenetic stimulation results the authors present. Furthermore, for the EC result, the authors assume the response they measure is not monosynaptic, which indeed is likely given the long delay, but to interpret this properly a few recordings with pharmacology would be helpful to really show monosynaptic connections (also for the CA3 inputs). One could also cut the inputs to the DG to show that the delayed EC inputs are abolished then (or instead they may be relayed via local CA1 pyramids receiving EC input). Either way, some additional line of evidence beyond simply the delay would be reassuring. A further worry for the EC result relates to the angle of slicing: can the authors give the reader some reassurance that the lack of monosynaptic inputs is not simply a result of cut connections in the slices they used? Especially since only 5 neurons were recorded with stimulation of presumed EC fibers, it is hard to rule out EC input based on the presented evidence. Related to this, one wonders why in Figures 1D and 1E there are no reported connections from EC to CA1 pyramidal cells (while the authors do include CA1 pyramidal cell recordings for the CA3 stimulation experiments); again this might suggest the connections are simply cut in the slice preparation.

For the connectivity results, the data seem to support the claims, but the conclusions would be improved if the terminology of "privileged" and "escaped" could be avoided. More importantly, the exact criteria for distinguishing between bistratified and basket cells are not fully clear; it seems that the amount of current needed to induce AP firing was the main criterion but there is no figure showing this data (only an example in S1A). The input resistance distributions are overlapping, so this was clearly not used as the main criterion. Showing some pictures of the filled cells as supplemental material would also be helpful to give the reader a bit more confidence that the classification is reliable. In the methods, it is mentioned that 10 cells were filled with biocytin, but the authors don't explicitly state (or show) that the identity was confirmed for all 10 filled cells, and what this was based on. Overall, a bit more info on the giant excitatory cells in the stratum radiatum would be helpful (e.g. soma locations, extent of dendrites relative to layers, density/nr of cells); a brief mention of this in the introduction or discussion would help the reader to place the work in context.

The number of tested pairs or cells is also a bit low (or unclear) in some cases. For instance, the relatively low number of recordings (n=30) between CA1 pyramidal cells and giant excitatory cells in the radiatum means a low connectivity rate on the order of a few percent cannot be ruled out; it has been shown that even in CA3, which is classically considered a "reciprocally connected" area, such low connectivity rates can still be functionally important (Guzman et al).

For the feedforward inhibition result, the concept of "amplification relay station" that was introduced is not so clear. It is not unexpected that when you strongly innervate BC cells and bring them to spike, as the giant cells in this study do, this activity will in turn inhibit pyramidal cells (and actually quite a lot of them, so that it is not surprising that you can measure IPSCs). Furthermore, the rationale for doing the silicon probe recordings is not well explained, and it would be helpful if the authors could discuss the significance of performing such LFP recordings in slices.

Conceptually, the presentation of perisomatic inhibition as simply silencing pyramidal and granule cells, forming a "burden" that needs to be "overcome" or "bypassed" via an alternative pathway (as in the example the authors give of having an axon coming from a dendrite instead of the presumably "blocked" soma), is not so convincing. Perisomatic inhibition is much more than that, particularly if one takes timing into account (indeed the authors point to its role in rhythmogenesis). This does not detract from the fact that the lack of perisomatic inhibition (at least from fast-spiking basket cells) is likely to have large functional implications, which the authors rightly emphasize.

Reviewer #2 (Public Review):

Summary:<br /> This manuscript describes the production of a mouse model for LAMA2-CMD. This mouse was produced using CRISPR-Cas9 and deleted exon 3 of the Lama2 gene. The mice exhibit reduced life expectancy, muscle pathology, and disruption of the gliovascular basal lamina assembly leading to defects in the blood-brain barrier. Single-cell RNAseq was used to explore the effect that loss of Laminin-211/221 had on gene expression.

Strengths:<br /> (1) The authors produced a mouse model of LAMA2-CMD using CRISPR-Cas9.

(2) The authors identify cellular changes that disrupt the blood-brain barrier.

Weaknesses:<br /> (1) The major weakness is the manuscript reads like this was the first-ever knockout mouse model generated for LAMA2-CMD. There are in fact many Lama2 knockout mice (dy, dy2J, dy4k, dyW, and more) which have all been extensively studied with publications. It is important for the authors to comment on these other published studies that have generated these well-studied mouse lines. Therefore, there is a lack of background information on these other Lama2 null mice.

(2) The phenotypes of dyH/dyH are similar to, if not identical to dy/dy, dy2J/dy2J, dy4k/dy4k, dyW/dyW including muscle wasting, muscle weakness, compromised blood-brain barrier, and reduced life expectancy. This should be addressed, and a comparison made with Lama2 deficient mice in published literature.

(3) Recent published studies (Chen et al., Development (2023), PMID 36960827) show loss of Itga7 causes disruption of the brain-vascular basal lamina leading to defects in the blood-brain barrier. This should be referenced in the manuscript since this integrin is a major Laminin-211/221 receptor in the brain and the mouse model appears to phenocopy the dyH/dyH mouse model.

Reviewer #2 (Public Review):

In this study, Nguyen et al. showed that cat saliva can robustly induce freezing behavior in mice. This effect is mediated through the accessory olfactory system as it requires physical contact and is abolished in Trp2 KO mice. The authors further showed that V2R-A4 cluster is responsive to cat saliva. Lastly, they demonstrated c-Fos induction in AOB and VMHdm/c by the cat saliva. The c-Fos level in the VMHdm/c is correlated with the freezing response.

Strength:

The study opens an interesting direction. It reveals the potential neural circuit for detecting cat saliva and driving defense behavior in mice. The behavior results and the critical role of the accessory olfactory system in detecting cat saliva are clear and convincing.

Weakness:

The findings are relatively preliminary. The identities of the receptor and the ligand in the cat saliva that induces the behavior remain unclear. The identity of VMH cells that are activated by the cat saliva remains unclear. There is a lack of targeted functional manipulation to demonstrate the role of V2R-A4 or VMH cells in the behavioral response to the cat saliva.

Reviewer #2 (Public Review):

We appreciate the authors revision of this manuscript and toning down some of the statements regarding "contradictory" results. We still have some concerns about the major claims of this paper which lead us to suggest this paper undergo more revision as follows since, in its present form, we fear this paper is misleading for the field in two areas. here is a brief outline:

(1) Despite acknowledging that the injections only occurred in the anteromedial aspect of the tubercle, the authors still assert broad conclusions regarding where the tubercle projects and what the tubercle does. for instance, even the abstract states "both D1 and D2 neurons of the OT project primarily to the VP and minimally elsewhere" without mention that this is the "anteromedial OT". Every conclusion needs to specify this is stemming from evidence in just the anteromedial tubercle, as the authors do in some parts of the the discussion.

(2) The authors now frame the 2P imaging data that D1 neuron activity reflects "increased contrast of identity or an intermediate and multiplexed encoding of valence and identity". I struggle to understand what the authors are actually concluding here. Later in discussion, the authors state that they saw that OT D1 and D2 neurons "encode odor valence" (line 510). We appreciate the authors note that there is "poor standardization" when it comes to defining valence (line 521). We are ok with the authors speculating and think this revision is more forthcoming regarding the results and better caveats the conclusions. I suggest in abstract the authors adjust line 14/15 to conclude that, "While D1 OT neurons showed larger responses to rewarded odors, in line with prior work, we propose this might be interpreted as identity encoding with enhanced contrast." [eliminating "rather than valence encoding" since that is a speculation best reserved for discussion as the authors nicely do.

The above items stated, one issue comes to mind, and that is, why of all reasons would the authors find that the anteromedial aspect of the tubercle is not greatly reflecting valence. the anteromedial aspect of the tubercle, over all other aspects of the tubercle, is thought my many to more greatly partake in valence and other hedonic-driven behaviors given its dense reception of VTA DAergic fibers (as shown by Ikemoto, Kelsch, Zhang, and others). So this finding is paradoxical in contrast to if the authors would had studied the anterolateral tubercle or posterior lateral tubercle which gets less DA input.

Reviewer #2 (Public Review):

Oemisch and Seo set out to examine the effects of low-dose ketamine on reinforcement learning, with the idea that alterations in reinforcement learning and/or motivation might inform our understanding of what alterations co-occur with potential antidepressant effects. Macaques performed a reinforced/punished matching pennies task while under effects of saline or ketamine administration and the data were fit to a series of reinforcement learning models to determine which model described behavior under saline most closely and then what parameters of this best-fitting model were altered by ketamine. They found a mixed effect, with two out of three macaques primarily exhibiting an effect of ketamine on the processing of losses and one out of three macaques exhibiting an effect of ketamine on processing losses and perseveration. They found that these effects of ketamine appeared to be dissociable from the nystagmus effects of the ketamine.

The findings are novel, and the data suggesting that ketamine primarily affects on the processing of losses (under the procedures used) are solid. However, it is unclear whether the connection between the processing of losses and the antidepressant effects of ketamine is justified, and the current findings may be more useful for those studying reinforcement learning than those studying depression and antidepressant effects. In addition, the co-occurrence of different behavioral procedures with different patterns of ketamine effects, with one macaque tested with different parameters than the other two exhibiting effects of ketamine that were best fit with a different model than the other two macaques, suggests that there may be difficulty in generalizing these findings to reinforcement learning more generally.

(1) First, the authors should be more explicit and careful in the connection they are trying to make about the link between loss processing and depression. The authors call their effect a "robust antidepressant-like behavioral effect." However, there are no references to support this or discussion of how the altered loss processing would relate directly to the antidepressant effects. A few statements about a link to antidepressant effects have been removed or moderated, but many remain, including those in the abstract. The authors provide little to no support for this link, so the current version represents solid evidence for an effect on loss processing and incomplete or weak evidence for an antidepressant effect.

(2) It appears that the monkey P was given smaller rewards and punishers than the other two monkeys, and this monkey had an effect of ketamine on perseveration that was not observed in the other two monkeys. This may be due to this monkey being trained and tested before the other animals, but it does raise the issue of the generality of the authors' findings. It seems possible that the procedures used for the other two monkeys (with no deviation at all) might support the best-fit model that the authors favor. However, if changes in the size of the rewards and punishments suddenly make ketamine affect perseveration, then it suggests that ketamine's effect is highly parameter-specific. For example, might there be some parameters where ketamine would only alter perseveration and not loss processing?

Reviewer #3 (Public Review):

In the manuscript titled "Structure and Evolution of Alanine/Serine Decarboxylases and the Engineering of Theanine Production," Wang et al. solved and compared the crystal structures of Alanine Decarboxylase (AlaDC) from Camellia sinensis and Serine Decarboxylase (SerDC) from Arabidopsis thaliana. Based on this structural information, the authors conducted both in vitro and in vivo functional studies to compare enzyme activities using site-directed mutagenesis and subsequent evolutionary analyses. This research has the potential to enhance our understanding of amino acid decarboxylase evolution and the biosynthetic pathway of the plant specialized metabolite theanine, as well as to further its potential applications in the tea industry.